Machine Learning Based Power Quality Enhancement System for Renewable Energy Sources
Keywords:
Renewable Energy, Machine Learning, Multilayer Perceptron, Support Vector RegressionAbstract
The uncertain and sporadic nature of the power that renewable energy sources provide, they are increasingly being integrated into the world's existing electrical networks in the contemporary day. Utilising soft computing methods for energy prediction is an essential part of the solution to these problems and is an intrinsic component of the solution. Because of its close connection to other forms of energy, such as natural gas and oil, generating an accurate prediction of the amount of electricity that will be used is one of the most important steps in the process of creating a national energy strategy. In this work, we use a wide range of Machine Learning methods, such as preprocessing historical load data and analysing the features of the load time series. We included and investigated use trends for both renewable and non-renewable forms of energy. The addition of active power filter capabilities makes it possible for the inverter to be controlled so that it may function as a tool that is capable of performing several functions. As a consequence of this, the inverter may function as both a power converter to add the energy produced by RES to the grid as well as a shunt active power filter (APF) to rectify current imbalance, load harmonics, load reactive power demand, and load neutral current. The option of completing these two jobs simultaneously or one after the other. As a consequence of this, the suggested controller either performs the function of an APF, regulates the flow of electricity between RES and the grid, or integrates all of these functions into a single piece of equipment. In order to assess the operating strategy as well as the control hypothesis, respectively, MATAB simulation and DSP experimentation are both used.
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