G DILLI BABU DATA SET

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.本研究的研究假说如下：相较于传统最大功率点跟踪（Maximum Power Point Tracking，MPPT）方法与变换器拓扑结构，集成电感网络开关电容（Inductive Network Switched Capacitor，INSC）变换器的自适应动态搜索优化（Dynamic Search Optimization，DSO）型MPPT控制器，可提升质子交换膜燃料电池系统（Proton Exchange Membrane Fuel Cell Systems，PEMFS）的效率、电压增益、动态响应与功率提取能力。 本数据集通过对不同燃料电池系统、变换器结构及MPPT算法在不同运行温度下的仿真分析采集得到。研究基于燃料类型、效率、运行温度、应用场景、优势与局限性，对质子交换膜燃料电池（Proton Exchange Membrane Fuel Cell，PEMFC）、固体氧化物燃料电池（Solid Oxide Fuel Cell，SOFC）、熔融碳酸盐燃料电池（Molten Carbonate Fuel Cell，MCFC）、碱性燃料电池（Alkaline Fuel Cell，AFC）、直接甲醇燃料电池（Direct Methanol Fuel Cell，DMFC）以及磷酸燃料电池（Phosphoric Acid Fuel Cell，PAFC）等不同燃料电池技术开展了对比研究。同时，本研究还以开关数量、电容、电感、电压增益及电流扰动等参数为指标，将所提出的变换器与IIBC、IQZSC、HSSC、DPIC、MMBC等传统变换器进行了对比。 本研究针对质子交换膜燃料电池系统的性能，采用自适应扰动观察法（Adaptive P&O）、增量电导法（Incremental Conductance，IC）、MPNN、RBFN、模糊逻辑（Fuzzy Logic）以及所提出的DSO方法等多种MPPT技术进行了评估。在280K、320K、350K、265℃、290℃、315℃、345℃及365℃的运行温度下，记录了源电压、源电流、源功率、负载电压、负载电流、负载功率、效率、调节时间及跟踪时间等参数。 数据集结果显示，所提出的基于DSO的MPPT方法始终表现出更优的性能。在350K工况下，DSO控制器的系统效率可达94.78%，而自适应扰动观察法的效率仅为85.56%。在365℃工况下，DSO方法的负载功率可达1283.90W，效率为98.75%，为所有测试方法中的最优表现。所提出的控制器还缩短了调节时间与跟踪时间，并最大限度抑制了最大功率点附近的振荡。相较于自适应扰动观察法3.5%的振荡幅度，DSO技术的振荡幅度仅为2.0%。 研究结果表明，基于智能优化的MPPT技术可显著提升燃料电池的能源利用率、变换器性能与系统稳定性。所提出的INSC变换器可在使用更少无源元件的前提下实现更高的电压增益，同时降低电流扰动，展现出更低的硬件复杂度与更优的能量转换能力。 本数据集可作为证明搭载DSO控制器的质子交换膜燃料电池系统更适用于电动汽车、便携式电源系统、分布式发电及备用能源系统等可再生能源应用场景的依据。同时，所提供的对比数据也可供其他研究人员用于验证MPPT算法、变换器设计以及燃料电池性能优化等相关研究。