Artificial Intelligence based Station Protection Concept for Medium Voltage Microgrids
Despite the rapid improvements in the field of microgrid protection, it continues to be one of the most important challenges faced by the distribution system operators. With the introduction of this new operation concept, the existing protection devices are not able to successfully identify, classify and localize different types of faults that occur in the microgrids due to their dynamic behaviour, especially in the islanded mode of operation. This paper presents a methodology that provides the station protection functionalities that include detection and classification of faults, isolation of the faulty feeder and fault location estimation. The proposed method is based on discrete wavelet transform and artificial neural networks. The test system based on the real data, completely developed in MATLAB Simulink, is used to demonstrate the accuracy of all functionalities of the station protection algorithm that can be easily applied in microgrids. The presented results demonstrated the method accuracy and showed that it can be used as an upgrade of the existing protection equipment for the future implementation of the advanced microgrid station protection system.