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In order to solve the coordinated optimization problem of distributed generation (DG), on‐load tap changer (OLTC) and capacitor banks, a dynamic reactive power optimization (DRPO) method based on fuzzy time clustering is proposed. In the method, fuzzy time clustering algorithm is used to cluster the static optimal switching sequence of OLTC and capacitor banks. The time decoupling of control variables is realized and it meets the constraints of the maximum switching times. Next, the influencing factors of DG reactive power limit are analysed. Considering the control ability of OLTC, capacitor banks and DG in voltage and reactive power optimization, a coordinated optimization method is proposed. Since the adjustment of OLTC tap position directly affects the voltage and reactive power distribution of the whole line, the switching time and tap position of OLTC are determined based on the static optimization results of each time and fuzzy time clustering algorithm. Then, the switching time of capacitor banks is determined by fuzzy time clustering algorithm. At last, the switching capacity of capacitor banks and DG reactive power are jointly optimized to get the final control scheme. The proposed method demonstrated good performance in the modified IEEE33 node system and two actual power grid examples. [ABSTRACT FROM AUTHOR]

The grid connection of wind farms affects the load flow of a power system and may introduce an unexpected threat to the power system small signal stability. This study presents an application of probabilistic eigenvalue sensitivity indices (PSIs) for optimal allocation of wind farm in a power system. For this purpose, firstly, probabilistic sensitivity indices are used to identify the strongly correlated electromechanical eigenvalues of generators and to facilitate the wind farm site selection. Two different cases are studied, wind farm replacing synchronous generators or being directly added to the power system. Afterwards, the influence of the wind farm location on power system small signal stability is examined. For this purpose, an index is proposed to measure the small signal stability of the system and is used for optimal allocation of the wind farm. Two examples, i.e. a 3‐machine 9‐bus and an 8‐machine 24‐bus power system, with a wind farm integrated based on PSIs are presented to demonstrate the proposed approach by employing the eigenvalue analysis and a dynamic time‐domain simulation. Finally, the results are compared with the traditional optimal allocation of a wind farm for power loss reduction. [ABSTRACT FROM AUTHOR]