Small-signal Stability Analysis And Improvement Of Power Sytsem With Wind Farm Integration Under Multi-operating Conditions

Large-scale wind power connection can change the structure and the power flow distribution of power systems, and generate dynamic interactions with the generators of the system, thus influence the small-signal stability of the power system. In addition, wind power has the characteristics of randomness and fluctuations, which make the conventional power system into the random-deterministic power system. Although the traditional deterministic theories and methods are still widely used for small-signal stability analys is of power systems, the solutions offered by using these methods for electromechanical oscillations generated by poor damping are applicable only in some specific operation modes, which have great limitations.Aiming at the small-s ignal stability problem in the power system with wind farm integration, this dissertation studys the probabilistic small-signal stability of power system with wind farm connected, taking into consideration multi-operaing conditions including the stochastic fluctuations of wind farm output, synchronous generator output, loads and nodal voltages, and proposes improvement measures. The primary contents and original contributions are listed as follows :(1) The plug-in modeling technology(PMT) is employed for building the complete mathematical model of the small-s ignal stability analysis model of the power system with a doubly-fed wind farm, inc lud ing the doubly-fed induction generator, synchronous generators, static VAR compensator(SVC) and their control system, which lays a foundation for the small-s ignal stability analysis under multiple operating conditions.(2) The probabilistic small-signal stability analysis approach based on numerical analys is is proposed, taking into consideration the multiple operating conditions of the power system. The effic iency of the proposed method is confirmed by the analysis of examples and comparing with the results of Monte Carlo simulation. And the characteristics of high precis ion and fast solving speed of the algorithm are also presented. Simulations on the test systems illustrate that the probabilistic method can better describe the statiscal attributes of the small-s ignal stability of the power system.(3) A power system stabilizer(PSS) is installed on the rotor side converter controller and the pitch angle control system respectively, for improving the probabilistic small-signal stability of the power system. The damping performances of PSS installed on the rotor side converter controller are compared under the four different input signals, which provide a reference for selecting the input signals.(4) Combined with the probabilistic approach, a modified fruit fly optimization algorithm(MFOA) is poroposed to coordinate and optimize the parameters of PSSs and SVC damping controller. Through the analysis of examples and comparision with three different scenarios including the test system with PSSs only, the test system with SVC damping controller only, the test system with uncoordinated PSSs and SVC damping controller, the test system with coordinated PSSs and SVC damping controller parameters can improve the probabilistic small-s ignal stability of power systems with large-scale wind generation better.