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Power grid online risk calculation method based on regression learning and feature mining

A risk calculation and power grid technology, applied in computing, computing models, machine learning, etc., can solve the problem of not finding the same or similar, and achieve the effect of improving prediction accuracy, generalization performance, and computing accuracy

Pending Publication Date: 2022-03-25
ELECTRIC POWER SCI & RES INST OF STATE GRID TIANJIN ELECTRIC POWER CO +2
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  • Application Information

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[0007] After searching, no documents of the prior art i

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  • Power grid online risk calculation method based on regression learning and feature mining
  • Power grid online risk calculation method based on regression learning and feature mining
  • Power grid online risk calculation method based on regression learning and feature mining

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Embodiment Construction

[0082] Embodiments of the present invention are described in further detail below:

[0083] An online risk calculation method of a power system based on regression learning and feature mining and counting time-varying operating states, comprising the following steps:

[0084] Step 1: based on the data collection of off-line modeling, obtain test data set and training data set;

[0085] The concrete steps of described step 1 include:

[0086] (1) Input operation-related parameters such as generator reliability parameters, generator equipment output, and annual load curve of the power system with known structure (see Table 1, Table 2, Table 3, and Table 4), Repair the two-state model of forced failure and establish the probability model of system component uncertainty factors, that is, establish the component unavailability rate U according to the formula (1), and use the Monte Carlo method to calculate the system state according to the uncertainty factor probability model and ...

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Abstract

The invention relates to a power grid online risk calculation method based on regression learning and feature mining. The power grid online risk calculation method comprises the following steps: step 1, data collection based on offline modeling; 2, selecting characteristic variables based on a multi-target particle swarm; 3, selecting an optimal parameter of the SVM model based on an improved ant colony algorithm; 4, establishing a regression vector machine model in combination with the key parameters of the training model, and inputting the obtained data into a regression model by using the model after feature screening to carry out one-time offline training; and step 5, performing feature screening on the system operation state according to the result of the step 2, and inputting the feature into the multiplexing network to obtain the node power failure risk in the corresponding state. And performing weighted summation according to the node risk and the state probability to obtain an annual expected risk index. The operation state covered by the method is more comprehensive, the uncertain information of the system can be more fully reflected, and the evaluation result is more systematic globality.

Description

technical field [0001] The invention belongs to the technical field of real-time evaluation of electric power system operation risk, and relates to an online risk calculation method of a power grid, in particular to an online risk calculation method of a power grid based on regression learning and feature mining. Background technique [0002] Modern power systems integrate supervisory control and data acquisition (SCADA) and phasor measurement units (PMU) and other equipment data acquisition systems. While providing more power system information for the operation and control of the power system, these massive data also provide power systems. The real-time performance of data processing and data mining capabilities put forward higher requirements. The scale and complexity of modern power systems are gradually increasing, and the difficulty of modeling and solving models with traditional model-based methods is also increasing; modern power systems are more often operating near...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06G06N3/00G06N20/00G06Q10/04
CPCG06Q10/0635G06Q10/06393G06Q50/06G06N3/006G06N20/00
Inventor 王天昊刘伟鄂志君马世乾董紫珩侯恺李振斌刘颂范瑞卿王珍珍马钢于光耀杨帮宇王坤宋国辰
Owner ELECTRIC POWER SCI & RES INST OF STATE GRID TIANJIN ELECTRIC POWER CO
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