Abstract

Aiming at the fact that the independent component analysis algorithm requires more measurement points and cannot solve the problem of harmonic source location under underdetermined conditions, a new method based on sparse component analysis and minimum conditional entropy for identifying multiple harmonic source locations in a distribution system is proposed. Under the condition that the network impedance is unknown and the number of harmonic sources is undetermined, the measurement node configuration algorithm selects the node position to make the separated harmonic current more accurate. Then, using the harmonic voltage data of the selected node as the input, the sparse component analysis is used to solve the harmonic current waveform under underdetermination. Finally, the conditional entropy between the harmonic current and the system node is calculated, and the node corresponding to the minimum condition entropy is the location of the harmonic source. In order to verify the effectiveness and accuracy of the proposed method, the simulation was performed in an IEEE 14-node system. Moreover, compared with the results of independent component analysis algorithms. Simulation results verify the correctness and effectiveness of the proposed algorithm.

Highlights

  • With the continuous penetration of new energy, power electronic equipment and non-linear loads, harmonic pollution in power systems is becoming increasingly serious

  • Aiming at the problem that Independent Component Analysis (ICA) requires many measurement nodes and cannot solve the problem of underdetermined blind source separation, a harmonic source localization method based on sparse component analysis is proposed

  • When the measurement device is smaller than the number of harmonic sources, that is, M < N, Fast-ICA cannot achieve the separation of all harmonic source signals, so the ICA algorithm has a significant disadvantage

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Summary

Introduction

With the continuous penetration of new energy, power electronic equipment and non-linear loads, harmonic pollution in power systems is becoming increasingly serious. The above method needs to constantly adjust the position and quantity of the measurement node to meet the observability requirements of the approach, which is complicated. These factors limit the application in harmonic source localization. The Independent Component Analysis (ICA) method can locate the harmonic source under the condition that the network topology and harmonic impedance are unknown, avoiding the need for complete electrical parameters in traditional HSE methods [8,9,10]. Aiming at the problem that ICA requires many measurement nodes and cannot solve the problem of underdetermined blind source separation, a harmonic source localization method based on sparse component analysis is proposed. Simulation results verify the correctness and effectiveness of the proposed method

Relationship between HSE Model and BSS Model
Independent Component Analysis Algorithm Using Fast-ICA
Two-Step Method to Obtain the Mixing Matrix and Source Signal Respectively
Clustering
Selection of Measurement Point Data
Identify the Location of the Harmonic n
Harmonic Source Localization Using CA and Minimum Conditional Entropy
REVIEW
Example Test
Comparison between SCA and Fast-ICA Configuration Schemes
Performance
Tables separated
Coefficient
Conclusions
13. Therefore the and the harmonic current
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