Customer loads connected to power distribution network may be broadly categorized as either linear or nonlinear. Nonlinear loads inject harmonics into the power network. Harmonics in a power system are classified as either load harmonics or as supply harmonics depending on their origin. The source impedance also impacts the harmonic current flowing in the network. Hence, any change in the source impedance is reflected in the harmonic spectrum of the current. This paper proposes a novel method based on artificial neural networks to isolate and evaluate the impact of the source impedance change without disrupting the operation of any load, by using actual field data. The test site chosen for this paper has a significant amount of triplen harmonics in the current. by processing the acquired data with the proposed algorithm, the actual load harmonic contribution of the customer is predicted. Experimental results confirm that attempting to predict the total harmonic distortion of a customer by simply measuring the customer's current may not be accurate. The main advantage of this method is that only waveforms of voltages and currents at the point of common coupling have to be measured. This method is applicable for both single- and three-phase loads.


Electrical and Computer Engineering


National Electric Energy Testing Research and Applications Center

Keywords and Phrases

Neural Networks; Power Quality; Power System Harmonics; Total Harmonic Distortion (THD); Harmonic analysis

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version

Final Version

File Type





© 2008 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Sep 2008