This paper presents a novel approach to diagnose the inter-turn short-circuit (ITSC) faults in three-phase induction motors using machine learning. Induction motors possess many attractive advantages such as high efficiency, cost-effectiveness, and high reliability. However, with their widespread applications, real-time online fault detection and maintenance play a vital role in providing continuous operation. This paper focuses on developing a real-time inter-turn fault detection of a three-phase induction motor using machine learning. The proposed method detects a minor ITSC fault in the starting stage itself. Therefore, the faults can be removed, or the faulty motors can be replaced with healthy ones to provide continuous operation. Also, using machine learning the severity of faults can be classified as taking adequate maintenance action. The ML-based fault detection method uses a convolutional neural network (CNN) and an Extreme Learning Machine (ELM) for training the diagnosis model with experimental data. The features required for training the model are selected from the faulty inter-turn model and vibration analysis of the three-phase induction motor. The proposed ML-based fault detection gives an accuracy of about 99%.

Decentralizing the electric market and adapting new loads such as electric vehicles and direct current (DC) energized loads have brought many developments in conventional grids. This modernization of utility grids has brought many non-conventional energy sources to satisfy energy demand. The operation of the old and new loads available on the utility grid is facilitated by using non-conventional energy sources. To operate these loads, different power converters are utilized. The popularity of DC loads has brought many DC microgrids, which are accompanied by solar and fuel cell sources. Voltage levels at the output of these non-conventional sources are very low compared to the voltage required to drive the loads. Therefore, DC-DC converters are utilized to boost the voltage at the output of the non-conventional sources. This paper discusses a new DC-DC step-up converter for boosting the output voltage from a non-conventional source. The DC-DC boost converter has a high voltage gain ratio, which gives four times the voltage gain at duty cycle 50%. Initially, the improvised boost converter is studied, and the performance of the converter’s behavior is tested. The converter enables the real-time use of voltage-boosting techniques by reducing the voltage stress across the MOSFET. The prototype of the converter is prepared, performance is verified, and it is found that the converter can provide a high gain voltage at the converter’s output.

In modern days, to achieve low emission of Green-House Gases (GHG), clean system, easy maintenance, high starting torque, and high efficiency, Internal Combustion (IC) engines are gradually being changed by electric motors. Smart vehicular systems (SV) either functioned from moderately or fully electrical energy generated from renewable sources. The energy storage, i.e., battery or ultra-capacitor system, gains energy from the IC engine. Alternative the electric grid, e.g., IC engines and electric grids are used to charge the battery in Hybrid Smart Electric Vehicle (H-SEV) and Plug-in Smart Electric Vehicle (PSEV), respectively. Power Electronics skills play an imperative role in the energy conversion between Grid-vehicle-Wheel (G-VW) and Wheel-vehicle-Grid (W-V-G). This letter discusses smart vehicular systems with their demand, curiosity, challenges, and power electronic skills.

The grid penetration of distributed generation systems is rapidly increasing to meet the energy demands. In the grid connected operation of these systems, the islanding scenarios are considered as a critical threat for stable operation of the utility. This paper aims at developing an islanding classification technique to efficiently detect the grid abnormalities and classify the islanding scenario. The process involves generation of data sets corresponding to various grid abnormalities, and training them with a machine learning classifier. To realize this development, a 10 kW three-phase grid connected photovoltaic (PV) system is simulated and compiled in Typhoon hardware-in-loop (HIL) environment to get different data sets for grid abnormality conditions. Further, the data is trained with the k-nearest neighbor (kNN) classified to develop the islanding classification mechanism. The efficiency of trained classifier is 94.2 %, and has the capability for efficient classification at a speed of approximately 17000 observations per second.

The promising growth of Electric Vehicles (EVs) places a mandate for a continuous increment in the power demand on the grid. To prepare for this directive, charging infrastructure plays a vibrant role. The future evolving technology for implementing charging infrastructure with the concept of no wait time to charge the battery is workable with a dynamic wireless charging system (DWCS). In this paper, the comprehensive assessment and performance analysis of solar PV-powered and the dc grid-powered DWCS is investigated. Further, the simulation is carried out through MATLAB software of version 2020a to examine and analyze the performance of the DWCS. Finally, it is observed that the solar PV-powered DWCS needs power help during the poor weather conditions which can be effectively executed by switching to the dc grid.

The global paradigm shift from conventional sources of energy to the renewable resources has propelled the adoption of Photovoltaic (PV) as an alternative energy source. With the increased focus on grid connected PV systems, the reliability and stability of solar inverters is a major area of interest. The existence of fault in inverter may severely affect the operation of the whole system leading to adverse effects at the grid end. To improve the system reliability, it is imperative to have a Fault Diagnostic Mechanism that is capable of identifying and classifying such failure conditions. The adoption of data driven techniques for fault classification has proved to be highly efficient. This paper proposes an intelligent data driven modified AlexNet based fault detection and classification technique for grid connected single phase PV Inverters. It incorporates global average pooling layer (GAP) as replacement of fully connected layer to realize improved network model. The fault diagnostic and classification scheme is implemented in MATLAB. The training accuracy obtained is 99.6% and testing accuracy is 99.2%. Hence, the proposed technique is effective and satisfies the requirements of a fault diagnostic classifier.

Electric Vehicles (EVs) have become prominent on our roadways. They are cost-effective, save our precious time and create a pollution-free environment. High efficiency inductive wireless charging systems for electric vehicles (EVs) have proved convenient and user friendly compared to their wired charging counterparts. Wireless EVs (WEVs) are unusual in most countries due to the associated techno-economic problems. Hence, it becomes essential to study in detail about the same before deploying a WEV charging infrastructure. This paper examines the economic aspects of wireless charging systems (WCS) for EVs. The impacts of the COVID-19 and real-estate availability for constructing and commissioning various (WCS) are discussed. Additionally, the cost involved in converters/ inverters, coils, battery for WCS are enumerated.

Multilevel inverters are the best suitable power electronics converters over conventional two/three-level inverters to provide high power quality ac waveforms. However, the quality of the ac power highly depends on the modulation scheme employed by the multilevel inverter. The use of a low-switching frequency modulation scheme is strongly emphasized over a high-switching frequency modulation scheme because of numerous advantages such as lesser power loss, lower switching stress, improved converter efficiency and enhanced converter lifetime. This paper presents a variant of the Nearest Level Modulation scheme, which is based on the geometric nature of variation of MLI switching angles. Hence, the proposed NLM scheme is termed as Geometric-NLM (G-NLM). A seven-level symmetric CCS-MLI topology is taken for the case study, and the proposed modulation scheme is implemented on it. Matlab/Simulink is used to carry out the simulation, and the results are presented. The G-NLM is found to have reduced the voltage THD and improved other MLI parameters compared to the conventional NLM scheme.

Loss of synchronization (LOS) in case of synchronous reference frame based phase-locked loop (SRFPLL) affects the low voltage ride-through performance of grid-connected power converters. To avoid this issue, this paper proposes a hybrid grid synchronization technique that includes two frequency and two phase-angle estimators. It uses the frequency and phase-angle estimation by the SRFPLL during normal grid operation and switches to the arc tangent based phase-angle and corresponding frequency estimation in the αβ-frame during grid faults. To avoid sudden mode transfer between these estimators a common transition algorithm is proposed as well, which is controlled by the phase-angle difference between them. The proposed hybrid grid synchronization transition is implemented in the frequency dependent proportional and resonant current controller of the converter. The controller is run with the low voltage ride-through strategy during the faults. It is observed that the converter having the proposed grid synchronization technique provides robust current controller performance as the fast tracking of grid current on the fault inception and recovery.

The recommended power quality (PQ) indices in electric power systems are defined based on the discrete Fourier transform (DFT). DFT is mostly evaluated applying fast Fourier transform (FFT) algorithm which requires signals to be periodic in nature. Hence, it evaluates PQ indices accurately for stationary PQ disturbances only. However, in case of nonstationary PQ disturbances signal is aperiodic and FFT results in erroneous assessment due to spectral leakage phenomena. Furthermore, FFT cannot provide time information of the PQ indices as computation is performed in frequency domain only. Which is significant shortcoming of the FFT when dealing with time-varying PQ disturbances to extract dynamic signature of the signal. Thereby, this paper presents a framework of redefining PQ indices for stationary and nonstationary PQ disturbances employing time-frequency distribution (TFD) method. Among all TFDs, reduced interference distribution (RID) represents the most suitable properties for analyzing time-varying PQ disturbances. Hence, in this work, it is employed to reformulate PQ indices typically used in electric power systems. The results of two synthetic examples considering stationary and nonstationary PQ disturbances imply that the RID-based redefined PQ indices are closer to the actual values. Also, they provide more accurate results than the existing transient PQ indices and traditional FFT-based method.

This paper proposes a new algorithm to detect and classify faults in electric power transmission line. The method is developed based on advanced digital signal processing (DSP) and time-frequency distribution (TFD) technique. Since it shows suitable properties to extract time-varying signature of non-stationary signals and high-frequency transients introduced by typical power quality (PQ) disturbances in electric power systems. The proposed method first separates fault disturbance component from the steady-state signal, and represents it in time-frequency domain. Thereby for feature extraction to single out faults from other common electric PQ disturbances such as voltage sags and oscillatory transients. Once fault is detected, TFD-based new index Instantaneous Fault Disturbance Ratio (IFDR), which provides energy information of fault disturbance compared to steady-state signal, is utilized to classify different types of faults. The analysis results show that the proposed method is able to classify faults successfully by setting up thresholds obtained via IFDR index for different types of faults. In this work, different types of fault signals are generated using PSCAD/EMTDC simulation software. Further, fault signal data are imported into MATLAB for post processing, and time-frequency analysis using Signal Processing Toolbox in MATLAB.

In this article, the conventional landsman DC-DC converter is modified to achieve the high inverting voltage conversion ratio for renewable energy applications. A new Switched Reactive Circuitry (SRC) made of one inductor, one capacitor, and two diodes are incorporated to lift the voltage conversion ratio. The characteristics waveform and operation of the proposed converter is discussed in detail. The proposed converter finds the major role in renewable energy integrated application where the high voltage demanded. The proposed landsman converter is compared with existing landsman converter and recently proposed converter in terms of voltage conversion ratio. The proposed configuration has higher voltage conversion ratio compared to conventional landsman converter. The simulation results match with theoretical analysis and validate the feasibility and functionality of the proposed converter.

In this paper, new modified configurations of CUK converter are proposed for high-voltage/low-current applications with the employment of multiple Voltage Lift Switched Inductor (VLSI) modules. Proposed CUK converter configurations are designed based on the placement of VLSI modules in modified CUK converter named as MCCVLSI-XYL, MCCVLSI-LYZ, MCCVLSI-XLZ, and MCCVLSI-XYZ. The mathematical evaluation of each configuration is done to find out the voltage conversion ratio. The outstanding qualities of the proposed CUK configurations are discussed in the paper. Also, the operating modes of MCCVLSI-XYZ configuration are discussed. Moreover, the proposed configurations are compared with non-inverting converters in terms of voltage gain and to each other in terms of reactive components, and semiconductor devices. The functionalities and performance of the proposed converters are verified by MatLab (R2016a) simulation results and which are always show a good agreement with the theoretical analysis.

In this paper, the aim is to test out the various existing load shedding approaches along with the proposed approach using Bus Priority Index (BPI). It is a robust hardware and server backed software framework. The complexity of the algorithm using Big O Notation is also discussed. The hardware framework consists of a relay with pseudo loads in the form of bulbs. The relay is driven by a raspberry pi which reads various system parameters from the incoming power line using Schneider PowerLogic Ion 7650 Power and Energy meter via TCP Modbus Protocol. The raspberry takes the decision based on the load shedding logic used and drives the relay accordingly to drop loads and transmits vital info to the server which displays the said data along with pertinent graphical representation in a web interface. The back-end architecture was developed using Python, the front end was made using HTML, CSS, JavaScript and jQuery while the analysis was done using MATLAB 2018b.

This paper presents a new multilevel breed of XY converter family, called LY Multilevel Boost Converter (LY-MBC) topologies for renewable energy applications. Four LY-MBC topologies are designed to obtain a high voltage conversion ratio and which also offer an operative solution for renewable energy systems with minimal number of components. Existing LY converter topologies includes L-L, L-2L, L-2LC and L-2LCm converter topologies and LY converter is one of the breed of the XY converter family. The proposed LY-MBC topologies are derived by attaching the Cockcroft Walton (CW) multiplier to the Y converter of LY converter topologies. The proposed LY-MBC converter topologies are compared with existing LY converter topologies in terms of voltage conversion ratio and number of components. The noticeable features of the proposed LY-MBC topologies are also discussed in details. The working operation, feasibility, and design of the proposed LY-MBC topologies are verified by simulations.