Photovoltaic Multiple Peaks Power Tracking Using Particle Swarm Optimization with Artificial Neural Network Algorithm

Abstract

Photovoltaic (PV) array may receive different level of solar irradiance, such as partially shaded by clouds or nearby building. Multiple peak power points occur when PV module is under partially shaded conditions, which would significantly reduce the energy produced by PV without proper control. Therefore, a Maximum Power Point Tracking (MPPT) algorithm is used to extract maximum available PV power from the PV array. However, most of the conventional MPPT algorithms are incapable to detect global peak (GP) power point with the presence of several local peaks (LP). A hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed in this article to detect the GP power. The PV system which consists of PV array, DC-DC boost converter, and a resistive load, were simulated using MATLAB/Simulink. The performance of the proposed algorithm is compared with that of the standard PSO algorithm. The proposed algorithm is tested and verified by hardware experiment. The simulation results and the experimental results are compared and discussed. It shows that the proposed algorithm performs well to detect the GP of the PV array under partial shaded conditions. In this work, the tracking efficiency of the proposed algorithm is in the range of 92.7-99.7 %.

Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques

“…(2) The introduction of bypass diodes in PV arrays has a side effect of creating multiple MPPs in the PV array output characteristics during partial shading conditions [6,47]. Figure 9(b) [47] illustrates the presence of multiple peaks in a PV array powervoltage characteristic curve.…”

“…(2) The introduction of bypass diodes in PV arrays has a side effect of creating multiple MPPs in the PV array output characteristics during partial shading conditions [6,47]. Figure 9(b) [47] illustrates the presence of multiple peaks in a PV array powervoltage characteristic curve. These multiple MPPs render most conventional MPP tracking algorithms ineffective for MPP tracking during partial shading conditions [48][49][50][51][52][53] (3) Increased temperature of bypass diodes as a result of long-term operation of the bypass diodes may result in the degradation of the adjacent cells of the PV module [27]…”

Grid-connected modular PV-Converter system with shuffled frog leaping algorithm based DMPPT controller

“…results or increased computational cost. A hybrid PSO and Artificial Neural Network (PSO-ANN) algorithm was also proposed in [36] to detect the global peak power point in the presence of several local peaks. Although this method has advantage of relative high tracking accuracy, it is complex in developing NN hence more costly in sample training and computational time.…”

A Two-Stage Particle Swarm Optimization Algorithm for MPPT of Partially Shaded PV Arrays

“…Recent research in this area has seen the application of various artificial intelligence schemes, such as fuzzy logic controller (FLC) (Alajmi et al 2011;Bendib et al 2014) and neural network (NN) (Bendib et al 2014;Zhang and Bai 2008;Boumaaraf et al 2015;Arthishri et al 2014;Ngan and Tan 2016). Though these methods have been shown to be effective in dealing with the nonlinear characteristics of the I-V curves, they incur considerable computational cost (Ishaque et al 2012).…”

“…The work in (Ishaque et al 2012) employed PSO to locate the nearest section where the global MPP may lie, and then used HillClimbing to find the exact point. A hybrid PSO and Artificial Neural Network (PSO-ANN) algorithm was proposed in this article to detect the global peak power point in the presence of several local peaks (Ngan and Tan 2016). The authors in (Phimmasone et al 2010) have updated the traditional PSO equations by adding various coefficients to improve the searching accuracy of the algorithm.…”