Power system dynamic estimation method based on unscented Kalman particle filtering

An unscented Kalman and power system technology, applied in computing, electrical digital data processing, computer-aided design, etc., can solve problems such as lack of particles

Active Publication Date: 2019-08-23
NORTHEASTERN UNIV
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a dynamic estimation method of power system based on unscented Kalman particle filter for the above-mention

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  • Power system dynamic estimation method based on unscented Kalman particle filtering
  • Power system dynamic estimation method based on unscented Kalman particle filtering
  • Power system dynamic estimation method based on unscented Kalman particle filtering

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[0092] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0093] Such as figure 1 As shown, the method of this embodiment is as follows.

[0094] The present invention provides a dynamic estimation method of power system based on unscented Kalman particle filter, comprising the following steps:

[0095] Step 1: Initialize the state variables of the current power system to obtain the particle swarm Φ at time k=0, where k represents time; the number of particles is N, Fori=1:N, from the prior probability density function p(X 0 ) to extract the initialization state As the initial state of the power system, calculate the initial state mean variance

[0096]

[0097]

[0098] where X0 =[v 0 θ 0 ],X 0 is the initial ...

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Abstract

The invention discloses a power system dynamic estimation method based on unscented Kalman particle filtering, and relates to the technical field of power system monitoring, analysis and control. Themethod comprises the following steps: step 1, initializing a current state variable of the power system, and calculating an initial state mean value and variance; 2, setting k as 1; 3, carrying out importance sampling to obtain a particle swarm and a weight at the k moment; 4, carrying out particle swarm splitting and weight adjustment on the particle swarm; 5, judging whether Neff<Nth is established or not, if yes, turning to the step 6, and if not, turning to the step 8; 6, copying and eliminating the particle swarm to obtain a new particle swarm and weight; 7, carrying out weight normalization again; 8, calculating a state estimation value of the power system; and step 9, judging whether k >= Omega is established or not, if not, setting k = k + 1, and turning to the step 3, and ending the dynamic estimation of the power system if so. According to the method, the suggested density distribution problem is improved, the estimation precision can be effectively improved, and the problemof particle shortage is effectively solved.

Description

technical field [0001] The invention relates to the technical field of power system monitoring, analysis and control, in particular to a power system dynamic estimation method based on unscented Kalman particle filter. Background technique [0002] With the continuous development of society, people have higher and higher requirements for the stability of power system. In order to improve the stability of the power system, we must improve the scheduling, control, safety assessment and other aspects of the power system. Power system dynamic estimation is the basis of power system dispatching, control, safety assessment, etc. Dynamic state estimation can carry out safety assessment of power system through state prediction, and realize online functions such as economic dispatching and preventive control. The importance is self-evident. In view of this, we must quickly and accurately conduct dynamic estimation of the power system. [0003] In the early 1970s, Debs and others pr...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06Q10/06G06Q50/06
CPCG06Q10/063G06Q50/06G06F30/20Y02E60/00
Inventor 黄博南李明肖军刘鑫蕊孙秋野杨珺刘振伟马大中胡经纬王睿
Owner NORTHEASTERN UNIV
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