This paper presents a parameter estimation technique for the hot-spot thermal model of power transformers. The proposed technique is based on the unscented formulation of the Kalman filter, jointly considering the state variables and parameters of the dynamic thermal model. A two-stage estimation technique that takes advantage of different loading conditions is developed, in order to increase the number of parameters which can be identified. Simulation results are presented, which show that the observable parameters are estimated with an error of less than 3%. The parameter estimation procedure is mainly intended for factory testing, allowing the manufacturer to enhance the thermal model of power transformers and, therefore, its customers to increase the lifetime of these assets. The proposed technique could be additionally considered in field applications if the necessary temperature measurements are available.

Relieving network congestions is a critical goal for the safe and flexible operation of modern power systems, especially in the presence of intermittent renewables or distributed generation. This paper deals with the real-time coordinated operation of distributed static series compensators (DSSCs) to remove network congestions by suitable modifications of the branch reactance. Several objective functions are considered and discussed to minimize the number of the devices involved in the control actions, the total losses or the total reactive power exchanged, leading to a non-convex mixed-integer non-linear programming problem. Then, a heuristic methodology combining the solution of a regular NLP with k-means clustering algorithm is proposed to get rid of the binary variables, in an attempt to reduce the computational cost. The proposed coordinated operation strategy of the DSSCs is tested on several benchmark systems, providing feasible and sufficiently optimal solutions in a reasonable time frame for practical systems.

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