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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.

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.

This paper proposes an algorithm for transmission expansion planning (TEP) which minimizes the congestion surplus calculated from optimized nonlinear (AC) Optimal Power Flow (OPF) and Locational Marginal Prices (LMPs). Uncorrelated and correlated uncertainties related to operating conditions of the future transmission network and expected costs of the submitted energy bids to the energy market are constrained by bounding hyper-ellipsoid around base case AC OPF solution, with assumption of additive uncertainties. Perturbed uncertain points inside a hyper-ellipsoid are selected by proposed quasi-random sampling algorithm. For these points, the linearized OPF around base case AC OPF solution is proposed. The Genetic Algorithm (GA) does selection of lines and years for transmission expansion, where the increments of the fitness function are calculated by proposed linearized AC OPF model. The results and practical aspects of the proposed methodology are illustrated on 12- and 118-bus test power system examples.

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]

In this paper is proposed a novel branch flow and weighted least square (WLS) based algorithm for state estimation in three phase distribution networks with distributed generation (DG) units. The basic formulation is simplified by the use of radial property of distribution network and later will be extended to meshed networks. Unmonitored (or partially monitored) loads are initially estimated from normalized daily load profiles (NDLPs) with lower weights (treated as the pseudo measurements). Also, the different types of monitored, partially monitored, or unmonitored DG units are included in state estimation. Their initial power outputs are calculated on the basis of external inputs, such as wind, sun and water inflow forecasts etc. (depending on DG unit type) or by normalized daily generation profiles (NDGPs) obtained from historical generation data. The pseudo measurements obtained by Initial Load/Generation Allocation are re-adjusted additionally by Optimal Load/Generation Reallocation procedure to fit real-time measurements inside WLS-based state estimation. The results and practical aspects of the proposed methodology are demonstrated on two real-life distribution networks.

This paper deals with the calculation of the electric field strength in high voltage (HV) substations comprising complex geometrical structures. Generalized charge simulation method is proposed for improving the precision of the calculation of the electric field strength. The objective of this analysis is to examine the influence of towers, HV apparatus and fences on the spatial electric field distribution. For this purpose, a three-dimensional generalized model of HV substation has been developed, including phase conductors, bypass busbars, HV apparatus, fences and towers (denoted as Full model). The obtained results of the calculation of the electric field strength are compared with the Simplified model, which only includes phase conductors connected to HV network. Verification of the proposed Full model performed by comparing the calculated and in-field measured values of the electric field strength within HV substations and in their vicinity gives very promising results.

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.