Using True RMS Current Measurements to Estimate Harmonic Impacts of Multiple Nonlinear Loads in Electric Distribution Grids

Flávia P. Monteiro, Suzane A. Monteiro, Maria E. Tostes and Ubiratan H. Bezerra
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Flávia P. Monteiro: Campus Oriximiná, Federal University of Western Para, Oriximiná, PA 68270000, Brazil
Suzane A. Monteiro: Campus Oriximiná, Federal University of Western Para, Oriximiná, PA 68270000, Brazil
Maria E. Tostes: Electrical Engineering Department, Federal University of Para, Belém, PA 66075-110, Brazil
Ubiratan H. Bezerra: Electrical Engineering Department, Federal University of Para, Belém, PA 66075-110, Brazil

Energies, 2019, vol. 12, issue 21, 1-21

Abstract: Currently, for analyzing harmonic impacts on voltage at a point of interest, due to multiple nonlinear loads, the literature recommends carrying out simultaneous and synchronized measurement campaigns in all suspicious points with the use of high cost energy quality analyzers that are usually not available at the customers’ facilities and very often also not at the electric utilities. To overcome this drawback this paper proposes a method of assessing the harmonic impact due to multiple nonlinear loads on the total voltage harmonic distortion using only the load current true RMS values which are already available in all customers’ installations. The proposed methodology is based on Regression Tree technique using the Permutation Importance indicator which is validated in two case studies using two different electrical systems. The first case study is to ratify the use of Permutation Importance to measure the impact factor of each nonlinear load in a controlled scenario, the IEEE-13 bus test system, using ATP simulation (Alternative Transient Program). The second is to apply the methodology to a real system, an Advanced Measurement Infrastructure System (AMI) implanted on a campus of a Brazilian University, using low cost meters with only true RMS current measurements. The results achieved demonstrated the feasibility of applying the proposed methodology in real electric systems without the need for additional investments in high-cost energy quality analyzers.

Keywords: current true RMS; harmonic distortion contribution; machine learning; power system analysis computing; total voltage harmonic distortion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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