Intelligent transient stability evaluation system for power grid

Abstract

The invention discloses an intelligent transient stability evaluation system for a power grid. The system mainly comprises a phasor measurement unit, a preprocessing unit, a stability classification module, a key generator module, a stability classification simple logic processing unit, a key generator simple logic processing unit and an auxiliary decision module. According to the evaluation system provided by the invention, the same PMU samples are used as input, transient stability classification and key generator identification are respectively used as output, a double-task stability evaluation model is established, and the stability classification and key generator identification given by the double-task stability evaluation model are subjected to simple logic processing and then are used as input of an auxiliary decision-making system, dual-task interaction verification discrimination logic is adopted, mutual check of prediction results is realized, and a final stability discrimination result and a key generator set are obtained; meanwhile, uncertain samples difficult to discriminate are screened out, so the prediction accuracy of intelligent transient stability assessment andthe reliability of the results can be remarkably improved, and safe operation and risk regulation of a power grid are facilitated.

Description

technical field [0001] The invention relates to the field of electric power technology, in particular to an intelligent transient stability evaluation system for a power grid. Background technique [0002] The intelligent transient stability assessment algorithm using machine learning technology does not need to rely on power grid models and simulation calculations. It adopts a data-driven method to learn the mapping relationship between power grid characteristics and stability results offline, which can realize fast online discrimination. However, due to the complex dynamic characteristics and strong nonlinearity of transient stability, it is difficult to accurately capture, intelligent transient stability assessment generally outputs a single information, and most of them are stable / unstable binary classifications, or stability indicators such as critical clearance time. When faced with operating modes or failures that are quite different from the training sample set, the ...

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Problems solved by technology

However, due to the complex dynamic characteristics and strong nonlinearity of transient stability, it is difficult to accurately capture, intelligent transient stability assessment generally outputs a single information, mostly stable / unstable binary classification, or stability indicators such as critical clearance time

When faced with operating modes or failures that are quite different from the training sample set, the reliability of the prediction results of the data-driven stability evaluation model may drop significantly, but it cannot self-test the credibility of the results given. Its application in practical engineering cannot contribute to the safe operation and risk control of the power grid.

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