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Multi-objective hybrid estimation of distribution algorithm-interior point method-based meter placement for active distribution state estimation

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IET GENERATION TRANSMISSION & distribution 2018年第3期12卷 767-779页

作者： Prasad, Sachidananda Mallesham, Vinod Kumar Dulla Natl Inst Technol Warangal Dept Elect Engn Warangal 506004 Telangana State India

This study proposes a new multi-objective hybrid estimation of distribution algorithm (EDA)-interior point method (IPM) algorithm to obtain the optimal location of measuring devices for state estimation (SE) in active distribution networks. The objective functions to be minimised are, the total network configuration cost, the average relative percentage error of bus voltage magnitude and angle estimates. As the objectives are conflicting in nature, a multi-objective Pareto-based non-dominated sorting EDA has been proposed in this study. Moreover, due to poor exploitation capability of the EDA, it is hybridised with IPM to improve its local searching ability in the search space. The hybridisation of EDA and IPM brings a higher degree of balance between the exploration and exploitation capability of the algorithm during the search process. Furthermore, the loads and generators are treated as stochastic variable and the impact of different type of distributed generations on SE performance has also been investigated. The efficiency of the proposed algorithm is tested on PG&E 69-bus system and Indian 85-bus radial distribution network. The obtained results are compared with conventional EDA, particle swarm optimisation, non-dominated sorting genetic algorithm and also with existing techniques in the literature such as dynamic programming and ordinal optimisation algorithm.

关键词： power distribution economics costing Pareto optimisation distributed power generation particle swarm optimisation genetic algorithms dynamic programming multiobjective hybrid estimation distribution algorithm-interior point method meter placement active distribution state estimation EDA-IPM algorithm measuring devices active distribution networks total network configuration cost average relative percentage error bus voltage magnitude angle estimates multiobjective Pareto-based nondominated sorting EDA local searching ability search process stochastic variable distributed generations SE performance PG& E 69-bus system 85-bus radial distribution network particle swarm optimisation nondominated sorting genetic algorithm dynamic programming ordinal optimisation algorithm

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Multi-objective hybrid decomposition and local dominance based meter placement for distribution system state estimation

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IET GENERATION TRANSMISSION & distribution 2020年第20期14卷 4416-4425页

作者： Chintala, Bhanu Prasad Vinod Kumar, D. M. Natl Inst Technol Warangal Dept Elect Engn Warangal Andhra Pradesh India

The study proposes a new hybrid multi-objective evolutionary optimisation algorithm based on decomposition and local dominance for meter placement in distribution system state estimation. The evenly distributed qualitative and diverse solutions on the Pareto front are required for a decision-maker for selecting a final optimal solution. Such a Pareto front can be achieved by obtaining the balance between convergence and diversity of multi-objective optimisation algorithm. Therefore, the proposed method combined dominance and decomposition techniques, modelled meter placement as a constrained combinatorial multi-objective optimisation. The meter placement is designed as a trade-off between three objectives that are minimising the cost of the meters, average relative percentage error (ARPE) of voltage magnitude and ARPE of voltage angle. As the meter placement problem is a combinatorial optimisation, the binomial distribution-based Monte Carlo method is utilised to initialise the population, which aims to improve the diversity, as a consequence it improves the convergence, which is a by-product of this method. The results of the proposed method are compared with multi-objective evolutionary algorithm based on decomposition, non-dominated sorting genetic algorithm-II and with multi-objective hybrid particle swarm optimisation-krill herd algorithm, multi-objective hybrid estimation of distribution algorithm-interior point method and demonstrated on PG&E 69-bus distribution system and Practical Indian 85-bus distribution system.

关键词： Monte Carlo methods genetic algorithms Pareto optimisation particle swarm optimisation power system state estimation average relative percentage error constrained combinatorial multiobjective optimisation modelled meter placement combined dominance optimal solution Pareto front evenly distributed qualitative solutions hybrid multiobjective evolutionary optimisation algorithm distribution system state estimation local dominance based meter placement multiobjective hybrid decomposition Indian 85-bus distribution system PG& E 69-bus distribution system distribution algorithm-interior point method multiobjective hybrid estimation multiobjective hybrid particle swarm optimisation-krill herd algorithm nondominated sorting genetic algorithm-II binomial distribution-based Monte Carlo method combinatorial optimisation meter placement problem ARPE

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