Multi-area state estimation in a distribution network using Takagi-Sugeno model estimated by Kalman filter

This study addressed the problem of multi-area state estimation in a clustered distribution system. Distribution networks are inherently expansive and comprise a multitude of nodes. This issue increases the state estimation computation time and makes it inapplicable for control of sophisticated distribution networks. Multi-area state estimation is a technique to reduce computation time while concerning computation accuracy. Many efforts are required to reach a perfect algorithm, followed by the optimization of different parameters of the proposed algorithms. This paper performed a precise mathematical analysis of the impact made by the common (shared) node exchanged information between the areas in the multi-area state estimation algorithm. Furthermore, a new iterative multi-area state estimation algorithm equipped with machine learning tools was designed based on analytical detections for enhancing the convergence speed and accuracy of the estimation results. The improvement was evaluated in two clustered networks with 356 and 711 nodes. The results indicated the benefits provided by the proposed modification in terms of convergence speed and accuracy with minimum data exchanges in an iterative multi-area distribution network.