Meta Heuristic Optimization Technique For Power Quality Monitoring And Feedback Control For Hybrid Renewal Energy System

Abstract

The percentage of energy generated from solar and wind power is always going up, which is a positive trend. The use of Hybrid Renewable Energy Systems (H.R.E.S.), also known as HRES, in the power generation system is gaining steam. A rise in the proportion of power coming from renewable sources has led to an increase in the number of Power Quality (P.Q.) problems experienced by users and consumers. As a result of this research, electrical engineers will be able to better understand the P.Q. disturbances caused in the power system by hybrid renewable power sources, in particular solar and wind power. The research will make a prediction as to what kinds of PQ disruptions are brought about during the generation of power. The stand-alone Hybrid Renewable Energies System will serve as the primary focus of the project (H.R.E.S.). The information pertaining to the energy system will be gathered and analysed in order to develop a Grey Wolf Optimization technique for the purpose of enhancing the ashington strategies in order to decrease P.Q. disruptions. The proposed method does an analysis on both the previously saved data and the ongoing data from a variety of sources. The data will be gathered, and then they will be used to drive the hardware so that the relevant measures may be taken at the mitigation level. The algorithms will make the system more efficient and will provide the designers with assistance in making the system more capable of being designed. Whenever various types of energy sources are employed to generate electricity, the surveillance system will be capable to help forecast the kinds of p.q. disturbances that are caused as a result. Such p.q. disturbances are monitored by the technology that is being suggested, and they are categorised according to the degree of their impact.