@article{cao-gao-che-hex-don-lin-22-aa-fuzzclu,
  author = {Cao, Huazhen and Gao, Chong and Chen, Peidong and He, Xuan and Dong, Zhihui and Lin. Lingxue},
  title = {Distribution Network Dynamic Reconfiguration Based on Improved Fuzzy {C}-Means Clustering with Time Series Analysis},
  journal = {IET Generation, Transmission {\&} Distribution},
  year = 2022,
  month = oct,
  volume = {17},
  number = {2},
  month = feb,
  pages = {174-182},
  doi = {doi.org/10.1002/tee.23504},
  comment = {Uses ``affine Taylor expansion to solve the interval power flow equations''. Is this affine arithmetic?}.
  abstract = {The rapid growth of distributed energy resources integrated in distribution systems leads to an increasing need of continuously and automatically changing the system topology to realize the economic operation of distribution networks. This paper proposes an optimization model of dynamic reconfiguration for distribution networks based on a new method of time series analysis. Equivalent daily curve considering time-varying nature of distributed generator and load demands is divided by an improved fuzzy C-means clustering algorithm, where the indicator of section function is set to find the optimal reconfiguration time intervals. The uncertainty of distributed generator outputs and load demands is described by the interval algorithm. Then the affine Taylor expansion is adopted to solve the interval power flow equations. The reconfiguration optimization model is solved with decimal particle swarm optimization algorithm based on loop search. The optimal dynamic reconfiguration of a modified 70-bus test system with distributed generators is carried out and the simulation results demonstrate the effectiveness and superiority of the proposed method}
}