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检索条件"主题词=whale optimization algorithms"

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whale optimization Algorithm Enhanced Multi-Layer Perceptron for Switch Cabinet Fault Diagnosis

Whale Optimization Algorithm Enhanced Multi-Layer Perceptron...

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Artificial Intelligence and Power Systems (AIPS), International Conference on

作者： Quan Shi Wang Ran Ming Fu Dong Fu Wenjie Xu Guangdong Yuedianke Testing Technology Co. Ltd. Guangzhou Guangdong China

ISBN: (数字)9798350392210

ISBN: (纸本)9798350392227

Switch cabinet fault diagnosis, a critical task in power system reliability, has traditionally utilized the Multi-Layer Perceptron (MLP), an approach that occasionally converges to local optima, thereby limiting diagnostic accuracy. The whale optimization Algorithm (WOA), known for its robust optimization capabilities, offers a promising solution to this challenge. This paper introduces a novel methodology that synergistically combines WOA with MLP to enhance switch cabinet fault diagnosis. Initially, the architecture of the MLP neural network is meticulously optimized by determining the optimal number of hidden layers and neurons per layer. Through a systematic evaluation of MLP's recognition performance, a configuration of 3 hidden layers with 12 neurons each is identified and implemented. Subsequently, the study conducts a comprehensive analysis to explore the impact of varying iteration counts and population sizes on the diagnostic accuracy of the integrated WOA-MLP model. This analytical process contributes to the refined precision of switch cabinet fault diagnosis. Finally, the paper employs real-world fault data from a power grid to validate the feasibility and applicability of the WOA-MLP algorithm. Experimental results substantiate the algorithm's efficacy, demonstrating a marked improvement in accuracy and robust stability, thereby affirming its potential as a valuable tool in fault diagnosis.

关键词： Fault diagnosis Accuracy Systematics Robust stability Neurons Switches Power system stability whale optimization algorithms optimization Biological neural networks

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optimization of WSNs Coverage for Power Monitoring Based on Multi-strategy Sand Cat Swarm optimization Algorithm

Optimization of WSNs Coverage for Power Monitoring Based on ...

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International Conference on Information, Communication and Networks (ICICN)

作者： Hongmei Fei Tao Luo Baitao Zhang Zheng Sun Pengwei Ma Jie Zhou College of Information Science and Technology Shihezi University Shihezi China

ISBN: (数字)9798350355802

ISBN: (纸本)9798350355819

With the rapid development of electric power technology, the application of wireless sensor networks (WSNs) in the electric power field has received widespread attention. The WSNs coverage optimization problem in the power field is one of the key factors to improve the power transmission efficiency and resource utilization. However, traditional optimization algorithms have some limitations in dealing with the deployment problem of large-scale sensor nodes. Therefore, this thesis proposes a multi-strategy sand cat swarm optimization algorithm (MSSCSO) to solve the WSNs coverage problem. First, this paper proposes an improved Tent chaos mapping to improve the search range and enhance the local search capability of the algorithm. Secondly, the reverse learning strategy is used to increase the population diversity and global search capability, which improves the convergence speed and optimization accuracy of the algorithm. Finally, the global search capability of the algorithm and the adaptability and robustness of the algorithm are enhanced by the adaptive strategy. In order to evaluate the performance of the proposed method, this paper conducts comparative experiments with Genetic Algorithm (GA), Firefly optimization Algorithm (FA), whale optimization Algorithm (WOA) and Gray Wolf optimization Algorithm (GWO), respectively. The experimental results show that MSSCSO's WSNs coverage optimization method can effectively improve the network coverage in power scenarios by 10.54%, 20.04%, 9.81%, and 6.43% over GA, FA, WOA, and GWO, respectively. Meanwhile, the method has strong adaptability and stability to the uncertainty and dynamic changes in the power environment.

关键词： Wireless sensor networks Uncertainty Heuristic algorithms Stability analysis whale optimization algorithms Robustness Particle swarm optimization optimization Genetic algorithms Convergence

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Fault response strategy for high proportion distributed energy resources distribution area based on whale optimization algorithm

Fault response strategy for high proportion distributed ener...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Hengyu Liu Liyang Liu Xiaoning Ma Dawei Hu Jingwei Han Tianwei Cao Jiazheng Sun Zhi Zhang Electric Power Research Institute of State Grid Liaoning Electric Power Co. Ltd. Shenyang China State Grid Liaoning Electric Power Supply Co. Ltd. Shenyang China The School of Electrical Engineering Shenyang University of Technology Shenyang China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

The proportion of distributed energy resources in the distribution area is increasing, and its output is uncertain. Therefore, considering the uncertainty, a fault recovery strategy combining island division and recovery and reconstruction is proposed. Firstly, a distributed energy resources output model and a load characteristic model are established, and a combination of breadth-first traversal and depth-first traversal is used to divide the islands according to the faults occurring at different time periods. Then, the network reconstruction model is established with the goal of minimum switching operation times and minimum network loss, and the improved whale optimization algorithm is used to solve it, and the residual power loss load in the distribution area is restored. Finally, the simulation analysis of IEEE33 nodes is carried out, and the experimental results show that the proposed method can restore power supply quickly and accurately.

关键词： Wind Uncertainty Power supplies Switches System integration Wind power generation whale optimization algorithms Stability analysis Distributed power generation Load modeling

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An Improved IIR Filter Design using Artificial Rabbits optimization Algorithm and its FPGA Implementation

An Improved IIR Filter Design using Artificial Rabbits Optim...

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Devices for Integrated Circuit (DevIC)

作者： Subhadeep Mondal Sajal Sarkar Raina Modak Aich Supriya Dhabal Palaniandavar Venkateswaran Dept. of ETCE Jadavpur University Kolkata West Bengal India Dept. of ECE Netaji Subhash Engineering College Kolkata West Bengal India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

This paper presents a meta-heuristic optimization algorithm known as Artificial Rabbits optimization (ARO) to get the optimal set of infinite impulse response (IIR) filter coefficients. Application of the ARO algorithm to design two real-life examples is used for performance testing. The proposed algorithm is simulated to get the coefficients of different types of linear and non-linear (with noise and without noise) problems with similar order and lower order, and the simulated results are compared with few recent optimization algorithms such as Salp Swarm Algorithm (SSA), whale optimization Algorithm (WOA), and Dragonfly Algorithm (DA). The proposed algorithm is much faster and efficient than the numerous standard algorithms. The Proposed ARO algorithm demonstrates a lower Mean Square Error (MSE) and superior convergence speed as compared to the other optimization techniques examined herein. FPGA implementation is performed to design IIR filters where ARO shows better performance.

关键词： Rabbits Noise Metaheuristics IIR filters Skin whale optimization algorithms Internet of Things Field programmable gate arrays Convergence Testing

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Fopid Controllers Based Forward DC to DC Converter With Modified Osprey optimization Algorithm for MPPT of PV System Under Partial Shading Conditions

Fopid Controllers Based Forward DC to DC Converter With Modi...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Naveen Kumar Bind K. Krishnaveni Burada Sankara Rao Vikrant Singh Sadu Munisekhar Neel Kamal Ch. Lenin Babu Department of Electrical Engineering JSS Academy of Technical Education Noida Uttar Pradesh India Department of EEE Chaitanya Bharathi Institute of Technology Hyderabad Telangana India CPGC Pvt. Ltd and ERG Foundation Anakapalli Visakhapatnam Andhra Pradesh India Department of Electrical Engineering O.P. Jindal University Raigarh Chhattisgarh India Electrical and Electronics Engineering Sri Venkatesa Perumal College of Engineering and Technology (SVPCET) Puttur India JNTU Ananthapuramu India Electrical & Electronics Engineering School of Engineering & Technology IIMT University Meerut U.P. India Department of EEE RSR Engineering College Kavali India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Everywhere, electric power is needed for a variety of functions and tasks. Creating electric power through renewable resources, like solar energy, is a practical way to solve a lot of issues. Typically, photovoltaic modules use solar radiation to generate electricity. To guarantee maximum usage or optimal efficiency, a photovoltaic system must be equipped with a maximum power point tracking converter (MPPTC). In this paper, a DC to DC forward converter is chosen to function as MPPTC. For MPPTC under uniform sun irradiances, a variety of traditional methods are available, such as incremental conductance, perturb, and observe. However, trees, shade, dust, birds, clouds, and other factors will cause photovoltaic modules - of which many are coupled to make a power production unit - to receive non-uniform irradiances. The system enters a partial shading condition (PSC) as a result of this event. Conventional algorithms may be failed to approach the maximum utilization point to increase energy extraction under PSC. Therefore, to operate the power unit at its optimal usage under the situation of PSC, an optimization method is needed. In this study on MPPTC, an optimization method called the “Modified Osprey optimization Algorithm (MOOA)” is designed to gather more energy during PSCs. This research also compares MOOA, Modified Invasive Weed optimization (MIWO), Modified Grey Wolf optimization (MGWO), and whale optimization Algorithm (WOA). OPALRT technology is used to create the Hardware-in-the-Loop, which gathers diverse outcomes for analysis under different operations.

关键词： Maximum power point trackers Renewable energy sources optimization methods Solar energy whale optimization algorithms Power systems Solar panels Solar radiation Voltage control Sun

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Analysis and Solution of Grid Configuration optimization Model Based on whale optimization Algorithm

Analysis and Solution of Grid Configuration Optimization Mod...

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IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Menglu Han Shichen Ren College of Electronic Information and Automation Tianjin University of Science and Technology Tianjin China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

This paper addresses the optimization of microgrid configurations using a whale optimization Algorithm (WOA). Microgrids, consisting of wind and solar power generation in conjunction with the main grid, supply power to the load. In order to maximize the proportion of renewable energy supplied to the load, a higher proportion of installed wind and solar power generation capacity is required. However, the temporal mismatch between the load and renewable power generation may lead to curtailment issues. Integrating energy storage can alleviate this temporal mismatch, thereby reducing curtailment. Considering the substantial cost of electrochemical energy storage, the investment and returns associated with energy storage deployment are crucial factors. This paper focuses on addressing the economic operation of various microgrids under scenarios without energy storage and with the deployment of $50 \text{kW} / 100 \text{kWh}$ energy storage systems. Initially, a linear programming model is established and solved using the whale optimization Algorithm to obtain optimized energy storage configurations, thereby enhancing the economic viability and resource utilization efficiency of the microgrids.

关键词： Renewable energy sources Microgrids Wind power generation Linear programming whale optimization algorithms Solar power generation Resource management optimization Energy storage Investment

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Golden Sine Strategy-Fire Hawk optimization for Energy-Efficient Resource Allocation in IoT-Based Cloud-Fog Environments

Golden Sine Strategy-Fire Hawk Optimization for Energy-Effic...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Kalyan C. Gottipati Deepika Maddineni Department of Information Technology Citizens Financial Group Arizona USA Department of Information Technology JPMorgan & Chase Co New York USA

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The rapid growth of Internet of Things (IoT) has created significant demand for effective resource management in cloud-fog platforms. Therefore, there is a need for efficient resource allocation in the IoT environment to avoid unnecessary delays. In this paper, a novel method named Golden Sine Strategy-Fire Hawk optimization (GSS-FHO) is proposed for resource allocation in IoT-based cloud-fog systems. GSS-FHO minimizes latency, improves resource usage, and decreases energy consumption by optimally allocating resources to end users. It also overcomes challenges of existing metaheuristic optimization algorithms by integrating golden ratio and sine functions, while also enabling balanced exploration-exploitation, thus improving the overall performance and solving the local optimum issues. Additionally, the performance of GSS-FHO is compared with that of the conventional algorithms like Particle Swarm optimization (PSO) and whale optimization Algorithm (WOA) for analysis. The experimental results showcase the effectiveness of GSS-FHO as a reliable solution for dynamic cloud-fog platforms. When compared to existing methods, GSS-FHO reduces the load to 0.003, energy consumption to 0.12, and migration cost to 0.002, thereby demonstrating the proposed method's effective strategy for resource allocation.

关键词： Cloud computing Energy consumption Costs Heuristic algorithms Metaheuristics whale optimization algorithms Resource management Reliability Internet of Things Particle swarm optimization

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A Novel Hybrid MPPT Algorithm Based on Super Twisting Sliding Mode and whale optimization Algorithm for PEMFC Systems

A Novel Hybrid MPPT Algorithm Based on Super Twisting Slidin...

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Electrical Engineering and Automatic Control (ICEEAC), International Conference on

作者： BADOUD Abd Essalam OULD BOUAMAMA Belkacem KHEMMOUDJ Youcef Aymen KOUADRI Ramzi Département d'Electrotechnique Laboratoire d’ Automatique de Sétif University Ferhat Abbas Sétif 1 Algérie CRIStAL UMR CNRS 9189 Université de Lille Villeneuve d’ Ascq France

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

The main objective of this work is to develop an improved approach for monitoring the maximum power point (MPPT) in hydrogen production systems (PEMFC) using super twisting sliding mode based on whale optimization algorithm. The suggested strategy can track the MPP in adverse conditions such as severe temperature and pressure variations. Additionally, this method improves effectiveness and lessens the effect of shattering. The effectiveness of the suggested algorithm is tested under various operational settings and compared to three other algorithms: Grey Wolf Optimizer (GWO), fuzzy logic (FL), and perturbation and observation technique (P&O). The simulation findings demonstrate that the suggested controller for MPPT of PEMFC outperforms previous techniques regarding rapid response, minimal oscillations, and slight overshoot and undershoot.

关键词： Fuzzy logic Electrical engineering Production systems Fluctuations Perturbation methods Hydrogen whale optimization algorithms

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whale optimization Algorithm Based on Chaotic Mapping and Dynamic Parameters

Whale Optimization Algorithm Based on Chaotic Mapping and Dy...

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Signal Processing and Intelligent Computing (SPIC), International Conference on

作者： Wei Liu Wenlv Yan Tengteng Ren Tong Li Chuang Zhang College of Information Science and Engineering Shenyang Ligong University Shenyang China Department of Information Science and Engineering Ocean University of China Qingdao China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

To tackle the shortcomings of the original WOA, including its sluggish convergence rate and tendency to approach local optima, an adaptive whale optimization algorithm (MWOA) combining chaotic maps and dynamic parameters is presented. First, a modified tent map was employed to create the whale population, increasing its diversity and distributing it more evenly. Then, to strengthen and balance the capabilities of global and local search, the adaptive weight coefficient is added and the linear convergence factor is modified. Finally, the findings of simulation trials on six test functions demonstrated that MWOA considerably increased convergence accuracy and speed.

关键词： Accuracy Heuristic algorithms Diversity reception Signal processing algorithms whales Signal processing whale optimization algorithms Stability analysis optimization Convergence

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Hybrid Golden Jackal and whale optimization Algorithm (HGJWOA) for Optimizing Resource and Service Allocation in Edge Computing Environments

Hybrid Golden Jackal and Whale Optimization Algorithm (HGJWO...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： M Saradha T Sasivanan C Monishkumar J Muralidharan K Nivitha Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchengode India Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Edge computing has emerged as a promising paradigm to address the latency and bandwidth constraints of traditional cloud computing by bringing computation and storage closer to the data source. In edge computing environments, efficient resource and service allocation are crucial for optimizing performance and meeting user requirements. This paper proposes a novel approach for optimizing resource and service allocation in edge computing environments using a hybrid algorithm (HGJWOA)combining Golden Jackal optimization (GJO) and whale optimization Algorithm (WOA). The hybridization of GJO and WOA aims to leverage the strengths of both algorithms to achieve superior optimization performance. We develop a framework for resource and service allocation, formulate the optimization problem, and define evaluation metrics. The hybrid algorithm is integrated into the framework and evaluated through extensive simulations. Experimental results demonstrate the effectiveness of the proposed approach in improving resource utilization, reducing latency, and enhancing service delivery in edge computing environments compared to traditional optimization algorithms. Additionally, case studies and real-world deployment scenarios illustrate the practical applicability and benefits of the hybrid algorithm. Overall, the proposed hybrid Golden Jackal and whale optimization Algorithm presents a promising solution for optimizing resource and service allocation in edge computing environments, contributing to the advancement of efficient and scalable edge computing systems.

关键词： Energy consumption Computational modeling Soft sensors whales whale optimization algorithms Space exploration Resource management

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