An Effective Salp Swarm Based MPPT for Photovoltaic Systems Under Dynamic and Partial Shading Conditions

Abstract

This study proposes a duty cycle-based direct search method that capitalizes on a bioinspired optimization algorithm known as the salp swarm algorithm (SSA). The goal is to improve the tracking capability of the maximum power point (MPP) controller for optimum power extraction from a photovoltaic system under dynamic environmental conditions. The performance of the proposed SSA is tested under a transition between uniform irradiances and a transition between partial shading (PS) conditions with a focus on convergence speed, fast and accurate tracking, reduce high initial exploration oscillation, and low steady-state oscillation at MPP. Simulation results demonstrate the superiority of the proposed SSA algorithm in terms of tracking performance. The performance of the SSA method is better than the conventional (hill-climbing) and among other popular metaheuristic methods. Further validation of the SSA performance is conducted via experimental studies involving a DC-DC buck-boost converter driven by TMS320F28335 DSP on the Texas Instruments Experimenter Kit platform. Hardware results show that the proposed SSA method aligns with the simulation in terms of fast-tracking, convergence speed, and satisfactory accuracy under PS and dynamic conditions. The proposed SSA method tracks maximum power with high efficiency through its superficial structures and concepts, as well as its easy implementation. Moreover, the SSA maintains a steady-state oscillation at a minimum level to improve the overall energy yield. It thus compensates for the shortcomings of other existing methods.

Imperative role of optimization techniques to reconfigure solar photovoltaic array systems to diminish shading effects

“…where x i (t+1) is the ith grasshopper's position at t+1 iteration, s is a function represents the social force strength, f stands for the attraction strength, r is a distance, ub d and lb d are the upper and lower bounds, d ij is the distance between ith and jth grasshoppers, shrinking factor is represented by C, and Td^is the target of d-dimensional position. 15,32,37,38 The steps involved in execution of GHO are shown in Figure 11.…”

“…30 In Yousri et al, 31 the hybrid fractional chaos (FC)-FRA method is tested under nonhomogeneous shading conditions and compared with FPA on a total of four PV modules with 300-1000 W/m 2 . The GMPP was observed at 213.46 W. In Jamaludin et al, 32 salp swarm algorithm (SSA) was used to reconfigure 4 Â 4 size PV array and compared with other MH techniques such as PSO, GOA, GWO, and BOA to reconfigure PV array connections. Among all the optimization algorithms, SSA is found best in terms of GMPP at 239.6 W under distinct shading scenarios.…”

Jellyfish Search Optimization Algorithm for MPP Tracking of PV System

“…Such segmentation helps the swarm to handle complex issues, which include cooperation. Some of the well-known swarm intelligence algorithms are (PSO) [22], the genetic algorithm (GA) [23], ant colony optimization (ACO) [24], evolutionary algorithms (EA) [25], artificial bee colony (ABC) [26], and salp swarm optimization (SSO) [27]. Each algorithm has its own pros and cons respective to a particular issue.…”

Maximum Power Point Tracking Using ANFIS for a Reconfigurable PV-Based Battery Charger Under Non-Uniform Operating Conditions

“…Also, this algorithm has been implemented with an ANFIS for MPPT of a PV pumping system [26]. To avoid the procedural complexity of the hybridized ANFIS-based techniques, [27] has proposed utilizing the salp swarm algorithm (SSA) to enhance the PV system efficiency by using the duty cycle boundary concept that directly searches for the global MPP. Despite the high MPPtracking efficiency and fast dynamic response achieved by the prescribed methods, the benefits that can be achieved by reconfiguring the PV system with the varying and non-uniform weather conditions are ignored.…”