Real-Time Transient Instability Identification in Power Systems using a PMU-Based EMS System

• Main Authors: Sarmin M.K.N.M., Saadun N., Azmi M.T., Abidin I.Z.
• Other Authors: 56177713500
• Format: Conference Paper
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Subjects: cascading analysis; hardware-in-the-loop simulation; linear state estimation; performance index; phasor measurement unit; Transient stability; Data Analytics; Electric power system interconnection; Electric power system protection; Energy management systems; Hardware-in-the-loop simulation; Learning systems; Real time systems; State estimation; Synthetic apertures; System stability; Wide area networks; Cascading analyse; Hardwarein-the-loop simulations (HIL); Linear state estimation; Operating condition; Operational challenges; Performance indices; Power; Real- time; Time transient; Transient instability; Phasor measurement units

Modern power systems are confronted with operational challenges that increase the risk of transient instability. Existing Dynamic Security Assessment (DSA) tools have limitations, necessitating accurate and timely assessment of transient stability. To address this, a novel approach for real-time identification of transient instability is introduced in this paper using a Thevenin equivalent network model. The proposed method leverages synchronized phasor measurements and incorporates PMU-based Energy Management System (EMS) with Linear State Estimation (LSE) alongside snapshots from existing EMS systems, cascading analysis application, and a performance index (PI) to rank cascading outages based on severity. A case study demonstrates the effectiveness of the proposed method to identify transient instabilities in a large interconnected power system through real-time hardware-in-the-loop (HIL) simulations. By offering enhanced accuracy and efficiency in real-time stability assessment, the method empowers grid operators to promptly act and prevent wide area outages during challenging operating conditions. Future research directions encompass integration with Wide-Area Monitoring, Protection, and Control (WAMPAC) system, incorporation of advanced machine learning techniques alongside data analytics, as well as scalability examination across diverse operating conditions and contingencies. � 2023 IEEE.