Research On Interactions Analysis And Control Strategy Of Multiple FACTS Controllers In Power System

It has been the common tendency in the world that the electric power system is developing towards the interconnected network of large scale, which makes its structure and operation more complicated. With the development of the economic and the advancement of the technology, the traditional power system is experiencing a progress along with the directions from the region power supply to the large-scale power systems' connection, the monopolization of trade to the competition of the market and an uncontrolled state to an entirely controlled state progressively.The technique of Flexible AC Transmission System (FACTS) appearing to provide an effective means, which can enhance the capacity of transmission line and improve the transient and dynamic stability of the system efficiently. Recently, FACTS controllers installed in power systems are increasing gradually, which makes a great economic benefit and at the same time it also makes a serious challenge to the security of the power system.With the wide applications of FACTS controllers in power systems, a potential risk, the interaction between FACTS controllers, is emerging and may impose negative influences on power system operation and control. And at present, the impact of the interaction among power system controls, especially FACTS controllers, is on the elementary stage, there still exist many problems to be explored thoroughly. So, exploring the new theory, new technology and new method, enhancing the level of the security of the power system through the coordination control of multi-FACTS elements is an important task for the discussion of the power system's stability and has great theoretical and engineering application. Focusing on this topic, the contents of this dissertation include the following parts:In the first part, the physical structure and mathematical models of four FACTS devices, i.e., Static Var Compensators (SVC), Static Synchronous Compensator (STATCOM), Thyristor Controlled Series Compensator (TCSC) and Unified Power Flow Controller (UPFC) are introduced in detail. And, a general procedure for dynamical modeling of a large-scale multi-machine power system installed with multiple FATCS devices is developed. A mathematical model included TCSCs, SVCs, STATCOMs and UPFCs in multi-machine power system are derived, which make a foundation for the digital simulation and the analysis on the next steps.In the second part, an analytical approach based on Singular Value Decomposition (SVD) is proposed for the analysis of interactions among multiple controllers. The proposed method can quantify the interaction among FACTS devices and indicate the optimal electrical parameters and placement for devices to weaken the influence of interaction. Based on the SVD analysis method, the influence caused by the system electrical parameters between two FACTS devices is investigated. The detailed simulation results in two study cases accord with the analysis results by SVD method, which demonstrates the effectiveness of SVD method in analyzing interactions of FATCS controllers.In the third part, a Modified Relative Gain Array (MRGA) method is proposed to quantitatively measure the interactions between FACTS normal control and its superimposed damping control function. It suggests that when the FACTS damping controller is designed, its possible interactions with FACTS normal control functions are estimated by the proposed modified RGA. The estimation can guide the design to avoid or minimise the interactions, hence achieving an effective design. Two application examples are presented to demonstrate how the proposed modified RGA can be used to (1) select the best voltage control function in an SVC and a STATCOM to put on a damping control function in Single-Machine and Infinite-Bus (SMIB) power system; (2) select the best normal control function of a UPFC to superimpose a damping control function in New England Test Power System (NETPS). The proposed modified RGA method is validated by simulation results in both application examples.In the fourth part, a UPFC structure-specified H_∞robust controller via genetic algorithm is presented. From the point view of control theory, the power system with UPFC forms a Multi-Input and Multi-Output (MIMO) control system. However, the multivariable control is usually complex and is difficult to implement in practical applications. The proposed method is to find an internally stabilizing controller that minimizes a H_∞performance index subject to some industry constraints. This problem can be considered as a problem of optimal tracking performance that is solved by genetic algorithms. The Pi-type controllers for UPFC are presented to illustrate the design procedure and validity of the proposed algorithm in the SMIB and NETPS test systems. Simulation results show that controllers designed by the proposed approach can performance well under different operating conditions.In the fifth part, a novel Multi-Objective hybrid evolutionary algorithm based on Evolutionary Programming (EP) and Particle Swarm Optimization (PSO), named MOEPPSO, is presented to coordinate multiple FACTS controllers in multi-machine power system. The coordinate design problem among multiple FACTS controllers is formulated as a multi-objective optimization problem, in which the system response is optimized by minimizing several system behavior measure criterions. Then, MOEPPSO is employed to search optimal controller parameters. Design of the multi-objective optimization aims to find out the Pareto optimal solution which is a set of possible optimal solutions for controller parameters. The proposed approach has been applied to a typical IEEE multi-machine power system with two TCSCs. Simulation results validate the proposed approach. And in comparison with separate design of the controllers, the better performance of the system is achieved.