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SUMMARY This paper examines the influence of correlated pseudo measurements on the three-phase (sequence component-based) micro-grid state estimation. Pseudo measurements are used as the external inputs to replace the unavailable real-time measurements on distributed generation (DG) units and loads to provide the minimum bus observability. Output powers of unmonitored DG (photovoltaic and wind-based) units and loads are evaluated using the weather (either measured or forecasted) data, historically recorded state estimation patterns and available real-time measurements. The historical data are classified into clusters by the Self-Organization Map Artificial Neural Network (SOM ANN). The correlation coefficients between dependent pseudo measurements are calculated from clustered weather data and corresponding powers from DG units or loads, where the Feed Forward Artificial Neural Networks (FF ANNs) with backpropagation are used for approximating the output active power of unmonitored elements. The results and practical aspects of the proposed three-phase state estimation methodology with correlated measurements are demonstrated on two (benchmark and real-world) micro-grids. Copyright © 2014 John Wiley & Sons, Ltd.

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.