Distributed Control And Line Outage Detection Of Complex Smart Grid

The smart grid (SG) is regarded as the next generation power grid. By utilizing modern information technologies, the SG is improved and much more efficient, secure and flexible than the traditional power systems. The smart grid is consisted by a large number of smart electrical devices and a complex topology structure. It can be viewed as a large-scale complex network. Therefore, the decentralized monitor and control method are much more suitable for the smart grid than the centralized way. In this paper, based on the complex dynamic network theory and multi-agent systems theory, we study the distributed control and multiple line outage detection of the smart grid. The details are presented as follows.Multiple line outages are common in blackouts. The detection of MLOs is very important for the robust and security of the power grid. Many insightful methods have been reported to handle with line outage detection, but few of them focus on the detection of MLOs that happen in a short period of time. To deal with this problem, based on the complex network theory, some novel algorithms are developed by using phasor measurement units information. By invoking virtual adaptive observers, the presented algorithms monitor the connectivity status between buses, which make the algorithms tolerant to the interaction effects between the multiple line outages. Besides, the proposed algorithms also address the reconstruction of the real-time adjacency matrix of the power transmission networks.The topology identification of wide area smart grid is very important for the state estimation of the whole grid. The convergence rate of the existing algorithm based on adaptive observers is not ideal, to solve this problem, we investigated topology identification method with the impulsive adaptive observer. We proposed a novel topology identification algorithm with the impulsive adaptive observer. The convergence rate of the algorithm is accelerated by adding the equal interval impulsive feed back in the control of the error system.In the fourth section, we focus on the controller design for multi-machine power systems to achieve both voltage regulation and transient stability. In practice, voltage regulation and transient stability are treated as different dynamic system and using different controller in different period of power systems stir. Taking this in consideration, we purposed a new state-dependent switched decentralized controller for the power system. The model of the power system established by us after linearizing take both phase and voltage for the parts of states. The switched controller is designed as a universalized robust controller with the help of linear matrix inequality (LMI) theory. The digital simulation studies show that each nodal can achieve transient stability and voltage regulation by implemented the compensator low using switched controller.The distributed algorithm for is needed for the economic dispatch problem in power system with distributed generators and responsive demands. The proposed algorithm is distributed and cooperative such that it eliminates the need for a central leader. Unlike centralized approaches, the proposed algorithm enables generators to collectively learn the mismatch between demand and the total amount of power generation. The estimated mismatch is then used as feedback to adjust current power generation by each generator. Finally, all generators can automatically minimize the total cost in a collective sense.Due to the limited data sensing and communication, in many practical situations, only sampled data are available for the cooperation of multi-agent systems. To overcome this problem, a distributed hybrid controller is presented for the cooperative output regulation, and cooperative output regulation is achieved by well designed state feedback law. Then it proposed a method for the designing of sampled data controller to solve the cooperative output regulation problem with continuous linear systems and discrete-time communication data. Finally, numerical simulation example for cooperative tracking and a simulation example for optimal control of micro-grids are proposed to illustrate the result of the sampled data control law.