Distributed harmonic source identification method based on single-frequency current transmission characteristics

Abstract

The invention relates to a distributed harmonic source identification method based on singe-frequency current transmission characteristics. The method comprises the following steps that (1) a current harmonic source identification model is established; (2) constraint conditions are exported; (3) a current harmonic observation matrix is established and preprocessed; (4) the harmonic source maximum identification frequency T and a non-Gaussianity threshold value F in an electricity network are set, and k is made to be equal to 0; (5) the k is made to be equal to k+1, and a FastICA method is used for solving a mixing matrix Wh; (6) the inverse matrix Wh-1 of the Wh is calculated, the phase position and the row order of the Wh-1 are adjusted according to the constraint conditions, the Wh-1 is denoted as Ph, if the Ph meets the constraint conditions, the step (7) is executed, and otherwise the step (5) is executed again; (7) the reverse matrix of the Ph is calculated, a demixing matrix Wh=Ph-1 is updated, the non-Gaussianity sum phi of identification results is calculated, if phi is no smaller than the threshold value F, the demixing matrix Wh is output, the harmonic source identification result is calculated, the positions of main harmonic sources are analyzed, iteration is ended, and otherwise the step (8) is executed; (8) when k is smaller than or equal to T, the step (5) is executed again, when k is larger than T, the demixing matrix Wh and the non-Gaussianity threshold value are output, and the positions of the main harmonic sources in the network are further analyzed.

Description

technical field [0001] The invention relates to a method for identifying a distributed multi-current harmonic source in a power network, in particular to a method for identifying a distributed harmonic source based on the transmission characteristics of a single-frequency alternating current. Background technique [0002] With the development of electronic technology and the widespread use of nonlinear equipment, a large number of harmonic disturbances are injected into the power grid and cause power quality problems such as voltage distortion, which seriously affects the safe and stable operation of the power network. Smart power consumption requires real-time monitoring and control of power quality, and the safety and stability of the power grid can be improved by formulating corresponding control strategies. Among them, the harmonic monitoring and harmonic control technology of power consumption network has received great attention. The identification and analysis of harm...

AI Technical Summary

Problems solved by technology

Since the linear unmixing model mainly judges whether the admittance matrix meets the requirements based on the non-Gaussian nature of the identification results, it lacks effective supervision on the magnitude and order of the admittance matrix, resulting in uncertain order and amplitude of the harmonic source identification results. Additional algorithms such as mutual information are used to adjust the order and phase of harmonic source identification results, which increases the complexity of the algorithm and reduces the accuracy and reliability of harmonic source identification

[0004] To sum up, the research on harmonic source identification is a key link to realize intelligent power consumption and improve the real-time and reliability of power harmonic control. The current harmonic source identification method may affect the Accuracy of source identification, or the accuracy and reliability of harmonic source identification results are affected due to the lack of supervised learning

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