Pin-SVM (Support Vector Machine)-based transient stability evaluation method for power system

A transient stability evaluation and power system technology, applied to electrical components, circuit devices, AC network circuits, etc., can solve problems such as difficulty in weight convergence, long model training time, and unsatisfactory classification performance, so as to achieve small impact and reduce The dimension of feature set and the effect of ensuring safe and stable operation

Active Publication Date: 2017-06-13
NORTHEAST DIANLI UNIVERSITY +1
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Problems solved by technology

[0003] In artificial intelligence methods, the Support Vector Machine (SVM) algorithm based on statistical principles has been widely used in power system transient stability assessment, but the method has problems in the construction, selection and classification of input features in practical applications. The problem of device construction still needs to be improved
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  • Pin-SVM (Support Vector Machine)-based transient stability evaluation method for power system
  • Pin-SVM (Support Vector Machine)-based transient stability evaluation method for power system
  • Pin-SVM (Support Vector Machine)-based transient stability evaluation method for power system

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[0028] A Pin-SVM-based power system transient stability evaluation method of the present invention will be described in detail below with reference to embodiments and drawings.

[0029] A Pin-SVM-based power system transient stability evaluation method of the present invention uses the response trajectory data of the power system to construct an original feature set composed of system indicators and projected energy function indicators, and uses the maximum correlation and minimum redundancy (maximal Relevance and Minimal Redundancy, mRMR) (from literature: Li Yang, Gu Xueping. Feature selection for transient stability assessment based on improved maximum correlation minimum redundancy criterion[J]. Chinese Journal of Electrical Engineering, 2013,33(34):179-186 .) The feature selection method performs feature compression, finds out the feature subsets with high sensitivity to dynamic changes in the power grid, and maps the sample set to a high-dimensional space; uses Pin-SVM fo...

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Abstract

The invention relates to a Pin-SVM (Support Vector Machine)-based transient stability evaluation method for a power system. The method comprises the following steps: establishing an original feature set comprising training sample data and test sample data; training a transient stability evaluation model of the power system by the training sample data; carrying out stability evaluation on a power system state simulated by the test sample data by using the transient stability evaluation model of the power system; for the fault type of the actual power system, classifying the faults according to the membership degree of a power system feature subset relative to the stable class and unstable class of the power system, if the power system belongs to the unstable class, then the fault being a serious fault; otherwise, the fault being a nonserious failure; obtaining an evaluation index when the fault screening and stability evaluation are completed. According to the method provided by the invention, the feature set dimension and the redundant information can be reduced, and the feature dimension can be reduced, and the evaluation accuracy is higher. The method can be applied to the online safety and stability evaluation of the regional power system in China, and the safe and stable operation of the complex power system is powerfully ensured.

Description

technical field [0001] The invention relates to a method for evaluating transient stability of a power system. In particular, it involves a Pin-SVM-based transient stability assessment method for power systems. Background technique [0002] Transient Stability Assessment (TSA) is an important part of power system security and stability analysis. With the continuous development of the power system and the continuous expansion of the interconnection scale of the regional power grid, the power system is facing more risks of safe and stable operation. In order to avoid the recurrence of blackouts similar to those in the United States, Canada and Western Europe, an accurate and stable evaluation method for the transient stability of the power system is sought is of great significance. [0003] In artificial intelligence methods, the Support Vector Machine (SVM) algorithm based on statistical principles has been widely used in power system transient stability assessment, but the...

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Application Information

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IPC IPC(8): H02J3/00
CPCH02J3/00H02J2203/20
Inventor 陈厚合王长江姜涛李雪李国庆
Owner NORTHEAST DIANLI UNIVERSITY
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