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Dynamic state estimation algorithm for power distribution network

A dynamic state estimation and state estimation technology, which is applied in calculation, calculation model, electrical components, etc., can solve problems such as not being able to provide better state estimation results, and achieve the goals of reducing algorithm time consumption, enhancing robustness, and improving filtering performance Effect

Pending Publication Date: 2021-08-27
GUIZHOU UNIV
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  • Application Information

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Problems solved by technology

The existing distribution network dynamic state estimation algorithm cannot provide better state estimation results in this non-Gaussian, highly random mutation distribution network environment

Method used

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  • Dynamic state estimation algorithm for power distribution network
  • Dynamic state estimation algorithm for power distribution network
  • Dynamic state estimation algorithm for power distribution network

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Embodiment Construction

[0082] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the associated drawings. Preferred embodiments of the invention are shown in the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0083] In addition, an element in the present invention is said to be "fixed" or "disposed on" another element, and it may be directly on another element or an intervening element may also exist. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and similar expressions are used herein for pur...

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Abstract

The invention discloses a dynamic state estimation algorithm for a power distribution network, which comprises the following steps of: firstly, controlling the distribution of Sigma sampling points by adaptively adjusting a proportional correction factor of unscented transformation and introducing a fading factor to adjust Kalman filtering gain on line so as to improve the quality of an importance density function, and then, realizing state estimation by using a particle filtering theory. The improved unscented Kalman filter (UKF) algorithm is combined with the particle filter (PF) algorithm to form a power distribution network state estimation algorithm with stronger robustness and higher filtering precision, and the number of sample particles is reduced to achieve state estimation with higher precision requirements, so that the algorithm time consumption is reduced; when the system has bad measurement data, the bad measurement data can be quickly sensed, and the predicted covariance is corrected through the introduced fading factor, so that the filtering gain is adjusted, and the robustness of the algorithm is enhanced.

Description

technical field [0001] The invention relates to the technical field of distribution network operation state estimation, in particular to a distribution network dynamic state estimation algorithm. Background technique [0002] The Unscented Kalman Filter (UKF) algorithm uses the unscented transform UT transform to obtain 2n+1 (n is the system dimension) sigma points through deterministic sampling, and the filtering accuracy can reach more than the second order. In order to ensure that the UKF algorithm can have high filtering accuracy under abnormal conditions, a biased and unbiased estimator is used to form a noise parameter statistical estimator to estimate the process noise statistical parameters in real time. The improved UKF algorithm not only has Strong robustness and good adaptive ability to state mutations; the above estimation algorithm can only get the best filtering effect when the noise is Gaussian distribution, and the filtering accuracy of strong nonlinear non-G...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06H02J3/00G06N3/00
CPCG06Q10/067G06Q10/06393G06Q50/06H02J3/00G06N3/006H02J2203/20
Inventor 刘敏罗永平
Owner GUIZHOU UNIV
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