Research On Analysis And Preventive Control Of Cascading Blackouts In Complex Power Systems

With the development of global economy, the interconnected power grid has become one of the largest and most complex modern artifical networks in the world. In this large-scale network, the capabilities that allow power to be transferred over hundreds of miles also enable the propagation of local failures into grid-wide events and even trigger serious consequence. Recently, a series of large blackouts caused by cascading failures have taken place and bring catastrophes to the national economy. Analysis and prevention of cascading blackouts have therefore attracted great attention and been treated as an important strategic problem to study all over the world.Conventional safety analysis constructs detailed model of every component of the system, and pays attention to dynamic behavior of individual component. Therefore, it is difficult to uncover the global dynamic characteristic while tend to deeply study the cascading failures and the mechanism of large blackouts. The complex theory, as a holism that emphasizes the whole rather than their constituent parts, can provide global and top-down perspectives of cascading blackouts. This dissertation focuses on the complex theory and cascading failure models, and employs them to make systematic and thorough studies on the topology of power grid, protection device failure as well as blackout prediction and preventive control. The main work is as follows.Firstly, the background and significance of cascading failure and blackout in power systems are introduced. In detail, the situation of current research on cascading failures analysis and prevention is summarized, and a novel approach which studies cascading failures via complex theory is emphasized especially. In addition, the main work and the chapter arrangement of this dissertation are briefly mentioned.In the second chapter, for the different time-scale of line outage, a dynamical cascading failure model for simulating lines outage is proposed at first. To compare the influence of different topology on cascading failures, two representative complex power grids, small-world network and scale-free network, are performed for line cascading failures. Simulation results prove the rationality of the dynamical evolution process of cascading failures. Moreover, according to the comparison results of the two power grids, the load loss caused by cascading failures in small-world network is much larger than that in scale-free network, and the spreading speed of cascading failures in small-world network is much faster than that in scale-free network. These results can be beneficial not only in areas such as major disturbance mitigation but also in system planning and upgrading.Traditional OPA model can simulate the cascading failures well but lack for considering the change of topology of the power grid. In the third chapter an improved OPA model is proposed, in which the size growing of the power grid is added to the slow dynamics. Simulation results from two typical complex networks show power grid always organizes itself to criticality in growing model of small-world network as well as scale-free network. Moreover, from the comparison results between two power grids with different growth ways, we find that the characteristic of small-world power grid makes larger load loss than that in scale-free power grid, and cascading failures in scale-free power grid often lead to small-scale lines outage and end with little load loss, which can help to release the system stress and decrease the probability of large blackouts. Furthermore, a novel conception of power flow entropy is defined to display the large heterogenous loading rate of the two grown power grids. The index of power flow entropy is one of the most important factors to judge whether the power system is at critical state.The fourth chapter presents a solution to find out the key lines affecting the cascading failures most. Based on the Hidden Failure model and risk theory an approach to estimate the risk of line outage and identify key lines causing blackouts is proposed for large-scale power systems. This model considers line overload, hidden failure, control strategy, operation condition and weights of tripped lines. Simulation results indicate that improving a few key lines identified at high loading can reduce the probability of blackouts, especially large blackouts; improving a few key lines identified at critical state can take the system out of critical state and avoid large blackouts. The fifth chapter investigates the mechanism of large blackouts and points out that in the process of blackouts, subsystems of the power grids are always competitive and cooperative. This process behaves in a manner of self-organized criticality and can be forecasted. Based on the synergetic theory of self-organized criticality, a syergetic model for power system blackout is proposed and an order parameter evolving equation for blackout is described by the order parameter theory and the slaving principle. Simulation results show that power system blackouts reveal self-organized criticality, and the proposed model has satisfying performance in the case study of North America blackouts and can be used to analyze the mechanism of cascading failures and blackouts.At last, to overcome the shortage of complex theory's application to real-time control of cascading failures in large-scale power grids, an online distributed computing control approach is proposed, and an optimal principle of load shedding and generator tripping is also given in the sixth chapter. In this dissertation, agent is placed at each bus of the power grid differing from traditional distributed method that takes subarea or substratum as an agent. Each agent employs model predictive control to solve its local problem based on locally available data and the global solution can be acquired with agents' cooperation. Further, a filtering method for control variable and constraint set is applied, and linear program method is used in every varying receding-horizon. The data for next varying receding-horizon control is acquired from the measured data of the real power grid. Simulation results on the IEEE118-bus system verify the fleetness and effectiveness of this method.