混合调制LLC谐振变换器的效率优化控制策略OA
Efficiency-optimized control strategy for hybrid-modulated LLC resonant converters
针对LLC谐振变换器宽范围运行与高效率难以兼顾,以及采用传统控制器进行环路设计时动态性能不佳、抗扰性能较差的问题,提出了一种混合调制LLC谐振变换器的效率优化控制策略.首先,分析变换器在变频控制和移相控制下的增益与软开关特性,设计了混合调制的控制方式.其次,提出了一种基于低通滤波器的改进自抗扰控制器,降低了扩张状态观测器的测量噪声,提高了系统的抗扰性能.然后,对变换器各部分的损耗进行分析,构建了效率优化模型,提出了一种基于山瞪羚算法的效率优化方法,实现了混合调制的效率最大化.通过对最优调制参数进行曲线拟合,降低了设计控制环路的复杂度.最后,搭建了实验平台进行理论验证,实验结果验证了所提效率优化方法和控制策略的有效性和可行性.
To address the difficulty of simultaneously achieving wide operating range and high efficiency in LLC resonant converters,as well as the poor dynamic performance and weak disturbance rejection associated with conventional controller-based loop design,an efficiency-optimized control strategy for hybrid-modulated LLC resonant converters is proposed.First,the gain and soft-switching characteristics of the converter under frequency and phase-shift control are analyzed,and a hybrid modulation control scheme is designed.Second,an improved self-disturbance rejection controller based on a low-pass filter is proposed to reduce measurement noise in the extended state observer and enhance the system's disturbance rejection capability.Then,the losses of each component of the converter are analyzed to construct an efficiency optimization model,and an efficiency optimization method based on the mountain gazelle optimization algorithm is proposed to achieve efficiency maximization under hybrid modulation.By curve fitting of the optimal modulation parameters,the complexity of control loop design is reduced.Finally,an experimental platform is built to validate the theoretical analysis,and experimental results confirm the effectiveness and feasibility of the proposed efficiency optimization method and control strategy.
常雨芳;张振;蒋煊焱;黄文聪;严怀成
太阳能高效利用及储能运行控制湖北省重点实验室(湖北工业大学),湖北 武汉 430068太阳能高效利用及储能运行控制湖北省重点实验室(湖北工业大学),湖北 武汉 430068太阳能高效利用及储能运行控制湖北省重点实验室(湖北工业大学),湖北 武汉 430068太阳能高效利用及储能运行控制湖北省重点实验室(湖北工业大学),湖北 武汉 430068华东理工大学信息科学与工程学院,上海 200237
LLC谐振变换器混合调制效率优化改进自抗扰控制山瞪羚优化算法
LLC resonant converterhybrid modulationefficiency optimizationimproved active disturbance rejection controlmountain gazelle optimization algorithm
《电力系统保护与控制》 2026 (1)
50-59,10
This work is supported by the National Natural Science Foundation of China(No.62473133). 国家自然科学基金项目资助(62473133)
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