Identification of Multiple Input-Single Output (MISO) Model for MPPT of Photovoltaic System

M.N.M. Hussain*, A.M. Omar*, A.A.A. Samat* * Faculty of Electrical Engineering, Universiti Teknologi MARA, MALAYSIA

najib830@ppinang.uitm.edu.my, omar_maliki@yahoo.com.sg, ahmadasri759@ppinang.uitm.edu.my

Abstract — This paper presents a modelling of Multiple Input–Single Output (MISO) model for tracking maximum point of Photovoltaic (PV) Systems. A system identification approach is used to obtain the model characteristics of existing structural systems through dynamic observations which incorporate with traditional direct control Maximum Power Point Tracking (MPPT) algorithm. The generate model characteristics was used in designing the MPPT of the Photovoltaic Systems. The need of an efficient mathematical model to characterize the dynamics system for PV is essential for implementing the system for control applications. In this paper, the transfer function relating the input parameters (Solar irradiance and cell temperature) and output parameter (DC current) of the PV module MF120EC3 was identify with the aid of MISO for Auto-Regressive with eXogenous input (ARX) model. The approach has concern on the estimation of the (Direct Current (DC) for photovoltaic system based on the real systems data from Pusat Tenaga Malaysia (Malaysia Energy Centre, MEC). A Fourth-order autoregressive (ARX) model using the ARX algorithm, (ARXQS) was chosen since the model output provide 93.42% best fit model criteria. The modelling is implemented using system identification toolbox of Matlab software.

Keywords-component; Photovoltaic (PV), Multiple Input–Single Output (MISO), Auto-Regressive with eXogenous input (ARX), System Identification, Maximum Power Point Tracking (MPPT).

I. INTRODUCTION An awe-inspiring of using Artificial Intelligent (AI)

technique replacing human capability of solving many scientific and engineering problems, having a significantly impact on power engineering fields especially in power electronics applications technologies. Bimal has discussed comprehensively on the applying of AI in [1] as a control signal approach to the power electronics applications and promoting of using such technique toward enhancing viable and inexpensive control or estimation for power electronics area. However, for the estimation and identification of finding a best suitable mathematical model for controlling of switching converter, system identification approach is seen as appropriate alternative.

For photovoltaic system (PV) applications, by means of system identification, the models can be practically use for better understanding of the system or to predict and simulate a system behavior based on the real systems. In particular of tracking maximum power point (MPPT) of PV modules, a black-box model is constructed from system input and output

data which is based on recorded input and output data without knowing of structure and parameters insight. A primary procedure of identify a system as Linear System (LS) from data collected in MEC in 2010 is establish as a multi-input single-output (MISO) system. After selecting the model structure, the inputs and outputs based on recorded data will be determined by an optimization process including minimization and maximization of a linear criterion to solve for best fitting model [2, 3].

The authenticated best-fit model will be further used for switching control of power electronics converter to harvest maximum power point deportment. These resultant transfer functions represent the best-fit model for continuous and time-invariant systems. Methodically, the system identification can be said as a solving problem of constructing the mathematical models of dynamic systems to describe the underlying mechanism of the observed data for a specific system.

II. MULTI INPUT-SINGLE OUTPUT SYSTEM System identification approach is the important tool for

technical areas which providing a mathematical–physical representation of a dynamic system through transfer functions. Basic identification steps are 1) Optimal experiment design and data collection. 2) Model structure selection. 3) Model estimation and 4) Model validation. The overall process is shown in Fig. 1. Analysis of the performance for a SISO control system can be performed in the frequency domain, considering the system's transfer function [4]. While for MISO or MIMO, generally, more complicated control systems should consider from the theoretical results devised for each control technique. Thus, system identification toolbox from MATLAB Software is a satisfactory alternative.

Most commonly used time domain models are the linear state-space models and the difference equation models, such as the ARX and Auto-Regressive Moving Average with eXogenous input (ARMAX) models [5]. These models are basic general-linear polynomial model which describe by equation as below.

, , (1)

Where and are the input and output of the system respectively. While is a zero-mean noise, or the

Research Management Institute, RMI and Photovoltaic Monitoring Center, PVMC of UiTM, Malaysia

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disturbance of the system, , is the transfer function of the deterministic part of the system, , is the transfer function of the stochastic part of the system.

A Fourth-order autoregressive (ARX) which also known as

ARXQS model is prefer for this system. ARXQS is a model which is adapted from ARX algorithm. The polynomial model for ARXQS is parametric as follows:

1 1 (2)

Where represents the output at time , represents the input at time . is the number of poles, is the number of parameters, is the number of samples before the input affects output of the system (called the delay time of the model), and is the noise disturbance. Using System Identification Toolbox in MATLAB software, the system will estimate the parameters … and … based on the given input and output data of the estimation data set.

Figure 1. Process of System Identification

III. MAXIMUM POWER POINT TRACKING ALGORITHM A fluctuated of solar irradiation to a PV module formulate

instable output power to the loads. Therefore MPPT technique is design to a PV module to track or produce maximum power that can be harvested from the module to the loads. Thus, tracking of the maximum power point is a vital task for PV system in a PV control system to signify the performance of the PV system. Numerous techniques have been introduced as an efficient method to track a maximum power point. Among all the tracking techniques, the Perturb and Observe (P&O) [6-8] and Incremental Conductance [9, 10] methods are the famous and preferred approach. They are categorized in direct methods.

An efficient of MPPT algorithm or approach for PV system should be considered in several factors. This factors including 1) for dynamic response: the speed of continuously tracking the MPP. If speed is very fast, then the output will increase. 2) for steady-state response: possible to minimize the oscillation of MPP for minimizing the power losses. 3) can match with all climatologic conditions: lacking of this criterion can make MPPT algorithm unstable and track in a wrong direction. 4) for partial shading: shading will effect a multiple peaks state, as a results, the algorithm get trapped in a local maximum thus delivering lower peak power. 5) simplicity: the simplicity of MPPT algorithm is essential to operate in short sampling periods thus faster tracking.

IV. SYSTEM IMPLEMENTATION

A. Model Identification in MATLAB The data was taken from (Malaysia Energy Centre, MEC),

Bangi, Malaysia based on system in Table I using Mitsubishi PV-MF120EC3 panels. Fig. 2 shows the solar irradiance and normalized DC power dataset for installed PV system. The analysis data only use 72 set for validation data set. A satisfactory of 72 set of data is selected based on the setting of irradiance at greater than 100 ⁄ .

TABLE I. INSTALLED PV SYSTEM AT MEC

Inverter Brand: IG500 Code: IG2

P nominal: 40,000 W P max: 40,000 W

V window: 210 to 420 V Vmax: 530 V

PV Brand: PV-MF120EC3 Series: 21

Parallel: 18 Vmp stc module/string: 17.6 / 369.6 V Voc stc module/string: 22.0 / 462 V Pmp stc module/array: 120 / 45360 Wp

Total module: 378 De-rating

factor - 88.18%

Construct the Experiment

Data Collection

Choosing Model Structure

Model Estimation

Model Validation

Model Selection

Model Application

Not Accepted

Accepted

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Figure 2. Solar irradiance and normalize DC power

The dataset of input to output signal is illustrated severally in Fig. 3 (a) and (b). From quick start of system identification toolbox, the model of arxqs is identifying as a best fit model since it give 93.42% accuracy model representing the real installed PV system at MEC. The measured and simulated model output is shown in Fig. 4.

Figure 3 (a). Irradiance to DC current signal

Figure 3 (b). Cell Temperature to DC current signal

Figure 4. Measured and simulated model output

The transfer function from input "cell temperature" to

output "DC current" and "irradiance" to output "DC current" for Multiple-Input Single-Output system is obtain severally as; 0.01358 0.02393 0.001419 0.011591.148 0.251 0.01503 0.0381 (1) and 0.05644 0.06703 0.002245 0.000391.148 0.251 0.01503 0.0381 (2)

B. System Integration for MPPT system The model establishes from best fit model then will employ

to the system to track maximum current, of the PV module. These models then incorporate with simple direct method of MPPT as demonstrate in Fig. 5.

Figure 5. Simple direct method

I (k+1) < Imp

Set Imp from IDENT

Measure I (k+1)

I (k+1) > Imp

I (k+1) =?

Imp = I (k+1)

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A steady value from system identification which establish on selected database is needed to avoid oscillations on MPP operation if only apply direct method to the system. A variation of output voltage and current from PV module will then is being recognized by simple direct method strategies. By means of this technique, the effect of variable solar irradiance to the cell temperature will be considered.

V. SIMULATIONS AND RESULTS OF THE PROPOSED SYSTEM The output of the solar PV module depends purposely on

its temperature during operation. When solar irradiance increase, the cell temperature is dramatically increase. This situation need a serious attention because, the output power will drastically decrease. At irradiance of 1000 ⁄ and 25 of ambient temperature, an IV and PV-characteristics of the PV-MF120EC3 are shown in Fig. 6 (a) and (b) severally. Both curves are identical with MF120EC3 datasheet (http://www.mitsubishielectric.com/bu/solar/).

Figure 6 (a). I-V Characteristics

Figure 6 (b). P-V Characteristics

For single simulation of PV module, the response of the

module for DC voltage, current and power are shown in Fig. 7. The curve represent the condition from short-circuit at time 0 until open-circuit value.

Figure 7. Output energy from solar module

Figure 8. MPP of DC current

By using the proposed system for tracking MPP, the

maximum current tracked for testing module is 6.618 A as shown in Fig. 8. It can be seen that the current is attain stabilize at less than 0.3 seconds from changing of DC current from curve in Fig. 7. The results of DC current is classify as MPP since the value of current is close by the current provide by the datasheet given by the manufacturer as illustrate in Fig. 9.

Figure 9. Electrical performance of PV-MF120EC3

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VI. CONCLUSION A Fourth-order autoregressive (ARX) which also known as

ARXQS model which establish from system identification approach possibly used as a tool for seeking the maximum power point (MPPT) of the PV module. Integrating with conventional current tracking will give efficient MPP current that need to adapted with dynamic response behavior and also match with any climatologically situation. It is beneficial for minimizing power losses and solves partial shading condition in PV system. The value of MPP current is similar with standard test conditions of the module.

ACKNOWLEDGEMENT This work was supported and assisted by UiTM (Universiti

Teknologi MARA) and PVSMC (Photovoltaic System Monitoring Centre), UiTM, Malaysia.

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[8] N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, "A Technique for Improving P&O MPPT Performances of Double-Stage Grid-Connected Photovoltaic Systems," Industrial Electronics, IEEE Transactions on, vol. 56, pp. 4473-4482, 2009.

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