Harmonic Source Identification in Power Distribution System and Meter Placement using Network Impedance Approach

Maryam Nabihah Zaidi; Dalila Mat Said; Aida Fazliana Abdul Kadir; Nasarudin Ahmad

doi:10.11113/elektrika.v19n2.228

Authors

Maryam Nabihah Zaidi UTM
Dalila Mat Said UTM
Aida Fazliana Abdul Kadir
Nasarudin Ahmad UTM

DOI:

https://doi.org/10.11113/elektrika.v19n2.228

Abstract

The growing use of non-linear loads in the electrical systems has made harmonics a serious problem. Harmonic disturbance leads to degradation of power quality by deforming the current or voltage waveforms, thus, necessitating the effective techniques for harmonics detection.Â The purpose of this study is to propose a method for a single harmonic source identification in power distribution system by implementing a network impedance technique, and optimize the meters allocation by optimum meter placement algorithm (OMPA). The main advantage of this technique is that it results in enhanced accuracy with minimum vulnerability towards deviations in the measurements. Moreover, it minimizes the number of nodes for meter allocations, thereby resulting in economic advantages. To validate the results and effectiveness of the proposed methodology, a standard IEEE 13-Bus industrial network is designed using ETAP software and the algorithm is developed in MATLAB software. The validation of proposed algorithm OMPA is done by comparing its results with Monte Carlo Algorithm (MCA) technique. The results show that without any deviation in the network impedances, OMPA gives 89% accuracy as compared to 75% accuracy of MC. With the deviations in the harmonic impedances, the accuracy of both algorithms is decreased. For the deviation value êº = 1^-13 in the harmonic impedances, the overall accuracy of OMPA stays at 75%, while that of MCA drops down to 56%. The developed algorithm OMPA is not only better in performance in harmonics identification with minimum number of meters, but also shows more resistance to the variations in the harmonic impedances as compared to MCA.

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