摘要
由于风电自身固有的波动和接入配电网时形成的差异,导致风电并网过程中容易出现电网失稳现象。针对风电并网设计了一种改进PSO无功补偿方案。首先对风电接入配电网时的风电场和风机单元功率平衡进行分析,采用STATCOM动态补偿应对风电场出力的时变特性。然后在风电并网模型基础上,将无功补偿转化为多目标寻优问题,从电压偏差、有功损耗与谐波畸变三个角度构造寻优模型,并分别基于有功、无功、电压和电流关系设计了相应的等式、不等式与机会约束。最后对PSO的惯性系数与加速系数进行优化设计,根据分布熵计算动态调整惯性系数,根据粒子密度动态调整加速系数,从而改善PSO的收敛时间与收敛精度。算例仿真结果表明,基于改进PSO的无功补偿能够获得更好的电流质量和网络损耗,明显提高了无功补偿速度,有效保证了风电接入配电网的稳定性。
Due to the inherent fluctuation of wind power and the difference formed when it is connected to the distribution network, it is easy to cause grid instability in the process of wind power integration. Therefore, an improved PSO reactive power compensation scheme is designed for wind power integration. Firstly, the power balance of wind farms and wind turbine units was analyzed when wind power was connected to the distribution network, and STATCOM dynamic compensation was used to deal with the time-varying characteristics of wind farm output. Then, based on the wind power integration model, the reactive power compensation was transformed into a multi-objective optimization problem. The optimization model was constructed from voltage deviation, active power loss and harmonic distortion. Based on the relationship between active power, reactive power, voltage and current, the corresponding equations, inequalities and chance constraints were designed. Finally, the inertia coefficient and acceleration coefficient were optimized, and the inertia coefficient was dynamically adjusted according to the distribution entropy, and the acceleration coefficient was dynamically adjusted according to the particle density, so as to improve the convergence time and convergence accuracy. The simulation results show that the improved reactive power compensation can obtain better current quality and network loss, the speed of reactive power compensation is improved, and the stability of wind power access to the distribution network is effectively ensured.
作者
戴琼洁
刘燕
刘吉成
DAI Qiong-jie;LIU Yan;LIU Ji-cheng(Ordos Institute of Technology,Ordos Inner Mongolia 017000,China;North China Electric Power University,Baoding Hebei 071003,China;Guizhou Institute of Technology,Guiyang Guizhou 550003,China)
出处
《计算机仿真》
北大核心
2021年第12期61-65,共5页
Computer Simulation
基金
云贵高原风电机组故障检测与诊断研究(黔科合基础[2020]1Y241)
内蒙古自治区高等学校科学研究项目:光伏-储能价值链价值增值路径及系统仿真研究(NJSY21144)
内蒙古自治区自然科学基金项目:光伏-储能价值链价值增值协同决策模型研究(2021BS07002)。
关键词
风电并网
动态无功补偿
粒子群优化算法
目标约束
Wind power integration
Dynamic reactive power compensation
Particle swarm optimization algorithm
Objective constraints