Power system transient state voltage stability evaluating method based on misclassification cost classified-learning

Abstract

The invention relates to a power system transient state voltage stability evaluating method based on misclassification cost classified learning, which belongs to the power system stability analyzing and evaluating field. Based on dynamic measurement datum of a synchronous phasor measuring unit, a key subsequence closely related to the power system state is extracted from a time sequence constituted of a large amount of dynamic measurement datum; by setting different misclassification costs of stable power system and unstable state, a weight coefficient is introduced into a learning sample; a decision-making tree algorithm infused with the sample weight coefficient is used to perform classified learning so as to acquire a decision-making tree model; and the decision-making tree is used for online monitoring to implement evaluation to the power system transient state voltage stable situation.

Description

technical field [0001] The invention relates to a power grid transient voltage stability evaluation method based on misclassification cost classification learning, and belongs to the field of power system stability analysis and evaluation. Background technique [0002] At present, the synchrophasor measurement technology that is widely popularized and applied in the power grid can provide a reliable source of synchronous data for power system stability analysis and monitoring based on big data. The advantages of big data methods in knowledge mining and exploration can provide new ideas for people to better solve the traditional problems of power grid transient voltage stability assessment. However, most of the existing methods extract characteristic variables from a single time section, which is difficult to be directly used in the evaluation of transient voltage stability of power grids with drastic changes in electrical quantities. In fact, the key variation trends and ch...

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Problems solved by technology

[0003] In the existing transient voltage assessment methods based on data mining, the particularity of transient voltage stability classification learning is not considered: the instability is divided into stability (missed judgment) and the stability error is divided into instability ( The cost of misjudgment) is completely different. The former often easily causes irreversible voltage collapse or even blackout accidents, causing huge economic losses, while the latter can usually be remedied in time by using corrective control measures, and the losses caused are much smaller.

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