Adaptive protection strategies for detecting power system out-of-step conditions using neural...

Adaptive protection strategies for detecting power system out-of-step conditions using neural networks

A.Y. A bd e I az i z M.R.lrving M.M.Mansour A.M. El-Ara baty A.I.Nosseir

Indexing terms: Neural networks, Protection strategies, Synchronous generators

Abstract: This paper presents new strategies for adaptive out-of-step (OS) protection of synchronous generators based on neural networks. The neural networks architecture adopted, as well as the selection of input features for training the neural networks, is described. A feed forward model of the neural network based on the stochastic backpropagation training algorithm is used to predict the OS condition. Two adaptive OS protection strategies are suggested. The first approach depends firstly on detecting the case of the system through case detection neural networks by some prefault local measurements at the machine to be protected, and then calculating the new OS condition through an adaptive routine. The second approach is based on creating a large neural network to be trained using different outage cases of the power system. The capabilities of the developed adaptive OS prediction algorithms are tested through computer simulation for a typical case study. The results demonstrate the adaptability of the proposed strategies.

List of symbols

cdi case detection decision from case detection neural network i

KE generator kinetic energy deviation at the instant of fault clearing

osi OS decision from OS prediction neural network i Pm generator mechanical input power

ora, generator average acceleration during fault /3 learning constant of a neural network y momentum constant of a neural network 0 IEE, 1998 TEE Proceedings online no. 19981994 Paper first received 15th January 1997 and in revised form 9th January 1998 A.Y. Abdelaziz, M.M. Mansour, A.M. El-Arabaty and A.1. Nosseir are with the Department of Electrical Power and Machines, Faculty of Engmeering, Am Shams University, Egypt M.R. Irving is with the Department of Electrical Engineering and Electronics, Brunel University, UK

1 introduction

The first requirement of reliable service is to keep the synchronous generators running in parallel and with adequate capacity to meet the load demand. Out-of- step (OS) conditions on a power system are caused by an attempt to transfer a given amount of power through an excessive impedance or by deficient voltage levels as a result of fault conditions, automatic or man- ual circuit switching or loss of machine excitation [l].

The principal relation of protective relays to the problem of power system transient stability is in their vital role of clearing faults as rapidly as possible to maintain stability. Therefore, system transient stability conditions strongly influence the strategies on which OS protection relays must operate to provide maximum reliability [2, 31.

Many techniques are introduced for OS protection. Conventional OS distance-type relaying schemes have been used in most utilities so far [4]. A new concept augmenting the measured apparent resistance (R) with its rate of change (R) has been introduced [5]. Another OS relaying concept has been presented in [6], where the relay scheme utilises the direct method of Lyapu- nov to determine when a disturbed system phase plane trajectory leaves the postdisturbed-system region of stability. Recently, artificial intelligence (AI) has been introduced in the OS field. The K-means clustering pattern recognition technique gives good results in detecting OS conditions [7]. The present authors [SI have given another prediction approach based on neural networks. Yabe et al. [9] have utilised the induction and abduction methodologies of AI to develop the logic for a smart relay with early detection of system instabili- ties. Advances in the field of microprocessor technology, and consequential improvements in digital relays and phasor measurement units (PMU) [IO] enable the implementation of sophisticated and more complex relay logic. Due to changes in the power system, such as generator and line outages and changes in load and generation, the performance of the protective relays can vary. To achieve correct operation an adaptive strategy must be introduced to make the relays more attuned to the prevailing power system conditions. Adap t ive relaying is a relatively new subject [10-13]. It proposes protection functions that will adapt to changing power system conditions. Recently the problem of adaptive protection has been considered, from various perspectives. The concept of adaptive transmission pro-

387 IEE Proc.-Gener. Transm. Distrib., Vol. 145, No. 4, July 1998

tection has been presented [ l l ] as ‘online activity that modifies the preferred protective response to a change in system conditions or requirements’. Horwitz et al. [12] have described the result of an investigation into the possibilities of using digital techniques to adapt transmission system protection and control to real-time power system changes. They define adaptive protection as ‘a protection philosophy which permits and seeks to make adjustments to various protection functions in order to make them more attuned to prevailing power system conditions’. Jampala et al. [13] have illustrated the concept of adaptive transmission protection with examples. They report efficient enhancement to the existing algorithms for achieving faster relay co-ordina- tion. Phadke et al. [lo] have presented an adaptive out- of-step relay which recognises power system changes and uses a modified system impedance matrix to change relay settings using phasor measurement techniques.

This paper presents two approaches for adaptive out- of-step prediction with the advantage of initiating early tripping for unstable swings while avoiding tripping on stable swings. These approaches are based on backpropagation trained neural networks. The major bene- fit of ANN is that, once trained, it can quickly classify a new pattern as belonging to a known predefined class of patterns. The prediction of the generator out-of-step condition requires a check for possible loss of synchro- nism due to a large transient disturbance. Using neural networks, the prediction of the OS condition is a classification problem with two classes. Compared with recent approaches [5-71 the algorithm presented is very straightforward for real-time implementation and has good classification performance.

2 Artificial neural networks

Neural networks are a relatively new information processing technique. They can be defined as ‘a com- puting system made up of a number of simple, highly interconnected processing elements, which processes information by its dynamic state response’. A neural network consists of a number of very simple and highly interconnected processors called neurodes which are the analogues of the neurons in the brain. The neurodes are connected by a large number of weighted links, over which signals can pass. In the present application, three-layer neural networks algorithms (having an input layer, a middle layer and an output layer) have been used, together with a sigmoidal activation function and supervised training via a backpropagation technique. The well known enhancement of introducing a momentum term in the weight updating formula has also been successfully applied to reduce training times and to help in avoiding premature convergence (i.e. to a local optima). Further details of artificial neural network methods, and the various enhancements which have been used here, can be found in [14].

3 prediction

Application of neural networks to OS

In this Section, the method and the procedures for application of neural networks to OS prediction are described. The example system under study is a nine- bus power system that has three generators and three loads. A one-line diagram for the system is shown in Fig. 1, and the system characteristics are given in [7].

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Generator no. 3 is selected to be protected by the proposed technique as shown in Fig. 1.

I 2 7

18kV

Fig. 1 Power system under study

3.7 Neural networks for OS prediction

3. I . I Generation of samples: It was assumed that the loads are randomly distributed and that they have a normal distribution shape with the following means:

[PA,P~,P~] = [l 25,0.9,1.0] P.U.

For load flow analysis, bus 1 is taken as the swing bus and buses 2 and 3 are voltage controlled buses with voltage magnitude of 1.025p.u. For each load sample, the loading of the generators is determined by econom- ical dispatch of the total load among generators, fol- lowed by a load flow analysis. A three-phase short circuit is assumed to occur at one line very close to one of the buses of the system and the fault is removed after 150ms by tripping out the faulted line. In HV networks, lOOms is preferred, but l5Oms is used here just to generate more unstable samples in the training process. The Runge-Kutta numerical integration approach is applied to find the class of each sample. A sample is classified as unstable ‘out-of-step’ if the rotor angle of the generator under study reaches 180” within 1s [16], and it is given a stability index of 0, otherwise the sample is classified as stable, and it is given a stability index of 1.

Generation of samples is performed by changing both the fault location and loading conditions of the system prior to the occurrence of the fault. For this study, a group of samples is generated at six different fault locations with three different load levels (1.6, 1.0 , 0.4) p.u. for each of the three loads of the power network under study resulting in 162 samples. The fault locations are:

busnumber 7 5 4 6 9 8 faulted line 7-5 5 4 4-6 6-4 9-6 8-9

To improve the classification performance, a normali- sation process is performed to all the variables of the training set (and the test set).

3.1.2 Selection of features: There are many important quantities which have a significant effect on stability and as a result would dominate the pattern of stable and unstable classes. Three features are chosen using the single ranking method based on previous experience of applying a pattern recognition technique to the same problem. The first is the prefault loading of the generator or the mechanical input power ‘Pm’. The second feature is the generator kinetic energy deviation KE or 0.5 M CL? at t = T,;. The third feature is the average acceleration during fault aav. It is the average value of the two rotor angular accelerations al at t = Tf’ and a2 at t = Tc{. Tfis the instant of fault and T,,

IEE Pro,.-Gener. Dansm. Distrib., Vol. 145, No. 4, July 1998

is the instant of fault clearing. P, is a direct outcome of the load flow results. The other two features are determined from a transient stability study using the second-order model of the machine. Full details of the reasons for choosing these features are given in [7].

3.1.3 Validity of classification using the backpropagation algorithm: The neural network used in this program consists of three layers as shown in Fig. 2. It is supposed to have an output value from the neural network between 0 and 1. Normally, the sample can be classified as unstable if its output value from the neural network is less than 0.5 and stable if the value is greater than 0.5.

input hidden output layer layer layer

Schematic diagram of adopted neural network Fig. 2

There is an important factor which measures the degree of success of the classification process, which is, the number of misclassified samples among the total number of samples in the training set. A misclassified sample is a stable sample classified as unstable, or an unstable sample classified as stable.

Table 1: Effect of varying the number of neurodes in the hidden layer and the number of iterations on the classification process of the training set

Number of neurodes Number of in hidden layer iterations

10 50 100

15 100 500

20 100 500

30 100 500

Number of misclassified samples

3(u) 2(s) 3(u) 4(s)

1(u) 2(s)

U unstable sample misclassified as stable s stable sample misclassified as unstable

When using the standard backpropagation algorithm in classification, with a learning constant f i = 0.2 and momentum constant y = 0.9, some unacceptable results are obtained. It is noticed that the output value from the neural network for all the samples of the training set ranged from 0.9 to 1. This might occur due to the sequence of the samples. It is concluded that care must be taken with the order in which the patterns are presented. For example, when using the same sequence repeatedly the network may become focused on the

using a permuted training method. The program is therefore modified to randomise the order of the samples in the training process. This operation is called the stochastic training approach [I 51.

f i r s t few patterns. This problem can be overcome by

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Table 1 shows the effect of varying the number of the neurodes in the hidden layer and the number of iterations on the performance of the classification process using the stochastic backpropagation algorithm.

3.1.4 Performance testing: The last step in the neural network approach is the generalisation process by which a complete verification of the capabilities of the neural network in predicting the class of unknown samples is performed. The generalisation ability is best stated in probabilistic terms as the probability of correct classification. It can serve as an index of satisfac- tory performance of the classification in unknown situations. This step is conducted by testing the chosen neural network using an adequate test set. The samples of the test set should cover a wide spectrum of operat- ing conditions and contingencies that the machine under study may be subjected to. The generation of samples for the test set was performed in a similar way to the training set. The test set was generated at six fault locations with four different load levels (1.75, 1.25, 0.75 , 0.25) p.u. for each of the three loads. This produces 384 (6 x 43) samples.

The prediction of the stability of each sample is obtained by running the chosen neural network for each sample of the test set and obtaining its output value. If the output value of the sample is less than 0.5 (as a threshold), it will be considered as unstable, otherwise, it will be considered as stable. Table 2 shows the resulting percentage of the correct classification and the number of misclassified samples for selected neural networks used. The correct classification percentage is defined as: [l - (total number of misclassified samples/ total number of samples in the test set)] x 100.

Table 2: Results of testing selected neural networks using the test set

Number of neurodes in the Number of Correct hidden layer for the neural misclassified classification network used samples %

10 (50 iterations) 14 96.4 10 (100 iterations) 30 92.2




3.2 OS prediction for different line outages of the power system It is concluded that a neural network is able to predict the OS condition of synchronous generators correctly for the original (intact) power network. It is then important to study the effect of making some significant changes to the power network on the neural network prediction capability to check the adaptive features of the proposed OS relay. For that purpose, four other network topologies are studied by removing a line of the six lines in the power system under test: (i) line 9-8 is removed, (ii) line 9-6 is removed, (iii) line 7- 5 is removed, and (iv) line 7-8 is removed. For each case, new samples are generated for training and testing. For each case, four fault locations are considered. When clearing these faults, care has to be taken in selecting the tripped lines because of the possible

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machine or load isolation conditions. Three load levels of the existing three loads (1.6, 1.0, 0.4) p.u. and four fault locations are considered to generate 108 samples for each case.

3.2. I Testing based on the intact case applied to the four outage cases: The OS prediction neural network based on the intact case is tested to check its ability to predict the OS condition for each of the four outage cases. Table 3 shows the resulting correct classification percentage for each case.

Table 3: Results of testing the four line outage cases by OS prediction neural network based on the intact case

Number of Correct

(based o n 108 samples) % Case to be tested misclassified samples classification

Line 9-8 removed 78 27.8




It is apparent from Table 3 that the OS prediction neural network based on the intact case fails to predict the OS condition with an acceptable percentage of correct classifications. Therefore, four different OS prediction neural networks corresponding to the four line outage cases are constructed. Each one is trained based on the corresponding case. The test samples are generated at the predefined four fault locations with four different load levels (1.75, 1.25, 0.75, 0.25) p.u. for each of the three loads resulting in 256 samples for each case. Table 4 shows selected results of the classification performance.

3.3 Case detection neural networks Five different cases for the power network under study are considered.

Case 0: the intact ‘original’ case Outage case 1: line 9-8 is removed Outage case 2: line 9-6 is removed Outage case 3 : line 7-5 is removed Outage case 4: line 7-8 is removed

After testing the five cases against the various trained neural networks, it is concluded that it is preferable to

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use each neural network for its appropriate case to obtain the optimal discrimination results. It is therefore essential to discriminate between these five different cases to optimise the relay response under widely varying network conditions. Five classifier neural networks were suggested to distinguish between the different cases using only local measurements from the machine in question. These classifier networks will be referred to as case detection neural networks.

3.3. I Feature selection of case detection neural networks: As mentioned before, five power system topologies are considered. Two of these involve removal of lines connected to the machine in question. Removal of other lines also affected the power flow through these lines and the voltage magnitude of the buses connected to these lines. Voltage magnitudes usu- ally vary in a narrow band near the rated values and are unlikely to be sufficiently discriminatory. So, it would be convenient to choose the active and reactive powers through lines connected to the machine as the appropriate features. The four variables chosen as inputs to the case detection neural networks are:

(1) The active power in line 9-6 (P9-6) (2) The reactive power in line 9-6 (Q9_6) (3) The active power in line 9-8 (P,_*) (4) The reactive power in line 9-8 (e9_*)

3.3.2 Samples generation of case detection neural networks: To generate the training set patterns, it is assumed that the loads are randomly distributed, having normal distributions with the following means:

[PA,PB,P~] = [1.25,0.9,1.0] P.U.

For each load sample, a load flow analysis is performed. The four selected features are calculated to represent each case. Generation of samples for training is done by changing the loading conditions of the system prior to the occurrence of a fault. The training set is generated with three different load levels [ 1.6, 1.0, 0.4 ] p.u. for each of the five cases resulting in 135 (27 x 5 ) samples. Five similar training sets are generated to be trained by five case detection neural networks. For each sample, an index is given according to the state of the power network. These indices are: 0.0 for the network configuration case which was required

Table 4: Results of testing selected neural networks for the four line outage cases

Neurodes in Number of ~ s ? ~ ~ : ~ . ~ d

samples % Case to be tested hidden layer iterations

Line 9-8 removed 5 780 9 96.5 12 560 10 96.1 20 500 11 95.7

Line 9-6 removed 8 550 11 95.7 12 4269 8 97 15 2141 9 96.5

Line 7-5 removed 16 784 9 96.5 20 239 10 96.1 24 239 9 96.5

Line 7-8 removed 3 3100 11 95.7 12 6573 10 96.1 15 7813 9 96.5

IEE Proc.-Gener. Transm. Distrib., Vol. 145, No. 4, July 1998

to be detected and 1.0 for other cases. Table 5 gives an example consisting of part of the first training set for discrimination between the intact case and the other topologies of the power network.

Table 5: Part of the first training set for discrimination between the intact case and the other topologies of the power network

p9-6 Qg-6 p9-8 Qg-8 Case Index

0.92 0.34 0.39 0.38 intact 0.0

1.31 0.34 0.00 0.00 line 9-8 removed 1.0

0.00 0.00 1.31 0.56 line 9-6 removed 1.0

2.17 0.88 -0.86 0.45 line7-5 removed 1.0

-0.33 0.57 1.65 0.97 line 7-8 removed 1.0 ~

3.3.3 Performance of the case detection neural networks used: The construction of the case detection neural networks used is the same as in Fig. 2 with four input features (case detection features) instead of three.

The stochastic backpropagation algorithm is used to classify the samples of the five training sets. Table 6 presents the results of testing the five case detection neural networks in detecting the five cases considered.

In Table 6, the classification performance index (CPI) is defined as the sum of squares of errors for all the samples and the error is the difference between the desired output value and the actual output value from the neural network. From the results listed in the Table, five successful case detection neural networks are obtained: a case detection neural network for detecting the intact case and four case detection neural networks for each of the four outage cases.

An adaptive approach to OS protection can then be proposed. The approach depends first on defining the case of the power network using the local power flow measurements through the different case detection neural networks. According to that decision, the appropriate OS prediction neural network will be selected to predict the OS condition for the machine in question. After detection of the instants of fault inception and clearance, the required measured values of the features for the machine are provided to the selected OS prediction neural network. The neural network would predict the OS condition.

The previous adaptive OS protection approach is valid for the relatively limited number of cases studied. However, if another outage case occurred and is not recognisable by any of the case detection neural networks, it would be difficult to choose which of the OS/ prediction neural networks to use in determining the possible OS condition. Therefore a more generalised approach is required to adapt itself to any probable case.

4 Adaptive out-of-step protection strategies

4. I Weighted-combination technique A modification is suggested to the previous adaptive OS protection approach. It depends on using all the case detection and OS prediction neural networks in a composite (or blended) manner. The following equa- tion is used to calculate the adaptive OS condition for any case:

where: blend is the adaptive OS decision; os, is the OS decision from OS prediction neural network i (0 = out- of-step, 1 = stable); ed, is the case detection decision from case detection neural network i (0 = the case, 1 = not the case); and n is the number of case detection neural networks used.

The proposed strategy depends on identifying the case for any sample by calculating the output values through the five different case detection neural networks. For calculating the OS condition, a greater weight is given to the OS prediction neural network which has the lowest output value from the case detection neural networks. For example, if the output value from any one of the case detection neural networks for any sample is zero, this means that this sample is confi- dently expected to belong to that case. The corresponding OS prediction neural network is then to be used. However, if the sample is not accurately classified as belonging to any one of the studied cases, then the sample may be approximately represented as a weighted-combination of many cases, according to the output values from the five case detection neural networks. The OS decision for the sample would be calculated from the five OS prediction neural networks using the previous formula which gives higher weights to the OS decision for the cases which are much closer to the sample. Obviously, if the blend value is less than 0.5, the sample would be classified as OS, otherwise it would be classified as stable.

Fig. 3 shows a schematic diagram of the adaptive OS protection scheme. For testing this weighted-combination adaptive OS prediction technique, a further line outage case is considered by removing line 4-5 of the power system under study, and 108 samples are then generated (four fault locations and three load levels for the existing three loads). The results reveal a very good classification perfoimance. The number of misclassified samples is just ten samples which is equivalent to (91 % correct classification percentage). Comparing that with results obtained when testing the same samples using the OS prediction neural network based on the intact case (36 % correct classification percentage) illustrates the adaptivity of the proposed technique. Table 7

Table 6: The results of testing the five case detection neural networks

Case detection neural network used

1

2

3

4

5

Number of neurodes in hidden layer

Case to be tested

intact 4 (300 iterations) line 9-8 removed 3 (100 iterations)

line 9-6 removed 4 (100 iterations)



Number of misclassified C.P.1 samples

1 0.318 0 3.7E-04

0 1.6E-04

1 0.99

0 8.1 E-03

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shows the results of testing selected samples of the previous case using the weighted-combination technique. In the Table: os1, os2, os3, os4 and os5 are the OS decisions of the five OS prediction neural networks; cdl, cdz, cd3, cd4 and cd, are the case detection decisions of the five case detection neural networks; index is the actual state of the sample (0 = out-of-step, 1 = stable); and blend is the result of the adaptive OS decision.

measurements for case detection measurements for out-of- step prediction

neural network neural network

signal to operate the relay

Fig. 3 tion scheme

Schematic diagram of weighted-combination adaptive OS protec-

The time for calculating the adaptive OS decision using the weighted-combination technique may be calculated as the maximum time for detecting the case of the sample by any of the case detection neural networks added to the maximum time for predicting the OS condition of the sample by any of the OS prediction neural networks plus the time for calculating the blend result. This calculation for the time of the adaptive OS decision is valid if parallel processors are used. Otherwise, it should be calculated as the sum of all the times for detecting the case by the different case detection neural networks plus the sum of all the times for predicting the OS condition by the different OS prediction neural networks plus the time for calculating the blend result. The time using a Digital ALPHA 4610

computer was 0.0022s (assuming parallel processing) and 0.0033 s (assuming serial processing).

the developed neural

OS technique I I I

reactive power flow

I I

Fig. 4 General block diagram for the proposed relay

4.1.1 Considerations for real-time implementation: The hardware requirements for the proposed neural network-based adaptive OS relay are relatively simple. Some transducers are required; one for the electric power output of the machine P,, one for its angular speed 03, one for the active power flow in each line connected to the machine, and one for the reactive power flow in each line connected to the machine. These transducers are to be interfaced to a microcontroller-based system and/or neurochip-based system. Fig. 4 represents a general block diagram for the proposed relay and Fig. 5 shows a flow chart for the microcontroller processes. The microcontroller would store the weights of the trained case detection and OS prediction neural networks, and the maximum and minimum values for each feature. Before the fault, the case detection features are measured and normalised. Using the weights provided, all the output values from the case detection neural networks are calculated. After the occurrence of a fault, the OS features are calculated and normalised. Using the weights provided, the output values of the OS prediction neural networks can be calculated, and hence the weighted-combination OS condition may be detected. Once this is determined, a signal is initiated for tripping the relay.

4.2 Training the OS prediction neural network using outage cases Another modified approach for adaptive OS prediction strategy is to combine all the samples of the four out-

Table 7: Results of using the weighted-combination formula for selected samples of line 4-5 outage case

Sample no. os1 os, os3 os4 os5 cdl cd2 cd3 cd4 cds Index Blend

1 0.000 0,000 0.000 0,001 0.000 1.000 1 000 1 000 0 000 1.000 0 0 001

18 0.252 0.000 0.000 0 005 0.000 1.000 1.000 1.000 0.000 1.000 0 0.005

30 1.000 0.010 0.000 0.091 0.000 1.000 1.000 0.419 0.000 1.000 0 0.057

37 0.001 0.000 0.000 0.001 0.000 0.155 1.000 1.000 1.000 0.027 0 0.001

38 1.000 0.243 0.000 0.128 0.000 0.155 1.000 1.000 1.000 0.027 0 0.464 45 0.395 0.000 0.000 0.001 0.000 0.109 1.000 1.000 1.000 0.001 0 0.186

61 1.000 0.000 0.000 0.001 0.000 0.999 1.000 0.232 1.000 0.000 0 0.001

69 1.000 0.000 0.000 0.001 0.001 0.000 1.000 0.367 1.000 1.000 1 0.612

90 1.000 0.044 0.000 0.016 0.001 0.187 1.000 1.000 1.000 0.000 0 0.448

107 1.000 0.000 0.000 0.000 0.007 0.000 1.000 0.999 1.000 0.226 1 0.566 ~

392 IEE Proc -Gener Transm Distrib , Vol 145, No 4, July 1998

age cases in addition to the samples of the intact case in a large training set (594 samples) to be trained by a new neural network with new input features.

at '=TI, read Pflow and Clflow in lines connected to the machine, P,, and W,

I at t=T: , read P,,

I at t=T; , read Pez o2

calculate the features for the OS prediction neural networks as in eqns 10-12

pln=p*, (1 0)

(1 1) (0," -0 : ) KE=M-

1 calculate the output values from the case detection neural networks

using the stored weights of the trained case detection neural networks

calculate the output values from the OS prediction neural networks using the stored weights of the trained OS prediction neural networks

I

calculate the weighted- combination OS decision 'blend'

Fig. 5 Flow churt for the microcontrollev processes

Table 8: Classification results of the large training set (594 samples) using five features

Neurodes in hidden layer Number Of Correct and iterations classification % misclassified

samples - 15 (1000 iterations) 48 92

20 (1000 iterations) 54 91

30 (1000 iterations) 52 91.2

50 (1000 iterations) 59 90.1

60 (1000 iterations) 54 91

70 (1000 iterations) 48 92

15 (10000 iterations) 19 96.8

20 (10000 iterations) 32 94.6

25 (10000 iterations) 17 97.1

30 (10000 iterations) 12 98

35 (10000 iterations) 18 97

40 (10000 iterations) 23 96.1

50 (10000 iterations) 20 96.6

As mentioned before, four outage cases beside the intact case are considered. Two of these cases involve removal of lines connected to the machine in question. Removal of other lines also affects the power flow through these lines. So, it would seem to be appropriate to choose the power flow in the lines connected to the machine as new features in addition to the previous

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three features used for OS prediction. Therefore, the selected input features for the new neural network are (PgZ, KE, aav, P9-6 and P9-8). Table 8 shows selected results of classifying the 594 samples of the large training set using different neural networks with the above five input features. Table 9 represents the results of classifying the test samples of outage case 5 (line 4-5 removed) using previous trained neural network.

Table 9: Results of testing outage case 5 (108 samples) using the large training set

Number of misclassified Correct Neurodes in hidden layer

and iterations samples classification %

15 (1000 iterations)










- 22

16

21

22

19

26

25

17

24

29

79.6

85.2

80.6

79.6

82.4

75.9

76.9

84.3

77.8

73.1

4.2.1 Comments about the modified approach: The new neural network needs a longer time for training because it contains many samples of different outage cases.

The advantage of this approach is that instead of using different case detection neural networks and OS prediction neural networks for each outage case, just one neural network is used to predict the OS condition with different topologies of the power system. Another advantage is that all the required features can be measured locally from the machine to be protected.

The correct classification percentage of the neural network for testing outage cases used in training is about 98% and for testing a different outage case is about 85%. The CPU time for calculating the OS condition of a sample of the tested cases ranged from 0.000185 to 0.00037s according to the neural network chosen using the Digital ALPHA 4610 computer. Com- pared with the weighted-combination technique, this approach is faster. The correct classification percentage for testing a similar outage case is slightly less than the weighted-combination approach.

5 Conclusions

Different algorithms for training neural networks are used to predict the OS condition of synchronous generators. It is concluded that the stochastic backpropagation algorithm gives acceptable results especially with an appropriate number of neurodes in the hidden layer.

Two adaptive OS protection strategies have been presented based on neural networks. The first approach depends on recognising the power network condition through case detection neural networks and then a new OS decision is calculated through an adaptive routine. The second approach depends on training a large neural network using samples from different outage cases. An advantage of these approaches is that all the required features can be measured locally from the machine to be protected.

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The use of neural networks for OS prediction has several advantages. Besides avoiding tripping on recov- erable swings, the capability is provided to initiate early tripping for nonrecoverable swings. The capabilities of the algorithms have been tested through numerical computer simulation. The hardware and software requirements for real-time implementation of a microprocessor based OS relay based on neural networks are expected to be simple especially with the frequent and rapid advances in micro-electronic technology.

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