Partial shading conditions lead to power mismatches among photovoltaic (PV) panels, resulting in the generation of multiple peak power points on the P-V curve. At this point, conventional MPPT algorithms fail to operate effectively. This research work mainly focuses on the exploration of performance optimization and harnessing more power during the partial shading environment of solar PV systems with a single-objective non-linear optimization problem subjected to different operations formulated and solved using recent metaheuristic algorithms such as Cat Swarm Optimization (CSO), Grey Wolf Optimization (GWO) and the proposed Chimp Optimization algorithm (ChOA). This research work is implemented on a test system with the help of MATLAB/SIMULINK, and the obtained results are discussed. From the overall results, the metaheuristic methods used by the trackers based on their analysis showed convergence towards the global Maximum Power Point (MPP). Additionally, the proposed ChOA technique shows improved performance over other existing algorithms.

This paper proposes the application of a metaheuristic algorithm inspired by the social behavior of chimps in nature, called Chimp Optimization Algorithm (ChOA), for the maximum power point tracking of solar photovoltaic (PV) strings. In this algorithm, the chimps hunting process is mathematically articulated, and new mechanisms are designed to perform the exploration and exploitation. To evaluate the ChOA, it is applied to some fixed dimension benchmark functions and engineering problem application of tracking maximum power from solar PV systems under partial shading conditions. Partial shading condition is a common problem that appears in the solar PV modules installed in domestic areas. This shading alters the power developed by the solar PV panel, and exhibits multiple peaks on the power variation with voltage (P-V) characteristic curve. The dynamics of the solar PV system have been considered, and the mathematical model of a single objective function has been framed for tuning the optimal control parameter with the suggested algorithm. Implementing various practical shading patterns of solar PV systems with the ChOA algorithm has shown improved solar power point tracking performance compared to other algorithms in the literature.

The power versus voltage curves of solar photovoltaic panels form several peaks under fractional (partial) shading conditions. Traditional maximum output power tracking (MPPT) techniques fail to achieve global peak power at the output terminals. The proposed Cat Swarm Optimization (CSO) method intends to apply MPPT techniques to extract the global maxima from the shaded photovoltaic systems. CSO is a robust and powerful metaheuristic swarm-based optimization technique that has received very positive feedback since its emergence. It has been used to solve a variety of optimization issues, and several variations have been developed. The CSO-based maximum power tracking technique can successfully tackle two major issues of the PV system during shading conditions, including random oscillations caused by conventional tracking techniques and power loss. The proposed techniques have been extensively used in comparison to conventional algorithms like the Perturb and the Observe (P and O) technique. The main objective is to achieve a tracking speed for extracting the Maximum Power Point (MPP) from the solar Photovoltaic (PV) system under fractional shading conditions by using CSO. Modeling of the solar photovoltaic array in the MATLAB/Simulink platform comprises a photovoltaic module, a switching converter (Boost Converter), and the load. The PSO and CSO techniques are applied to the PV module under different weather conditions. The PSO algorithm is compared to the CSO algorithm according to simulation results, revealing that the CSO algorithm can provide better accuracy and a faster tracking speed.

The global demand for electrical energy has witnessed a substantial increase, presenting a challenge for power systems worldwide. In addition to technical considerations, the escalating issue of global warming has become a paramount concern in the planning studies of various sectors. The formulation and resolution of a single-objective non-linear optimization problem are carried out, considering different operational scenarios. Recent heuristic algorithms, including Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), Teaching Learning Based Optimization (TLBO), Grey Wolf Optimization (GWO) and Chimp Optimization algorithm (ChOA) are employed to address the complexities associated with maximizing power output under partial shading conditions in solar PV systems. The inherent challenges of achieving MPPT under such conditions make conventional analytic approaches computationally intensive. Hence, this study leverages heuristic algorithms to optimize solar PV system performance, providing efficient solutions to the associated optimization problems. The current research work was performed on a test system using a MATLAB/SIMULINK environment and the results are presented and discussed. From the simulation results, it was found that ChOA have shown higher conversion efficiency of 99.63% with maximum power output of 525.13 W when compared to other optimization algorithms for the given shading pattern condition. Further, ChOA offers easy implementation and faster convergence, outperforming established methods in GMPP search by reducing power oscillations and achieving precise MPP convergence.

Solar energy is a sustainable source of energy that is renewable and inexhaustible. It is clean and pollution free in nature; its usage is strongly influenced by environmental and technological aspects. The present work mainly focuses on the application of novel optimization technique for extracting maximum solar energy with the solar Photovoltaic (PV) module. The solar radiation and temperature fluctuate throughout the day; hence the maximum power extraction using PV panel is always variable which results in decreasing the efficiency of PV module. Generally, Maximum Power Point Tracking (MPPT) method is widely used for extraction of optimum energy from the solar PV panels under certain conditions. Numerous conventional MPPT methods were developed by the earlier researchers such as Perturbation and Observation, Incremental Conductance and hill climbing for extracting maximum energy at a uniform irradiation level. Most of the conventional MPPT methods are generating errors under certain circumstances and this leads to further reduction in the efficiency of the PV systems. In this context, the present research work explores an evolutionary technique referred as Particle Swarm optimization (PSO) employed in MPPT detection and in harnessing maximum solar power from the solar PV module in order to enhance its efficiency. The present work also depicts the superiority of particle swarm optimization of MPPT method over Perturbation and Observation approach.

The rapid developing smart cities in India increase the great demand for electric power generation using renewable energy sources. Sun irradiation that fall on the solar photovoltaic (PV) panel has different P-V and I-V characteristics. This work presents the extraction of global maximum power point tracking of a PV system using the novel population based optimization method called teaching learning based optimization (TLBO) technique inspired by class room teaching environment. The proposed method overcomes the shortfalls of other conventional MPPT tracking methods in view of their tracking efficiency, steady state oscillations. TLBO and PSO algorithms were compared based on the number of fitness evaluations rather than that of number of population under different environmental conditions. Also, the proposed research work analysed in qualitative and quantitative approach through matlab/simulink respectively. It is found TLBO met heuristic method shown better performance over PSO method with less computational effort and good consistency.