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Explicit small-interference stability constraint generation and application method based on data driving

A stable, data-driven technology with little interference. It is applied to AC networks, AC network circuits, electrical components, etc. with the same frequency from different sources. Difficulty, avoiding huge burdens, reducing the effect of nonlinear relationships

Pending Publication Date: 2022-06-17
CHONGQING UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the calculation of eigenvalues ​​is very time-consuming, making it difficult for existing methods to meet the application requirements.

Method used

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  • Explicit small-interference stability constraint generation and application method based on data driving
  • Explicit small-interference stability constraint generation and application method based on data driving
  • Explicit small-interference stability constraint generation and application method based on data driving

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Experimental program
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Embodiment 1

[0121]see figure 1 and figure 2 , a data-driven explicit small-disturbance stabilization constraint generation and application method, including the following steps:

[0122] 1) Establish an optimal power flow model, and calculate the initial economic dispatch calculation result; if the initial dispatch result cannot meet the system's small disturbance stability requirements, go to step 2);

[0123] The optimization objective of the optimal power flow model is as follows:

[0124]

[0125] In the formula, P g is the active output of the gth generator; Ξ is the generator set; and are the coefficients of the generator cost function.

[0126] The constraints of the optimal power flow model include node power balance constraints, branch power flow constraints, and system operation constraints;

[0127] The node power balancing constraints are as follows:

[0128]

[0129]

[0130] In the formula, Q g is the reactive output of the gth generator; P i,d and Q ...

Embodiment 2

[0230] see figure 1 and figure 2 , a data-driven explicit small-disturbance stabilization constraint generation and application method, including the following steps:

[0231] 1) Calculate the optimal power flow without considering the small disturbance stability constraints, and obtain the initial economic dispatch calculation results. If the initial scheduling result cannot meet the requirement of system small disturbance stability, the method proposed in the present invention is executed.

[0232] 2) Compress the sampling space based on sensitivity analysis. Based on the compressed sampling space, samples are generated by the Latin hypercube sampling method.

[0233] 3) The samples are trained by SVM to generate stable constraints that show small disturbances. The misclassification compensation strategy is used to reduce the misjudgment probability of unstable samples.

[0234] 4) Establish an optimal power flow model that includes the constraints that show stability ...

Embodiment 4

[0352] The verification experiment of the generation and application method of explicit small disturbance stability constraints based on data-driven, including:

[0353] 1) Test system

[0354] In this embodiment, the IEEE 39 node system is used as the test system, and the system has a total of 10 generators. The dynamic models of generators, excitation systems and governors are described by the built-in dynamic models of DIgSILENT / PowerFactory. The excitation system adopts the IEEE I type excitation model, and the governor adopts the IEEE I type speed regulation model. The parameters involved in this paper are shown in the following table:

[0355] Table 1 Parameter settings

[0356] k max

Δζ c

ε 1

n ΔC -

R l max

τ 10 0.001 0.01 100 0.5 100％ 50 1

[0357] Set 70% of the samples as training samples and 30% of the samples as test samples.

[0358] The method was simulated by Digsilent / Power Factory. The initial scheduli...

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Abstract

The invention discloses an explicit small-interference stability constraint generation and application method based on data driving, and the method comprises the steps: 1), building an optimal power flow model, and carrying out the calculation to obtain an initial economic dispatching calculation result; 2) compressing the sampling space based on sensitivity analysis, and generating a sampling sample by using a Latin hypercube sampling method; 3) training the samples by using an SVM method, distinguishing stable samples from unstable samples, and establishing an optimal power flow model containing small interference stability constraints; and 4) inputting the sample into the optimal power flow model of the small interference stability constraint, and resolving to obtain a rescheduling result meeting the small interference stability requirement. The invention provides an explicit small-interference stability constraint generation method based on data driving, the voltage is used as a control variable, the application of the voltage in optimal power flow calculation is researched, and the solving efficiency of the optimal power flow considering the small-interference stability constraint is greatly improved.

Description

technical field [0001] The invention relates to the field of power system and automation thereof, in particular to a method for generating and applying an explicit small disturbance stability constraint based on data drive. Background technique [0002] In the operation calculation of the power system, the power flow constraints and equipment operation constraints are usually considered first, and the economic dispatch results are obtained through the optimal power flow calculation. Then, based on the economic dispatch results, the system is checked for safety, and the dispatch results are adjusted to ensure the safe operation of the system. In large-scale interconnected power systems, the rapid development of ultra-high voltage AC transmission and the increasing popularity of renewable energy make the stability of power system small disturbances face new challenges, which may lead to large-scale power outages. During the safety check, if the economic scheduling results can...

Claims

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Application Information

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IPC IPC(8): H02J3/00H02J3/06
CPCH02J3/0075H02J3/06H02J2203/10H02J2203/20
Inventor 杨知方刘珏麟余娟
Owner CHONGQING UNIV
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