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

Netzah Calamaro,Avihai Ofir,Doron Shmilovitz

doi:10.3390/en14113275

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, 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.

Highlights

The objective of this study is to provide a systematic study by example of an adaptation of a multivariate problem to E-Currents’ Physical Components (CPC) and artificial intelligence (AI) feature generation
Theoretical results were generically formulated by fifteen theorems majorly to linear loads but extended to harmonic generating load (HGL)
The main focus of current paper was on CPC

Summary

Introduction

Detection attracts considerable research efforts, mostly by means of analytic models [1,2,3]. Work was performed regarding various types of anomaly detectors and various objectives such as: Vacca’s review book about anomaly detection on computer networks intrusion detection systems [4], Kovanen et al performed anomaly detection survey work on encrypted traffic [5], part of a book “Lecture Notes in Computer Science”. By Springer, and Kamat et al performed an anomaly detection survey work for preventive maintenance [6]. Multi-channel anomaly is a more sophisticated since it requires multiplicative computation resources and sometimes a collaboration of the channels: generation of a collaborative view. By “collaborative” it is meant that this is an aggregative view

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Conclusion