Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems

Abstract

In the current energy usage scenario, the demands on energy load and the tariffs on the usage of electricity are two main areas that require a lot of attention. Energy forecasting is an ideal solution that would help us to better understand future needs and formulate solutions accordingly. Some important factors to investigate are the quantity and quality of smart grids as they are significantly influenced by the transportation, storage, and load management of energy. This research work is a review of various machine learning algorithms for energy grid applications like energy consumption, production, energy management, design, vehicle-to-grid transfers, and demand response. Ranking is performed with the help of key parameters and is evaluated using the Rapid Miner tool. The proposed manuscript uses various machine learning techniques for the evaluation of power quality performance to validate an efficient algorithm ranking in a grid-connected system for energy management applications. The use of renewable energy resources in grid-connected systems is more common in modern power systems. Universally, the energy usage sector (commercial and non-commercial) is undergoing an increase in demand for energy utilization that has substantial economic and ecological consequences. To overcome these issues, an integrated, ecofriendly, and smart system that meets the high energy demands is implemented in various buildings and other grid-connected applications. Among various machine learning techniques, an evaluation of seven algorithms—Naïve Bayes, artificial neural networks, linear regression, support vector machine, Q-learning, Gaussian mixture model, and principle component analysis—was conducted to determine which algorithm is the most effective in predicting energy balance. Among these algorithms, the decision tree, linear regression, and neural networks had more accurate results than the other algorithms used. As a result of this research, a proposal for energy forecast, energy balance, and management was compiled. A comparative statement of various algorithms concludes with results which suit energy management applications with high accuracy and low error rates.