SSO risk evaluation method for grid-connected wind farm systems based on LHS and extended Prony

As the proportion of installed wind power is increasing every year, the dynamic characteristics of the grid-connected system for wind farms are increasingly becoming complex, and the system faces the risk of subsynchronous oscillation (SSO). The uncertainty of wind speed and series compensation degree has significant effects on the SSO of the series-compensated grid-connected system of doubly-fed induction generator (DFIG)-based wind farms. The oscillation risk of the grid-connected wind power systems is highly stochastic and time-varying and is difficult to evaluate accurately and quantitatively. Given the above problems, this paper proposes a risk evaluation method for SSO of the grid-connected system for series-compensated DFIG-based wind farms considering the uncertainty of wind speed and series compensation degree. First, the mechanism and time-varying characteristics of the subsynchronous oscillation of the grid-connected wind power systems are analyzed, and the risk quantification index is accordingly selected. Secondly, according to the oscillation analysis model of the doubly-fed wind farm grid-connected system, the extended Prony and Latin hypercube sampling are used to identify the SSO signal, and the least-squares polynomial method is applied to obtain the indicator model for the corresponding mode. Then, the risk matrix is used to quantify the probability and severity of the occurrence of subsynchronous oscillation risk caused by the uncertainty of the grid-connected wind farm systems. Finally, examples are presented to verify the effectiveness and viability of the proposed method.