Department of Electrical and Electronics Engineering

This paper presents a boost-integrated T-type multilevel inverter (MLI) to address the issue of neutral point voltage unbalancing. The proposed converter is designed by modifying the neutral point connection of the T-type boost inverter to the positive DC supply, resulting in asymmetric voltage levels at each pole. Due to the varying nature of one pole voltage, conventional space vector modulation is not feasible. Hence, a novel asymmetric space vector modulation (ASVM) is proposed by utilizing the asymmetric voltage levels while integrating the feature of DC-bus clamping. To validate the proposed approach, a 1 kW prototype is developed, and the control techniques are tested across diverse duty cycles and modulation indices to achieve the desired output voltage, emphasising the importance of selecting the optimal operating point. Experimental results and efficiency analysis demonstrate the effectiveness of the proposed PWM technique in utilising asymmetric voltage levels, achieving lower THD and reduced switching losses at the output.

Recently, onboard chargers with bidirectional power features have played a key role in minimizing energy imbalance at the grid for improved power system stability. This article proposes a new family of buck-boost bidirectional dc–dc converters inspired from the synchronously operated cascaded Ćuk converter topology. A comprehensive investigation of the operating theory and steady-state parameters of the proposed family of bidirectional converters highlights the features of wide voltage gain in both forward and backward modes. Furthermore, the continuous nature of currents at the input and output sides of the proposed converters enhances the battery’s lifetime and reduces the size of the dc-link capacitor. Here, the proposed converters exhibit the same quadratic voltage gain with a common ground, but the voltage and current stress on the switches vary, providing flexibility to choose the suitable option for the charge applications of diverse EVs. A 300 W laboratory prototype of the proposed onboard EV charger is made, and real-time experimentations are performed to verify the theoretical findings and the nature of dynamic reference tracking. The proposed converters demonstrate promising performance, exhibiting a higher effective index, lower normalized voltage stress, lower current ripple, and impressive peak efficiencies.

A p-type modified quadratic gain buck-boost (PMQBB) converter is proposed in this paper. PMQBB converter topology evolution is based on the integration of a modified quadratic boost configuration with the p-type converter structure. Both of the inductors are in continuous conduction mode (CCM). The proposed PMQBB converter’s key features include a reduced component count, lower order, high voltage gain, and continuous input current. The proposed PMQBB converter exhibits a buck capability at a duty ratio D ≤ 0.2929. This paper provides a comprehensive description of the PMQBB converter, including its steady-state analysis, operating modes, and analysis of semiconductor voltage and current stress. To emphasize the PMQBB converter, a detailed comparative study is presented. A 40/400 V, 300 W hardware prototype is tested to authenticate the converter's performance. The experimental outcomes validate the superior performance and efficiency of the PMQBB converter, highlighting its suitability for high-gain applications.

High-performance applications extensively use permanent magnet synchronous motor (PMSM) drives because of their high torque density and efficiency. However, conventional PI controllers employed in the outer speed control loop of direct torque control with space vector modulation (DTC-SVM) are limited by parameter sensitivity, poor adaptability under dynamic conditions, and the need for extensive manual tuning. To overcome these challenges, a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent is introduced, incorporating a customised reward function to ensure precise torque reference generation. The TD3 agent is trained in MATLAB/Simulink using random speed and load profiles and deployed on a TMS320F28379D digital signal processor. Real-Time validation is carried out using an OPAL-RT 4512 simulator under a hardware-in-the-loop (HIL) framework. The inner-loop DTC operates at 20 kHz for torque and flux control, while the TD3 agent regulates speed at 2 kHz. Experimental results on 4.5 kW and 7.5 kW PMSMs show a 50% reduction in settling time, elimination of overshoot, and stable current responses without requiring controller retuning. The proposed method demonstrates robust and adaptive performance, confirming its effectiveness for embedded motor drive applications.

Recently, multilevel inverters (MLIs) have been extensively used in different renewable energy systems due to various reasons, including enhanced efficiency, improved power handling capability, improved output quality, etc. However, as the number of switching devices increases to achieve multilevel voltage output, ensuring the reliability of these converters present a growing challenge. Hence, this paper proposes a novel fault-tolerant multilevel inverter that offers resilience against various switch faults and can operate in both symmetric and asymmetric voltage modes. The proposed topology can achieve fault tolerance by incorporating a redundant unit consisting of switches and triacs to maintain rated power. Additionally, a new parameter is introduced to evaluate the fault-tolerant capabilities of converter topologies, providing deeper insights into their reliability. Experimental validation of the proposed converter is conducted on a 500 W prototype to validate the robustness of the presented MLI under diverse fault conditions. Further, comparative analysis has been demonstrated to underscore the advantages of the proposed topology, highlighting its superior performance across multiple metrics such as fault-tolerant efficacy, reliability, and efficiency. Furthermore, the generalized structure of the proposed fault-tolerant MLI is presented to emphasize its versatility and scalability.

Deep reinforcement learning (DRL) has emerged as a promising paradigm for the intelligent control of power electronic converters. It offers adaptability, model-free operation, and real-time decision making in complex, nonlinear, and dynamic environments. This review provides a comprehensive analysis of the state-of-the-art in DRL-based control strategies for various power converter applications. It includes voltage regulation in DC-DC converters connected to DC microgrids, speed control of permanent magnet synchronous motors (PMSM), voltage regulation and frequency modulation in dual active bridge (DAB) converters, maximum power point tracking (MPPT) in solar pv systems, and grid-connected inverter control in both grid-following and grid-forming modes. The paper systematically categorizes the recent literature based on converter topology, control objectives, DRL algorithms used, and implementation frameworks, highlighting the strengths and limitations of each approach. Special attention is given to the design of reward functions and action-state representations. Furthermore, the review identifies key challenges including stability assurance, sample inefficiency, hardware deployment constraints, and lack of standardized benchmarking environments. Finally, research gaps and future directions are outlined, emphasizing the need for physics-informed learning, safe exploration strategies, and hybrid model-based approaches to bridge the gap between academic advances and real-world deployment in power electronic systems.

The research presents a novel Bidirectional Switched Capacitor DC-DC (BSCD) Converter and demonstrates its application in integrating a battery with an electric vehicle's (EV) traction motor. During discharging, the motor is powered by the battery through the converter, and during charging, the traction motor functions as a generator, returning the recovered energy to the battery via the converter. The recommended converter employs a two-duty cycle operation to enhance voltage gain while minimizing circuit components. It utilizes a switched capacitor (SC) cell, enhancing the voltage transfer ratio by operating capacitors CS1 and CS2 in parallel or series. The work includes analysis of the converter's steady state, mathematical approach, state-space modelling, stability, and efficiency. The proposed converter achieves an efficiency of 90.66 % in charging mode and 96.6 % in discharging mode, with a Gain Margin of 54.4 dB and Phase Margin of 8.09°, indicating stability. Comparative evaluations with existing BDCs are also provided. The implementation of a closed-loop simulation using MATLAB/Simulink and dSpace software validates the performance of the suggested converter-based drive. Furthermore, an experimental investigation of a 200 W, 30 V/430 V configuration confirms the converter's practical viability.

This paper presents an allocation planning approach to optimally allocate DSTATCOM to improve the operational performance of microgrids (MGs) with plug-in electric vehicle charging stations (PEVCSs). A two-stage optimisation problem is formulated for this. The first stage is used to minimise the total cost of MG to optimally design MG and the second stage is used to optimally allocate PEVCSs and DSTATCOM in MGs by minimising the operational performance index, which is a weighted sum of energy loss and voltage deviation indices. Differential evolution (DE) and particle swarm optimisation (PSO) algorithms are used to solve the MG design optimisation problem. An exhaustive search-based approach is also employed to show the impact of the placement of a PEVCS in sub-optimal locations (one at a time) of MGs on the operational parameters of MGs. A case study is also conducted with multiple placements of PEVCSs in MGs. The proposed approaches are validated on 33-bus, 69-bus and 52-bus Indian practical distribution networks. The results show that the placement of PEVCS in remote locations greatly deteriorates the operational parameters of MGs. With the optimal allocation of DSTATCOM, a significant reduction in the operational performance index of MGs with PEVCSs is observed.

Sensorless control of Brushless DC (BLDC) motors is a cost-effective and reliable alternative to traditional Hall sensor-based methods, eliminating the need for additional hardware while enhancing system robustness. This study integrates a proportional-integral (PI) controller with a robust closed-loop sensorless speed control strategy for a BLDC motor. Back-EMF Zero-Crossing Detection (ZCD). By introducing a 30° phase delay for exact commutation and collecting rotor position information from the back-EMF of the unexcited phase, the suggested method eliminates the need for position sensors. By dynamically modifying the PulseWidthModulation (PWM) duty cycle of the VoltageSource Inverter (VSI) based on real-time speed error, an API controller is built to control motor speed. MATLAB/Simulink is used to model and simulate the system, which consists of a BLDC motor, VSI, DClink capacitor, and AC rectifier. Real-time implementation using dSPACE further validates the suggested control strategy by demonstrating stable speed control, fast dynamic response, and decreased steady-state error. The sensorless control method provides a cost-effective, efficient, and reliable solution, making it highly suitable for industrial automation, electric vehicles, and renewable energy applications.

This study examines the integration of permanent magnet synchronous motors (PMSM) with renewable energy sources, focusing on solar photovoltaic (SPV) arrays to improve efficiency and sustainability in electric vehicle (EV) applications. PMSM, renowned for its high efficiency, silent operation, and precise control, is managed using a proportional-integral (PI) controller to handle variable load conditions, including fluctuations in torque and current. By fine-tuning the PI controller's gains, the desired motor speed is achieved efficiently. A DC-DC Buck-Boost converter serves as an intermediary power conditioning unit, optimizing energy extraction from the SPV array and enhancing system efficiency. This setup ensures that PMSM meets the power and operational demands of EVs. Additionally, a voltage source inverter (VSI) facilitates electronic commutation of the PMSM, providing accurate control using fundamental frequency pulses. The system is modelled and simulated in MATLAB/Simulink, demonstrating its reliability under diverse load conditions. The findings underscore the potential of this approach in promoting renewable energy integration in EVs, paving the way for cleaner and more sustainable transportation solutions.

This article presents an artificial neural network (ANN)-based hybrid design methodology for motors used in electric vehicle applications. The proposed method uses ANN to achieve a semi-optimized motor geometry, followed by the drive cycle analysis for the desired vehicle. For this, a large pool of motor design data is used as a training set for the ANN. The semi-optimized motor geometry is further processed for power factor improvement, overall motor efficiency, and electromagnetic noise reduction. The proposed method reduces the overall complexity of the iterative motor design and optimization process. The implementation of the method is demonstrated with a case study wherein a 110 kW three-phase induction motor is designed for an electric bus using the NREL drive cycle. The performance of the motor is verified using a finite element analysis motor using Maxwell ANSYS. The work described in this article was motivated by the complexities of the iterative motor design process, which involves a high level of human resources engagement and time consumption. To address this, the presented work proposes a design approach that bypasses all the complex parts of the work by applying machine learning. The main feature of the approach is that it adopts an ANN-based method that provides a primitive set of motor design parameters for different structures/models of the motor. It eases the work of the motor designer, who has to select the best possible motor structure among these structures and revamp it for further improvement of motor performance. The application of the method is more prolific if the motor is designed for an electric vehicle that exhibits variable loading conditions. The assessment of the proposed model by designing a heavy-duty exhibit shows a significant reduction in the process complexities.

The rapid rise in electric vehicle (EV) adoption highlights the critical need for a reliable charging infrastructure to ensure the stability of power distribution networks. This research introduces a fuzzy inference system (FIS) designed to forecast daily EV loads essential for developing microgrids to meet the increasing demand for EVs. The present work considers four factors for FIS designing: travel distance, parking duration, battery state of charge (SoC), and expected arrival times at charging stations. By developing fuzzy logic rules for these variables, a probabilistic charging is generated, improving both the precision and adaptability of load forecasts. This study also explores the impact of future EV adoption on microgrid load demand, analyzing adoption rates of 53%, 68%, and 84%, providing crucial insights for planning microgrids. The discrepancy between estimated and actual EV loads is found to be 0.078, demonstrating a reduction in prediction error. This effectively mitigates uncertainties related to EV user behavior and supports the design of resilient and flexible microgrid systems.

This article presents a novel mathematical model for accurately predicting the net core loss of inverter-fed induction machines (IMs). Rotating field waves generated by all the sources such as permeance variations, source harmonics, and magnetic saturation are derived using the material characteristics. Analytical expressions for additional surface core loss and pulsation losses generated by the saturation as well as the losses incurred by augmented teeth flux densities with leakage fluxes are derived. For accurate estimation of these losses, instantaneous filed densities in various iron segments at different loading conditions are determined with on-load magnetizing current in inverter-fed operation, calculated using time-domain variation of magnetizing inductance with flux linkage. Magnitudes of saturation caused field waves are then determined iteratively using the iron magnetization profile. The accuracy of the loss model is validated by comparing the measured and simulated core loss of 11 kW IM under no-load and on-load conditions. In the pursuit of achieving net-zero carbon emissions, advancing transportation electrification stands as a crucial milestone, necessitating the utilization of traction motors tailored. As such, a precise iron core loss model is proposed, capable of effectively accounting for frequency-dependent impacts in forecasting no-load and on-load core loss.

This paper presents an AC-DC converter system tailored for grid-to-vehicle (G2V) applications, aimed at facilitating efficient power flow while achieving a Unity power factor (UPF). The system employs a rectifier for AC-DC conversion, which effectively steps up a 230V AC input to a 380V DC output. This DC output can be further regulated using a buck converter to meet specific load requirements. A Proportional-Integral (PI) controller is implemented to oversee the voltage and current regulation, thereby minimizing harmonic distortion and enhancing the overall power factor. By actively managing the input voltage and current, the controller ensures that the system operates within desired parameters, thus optimizing power quality. Comprehensive simulation results validate the system's performance, demonstrating its capability to maintain a UPF in G2V mode. The findings indicate significant reductions in total harmonic distortion (THD), reinforcing the system's effectiveness in managing power quality. This AC-DC converter design not only enhances the efficiency of power flow in electric vehicle charging systems but also contributes to the stability of the grid by minimizing reactive power and harmonics. Overall, this work represents a significant advancement in converter technology for sustainable transportation and energy management.

Accurate wind power forecasting is essential for the effective integration of wind energy into power grids. Yet, the inherent variability of wind and the intricate interplay of meteorological factors make prediction a challenging task. This study introduces a novel short-term wind power forecasting method, improving the traditional convolutional neural network and long short-term memory (CNN-LSTM) model through two significant innovations. First, we introduce a parallel convolutional architecture that employs both 1dimensional (1D) and 2-dimensional (2D) convolutions to simultaneously capture temporal patterns and inter-variable relationships in wind power data. This structure, inspired by Explainable-CNNs, enables more comprehensive feature extraction. Second, we integrate an attention mechanism that dynamically weights the importance of different input features and time steps, improving both forecast accuracy and model interpretability. The proposed model is evaluated using data from two wind farms in Croatia, comparing its performance against benchmark models including standard CNN-LSTM, LSTM, and gated recurrent unit (GRU) networks. Results demonstrate that our enhanced CNN-LSTM model achieves superior forecasting accuracy, with improvements in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 15% and 12% respectively, compared to the best-performing benchmark. Furthermore, the attention mechanism provides valuable insights into the relative importance of different features over time, offering a new level of interpretability in wind power forecasting models. This work contributes to the advancement of accurate and explainable wind power prediction, supporting more efficient renewable energy integration and grid management.

In this paper, A new customized multi-level inverter (MLI) configuration is proposed for induction motor drive, aiming to lower the requirement of DC bus voltage magnitude. This method utilizes pole pair winding coils separately to generate multi-level voltage waveform across the total stator phase windings. As the inverter requires lower input voltage it eliminates the requirement of boost converters when it is used in the EV applications. The inherent advantages of this topology significantly reduce control complexity in the battery systems by reducing the number of series-connected battery cells. The conventional Level-Shifted Sine Triangle PWM technique proficiently shifts low-frequency harmonics to the carrier frequency, enhancing power quality and minimizing electromagnetic interference. Through MATLAB simulation, this new customized multi-level inverter-fed open-end stator winding Induction motor is simulated and results are presented to validate the proposed concept. Ultimately, our research aims to contribute to advancing electric vehicle technology by operating the induction motor with minimal input DC source voltage, and substantial output gain.

The rapid adoption of electric vehicles (EVs) offers ecological and economic benefits but also introduces challenges to power distribution networks, including increased energy losses, voltage fluctuations, reduced reliability, and higher peak demand. Uncoordinated deployment of charging stations (EVCSs) may further deteriorate grid performance. While existing studies have examined EVCS siting or renewable energy integration separately, few provide a holistic framework that simultaneously considers EVCS planning, renewable generation, storage-based energy management, and user behavior under uncertainty. The objective of this study is to develop an integrated planning model that determines the optimal locations and sizes of EVCSs, aiming to minimize energy losses, investment costs, and driver travel costs, while reducing peak demand and maximizing renewable energy utilization. To achieve this, a hybrid Gray Wolf Optimization–Particle Swarm Optimization (GWO–PSO) algorithm is applied for multi-objective optimization, chosen for its effective balance of global exploration and local exploitation. Photovoltaic (PV) systems are incorporated at selected distribution nodes, and energy management strategies (EMSs) are designed to coordinate energy storage system (ESS) operations. Uncertainties in PV generation and EV charging demand are addressed using Monte Carlo Simulation (MCS). The methodology is validated on the IEEE 33-bus distribution system under a 24-hour simulation. Results show that integrating EMS with optimally located EVCSs reduces average energy losses by up to 15 % and lowers peak power demand by 20 %. These findings demonstrate that the proposed approach provides a robust, cost-effective, and sustainable pathway for EVCS infrastructure planning.

This paper presents a novel droop control strategy for an islanded DC microgrid that integrates renewable energy resources, including photovoltaic (PV) panels, a wind turbine, and fuel cells as generation sources, along with a battery storage system (BSS) and a current-loop-controlled electric vehicle (EV) as flexible storage units. The EV contributes flexibility by charging during low-demand periods and discharging during peak or overload conditions. Unlike conventional proportional–integral (PI) control, which offers accurate voltage regulation but limited dynamic performance, and classical droop methods, which suffer from a trade-off between current sharing accuracy and voltage deviation, the proposed strategy uses adaptive droop coefficients to achieve a balance between bus voltage stability and equitable power sharing without requiring large droop gains. The main contributions are as follows: (i) formulation of an improved droop framework that mitigates the classical trade-off between voltage deviation and sharing accuracy; (ii) coordinated integration of RES, BSS, and EV into a unified control system, enabling dynamic charging and discharging of the EV in response to load fluctuations; and (iii) comparative evaluation with conventional PI control to highlight the practical advantages of the proposed method. Two case studies are investigated: (a) renewable variability and load disturbances, including irradiance fluctuations, wind speed changes, sudden load variations, and load shedding, and (b) different load levels ranging from underload to overload conditions. Simulation studies, carried out in MATLAB/Simulink with a Nissan Leaf battery EV model, confirm that the proposed droop control strategy maintains DC bus voltage stability, reduces current stress in overload conditions, and achieves balanced power distribution more effectively than the conventional PI approach. The results demonstrate that the strategy enhances reliability, dynamic performance, and operational sustainability, establishing it as a robust and scalable solution for future islanded DC microgrids.

With the technological revolution, the Internet of medical things (IoMT) has developed to be of immense benefit. In IoMT, medical images and patients’ data are widely transmitted through private/public network. An ideal transmission should not jeopardize the security, confidentiality, authenticity, authorization, or integrity of medical data/images. To ensure effective transmission and address the aforementioned issues, this paper proposes a blind region based medical image watermarking approach where a medical image is partitioned into region of interest (ROI) and region of non-interest (RONI). To ensure ROI intergrity, localized tamper detection and recovery bits (LTDRB) are generated. For precise diagnosis, patient’s electronic health record (EHR) and LTDRB are embedded in RoNI using hybrid DWT-SVD. No embedding is done in RoI to maintain its integrity and high visual quality. To ensure the security and confidentiality of EHR, a novel encryption scheme using Magic Square technique with low computational cost is proposed. Experimental results demonstrates that the proposed scheme provides high imperceptibility (Avg. PSNR>55 dB, SSIM ≈ 1 and BER ≈0), robustness, security at low computational cost and high accuracy in tamper detection and recovery. A comparative study with some of the latest related research shows that the proposed scheme provides imperceptibility and robustness at par. However, the proposed scheme shows superior performance by providing higher EHR security at low computational cost and higher accuracy in ROI tamper detection and recovery, which other schemes have overlooked.

The Distributed Static Compensator (DSTATCOM) is being recognized as a shunt compensator in the power distribution networks (PDN). In this research study, the superconducting magnetic energy storage (SMES) is deployed with DSTATCOM to augment the assortment compensation capability with reduced DC link voltage. The proposed SMES is characterized by a DC-DC converter with different circuit elements like one inductor, two diodes and two insulated gate bipolar transistors. The Deep Bayesian Active Learning algorithm is suggested to operate SMES supported DSTATCOM for the elimination of harmonics under different loading scenarios. Apart from this, the other benefits like improvement in power factor, load balancing, potential regulation are attained. The simulation studies obtained from the proposed method demonstrates the correctness of the design and analysis compared to the DSTATCOM. To show the power quality effectiveness, balanced and unbalanced loading are considered for the shunt compensation as per the guidelines imposed by IEEE-519-2017 and IEC- 61000-1 grid code.