Vasudeva Naidu Pudi’s research while affiliated with Matrusri Engineering College and other places

This study presents a novel approach that employs a mixture of the tunable-Q wavelet transform (TQWT) and enhanced AdaBoost to address the issue of high impedance fault (HIF) recognition in power distribution networks. Traditional overcurrent protection relays frequently have lower fault current levels than normal current, making it exceedingly difficult to detect this HIF problem with the necessity to use a quick and effective approach to find HIF problems. Since the TQWT performs better with signals that exhibit oscillatory behavior, it has been utilized to extract special features for the training of the improved AdaBoost model. The procedure is accelerated by calculating the Kourtosis (K) value for each level and selecting the ideal level of decomposition to minimize computing work. Faulted zones are categorized using an enhanced AdaBoost approach. Under normal, noisy, and unbalanced conditions, the recommended approach is applied to an imbalanced 123-bus test system and an IEEE 33-bus test system. The efficiency of the recommended method is also being assessed for imbalanced distribution networks incorporating dispersed generation into real-time platforms. This procedure is quick compared to previous methods since it uses an upgraded AdaBoost classifier and optimal decomposition level.

This study proposes an innovative approach to enhance the performance of photovoltaic-unified power quality conditioner (PV-UPQC) system by replacing traditional synchronous reference frame control with a sophisticated gated recurrent unit (GRU) network controller. This innovative framework achieves a reduction in system expenditure and intricacy by neglecting mathematical operations. The GRU-based controller dynamically adjusts voltage source converter control signals, effectively mitigating voltage irregularities caused by nonlinear loads and harmonic supply voltages, thus improving overall power quality (PQ). The GRU's proficiency in retaining and adopting historical information renders it well-suited for managing the dynamic and time-varying attributes inherent in power systems. Moreover, a prairie dog optimized adaptive neuro-fuzzy inference system controller is employed for precise DC bus voltage regulation. The PV-UPQC system is reinforced by both PV and battery energy storage system (BESS) support, enhancing reliability and sustainability in grid-connected microgrids. In typical scenarios, PV furnishes active power to the load, seamlessly transitioning to BESS during prolonged voltage interruptions. In contrast to stand-alone PV-UPQC systems, the hybrid PV-BESS configuration ensures heightened reliability, alleviating instability and environmental dependencies. A comprehensive evaluation of this proposed approach, conducted through MATLAB simulations and validated experimentally, substantiates its effectiveness in advancing PQ, reliability and sustainability by generating a reduced settling time of 0.18 s, simulation THD of 0.60% and hardware THD of 1.22%. Moreover, the proposed work exhibits an improved power factor of 0.999 with enhanced dynamic response and reduced complexity.

This research introduces a novel high gain interleaved boost-cuk converter, tailored specifically for powering the brushless direct control motor (BLDC) in an electric vehicle (EV). The proposed converter aims to efficiently harness and regulate photovoltaic (PV) power to optimize the performance of EV. The converter's design ensures compatibility with the unique characteristics and requirements of BLDC motor, resulting in an optimized power delivery system for electric propulsion. To achieve this, a hybridized approach combining whale optimized spider monkey optimization algorithm is employed, which assists the proportional-integral (PI) controller. The hybridized optimization approach ensures adaptability to dynamic operating conditions of the EV and varying PV power generation. The ability to dynamically adjust the PI controller parameters in real-time enhances the converter's response to fluctuations in energy availability, thereby maintaining a stable and efficient DC link voltage. Additionally, grid connection is incorporated, it stores excess PV power during peak generation periods, and during times of insufficient PV supply, it provides supplementary power to EV system. MATLAB/Simulink is used to assess the suggested system's efficacy and performance. The simulation results provide valuable insights into system's behavior, efficiency, and energy management capabilities under various driving conditions and environmental scenarios.

Researchers from all around the world had worked tirelessly to find ways to lower the cost of solar panels, create more efficient new goods, boost their fuel efficiency, and create innovations and largely dependent on photovoltaic system technology. When compared to other forms of non-conventional energy, such as wind and tidal, solar has been one of the most widely employed resources. To harness the power of the sun using photovoltaics, one needs a photovoltaic system. Research into improving the cost-effectiveness of solar panels, which play a crucial part in photovoltaic systems, is a global endeavour. The process of choosing solar panels is nuanced, encompassing a wide range of subjective and objective considerations. To choose the best solar cell for a PV array, we use the VIKOR (VIekriterijumsko KOmpromisno Rangiranje) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) techniques. Using the Analytical Hierarchy Process (AHP) and a resemblance to ideal result ordering technique, the study’s goals were satisfied. VIKOR and other MCDM tools like the AHP and TOPSIS are used to rank candidates in terms of performance. An in-depth case study was conducted using six different kinds of solar panels to show how well the approaches work.

In this paper, a model of an electric vehicle transfer function using GA and a model-reduced order discrete time realization (DRA) algorithm is presented. The electric vehicle (EV) control system regulates vehicle speed according to the driver’s command signal and brings the vehicle to its equilibrium point, i.e., the desired speed under any abnormal conditions. The controller transfer function is designed based on EV's dynamic differential equations. An infinite-order transcendental transfer function for the EV model is approximated to find high-reliability discrete-time state-space reduced-order models (ROMs). Reduced the robustness and increased the predictions' accuracy of ROM output in terms of accurate numerical simulations of the EV transfer function. Also, the design of the PID controller parameter using optimization techniques (OT) for the tuning of conventional controllers for the desired speed is done using a linearized model. In this study, the performance of a conventional PID controller is compared with that of a GA-based PID controller. In terms of maximum overshoot, peak time, rising time, settling time, and steady state error, the simulation results show that the suggested controller is preferable.