Comparative Analysis between Wind and Solar Forecasting Methods Using Artificial Neural Networks and Fuzzy Logic
The main objective of the study is to suppress the Renewable energies, with effects in supporting sustainable development. Today, Increase the demand for Renewable energy resources (RES) in distribution systems. In this paper presents a control strategy of three-phase grid interfacing inverter to utilize the renewable energy Source with a grid efficiently. Controlling of the inverter in such a way that to utilize the following compensate load current, compensate load voltage, compensate reactive load power and load neutral Current. The Renewable Energy Source may be Solar, or Wind depends on distribution system voltage level. All these works of the inverter are done either individually or combined to overcome the unbalanced effects of all types of linear, non-linear, balance or unbalance loads at the distribution level. The increasing use of renewable energy contributes to lower pollution generated by other methods of producing electricity, preserves other types of fuels with an effect on environmental protection and also lowers the price of electricity to consumers. Through its components and models, the prototype will have a positive impact on decision support process in the renewable energy field for both producing units and regulators. The forecasting methods using neural networks. An artificial neural network (ANN)and fuzzy logic is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the (ANN) and fuzzy logic artificial neural network because a neural network changes - or learns, in a sense - based on that input and output.
Author Name: Reju Roy and N. Vinothini
Author Email: email@example.com
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Keywords: Renewable Energy Integration, Neural Network, Wind Power Plants, Photovoltaic Generation