Mitigating Future Blackouts via Smart Relays: A Machine Learning Approach

As the electric power systems in the United States become increasingly large, complex, and interconnected, the conventional relays and protection systems are proving to be inadequate during some abnormal conditions. In particular, there exists a significant history of relay protection schemes malfunctioning and, ultimately, leading to the infamous system-wide failures, known as electric power blackouts. The malfunctioning ranges from: (1) disconnecting a functional equipment component because of ’false alarms’ which are caused by abnormal conditions elsewhere in the system, and triggering cascading failures of other components; (2) not clearly differentiating the equipment failures from unusually large load demand deviations; and, (3) not providing sufficient coordination of the affected components to disconnect service only to the minimal number of customers and to isolate the rest of the system from the effects of the triggering events. Considering the possibility of carefully planned malicious attacks on the electric power system, today’s protection systems would be inadequate during such conditions, as well. More intelligent relays are, therefore, needed to meet both security and reliability requirements of the current and future electric power grids. In this dissertation, we investigate the existing logic of protection relays in electric power systems and their roles in preventing or mitigating large-scale blackouts. We review several proposed solutions to this problem which employ communications and intelligent algorithms. After reviewing such solutions, we propose a new machine learning based approach for the design of smart protective relays. The goal of smart relays is to classify and discriminate normal conditions from fault conditions based on local measurements. It is shown that the proposed SVM-based smart relays can detect the location of an initial fault (in terms of which zone it belongs to) using local current, voltage, real power and reactive power measurements; and by continuing to monitor these metrics they can make a correct decision even when the state of the system changes after some equipment failure. By making an intelligent decision on whether and when to trip, and communicating the changes observed to SCADA for quick and intelligent decision making, SVM-based smart relays have the potential to mitigate large-scale blackouts and confine them to much smaller areas. Notably, we show that by using SVM-based smart relays only at relatively few critical locations where they have the highest probability to be tripped incorrectly, the probability of cascade of failures and a blackout can be substantially reduced.