Abstract
Massive use of power electronic devices which are nonlinear and mostly unbalanced in nature has influenced the power quality (PQ) in electric distribution networks. They not only create significant harmonic pollution in the electric power system but also degrade the system along the grid. Prediction of the sources feeding the electrical system with polluted disturbances is a pivotal point in the estimation of the power quality. This paper proposes an fast and accurate approach based on artificial neural network (ANN). Unique identification of various types of devices along with distinct harmonic signatures is achieved via ANN utilizing feature extraction through input current waveform. To achieve feature extraction, multilayer perceptron with certain parameters is constructed and trained through backpropagation algorithm with performance compared and evaluated. The ANN incorporating MLP architecture with supervised learning is simulated in MATLAB. The results validate the ability of the proposed architecture for efficient classification of device signature for harmonic contributions with reasonably fast response.
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References
Arrillaga J (1997) Power system harmonic analysis. Wiley, New York
Hartana RK, Richards GG (1990) Harmonic source monitoring and identification using neural network. IEEE Trans Power Syst 5(4):1098–1104
Lin W-M, Lin C-H, Tu K-P, Wu C-H (2005) Multiple harmonic source detection and equipment identification with cascade correlation network. IEEE Trans Power Syst 20(3), July 2005, pp 2166–2173
Srinivasan D, Ng WS, Liew AC (2006) Neural-network-based signature recognition for harmonic source identification. IEEE Trans Power Delivery 21(1):398–405
Fernandes RAS, da Silva IN, Oleskovicz M (2011) Data mining applied to harmonic current sources identification in residential consumers. IEEE Latin Am Trans 9(3):302–310
Heydt GT (1989) Identification of harmonic sources by a state estimation technique. IEEE Trans Power Deliv 4(1), 569–576
Saxena D, Bhaumik S, Singh SN (2014) Identification of multiple harmonic sources in power system using optimally placed voltage measurement devices. IEEE Trans Ind Electron 61(5):2483–2492
Jain P, Tiwari AK, Jain SK (2017) Harmonic source identification in distribution system using ESPRIT-THP method. Trans Inst Measur Control. https://doi.org/10.1177/0142331217721316
Safargholi F, Malekian K, Schufft W (2017a) On the dominant harmonic source identification—Part I: review of methods. IEEE Trans Power Deliv. https://doi.org/10.1109/TPWRD.2017.2751663
Safargholi F, Malekian K, Schufft W (2017b) On the dominant harmonic source identification—Part II: application and interpretation of methods. IEEE Trans Power Deliv. https://doi.org/10.1109/TPWRD.2017.2751673
Pomilio JA, Deckmann SM (2007) Characterization and compensation of harmonics and reactive power of residential and commercial loads. IEEE Trans Power Deliv 22(2):1049–1055
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Tayal, A., Dewan, L., Lather, J.S. (2021). Artificial Neural Network-Based Source Identification Producing Harmonic Pollution in the Electric Network. In: Dewan, L., C. Bansal, R., Kumar Kalla, U. (eds) Advances in Renewable Energy and Sustainable Environment. Lecture Notes in Electrical Engineering, vol 667. Springer, Singapore. https://doi.org/10.1007/978-981-15-5313-4_6
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DOI: https://doi.org/10.1007/978-981-15-5313-4_6
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