Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms.

This article discusses the optimal placement of distributed generation (DG) in power distribution systems and the evaluation of losses and voltage using machine learning algorithms. The proposed algorithm accurately predicts DG positions using Support Vector Machine, Random Forest, and Radial Neural Networks. The research highlights the effectiveness of these algorithms in enhancing system performance and providing valuable insights for decision-making in the field of distributed generation. The proposed algorithm effectively addresses voltage imbalance, line loading, and power losses in the distribution system, offering advantages over other algorithms. The performance of the algorithm is evaluated using metrics such as R-squared and Mean Absolute Percentage Error, and it consistently outperforms existing methods in terms of prediction accuracy. The findings suggest that the proposed model can effectively address the challenge of predicting distribution systems. The given text is a list of references to various research papers and articles related to distributed generation in power systems, providing valuable insights for researchers and library patrons interested in understanding and exploring the field. [Extracted from the article]