Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gains

Self Cite

Integration renewable energy sources into current power generation systems necessitates accurate forecasting to optimize and preserve supply–demand restrictions in the electrical grids. Due to the highly random nature of environmental conditions, accurate prediction of PV power has limitations, particularly on long and short periods. Thus, this research provides a new hybrid model for forecasting short PV power based on the fusing of multi-frequency information of different decomposition techniques that will allow a forecaster to provide reliable forecasts. We evaluate and provide insights into the performance of five multi-scale decomposition algorithms combined with a deep convolution neural network (CNN). Additionally, we compare the suggested combination approach's performance to that of existing forecast models. An exhaustive assessment is carried out using three grid-connected PV power plants in Algeria with a total installed capacity of 73.1 MW. The developed fusing strategy displayed an outstanding forecasting performance. The comparative analysis of the proposed combination method with the stand-alone forecast model and other hybridization techniques proves its superiority in terms of forecasting precision, with an RMSE varying in the range of [0.454–1.54] for the three studied PV stations.

“…( 10 ). The can be described as: where n represents the iteration numbers, , , and indicate the form after , , , and Fourier transform 61 . …”

Section: Methodsmentioning

confidence: 99%

An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques

Guermoui,

Fezzani,

Mohamed

et al. 2024

Self Cite

“…Mutation operators maintain diversity by adding randomness. By using this operator, the GA algorithm avoids local solutions and prevents solutions from becoming similar 43,79,89 , 90 .…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems

Deghfel,

Badoud,

Merahi

et al. 2024

Self Cite

Recently, the integration of renewable energy sources, specifically photovoltaic (PV) systems, into power networks has grown in significance for sustainable energy generation. Researchers have investigated different control algorithms for maximum power point tracking (MPPT) to enhance the efficiency of PV systems. This article presents an innovative method to address the problem of maximum power point tracking in photovoltaic systems amidst swiftly changing weather conditions. MPPT techniques supply maximum power to the load during irradiance fluctuations and ambient temperatures. A novel optimal model reference adaptive controller is developed and designed based on the MIT rule to seek global maximum power without ripples rapidly. The suggested controller is also optimized through two popular meta-heuristic algorithms: The genetic algorithm (GA) and the whale optimization algorithm (WOA). These meta-heuristic approaches have been exploited to overcome the difficulty of selecting the adaptation gain of the MRAC controller. The reference voltage for MPPT is generated in the study through an adaptive neuro-fuzzy inference system. The suggested controller’s performance is tested via MATLAB/Simulink software under varying temperature and radiation circumstances. Simulation is carried out using a Soltech 1sth-215-p module coupled to a boost converter, which powers a resistive load. Furthermore, to emphasize the recommended algorithm’s performance, a comparative study was done between the optimal MRAC using GA and WOA and the conventional incremental conductance (INC) method.

“…Many studies have evaluated various meta-heuristic optimization strategies for tracking the maximum power point in renewable energy (RE) plants. FSSO 67,68 , and CS 69 are better at efficiency, accuracy, resilience, and time to convergence than Genetic Algorithms (GA) 70,71 , Differential Evolution (DE) 72,73 , or Particle Swarm Optimization (PSO) [74][75][76] .…”

Section: Evaluation Of Performancementioning

confidence: 99%

Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments

Bouguerra,

Badoud,

Mekhilef

et al. 2024

Self Cite

This study looks into how to make proton exchange membrane (PEM) fuel cells work more efficiently in environments that change over time using new Maximum Power Point Tracking (MPPT) methods. We evaluate the efficacy of Flying Squirrel Search Optimization (FSSO) and Cuckoo Search (CS) algorithms in adapting to varying conditions, including fluctuations in pressure and temperature. Through meticulous simulations and analyses, the study explores the collaborative integration of these techniques with boost converters to enhance reliability and productivity. It was found that FSSO consistently works better than CS, achieving an average increase of 12.5% in power extraction from PEM fuel cells in a variety of operational situations. Additionally, FSSO exhibits superior adaptability and convergence speed, achieving the maximum power point (MPP) 25% faster than CS. These findings underscore the substantial potential of FSSO as a robust and efficient MPPT method for optimizing PEM fuel cell systems. The study contributes quantitative insights into advancing green energy solutions and suggests avenues for future exploration of hybrid optimization methods.