Design of adaptive protection coordination scheme using SVM for an AC microgrid

Abstract

Microgrids have gathered a significant amount of attention within the past decade and becoming an essential asset in the energy industry. The ability to integrate sustainable renewable energy generation into the distribution network is one of the main reasons for microgrids' popularity. Besides technical advantages, renewable energy sources integration with the grid poses many technical challenges. Due to randomness in generation and different modes of operation, integrating renewable energy sources into power systems will be challenging. Reliable operation of renewable energy sources integrated microgrids even when faults occur, requires an adaptive protection scheme and relay coordination. In order to tackle those issues, this paper developed an adaptive protection coordination scheme using numerical overcurrent relays and support vector machines with a particle swarm optimization approach. Renewable energy sources based on microgrids use power electronics systems that require relays to operate in milliseconds, and the power system will be safe. particle swarm optimization's program execution time exceeds the primary relays. The machine learning algorithm is faster in computation time. This article proposes a method, particle swarm optimization, to estimate the optimum relay operating time. The machine learning model having a support vector machine is proposed to estimate the relay operating time. The proposed research is developed and tested on an IEEE12BUS and IEEE35BUS system. The support vector machine -based on adaptive protection coordination schemes estimates the operating time of directional overcurrent relays, with a lesser program execution time and a low percentage error rate. The derived result defined that the proposed approach is well-suitable for real-time applications of an AC microgrid system.