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[Advances in Intelligent Systems and Computing] Applications of Artificial Intelligence Techniques in Engineering Volume 698 (SIGMA 2018, Volume 1) || Predictive Control of Energy Management System for Fuel Cell Assisted Photo Voltaic Hybrid Power System
摘要： Distributed generation systems also known as hybrid power systems which involve renewable energy sources are extensively used due to their ef?ciency and green interface. Considering the varying environmental conditions, these systems are prone to many disadvantages and limitations. In order to overcome these constraints, intelligent techniques which can achieve steady process and power balance are to be implemented. This paper provides an intelligent control using fuzzy inference system and energy management algorithm for Fuel cell assisted PV Battery system. The supervisory control was implemented to achieve utmost feasible ef?- ciency despite varying conditions such as irradiance and Hydrogen levels. With Lev- elized cost being adapted, an ef?cient energy management system attributes for even power distribution throughout the day can be implemented. Our thought process was demonstrated, and ?nal software interface was simulated using MATLAB/Simulink to obtain results which con?rm the effectiveness of the developed system.
关键词： MPPT,Inference systems,Fuzzy logic controller,Energy management,Fuel cell,PVFC hybrid system
更新于2025-09-23 15:21:01
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Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model
摘要： The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.
关键词： solar irradiation,adaptive neuro-fuzzy inference systems,PVs power output forecasting,particle swarm optimization-artificial neural networks
更新于2025-09-19 17:13:59