Natarajan Prabaharan

This paper proposes a novel magnetic coupler, a segmented bipolar pad (SBP) that outperforms the conventional bipolar pad (BP) with symmetrical geometrical dimensions. The performance parameters: mutual inductance (M_TR), coupling coefficient (k), output power (P_O), and DC-DC efficiency (η). The performance evaluation of the proposed pad is compared with the conventional pad under cases: (1) lateral misalignment (ΔY), and (2) interoperability with non-polarized pad (NPP) and polarized pad (PP). A 4.7 kW inductive power transfer (IPT) system is designed with an inductor–capacitor–capacitor-series (LCC-S) compensation network. For case 1, the M_TR of the SBP at ΔY = ±90 mm is the same as the M_TR of BP at ΔY = 0 mm, ensuring better misalignment tolerance capability of SBP. The maximum η of SBP is 93.64%, which is 4.96% greater than the highest η of BP. For case 2, the M_TR of the SBP with NPP is 22–24% and with PP is 20–25% higher than the BP interoperable performance. The obtained η shows maximum improvement of 2.46% for SBP with NPP, and 3.7% for SBP with PP when compared to the interoperable results of BP. SBP gives enhanced performance for both cases compared to the conventional pad at no additional pad design cost. The proposed work is validated through an experimental setup.

Hybrid Microgrids (HMGs) are more predominant due to their ability to integrate Distributed Renewable Energy Resources (DREs) with existing utility grids, minimize power conversion losses, enhance energy efficiency, and improve system stability. Due to the fluctuating and unpredictable output of DRES power generation, varying load demands negatively affect HMG’s voltage quality and power-sharing. These fluctuations can be reduced by ensuring a proper balance between the power generation and the load demand. Therefore, this paper presents the robust architecture of HMG comprised of DC microgrid (DCMG) and AC microgrid with Particle Swarm Optimization (PSO) based control algorithm for a Bidirectional Interlinking Converter. The proposed HMG concept consists of a Doubly Fed Induction Generator and Permanent Magnet Synchronous Generator based wind turbines, Photovoltaic (PV) Source (PV), diesel generator, and Battery Energy Storage System (BESS) tied at the AC bus and DC bus. The proposed system employs an Adaptive Neuro-Fuzzy Inference System for a Maximum Power Point Tracking controller in the DC–DC converter of a PV system to achieve maximum output power during various irradiation scenarios due to its accuracy and fast-tracking speed. The Model Predictive Control technique is employed to control the switching of the bidirectional converter of BESS in DCMG due to its non-linear and multi-input multi-output control. It improves DC bus voltage regulation and ensures the smoothing of varying output power generation from RES. The HMG is analyzed for power management under varying source and load conditions. The proposed system is verified in the MATLAB Simulink environment to validate HMG’s grid-connected and islanded operation, and a comparative analysis with and without optimization is presented. The result analysis proves that the PSO-based control algorithm performs better than the conventional controller, significantly enhancing the system’s dynamic conditions. Specifically, the overshoot and undershoot values are reduced from 28.5 to 21.5% and from 17.6 to 9.5% at various transient conditions with less oscillations. These improvements show the effectiveness of the proposed optimization-based control strategy in enhancing transient stability, power sharing, and voltage regulation. These results confirm the effectiveness of the proposed optimization-based control strategy for next-generation hybrid microgrids.

A Multiple Output Switched Mode Power Supply (MOSMPS) with Artificial Neural Network (ANN) control for Power Quality (PQ) improvement and output voltage regulation is proposed for personal computers in this paper. The proposed MOSMPS eliminates the conventional diode bridge rectifier at the input by incorporating a bridgeless converter to minimize conduction losses and improve thermal management. The bridgeless converter is modeled to operate in Discontinuous Conduction Mode (DCM) for advantages like improved Power Factor (PF), zero-current switching of power switches, and several sensors to implement the control strategy effectively. The proposed ANN controller is simulated and experimentally verified for various source and load conditions. An FPGA processor is implemented to experimentalize ANN control. The efficacy of the proposed configuration with an ANN controller is validated in terms of PQ indices by comparing the simulation and experimental results with those of another topology and conventional controller.

Power generation from renewable energy sources (RES) due to its uncertain nature and load variations in electrical power grids can lead to inertia reduction and, consequently, frequency deviation. In this study, to solve this problem and regulate the frequency, an automatic generation control (AGC) system along with a battery energy storage system (BESS) based on virtual inertia control (VIC) is used. In order to reduce the effects of inter-area oscillations (IAO) in power systems, the approach used is that a linear quadratic controller (LQC) is used to optimize the parameters of the PI controller. This control configuration is designed as a dual structure: in the first layer, the state errors of the PI controller are improved by minimizing the integral square error (ISE) criterion. Then, in the second layer, a LQC controller, acting as a supervisor, adjusts the optimal values of the PI parameters in the lower layer. Due to the presence of noise and the lack of direct measurement of all state variables, the Kalman Filter (KF) is used as an observer to estimate inaccessible states. The objective functions for frequency tuning are also optimized using the Firefly Algorithm (FA) in the MATLAB software platform. The data obtained from the simulations, which have been evaluated under various conditions, indicate that the proposed solution, in addition to improving the efficiency of frequency tuning, has a higher accuracy in reducing the size of the overshoot, subsidence, and settling time compared to other solutions.

This article proposes a new non-isolated dc–dc converter without coupled inductors to attain a higher voltage gain at a low duty ratio, less voltage stress across the switch, and improved reliability. The operation of the proposed converter is examined through the steady-state mathematical equations in continuous conduction mode and discontinuous conduction mode. The boundary condition mode of the proposed topology is also discussed. Reliability analysis is performed using the MIL-HDBK-217F standard to predict the failure rate of the individual components. A laboratory-based prototype is developed and tested with 250 W to validate the operation of the proposed converter. Also, the proposed converter is tested with steady-state and dynamic conditions by changing the duty ratio and load values. The efficiency and power density of the proposed converter are 91.8% and 37.71 W/in$^{3}$ (2.3 kW/L).

The increasing energy demand and rising fossil fuel prices are accelerating the transition to renewable energy, supported by government initiatives due to their environmental and economic advantages. However, challenges such as limited capacity and stability constraints hinder the widespread adoption of distributed energy resources (DERs). Virtual Power Plants (VPPs) enhance market participation by aggregating DERs, while electric vehicles (EVs) contribute to environmental sustainability by reducing emissions. Additionally, integrating distribution static compensators (DSTATCOMs) within VPPs improves microgrid stability and reactive power support. This study proposes a two-stage optimization approach to enhance network resilience and VPP profitability in a radial distribution network (RDN). The first stage focuses on minimizing resilience-related costs and energy not supplied (ENS) during natural disasters, while the second stage optimizes VPP profit using a three-phase bidding strategy, which includes the day-ahead market, real-time market, and overall market. A hybrid improved grey wolf optimization-particle swarm optimization (IGWO-PSO) algorithm is developed to solve this complex optimization problem. To demonstrate the effectiveness of the proposed approach, IGWO-PSO is compared with other hybrid optimization algorithms. Validation on a modified IEEE 33-bus RDN confirms that the proposed model enhances VPP placement and sizing, leading to improved economic, operational, and resilience metrics. Furthermore, the model accounts for uncertainties in load demand, renewable generation, energy prices, and equipment availability, ensuring a robust and adaptable energy management strategy.

Deploying electric vehicles (EVs) is essential for reducing greenhouse gas emissions and other harmful gases generated by conventional engines. Unprecedented hike in fuel prices and environmental concerns have increased the usage of EVs for last‐mile connectivity. An increase in the adoption of EVs also leads to an increase in the grid power demand. A typical EV docking station (EVDS) hosting EVs for last‐mile connectivity is considered in this research. Hence, there is a need to locate EVDS, considering the optimal charging pattern of the EVs and the distance traveled. Initially, a ride‐hailing company EVDS is modeled based on the type and level of charging. Based on charging profile observations from the advanced metering system, the EV charging system provider (CSP) must propose a suitable location for placing the docking stations corresponding to the energy required for charging the EV fleet. The proposed approach integrates an IEEE 69 Bus distribution network with a solar‐powered EVDS. Integration makes use of solar energy as a primary or supplementary source for charging EVs, promoting sustainability and reducing reliance on the conventional grid. Solar photovoltaic (SPV) system‐powered EVDS improves reliability and efficiency by reducing power losses. EVDS remains functional during peak demand or grid outages due to the distributed nature of SPV systems. The optimal placement of a solar‐based EVDS in the IEEE 69 bus system will improve the charging time, system reliability, and greater utilization of renewable energy sources. Finally, the reliability test was carried out for the optimal placement of solar EVDS within an IEEE 69 BUS system.