An agent‐based fast concurrent prediction scheme for transient and short‐term voltage instability. Issue 22 (21st July 2021)

• Record Type: Journal Article
• Authors: Lashgari, Mahmoud; Shahrtash, Seyed Mohammad
• Is Part Of: IET generation, transmission & distribution. Volume 15:Issue 22(2021)
• Page Start: 3176
• Page End: 3187
• Publication Date: 2021-07-21
• Subjects: Interpolation and function approximation (numerical analysis) -- Power system control -- Power system measurement and metering -- Stability in control theory -- Control of electric power systems -- Interpolation and function approximation (numerical analysis); Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/gtd2.12250
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract: A novel agent‐based online prediction method is presented in this paper, to predict the status of both transient and short‐term voltage (STV) stability, against fault occurrence. In the proposed method, the trajectories of the relative frequency deviation (Δ F ) and the power imbalance (Δ P ) are estimated by employing the third‐degree polynomial curve fitting method. By tracking the estimated trajectories on Δ F –Δ P plane and checking some simple defined rules, an early prediction of both transient and STV instability is achieved in an organized multi‐agent system (MAS). In order to evaluate the performance of the proposed algorithm, the method has been tested on IEEE 39‐bus system, IEEE 118‐bus system and IEEE Nordic test system. Based on the obtained results, the proposed algorithm has an overall accuracy of 99.5% under symmetrical and asymmetrical faults, PMU measurement error, different operating points and topological changes.