Local Volt-Var Control Applied in an Islanded Microgrid Using Supervised Learning Techniques

The electrical sector pursuit of technical and ecological alternatives makes it possible to integrate and cooperatively optimize dispersed energy resources, enhancing the stability, dependability, and resilience of contemporary energy systems. Microgrids and artificial intelligence are two ideas that could be included into contemporary power grids in an effort to lower costs and pollution emissions. This work proposes a new energy control and management strategy based on smart devices in this context. It explores machine-learning techniques for implementing supervised learning algorithms to perform automatic volt-var control adjustments and mitigate voltage fluctuations at the point of common coupling using smart inverters. The techniques explored and compared in this study include multilayer perceptron, SVM, and random forest. The results were consistent, with average accuracies above 90%, indicating the relevance of the analyzed models for this application. Thus, this research seeks to improve power quality in islanded microgrids with high penetration of distributed generation and explore the potential of artificial intelligence in decision-making processes.