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

• Published in: Energies (Basel) 2021-06, Vol.14 (11), p.3275
• Main Authors: Calamaro, Netzah, Ofir, Avihai, Shmilovitz, Doron
• Format: Article
• Language: English
• Subjects: Accuracy; AI—artificial intelligence; Anomalies; CNN—convolution neural network; Collaboration; Computer networks; CPC–currents’ physical components; Data centers; Decision making; Deep learning; Dismantling; Electrical impedance; Electrical measurement; Electricity; Electricity distribution; HGL—harmonic generating load; Intrusion detection systems; MDMS—meter data management system; Neural networks; Noise; Nuclear power plants; Preventive maintenance; Remote sensors; RNN—recurrent neural network; Sensors; Transfer functions
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en14113275

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, Yi(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.