Research On The Optimal Location Of Nonlinear Controller In Power Systems

With the wide application of the power electronic equipment and the integration of large-scale renewable energy in power systems, the number of nonlinear components in power systems is increasing. As a result, the nonlinearity of power systems will become much stronger, which will be a great threat to the safe and stable operation of power systems. Meanwhile, Traditional linear controller is designed for the system’s particular operating point, and it could hardly consider the impact of nonlinearity explicitly. If there is a large disturbance, the system’s operating point changes over a large range frequently, and the traditional linear controller is not adaptable. It will affect the safe and stable operation of power systems. Therefore, it is necessary to design controller based on nonlinear control theory. In this case, the safety and reliability of power systems could be ensured.There have been some achievements about designing nonlinear controller in power systems. However, with the developing of nonlinear controller, it also brings a new research subject:how much the location and parameter setting of nonlinear controller will affect the performance of power systems. In this paper, the optimal location and parameter setting of nonlinear controller are studied. The main contents and results are as follows:1. A general method of the optimal location and parameter setting of nonlinear controller is proposed. Take the terminal voltage nonlinear excitation controller for example. First, design nonlinear excitation controller based on inverse system theory and reduce the complex model. Then, establish the optimization model based on the evaluation index. Finally, solve this problem by an appropriate method.2. For the optimal location of nonlinear excitation controller, a method is proposed to obtain the optimal location and parameter of nonlinear excitation controller. A linear model of multi-machine system is derived based on small signal stability analysis method. Thus, an optimization model is established to mitigate the small signal stability problem. Finally, the results of simulation have well verified the validity of the proposed method.3. For the joint allocation of nonlinear excitation controller and other component in power systems, a multi-objective optimal method is proposed. A mixed integer multi-objective optimal model is established to mitigate the small signal stability problem and reduce the active power loss at the same time. The particle swarm optimization is used to investigate this problem. Finally, the results of simulation have well verified the validity of the proposed method. The system oscillation can be restrained effectively and the active power loss can be reduced to some extent.