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Electrical power system dynamic state estimation method base on unscented transformation Kalman filter

A technology of dynamic state estimation and Kalman filtering, which is applied in the direction of electrical components, circuit devices, AC network circuits, etc., can solve the problems of affecting filtering accuracy, filtering divergence, and unsatisfactory accuracy

Inactive Publication Date: 2009-12-30
HOHAI UNIV
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Problems solved by technology

However, the power system itself is a typical nonlinear system, and the EKF algorithm is a linear filtering method, which linearizes the system state transition equation and measurement equation around the state estimation, and the linearization error of the model often seriously affects the final The filtering accuracy of the filter is even leading to the divergence of the filtering, and the accuracy of prediction and estimation is unsatisfactory.

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  • Electrical power system dynamic state estimation method base on unscented transformation Kalman filter
  • Electrical power system dynamic state estimation method base on unscented transformation Kalman filter
  • Electrical power system dynamic state estimation method base on unscented transformation Kalman filter

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[0045] The technical scheme of the invention will be described in detail below in conjunction with the drawings:

[0046] Since R.E.Kalman proposed the Kalman filter method in 1960, Kalman filter has been widely used in many engineering fields due to its wide applicability and recursive algorithm structure.

[0047]Kalman filtering is a linear filtering method based on the minimum mean square error criterion. For linear systems, Kalman filtering uses the following facts:

[0048] 1. Only the mean and variance (covariance) of a distribution are given. The most conservative assumption for this distribution is that the distribution is Gaussian, and its mean and variance are the given mean and variance.

[0049] 2. The Gaussian distribution is still Gaussian after linear operation.

[0050] Based on these two assumptions, Kalman filter gives the best possible estimate of the mean and variance of the state.

[0051] In fact, almost all actual engineering problem values ​​have a certain...

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Abstract

The invention discloses an electrical power system dynamic state estimation method based on an unscented transformation Kalman filter, which comprises the following steps: selecting a sigma point sampling strategy according to statistics of input variable to obtain a sigma point set of the input variable and corresponding weight; secondly, performing non-linear transformation on each sigma point in the sampled sigma point set of the input variable to obtain a transformed sigma point set; and finally, performing weight processing on the transformed sigma point set to obtain the statistics of output variable. The method reduces the application difficulty, and simultaneously can obtain higher accuracy and robustness.

Description

Technical field [0001] The invention relates to a power system dynamic state estimation method based on unscented transform Kalman filter, which belongs to the technical field of power system operation and control. Background technique [0002] Energy Management System (EMS) is a computer-based modern power dispatch automation system. The most fundamental change it brings is from traditional empirical dispatch to analytical dispatch, thereby improving the safety and economy of power system operation . With the implementation of west-to-east power transmission and the rapid implementation of the power market, UHV, long-distance, AC and DC hybrid transmission technology has developed rapidly in my country's power grid, so the requirements for EMS in system analysis and decision-making are getting higher and higher. State estimation technology, as a means of computer real-time data processing, has become an important part of EMS since the introduction of space technology into the po...

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Application Information

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IPC IPC(8): H02J3/00
Inventor 卫志农孙国强庞博
Owner HOHAI UNIV
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