A novel recursive approach for real-time transient stability assessment based on corrected kinetic energy

2016 ◽  
Vol 48 ◽  
pp. 660-671 ◽  
Author(s):  
Mehdi Zareian Jahromi ◽  
Shahram Montaser Kouhsari
Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6893
Author(s):  
A. F. Diaz-Alzate ◽  
John E. Candelo-Becerra ◽  
Albert Deluque-Pinto

Real-time transient stability studies are based on voltage angle measures obtained with phasor measurement units (PMUs). A more precise calculation to address transient stability is obtained when using the rotor angles. However, these values are commonly estimated, which leads to possible errors. In this work, the kinetic energy changes in electric machines are used as a criterion for evaluating and correcting transient stability, and to determine the precise time of insertion of a special protection system (SPS). Data from the PMU of the wide-area measurement system (WAMS) are used to construct the SPS. Furthermore, it is assumed that a microcontroller can be located in each generation unit to obtain the synchronized angular velocity. Based on these measurements, the kinetic energy of the system and the respective control action are performed at the appropriate time. The results show that the proposed SPS effectively corrects the oscillations fast enough during the transient stability event. In addition, the proposed method has the advantage that it does not depend on commonly proposed methods, such as system models, the identification of coherent machine groups, or the structure of the network. Moreover, the synchronized angular velocity signal is used, which is not commonly measured in power systems. Validation of the method is carried out in the New England power system, and the findings show that the method is helpful for real-time operation on large power systems.


2010 ◽  
Vol 108-111 ◽  
pp. 765-770
Author(s):  
Lin Niu ◽  
Jian Guo Zhao ◽  
Ke Jun Li ◽  
Zhen Yu Zhou

One of the most challenging problems in real-time operation of power system is the prediction of transient stability. Fast and accurate techniques are imperative to achieve on-line transient stability assessment (TSA). This problem has been approached by various machine learning algorithms, however they find a class decision estimate rather than a probabilistic confidence of the class distribution. To counter the shortcoming of common machine learning methods, a novel machine learning technique, i.e. ‘relevance vector machine’ (RVM), for TSA is presented in this paper. RVM is based on a probabilistic Bayesian learning framework, and as a feature it can yield a decision function that depends on only a very fewer number of so-called relevance vectors. The proposed method is tested on New England power system, and compared with a state-of-the-art ‘support vector machine’ (SVM) classifier. The classification performance is evaluated using false discriminate rate (FDR). It is demonstrated that the RVM classifier can yield a decision function that is much sparser than the SVM classifier while providing higher classification accuracy. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time implementation.


2018 ◽  
Vol 7 (1) ◽  
pp. 26-37 ◽  
Author(s):  
Wei HU ◽  
Zongxiang LU ◽  
Shuang WU ◽  
Weiling ZHANG ◽  
Yu DONG ◽  
...  

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