G DILLI BABU DATA SET
Description
The research hypothesis of this study is that an Adaptive Dynamic Search Optimization (DSO)-based Maximum Power Point Tracking (MPPT) controller integrated with an Inductive Network Switched Capacitor (INSC) converter can improve the efficiency, voltage gain, dynamic response, and power extraction capability of Proton Exchange Membrane Fuel Cell Systems (PEMFS) compared with conventional MPPT methods and converter topologies. The data was gathered through simulation-based analysis of different fuel cell systems, converter structures, and MPPT algorithms under varying operating temperatures. Different fuel cell technologies such as PEMFC, SOFC, MCFC, AFC, DMFC, and PAFC were comparatively studied based on fuel type, efficiency, operating temperature, applications, advantages, and limitations. The proposed converter was also compared with conventional converters such as IIBC, IQZSC, HSSC, DPIC, and MMBC using parameters including number of switches, capacitors, inductors, voltage gain, and current disturbances. The PEM fuel cell system performance was evaluated using different MPPT techniques including Adaptive P&O, Incremental Conductance (IC), MPNN, RBFN, Fuzzy Logic, and the proposed DSO method. Parameters such as source voltage, source current, source power, load voltage, load current, load power, efficiency, settling time, and tracking time were recorded at operating temperatures of 280K, 320K, 350K, 265°C, 290°C, 315°C, 345°C, and 365°C. The data shows that the proposed DSO-based MPPT method consistently achieved superior performance. At 350K, the DSO controller achieved 94.78% system efficiency, compared with 85.56% for Adaptive P&O. At 365°C, the DSO method produced 1283.90W load power with 98.75% efficiency, which was the highest among all techniques. The proposed controller also reduced settling time and tracking time while minimizing oscillations around the maximum power point. The DSO technique showed only 2.0% oscillation compared with 3.5% in Adaptive P&O. The results indicate that intelligent optimization-based MPPT techniques can significantly improve fuel cell energy utilization, converter performance, and system stability. The proposed INSC converter achieved higher voltage gain with fewer passive components and lower current disturbances, demonstrating reduced hardware complexity and improved energy conversion capability. The dataset can be interpreted as evidence that DSO-controlled PEM fuel cell systems are more suitable for renewable energy applications such as electric vehicles, portable power systems, distributed generation, and backup energy systems. The provided comparative data can also be used by other researchers for validating MPPT algorithms, converter designs, and fuel cell performance optimization studies.