Abstract
Kalman filter (KF) is often based on two models, which are phase angle vector (PAV) model and orthogonal vector (OV) model, in the application of distorted grid AC signal detection. However, these two models lack rigorous and detailed derivation from the principle of dynamic modeling. This paper presents a phase angle vector dynamic (PAVD) model and an orthogonal vector dynamic (OVD) model, which are combined with Kalman filter for detecting distorted grid AC signal. They reveal that the state noise covariance of the dynamic model−based KF is related to the sampling cycle, and overcome the defect of more detecting error for conventional model−based KF. Experiment and evaluation results show that the proposed KF algorithms are reasonable and effective. Therefore, this paper contributes a guiding significance for the application of KF algorithm in harmonic detection.
Highlights
Energies 2021, 14, 8175. https://The Kalman filter (KF) plays an increasingly important role in the real−time detection of grid AC signal [1]
We greatly improve the detecting accuracy of fundamental and harmonics components, enabling the detection results to meet the requirements of modern engineering applications of AC current and voltage signal
We know that detecting harmonics components for the conventional algorithm are big, though the of the harmonics components for the conventional orthogonal vector (OV)−KF algorithm are big, though error seems smaller compared to Corresponding to it, the detecting accuracies the error seems smaller compared to phase angle vector (PAV)−KF
Summary
The Kalman filter (KF) plays an increasingly important role in the real−time detection of grid AC signal [1]. It mainly applies to the following fields: fundamental and harmonic components detection of grid voltage/current [5,6,7,8,9,10], phase−locked loop synchronization [11,12,13,14,15], power quality detection and compensation equipment [16,17,18,19,20], power disturbance feature extraction and machine classification [21,22,23,24], and etc In these applications, two conventional models, phase angle vector (PAV) model and orthogonal vector (OV) model, are mainly used. KF (PAVD−KF) algorithm and the orthogonal vector dynamic (OVD) model−based KF algorithm (OVD−KF) through model derivation according to the stochastic process theory [12] It reveals that the state noise covariance of the conventional detecting model is related to the sampling cycle.
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