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This paper proposes a new adaptive method for online tuning of process noise covariance matrix in the Extended Kalman Filter based three-phase distribution system state estimator. Specifically, a new form of exponential function is proposed for tuning the process noise covariance matrix, adapting it to the level of state changes. A new indicator derived from normalized innovations of the available real-time and virtual measurements is developed for tracking the level of state changes. The proposed method relies on measurements of the existing distribution networks, and therefore can be easily implemented in the advanced distribution management system. The method efficiently adapts process noise to system state variations and can deal with both quasi steady-state and unexpected sudden state changes caused by changes in the system topology, generation or demand. Comparison results with other state-of-the-art adaptive methods on the modified IEEE 37-bus and IEEE 123-bus distribution systems show that the proposed method achieves better accuracy under quasi steady-state condition while being more robust to unexpected sudden state changes.

This paper presents a method for determining optimal arrangements of parallel independent overhead power lines aimed to decrease electric and magnetic field emissions. The Genetic Algorithm (GA) is used to find the optimal placement of conductors. The Monte Carlo approach implemented in GA allows consideration of uncertain phase shifts between independent overhead power lines. The results and practical aspects of the proposed methodology are illustrated on two different configurations of both independent 400 kV singlecircuit and double-circuit overhead power lines. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. III-42009]

Summary This paper proposes a novel method for determining optimal arrangements of overhead double-circuit power line conductors aimed to reduce electric and magnetic field emissions. Extremely low frequency electric and magnetic field optimization problems have multiple objectives. Multiple-objective optimization allows the simultaneous optimization of two or more conflicting objectives and determination of mutually non-dominated solutions. The multi-objective optimization technique used in this paper is based on Genetic Algorithm to obtain a set of mutually non-dominated solutions (Pareto optimal set) for the two objectives: the electric and magnetic field strengths near overhead double-circuit power line. Also, additional assessment criteria were defined for the selection of several decision scenarios from the Pareto optimal set. The results and practical aspects of the proposed methodology are demonstrated on two typical 400-kV double-circuit overhead power lines. The efficiency of the proposed multiple-objective optimization method is compared with the existing one based on single-objective optimization function. Copyright © 2016 John Wiley & Sons, Ltd.