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Electric vehicles (EVs) have been receiving greater attention as a tool for frequency control due to their fast regulation capability. The proliferation of EVs for primary frequency regulation is hampered by the need to simultaneously maintain industrial microgrids dispatch and EV state of charge levels. The current research aims to examine the operative and dominating role of the charging station operator, along with a vehicle to grid strategy; where, indeterminate tasks are executed in the microgrid without the EVs charging/discharging statistics. The role of the charging station operator in regulation is the assignment of the job inside the primary frequency control capacity of electric vehicles. Real-time rectification of programmed vehicle to grid (V2G) power ensures electric vehicles’ state of charge at the desired levels. The proposed V2G strategy for primary frequency control is validated through the application of a two-area interconnected industrial micro-grid and another microgrids with renewable resources. Regulation specifications are communicated to electric vehicles and charging station operators through an electric vehicle aggregator in the proposed strategy. At the charging station operator, V2G power at the present time is utilized for frequency regulation capacity calculation. Subsequently, the V2G power is dispatched in light of the charging demand and the frequency regulation. Furthermore, V2G control strategies for distribution of regulation requirement to individual EVs are also developed. In summary, the article presents a novel primary frequency control through V2G strategy in an industrial microgrid, involving effective coordination of the charging station operator, EV aggregator, and EV operator.

Time difference of Arrival (TDOA)-based localization method, although used widely, calls for a fast and accurate solution owing to its time inefficiency and sensitivity to time delay estimation. In order to speed up the solution for nonlinear TDOA equations, while guaranteeing the location accuracy, this paper presents a hybrid approach namely multi-deep neural network model based on a virtual measurement method (MDNNM-VMM). Data consisting of multiple time difference values, resulting from a virtual measurement method (VMM), are fed to a pretrained multi-deep neural network model (MDNNM). These 'n' number of virtually generated sequences of time delays are obtained from a single set of TDOA equations, while conforming with a uniform distribution. The multi-DNN model using these data, outputs the required partial discharge (PD) coordinates that help determine accurate PD location. While applying a measurement error of 24 ns, the average error values r, θ , , and d for the proposed method, compared to a multi-DNN method, see a significant percentage decrease of 32 %, 24 %, 39 %, and 44 %, respectively. Additionally, varying simulated error, different array designs, and certain other parameters are studied to make the PD localization process more efficient and multifaceted. INDEX TERMS Deep neural network (DNN), partial discharge (PD) localization, time difference of arrival (TDOA), ultra-high frequency (UHF), virtual measurement method (VMM).

Time difference of arrival-based localisation method has been extensively used by researchers for the partial discharge (PD) diagnosis despite being time-inefficient and sensitive to time delay estimation. Most of the contemporary work focuses on overcoming these problems by using data-driven approaches and/or statistical simulation methods. When used simultaneously, statistical simulation-based methods facilitate the data-driven approaches in terms of providing them with large amounts of data during the testing phase. However, the present work introduces a novel training phase strategy for a multi-deep neural network model (MDNNM). In this method, 'N' number of randomly generated PD sources in 3-D space are obtained statistically through virtual measurement method (VMM). Time delays amongst sensors, for the received ultra-high frequency signals from these 'N' PDs, are used in training of the MDNNM. This enhances the model's PD detection ability, as the obtained time delays realise the measurement error beforehand; and consequently, the model learns to predict the PD coordinates accurately. After applying this MDNNM trained with a novel VMM practically, the experimental results show that a location accuracy of 1 • can be obtained for a system error value of time difference up to 10 ns. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.