基于改进粒子群的储能变流器低电压穿越控制建模及参数辨识

Modeling and Parameter Identification of LVRT Control for Energy Storage Converters Based on Improved Particle Swarm Optimization

中文关键词:电池储能  变流器  低电压故障穿越  改进粒子群  参数辨识

keywords:battery energy storage  converter  low voltage ride-through  improved particle swarm optimization  parameter identification

中文摘要:针对储能变流器（power conversion system，PCS）低电压穿越（low voltage ride-through，LVRT）控制模型参数具有非线性与强时变性，导致传统辨识方法精度不足的问题，提出一种基于改进粒子群优化（improved particle swarm optimization algorithm，PSO）算法的LVRT控制建模及参数辨识方法。首先，基于电力系统仿真软件建立PCS在LVRT过程中的数学模型，明确模型的核心控制参数；其次，通过引入自适应惯性权重动态调整机制与混沌变异算子，优化传统粒子群算法的全局搜索能力及跳出局部最优解的性能，结合实测数据对LVRT控制策略中的待辨识参数进行全局寻优；最后，将辨识得到的最优参数代入LVRT控制模型，通过计算模型输出数据与实际测量数据的偏差，验证参数辨识结果的准确性。仿真结果表明，与传统PSO算法相比，改进PSO算法的各项误差指标均实现30%以上的削减，显著提升了参数辨识精度。

Abstract:The control model parameters of power conversion system （PCS） under low voltage ride-through （LVRT） have strong nonlinearity and time-varying characteristics，which lead to low accuracy of traditional parameter identification methods. To solve this problem，this paper proposes a modeling and parameter identification method for LVRT control of energy storage converters based on improved particle swarm optimization （PSO） algorithm. First，the mathematical model of PCS in the LVRT process is established based on power system simulation software，and the core control parameters of the model are clarified. Second，by introducing the adaptive inertia weight dynamic adjustment mechanism and chaotic mutation operator，the global search ability and the performance of jumping out of local optimal solutions of traditional PSO are optimized. Combined with measured data，the global optimization of parameters to be identified in the LVRT control strategy is carried out. Finally，the optimal identified parameters are substituted into the LVRT control model. The accuracy of parameter identification results is verified by calculating the deviation between model output data and actual measured data. Simulation results show that compared with the traditional PSO algorithm，the improved PSO algorithm reduces each error index by more than 30%，and significantly improves the accuracy of parameter identification.