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