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.

Energy fraud detection bears significantly on urban ecology. Reduced losses and power consumption would affect carbon dioxide emissions and reduce thermal pollution. Fraud detection also provides another layer of urban socio-economic correlation heatmapping and improves city energy distribution. This paper describes a novel algorithm of energy fraud detection, utilizing energy and energy consumption specialized knowledge poured into AI front-end. The proposed algorithm improves fraud detection’s accuracy and reduces the false positive rate, as well as reducing the preliminary required training dataset. The paper also introduces a holistic algorithm, specifying the major phenomena that disguises as energy fraud or affects it. Consequently, a mathematical foundation for energy fraud detection for the proposed algorithm is presented. The results show that a unique pattern is obtained during fraud, which is independent of a reference non-fraud pattern of the same customer. The theory is implemented on real data taken from smart metering systems and validated in real life scenarios.

The central problems of some of the existing Non-Intrusive Load Monitoring (NILM) algorithms are indicated as: (1) higher required electrical device identification accuracy; (2) the fact that they enable training over a larger device count; and (3) their ability to be trained faster, limiting them from usage in industrial premises and external grids due to their sensitivity to various device types found in residential premises. The algorithm accuracy is higher compared to previous work and is capable of training over at least thirteen electrical devices collaboratively, a number that could be much higher if such a dataset is generated. The algorithm trains the data around 1.8×108 faster due to a higher sampling rate. These improvements potentially enable the algorithm to be suitable for future “grids and industrial premises load identification” systems. The algorithm builds on new principles: an electro-spectral features preprocessor, a faster waveform sampling sensor, a shorter required duration for the recorded data set, and the use of current waveforms vs. energy load profile, as was the case in previous NILM algorithms. Since the algorithm is intended for operation in any industrial premises or grid location, fast training is required. Known classification algorithms are comparatively trained using the proposed preprocessor over residential datasets, and in addition, the algorithm is compared to five known low-sampling NILM rate algorithms. The proposed spectral algorithm achieved 98% accuracy in terms of device identification over two international datasets, which is higher than the usual success of NILM algorithms.

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This paper describes an electricity technical/nontechnical loss detection method capable of loss type identification, classification, and location. Several technologies are implemented to obtain that goal: (i) an architecture of three generative cooperative AI modules and two additional non-cooperative AI modules for data knowledge sharing is proposed, (ii) new expert consumption-based knowledge of feature collaboration of the entire consumption data are embedded as features in an AI classification algorithm, and (iii) an anomaly pooling mechanism that enables one-to-one mapping of signatures to loss types is proposed. A major objective of the paper is an explanation of how an exact loss type to signature mapping is obtained simply and rapidly, (iv) the role of the reactive energy load profile for enhancing signatures for loss types is exemplified, (v) a mathematical demonstration of the quantitative relationship between the features space to algorithm performance is obtained generically for any algorithm, and (vi) a theory of “generative cooperative modules” for technical/nontechnical loss detection is located and mapped to the presented system. The system is shown to enable high-accuracy technical/nontechnical loss detection, especially differentiated from other grid anomalies that certainly exist in field conditions and are not tagged in the universal datasets. The “pooling” architecture algorithm identifies all other loss types, and a robotic process automation module obtains loss type localization. The system feeds from the entire smart metering data, not only the energy load profile. Other solutions, such as a stand-alone algorithm, have difficulty in obtaining low false positive in field conditions. The work is tested experimentally to demonstrate the matching of experiment and theory.