Transient stability evaluation method for Bayesian optimization LightGBM

A transient stability evaluation and transient stability technology, applied in the direction of AC network circuits, electrical components, circuit devices, etc., can solve the problem of input feature extraction ability limitation, support vector machine noise sensitivity, weak big data processing ability, etc. Good feature extraction ability, eliminating redundant features, preventing system transient stability and collapse

Active Publication Date: 2020-01-21
STATE GRID SICHUAN ECONOMIC RES INST
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Problems solved by technology

However, decision trees are prone to overfitting; when dealing with large-scale power system data, the ability of extreme learning machines to extract input features is limited; support vector machines are easily sensitive to noise and have weak processing capabilities for large data; random forests Can handle high-dimensional data samples, bu

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  • Transient stability evaluation method for Bayesian optimization LightGBM
  • Transient stability evaluation method for Bayesian optimization LightGBM
  • Transient stability evaluation method for Bayesian optimization LightGBM

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Embodiment

[0058] like Figure 1-7 as shown,

[0059] A transient stability evaluation method for Bayesian optimization LightGBM, comprising the following steps:

[0060] S1: Obtain the response trajectory data of the power system, and select the offline or online monitoring data of the transient stability state of the power system to obtain a transient stability data set;

[0061] S2: Use the data in the transient stability data set in step S1 to train Bayesian-optimized LightGBM to obtain the optimal parameters of LightGBM, and then obtain the trained LightGBM;

[0062] S3: Obtain online the data used to evaluate the transient stability of the power system after the power system fault, preprocess the acquired data and input it into the trained LightGBM in step S2, and obtain the power system transient stability after the power system fault evaluation result.

[0063] A Bayesian optimization LightGBM transient stability evaluation method proposed by the present invention selects bus ...

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Abstract

The invention discloses a transient stability evaluation method for Bayesian optimization LightGBM. The method comprises the following steps: obtaining a transient stability data set of a power system; training the Bayesian optimized LightGBM by using the data in the transient stability data set to obtain an optimal parameter of the LightGBM, and then obtaining the trained LightGBM; obtaining thedata used for evaluating the transient stability of the power system on line after the power system has a fault, preprocessing the obtained data, and inputting the preprocessed data into the trained LightGBM to obtain a power system transient stability evaluation result after the power system has the fault. According to the method, the transient stability state under multiple complex uncertain factor 'combinatorial number explosion' can be quickly and accurately evaluated, and the online evaluation of the transient stability of the power system is facilitated.

Description

technical field [0001] The invention relates to a method for evaluating transient stability of a power system, in particular to a method for evaluating transient stability of a Bayesian optimized LightGBM. Background technique [0002] With the large-scale grid integration of wind power, photovoltaic and other renewable energy sources, their volatility, randomness, and low inertia characteristics make the power system face great challenges in transient stability assessment and control. As we all know, the early stages of power system accidents are often accompanied by transient faults. Once the dispatcher cannot make appropriate decisions on transient faults and intervene in time, the transient stability of the system may be destroyed, and even develop into a subsequent Cascading failures, which will lead to mass blackouts. Due to the rapid development of transient accidents and the short response time, it is difficult to make correct judgments and decisions in a very short...

Claims

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Application Information

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IPC IPC(8): H02J3/00
CPCH02J3/00
Inventor 汪荣华苟竞刘方苏韵掣欧阳雪彤陈谦唐权胥威汀李婷王云玲
Owner STATE GRID SICHUAN ECONOMIC RES INST
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