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The emergence and augmentation of nontechnical losses (NTLs) in a power distribution system has always been considered a critical issue in the global electricity market. Being considered as uncharged, unlawfully or unfairly charged consumed electricity, NTLs due to fraudulent activities can impose a serious burden on the grid and revenue loss in the state’s budget. The detection of NTLs based on the analysis of huge consumer dataset using machine learning is an acknowledged approach due to its expedient performance compared to manual inspection. This paper addresses a comprehensive investigation of monthly electricity consumption data of ~15000 consumers over three years using machine learning for NTL detection. The data is acquired by meters having automatic meter reading (AMR) capability and is processed using support vector machine (SVM) classifier, deep neural network (DNN), gradient boosted reinforcement learning (GBRL) and apriori algorithm. The results of ML algorithms are assessed by various performance metrics based on the confusion matrix and compared among themselves as well as with the findings from the published works. The least recall score of 0.8926 is exhibited by SVM classifier and poorest scores of accuracy (0.9376), specificity (0.9451), precision (0.7521), F1 (0.8183) and false positive rate (0.0549) are given by DNN program. With apriori algorithm, the scores of accuracy, recall, specificity, precision, F1 and false positive rate are observed as 0.9964, 0.9927, 0.9971, 0.9843, 0.9885, 0.0029 respectively. In all the performance based six domains, a significant improvement of 6%, 10%, 5%, 23%, 17% and 5% respectively compared to the least scores is demonstrated by the apriori algorithm. Therefore, in all these domains, the apriori algorithm being reported for the first time in this research work outperforms all other methods of this paper as well as the similar published works.

Mineral oil is the most commonly used insulating oil in power transformers due to its low cost, universal availability and high dielectric strength. The potential of vegetable oils to serve as an alternative to the conventional mineral oil for power transformers is being considered due to its better dielectric strength and high biodegradability. Furthermore, the addition of small quantity of nanotubes to vegetable oils has the potential to considerably enhance their dielectric strength. In this paper, a detailed experimental investigation on the dielectric characteristics of Mustard oil infused with Titanium dioxide (TiO2) nanotubes has been carried out. The investigation covers the analysis of physical properties including moisture content, specific gravity, pour-point, flash point, viscosity, and acidity. Additionally, the breakdown strength and partial discharges of Mustard oil and nanofluid were also examined. All the tests were performed according to ISO and IEC standards. The detailed characterization of nanotubes was investigated with the help of X-ray diffraction and Scanning Electron Microscopy. The TiO2 nanotubes were mixed with pure Mustard oil at the concentration levels of 0.02g/L, 0.05g/L, and 0.1g/L. The experimental results indicate that the nanofluid with a concentration of 0.05g/L exhibits better properties compared to the other samples in its potential as a replacement of the traditional mineral oil. The investigation also revealed that the AC breakdown voltage increased by approximately 40%, whereas a three-fold rise in the partial discharge extinction and inception voltages was observed when 0.05g/L TiO2 was added into the Mustard oil compared to the pure mineral oil. A comprehensive evaluation of the dielectric properties of this mixture, in comparison to the mineral oil and other vegetable oils, underscores its cost-effectiveness as a viable alternative for power transformers. Hence, the addition of 0.05g/L TiO2 nanofluids into Mustard oil can be considered as a practical and economically feasible substitute to both mineral and vegetable oils.

In modern power systems, condition based monitoring and diagnosis is essential to ensure the effective and reliable operation of different high voltage equipment (HVE). Compared to other monitoring techniques, partial discharges (PD) measurement is considered as a key method for assessing the insulation health condition. The benefits of PD condition monitoring of HVE can be extended by proper detection, identification, and interpretation of PD signal. Among both online and offline PD monitoring techniques, online PD monitoring is a very promising technique that assists in robust monitoring system which reduces the power failure incidents in power system components. Therefore, to understand recent developments and trends in theory and in practice, it is necessary to establish a holistic analysis of current online PD monitoring techniques for HVE in power systems. This paper presents an intensive literature review of current online PD monitoring techniques used for different high voltage electric components in power system. Finally, a smart PD monitoring techniques based on wireless sensor board is proposed. The proposed smart PD monitoring framework may be used to correctly estimate the insulation degradation in HVE and enhance the overall performance of power systems.

The recent increase in electricity prices and the usage of single-phase induction motors (SPIMs) provide a stimulus for a focused research on energy-efficient optimization of SPIM load such as air-conditioners and refrigerators. Variable speed control of SPIM provides a promising way forward to reduce its power consumption. However, during variable speed operation under the popular constant V/f method, SPIM is required to operate at non-rated conditions. The operation of SPIM at non-rated conditions disturbs its symmetrical and balanced operation, thus degrading its efficiency. Moreover, soft-starting of SPIM at non-rated conditions is also challenging due to the resulting reduction in starting-torque. In this article, after a detailed analysis of SPIM energy-efficiency, an improved sensor-less optimal speed control strategy is developed to enable the symmetrical and balanced operation of SPIM at all the operating points over the entire speed-range to improve its performance. A novel algorithm, termed as the phase-shift algorithm, is also devised for efficient implementation of the proposed optimal speed control strategy. In addition, a unique framework for efficient soft-starting of SPIM at very low frequencies is also developed. The simulation-based results of the motor operated through the proposed phase-shift algorithm validate the energy-saving potential of the proposed control strategy.

Partial discharges (PD) are produced in the stator insulation of electrical machines while energized. The sources of PD are internal voids, crack, and manufacturing defects, which keep on extending with its normal operation, leading to the complete failure of insulation. The intensity of PD is considerably influenced by the environmental factors, including surrounding temperature, pressure, humidity, and contamination. This paper investigates the influence of these factors on the PD activity in electrical machines and estimates the lifetime of insulation. The PD measurements were conducted by placing the motors in a climate-controlled chamber. The PD trend under varying environmental stresses was investigated over time. Accordingly, PD inception voltage and various discharge characteristic parameters such as accumulated apparent charge, cumulative energy function, average discharge current, discharge power, and quadratic rate have been determined. The combined effect of various environmental stresses on the PD activity was also investigated. The mathematical relationships were developed to estimate the discharge characteristics as the functions of pressure and temperature using the Gaussian model and the Exponential model. Finally, the endurance of insulation was estimated under different environmental conditions based on the average discharge current to differentiate between correct and incorrect PD measurements and to develop a right maintenance schedule for motors in service.