Post‐disturbance transient stability assessment of power systems by a self‐adaptive intelligent system. Issue 3 (1st February 2015)

• Record Type: Journal Article
• Authors: Zhang, Rui; Xu, Yan; Dong, Zhao Yang; Wong, Kit Po
• Is Part Of: IET generation, transmission & distribution. Volume 9:Issue 3(2015)
• Page Start: 296
• Page End: 305
• Publication Date: 2015-02-01
• Subjects: power system transient stability -- phasor measurement -- learning (artificial intelligence) -- power engineering computing -- power system faults -- decision making; post‐disturbance transient stability assessment -- power systems -- self‐adaptive intelligent system -- synchronous phasor measurements -- fault clearance -- extreme learning machines -- post‐disturbance generator voltage trajectories -- self‐adaptive TSA decision‐making mechanism -- ELM ensemble classiflers -- online pre‐disturbance TSA -- IEEE 50‐machine system -- New England system; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2014.0264
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Intelligent system (IS) using synchronous phasor measurements for transient stability assessment (TSA) has received continuous interests recently. For post‐disturbance TSA, one pivotal concern is the response time, which was reported in the literature as a fixed value ranging from 4 cycles to 3 s after fault clearance. Since transient instability can develop very fast, there is a pressing need for faster response speed. This paper develops a novel IS to balance the response speed and accuracy requirements. A set of classifiers are sequentially organised, each is an ensemble of extreme learning machines (ELMs), whose inputs are post‐disturbance generator voltage trajectories and outputs are the classification on the stable/unstable status of the post‐disturbance system and an evaluation of the credibility of the classification. A self‐adaptive TSA decision‐making mechanism is designed to progressively adjust the response time, such that the IS can do the classification faster, thereby allowing more time for emergency controls. The ELM ensemble classifiers can also be updated by on‐line pre‐disturbance TSA results due to its very fast learning speed. Case studies on the New England system and IEEE 50‐machine system have validated the high efficiency and accuracy of the IS.