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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 |
A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning
الگوریتم جدید بهینه سازی یادگیری تقویتی گرگ خاکستری برای برنامه ریزی مسیر وسایل نقلیه هوایی بدون سرنشین (پهپاد)-2020 Unmanned aerial vehicles (UAVs) have been used in wide range of areas, and a high-quality path
planning method is needed for UAVs to satisfy their applications. However, many algorithms reported
in the literature may not feasible or efficient, especially in the face of three-dimensional complex flight
environment. In this paper, a novel reinforcement learning based grey wolf optimizer algorithm called
RLGWO has been presented for solving this problem. In the proposed algorithm, the reinforcement
learning is inserted that the individual is controlled to switch operations adaptively according to the
accumulated performance. Considering that the proposed algorithm is designed to serve for UAVs
path planning, four operations have been introduced for each individual: exploration, exploitation,
geometric adjustment, and optimal adjustment. In addition, the cubic B-spline curve is used to smooth
the generated flight route and make the planning path be suitable for the UAVs. The simulation
experimental results show that the RLGWO algorithm can acquire a feasible and effective route
successfully in complicated environment. Keywords: Unmanned aerial vehicles (UAVs) | Three-dimensional path planning | Reinforcement learning | Grey wolf optimizer |
مقاله انگلیسی |
3 |
A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy
چارچوب پیش بینی نیروی باد مکانی و مکانی رمان بر اساس ماشین بردار پشتیبانی چند خروجی و استراتژی بهینه سازی-2020 The integration of a large number of wind farms poses big challenges to the secure and
economical operation of power systems, and ultra-short-term wind power forecasting is an
effective solution. However, traditional approaches can only predict an individual wind farm
power at a time and ignore the spatio-temporal correlation of wind farms. In this paper, a novel
ultra-short-term forecasting framework based on spatio-temporal (ST) analysis, multi-output
support vector machine (MSVM) and grey wolf optimizer (GWO) which defined
ST-GWO-MSVM model is proposed to predict the output wind power from multiple wind farms;
the ST-GWO-MSVM model includes data analysis stage, parameters optimization stage, and
modeling stage. In the data analysis stage, the person correlation coefficient and partial
autocorrelation function are used to analyze the spatio-temporal correlation of wind power. In the
parameters optimization stage, to avoid obtaining the unreliable forecasting results due to the
parameters are chosen empirically, the GWO algorithm is used to optimize the kernel function
parameters of the MSVM model. In the modeling stage, an innovative forecasting model with
optimal parameter of MSVM is proposed to predict the output wind power of 15 wind farms.
Results show that the performance of ST-GWO-MSVM is better than other benchmark models in
terms of multiple-error metrics including fractional bias, direction accuracy, and improvement
percentages. Keywords: wind power forecasting | Spatio-temporal correlation | Multi-output support vector machine | Grey wolf optimizer | Combined forecasting approaches |
مقاله انگلیسی |