A synchronous condenser(SC)isusedto maintain grid voltage stability owing to its bidirectional fast reactive power regulation ability and good dynamic characteristics.To address the issue of dynamic voltage instabilit...A synchronous condenser(SC)isusedto maintain grid voltage stability owing to its bidirectional fast reactive power regulation ability and good dynamic characteristics.To address the issue of dynamic voltage instability inpower systemduring failuresor heavy inductive loads,an SC reactive power regulation optimization method based onsingle neuron adaptive PID(SNA-PID)combined with whale optimization algorithm(WOA)is proposed.This approach aimsto overcome the limitationsof normal PID controllers.Asimulation model of the SC reactive power regulation system,based on SNA-PID combined with the WOA,is established using Matlab.The parameters of the SNA-PID are optimizedbythe WOAwith the ITAE criterion under two typical operation situationsof the power system:one is to set three different degrees of short-circuit ground faults,and the other isto accessthree different three-phase resistive loads.Compared to conventional PID control,asthe degree of short-circuit ground faults increases and the three-phase resistive load resistance decreases,the SC reactive power regulation optimization method based on SNA-PID combined with the WOA can still reduce the voltage recovery time and voltage oscillation,while maintaining voltage stability.Simulation results show that the proposed method exhibits better dynamic adjustment characteristics and adaptive ability.展开更多
针对电动调节阀控制系统在实际生产过程中存在的非线性、多扰动等问题,提出一种基于改进蚁群算法优化单神经元PID(proportional integral derivative)的控制方法并将其应用于阀门开度控制中。该方法利用单神经元网络的自学习和自适应能...针对电动调节阀控制系统在实际生产过程中存在的非线性、多扰动等问题,提出一种基于改进蚁群算法优化单神经元PID(proportional integral derivative)的控制方法并将其应用于阀门开度控制中。该方法利用单神经元网络的自学习和自适应能力,实现PID控制参数的在线整定,并采用改进的蚁群优化算法优化单神经元PID中的学习速率和神经元比例系数,有效克服了单神经元PID中的学习速率和神经元比例系数因经验设定而无法达到预期控制效果的不足。仿真对比结果显示,相比于传统PID、单神经元PID以及基于蚁群优化算法优化单神经元PID 3种控制方法,本文提出的控制方法超调量分别减少了10.2%、6.1%和1.8%,同时调节时间也相应缩短了0.22、0.07、0.03 s,并且表现出更强的自适应和抗干扰能力,能够使阀门开度控制更加稳定可靠。展开更多
基金Supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX21_0474).
文摘A synchronous condenser(SC)isusedto maintain grid voltage stability owing to its bidirectional fast reactive power regulation ability and good dynamic characteristics.To address the issue of dynamic voltage instability inpower systemduring failuresor heavy inductive loads,an SC reactive power regulation optimization method based onsingle neuron adaptive PID(SNA-PID)combined with whale optimization algorithm(WOA)is proposed.This approach aimsto overcome the limitationsof normal PID controllers.Asimulation model of the SC reactive power regulation system,based on SNA-PID combined with the WOA,is established using Matlab.The parameters of the SNA-PID are optimizedbythe WOAwith the ITAE criterion under two typical operation situationsof the power system:one is to set three different degrees of short-circuit ground faults,and the other isto accessthree different three-phase resistive loads.Compared to conventional PID control,asthe degree of short-circuit ground faults increases and the three-phase resistive load resistance decreases,the SC reactive power regulation optimization method based on SNA-PID combined with the WOA can still reduce the voltage recovery time and voltage oscillation,while maintaining voltage stability.Simulation results show that the proposed method exhibits better dynamic adjustment characteristics and adaptive ability.
文摘针对电动调节阀控制系统在实际生产过程中存在的非线性、多扰动等问题,提出一种基于改进蚁群算法优化单神经元PID(proportional integral derivative)的控制方法并将其应用于阀门开度控制中。该方法利用单神经元网络的自学习和自适应能力,实现PID控制参数的在线整定,并采用改进的蚁群优化算法优化单神经元PID中的学习速率和神经元比例系数,有效克服了单神经元PID中的学习速率和神经元比例系数因经验设定而无法达到预期控制效果的不足。仿真对比结果显示,相比于传统PID、单神经元PID以及基于蚁群优化算法优化单神经元PID 3种控制方法,本文提出的控制方法超调量分别减少了10.2%、6.1%和1.8%,同时调节时间也相应缩短了0.22、0.07、0.03 s,并且表现出更强的自适应和抗干扰能力,能够使阀门开度控制更加稳定可靠。
