The increasing demand for reliable and efficient renewable energy integration requires advanced control and conversion schemes in photovoltaic (PV) systems. This paper presents a comprehensive quantitative evaluation of a PV based grid-connected power generation system incorporating a Quadratic High Gain Boost Converter (QHGBC) and a Lemur Optimized Artificial Neural Network (LO-ANN) based Maximum Power Point Tracking (MPPT) system. The proposed QHGBC ensures high voltage gain and efficient power conversion from PV arrays, while the LOANN-based MPPT provides rapid and accurate tracking of PV maximum power. The bidirectional converter allows seamless charging and discharging of the battery, ensuring stable power delivery to grid applications. Simulation results from MATLAB/Simulink demonstrate significant improvements in energy efficiency as 95%, stability and adaptability, validating the system’s potential for sustainable and reliable grid-connected PV power generation compared to the other recently utilized models.
L. Chitra, S. Prakash, K. Dinesh et al.· International Conference on...· 0 citations
Strong power system infrastructure is more important than earlier due to the growing demand for electricity in the generating, transmission, and distribution sectors. Specifically, the extensive and uncontrolled usage of both linear and nonlinear loads in distribution networks creates significant power quality issues. High-voltage power systems that incorporate Static Synchronous Compensators (STATCOMs) play a crucial role in reactive power compensation, voltage management, and power quality enhancement. A unique 17-level Cascaded H-Bridge (CHB) inverter topology is proposed in this study to improve dynamic response and maintain system efficiency. A Renewable Energy Source (RES) is added to the system through a solar PV array interfaced by a Modified Luo DC-DC converter, which supports the inverter control technique and enables effective power extraction under changeable temperature and irradiance conditions. An Invasive Weed Optimization (IWO)-tuned PI controller governs the converter, dynamically adjusting the duty cycle to maintain a steady DC-link voltage for the STATCOM. With effective adjustment of the positive, negative, and harmonic components of current under dynamic grid conditions, the suggested system is put into practice using MATLAB simulation.
L. Chitra, K. Boopathy, S. Ajith et al.· International Conference on...· 0 citations
Modern energy storage systems depend on Battery Management Systems (BMS) to be safe, effective and long-lasting. In order to improve battery performance and reliability, this paper integrate a Smart BMS with Phase Change Material (PCM) Cooling Technique and Predictive Maintenance. For real-time monitoring and control, the system uses a number of sensors, including temperature, voltage, current and flame sensors, which are connected to an Arduino Uno and an ESP32-based Internet of Things module. Sensor data is analyzed to anticipate any malfunctions, allowing for proactive maintenance and preventing unplanned downtime. By efficiently controlling battery temperature, PCM cooling method reduces thermal runaway and lengthens battery life. Real-time warnings and remote monitoring are made possible by IoT architecture through a web or mobile interface. Improved safety, operational effectiveness and battery lifespan are demonstrated by experimental evaluation, which makes this strategy a viable option for cutting-edge energy storage applications.
P. Geethi, L. Chitra, A. Udhaya Kumar et al.· International Conference on...· 0 citations