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A short-term voltage stability evaluation method for deep reinforcement learning based on incomplete data

A voltage stability and reinforcement learning technology, applied in data processing applications, instruments, biological neural network models, etc., can solve the problems of missing bus voltage data and poor accuracy of evaluation results.

Active Publication Date: 2022-07-29
HEILONGJIANG UNIV
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the lack of collected bus voltage data leads to poor accuracy of the evaluation results when the failure is not considered in the dynamic evaluation method of short-term voltage stability in the prior art

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  • A short-term voltage stability evaluation method for deep reinforcement learning based on incomplete data
  • A short-term voltage stability evaluation method for deep reinforcement learning based on incomplete data

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Embodiment Construction

[0034] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

[0035] It should be noted that the embodiments of the present invention and the features of the embodiments may be combined with each other under the condition of no conflict.

[0036] see figure 1 and figure 2This embodiment will be described. The incomplete data-based deep reinforcement learning short-term voltage stability evaluation method described in this embodiment is used to evaluate the voltage stability of a multi-bus power system....

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Abstract

A deep reinforcement learning short-term voltage stability assessment method based on incomplete data relates to the field of voltage stability assessment in multi-bus power systems. The present invention solves the problem of poor accuracy of the evaluation result caused by the lack of the collected bus voltage data when the fault occurs in the dynamic evaluation method of the short-term voltage stability in the prior art. In the method of the present invention, in the case of data loss, the lost data is predicted by combining the associated historical data corresponding to the faulty busbar and the trained LSTM neural network model to obtain the busbar voltage predicted value of the faulty busbar, and the predicted busbar is used. The voltage prediction fills the sampling results with data, and finally uses the filled data to evaluate the system stability. The invention is mainly used for evaluating the voltage stability in a multi-busbar power system.

Description

technical field [0001] The invention relates to the field of voltage stability evaluation in a multi-busbar power system. Background technique [0002] In order to solve the problem of short-term voltage stability in power systems, there are two types of solutions. In terms of hardware equipment, we can use dynamic VAR compensation equipment. This is a reactive power compensator, which makes the whole smart grid system more stable, but cannot be put into use in all grids due to its high price. In terms of system management control, we can perform short-term voltage stability assessment and perform emergency control (EC) of the system after the assessment, so the accuracy of the assessment will affect our subsequent control of the system. [0003] For short-term voltage stability assessment, there are generally two approaches. The first is an event-based STVS assessment, and the other is a response-based STVS assessment. The first evaluation is to pre-set possible faults ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06Q50/26G06N3/04
CPCG06Q10/06393G06Q10/067G06Q50/26G06N3/044
Inventor 关心刘永楠刘安琪
Owner HEILONGJIANG UNIV