An Improved Recursive Bayesian Approach for Transformer Tap Position Estimation

作者：Chen Yanbo^*; Liu Feng; Mei Shengwei; He Guangyu; Lu Qiang; Fu Yanlan

来源：IEEE Transactions on Power Systems, 2013, 28(3): 2830-2841.

DOI：10.1109/TPWRS.2013.2248761

• Parameter estimation
• recursive Bayesian estimation
• state estimation
• transformer windings

In this paper, a reliable and efficient methodology based on the recursive Bayesian approach (RBA) and its improved version (SRBA) is proposed for the transformer tap position (TTP) estimation. By recursively computing the posteriori probabilities of all the tap positions of the suspicious transformer, the proposed approach can find the correct TTP reliably. Furthermore, we remarkably improve the computational efficiency of SRBA from the following aspects: 1) reducing the number of transformers to be estimated by identifying suspicious tap positions; 2) proposing a fast prediction-correction algorithm to calculate the residuals; 3) reducing the set including the correct tap position by using a heuristic method during the recursive process; 4) reducing iteration numbers by proposing a stopping criterion with solid theoretical foundation. Simulations are carried on the IEEE 14-bus system and a real power grid of China, illustrating that our methodology is reliable with high efficiency.

• 出版日期2013-8
• 单位清华大学