Application Of Robust Least Squares In Power System State Estimation

Along with the development of power systems in our country, the structure and run-mode of which are more and more complex, and the automation level in the control center is also developing from base to advance. To the computer, which plays an important part in modern control center, the advanced automation control depend on the functions of the program installed, so we should pay sufficient attention to the reliability of state estimation algorithm.At present the most of state estimation algorithms in use follow the classic weighted least squares (WLS), whose precondition is that random noises submit normal distribution. When bad data exists, WLS can not get better estimation results until it combined with other bad data detection and identification methods. Due to that measure noise can' t strict submit normal distribution, integrated with other detection and identification methods makes the program more complex, and prolongs the computing time of the algorithm. To this problem, the idea applying robust least square estimation to state estimation of power system was proposed. Compared with WLS, it indicates that robust least square is effective and of better efficiency.Different equivalent weight function has different abilities to resist gross. Considering that in the same condition, huber transform works better than IGG transform, some new ways was put forward. Three new equivalent weight functions was built based on the rule of choosing equivalent weight functions. After that, the new schemes were put in use in the experiments of state estimation. The results show that different equivalent weight functions are all able to resist gross, but their ability of resisting gross are different. Scheme. 1 and Scheme. 3 are better than huber transform, on the contrary Scheme. 2 is worse. So we can safely draw conclusion that choosing a good equivalent weight function is an important way to improve the efficiency of state estimation arithmetic too.The implementations of gross resistance capability of estimation rely on the changes of weight during computing. The coefficient matrix of iterative amending equation changes with measurement noise, and it may cause the instability of estimation results. So the proposal of using orthogonal transformation to robust least square was detailed. Experiment results show that numerical solutions of state estimation are of better stability.state estimation;robust least square;equivalent weight function;orthogonal transformation...