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

Partial shading is an unavoidable severe problem experienced by a photovoltaic system. The P‐V characteristics are highly non‐linear and exhibit multiple peaks under partial shading conditions (PSC). The conventional MPPT techniques are unable to track global maximum power point (GMPP) under PSC. Various bio‐inspired optimization methods employed for MPPT were presented in the literature. The operation of each algorithm differs from one another when tracking the GMPP. Therefore, this paper proposes a hybrid of adaptive particle swarm optimization (APSO) and Nelder Mead (NM) in searching for the global maximum power point. In this hybrid method, the unnecessary movement of APSO particles is minimized, leading to rapid convergence without the local trapping of the NM process. Thus, the proposed algorithm can balance both explorations by APSO and exploitation by NM. Extensive simulations are performed using Matlab/Simulink, which shows that this method successfully tracks global power under various shading patterns. The proposed method is compared with existing advanced optimization algorithms. Further validation is done via experimental analysis, and it confirms that the proposed method performs better in tracking maximum global power, less steady‐state oscillations, and fast‐tracking speed. The results demonstrate the effectiveness of proposed method in tracking GMPP with an average efficiency of 99.32% and an average tracking time of 0.513 s.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An effective maximum power point tracking technique for partially shaded photovoltaic system under rapidly changing atmospheric conditions

Reddy

Robinson

2022

“…Among the existing renewable resource, the solar energy is designated as the representative due to its convenience, high economic merits, and infinity of energy storage. The development of photovoltaic (PV) power generation technology effectively relieves the pressure of traditional thermal power generation, and hence, it has received wide attention in the research community 1–3 …”

Section: Introductionmentioning

confidence: 99%

“…The development of photovoltaic (PV) power generation technology effectively relieves the pressure of traditional thermal power generation, and hence, it has received wide attention in the research community. [1][2][3] The PV power generation systems can be implemented by the centralized structure, group serial structure, or micro-module integrated structure. In the first two systems, the power generated by the PVs concentrates into the grid-connected inverter from a lot of nodes.…”

Section: Introductionmentioning

confidence: 99%

A novel compensation power‐decoupling strategy for single‐phase three‐level flying capacitor PV micro‐inverters

Luo

et al. 2022

The traditional power‐decoupling controller for single‐phase photovoltaic (PV) micro‐inverters suffers from complex control structure, complicated parameter design and poor stability. In this paper, a novel compensation power‐decoupling strategy is proposed for a single‐phase three‐level flying capacitor PV micro‐inverter. The proposed strategy is aimed at tackling the problem of unbalanced charging and discharging of the flying capacitor, and hence makes the micro‐inverter highly stable and reliable. First, the topology and operating principle of the studied micro‐inverter are discussed, followed by the mathematical models established. Afterwards, the state‐space equations of the micro‐inverter are established for all the operating states, of which two transition variables are introduced to realize a linear presentation. On this basis, a double closed‐loop control method is designed to regulate the DC‐link voltage and current. Meanwhile, the flying capacitor voltage is controlled in a closed‐loop form to do the job of a power pulsating buffer (PPB). Following this, the proposed compensation power‐decoupling strategy is elaborated. Finally, some experimental results are presented to indicate the high performance of the studied micro‐inverter and verify the effectiveness of the proposed strategy.

“…In order to overcome the above-mentioned drawbacks, soft computing techniques are introduced in the literature. 10,11 Although artificial neural network, 12 fuzzy logic, 13 and adaptive-network-based fuzzy inference system (ANFIS) 14 track global maximum power point (GMPP), it depends upon extensive training of the model which gives rise to computation burden on the system. Moreover, the system is burdened with large memory storage.…”

mentioning

confidence: 99%

A novel constraint‐based improved equilibrium optimization for global peak tracking of photovoltaic system under complex shading conditions

Reddy

2023

The P-V characteristics of photovoltaic (PV) array exhibit multiple peaks during partial shading conditions (PSCs). Traditional maximum power point tracking (MPPT) approaches are unable to locate the global power point and frequently converge to the local power point. In recent years, evolutionary techniques have given a satisfactory result by tracking global maximum power point (GMPP) but at the cost of ample memory storage and computational burden. Therefore, this paper proposes an improved equilibrium optimization (IEO) technique to track maximum power from the solar photovoltaic system under PSC. The reduced search space exploration by using the skipping method is implemented to improve the convergence speed. The proposed IEO algorithm is capable of differentiating between uniform shading, different PSCs, and solar intensity variation with fast tracking speed. Similarly, the proposed algorithm has been compared with deterministic Jaya (DM-Jaya), crow search algorithm (CSA), and equilibrium optimization (EO) algorithms. The effectiveness of the proposed IEO technique-based MPPT has been validated through simulations and experiments. The results demonstrate the capability of IEO in tracking GMPP with an average efficiency of 99.72% and average tracking time of 0.708 s. Further, the proposed algorithm has been implemented for grid integration under PSC. The results reveal that the proposed method has high PV tracking efficiency, negligible steady-state error, a low MPPT period, and a quick convergence velocity.