Machine Learning Based Power Distribution System Reliability Improvement
Keywords:
Artificial Neural Network (ANN), Machine Learning (ML), MATLABAbstract
The task of maintaining optimum values of reliability by assessing parameters is becoming an ever-increasing challenge for utilities. This study focuses on optimizing the values of SAIDI and SAIFI indices by implementing a Machine Learning (ML) based method known as Artificial Neural Network (ANN) on the IEEE 9 bus system. The system is modeled in the Simulink environment of MATLAB. The load buses of the system are then subjected to different faults that occur on the distribution network, which affects the reliability of distribution systems. The data collected by this process is used to train the ANN so it can detect and classify these faults. Since the focus of this study is on the distribution section, this means that only the three load buses are being considered for analysis. After the faults had been correctly detected and classified by the ANN, these results were used to optimize SAIDI and SAIFI. In the next phase, software named Windmill was used for reliability analysis. The fault detection time calculated previously was used here to observe the updated conditions of the system and in the calculation of the improved values of SAIFI and SAIDI.
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