Optimization and Performance Improvement of Grid-Connected PV Plant Based on ANN-PSO and P&O Algorithms

Abstract

This research investigated the performance of a 5 MW PV grid-connected plant in Al Fashir City, Sudan. The research aims to improve the performance and increase the efficiency of the Al Fashir plant by identifying the maximum power point and increasing the tracking efficiency based on the algorithms developed. The PV systems benefit from MPPT approaches because they improve power output and energy delivery to the load while also extending the useful life of the PV system. The P&O algorithm performance is compared to the ANN trained by the PSO method by a set of solar radiation values. However, time response, oscillation, and stability are the three most important factors to consider when evaluating the effectiveness of any MPPT algorithm. The results show that the ANN trained by the performance of the PSO algorithm was better in time response, tracking speed, and oscillation than the P&O algorithm and could identify the new power point quickly. The results of this study will assist in resizing the PV plant and improve the operation performance and efficiency to provide affordable and reliable power accessible to the people in Al Fashir city.

A Multistep Iterative Ranking Learning Method for Optimal Project Portfolio Planning of Smart Grid

“…When applied specifcally to the infrastructure project portfolio planning scenario of power, the input historical infrastructure project data contain multiple project features and the infrastructure projects are combined in pairs to form a sample, each sample including all features of two infrastructure projects (x (1) i , x (2) i ) and a label y i indicating which infrastructure project should be ranked frst.…”

“…Te optimal project portfolio planning problem of power grid is a nonconvex, multitemporal coupling, strong constrained, multiobjective complex portfolio optimization problem [2,3]. Most existing methods applied Pareto optimization [4], fuzzy multicriteria-decision-making techniques [5], integer linear programming [6], dynamic modelling [7], or expert experience to solve the portfolio optimization problem.…”

Comparative study of nature-inspired maximum power point tracking algorithms for partially shaded photovoltaic systems

“…However, this tracking scheme may be ineffective if the PV panels are partially shaded. Therefore, researchers are exploring efficient and reliable MPPT algorithms, and there has been a shift toward nature-inspired search algorithms [10] in recent years. In this context, the novelty of this study lies in presenting a comparative analysis of different nature-inspired MPPT algorithms for mitigating the challenges faced by PV systems due to fluctuations in temperature and irradiation [11].…”

Performance investigation for analysis of energy management in renewable energy by using particle swarm optimization

“…Some examples renewable energy source is sun, wind, ocean, biomass, and tidal etc. solar power is more power in comparison to other renewable energy because it is most reliable, less maintenance, noiseless and less cost, environment friendly and also reducing fossil fuels dependency more than one third [2][3]. The investments in renewable energy have reaching record-high annual investments exceeding USD 0.5billion in 2021, investment in off-grid renewable solution falls chart of USD 23billion needed annually in off grid solar products alone (mini grids not including between 2021 & 2030 [4].Today, urban areas are home to more than half of the world's population.…”