Transient stability evaluation method and system based on automation and interpretable machine learning

Abstract

The invention discloses a transient stability evaluation method and system based on automatic and interpretable machine learning, and the method comprises the steps: firstly obtaining monitoring data,extracting key feature data, and inputting the key feature data into an automatic and interpretable machine learning model; evaluating the transient stability condition of the power system by the transient stability evaluation sub-model, and calculating the contribution degree of each piece of key feature data to an evaluation result by the interpretable sub-model; when the transient stability evaluation sub-model is trained, screening original data, extracting low-dimensional key characteristic data, using a Bayesian optimization model to automatically adjust and optimize hyper-parameters ofthe transient stability evaluation sub-model, and evaluating evaluation performance of the transient stability evaluation sub-model according to accuracy, recall rate and area below an ROC curve. Thekey feature data can be automatically extracted, the hyper-parameters of the evaluation model can be automatically adjusted, the transient stability evaluation of the power system is realized, and the transient stability evaluation result is explained.

Description

technical field [0001] The invention relates to the technical field of power system safety, in particular to a transient stability evaluation method and system based on automatic and interpretable machine learning. Background technique [0002] As the penetration rate of renewable energy gradually increases and large-capacity UHV DC is fed into the grid, a large number of coal-fired synchronous generators are gradually replaced. In DC and new energy high-penetration power grids, the moment of inertia is significantly reduced, and the operating mechanism is becoming more complex, which brings unprecedented challenges to the evaluation of power system transient stability. [0003] In the prior art, the transient stability assessment of the power system generally adopts the time-domain simulation method or the direct method. The time-domain simulation method uses a set of high-dimensional nonlinear differential equations to describe the response process of the power system, an...

AI Technical Summary

Problems solved by technology

Although the transient stability assessment method based on machine learning has made some progress, it is still difficult to directly apply it to the planning and dispatching of the actual power system. The main reasons are as follows: (1) In order to apply machine learning Transient stability assessment requires repeated manual feature selection and heuristic parameter adjustment based on human experience, which is cumbersome, inefficient and time-consuming, which hinders the deployment and application of machine learning in power systems

(2) Existing machine learning models applied to transient stability assessment have poor interpretability

Because they are often mapped through black-box models, it is difficult for power system dispatchers to interpret the behavior of complex machine learning models and understand how these models make specific decisions

The lack of automation and interpretability severely limits the application of machine learning methods to online transient stability assessment

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