Several optimization methods,such as Particle Swarm Optimization(PSO)and Genetic Algorithm(GA),are used to select the most suitable Static Synchronous Compensator(STATCOM)technology for the optimal operation of the po...Several optimization methods,such as Particle Swarm Optimization(PSO)and Genetic Algorithm(GA),are used to select the most suitable Static Synchronous Compensator(STATCOM)technology for the optimal operation of the power system,as well as to determine its optimal location and size to minimize power losses.An IEEE 14 bus system,integrating three wind turbines based on Squirrel Cage Induction Generators(SCIGs),is used to test the applicability of the proposed algorithms.The results demonstrate that these algorithms are capable of selecting the most appropriate technology while optimally sizing and locating the STATCOM to reduce power losses in the network.Specifically,the optimized STATCOM allocation using the Particle Swarm Optimization(PSO)achieves a 7.44%reduction in total active power loss compared to the optimized allocation using the Genetic Algorithm(GA).Furthermore,the voltage magnitudes at buses 4,9,and 10,which initially had exceeded the upper voltage limit,were reduced and brought within acceptable ranges,thereby improving the system’s overall voltage profile.Consequently,the optimal allocation of the STATCOM significantly enhances the efficiency and performance of the power network.展开更多
基于模块化多电平换流器的静止同步补偿器(stationary synchronous compensator based on modular multilevel converters,MMC-STATCOM)是高压电力系统中无功补偿的关键设备,其传统线性控制器性能会因运行点的大范围变化而恶化。针对该...基于模块化多电平换流器的静止同步补偿器(stationary synchronous compensator based on modular multilevel converters,MMC-STATCOM)是高压电力系统中无功补偿的关键设备,其传统线性控制器性能会因运行点的大范围变化而恶化。针对该问题,该文提出了一种基于滑模状态反馈精确线性化的非线性智能控制策略,首先通过选择合适的输出函数、坐标变换,将不做任何简化的3阶MMC-STATCOM非线性模型转化为一个可控的Brunovsky标准型线性系统,并通过数学理论证明了该模型满足精确线性化条件。然后采用改进的粒子群算法配置其反馈增益矩阵,利用积分滑模控制计算其平衡点。最后通过状态反馈使各个状态变量快速收敛至平衡点。将该控制策略与传统PI控制、LQR状态反馈控制通过硬件在环实时仿真平台进行对比实验,结果表明该控制策略具有更好的动态特性、暂态稳定性、鲁棒性,尤其适用于运行环境发生大扰动时。此外,该控制策略的设计过程可以拓展应用于其他类型的柔性交流输电装置。展开更多
文摘Several optimization methods,such as Particle Swarm Optimization(PSO)and Genetic Algorithm(GA),are used to select the most suitable Static Synchronous Compensator(STATCOM)technology for the optimal operation of the power system,as well as to determine its optimal location and size to minimize power losses.An IEEE 14 bus system,integrating three wind turbines based on Squirrel Cage Induction Generators(SCIGs),is used to test the applicability of the proposed algorithms.The results demonstrate that these algorithms are capable of selecting the most appropriate technology while optimally sizing and locating the STATCOM to reduce power losses in the network.Specifically,the optimized STATCOM allocation using the Particle Swarm Optimization(PSO)achieves a 7.44%reduction in total active power loss compared to the optimized allocation using the Genetic Algorithm(GA).Furthermore,the voltage magnitudes at buses 4,9,and 10,which initially had exceeded the upper voltage limit,were reduced and brought within acceptable ranges,thereby improving the system’s overall voltage profile.Consequently,the optimal allocation of the STATCOM significantly enhances the efficiency and performance of the power network.
文摘基于模块化多电平换流器的静止同步补偿器(stationary synchronous compensator based on modular multilevel converters,MMC-STATCOM)是高压电力系统中无功补偿的关键设备,其传统线性控制器性能会因运行点的大范围变化而恶化。针对该问题,该文提出了一种基于滑模状态反馈精确线性化的非线性智能控制策略,首先通过选择合适的输出函数、坐标变换,将不做任何简化的3阶MMC-STATCOM非线性模型转化为一个可控的Brunovsky标准型线性系统,并通过数学理论证明了该模型满足精确线性化条件。然后采用改进的粒子群算法配置其反馈增益矩阵,利用积分滑模控制计算其平衡点。最后通过状态反馈使各个状态变量快速收敛至平衡点。将该控制策略与传统PI控制、LQR状态反馈控制通过硬件在环实时仿真平台进行对比实验,结果表明该控制策略具有更好的动态特性、暂态稳定性、鲁棒性,尤其适用于运行环境发生大扰动时。此外,该控制策略的设计过程可以拓展应用于其他类型的柔性交流输电装置。
