Measured data-based load model and parameter identification optimization method

A technology of load model and measured data, which is applied in the direction of gene model, calculation model, biological model, etc., can solve the problems of complex load model structure, low identification accuracy of load model parameters, and difficult identification of parameters

Inactive Publication Date: 2018-09-21
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

[0005] The invention provides a load model and parameter identification optimization method based on measured data, which is used to solve the problem of low identification accuracy of load model parameters in power grid operation and due to

Method used

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  • Measured data-based load model and parameter identification optimization method
  • Measured data-based load model and parameter identification optimization method
  • Measured data-based load model and parameter identification optimization method

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Embodiment 1

[0236] Embodiment 1: as Figure 1-6 As shown, a load model and parameter identification and optimization method based on measured data, the specific steps of the method are as follows:

[0237] (1) Establish a load model

[0238] 1) Static load model

[0239] Considering the frequency characteristic parameters, a static load model as shown in formula (1) is established.

[0240]

[0241] In the formula, P 1 is the static load active power, Q 1 is the static load reactive power, U is the voltage, U 0 is the initial value of the voltage, f is the frequency, f 0 is the initial value of the frequency, Δf is the variation of the frequency, where Δf=f-f 0 , P 0 is the initial value of active power, Q 0 is the initial value of reactive power, L DP is the active frequency characteristic parameter, L DQ is the reactive frequency characteristic parameter, P Z ,P I ,P P is the proportion of constant impedance, constant current and constant power in the active load, Q Z ,...

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Abstract

The invention relates to a measured data-based load model and parameter identification optimization method, and belongs to the technical field of electric power system load modeling. The method comprises the following steps of: establishing a static load model, a dynamic load model and a comprehensive load model, and determining a to-be-identified parameter of each load model according to three load model expressions; respectively identifying the to-be-identified parameters in the three load models by using a genetic algorithm on the basis of measured data; and carrying out error analysis on parameter identification results of the three load models and finally selecting the load model corresponding to a minimum load model identification error as an optimized load model. According to the method, three common load models are comprehensively considered, and load model optimization is carried out while parameter identification is carried out on the load models, so that benefit is brought to improve the load modeling precision and the effect is favorable.

Description

technical field [0001] The invention relates to a load model based on measured data and a parameter identification and optimization method, belonging to the technical field of power system load modeling. Background technique [0002] With the advancement of asynchronous networking, the scale of the power grid is becoming larger and more complex, and the impact of the load model on the simulation results of the power system has become increasingly sensitive, especially the selection of the load model, the determination of parameters, and the simulation of the distribution network. It has a significant impact on the stable calculation results of networked systems. Load modeling is a very complex problem: the load of the power system is composed of many different electrical equipment, and there are many types; the load composition and load quantity change with time; there is a lack of accurate data on the load composition; The accuracy of the model will directly affect the sim...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/00G06N3/12
CPCG06N3/006G06N3/126G06Q10/04G06Q50/06
Inventor 孙德娟孙士云安德超海世芳王杨范小红张雪娟
Owner KUNMING UNIV OF SCI & TECH
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