A robust experimental-based artificial neural network approach for photovoltaic maximum power point identification considering electrical, thermal and meteorological impact

Samer Gowid,Ahmed Massoud

doi:10.1016/j.aej.2020.06.024

Abstract

This paper aims to develop a robust and practical photovoltaic (PV) Maximum Power Point (MPP) identification tool developed using reliable experimental data sets. The correlations between the voltage and the current (Vmp and Imp) at maximum power from one side, and the irradiance information, electrical parameters, thermal parameters and weather parameters from another side, are investigated and compared. A comparative study between a number of input scenarios is conducted to minimize the MPP estimation error. Four scenarios based on a combination of various PV parameters using various Artificial Neural Network (ANN)-based MPP identifiers are presented, evaluated using the most common regression measure (Mean Squared Error (MSE)), improved in terms of the accuracy of the identification of MPP, and then compared. The first scenario is divided into two parts I(a) and I(b) and considers the irradiance information in addition to the highest correlated parameters with Imp and Vmp, which are circuit current (Isc) and open-circuit voltage (Voc), respectively. The second scenario considers irradiance information and the electrical parameters only. The irradiance information, in addition to the electrical, thermal, and weather parameters, are considered in the third scenario using a single layer network, while the irradiance information, in addition to the electrical, thermal, and weather parameters, are considered in the fourth scenario using a two-layer ANN network. Although the correlation study shows that the Vmp and Imp have the best correlation with the open-circuit voltage and the short circuit current (scenario I), respectively. Nonetheless, the consideration of irradiance, electrical, thermal, and weather parameters (scenario IV) yielded higher identification accuracy. The results showed a decrease in the MSE of Vmp by 74.3% (from 1.6 V to 0.411 V), and in the MSE of Imp by 95% (from 4.4e−6 A to 2.16e−7 A), respectively. In comparison to the conventional methods, the proposed concept outperforms their performances and dynamic responses. Moreover, it has the potential to eliminate the oscillations around the MPP in cloudy days. The MPP prediction performance is 99.6%, and the dynamic response is 276 ms.

Highlights

Photovoltaic (PV) solar systems exist in different configurations for a broad range of applications such as gridconnected mode and isolated mode of operation
The prediction results are significantly affected by the input parameters combination, and, unlike the previous work, this paper considers a large number of different PV parameters
The results showed that 99.25% performance could be achieved when using a variable step-size algorithm applied to a Cascaded Multilevel Converter (CMC) converter and that the performance remained unchanged with the variation of solar irradiance

Summary

Introduction

Photovoltaic (PV) solar systems exist in different configurations for a broad range of applications such as gridconnected mode and isolated mode of operation. The major conventional MPPT techniques provide acceptable performance, Artificial Neural Network (ANN)-based MPPT techniques demonstrated faster and more accurate MPP identification results, when considering partial shading or fast environmental changes This comes in agreement with the strength of ANN in solving non-linear problems [3,9]. ANN-based PV MPPT methods have been addressed extensively in the literature, and ANN algorithms demonstrated a number of capabilities such as (a) non-linear mapping, (b) fast response, (c) reliable and stable operation, (d) compact and accurate solution for multivariable problems, (e) off-line training and (f) reduced computational burden [12] In this context, the main contribution of the paper can be summarized in the following bullets: Investigate the correlation between the voltage and current at maximum PV power on the one hand, and the electrical, thermal, and weather parameters, on the other hand. Imp was found in a good correlation with Isc and Irradiance, while Vmp was found in a good correlation with Voc and irradiance (the two highest correlated parameters)

Performance of improved conventional MPPT methods

Method

MPP prediction using ANN

Findings

Conclusion