Distributed adaptive anti-disturbance control for power systems based on multi-agents
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摘要:
针对多机电力系统中不可避免存在非线性、不确定特性和动态干扰等问题，基于径向基神经网络(RBFNN)和非线性干扰观测器(NDO)提出一种分布式自适应抗干扰控制器，以增强多机电力系统的暂态稳定性和鲁棒性。采用RBFNN处理系统中的未知非线性问题，并基于神经网络的输出设计NDO以实现对复合扰动的在线估计；在多智能体框架下为多机电力系统提出一种分布式自适应抗干扰控制策略，实时接收通信网络测量的数据并控制储能装置动作，在外部扰动下实现各电机转速的快速同步与跟踪，并利用Lyapunov稳定性理论证明闭环系统信号的收敛性。仿真实验表明：所提策略有效可行。
Abstract:In response to problems like the inevitable nonlinearities, uncertainties and dynamic external disturbances in multi-machine power systems, a distributed adaptive anti-disturbance control scheme is proposed based on radial basis function neural networks (RBFNN) and nonlinear disturbance observers (NDO) to enhance transient stability and robustness. The unknown nonlinearities of the system are approximated by RBFNN, and NDO are designed based on the output of RBFNNs to estimate the compounded disturbances on-line. A novel distributed adaptive anti-disturbance control scheme for multi-machine power systems is developed with multi-agents' framework, which can receive real-time data measured by communication networks, and control the action of energy storage devices. The speed synchronization of each motor is guaranteed in the presence of external disturbances, and the stability of the closed-loop system is proven based on the Lyapunov stability theory. The simulation results verify the effectiveness of the proposed scheme.
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图 1 非线性多智能体电力系统测试系统图
Figure 1. Test system diagram of nonlinear multi-agent power system
图 2 非线性多智能体电力系统通信拓扑图及其$ {\boldsymbol{A}} $矩阵
Figure 2. Topology diagram of nonlinear multi-agent power system and A-matrix
图 6 复合扰动$ {D_i} $及其估计$ {\hat D_i} $
Figure 6. Composite disturbance $ {D_i} $ and its estimate $ {\hat D_i} $
图 9 断路故障下电力系统同步发电机转子转速
Figure 9. Power system synchronous generator rotor speed with fault
图 10 断路故障下一致性误差$ {{\textit{z}}_i} $
Figure 10. Consensus error $ {{\textit{z}}_i} $ with fault
表 1 电力系统参数
Table 1. Parameters of power system
${M_i}$ ${\varPi _i}$ ${E_i}$ ${E_j}$ ${G_{ij}}$ ${B_{ij}}$ 30 30 1 1 100 25 
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