Self-Organized Critical State Identification of Power Systems Based on Structural Equilibrium Theory
Gu Xueping1, Liu Yumemg1, Wang Tao1, Qin Xiaohui2
1. School of Electrical & Electronic Engineering North China Electric Power University Baoding 071003 China; 2. China Electric Power Research Institute Beijing 100192 China
Abstract Self-organized critical state identification has the important value for preventive control of cascading failures and power network planning. First, the transmission lines are represented as virtual nodes and the power flow interactive relations between the transmission lines as virtual links. Synthesizing the initial load rate, load rate increment and average of load rate increment, this paper defines the sign properties of the virtual links and then constructs a state correlation networks where importance degree of lines is used as node weight and the link sign properties as link weight. Based on the structural equilibrium theory of signed networks, the balance structure pattern and classification of power system operating states is established and analyzed. On this basis, a comprehensive of power network imbalance evaluation index is proposed to represent the power flow distribution characteristic and quantify power system disturbance dissolving ability, which can identify the self-organized criticality of a power system. Numerical results indicate that the defined imbalance index can effectively indicate the self-organized criticality of a power system in different network topologies, load rates and power flow entropy, and especially it works well when the system load rate and power flow entropy is lower.
Gu Xueping,Liu Yumemg,Wang Tao等. Self-Organized Critical State Identification of Power Systems Based on Structural Equilibrium Theory[J]. Transactions of China Electrotechnical Society, 2018, 33(17): 4136-4145.
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2018, Vol. 33 
