Application of Enhanced CPC for Load Identification, Preventive Maintenance and Grid Interpretation

Netzah Calamaro, Avihai Ofir and Doron Shmilovitz
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Netzah Calamaro: School of Electrical and Electronics Engineering, Tel-Aviv University, Tel-Aviv 39040, Israel
Avihai Ofir: School of Electrical and Electronics Engineering, Tel-Aviv University, Tel-Aviv 39040, Israel
Doron Shmilovitz: School of Electrical and Electronics Engineering, Tel-Aviv University, Tel-Aviv 39040, Israel

Energies, 2021, vol. 14, issue 11, 1-41

Abstract: Currents’ Physical Components (CPC) theory with spectral component representation is proposed as a generic grid interpretation method for detecting variations and structures. It is shown theoretically and validated experimentally that scattered and reactive CPC currents are highly suited for anomaly detection. CPC are enhanced by recursively disassembling the currents into 6 scattered subcomponents and 22 subcomponents overall, where additional anomalies dominate the subcurrents. Further disassembly is useful for anomaly detection and for grid deciphering. It is shown that the newly introduced syntax is highly effective for identifying variations even when the detected signals are in the order of 10 −3 compared to conventional methods. The admittance physical components’ transfer functions, Y i (ω), have been shown to improve the physical sensory function. The approach is exemplified in two scenarios demonstrating much higher sensitivity than classical electrical measurements. The proposed module may be located at a data center remote from the sensor. The CPC preprocessor, by means of a deep learning CNN, is compared to the current FFT and the current input raw data, which demonstrates 18% improved accuracy over FFT and 45% improved accuracy over raw current i ( t ). It is shown that the new preprocessor/detector enables highly accurate anomaly detection with the CNN classification core.

Keywords: CPC–currents’ physical components; MDMS—meter data management system; HGL—harmonic generating load; RNN—recurrent neural network; AI—artificial intelligence; CNN—convolution neural network; IDS—intrusion detection system; WGN—white gaussian noise; head end system—HES (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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