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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.

Summary: The long term and large scale energy storage operations require quick response time and round-trip efficiency, which are not feasible with conventional battery systems. To address this issue while endorsing high energy density, long term storage, and grid adaptability, the hydrogen energy storage (HES) is preferred. This proposed work makes a comprehensive review on HES while synthesizing recent research on energy storage technologies and integration into renewable energy (RE) applications. The proposed research also identifies critical challenges related to system optimization, energy management strategies, and economic viability while featuring emerging technologies like artificial intelligence (AI) and machine learning (ML) for energy management. The proposed survey also discusses key advancements in battery technologies (lithium-ion, Ni-Cd, Ni/MH, and flow batteries) that are examined alongside innovations in HES methods. The proposed survey utilizes an extensive list of publications to date in the open literature to canvass and portray various developments in this area.

In the absence of portable charging devices, sectors such as transportation, communication, and emergency services deal with various challenges towards electric power needs while compromising on (1) operational efficiency, (2) insufficient portable charging solutions, and (3) limited versatility. This highlights the critical need for reliable and multi-functional power solutions. To provide a portable charging solution across diverse sectors, this paper proposes an innovative development of a solar-powered multi-functional portable charging device (SPMFPCD) with internet- of-thing (IoT)-based monitoring capabilities. The proposed scheme introduces a comprehensive model integrating advanced technologies which include a highly efficient solar panel, charge controller, sensors, and IoT module. The proposed system facilitates versatile charging solutions for a wide range of power requirements with real-time monitoring and data analysis through the IoT platform. Moreover, the proposed work explores the applications of the SPMFPCD in (1) emergency medical scenarios, (2) outdoor adventures, (3) disaster management, and 4) public spaces. Performance evaluation was made by proposing case studies to validate the (1) economic viability, (2) power management, and (3) environmental impact of widespread deployment of SPMPFCD in public spaces. Furthermore, detailed analysis of battery energy storage system (BESS) and photovoltaic (PV) integration for load management, seasonal dynamics, and renewable energy integration (REI) contribute to a comprehensive understanding of the proposed solution.

Wind energy systems have been flourishing on a rapid scale as a reliable renewable energy source in several countries. However, as the demand for these systems continues to grow, it becomes crucial to have a reliable and cost-effective condition monitoring system to enhance their availability. Condition monitoring plays a crucial role in achieving economic and reliable wind turbine operation. Recently, online monitoring and fault detection have been gaining greater interest in protecting these systems. Innovative and novel methods are developed to achieve real-time and remote monitoring of wind turbines. Online monitoring can offer real-time data to indicate the health condition of these systems while in service. This reduces (1) loss of production and downtime, and (2) maintenance and operational costs incurred due to system failures. This proposed work presents a comprehensive review of wind turbine online condition monitoring methods and techniques presented in the literature. Moreover, the (1) structure, (2) failures, and (3) signal processing methods of wind turbine systems were presented. Furthermore, a brief comparison was presented of (1) online monitoring techniques, and (2) signal processing methods utilized for wind turbine systems. A detailed list of published work to date has been gathered to portray a complete picture of addressed developments in this area.

Abstract Partial discharge (PD) diagnostics test is a reliable solution for estimating insulation health conditions in power system components. During laboratory PD diagnostics, the effect of harmonic components in the voltage waveform generated by variable frequency drives (VFD) fed electric motors (EMs) is often overlooked. As VFD‐fed EMs operate at low speeds, the harmonic concentration in the voltage increases. It has been found that PD activity in VFD‐fed EM is significantly affected by the addition of harmonic components in the voltage, thus making it necessary to consider their impact when performing true PD diagnostics. This paper investigates the influence of voltage harmonic distortion produced in VFD‐fed EM on PD severity and the subsequent insulation degradation. Eight VFD‐fed EM are used to study the PD behaviour under different levels of harmonic pollution. The PD characteristics, such as inception voltage, pulse repetition rate, accumulated apparent charge, average discharge current, discharge power, and quadratic rate, are determined. Using probability distribution functions, PD activity and severity at various voltage harmonic levels are estimated. The proposed technique can be used to plan the maintenance activities that ensure the reliability for industrial applications dealing with the voltage harmonic distortion.

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.

The efficient operation of power-electronic-based systems heavily relies on the reliability and longevity of battery-powered systems. An accurate prediction of the remaining useful life (RUL) of batteries is essential for their effective maintenance, reliability, and safety. However, traditional RUL prediction methods and deep learning-based approaches face challenges in managing battery degradation processes, such as achieving robust prediction performance, to ensure scalability and computational efficiency. There is a need to develop adaptable models that can generalize across different battery types that operate in diverse operational environments. To solve these issues, this research work proposes a TransRUL model to enhance battery RUL prediction. The proposed model incorporates advanced approaches of a time series transformer using a dual encoder with integration positional encoding and multi-head attention. This research utilized data collected by the Centre for Advanced Life Cycle Engineering (CALCE) on CS_2-type lithium-ion batteries that spanned four groups that used a sliding window technique to generate features and labels. The experimental results demonstrate that TransRUL obtained superior performance as compared with other methods in terms of the following evaluation metrics: mean absolute error (MAE), root-mean-squared error (RMSE), and R2 values. The efficient computational power of the TransRUL model will facilitate the real-time prediction of the RUL, which is vital for power-electronic-based appliances. This research highlights the potential of the TransRUL model, which significantly enhances the accuracy of battery RUL prediction and additionally improves the management and control of battery-based systems.

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.

Partial discharge (PD) measurements have proved their reliability for health monitoring of insulation systems in power system components including synchronous generators, power transformers, switchgear and cables etc. Online condition monitoring and pro-active detection of PD faults have been highly demanded over the last two decades. This paper provides results from a research project to develop advanced non-intrusive sensing technologies that are cost effective, reliable and efficient for early detection of PD faults in medium voltage (MV) and high voltage (HV) air-insulated switchgear. Three sensors (high frequency E-field (D-dot) sensor, Rogowski coil and loop antenna) have been developed and tested under various PD faults and their performance were evaluated in comparison with high frequency current transformer (HFCT) which is being used commercially for PD testing and measurement. Among these three sensors, it is shown that D-dot sensor and Rogowski coil are more dependable when it comes to the PD measurements due to their high signal to noise ratio and hence high accuracy. These sensors can be customized according to a specific application and can be connected together with one data acquisition device while developing an online condition monitoring system.

Electricity demand in a certain locality varies during the day, depending on weather conditions, daily life routines, or a social event in a town. During high/peak demands, expensive power plants are put into operation, which affects electricity prices. Moreover, power lines are overloaded. If generation capacity is insufficient, a blackout may result. Demand response (DR) programs are widely proposed in energy research to tackle these problems. Although the benefits of DR programs are well known, customer response levels to these programs is low. This is due to the small fraction of benefits they receive against the loss of comfort, lost leisure time, and other inconveniences. The objective of this work is to study DR costs from the customer perspective by considering these factors. A customer survey-based direct approach is used to evaluate the willingness of customers to accept (WTA) a certain compensation when shifting the load is adopted. Two different methods are used to calculate DR costs: percentage compensation, which customers are WTA, and one based on a macroeconomic model, which considers the dependency factor of customers on loads and hourly wage. A linear mathematical model is presented based on both these techniques. This study reveals that DR costs are much less than interruption costs paid by the utility company, and hence is in the best interests of all stakeholders, i.e., customers, utility company, and transmission company.