Measurement devices allocation in distribution system using state estimation: A multi‐objective approach

Abstract

Optimal allocation of measurement devices is a necessity in order to carry out state estimation of a distribution system. In this paper, the placement problem of power measurement devices is modeled using a multi-objective method.The objectives of the problem are to minimize measurement devices' costs while increasing the accuracy of state estimation and improving the state estimation quality. Also, operational priorities are considered as another objective, which are based on power losses, lines' capacities, the number of lines connected to a specific line, and the change in lines' flows direction. A multiobjective evolutionary algorithm based on decomposition (MOEA/D) is used to optimize the allocation of measurement devices within the problem of distribution system state estimation. The state estimation problem is optimized by particle swarm optimization (PSO) algorithm and the Monte Carlo simulation is used to develop some conditions within the network to guarantee the robustness of the proposed method. The method is tested by simulation results on an IEEE 33-bus and IEEE 123-bus radial network.

Meter placement algorithms to enhance distribution systems state estimation: Review, challenges and future research directions

“…Existing literature for the meter placement in the distribution networks with the aim of state estimation use a variety of objective functions for optimization problem, including minimizing investment costs or the number of meters [19,24,25], minimizing estimation error [26][27][28], uncertainty [29,30], observability [31,32] and multi-objective functions [33][34][35] (e.g. both cost and error).…”

“…Comprehensive exploration increases convergence time, and high exploitation causes the algorithm to get stuck at a local optimal point and not be able to get close to the global optimal solution. In [3,17,19,41,45,75], meta-heuristic methods such as genetic algorithm (GA), ant colony optimization (ACO), bacterial foraging search, simulated annealing (SA), Tabu search (TS) and particle swarm optimization (PSO) are proposed for meter placement in the DSSE problem.…”

“…So far, various targets have been proposed to address this issue in the literature such as improving system observability, minimizing installation costs, capability of bad data detection, and increasing DSSE accuracy. In addition, different methods have been used to solve the optimal meter placement problem including heuristic algorithms [12,13], mixed integer linear programming (MILP) [14,15], metaheuristic algorithms [16,17], mixed integer semi-definite programming (MISDP) [18] and multi-objective evolutionary (MOE) [19] methods.…”

Multiobjective meter placement in active distribution system state estimation using inverse‐model ‐based multilabel Gaussian classification with adaptive reference point method

“…Moreover, the authors applied the same Pareto‐based hybrid multiobjective evolutionary algorithm to different objectives for meter placement in distribution system state estimation 20 . In Reference 21, authors proposed decomposition‐based multiobjective meter placement with different objectives. Reference 22, proposed a hybrid dominance and decomposition‐based MOEA for optimal meter placement to enhance the accuracy of the distribution system state estimation and to minimize the metering cost.…”