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

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.

Algorithms that transfer between different energy metering methods for simplification of energy trading and unified billing

“…A meter that's implemented at time axis shall yield same results – this may be theoretically shown by integrating Fourier series of . A paper by this group [38] explains how to separate net current into current+ and current- components using Current physical components theory (CPC). Eq.…”

“…Eq. (24) is implemented at work [38] for anomaly detection for preventive maintenance and grid analytics.…”

An Energy-Fraud Detection-System Capable of Distinguishing Frauds from Other Energy Flow Anomalies in an Urban Environment

“…For intrusion detection and preventive maintenance, another algorithm taking multisensor dimensional space, including fusion of electricity knowledge with deep learning, was applied [30] using a patented technology. The other sub-algorithm is more physically oriented to electric components failure types and grid-interpreting.…”

“…Now, let us review the "if's" which are again specified in Table 6. [55,56] are recommended to scientific community judgement, to be enhanced to include varieties of the "use cases", reported for example by current research and by [55] or to be tagged as additional anomalies [30]. 3.…”

“…(II) The second type of grid anomalies that are not fraud are related to preventive-maintenance and cyber intrusion detection, and these should also be presented; there are nations that are occasionally cyber attacked. Consequently, in a previous work by this group [30], a reference is presented, and it is assumed differentiated herein that these anomalies are separated. (III) There are several methods of electricity fraud such as: magnetic tampering, phase reverse and phase disconnect or consumption biasing the phase.…”