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Synchronous generator state estimation method capable of processing non-Gaussian noise and bad data

A synchronous generator, non-Gaussian noise technology, applied in the field of synchronous generator state estimation, can solve problems such as bad data, measurement data inclusion, etc., to achieve the effect of improving robustness, clear algorithm flow, and easy engineering application

Pending Publication Date: 2022-04-29
SHENZHEN RES INST OF XIAMEN UNIV +1
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  • Application Information

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Problems solved by technology

However, compared with Supervisory Control And Data Acquisition (SCADA), the accuracy of MU measurement data is improved, but it will still be affected by factors such as communication noise, environmental interference, and electromagnetic field environment, resulting in measurement data containing bad data

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  • Synchronous generator state estimation method capable of processing non-Gaussian noise and bad data
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  • Synchronous generator state estimation method capable of processing non-Gaussian noise and bad data

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Embodiment Construction

[0058] The present invention will be further described below through specific embodiments.

[0059] The method for estimating the state of a synchronous generator that can handle non-Gaussian noise and bad data proposed by the present invention includes the following steps:

[0060] 1) Initialize the estimator parameter values, including the initial state vector Covariance matrix W of system noise and measurement noise 0 and G 0 , the initial state estimate covariance matrix Φ 00 .

[0061] Among them, the estimator has a process model and a measurement model of the synchronous generator, which are expressed as:

[0062] x k =f(x k-1 ,u k )+w k

[0063] z k =h(x k ,u k )+v k

[0064] where x k =[δωE q′ E. d′ ] T Respectively represent the state vector of the synchronous generator, δ represents the phase angle, ω represents the angular velocity, E q′ E. d′ Represents the transient electromotive force of the q-axis and d-axis, u k =[E fd T m ] T Indicate...

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Abstract

The invention discloses a synchronous generator state estimation method capable of processing non-Gaussian noise and bad data. The method comprises the following steps: 1) initializing parameter values of an estimator; 2) calculating a state prediction value and a state prediction error covariance matrix at the moment k by utilizing a prediction step of an unscented Kalman filtering method; (4) calculating an interactive covariance matrix between state prediction and measurement prediction at the moment k, and establishing a linear batch processing regression model and performing white noise processing by combining the state prediction value and the measurement value with an interactive covariance matrix between the state prediction and the measurement prediction at the moment k; 5) solving the linear batch processing regression model after white noise processing by using an improved algorithm based on an index absolute value to obtain a state estimation result; 6) repeating the steps 2)-5) until the state estimation result is satisfied, and stopping iteration; and 7) outputting a dynamic state estimation result and calculating an estimation error covariance at the moment k by using an influence function. The method can effectively inhibit the influence of bad data, non-Gaussian noise and the like.

Description

technical field [0001] The invention relates to the technical field of power system monitoring and analysis, in particular to a method for estimating the state of a synchronous generator that can handle non-Gaussian noise and bad data. Background technique [0002] Due to the large-scale application of synchrophasor measurement units (Phasor Measurement Unit, PMU) in power systems, power systems are gradually developing from static state estimation to dynamic state estimation. The so-called power system dynamic state estimation generally refers to estimating the state of a synchronous generator, and the state quantity involves variables such as the angular velocity of the generator rotor. However, compared with Supervisory Control And Data Acquisition (SCADA), the accuracy of MU measurement data is improved, but it will still be affected by factors such as communication noise, environmental interference, and electromagnetic field environment, resulting in measurement data co...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G01R31/34G06Q10/06G06Q50/06G06F119/10
CPCG06F30/20G01R31/343G06Q10/0639G06Q50/06G06F2119/10
Inventor 陈腾鹏任和卿新林李钷
Owner SHENZHEN RES INST OF XIAMEN UNIV