Dynamic signal parameter identification method based on EKF and FSA

Abstract

The invention discloses a dynamic signal parameter identification method based on an EKF and an FSA. The method comprises the steps: firstly obtaining a state space expression, containing estimation parameters, in a state vector component; secondly giving a state estimation value and an initial value of a state estimation error covariance, carrying out one-step estimation through employing the EKF in the largest range of iteration, and obtaining an identification result at the next moment; thirdly judging whether the identification result at this moment meets a constraint condition or not; directly carrying out iteration identification again through employing the EKF if the identification result at this moment meets the constraint condition; carrying out optimizing at this moment through employing the FSA if the identification result at this moment does not meet the constraint condition, obtaining the identification result, meeting the constraint condition, at this moment, and carrying out the iteration identification at the next moment on the basis. Through the combination of an EKF (extended Kalman filtering) state estimation method and a fish school optimizing algorithm, the method solves a problem of dynamic signal parameter identification under the actual constraint condition, and enlarging the application range of a fish school optimizing algorithm.

Description

technical field [0001] The invention relates to a dynamic signal parameter identification method based on EKF and FSA, which belongs to the field of signal analysis and parameter identification. Background technique [0002] Electromechanical oscillation is one of the properties of large-scale interconnected power systems, and these electromechanical oscillation signals can provide important information about the operating mode of the power system. Fast and effective on-line identification of electromechanical oscillation signals helps to judge the real-time stability of large-scale interconnected power systems and helps prevent power system collapse. [0003] Due to the importance of electromechanical oscillation signal identification, researchers have proposed a variety of online parameter identification methods for electromechanical oscillation signals, such as matrix constraint method, maximum likelihood method, and Prony method. However, most of these methods are not s...

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Problems solved by technology

However, most of these methods are not suitable for real-time identification of electromechanical oscillation signals, and do not consider the practical constraints of parameters

Cannot effectively solve the problem of parameter identification of dynamic signals in electromechanical oscillations under constrained conditions

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