Investigation of power distribution systems is an attractive field in the power system analysis. The location and size of Distributed Generation (DG) are important and an uncontrollable choice might result in a negati...
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Investigation of power distribution systems is an attractive field in the power system analysis. The location and size of Distributed Generation (DG) are important and an uncontrollable choice might result in a negative impact on the system's behavior. This paper presents an improved approach for optimum simultaneous DG placement and sizing to reduce power loss and improve the voltage profile and the stability of distribution networks. Numerous methods have been used for solving this difficult combinatorial problem like Particle Swarm Optimization (PSO) algorithm used in this work. The suggested mopso method combined with MATPOWER toolbox was tested on standard IEEE 33-bus and IEEE 69-bus radial distribution networks (RDN) throw different scenarios. The major contribution of this paper was that the procedure was applied to the Tunisian electricity distribution network (ASHTART) of the SEREPT (Society for Research and Exploitation of Petroleum) to prove the efficiency of applying this method on real-world systems. This paper will also compare the proposed method with previous findings methods and the obtained results testify to the robustness and effectiveness of the improved mopso algorithm in terms of reduction in total active power losses (TAPL), maximization of a percentage of minimum voltage improvement (%MVI), amelioration in voltage profiles and also maximization in voltage stability index (VSI) while finding the optimal location and size of the DG units (OPSDG). (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
Managing construction uncertainties, especially those affecting safety and the environment, is critical in optimizing deep excavation projects. This paper introduces an integrated framework that leverages Building Inf...
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Managing construction uncertainties, especially those affecting safety and the environment, is critical in optimizing deep excavation projects. This paper introduces an integrated framework that leverages Building Information Modeling (BIM) to aggregate essential data on construction cost and duration. A multi-objective optimization model is proposed, incorporating the critical path method, system reliability metrics, rewardpenalty mechanisms, and environmental impact considerations to balance the objectives of duration, cost, safety, and environment. An improved Multi-Objective Particle Swarm Optimization (mopso) algorithm is applied to solve this complex problem. The effectiveness of this algorithm is demonstrated through statistical tests, showing a significant improvement in the solution quality and a reduction in the mean square error of particle density distance by over 85%. A case study from a project in Hangzhou, China, illustrates the practical application of this method, achieving compliance with safety and environmental regulations while reducing the duration by 22 days and saving over 28,350.
Aiming at the multi-objective polarity design of Mixed-polarity Reed-Muller(MPRM) circuit,such as small area and low power consumption, an integrated polarity optimization scheme based on improved Multi-objective part...
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Aiming at the multi-objective polarity design of Mixed-polarity Reed-Muller(MPRM) circuit,such as small area and low power consumption, an integrated polarity optimization scheme based on improved Multi-objective particle swarm optimization(mopso) is proposed. In the improvedmopso(Imopso) algorithm,particles in the external archive can be actively evolved through self-learning operations to find better circuit polarity. The particles in the population achieve selflearning fractals by comparing the differences between their own states and individuals in external archive to enhance the evolutionary level of the population. A multiobjective decision model of area and power consumption is established according to the characteristics of MPRM circuit. The tabular technique and the IMPOPO algorithm are combined to obtain the Pareto optimal polarity set of the MPRM circuit for area and power consumption. The MCNC Benchmark circuit is used to test the performance of the algorithm. The results verify the effectiveness of the proposed algorithm.
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