Online Transient Stability Assessment Implementing the Weighted Least-Square Support Vector Machine with the Consideration of Protection Relays

被引:7
作者
Poursaeed, Amir Hossein [1 ]
Namdari, Farhad [2 ]
机构
[1] Lorestan Univ, Fac Engn, Dept Elect Engn, Khorramabad 465, Iran
[2] Univ Exeter, Fac Environm Sci & Econ, Dept Engn, Exeter EX4 4QF, England
关键词
Support vector machines; Accuracy; Stability criteria; Protective relaying; Power system stability; Phasor measurement units; Vectors; Real-time systems; Transient analysis; Protection; Transient stability assessment; weighted least-square support vector machine; directional overcurrent relay; phasor measurement unit; PREDICTION;
D O I
10.23919/PCMP.2023.000032
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Weighted least-square support vector machine (WLS-SVM) is proposed in this research as a real-time transient stability evaluation method using the synchrophasor measurement received from phasor measurement units (PMUs). This method considers the directional overcurrent relays (DOCRs) for the transmission system, whereas in previous studies, the effect of protective mechanisms on the transient stability was largely ignored. When protective relays are activated in power system, the configuration of the power system is altered to mitigate the risk of the power system becoming unstable. The present study considers the operation of DOCRs in transmission lines for the transient stability so that the proposed method can respond to changes in the configuration of the case study system. In addition, WLS-SVM is employed for an online assessment of the transient stability. WLS-SVM not only is effective in response due to its faster speed, but also is resistant to noise and has excellent performance against the measurement errors of PMUs. To extract the characteristics of the vectors that are fed into the WLS-SVM algorithm, principal component analysis is used. The findings of the suggested technique reveal that it has higher accuracy and optimum performance, as compared to the extreme learning machine method, the adaptive neuro-fuzzy inference system method, and the back-propagation neural network method. The proposed technique is validated in the New England 39-bus system and the IEEE 118-bus system.
引用
收藏
页码:1 / 17
页数:17
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