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Construction method and application of dominant instability mode recognition model of power system

A power system and construction method technology, applied in the field of power system dominant instability pattern recognition model construction, can solve the problems of increasing labeling sample costs, expensive labeling costs, and increasing costs, so as to improve recognition accuracy, reduce costs, and reduce dependencies. Effect

Pending Publication Date: 2022-03-11
HUAZHONG UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the labeling cost of the identification of the dominant instability mode of the power system is relatively expensive, and some samples where power angle instability and voltage instability are intertwined often need to be cut off multiple times or load shedding simulations to determine their dominance , will greatly increase the cost of labeling samples. Taking my country's Northeast Power Grid (thousand-node level) as an example, tens of thousands of samples are needed to achieve a high judgment accuracy rate, which greatly increases the cost of sample labeling

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  • Construction method and application of dominant instability mode recognition model of power system
  • Construction method and application of dominant instability mode recognition model of power system
  • Construction method and application of dominant instability mode recognition model of power system

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Embodiment 1

[0051] The present invention adopts a method based on semi-supervised learning to build a power system dominant instability pattern recognition model. The method can construct a complex mapping relationship from original data to dominant instability patterns, and a well-trained model can quickly and accurately judge the dominant instability pattern of the system. The stability mode can effectively distinguish the two types of instability, voltage instability and power angle instability, and provide a basis for the subsequent formulation of control decision tables in the analysis of simulation data.

[0052] It should be noted that power angle stability can be subdivided into large disturbance power angle stability and small disturbance power angle stability according to the size of the disturbance. Similarly, voltage instability can also be divided into small disturbance voltage stability (static voltage stability) and large disturbance power angle stability. The disturbance vo...

Embodiment 2

[0098] Further analysis found that the application of disturbance in the model is random, which will cause the model to not make good use of the information of unlabeled samples, and the model should be able to adapt to small input changes, and if the model is overfitting, then there will be small A perturbation of the input causes a large change in the output. Therefore, during the training process, adding disturbances to the input features of unlabeled samples makes the model have the ability to resist disturbances, and at the same time strengthens the decision boundary, making the sample distribution near the decision boundary sparse, that is, strengthening the classification ability of the model. The virtual adversarial training method introduced in this embodiment is to find a direction that causes the largest change in the model output due to small disturbances, which is called the maximum disturbance direction. Applying a certain disturbance in this direction is better f...

Embodiment 3

[0111]A machine-readable storage medium. The machine-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the dominant failure in Embodiment 1 or 2. A construction method for stable pattern recognition and / or a dominant unstable pattern recognition method. Related technical features are the same as those in Embodiment 1 or 2.

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Abstract

The invention discloses an electric power system dominant instability mode recognition model construction method and application, and belongs to the field of electric power system stability judgment. Comprising the steps of obtaining a small number of labeled samples and a large number of unlabeled samples to form a training set; a training set is adopted to train a model, and the model comprises two parallel neural networks of the same structure, namely a first sub-network and a second sub-network; in the training process, inputting a labeled sample and an unlabeled sample into the second sub-network, and training and updating the network weight by calculating the cross entropy loss of supervised learning of the labeled sample and the uniformization loss of the unlabeled sample; inputting the same unlabeled samples into the first sub-network, and updating the weight of the first sub-network after weighting the historical weight of the first sub-network and the weight of the second sub-network; when the output of the second sub-network is consistent with the output of the first sub-network, a trained model is obtained, and the dominant instability mode with the maximum probability is output, the dependence on labeled samples can be reduced, and the recognition precision is high.

Description

technical field [0001] The invention belongs to the field of power system stability judgment, and more specifically relates to a method for building a dominant instability pattern recognition model of a power system and its application. Background technique [0002] The stability of power system operation is closely related to the sustainable development of social economy. In order to ensure that the power system operates in a safe and stable state, the power company needs to carry out large-scale digital simulation work every year. Digital simulation provides strong guidance for the operation mode of the power system and the formulation of stability control measures, which is of great importance in practical engineering. significance. [0003] Power grid simulation can be divided into two parts: simulation calculation and simulation data analysis, in which the former provides data support for the latter. During the simulation calculation process, a large amount of high-di...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06Q50/06
CPCG06Q50/06G06N3/08G06N3/047G06N3/045G06F18/2155G06F18/24143G06F18/2411
Inventor 姚伟张润丰石重托文劲宇
Owner HUAZHONG UNIV OF SCI & TECH