A Novel Identification Method for Self-Organized Critical State of Power System with Wind Power Integrated

With the increasing complexity of the power grid and the integration of large-scale wind power, the uncertainty of power grid continues to grow. Accurate judgment of the self-organized critical state of the power system has important application value for the prevention and control of cascading failures. To tackle this challenge, from a data-driven perspective, a new self-organized critical state identification algorithm for power systems with wind power integrated based on random matrix spectrum analysis theory is proposed. Firstly, the initial sample random matrix is established by using fluctuating line load rate data. Secondly, the self-organized critical state discriminant matrix is constructed on the basis of the stable initial sample random matrix. Then, the matrix linear eigenvalue statistics and the inner ring radius in the single ring theorem are used to construct the self-organized critical state discrimination index and related criteria. The simulation on the IEEE-39 bus system shows that the proposed discrimination index can track the changes of the verification index more effectively than other methods. And it can give the threshold for the system to enter the critical state of self-organization.

I. Introduction

In recent years, with the increasing complexity of power grids, major blackouts worldwide have occurred from time to time [1]–[3]. The analysis of the blackouts data over the years shows that the power grid blackouts have self-organized criticality [4]-[5]. Self-organized criticality means that a complex system undergoes a long self-organized process and can evolve from a normal state to a critical state. In this state, a small local disturbance may be amplified by a mechanism wsimilar to the "Domino effect". It may extend to the entire system and eventually cause the system to collapse [6]. With the integration of large-scale wind farms, the uncertainty in power system has greatly increased. The randomness, intermittency and volatility of uncertain power sources such as wind power can be regarded as another driving factor in the evolution of the self-organized critical state. But the direction of this driving force is uncertain. Therefore, judging whether the state of the current system is close to the self-organized critical state is of great significance to the dispatching operation.