scholarly journals A Multifeature Fusion Approach for Power System Transient Stability Assessment Using PMU Data

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yang Li ◽  
Guoqing Li ◽  
Zhenhao Wang ◽  
Zijiao Han ◽  
Xue Bai
Keyword(s):  
Power Systems ◽  
Power System ◽  
Transient Stability ◽  
Area Measurement ◽  
Feature Subset ◽  
Stability Assessment ◽  
Wide Area Measurement System ◽  
Feature Spaces ◽  
Proposed Model ◽  
Fusion Approach

Taking full advantage of synchrophasors provided by GPS-based wide-area measurement system (WAMS), a novel VBpMKL-based transient stability assessment (TSA) method through multifeature fusion is proposed in this paper. First, a group of classification features reflecting the transient stability characteristics of power systems are extracted from synchrophasors, and according to the different stages of the disturbance process they are broken into three nonoverlapped subsets; then a VBpMKL-based TSA model is built using multifeature fusion through combining feature spaces corresponding to each feature subset; and finally application of the proposed model to the IEEE 39-bus system and a real-world power system is demonstrated. The novelty of the proposed approach is that it improves the classification accuracy and reliability of TSA using multifeature fusion with synchrophasors. The application results on the test systems verify the effectiveness of the proposal.

Real-time transient stability assessment using stacked auto-encoders

2020 ◽  
Vol 39 (4) ◽  
pp. 971-990
Author(s):  
Masoud Azarbik ◽  
Mostafa Sarlak
Keyword(s):  
Power System ◽  
Measurement System ◽  
Design Methodology ◽  
Transient Stability ◽  
Area Measurement ◽  
Stability Assessment ◽  
Content Type ◽  
Wide Area Measurement System ◽  
Measurement Units ◽  
System Topology

Purpose This paper aims to report how one can assess the transient stability of a power system by using stacked auto-encoders. Design/methodology/approach The proposed algorithm works in a power system equipped with the wide area measurement system. To be more exact, it needs pre- and post-disturbance values of frequency sent from phasor measurement units. Findings The authors have investigated the performance of the proposed method. Going through details, the authors have simulated many contingencies, and then have predicted the transient stability in each of which by using the proposed algorithm. Originality/value The results demonstrate that the algorithm is fast, and it has acceptable performance under different circumstances including the change of system topology and failures of telecommunication channels.

scholarly journals Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

2021 ◽  
Vol 13 (12) ◽  
pp. 6953
Author(s):  
Yixing Du ◽  
Zhijian Hu
Keyword(s):  
Power Systems ◽  
Power System ◽  
Transient Stability ◽  
Short Term Memory ◽  
Risk Function ◽  
Stability Margin ◽  
Risk Level ◽  
Stability Assessment ◽  
Lstm Network ◽  
Hierarchical Assessment

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control.

An Overview of Wide Area Measurement System and Its Application in Modern Power Systems

2019 ◽  
pp. 289-307
Author(s):  
H. H. Alhelou
Keyword(s):  
Power Systems ◽  
Power System ◽  
General System ◽  
Area Measurement ◽  
Wide Area ◽  
Measurement Unit ◽  
Monitoring And Control ◽  
Wide Area Measurement System ◽  
Power System Monitoring ◽  
Recent Developments

In this chapter, wide area measurement systems (WAMS), which are one of the cornerstones in modern power systems, are overviewed. The WAMS has great applications in power system monitoring, operation, control, and protection systems. In the modern power systems, WAMS is adopted as a base for the modern monitoring and control techniques. Therefore, an introduction of WAMS is firstly provided. Then, phasor measurement unit (PMU), which is the base of WAMS, is described. Afterward, the most recent developments in power system estimation, stability, and security techniques, which are based on WAMS, are introduced. Later, general system setup for WAMS-based under-frequency load shedding (UFLS) is provided. Finally, the required communications infrastructures are comprehensively discussed.

scholarly journals An Improved Power System Transient Stability Prediction Model Based on mRMR Feature Selection and WTA Ensemble Learning

2020 ◽  
Vol 10 (7) ◽  
pp. 2255
Author(s):  
Jun Liu ◽  
Huiwen Sun ◽  
Yitong Li ◽  
Wanliang Fang ◽  
Shuanbao Niu
Keyword(s):  
Feature Selection ◽  
Power Systems ◽  
Power System ◽  
Prediction Model ◽  
Ensemble Learning ◽  
Prediction Accuracy ◽  
Transient Stability ◽  
Stability Assessment ◽  
Stability Prediction ◽  
Electromagnetic Power

Fast online transient stability assessment (TSA) is very important to maintain the stable operation of power systems. However, the existing transient stability assessment methods suffer the drawbacks of unsatisfactory prediction accuracy, difficult applicability, or a heavy computational burden. In light of this, an improved high accuracy power system transient stability prediction model is proposed, based on min-redundancy and max-relevance (mRMR) feature selection and winner take all (WTA) ensemble learning. Firstly, the contributions of four different series of raw sampled data from all of the three-time stages, namely the pre-fault, during-fault and post-fault, to transient stability are compared. The new feature of generator electromagnetic power is introduced and compared with three conventional types of input features, through a support vector machine (SVM) classifier. Furthermore, the two types of most contributive input features are obtained by the mRMR feature selection method. Finally, the prediction results of the electromagnetic power of generators and the voltage amplitude of buses are combined using the WTA ensemble learning method, and an improved transient stability prediction model with higher accuracy for unstable samples is obtained, whose overall prediction accuracy would not decrease either. The real-time data collected by wide area monitoring systems (WAMS) can be fed into this model for fast online transient stability prediction; the results can also provide a basis for the future emergency control decision-making of power systems.

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