RBFNN assisted transient instability-based risk assessment of cyber-physical power system. (May 2022)

• Record Type: Journal Article
• Authors: Milan Khalkho, Anant; Kumar Mohanta, Dusmanta
• Is Part Of: International journal of electrical power & energy systems. Volume 137(2022)
• Publication Date: 2022-05
• Subjects: Extended Kalman filter (EKF) -- Phasor measurement unit (PMU) -- Transient stability assessment (TSA) -- Radial basis feed forward neural network (RBFNN) -- Power system-based cyber-physical system (PSCPS); Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107787
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A synchrophasor data analytics based predictive algorithm for identifying vulnerable generating units transgressing the stability boundaries has been formulated for proactive control actions. A predictive assessment of degree of instability of different generating units has been investigated using RBFNN in combination with EKF. A faster real-time stability prediction of generating units after 10 cycles of operation of circuit breaker has been validated through case studies of IEEE-39 bus system. Assessment of risk pertaining to transient instability as well as loss of observability has been formulated for commensurate proactive control actions. Abstract: Wide area monitoring and control of modern power system sprawling over large geographical area, has emerged as a viable technology employing phasor measurement units (PMUs) as its backbone. The PMUs are optimally placed to make the whole power system observable. Due to tripping of some generating units, or transmission lines the power balance gets disrupted. As a consequence, the power system becomes prone to risk of transient instability. Also, loss of observability becomes an additional risk due to tripping of transmission lines, which is instrumental for contributing to indirect observability. The purpose of this paper is to develop a predictive approach for identifying vulnerable generating units for proactive control actions to ensure transient stability. The methodology involves use of extended Kalman filter (EKF) for processing real-time PMU data for predicting the dynamic capability of generating unit so as to assess the power swings during transients arising due to contingencies. The radial basis feed-forward neural network (RBFNN) assisted algorithm categorizes generating units into marginal and risky categories based in their trend of swings with respect to dynamic capability curve of generating unit. The case-studies pertaining to IEEE-39 bus system under different contingencies, validate the efficacy of the proposed approach for real world applications. Since, the PMUs communicate synchronized data to PDCs the cyber system is an integral part of the wide area monitoring and control system. Therefore, a cyber physical approach takes into account the loss of observability as a risk from the cyber perspective. Both the risk associated with the physical as well as the cyber layers have been investigated.