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Electric power system safety evaluation method based on correlation and redundancy detection

A technology for power system and safety assessment, applied in the direction of neural learning methods, gene models, genetic rules, etc., can solve the problem of low assessment accuracy

Pending Publication Date: 2020-10-23
CHINA THREE GORGES UNIV
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Problems solved by technology

However, this technology shows the problem of low evaluation accuracy during dynamic evaluation

Method used

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  • Electric power system safety evaluation method based on correlation and redundancy detection
  • Electric power system safety evaluation method based on correlation and redundancy detection
  • Electric power system safety evaluation method based on correlation and redundancy detection

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Experimental program
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Effect test

Embodiment

[0080] The invention was tested in an IEEE 30 node system. The IEEE 30-node system contains 30 nodes, 6 generators and 41 transmission lines. The simulation software adopts PSS / E, the generator model is a sixth-order model, and the load model is a constant impedance model. Simulate a three-phase short-circuit fault, the fault removal time is 1 second, and 6000 samples are generated, 80% of which are used for training, and the remaining 20% ​​of the samples are used for testing. All tests were performed on a computer with an Intel Core i7 processor and 8GB of RAM.

[0081] Accuracy rate (A cc ), precision (P re ), recall (R ec ) and F 1 value to evaluate the performance of the model, as shown in formulas (7)-(10):

[0082]

[0083]

[0084]

[0085]

[0086] In the formula: T 11 ,T 01 ,T 10 ,T 00 Respectively represent the number of stable samples judged as stable, the number of unstable samples judged as stable, the number of stable samples judged as unst...

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Abstract

The invention discloses an electric power system safety evaluation method based on correlation and redundancy detection. The method comprises the steps that 1, an original database containing a largenumber of power system operation variables and dynamic security classification tags is constructed based on historical operation data of a power system and dynamic simulation under a large number of anticipated accidents; 2, performing feature selection on the original database, and selecting a variable with high relevancy with the class label from a large number of operation variables as a key feature to form an efficient database; 3, constructing a dynamic safety evaluation model of the power system by combining a genetic algorithm and a BP neural network, and carrying out offline training and updating on the evaluation model by utilizing an efficient database; and step 4, based on real-time measurement data of a synchronous phasor measurement unit (PMU), carrying out online dynamic safety evaluation on the power system by utilizing the evaluation model. The method can quickly achieve the discrimination of the stability of the power system, guarantees the higher accuracy and reliability, and meets the online evaluation requirements of the power system.

Description

technical field [0001] The invention relates to the field of dynamic safety assessment of power systems, in particular to a power system safety assessment method based on correlation and redundancy detection. Background technique [0002] With the rapid development of the economy, the scale of the modern power system is getting bigger and bigger, the structure is becoming more and more complex, and the connection between the power systems in various regions is getting closer and closer. Although the large-scale interconnected power system can greatly improve the economy of system operation, it also significantly increases the impact of local faults on the power system. In recent years, due to the integration of renewable energy such as wind energy and solar energy into the modern power system, its intermittency and uncontrollability have brought a lot of challenges to the safe operation of the power system. Therefore, it is necessary to seek a fast, accurate and real-time o...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06K9/62G06N3/04G06N3/08G06N3/12G06F16/23G06Q50/06
CPCG06F30/27G06N3/084G06N3/126G06F16/23G06Q50/06G06N3/045G06F18/24
Inventor 刘颂凯刘礼煌陈少雄毛丹张涛李文武李欣郭攀锋刁良涛江进波曹成王丰李丹
Owner CHINA THREE GORGES UNIV
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