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1 |
Reconfiguration of electrical distribution network-based DG and capacitors allocations using artificial ecosystem optimizer: Practical case study
پیکربندی مجدد تخصیص DG و خازن مبتنی بر شبکه توزیع الکتریکی با استفاده از بهینه ساز اکوسیستم مصنوعی: مطالعه موردی عملی-2021 In this article, a new implementation of Artificial Ecosystem Optimizer (AEO) technique
is developed for distributed generators (DGs) and capacitors allocation considering the Reconfiguration of Power Distribution Systems (RPDS). The AEO is inspired from three energy transfer
mechanisms involving production, consumption, and decomposition in an ecosystem. In the production mechanism, the production operator allows AEO to produce a new individual randomly,
whereas the search space exploration can be improved as illustrated in the consumption mechanism
and exploitation can be performed in the decomposition. A practical case study of 59-bus Cairo distribution system in Egypt is simulated with different loading percentages. For optimizing the performance of that practical network, the AEO algorithm is employed for different scenarios. Besides,
the results obtained by recent optimization techniques which are Jellyfish Search Optimizer (JFS),
Supply Demand Optimizer (SDO), Crow Search Optimizer (CSO), Particle Swarm Optimization
(PSO), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA) are compared
with the developed AEO. The simulation results demonstrate the efficacies and superiority of the
AEO compared to the others. It surpasses the other algorithms in terms of obtaining the best, mean,
worst, and standard deviations. After optimal RPDS and DGs placements, the power losses are
decreased by 78.4, 77.84 and 71.4% at low, nominal and high levels, respectively. However, the best
scenario with its application prospects is mentioned after optimal RPDS, DGs, and capacitors
where the power losses are decreased by 68.8, 85.87 and 89.91% at low, nominal and high levels,
respectively.
KEYWORDS: Artificial ecosystem optimizer | Distributed generators | Electrical systems | Power losses | Reconfiguration |
مقاله انگلیسی |
2 |
Information and Measurement System for Electric Power Losses Accounting in Railway Transport
اطلاعات و سیستم اندازه گیری برای حسابداری تلفات برق در حمل و نقل ریلی-2021 The purpose of the presented research is to minimize the loss of electricity during the operation of railway power systems. Losses
are defined as an unbalance between the released and consumed electricity, which is recorded by means of commercial electricity
ccounting. Given that electricity losses are divided into technical and non-technical (commercial) components, there are
currently no technical tools that can analyze the components of electricity losses in detail, and therefore prevent their occurrence.
To achieve this goal, the factors inherent in commercial electricity accounting systems in various areas of production activity that
affect the growth of electricity losses are identified. An algorithm is proposed that allows determining the presence of abnormal
power losses in real time for making organizational and technical decisions to reduce them. A block diagram of the information
and measurement system for accounting of power losses has been developed, which allows using the existing equipment without
replacement or modernization, which allows obtaining new technical capabilities. The method of intellectualization of the
process of classification of factors that cause the growth of abnormal power losses, based on artificial neural networks, is
posed. The intelligent module allows replacing the person who makes organizational and technical decisions, minimizing the
consequences of abnormal situations that lead to the growth of abnormal losses, applying the proposed solutions in departments
that do not have qualified specialists. The results of training an artificial neural network are considered, and the main parameters
of the efficiency of the information and measurement system for loss accounting on a real railway transport object are
determined.
Keywords: Power Loss | Artificial Neural Networks. |
مقاله انگلیسی |