Transient Stability Analysis of a Power System Having ...

Transient Stability Analysis of a Power System Having Large

Scale Wind Generation

Author

Orenge Roy Samwel

MASTER OF SCIENCE IN ELECTRICAL ENGINEERING

PAN AFRICAN UNIVERSITY INSTITUTE FOR BASIC SCIENCES

TECHNOLOGY AND INNOVATION

August 2014

Transient Stability Analysis of a Power System Having Large

Scale Wind Generation

Orenge Roy Samwel

EE300-0002/2012

A thesis submitted to Pan African University, Institute for Basic Sciences Technology

and Innovation in partial fulfillment of the requirement for the degree of M.Sc. in

Electrical Engineering

August 2014

Declaration

This thesis is my original work and to the best of my knowledge it has not been

presented for a degree award in this or any other university.

Signature : ....................................... Date : ................................................

Name : Orenge Roy Samwel

This thesis report has been submitted for examination with our approval as University

supervisors.

Signature : ....................................... Date : ................................................

Dr. C. Maina Muriithi

Lecturer, Electrical and Electronic Engineering Department, JKUAT

Signature : ....................................... Date : ................................................

Prof. George Nyakoe

Associate Professor, Electrical and Electronic Engineering, JKUAT

i

Acknowledgement

This work has been carried out with the help of many individuals and organizations others

who even don’t know how much they helped. I could not have made it without the help,

words of wisdom, and emotional support by a number of people whom I would like to

thank.

First of all, I would like to express my deep and sincere gratitude to my supervisors Dr. C.

Maina Muriithi and Prof. George Nyakoe for their excellent supervision and constructive

advice during this work.

I would like to thank my sponsors profusely, the African Union through the Pan African

University, Institute Of Basic Sciences, Innovation and Technology. It went a long way in

facilitating the smooth running of the whole Masters program.

I would also like to thank all the members of staff at the institute, really the African

dream can not be realized without such a dedicated staff.

Finally, I would like to thank all my classmates for being there when needed and I will

forever appreciate your efforts. God bless you all.

ii

Abstract

Energy is one of the most important factors that continue to influence the shape of civ-

ilization in the 21st Century. The cost and availability of energy significantly impacts

our quality of life, the health of national economies and the stability of our environment.

In recent years there has been a significant global commitment to develop clean and al-

ternative forms of energy resources. It is envisioned that by 2020, 10% of world energy

demand will be supplied from renewable resources. It is expected that this figure will

grow to 50% by 2050. Among renewable energy resources, wind generation technology

has matured considerably. Wind is fairly distributed around the globe and therefore avail-

able to everyone in the world. In the last decade, wind generation has been the fastest

growing energy source globally. However more penetration of wind energy into existing

power networks raises concern for power system operators and regulators. This research

was aimed at carrying out transient stability analysis of a power system which has a large

scale penetration of wind power. The Kenyan power system was used in this research.

The proposed Lake Turkana Wind Project (LTWP) which is aimed at generating 300 MW

of wind power forms the basis of the research. Analysis was carried out first without the

wind integration and then with wind integration. Comparison was carried out on the two

commonly used wind generation technologies (SCIG and DFIG) to determine their effect

on the grid transient stability. This system was established and all the simulations and

analysis carried out in the power system analysis tool DIgSILENT PowerFactory. The

investigations were carried out with two excitation control configurations; first with man-

iii

ual excitation control only and secondly with excitation controlled by automatic voltage

control (AVR) together with a power system stabilizer (PSS)

Simulation results show that the inclusion of wind power from a DFIG based wind farm

has less impact on transient stability of the Kenyan power system as compared to a SCIG

based wind farm. The inclusion of the excitation controller improves system damping

which enhances system transient stability.

iv

Contents

Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

Aknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

1 Introduction 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3.1 General objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3.2 Specific objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.4 Contributions of the research . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.5 Organization of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

v

Contents

2 Literature Review 5

2.1 Definitions and classification of power system stability . . . . . . . . . . . . 5

2.1.1 Rotor angle stability . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.1.2 Voltage stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1.3 Frequency Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1.4 Transient Stability Indices . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Wind Energy Generating Systems . . . . . . . . . . . . . . . . . . . . . . . 8

2.2.1 Wind Turbine Generator Technologies . . . . . . . . . . . . . . . . 11

2.3 Power System Excitation Control . . . . . . . . . . . . . . . . . . . . . . . 21

2.3.1 Automatic Voltage Regulator (AVR) . . . . . . . . . . . . . . . . . 22

2.3.2 Power System Stabilizer (PSS) . . . . . . . . . . . . . . . . . . . . . 23

2.4 Recent Related Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3 Methodology 29

3.1 Power System Model Development in DIgSILENT . . . . . . . . . . . . . . 29

3.2 Comparison of SCIG and DFIG wind generation technologies . . . . . . . . 35

3.3 Analysis with the inclusion of the AVR and PSS . . . . . . . . . . . . . . . 38

4 Results and Discussions 40

4.1 Transient Stability Analysis With and Without the Wind Farm . . . . . . 42

4.2 Comparison of the effect of SCIG and DFIG . . . . . . . . . . . . . . . . . 44

4.3 Transient Stability Analysis With the Excitation Control . . . . . . . . . . 51

vi

Contents

5 Conclusions and Recommendations 53

5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

References 55

A Kenya Power System Data 62

B Vestas V52-850 kW Wind Turbine Rated Electrical Data 72

C IEEE 9 Bus Data 73

D Author’s Publications 76

Appendix 62

vii

List of Figures

2.1 Classification of power system stability. . . . . . . . . . . . . . . . . . . . . . 6

2.2 Conversion of wind power to electrical power. . . . . . . . . . . . . . . . . . . 9

2.3 Power curve for a 2MW wind turbine. . . . . . . . . . . . . . . . . . . . . . . 10

2.4 Grid connected Squirrel cage induction generator. . . . . . . . . . . . . . . . 12

2.5 dq theory of machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.6 Equivalent circuit of squirrel cage induction generator dynamic model. . . . . . 15

2.7 Components of DFIG wind turbine. . . . . . . . . . . . . . . . . . . . . . . . 18

2.8 Power converter in DFIG wind turbine. . . . . . . . . . . . . . . . . . . . . . 20

2.9 Crowbar arrangements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.10 Model of AVR in excitation system. . . . . . . . . . . . . . . . . . . . . . . . 23

2.11 Model of PSS in excitation system. . . . . . . . . . . . . . . . . . . . . . . . 24

3.1 IEEE standard 9 bus system. . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.2 Kenyan Power system one line diagram. . . . . . . . . . . . . . . . . . . . . . 31

3.3 Kenyan Power System Transmission Network . . . . . . . . . . . . . . . . . . 33

3.4 Single line diagram of the DFIG in DIgSILENT . . . . . . . . . . . . . . . . . 34

viii

Contents

3.5 Kenyan Power System Transmission Network with DFIG based Windfarm . . . 37

4.1 Active power response before and after LTWP wind farm at Gitaru. . . . . . . 42

4.2 Rotor Angle before and after inclusion of LTWP wind farm at Gitaru. . . . . . 43

4.3 Active power before and after inclusion of LTWP wind farm at Kamburu. . . . 43

4.4 Rotor Angle before and after inclusion of LTWP wind farm at Kamburu. . . . 44

4.5 G2 Rotor angle with no excitation control. . . . . . . . . . . . . . . . . . . . 45

4.6 Active power of Gitaru Station for both wind farms. . . . . . . . . . . . . . . 46

4.7 Active power of Turkwell Station for both wind farms. . . . . . . . . . . . . . 46

4.8 Reactive power of Gitaru station for both wind farms. . . . . . . . . . . . . . 47

4.9 Reactive power of Turkwell station for both wind farms. . . . . . . . . . . . . 48

4.10 Rotor angle of Gitaru station for both wind farms. . . . . . . . . . . . . . . . 49

4.11 Rotor angle of Turkwell for both wind farms. . . . . . . . . . . . . . . . . . . 49

4.12 Voltage response of Gitaru for both wind farms. . . . . . . . . . . . . . . . . 50

4.13 Voltage response of Turkwell for both wind farms. . . . . . . . . . . . . . . . 50

4.14 Active power of Gitaru Station with AVR and PSS. . . . . . . . . . . . . . . 51

4.15 Active power of Kipevu I Station with AVR and PSS. . . . . . . . . . . . . . 52

ix

List of Tables

2.1 Wind turbine products with DFIG concept. . . . . . . . . . . . . . . . . . . . 17

3.1 IEEET1 AVR setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.2 IEEEST PSS setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

A.1 Branches Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

A.2 Bus Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

A.3 Transformer Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

A.4 Machine Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

A.5 AVR Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

C.1 Generator Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

C.2 Load Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

C.3 Line Data in p.u . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

C.4 Operating Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

x

Abbreviations

AC Alternating Current

AVR Automatic Voltage Regulator

CCT Critical Clearing Time

DC Direct Current

DIgSILENT DIgital SImuLation and Electrical NeTwork

DFIG Doubly Fed Induction Generator

FRC Fully Rated Converter

FRT Fault Ride Through

GSC Grid Side Converter

GWEC Global Wind Energy Council

ICREPQ International Conference on Renewable Energies and Power Quality

IEEE Institution of Electrical and Electronics Engineers

IET Institution of Engineering and Technology

JATIT Journal of Theoretical and Applied Information Technology

JEMAA Journal of Electromagnetic Analysis and Applications

LTWP Lake Turkana Wind Project

MW Mega Watt

NREL National Renewable Energy Laboratory

PF Power Factor

PSS Power System Stabilizer

xi

SCIG Squirrel Cage Induction Generator

SMIB Single Machine Infinite Bus

STATCOM Static Synchronous Compensators

SVC Static Var Compensators

WASET World Academy of Science, Engineering and Technology

xii

Nomenclature

Pwind Instantaneous power from wind

ρair Mass density of air

νw Velocity of the wind

Cp Power performance coefficient

PBetz Betz limit

u Space vector for voltage

i Space vector for current

ψ Space vector for flux

ωsyn Synchronous speed

ωr Angular speed of the rotor

Lm Magnetizing reactance

Ls Stator inductance

Lr Rotor inductance

J Generator inertia

Te Electrical torque

Tm Mechanical torque

T ′q0 Direct axis transient time constant

T”d0 Direct axis subtransient time constant

T ′q0 Quadrature axis transient time constant

T”q0 Quadrature axis subtransient time constant

xiii

Xd Direct axis synchronous reactance

Xq Quadrature axis synchronous reactance

X ′d Direct axis transient reactance

X ′q Quadrature axis transient reactance

X”d Direct axis subtransient reactance

X”q Quadrature axis subtransient reactance

Xl Leakage reactance

xiv

Chapter 1

Introduction

1.1 Background

Focus on renewable energy sources is increasing in the world today mainly due to deple-

tion of fossil fuels which were being used widely traditionally. In the near future renewable

sources of energy will become the major contributors of the world’s energy supply. Wind

energy is one of these sources and currently it is the most widely exploited renewable

energy resource [1, 2].

Wind power has been used in the ancient times for applications such as: pumping wa-

ter grinding mills and in propelling boats.The remarkable contributions in the electricity

supply began in the mid 1980s and its now firmly established as one of the major tech-

nologies for electricity generation in the world. It is one of the fastest growing electricity

generating technologies and features in energy plans across the world, both in the devel-

oped and the developing world. According to the World Wind Energy Association, over

1

1.2. PROBLEM STATEMENT

282 GW of capacity is now installed worldwide with China, for instance having over 75

GW installed capacity [3, 4]. Some countries have high penetration levels like Denmark

which meets about 25% of its power demand from wind. It is also evident that the in-

stalled wind energy capacity has been increasing significantly around the world in the

recent past [3]. Wind power’s rapid expansion has been driven by a combination of its

environmental benefits, various state and federal policies and incentives, and improving

cost-competitiveness with other traditional generation technologies [5, 6]. Integration of

Large quantities of wind power has however presented some challenges such as absorption

of reactive power from the grid during faults which affects on system stability especially

in weak power grids.

1.2 Problem statement

Wind power as a generation source, differs in several respects from conventional sources

of energy such as hydro and thermal, the major difference being that wind generators are

usually based on induction generator technology instead of the conventional synchronous

generators. The induction generators , are known to consume reactive power (like in-

duction motors) during system contingency, which in turn affect the stability of a power

system[7]. This research analysed the transient stability of the Kenyan power system in

the presence of wind power which will be generated from the proposed Lake Turkana

Wind Project (LTWP). Different Scenarios were analysed first comparing the effect of

two wind generation technologies i.e the squirrel cage induction generators (SCIG) and

2

1.3. OBJECTIVES

the doubly fed induction generators (DFIG). The parameters that were observed are the

rotor angle, active power, voltage and reactive power at different generation stations in

the even of a system fault.

1.3 Objectives

1.3.1 General objective

The main objective of this research is to carry out the transient stability analysis of a

power system, which has a large proportion of wind power generation.

1.3.2 Specific objectives

i. To develop the Kenyan power system model for transient stability analysis with the

LTWP wind farm.

ii. To analyze the transient stability of the system using two wind generator technologies,

i.e, SCIGs and DFIGs.

iii. To analyze the transient stability of the system when the AVR and PSS are used.

1.4 Contributions of the research

This research provides a detailed transient stability analysis of the Kenyan power system

with the inclusion of the proposed Lake Turkana Wind Project (LTWP) that aims to

3

1.5. ORGANIZATION OF THE THESIS

provide 300 MW of wind power to the Kenyan national grid which equivalent to approx-

imately 20% of the current installed electricity generating capacity. With this ambitious

project it is therefore important for the stake holders in the Kenyan power industry to

know what to expect from this project.

1.5 Organization of the thesis

In Chapter 2, theoretical background on different aspects of power system stability are

presented with their causes. The two main technologies in wind power generation are also

introduced in the chapter. The chapter also gives some literature related to this work.

In Chapter 3, a description of the two power systems used in this research is given. The

methods used and the procedure for carrying out transient stability analysis are outlined.

In Chapter 4, the simulation results are presented and discussed. The results and discus-

sions are presented in line with the specific objectives

In Chapter 5, the conclusions and recommendations of the study are given.

4

Chapter 2

Literature Review

In this chapter different aspects of power stability are introduced and their causes. The

two major technologies employed in wind power generation are also discussed here. Var-

ious related research on the subject are also discussed in this chapter.

2.1 Definitions and classification of power system sta-

bility

Power system stability is that property of a power system that enables it to remain in

a state of equilibrium under normal operating conditions and to regain an acceptable

state of equilibrium after being subjected to a disturbance[8, 9]. Figure 2.1 shows the

classifications of power system stability.

5

2.1. DEFINITIONS AND CLASSIFICATION OF POWER SYSTEM STABILITY

Figure 2.1: Classification of power system stability.

2.1.1 Rotor angle stability

Rotor angle stability refers to the ability of synchronous machines of an interconnected

power system to remain in synchronism after being subjected to a disturbance [8, 9, 10].

It can be categorized into the following subcategories:

i. Transient Stability

This is the ability of the power system to maintain synchronism after a large distur-

bance e.g. loss of a generator or major load, system fault [9]. In transient stability

analysis the time frame of interest is usually 3 to 5 seconds following the disturbance.

It may extend to 10 to 20 seconds for very large systems with dominant inter-area

swings[8, 11].

ii. Steady State Stability

This is the ability of the power system to maintain synchronism when the system is

subjected to slow gradual disturbances such as gradual power changes [10, 12]. It is

6

2.1. DEFINITIONS AND CLASSIFICATION OF POWER SYSTEM STABILITY

basically concerned with the determination of the upper limit of the machine loading

before losing synchronism provided the loading is increasing gradually [13].

iii. Dynamic Stability

This is the ability of the power system to maintain synchronism after a small distur-

bance lasting for a long time after the action of the voltage regulators and turbine

governors [10, 13]. Dynamic stability analysis covers longer intervals of 5 to 10 seconds

and occasionally up to 30 seconds.

2.1.2 Voltage stability

This can be defined as the ability of a power system to maintain steady voltages at all

buses in the system under normal operating conditions, and after being subjected to a

disturbance [8, 9]. The main factor causing voltage instability is the inability of the power

system to meet the reactive power demand [9].

2.1.3 Frequency Stability

This refers to the ability of a power system to maintain steady frequency following a

severe system upset resulting in a significant imbalance between generation and load [8].

The main factor causing frequency instability is the inability of the power system to meet

the real power demanded by the load.

7

2.2. WIND ENERGY GENERATING SYSTEMS

2.1.4 Transient Stability Indices

They are used to indicate or asses the extent of transient instability of a power system.

The critical clearing time (CCT) is one of the indicators of transient stability. CCT is

the longest fault duration allowable for stability to be maintained. The higher the CCT

the more stable the system. For a given fault, it is generally considered as the best

measurement of severity of a contingency and thus widely used for ranking contingencies

in accordance with their severity [2]. Another way of telling the severity of a system fault

is looking at how long it takes for the system to regain its initial state of operation given

by the settling time. This can only be realized if a fault is cleared before reaching the

critical clearing time. A system which takes less time to settle is said to be more stable

and vice versa.

2.2 Wind Energy Generating Systems

Conversion of wind energy is achieved by wind turbines which produce electricity using the

power of the wind to drive an electrical generator. Wind passes over the blades, exerting

a turning force. The rotating blades turn a shaft inside the nacelle (housing), which goes

into a gearbox. The gearbox increases the rotational speed to that which is appropriate for

the generator. The mechanical power is converted into electrical power in the generator.

The principle of wind power conversion to electrical power can be summarized as shown

in figure 2.2. The instantaneous power Pwind available in the wind flowing through an

8


Figure 2.2: Conversion of wind power to electrical power.

area Av can be described as:

Pwind =1

2ρair.Av.ν

3w (2.1)

where ρair is the mass density of air and νw is the velocity of the wind.

The fraction of the wind captured by a wind turbine is given by the power performance

coefficient (Cp). This coefficient is based on the theoretical maximum power that can be

extracted from the wind, the so called Betz limit. It can be expressed as:

PBetz =1

2ρair.Av.ν

3wCp (2.2)

Near the earths surface, wind speed is reduced by friction. This means that the higher

the wind turbine tower, the greater the annual average wind speed [14]. This implies that

more wind power can be obtained if the turbines are raised well enough above the ground

level.

The power output of a wind turbine at various wind speeds is conventionally described

by its power curve. The power curve gives the steady-state electrical power output as

a function of the wind speed at the hub height and is generally measured using 10 min

9


average data [15]. An example of a power curve for a 2 MW wind turbine is given in

Figure 2.3. The power curve has three key points on the velocity scale:

Figure 2.3: Power curve for a 2MW wind turbine.

• Cut-in wind speed: The minimum wind speed at which the machine will deliver

useful power.

• Rated wind speed: The wind speed at which rated power is obtained (rated power

is generally the maximum power output of the electrical generator).

• Cut-out wind speed: The maximum wind speed at which the turbine is allowed to

deliver power (usually limited by engineering loads and safety constraints).

Below the cut-in speed, the wind turbine remains shut down as the speed of the wind

is too low for useful energy production. Then, once in operation, the power output in-

10


creases following the relationship in Equation 2.1 until rated wind speed is reached. Power

curves for existing machines can normally be obtained from the turbine manufacturer [15].

Wind turbine blades are shaped to generate the maximum power from the wind at the

minimum cost. Primarily, the design is driven by the aerodynamic requirements, but

economics mean that the blade shape is a compromise to keep the cost of construction

reasonable. In particular, the blade tends to be thicker than the aerodynamic optimum

close to the root, where the stresses due to bending are greatest [16].

Modern electricity-generating wind turbines now use three-bladed upwind rotors, although

two-bladed, and even one-bladed rotors were used in earlier commercial turbines. Reduc-

ing the number of blades means that the rotor has to operate at a higher rotational speed

in order to extract the wind energy passing through the rotor disk. Although a high rotor

speed is attractive in that it reduces the gearbox ratio required, a high blade tip speed

leads to increased aerodynamic noise and increased blade drag losses. More than three

blades increases the cost of the turbine with no significant gain in efficiency, therefore 3

blades is the optimum number [15, 17].

2.2.1 Wind Turbine Generator Technologies

A generator is a device which converts mechanical energy into electrical energy. It uses

magnetic fields to convert the rotational energy into electrical energy. The power output

goes to a transformer, which converts the power from the generator at around 700V

to the appropriate voltage for the power collection system [15, 18]. Asynchronous or

11


induction generators are mostly used in wind turbines as they can be operated at variable

speed unlike synchronous generator. Induction generators are also very common in small

renewable energy power plants because they are widely and commercially available and

inexpensive [19].

Two kinds of induction generators are used in wind turbines namely:

i. Squirrel cage induction generators (SCIGs)

ii. Doubly fed induction generator (DFIGs).

Squirrel Cage Induction Generators

In this technology the generators are directly coupled to the grid as shown in Figure 2.4

below.

Figure 2.4: Grid connected Squirrel cage induction generator.

The slip, and hence the rotor speed of a squirrel cage induction generator varies with

the amount of power generated. These rotor speed variations are, however, very small,

12


approximately 1 to 2 per cent. Therefore, this wind turbine type is normally referred to

as a constant speed or fixed speed turbine. A squirrel cage induction generator always

consumes reactive power during a system fault. In most cases, this is undesirable, par-

ticularly in case of large turbines and weak grids. Absorption of reactive power from the

system lowers the system voltage which can eventually lead to voltage collapse. Reactive

power consumption of the squirrel cage induction generator is nearly always partly or

fully compensated by capacitors in order to achieve a power factor close to one [20].

The function of the soft-starter unit is to build up the magnetic flux slowly and so minimize

transient currents during energization of the generator. Also, by applying the network

voltage slowly to the generator, once energized, it brings the drive train slowly to its

operating rotational speed [15].

In modeling an induction generator, a number of conventions are used in this report as

follows [21]:

• The model is written based on a direct and quadrature (dq) reference frame fixed

to a synchronous reference frame as shown in figure 2.5.

• Direct axis is aligned with the rotor’s pole. Quadrature axis refers to the axis whose

electrical angle is orthogonal to the electric angle of direct axis.

• The d -axis and q-axis are chosen as the real and imaginary parts of the complex

quantities, respectively.

• The stator current is assumed to be positive when it flows into the generator. This

13


Figure 2.5: dq theory of machines

convention is normally adopted in a motor model rather than in a generator model.

This convention is preferred because, in most literature, an induction machine exists

as a motor rather than as a generator. Hence, representation of the model using a

motor convention is used because of familiarity.

• All parameters are given in per unit quantities.

The following assumptions are also applicable: First, no zero-sequence current is present.

Second, the generator parameters in each phase are symmetrical and the windings are

assumed to be equivalent sinusoidally distributed windings. Air-gap harmonics are there-

fore neglected.

The detailed model of an induction generator involves both stator and rotor dynamics.

This model is also referred to as the fifth-order induction generator model, since it consists

of five derivatives, i.e. four electrical derivatives and one mechanical derivative.

14


The equivalent circuit of SCIG dynamic model is shown in Figure 2.6

Figure 2.6: Equivalent circuit of squirrel cage induction generator dynamic model.

Stator and rotor voltage equations can be written as follows

us = Rsis + jωeψs +dψs

dt(2.3)

ur = 0 = Rrir + j(ωe − ωr)ψr +dψr

dt(2.4)

where u, i, and ψ are space vectors for the voltage, current and flux, respectively. ωsyn is

the synchronous speed, and ωr is the angular speed of the rotor. The subscripts s and r

refer to quantities of the stator and rotor, respectively.

The relation between flux and currents is given by

us = Lsis + Lmir (2.5)

ur = Lrir + Lmis (2.6)

15


where Lm is the magnetizing reactance, Ls and Lr stand for the stator and rotor induc-

tances, respectively. The two parameters are given by

Ls = Lsl + Lm (2.7)

Lr = Lrl + Lm (2.8)

where Lsl and Llr are the stator and rotor leakage inductances, respectively.

The electromagnetic torque produced by the generator can be calculated as a cross product

of flux and current vectors

Te = ψ∗s × is (2.9)

This is equivalent to

Te = =[ψ∗s is] (2.10)

The complex power of the stator is given by

S = u∗sis (2.11)

The dynamic model of the induction generator is completed by the mechanical equation:

Jω̇r = Te − Tm (2.12)

where J is generator inertia, Te is the electrical torque, Tm is the mechanical torque.

Doubly-fed induction generator (DFIG)

Among wind turbine concepts, a DFIG wind turbine is the most dominant concept in the

market. Doubly fed induction generators have windings on stator and rotor where both

16


of the windings transfer significant power between the shaft and the electrical system

[22, 23]. Table 2.1 shows examples of DFIG wind turbine products from a number of

manufacturers. DFIG based wind turbines are designed to avoid disconnection from the

Table 2.1: Wind turbine products with DFIG concept.

Manufacturer Power (MW) Nom wind

(m/s)

Turb Speed

(rpm)

Gear Ratio

Nordex N100/2500 kW 2.5 12.5 9.6 - 14.9 1:77.4

Nordex N80/2500 kW 2.5 15 10.9 - 19.1 1:68

Vestas V80-2.0, 50 Hz 2.0 15 9.0 - 20.7 1:92.6

GE 1.5sle, 60 Hz 1.5 14 12.1 - 22.2 1:72

grid during a fault. This provides them with the so-called fault ride-through capability.

The grid code of most countries requires wind generators to stay connected in the case of

network faults (Low voltage ride-through capability (LVRT) or fault ride through capa-

bility (FRT)). It is of particular importance to transmission system operators, that wind

farms stay connected in case of faults at major transmission levels leading to a voltage

depression in a wide area, which could lead to a major loss of wind generation if wind

farms were not equipped with LVRT capability. Therefore, LVRT capability is a definite

requirement for all larger wind farms [24, 25].

Components of a DFIG wind turbine are shown in Figure 2.7 The stator of the DFIG

is connected directly to the network meaning that it operates synchronously at grid fre-

17


quency [21, 26]. A model of a DFIG wind turbine basically consists of a generator and

Figure 2.7: Components of DFIG wind turbine.

drive train, a turbine rotor model, a grid-side converter and dc-link capacitor, a pitch

controller, and a rotor-side controller that controls the active and reactive power of the

generator. The stator and rotor are usually coated in a closed assembly to protect the

machine from dust, damp and other unwanted intrusions. In wind turbine applications,

this generator is mounted in the nacelle of the wind turbine system. The typical stator

voltages for the megawatt-class of wind turbines are 690 and 960 V [27].

The stator and rotor voltage equations can be written as

us = Rsis + jωeψs +dψs

dt(2.13)

ur = 0 = Rrir + j(ωe − ωr)ψr +dψr

dt(2.14)

18


The complex power is given as

S = u∗sis + u∗rir (2.15)

Similar to the SCIG based wind turbine, the generator model of a DFIG wind turbine

can be represented in detail using a fifth-order model [21].

Drive-train

The generator in a DFIG wind turbine is driven by the wind turbine through a gearbox

system to attain a suitable speed range for the rotor. By means of the gearbox, the low

rotational speed of the wind turbine (9-21 rpm) is transformed into high rotational speed

on the generator side (900-2000 rpm for 50 Hz system frequency). For 2- 3 MW wind

turbines, a gearbox ratio of around 80-100 is common. The actual gearbox ratio is chosen

considering the optimum operation speed of the generator. The optimum speed of the

generator is selected based on two factors, namely the annual wind speed distribution and

the size of the power converter. annual effciency of the generator is somehow influenced

by the operating speed of the generator, whether it operates at sub-synchronous or super-

synchronous speed. Another aspect to be considered when selecting a gearbox ratio is the

weight of the gearbox, which increases along with gearbox ratio [28, 21].

Power converter

As shown in Figure 2.8, the power converter is made up of a back-to-back converter

connecting the rotor circuit and the grid. The rotor-side converter can be seen as a

19


current controlled voltage source converter. The rotor current can be controlled in several

ways. The most common method is by utilizing a PWM modulation.

Figure 2.8: Power converter in DFIG wind turbine.

The objective of the grid side converter (GSC) is to maintain the voltage at the DC link

between both power converters [29]. The converter can also be utilized to support grid

reactive power during a fault [30]. The grid-side converter can also be used to enhance

grid power quality. However, these abilities are seldom utilized since they require a larger

converter rating [21].

Crawbar

A rotor crowbar is designed to bypass the rotor-side converter, i.e. to short-circuit the

20

2.3. POWER SYSTEM EXCITATION CONTROL

rotor, in order to avoid overcurrent on the rotor-side converter as well as overvoltage on

the dc-link capacitor. The crowbar consists of thyristor(s) with an external resistance

insertion. As shown in Figure 2.9, the crowbar can be constructed by placing two pairs

of anti-parallel thyristors between the phases or by using a combination of a diode bridge

and a single thyristor [21].

Figure 2.9: Crowbar arrangements.

2.3 Power System Excitation Control

In most power systems around the world, generation is mainly dominated by synchronous

generators. The main reason for this fact is its good controlling capabilities, high ratings

and a low inrush current [9, 31]. All power systems rely mainly on control systems so

as to ensure that their operation is secure. These controls include the speed governing

and voltage regulation systems at all the major generating stations. The main purpose

of these control systems is to act on dynamical changes in the system, such as in load,

in order to minimize voltage and frequency changes. Thus their operation is essential in

ensuring quality of supply by the power system. The governors for instance adjust the

21


flow of the prime mover to a turbine which drives the generator, in order to keep rotor

speed constant [32]. The excitation control of a generator generally helps in improving

the performance of a system by supporting the voltage, enhancing transient stability and

damping oscillations [33].

2.3.1 Automatic Voltage Regulator (AVR)

The automatic voltage regulator (AVR) systems adjust the field current excitation in the

generator, in order to keep terminal voltage constant. The main function of the AVR is

to keep the generator voltage at fixed nominal value. An AVR model depends on the type

of DC current injection source to the excitation system. An important part of the AVR

consists of amplifiers, exciter, excitation voltage limiters, generators, and transducers [34].

The AVR transfer function can be written as in Equation 2.16.

VR(s)

VC(s)=

KA

1 + sTA(2.16)

where VR(s), VC(s), KA and TA are the output amplifier, control signal, amplifier gain

and time constant interval, respectively. These parameters have values ranging between

10 - 400 pu and 0.02 - 0.1 sec for the KA and TA, respectively. Excitation system voltage

is limited by using a limiter to avoid over excitation or under excitation. Linear model of

AVR in exitation system is shown in Figure 2.10.

22


Figure 2.10: Model of AVR in excitation system.

2.3.2 Power System Stabilizer (PSS)

The basic function of a PSS is to add damping to generator rotor oscillations by controlling

its excitation using auxiliary stabilizing signal. To provide damping, the stabilizer must

produce a component of electrical torque in phase with rotor speed deviation. However,

for the practical implementation, other signals such as bus frequency, electrical power,

accelerating power are also used. The latter signal is actually synthesized by a combination

of electrical and mechanical power signals. The mechanical power signal can be obtained

from the gate position in a hydraulic turbine or steam pressers in steam turbine. The

choice of control signal for PSS can be based on the following criteria;

i. The signal must be obtained from local measurements and easily synthesized.

ii. The noise content of the signal must be minimal. Otherwise complicated filters are

required which can introduce their own problems.

iii. The PSS design based on a particular signal must be robust and reject noise. This

implies that lead compensation must be kept to a minimum to avoid amplifying the

noise [35].

23


All the control signals considered such as rotor speed, frequency, electrical power are lo-

cally available. The speed signal can be obtained from a transducer using a tooth wheel

mounted on the shaft

citemahiraj. Alternately it can be obtained from the angle of the internal voltage which

can be synthesized. The bus frequency signal can be obtained by measuring the period

using zero crossing detection. The power signal can be derived from a Hall Effect trans-

ducer. PSS representation (shown in Figure 2.11) consists of five blocks: gain, a signal

washout block, phase compensation block, torsional filter and limiter.

Figure 2.11: Model of PSS in excitation system.

The stabilizer gain KSTAB determines the amount of damping introduced by the PSS. The

signal washout block serves as a high pass filter with the time constant TW high enough

to allow signals associated with oscillation in ωr to pass unchanged. Without it, steady

changes in speed would modify the terminal voltage. The value of TW is not critical can

be in the range of 1 to 20 sec. The phase compensation block provides the appropriate

phase lead characteristics to compensate for the phase lag between the exciter input and

the generator electrical torque. The torsional filter in the PSS is essentially a band reject

or a low pass filter to attenuate the first torsional mode frequency [35].

24

2.4. RECENT RELATED STUDIES

When wind power integrated system is at run, the stability of system will deteriorate,

especially after the system suffers from great disturbance, rotor angle difference between

synchronous generators will be a process with oscillation properties [36].

2.4 Recent Related Studies

Several researchers have carried out work related to this topic of research.

Sujith and Ashwani 2011 [37] addressed the transient stability analysis of a power

system with wind generation. The effects of automatic voltage regulators, power system

stabilizers, and static synchronous compensators on transient stability of a power sys-

tem were investigated. The transient stability was determined when a wind farm was

integrated in the power system. The wind penetration into the system caused deteriora-

tion in stability depending on the penetration level into the system. They showed that

the stability of the wind integrated system during the network disturbance under normal

condition is improved by incorporating a PSS with the exciter circuit of the alternator,

which damps out oscillations and hence stabilizing the system. The installation of a shunt

device like STATCOM was seen to further enhance the stability by reducing the settling

time of oscillations both in voltage and rotor angle.

Lin Xu [38] presented coordinated control for SVC and PSS for power swing damping

for the multimachine power system. They simulated the system using MATLAB and the

effect of PSS and SVC on the dynamic response of the system under single-phase and

three-phase faults were simulated. The simulation results revealed that the coordinated

25


control of the SVC and the generic/multi-band PSS is an effective solution to damp low

frequency oscillation for multimachine power system and enhance global stability of the

large inter-connected power system.

Wei Qiao and Ronald G. Harley [39] investigated the effect of grid-connected wind

turbines driving doubly-fed induction generators (DFIGs) on the transient stability of

power systems. Simulation studies were carried out to demonstrate and compare the

transient performance of the IEEE 10-machine 39-bus system. In the first case simulation

was carried out with 9% wind power and secondly without wind power integration during

a severe grid fault. It was shown that by using an uninterrupted operation scheme and

the fast control of the DFIG converters, the DFIG wind turbines can successfully ride

through grid faults and have no problem of angular stability associated with the conven-

tional synchronous generators. They concluded that DFIG wind turbines brings some

operation and control benefits on power system transient performance and stability. This

is because results showed that better transient performance and stability are achieved by

the 39-bus system with wind power integration.

Ch. Eping et al [40] focuses on transient stability issues and analyses the impact of

various aspects like location of wind generators, connection points, distributed generation

etc. separately for getting a thorough understanding about the impact of these aspects on

transient stability. The location of wind generators was found to have a very large impact

on transient stability. Especially when high wind resources are located in one particu-

lar area leading to highly modified power flows including increased tie-line flows, critical

26


fault clearing times can be considerably reduced. The actual generator technology has a

considerable impact on transient stability. In this study, variable speed wind generators

(DFIGs) with 12% penetration was analyzed. It was shown that this technology is able to

improve transient stability margins, when being equipped with low voltage ride-through

capability, reactive current boosting and ideally with fast voltage control. In terms of

connection point it was shown that integration of wind generation into subtransmission

and distribution systems has a negative impact on transient stability, because the reactive

contribution is highly limited due to reactive losses in subtransmission and distribution

systems. They concluded that, in actual cases, there will always be a superposition of

the above mentioned aspects, including a variety of generator types and voltage levels to

which wind generators are connected. So there is no general statement possible, if wind

generation improves transient stability margins or if the impact is rather negative. The

answer depends on system properties, location of wind resources and generator technolo-

gies and the problem has to be analyzed individually for each case.

M. El-Sayed and Effat Moussa [41] investigated the effects of wind farms of different

sizes on the Egyptian power system. The wind farm is aggregated into minimal set of

equivalent wind generator models combining all turbines with the same mechanical nature

frequency into single equivalent turbine. Power system dynamics simulation software is

used to study the impact of increasing wind turbine penetration on system performance.

The study was also carried out considering different contingencies i.e. transmission line

outages, loss of generation units and finally a combination of loss of generation and trans-

27


mission lines. The result shows that the Egyptian system with a total installed capacity

of 20400 MW can withstands wind farms of size up to 900 MW.

Clemens Jauch et al [42] looked at the effect of wind power on the transient fault

behavior of the Nordic power system. The Nordic power system is the interconnected

power system of the countries Norway, Sweden, Finland and Denmark.

The simulations yield information on:

i. How the faults impact on the wind turbines

ii. How the response of the wind turbines influences the post-fault behavior of the Nordic

power system.

Their conclusion was that an increasing level of wind power penetration leads to stronger

system oscillations in case of fixed speed wind turbines. It was found that fixed speed

wind turbines that merely ride through transient faults have negative impacts on the

dynamic response of the system.

28

Chapter 3

Methodology

This chapter addresses how the research was carried out step by step. The IEEE 9 bus

system was used first before applying the same procedure in the Kenyan power system.

All the simulations in this work were performed using power system simulation software

DIgSILENT Powerfactory. This is a computer aided engineering tool for the analysis of

industrial, utility, and commercial electrical power systems. It has been designed as an

advanced integrated and interactive software package dedicated to electrical power system

and control analysis in order to achieve the main objectives of planning and operation

optimization.

3.1 Power System Model Development in DIgSILENT

The first system that was developed in this work was the IEEE 9 bus system. The relevant

data of this test system was used to model it as shown if Figure 3.1. All the data used

29

3.1. POWER SYSTEM MODEL DEVELOPMENT IN DIGSILENT

in modelling this system and the operating conditions are given in the Appendix C as

found in [43]. The synchronous generator G3 which generates about 20% of the system

installed capacity was replaced with a wind farm. The wind farms were based SCIGs

and DFIGs each at a time. The Kenyan Transmission system data was collected. This

Figure 3.1: IEEE standard 9 bus system.

was made up of the Generators data, Transmission lines data, Transformers data and the

excitation data. This data was obtained from the Kenya power Company 2011. Load and

generation projections was updated with the help of the Ministry of Energy’s Updated

Least Cost Development Plan (LCPDP) for the study period 2011 - 2030 [44]. All the

data used to model this system is as appearing in the Appendix. The Schematic diagram

of the Kenyan power system before it was updated is as shown in figure 3.2.

30


Fig

ure

3.2:

Ken

yan

Pow

ersy

stem

one

lin

ed

iagra

m.

31


The Kenyan system was drawn in DIgSILENT power factory, as shown in Figure 3.3.

In this network only the 220 kV buses and 132 kV buses were represented. The green

buses in the diagram represent the 132 kV buses and lines, the red part represent the 220

kV buses and lines, while the blue line represent the 11 kV buses and lines. It is noted

the the blue parts are mostly generation.

The proposed wind farm under consideration in this work consists of 365 individual wind

turbines each rated 850 kW of power with the details as shown in the Appendix. Modelling

all these wind turbines separately was to take a lot of space therefore it was important

to come up with an aggregated model representing an entire wind farm by an equivalent

generator.

An assumption was made that wind speed is constant, all the wind turbines are exposed

to the same wind speed and turbulence level. these assumptions are necessary so as the

wind turbines can be considered to be producing their maximum rated power [45].

To model a SCIG in DIgSILENT, a normal induction generator is used with the type of

prime mover put as wind. Figure 3.4 illustrates the single line diagram of DFIG config-

uration in DIgSILENT. It contains a DFIG built-in model (the usual induction machine

model extended with the PWM rotor side converter), a common DC busbar, an IGBT

grid side converter (independent component in DIgSILENTs library) and an inductor in

series with the grid converter, used to smooth the converter currents. This inductor may

also be integrated into the transformer [46].

32


Fig

ure

3.3:

Ken

yan

Pow

erS

yst

emT

ran

smis

sion

Net

wor

k

33


Figure 3.4: Single line diagram of the DFIG in DIgSILENT

It contains a DFIG built-in model (the usual induction machine model extended with

the PWM rotor side converter), a common DC busbar, an IGBT grid side converter

(independent component in DIgSILENTs library) and an inductor in series with the grid

converter, used to smooth the converter currents. This inductor may also be integrated

into the transformer [46].

After the wind farm has been modelled it was then integrated into the Kenyan Power

system in Figure 3.3. The output voltage from LTWP was stepped up to 400 kV. The

wind farm was connected at Suswa substation which is 428 Km away from it. It was

achieved by the aid of a 400 kV AC transmission line. At Suswa substation the voltage

34

3.2. COMPARISON OF SCIG AND DFIG WIND GENERATION TECHNOLOGIES

is stepped down to 220 kV and then this finally transmitted to Nairobi’s 220 kV ring.

Figure. 3.5 shows the Kenyan power system after the introduction of LTWP and how

this power is finally integrated into the system.

3.2 Comparison of SCIG and DFIG wind generation

technologies

The two main technologies available in wind power generation which are the squirrel

cage induction generator (SCIG) and the double fed induction generator (DFIG) were

compared in this section. The IEEE 9 bus system was used first. Here generator G3

was replaced by a wind farm based on the two technologies each at a time as mentioned

earlier. A three phase short circuit was applied on the transmission line connecting buses

7 and 8 at 5% distance from bus 7. The fault was cleared by tripping the line 7-8 at both

ends before the system reached the critical clearing time. Transient stability analysis on

each of the above generator technologies was carried out separately. G2 was the nearest

to the fault location and therefore was the most affected. For this reason we considered its

post fault behavior in this analysis. At this point the excitation control was not included

in the system. The post fault behaviour of this system was recorded for rotor angle of

generator G2. In the case of the Kenyan power system to find out which of the two

commonly available wind generators will be more appropriate in this case, The wind farm

was modelled with both the SCIG and DFIG technologies. To analyse and compare the

35


impact of the two wind farms separately, the transient response of the system to a fault

with each technology was monitored separately. An assumption was made that wind

speed is constant and the wind turbines were producing their maximum rated power. It

is also assumed that all the 300 MW is injected at once. A three phase short circuit was

applied on the Dandora to Nairobi North 220 KV transmission line at 50% distance. The

fault was was introduced after 2 seconds c cleared after 2.01 seconds by tripping the line

at both ends. This was repeated in both cases (i.e. with each technology). Analysis on

each of the above cases was carried out separately. The post fault behaviour of different

generators in the system was observed considering different parameters such as the active

power, reactive power, terminal voltage and the rotor angle of these generators.

The action of the excitation control was initially not considered. After the simulations

were carried out without the excitation control and the results recorded the technology

that gave a better transient response was established. three phase short circuit was applied

on the Dandora to Nairobi

36


Fig

ure

3.5:

Ken

yan

Pow

erS

yst

emT

ran

smis

sion

Net

wor

kw

ith

DF

IGb

ased

Win

dfa

rm

37

3.3. ANALYSIS WITH THE INCLUSION OF THE AVR AND PSS

3.3 Analysis with the inclusion of the AVR and PSS

The excitation control comprising of the automatic voltage regulator (AVR) and the power

system stabilizer (PSS) was then included in the system and further simulation carried

so as to ascertain their effect. The type of AVR used was the EEET1 whose parameter

setting is provided in table 3.1.

Table 3.1: IEEET1 AVR setting

Parameter Setting

Tr, Measurement Delay (s) 0.02

Ka, Controller Gain (pu) 150

Ta, Controller Time Constant (s) 0.03

Ke, Exciter Constant (pu) 0.2

Kf, Stabilization Path Gain (pu) 0.05

Tf, Stabilization Path Time Constant (s) 1.5

E1, Saturation Factor 1 (pu) 3.9

Se1, Saturation Factor 2 (pu) 0.1

E2, Saturation Factor 3 (pu) 5.2

Se1, Saturation Factor 4 (pu) 0.5

Vmin, Controller Minimum output (pu) -10

Vmax, Controller Maximum output (pu) 10

38

3.3. ANALYSIS WITH THE INCLUSION OF THE AVR AND PSS

IEEEST PSS was used whose parameter setting are provided in table 3.2.

Table 3.2: IEEEST PSS setting

Parameter Setting

Ics, Input Selector (1-6) 2

T2, Lead-lag 2nd Delay Time Constant (s) 0.3

T1, Lead-lag 1st Derivative Time Constant (s) 0.05

T4, Lead-lag 4th Delay Time Constant (s) 1

T3, Lead-lag 3rd Derivative Time Constant (s) 1.3

Ks, Stabilizer Gain (pu) 130

T5, Stabilizer Derivative Time Constant (s) 1

T6, Stabilizer Time Constant (s) 1

A1, Filter 1st Time Constant (s) 0

A2, Filter 2nd Time Constant (s) 0

A3, Filter 3rd Time Constant (s) 0.5

A4, Filter 4th Time Constant (s) 1



Kd, Derivator Factor (pu) 1

Lsmin, Controller Minimum output (pu) -1

Vcl, Controller Minimum Limit (pu) 0.8

39

Chapter 4

Results and Discussions

The transient stability analysis with wind power generation was carried out using two

power systems.

• IEEE 9 bus system

• Kenyan power system

In this chapter the simulated results of these two systems are presented. First the results

comparing presence and absence of wind power in the case of the Kenyan power system

is shown. Secondly the comparison of the two wind generation technologies (SCIG and

DFIG) is presented and finally the effect of the excitation control (AVR and PSS) is

considered.

To investigate the impact of wind from the LTWP on the Kenyan power system, the

transient response of the system to a fault before and after the integration of the wind

farm was considered. In order to ensure that the operating conditions are the same in

40

both cases a conventional synchronous generator of equal capacity as the wind farm was

included in the absence of the power from wind.

An assumption was made that wind speed is constant and the wind turbines were pro-

ducing their maximum rated power. It is also assumed that all the 300 MW is injected at

once. The main fucus of this work is to look at the effect of increased power from wind as

compared to conventional power generation technologies. For the comparison to be more

realistic then these assumptions must be made

A three phase short circuit was applied on the Dandora to Nairobi North 220 kV trans-

mission line at 50% distance. The fault was cleared by tripping the line at both ends. This

was repeated in both cases (i.e. with and without the wind farm integration). Analysis

on each of the above cases was carried out separately.

The choice of this line was influenced by the fact that it is one of the major lines in the

system as the Dandora Bus is directly linked to the largest generating stations. However

similar results were obtained even if the fault location was changed. The post fault be-

haviour of different generators in the system was observed. This was achieved by looking

at different parameters such as the active power, reactive power, terminal voltage and the

rotor angle of these generators.

41

4.1. TRANSIENT STABILITY ANALYSIS WITH AND WITHOUT THE WIND

FARM

4.1 Transient Stability Analysis With and Without

the Wind Farm

The simulations were carried out initially without the wind farm. The excitation control

was also not modelled here. Though four parameters were monitored, for the purposes

this thesis the active power and rotor angle after a fault of the Gitaru and Kamburu

generation stations were considered as shown in figures below. These generators are the

closest to the point where the fault was introduced and hence expected to be most affected

by the fault.

Figure 4.1: Active power response before and after LTWP wind farm at Gitaru.

42

4.1. TRANSIENT STABILITY ANALYSIS WITH AND WITHOUT THE WIND

FARM

Figure 4.2: Rotor Angle before and after inclusion of LTWP wind farm at Gitaru.

Figure 4.3: Active power before and after inclusion of LTWP wind farm at Kamburu.

The other two parameters i.e. reactive power and terminal voltage behaves similar to

the active power and the rotor angle even when different generators in the system are

considered.

43

4.2. COMPARISON OF THE EFFECT OF SCIG AND DFIG

Figure 4.4: Rotor Angle before and after inclusion of LTWP wind farm at Kamburu.

From the results shown in Figures 4.1-4.4 it can be seen that with the inclusion of wind

power from LWTP, both the Active power and the Rotor angle takes a longer time to

settle after the fault. This is when compared to the case where the wind farm is replaced

with an equivalent synchronous generator.

4.2 Comparison of the effect of SCIG and DFIG

This comparison was initially carried out using IEEE 9 bus system where the synchronous

generator G3 in the 9-bus system (Figure 3.1) was replaced by a wind farm based on squir-

rel cage induction generators (SCIG) and then the doubly fed induction generator (DFIG)

technology each at a time. The wind farms were made to generate the same amount of

power as that generated by the synchronous generator G3 with the same operating con-

ditions. Figure 4.5 compares the the rotor angle response of Generator G2 when the two

44


wind technologies are used each at a time. As can be seen from Figure 4.5 it takes longer

for the system which is integrated with the SCIG based wind farm to settle as compared

to the one with the DFIG based wind farm. This is therefore a clear indication that the

double fed induction generator (DFIG) based wind farm has a better transient response

as compared to that of a similar SCIG.

Figure 4.5: G2 Rotor angle with no excitation control.

Considering the Kenyan power system simulations were carried out to compare the tran-

sient response of the active power, reactive power, rotor angle and voltage magnitude

with the LTWP wind farm for the two technologies i.e DFIG and SCIG. Here the active

power, reactive power, rotor angle and voltage magnitude after a fault of the Gitaru and

Turkwell generation stations were considered.

Figures 4.6 and 4.7 compares the active power response of Gitaru and Turkwell power

stations respectively. It takes about 16 seconds and 14 seconds for the active power to

45


Figure 4.6: Active power of Gitaru Station for both wind farms.

Figure 4.7: Active power of Turkwell Station for both wind farms.

settle in Gitaru and Turkwel respectively in the case of DFIG wind farm by the end of

30 seconds the system will not have fully settled from the effect of the fault.

46


Figures 4.8 and 4.9 are looking at the reactive power of the two power stations whereby

Figure 4.8: Reactive power of Gitaru station for both wind farms.

it can be seen than in the case of DFIG wind power integration Gitaru power station

will have settled at about 12 seconds while it takes up to about 23 seconds for the same

station to regain its normal operation when the system has SCIG based wind farm. On

the same figure i.e Figure 4.9, it was observed that by the 16th second the reactive power

in Turkwel had settled considering DFIG wind farm whereas in the case of SCIG the full

settling occurs at about the 22nd second.

47


Figure 4.9: Reactive power of Turkwell station for both wind farms.

It can be seen from Figures 4.10 and 4.11 that it the time it takes for the rotor angle

response for the two power station is significantly reduced in the case of DFIG wind farm

as compared to the SCIG based wind farm.

From Figures 4.12 and 4.13, it can be seen that with the inclusion of wind power from SCIG

based wind farm, the voltage magnitude takes a longer time to settle after the fault. This

is when compared to the case where the wind farm is replaced with an equivalent DFIG

technology. Similar results were also obtained when different generators were considered.

48


Figure 4.10: Rotor angle of Gitaru station for both wind farms.

Figure 4.11: Rotor angle of Turkwell for both wind farms.

49


Figure 4.12: Voltage response of Gitaru for both wind farms.

Figure 4.13: Voltage response of Turkwell for both wind farms.

50

4.3. TRANSIENT STABILITY ANALYSIS WITH THE EXCITATION CONTROL

4.3 Transient Stability Analysis With the Excitation

Control

The excitation controllers(the AVR and PSS) were modelled in all the generators in the

system. The type and the settings of these two controllers are given in Tables 3.1 and

3.2. The controllers were included with the DFIG based wind farm present in the system.

The active power response as observed at the Gitaru power stations is as shown by Figure

4.14.

Figure 4.14: Active power of Gitaru Station with AVR and PSS.

51

4.3. TRANSIENT STABILITY ANALYSIS WITH THE EXCITATION CONTROL

The active power response at Kipevu I station with the DFIG based wind farm is as

shown by Figure 4.15.

Figure 4.15: Active power of Kipevu I Station with AVR and PSS.

In both figures 4.14 and 4.15 it can be clearly seen that the inclusion of the AVR and

PSS further improves the system transient stability. This is because the excitation control

introduces more damping. This enables the system to achieve the steady state operating

condition in about 6 seconds in both cases

52

Chapter 5

Conclusions and Recommendations

This chapter gives the closing remarks of the whole thesis giving a final opinion arrived

at after the simulation results have been analyzed. It also shows some areas that can be

considered by future researchers.

5.1 Conclusion

This thesis investigated an efficient method of analyzing the impact of 300 MW wind power

from the proposed Lake Turkana Wind Power Project (LTWP) on transient stability

performance of the Kenyan power system. The performance of the system without wind

power was studied first. This performance was compared with the case when the wind

power is present considering the two main generator technologies (SCIG and DFIG). From

the simulations carried out it can be seen that the transient stability of the Kenyan power

system will be impacted negatively with the SCIG wind farm. The DFIG based wind

53

5.2. RECOMMENDATIONS

farm is seen to least impact the system. The conclusion was arrived at after considering

the settling time of different parameters after a system fault. With the DFIG being

seen as a better option, the operation of the system can be further enhanced if the

excitation controllers (AVR and PSS) is modelled and included in the system. The findings

in this thesis provide useful information for the power system planning, especially the

stakeholders in the Kenyan power system who are considering integrating this large wind

farm into the system.

5.2 Recommendations

Power system stability studies deal with a large number of different aspects and the com-

plexity is increased when combined with wind power studies. The scope of this thesis

concentrated on analyzing the transient stability of a system with large scale wind inte-

gration. It is therefore necessary for studies to be carried out to investigate the impact

of Wind power generation on other aspects of stability such as Voltage and Frequency

stability of the Kenyan Power system.

54

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61

Appendix A

Kenya Power System Data

Table A.1: Branches Data

LINE VOLT R X RATING LENGTH

FROM TO (V) (PU) (PU) (MVA) (KM)

DANDORA EMBAKASI 220 0.0026 0.0108 250 12.5

DANDORA KAMBURU 220 0.0164 0.0977 230 109.5

DANDORA NRB NORTH 220 0.0072 0.0448 150 46.3

DANDORA JUJA RD 132 0.0008 0.0048 150 3

EMBAKASI KIAMBERE 220 0.0293 0.0568 250 140

JUJA RD KINDARM 132 0.1229 0.2868 73 119

JUJA RD SULTAN 132 0.1291 0.3069 81 125

Continued on next page

62

Table A.1 – Continued from previous page

LINE VOLT RATING LENGTH

FROM TO (kV) R(PU) X(PU) (MVA) (KM)

JUJA RD RUARAKA 132 0.0058 0.012 81 1

KAMBURU GITARU 220 0.0014 0.008 250 9

KAMBURU KIAMBERE 220 0.0074 0.0302 210 35

MASINGA KIGANJO 132 0.1061 0.2142 101 88.5

KIAMBERE RABAI I 220 0.0925 0.3752 210 440

RABAI 2 KIPEV I 132 0.0204 0.0411 73 18

RABAI 2 KIPEV II 132 0.0176 0.0411 73 18

RABAI 2 KIPEV III 132 0.0176 0.0411 73 18

RABAI 2 BAMBURI 132 0.0393 0.08 81 33

BAMBURI KILIFI 132 0.0544 0.1099 81 48.6

KIGANJO NANYUKI 132 0.0617 0.1246 81 51.5

NAIVASHA LANET 132 0.0803 0.1626 81 67

NAIVASHA RUARAKA 132 0.0828 0.1725 81 71.5

LANET LESSOS 1 132 0.137 0.3043 81 126

LESSOS 1 ELDORET 132 0.0385 0.0779 81 32.1

LESSOS 1 MUSANGA 132 0.0785 0.1601 81 66

LESSOS 1 MUHORONI 132 0.068 0.1376 81 56.7


63


LINE VOLT RATING LENGTH

FROM TO (kV) R(PU) X(PU) (MVA) (KM)

LESSOS 2 TURKWEL 220 0.0479 0.1965 250 228

N NORTH OLKARIA 2 220 0.0103 0.615 250 66.3

OLKARIA 1 OLKARIA 2 132 0.0125 0.0075 150 3

OLKARIA 1 NAIVASHA 132 0.0097 0.0559 150 23.1

OLKARIA 2 OLKARIA 3 220 0.0119 0.0071 250 8

MUSAGA MUMIAS 132 0.0142 0.0846 81 30

MUSAGA WEBUYE 132 0.0216 0.0437 81 18

MUSAGA TORORO 132 0.0845 0.171 81 70.5

MUHORONI KISUMU 132 0.05816 0.1177 81 48.5

KISUMU SONDU 132 0.068 0.1376 81 50

Table A.2: Bus Data

BUS NAME NOMINAL VOLTAGE (kV) BUS TYPE

DANDORA 220 SWITCHING

DANDORA 132 SWITCHING


64



EMBAKASI 220 SWITCHING

JUJA ROAD 132 SWITCHING

KAMBURU 220 SWITCHING

KAMBURU 132 SWITCHING

GITARU 220 SWITCHING

GITARU 132 SWITCHING

MASINGA 132 SWITCHING

KINDARUMA 132 SWITCHING

KIAMBERE 220 SWITCHING

RABAI 1 220 SWITCHING

RABAI 2 132 SWITCHING

KIPEVU 1 132 SWITCHING



KIGANJO 132 SWITCHING

NANYUKI 132 SWITCHING

NAIVASHA 132 SWITCHING

LANET 132 SWITCHING


65



LESSOS 1 132 SWITCHING

LESSOS 2 220 SWITCHING

TURKWEL 220 SWITCHING

TURKWEL 11 GENERATOR

KIAMBERE 11 GENERATOR

KAMBURU 11 GENERATOR

N NORTH 220 SWITCHING

OLKARIA 1 132 SWITCHING



OLKARIA 1 11 GENERATOR



ELDORET 132 LOAD

MUSAGA 132 SWITCHING

MUMIAS 132 SWITCHING

MUMIAS 11 GENERATOR

WEBUYE 132 SWITCHING


66



MUHORONI 132 SWITCHING

KISUMU 132 LOAD

SONDU 132 SWITCHING

SONDU 11 GENERATOR

GITARU 11 GENERATOR

KINDARUMA 11 GENERATOR

MASINGA 11 GENERATOR

RABAI 11 GENERATOR

KIPEVU 1 11 GENERATOR



BAMBURI 11 GENERATOR

IBERAFRICA 11 GENERATOR

TANA 11 GENERATOR

RUARAKA 132 SWITCHING

67

Table A.3: Transformer Data

TRANSFORMER TRANSFORMATION WINDING RATING

NAME RATIO CONNECTION (MVA)

DANDORA 220/132 2 WINDING 200

KAMBURU 220/132 2 WINDING 270

KAMBURU 132/11 2 WINDING 37.5

GITARU 220/11 2 WINDING 95

MASINGA 132/11 2 WINDING 25

KINDARUMA 132/11 2 WINDING 23.5

KIAMBERE 220/11 2 WINDING 87.5

RABAI 220/132 2 WINDING 90

RABAI 132/11 2 WINDING 100

KIPEVU 1 132/11 2 WINDING 45



LESSOS 220/132 2 WINDING 75

TURKWEL 220/11 2 WINDING 62.5

OLKARIA 1 132/11 2 WINDING 90



68


TRANSFORMER TRANSFORMATION WINDING RATING

NAME RATIO CONNECTION (MVA)


OLKARIA 2-3 220/132 2 WINDING 80

MUMIAS 132/11 2 WINDING 40

SONDU 132/11 2 WINDING 40

Table A.4: Machine Data

GEN MVA PF T’do T”do T’qo T”qo H xd xq x’d x’q x”d xl

OLK 1 56 .9 6 .05 1 .05 4 2 1.9 .21 .6 .12 .1

OLK 2 42 .8 7.8 .05 1 .05 4 2 1.9 .21 .6 .12 .1

RABAI 90 .85 9.1 .05 1 .05 2.5 1.8 1.9 .3 .6 .13 .12

KPV 1 54 .8 4.7 .05 1 .05 2 2 2 .3 .6 .16 .14

KPV 2 36 .8 4.8 .05 1 .05 2 1.9 1.8 .27 .6 .13 .12

KPV 3 72 .8 9.1 .05 1 .05 2 1.9 1.8 .27 .6 .13 .12

MUMS 38 .8 5 .04 1.2 .07 2 1.8 1.8 .3 .6 .2 .12

KAMBR 38 .85 5 .06 .06 3 .97 .57 .3 .18 .17 .12


69


GEN MVA PF T’do T”do T’qo T”qo H xd xq x’d x’q x”d xl

TUKWL 62 .85 7.6 .13 .06 3 1.6 .94 .27 .18 .14 .1

KIAMB 85 .85 6.6 .13 .13 3 2 1.1 .66 .25 .17 .1

SONDU 80 .8 6.6 .13 .13 3 .97 1.1 .66 .25 .17 .1

GTARU 85 .85 9.2 .06 .06 3 1.11 .73 .22 .3 .13 .1

KINDA 47 .85 5 .06 .06 3 .9 .9 .27 .4 .14 .12

MSNGA 47 .85 4.1 .06 .06 3 .9 .65 .25 .4 .17 .1

TANA 30 .85 5 .13 .06 3 .96 .67 .23 .18 .18 .1

Table A.5: AVR Data

GEN TR TB TC KA TA VR VR TE KF TF KC KD KE E1 E2

max min

OLK 1 .015 9 3 400 .1 5 -.2 .5 .05 1 .2 .5 1 5.5 7.5

OLK 2 .015 9 3 400 .1 5 -.2 .5 .05 1 .2 .5 1 5.5 7.5

RABAI .015 9 3 400 .1 5 -.2 .5 .05 1 .2 .5 1 5.5 7.5

KPV 1 .015 9 3 400 .1 5 -.2 .5 .05 1 .2 .5 1 5.5 7.5

KPV 2 .015 9 3 400 .1 5 -.2 .5 .05 1 .2 .5 1 5.5 7.5


70


GEN TR TB TC KA TA VR VR TE KF TF KC KD KE E1 E2

max min

KPV 3 .015 9 3 400 .1 5 -.2 .5 .05 1 .2 .5 1 5.5 7.5

MUMS .015 9 3 400 .1 5 -.2 .5 .05 1 .2 .5 1 5.5 7.5

KAMBR .01 0 0 20 .055 3.5 -2.5 .5 .125 -.1 -.1 4.5 6

TUKWL .02 5 1 200 0 .2 -.2 .01 .1

KIAMB .02 5 1 200 0 .2 -.2 .01 .1

SONDU .02 5 1 200 0 .2 -.2 .01 .1

GTARU .02 5 1 200 0 .2 -.2 .01 .1

KINDA .02 5 1 200 0 .2 -.2 .01 .1

MSNGA .02 5 1 200 0 .2 -.2 .01 .1

TANA .01 0 0 20 .055 3.5 -2.5 .5 .125 -.1 -.1 4.5 6

71

Appendix B

Vestas V52-850 kW Wind Turbine

Rated Electrical Data

Power: 850 kW

Generator Type: Asynchronous with wound rotor.

Voltage: 690 V

Frequency: 50 Hz

Number of poles: 4

Generator power factor : 0.98 capacitive to 0.95 inductive

Available reactive power: +172/-279 kVAr

72

Appendix C

IEEE 9 Bus Data

All data in per unit on the bases of 100 MVA and 230 kV.

Table C.1: Generator Data

Generator G1 G2 G3

Xd 0.146 0.8958 1.3125

X ′d 0.0608 0.1198 0.1813

Xq 0.0969 0.8645 1.2578

X ′q 0.0969 0.1969 0.25

Xl 0.0336 0.0521 0.0742

τ ′d0 8.96 6 5.89

τ ′q0 0 0.535 0.6


73

Table C.1 – Continued from previous page

Generator G1 G2 G3

τ”d0 0.015 0.075 0.075

τ”q0 0.024 0.075 0.075

X”d 0.04 0.112 0.12

X”q 0.024 0.112 0.2

Inertia Time Constant(H) 9.5438 6.214 2.3516

Table C.2: Load Data

Bus Load (MW) Load (MVAr)

5 125 50

6 90 30

8 100 35

74

Table C.3: Line Data in p.u

From (Bus) To (Bus) Resistance

(R)

Reactance (X)

1 4 0 0.0576

2 7 0 0.0625

3 9 0 0.0586

4 5 0.01 0.085

4 6 0.017 0.092

5 7 0.032 0161

6 9 0.039 0.17

7 8 0.0085 0.072

8 9 0.0119 0.1008

Table C.4: Operating Conditions

P Q V (p.u)

G1 247.5 0 1

G2 163.2 28.8 1

G3 108.8 19.2 1

SCIG 119.45 8.86 1

DFIG 114 12.654 1

75

Appendix D

Author’s Publications

1. Orenge R. S., Muriithi C. M. and Nyakoe G. N. Comparative analysis of SCIG and

DFIG Based Wind Generation on Transient Stability of the Kenyan Power System,

presented at 2014 Sustainable Research and Innovation Conference held

on 7th -9th May 2014 at AICAD (within the JKUAT Main Campus)

2. Orenge R. S., Muriithi C. M. and Nyakoe G. N., Transient Stability Analysis of

a System with Wind Power Generation in DIgSILENT PowerFactory, to be Pre-

sented at the KSEEE-JSAEM International Conference on September

10th 2014 at Technical University of Mombasa, Mombasa.

3. Orenge R. S., Muriithi C. M. and Nyakoe G. N, Transient Stability Analysis of the

Kenyan Power System With Different Wind Generation Technologies, submitted

to the Journal of Scientific Research in Engineering in June 2014.

76

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