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Multi-extreme learning machine-based post-fault transient stability hierarchical evaluation method for power system

An extreme learning machine and power system technology, which is applied in the field of layered evaluation of transient stability after power system failure based on multiple extreme learning machines, can solve the problem that the transient stability evaluation cannot take into account the evaluation speed and evaluation accuracy, and achieve a comprehensive safety situation. , reasonable evaluation, and the effect of improving evaluation efficiency

Pending Publication Date: 2020-01-24
CHINA THREE GORGES UNIV
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Problems solved by technology

[0005] In order to solve the above-mentioned technical problems, the present invention provides a multi-extreme learning machine-based hierarchical evaluation method for transient stability of a power system after a fault, which solves the problem that the traditional transient stability evaluation cannot take into account the requirements of both evaluation speed and evaluation accuracy. , to meet higher overall evaluation accuracy requirements

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  • Multi-extreme learning machine-based post-fault transient stability hierarchical evaluation method for power system
  • Multi-extreme learning machine-based post-fault transient stability hierarchical evaluation method for power system
  • Multi-extreme learning machine-based post-fault transient stability hierarchical evaluation method for power system

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Embodiment

[0056] In this embodiment, the 1684-node power system is taken as the research object to determine its transient stability under different fault scenarios. The overall framework of the embodiment is as figure 1 shown. The above method mainly includes two parts: hierarchical training and hierarchical evaluation of multi-layer multiple extreme learning machines. The sample training part of each layer of multiple extreme learning machines is characterized by: power grid transient key feature extraction and centralized training of ELMs. The evaluation part of each layer of multiple extreme learning machines is characterized by: extracting key features from large data, and then sending them into a cluster of EMLs that have been trained, and finally comprehensively analyzing their outputs, giving transient stability evaluation results, and Send unconfident samples to the next layer.

[0057] As a preferred solution of the present invention, the flow chart of each layer of multipl...

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Abstract

The invention discloses a multi-extreme learning machine-based post-fault transient stability hierarchical evaluation method for a power system. The multi-extreme learning machine-based post-fault transient stability hierarchical evaluation method comprises the steps: extracting key features capable of representing the transient stability condition of the current power system from historical big data of the power system according to a maximum correlation minimum redundancy algorithm MRMR; carrying out hierarchical learning training on the ELMs by using the extracted key features of the transient stability condition of the power system; and then inputting the key feature group of the power system after the fault into the trained ELMs of each layer, and finally obtaining a final transient stability evaluation result by utilizing a decision evaluation criterion of each layer. The multi-extreme learning machine-based post-fault transient stability hierarchical evaluation method has high precision and meets the rapidity requirement of big data of the smart power grid.

Description

technical field [0001] The invention relates to the technical field of power system transient safety evaluation, in particular to a hierarchical evaluation method for power system transient stability after a fault based on multiple extreme learning machines. Background technique [0002] With the advancement of smart grid construction, the utilization rate of power system equipment will be further improved on the existing basis, which will inevitably lead to the operation of the power system getting closer and closer to its stable operating limit, which will cause successive failures of modern power systems. The possibility is getting higher and higher, and transient stability damage is one of the important factors that induce successive faults to evolve into blackout accidents. Whether it can be quickly and accurately evaluated is important for preventive control, emergency control and correction in the blackout defense system. Effective implementation of control measures i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G06Q50/06
CPCG06N3/08G06Q50/06G06N3/045G06F18/217G06F18/214
Inventor 李欣秦成龙郑之艺桂德钟浩花雅文
Owner CHINA THREE GORGES UNIV
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