A critical review on advanced reconfigured models and metaheuristics-based MPPT to address complex shadings of solar array

The classic Photovoltaic system maximum power point tracking technique cannot concurrently take into account the dynamic response speed and steady-state accuracy when the light intensity changes. To address this issue, a new composite variable step MPPT control algorithm is developed in this study. Based on the three-stage variable step incremental conductance method, the algorithm adds the Kalman filtering algorithm to pre-process the photovoltaic cells output signal, and uses a new calculation approach to adjust the variable step coefficient. As a result, the perturbation step can be automatically modified according to changes in the external environment, which resolves the issues with poor dynamic reaction speed when the classic variable step algorithm started and the light changed. Compared to conventional MPPT control algorithms, the improved MPPT strategy can be easily realized using a hardware control system since it has a simplified control logic and requires less data to be calculated. In this study, the hardware circuit of the enhanced MPPT control algorithm is built using the ESP32 as the primary control chip. This chip can be utilized in conjunction with the Internet of Things to enable remote monitoring of the solar power system’s operational state. According to test results, the algorithm can instantly detect the maximum power point in all lighting circumstances with tracking accuracy of up to 99.6% and a reduction in dynamic response time of the system to 0.12 s.

/pdf/an-efficient-variable-step-solar-maximum-power-point-34h0nfef.pdf

An Efficient Variable Step Solar Maximum Power Point Tracking Algorithm

Photovoltaic (PV) panels are devices capable of transforming solar energy into electricity without emissions. They are still a trending technology in the market not only because of the renewable features but also due to the avoidance of movable parts, which makes them an option with low maintenance. If the output voltage is insufficient or needs to be regulated, a boost converter is commonly connected to a PV panel. In this article, a commercial PV with a boost converter is controlled through a dSPACE platform for a maximum power point tracking (MPPT) task. Due to previous related experience, a fuzzy logic technique is designed and tested in real-time. The results are compared with an incremental conductance (IncCond) algorithm because it is a feasible and reliable tool for MPPT purposes. The outcomes show enhancement (in comparison with IncCond) in the steady-state oscillation, response time and overshoot values, which are 73.2%, 81.5% and 52.9%, respectively.

https://www.mdpi.com/1996-1073/16/2/748/pdf?version=1673249630

A Fuzzy Logic Control for Maximum Power Point Tracking Algorithm Validated in a Commercial PV System

The performance of a photovoltaic system is strongly affected by the environmental conditions which it is subjected such as random atmospheric variations. In order to improve the performance of a photovoltaic system, the work of this paper is devoted to the comparative study between the following MPPT algorithms: the perturbation and observation algorithm (P&O) and the particle swarm optimization algorithm PSO. These two algorithms are tested under various atmospheric conditions and evaluated in terms of efficiency, stability, speed, and robustness. The obtained simulation results show the effectiveness of the PSO than the P&O algorithm.

Comparative Study of P&O and PSO Particle Swarm Optimization MPPT Controllers for Photovoltaic Systems

In this paper, a novel hybrid Maximum Power Point Tracking (MPPT) algorithm using Particle-Swarm-Optimization-trained machine learning and Flying Squirrel Search Optimization (PSO_ML-FSSO) has been proposed to obtain the optimal efficiency for solar PV systems. The proposed algorithm was compared with other well-known methods viz. Perturb & Observer (P&O), Incremental Conductance (INC), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CSO), Flower Pollen Algorithm (FPA), Gray Wolf Optimization (GWO), Neural-Network-trained Machine Learning (NN_ML), Genetic Algorithm (GA), and PSO-trained Machine Learning. The proposed algorithm was modelled in the MATLAB/Simulink environment under different operating conditions, for example, with step changes in temperature, solar irradiance, and partial shading. The proposed algorithm improved the efficiency up to 0.72% and reduced the settling time up to 76.4%. The findings of the research highlight that PSO_ML-FSSO is a potential approach that outperforms all other well-known algorithms tested herein for solar PV systems.

/pdf/a-novel-hybrid-mppt-approach-for-solar-pv-systems-using-18cnrxa9.pdf

A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization

Of all the renewable energy sources, solar photovoltaic (PV) power is estimated to be a popular source due to several advantages such as its free availability, absence of rotating parts, integration to building such as rooftops, and less maintenance cost. The nonlinear current-voltage (I–V) characteristics and power generated from a PV array primarily depend on solar insolation/irradiation and panel temperature. The extracted PV output power is influenced by the accuracy with which the nonlinear power–voltage (P–V) characteristic curve is traced by the maximum power point tracking (MPPT) controller. In this paper, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). To validate the usefulness of the RIO MPPT algorithm, MATLAB/Simulink simulations are performed under varying environmental conditions, for example, step changes in solar irradiance, and partial shading of the PV array. Furthermore, the search performance of the RIO algorithm is examined on different unconstrained benchmark functions, and it is that realized that the RIO algorithm has improved convergence characteristics in terms of finding the optimal solution than Particle swarm optimization (PSO). The results demonstrated that the RIO-based MPPT performs remarkably in tracking with high accuracy as the PSO-based MPPT.

https://www.preprints.org/manuscript/202202.0126/v1/download

Nicholas Kakra Ntiakoh

Papers

Roach Infestation Optimization MPPT Algorithm for Solar Photovoltaic System