Research on rolling optimization of PSS parameters to search for small signal stability boundary strategy
DOI：10.7667/PSPC171298
Key Words:low-frequency oscillations  power system stabilizer  improved PGSA  rolling optimization  extreme boundary
Author NameAffiliation
SHENG Yibiao School of Electrical Engineering, Wuhan University, Wuhan 430072, China 
LIN Tao School of Electrical Engineering, Wuhan University, Wuhan 430072, China 
CHEN Rusi School of Electrical Engineering, Wuhan University, Wuhan 430072, China 
CHEN Baoping School of Electrical Engineering, Wuhan University, Wuhan 430072, China 
XU Xialing Central China Electric Power Dispatching and Communication Center, Wuhan 430077, China 
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Abstract:Because power system exists the weak interconnection among large areas and the fast excitation system with high magnification is widely used, low-frequency oscillation is easy to happen. The small signal stability region represents the set of equilibrium points that can maintain the small signal stability of the system in the parameter space, which is important for the safe and stable operation of the power grid. However, the existing research on the boundary of small signal stability region is short of taking the influence of damping controller parameters into account so that the boundary tends to be conservative. In order to extend the stability region boundary and improve the security and economy of operation, which is valuable to the off-line analysis of power system planning and operation mode checking, this paper takes PSS as research objects and establishes a multi-PSS parameter coordination and optimization model with maximum damping ratio. And an improved PGSA optimization algorithm is used to solve the problem. On this basis, this paper proposes a searching strategy of the small signal stability extreme boundary points by rolling optimization of controllers’ parameters. Finally, based on the 4-machine-2-area benchmark, the validity and superiority of the proposed strategy are verified by comparing with the conventional method. This work is supported by National Key Research and Development Program of China (No. 2017YFB0902600 and 2017YFB0902604).
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