Zonal Machine Learning-Based Protection for Distribution Systems

Abstract

Adaptive protection is defined as a real-time system that can modify the protective actions according to the changes in the system condition. An adaptive protection system (APS) is conventionally coordinated through a central management system located at the distribution system substation. An APS depends significantly on the communication infrastructure to monitor the latest status of the electric power grid and send appropriate settings to all of the protection relays existing in the grid. This makes an APS highly vulnerable to communication system failures (e.g., broken communication links due to natural disasters as well as wide-range cyber-attacks). To this end, this paper presents the addition of local adaptive modular protection (LAMP) units to the protection system to guarantee its reliable operation under extreme events when the operation of the APS is compromised. LAMP units operate in parallel with the conventional APS. As a backup, if APS fails to operate because of an issue in the communication system, LAMP units can accommodate a reliable fault detection and location on behalf of the protection relay. The performance of the proposed APS is verified using IEEE 123 node test system.

Adaptive Machine-Learning-Based Transmission Line Fault Detection and Classification Connected to Inverter-Based Generators

“…Identification using machine learning: ML can be used to detect and identify the system configuration changes by collecting measurements at different locations of the power system, such as lines, generators, and supplementary devices (fault current limiters, reactive power compensators, and others). The SVM classifier was used in [18], where the three-phase voltage and current, RMS values, and the zero-sequence current were the input features measured at different locations in the IEEE 123-node distribution test system. The authors in [19] proposed using several ML classifiers (SVM, k-NN, and ensemble algorithms) to identify the system configurations of a simulated standard power distribution system.…”

Protection Analysis of a Traveling-Wave, Machine-Learning Protection Scheme for Distributions Systems With Variable Penetration of Solar PV

“…These new approaches typically suggest the establishment of several set points for different operation conditions. This can be achieved by locally pre-defined decision rules [19], [20], or by creating complex optimization models and an appropriate communication system [21]- [25]. Some of these methods propose centralized adaptive protection with communication to monitor the system status and periodically update the relay settings when changes in the system occur, such as renewable generation or significant load variations [26].…”