Convolutional neural network-based power system transient stability assessment and instability mode prediction. (1st April 2020)

• Record Type: Journal Article
• Authors: Shi, Zhongtuo; Yao, Wei; Zeng, Lingkang; Wen, Jianfeng; Fang, Jiakun; Ai, Xiaomeng; Wen, Jinyu
• Is Part Of: Applied energy. Volume 263(2020)
• Publication Date: 2020-04-01
• Subjects: Transient stability assessment -- Synchronizing torque -- Damping torque -- Convolutional neural network -- Phasor measurement unit; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.114586
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: CNN is designed for both transient stability and instability mode prediction. Stochastic gradient descent with warm restarts training algorithm is employed. Case studies are conducted on both benchmark and practical larger power system. Superior accuracy and robustness to input signal noise and loss. Abstract: Online transient stability assessment (TSA) is vital for power system control as it provides the basis for operators to decide emergency control actions. But none of previous TSA research has taken into consideration the difference between two instability modes (aperiodic instability and oscillatory instability), which may threaten secure operation of power system. To address this problem, a TSA and instability mode prediction method based on convolutional neural network is proposed. The method takes the bus voltage phasor sampled by phasor measurement units (PMUs) during a short observation window after disturbance as input, and outputs the prediction result promptly: stable, aperiodic unstable or oscillatory unstable. The end-to-end model automatically extracts needed features from the raw measurement data, thus freeing itself from reliance on expertise. At the offline training stage, stochastic gradient descent with warm restart (SGDR) optimization algorithm is employed so that the model tends to converge to 'flat' and 'wide' minima with better generalization ability. Case studies conducted on New England 39-bus system and Western Electricity Coordinating Council (WECC) 179-bus system demonstrate superior accuracy, adaptability and scalability of the proposed method compared with conventional machine learning methods. Furthermore, the proposed model is empirically proven to be robust to PMU noise and loss.