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Estimating critical clearing time of Grid Faults Using DA of State-Reduction Model of Power Systems

CSEE Journal of Power and Energy Systems 2024年第2期10卷 807-820页

作者： Yang Liu Zhongyang Chen Huanjin Yao Lin Yi Q.H.Wu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China China Southern Power Grid Research Institute Guangzhou 510663China

This paper proposes a critical clearing time (CCT) estimation method by the domain of attraction (DA) of a state-reduction model of power systems using sum of squares (SOS) programming. By exploiting the property of the Jacobian matrix and the structure of the boundary of the DA, it is found the DA of the state-reduction model and that of the full model of a power system are topological isomorphism. There are one-to-one correspondence relationships between the number of equilibrium points, the type of equilibrium points, and solutions of the two system models. Based on these findings, an expanding interior algorithm is proposed with SOS programming to estimate the DA of the state-reduction model. State trajectories of the full model can be transformed to those of the state-reduction model by orthogonal or equiradius projection. In this way, CCT of a grid fault is estimated with the DA of the state-reduction model. The calculational burden of SOS programming in the DA estimation using the state-reduction model is rather small compared with using the full model. Simulation results show the proposed expanding interior algorithm is able to provide a tight estimation of DA of power systems with higher accuracy and lower time costs.

关键词： critical clearing time domain of attraction expanding interior algorithm state-reduction model SOS programming

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Hybrid Analytical and Data-Driven Model Based Instance-Transfer Method for Power System Online Transient Stability Assessment

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1664-1675页

作者： Feng Li Qi Wang Yi Tang Yan Xu Jie Dang School of Electrical Engineering Southeast UniversityNanjing 210000China Center for Power Engineering(CPE) School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore 639798Singapore Central China Branch of State Grid Corporation of China Wuhan 430077China

Data-driven methods are widely recognized and generate conducive results for online transient stability ***,the tedious and time-consuming process of sample collection is often *** functioning of power systems involves repetitive sample collection due to the constant variations occurring in the operation mode,thereby highlighting the importance of collection *** a means to achieve high sample collection efficiency following the operation mode change,we propose a novel instance-transfer method based on compression and matching strategy,which facilitates the direct acquisition of useful previous samples,used for creating the new sample ***,we present a hybrid model to ensure rationality in the process of sample similarity comparison and selection,where features of analytical modeling with special significance are introduced into data-driven *** the same time,a data-driven method can also be integrated in the hybrid model to achieve rapid error correction of analytical models,enabling fast and accurate post-disturbance transient stability *** a paradigm,we consider a scheme for online critical clearing time estimation,where integrated extended equal area criterion and extreme learning machine are employed as analytical model part and data-driven error correction model part,*** results validate the credible efficacy of the proposed method.

关键词： critical clearing time extreme learning machine instance-transfer method transient stability assessment

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Analytical Representation of Data-driven Transient Stability Constraint and Its Application in Preventive Control

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Journal of Modern Power Systems and Clean Energy 2022年第5期10卷 1085-1097页

作者： Yiwei Fu Xiaohua Zhang Lei Chen Zengyao Tian Kaiyuan Hou Haikuan Wang State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China State Grid Jibei Electric Power Company Beijing 100053China Northeast China Branch of State Grid Corporation of China Shenyang 110180China

Accurate transient stability assessment(TSA) and effective preventive control are important for the stable operation of power systems. With the superiorities in precision and efficiency, data-driven methods are widely used in TSA nowadays. Data-driven TSA model can be adopted in the stability constraints of preventive control optimization, but existing methods are mostly iteration-based ones, which may result in low efficiency, sometimes even non-convergence. In this paper,an analytical representation method of data-driven transient stability constraint is proposed based on a non-parametric regression model built for TSA. Key feature extraction and dominant sample selection are proposed to reduce the scale of the TSA model, and bi-level linearization is applied to further modify *** preventive control model is then formulated as a mixed-integer linear program(MILP) problem with the linearized analytical data-driven transient stability constraint, which can be solved without iterations. An overall procedure of datadriven TSA and preventive control is finally developed. Case studies show that the proposed method has high accuracy in TSA and can achieve effective preventive control of power system with high efficiency.

关键词： Data-driven critical clearing time transient stability constraint non-parametric regression optimal preventive control

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Data-driven Transient Stability Assessment Model Considering Network Topology Changes via Mahalanobis Kernel Regression and Ensemble Learning

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Journal of Modern Power Systems and Clean Energy 2020年第6期8卷 1080-1091页

作者： Xianzhuang Liu Xiaohua Zhang Lei Chen Fei Xu Changyou Feng State Grid Corporation of China State Grid Jibei Electric Power Company the State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical EngineeringTsinghua University

Transient stability assessment(TSA)is of great importance in power system operation and *** of the usual tasks in TSA is to estimate the critical clearing time(CCT)of a given fault under the given network topology and pre-fault power ***-driven methods try to obtain models describing the mapping between these factors and the CCT from a large number of ***,the influence of network topology on CCT is hard to be analyzed and is often ignored,which makes the models inaccurate and *** this paper,a novel data-driven TSA model combining Mahalanobis kernel regression and ensemble learning is proposed to deal with the *** model is a weighted sum of several *** sub-model only uses the data of one topology to construct a kernel *** weights are determined by both the topological similarity and numerical similarity between the *** similarities are decided by the parameters in Mahalanobis distance,and the parameters are to be *** reduce the model complexity,sub-models within the same topology category share the same *** estimating CCT,the model uses not only the sub-model which the sample topology belongs to,but also other ***,it avoids the problem that there may be too few data under some *** also efficiently utilizes information of data under all the ***,its decision-making process is clear and understandable,and an effective training algorithm is also *** results on both the IEEE 10-machine 39-bus and a real system verify the effectiveness of the proposed model.

关键词： Transient stability assessment critical clearing time network topology change Mahalanobis kernel regression ensemble learning data-driven

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