This article proposes a simple yet fully adaptive particle swarm optimization (PSO) algorithm to find the global peak (GP) of a photovoltaic array under partial shading condition. It exploits a constrained optimization approach having a very simple PSO structure with two adaptive parameters. The first one that controls the magnitude of velocity is adaptively varied according to the absolute distance of each particle with respect to best particle's position. The other parameter controls the search space, has been varied based on a penalty condition that decides the participation of particle in the next iteration. Regardless of a wide range of population size, the proposed scheme precisely locates the GP without jeopardizing the tracking speed—consumes a maximum of 16 perturbations. The algorithm is implemented on a Cuk converter and compared to two well-known tracking methods. It is also validated through run length distribution (RLD) test. The obtained RLD results reveal that proposed PSO outperforms other two methods, in terms of convergence speed and success rate. It always obtains 100% rate by utilizing fewer perturbations of voltage. When tested for a whole day environmental profile, the average efficiency of proposed algorithm is found to be 99.65%.

This paper proposes a new parameter extraction method of photovoltaic cell, based on the differential evolution (DE) technique. The proposed method requires very few control parameters and converges rapidly to a solution. Furthermore, it can fit the I-V curve very accurately irrespective of the values of the initial parameters guesses. The performance of DE is evaluated against the well known genetic algorithm (GA) using a synthetic and experimental I-V data set. It is found that the DE method fits the I-V curve better than GA, has a lower fitness function value and faster execution time.

In the last decade, there has been a substantial surge in the advancement of research into the maximum power point tracking (MPPT) controller. The MPPT approaches, on the other hand, continue to be in high demand due to the ease and simplicity with which tracking techniques can be implemented on the maximum power point (MPP). Diverse MPPT approaches and their modifications from various literature are categorized and thoroughly explored in this work, which is divided into two sections. The discussions are centered on the primary goal of attaining the most extraordinary feasible MPPT technique that produces the best results at the lowest possible expense. In order to determine which MPPT approaches to use, evaluations from earlier literature are used to guide the decision. In this section, we will examine the evaluation of the MPPT technique in two sections. Previously, in Part I, we explored the MPPT techniques based on constant parameters and trial-and- error. Part II of this article will examine the MPPT technique, which is based on mathematical computation, measurement, and comparison, and the algorithm development that has occurred in recent years. Furthermore, this section’s assessment for selecting MPPT approaches is based on previous literature reviews. To aid with this selection, the following criteria for the MPPT approach are proposed: sensors and analog/digital requirements, costeffectiveness, simplicity, stability, efficiency, and tracking speed. This enables the reader to select the MPPT technique that is most appropriate for their application.

The development of research on the maximum power point tracking (MPPT) controller has increased significantly in this decade. The MPPT technique, however, is still demanding because of the ease and simplicity of implementing tracking technique on the maximum power point (MPP). In this paper, MPPT techniques and their modifications from various literature are classified and examined in detail. The discussions are focused on the main objective of obtaining the best possible MPPT technique with the best results at a low cost. The assessment for the selection of MPPT techniques is based on assessments from the previous literature. The discussion of the MPPT technique assessment is divided into two parts. In Part I, the MPPT technique based on constant parameters, and trial-and-error will be discussed in detail, along with its algorithm development in recent times.

Particle swarm optimization (PSO) often stagnates in photovoltaic (PV) parameter extraction due to premature convergence and velocity decay. This paper proposes Random Reinitialized PSO (R²-PSO), which periodically reinitializes particle positions and velocities to restore diversity and sustain exploration without disrupting convergence. Performance is evaluated using the run-length distribution (RLD), a probabilistic benchmark that jointly characterizes convergence dynamics and steady-state success. In PV parameter extraction, R²-PSO achieves RMSE = 10⁻⁹, 100% success over 100 runs, and convergence in ≈1.05M function evaluations. Experimental validation on multi-crystalline (SM55), monocrystalline (S75), and thin-film (ST40) modules shows close agreement with manufacturer datasheets. Generalization capability is confirmed through the CEC-2022 single-objective benchmark suite, where R²-PSO attains the lowest total rank and 1st overall position among five competitive PSO-based algorithms, securing best-mean performance on 8 out of 12 functions. Statistical comparisons across both application and benchmark domains indicate uniformly lower error metrics (SSE, MAE, MSE, AE, SE) relative to competing methods. Sensitivity analyses further demonstrate robustness, maintaining 100% success under variations in the VTR threshold, population size, maximum velocity, and epoch initialization frequency. These results establish R²-PSO as an accurate, reliable, and computationally efficient optimizer for PV parameter extraction, while highlighting RLD as a rigorous standard for evaluating stochastic search algorithms.

Microcontrollers (μCs) are extremely useful in renewable energy (RE) converters, where numerous onboard control actions need to be executed at low cost. This paper focusses on a cost‐effective implementation of a fuzzy controller (FC) for the regulation of converters that are normally employed in RE applications such as solar, wind, and tidal. The μC realization has been achieved through simplification of a dual‐input FC (DFC) into a single‐input FC (SFC) using the signed distance approach, followed by the piece‐wise linear (PWL) simplification of SFC named as piece‐wise‐linear single input FC (PWL‐SFC). Despite the elimination of the fuzzification, knowledge inference, and defuzzification stages, PWL‐SFC exhibits a similar control performance to that of DFC. The proposed PWL‐SFC is tested through modeling and simulation using the MATLAB Simulink platform and experimentally validated through a low‐cost dsPIC μC. The results reveal that the proposed PWL‐SFC requires negligible tuning effort and uses three orders of magnitude less computational power compared to DFC.