Machine learning for protection of distribution networks and power electronics-interfaced systems

Distribution network protection is becoming more sophisticated in the wake of ever-changing landscape of power systems driven by the vast renewable energy integration mostly sited behind the meters, growing uncertainty and volatility subsequent to smart demand response and renewable energy integrations, further fusion of power electronics-interfaced equipments, and more constrained distribution branches as the result of load growth and limited investments. In the opposite side, the massive deployment of smart meters, proliferation of advanced measuring devices such as phasor measurement units, emerging electric and not-electric sensors, and IoT-enabled data gathering platforms continually expand/nourish the databases; they hence offer unprecedented opportunities to take the advantage of data-driven techniques. Machine learning (ML) as a principal class of artificial intelligence is the perfect match solution to this need and has newly revoked many researchers’ interests to tackle the problems excluding their exact/detailed models. This paper discusses applications of ML techniques in protection and dynamic security assurance of active distribution network, microgrids, and power electronics-based systems.