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International Conference on Power Electronics and Artificial Intelligence (PEAI)

作者： Xie, Chen Yang, Ling Zhu, Difan Li, Jiewen Hu, Wenbo Guangdong Univ Technol Sch Automat Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798400716638

To address the issues of existing short-term load forecasting models that cannot effectively mine key information for load prediction, lack of extraction of time series patterns, and insufficient prediction accuracy, a short-term electric load forecasting model is proposed that combines Bidirectional Gated Recurrent Unit (BiGRU), Temporal Pattern Attention (TPA)and Crisscross whale optimization algorithm (CSWOA). Firstly, BiGRU is used to obtain time series features in the raw data and effectively capture the changing patterns of key feature vectors. Secondly, TPA adaptively weights the state vectors output by BiGRU to further mine the hidden relationships among different variables at different time steps and solve the blindness of artificially selecting the influencing factors of load forecasting. Finally, the CSWOA algorithm is used to optimize the weight coefficients and bias parameters of the fully connected layer in the TPA-BiGRU model, solving the problem that neural networks using gradient descent are prone to getting stuck in local optima when updating parameters. The model was tested on electric load data provided by the 9th National University Student Electricity and Mathematics Modeling Competition. The experimental results showed that the model had lower percentage error (MAPE), root mean square error (RMSE), and higher determination coefficient (R2).

关键词： Power load forecasting Attention mechanism whale optimization algorithm Crisscross optimization Bidirectional gated recurrent unit

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Utility-Scale Bifacial Solar Photovoltaic System: Optimum Sizing and Techno-Economic Evaluation

Utility-Scale Bifacial Solar Photovoltaic System: Optimum Si...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Magableh, Sharaf K. Wang, Caisheng Lin, Feng Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

ISBN: (纸本)9798350381849;9798350381832

Classical monofacial solar photovoltaic systems have gained prevalence and are widely reported in the literature because they have a lower initial cost compared with bifacial systems. However, limited investigation of both systems has been done on a utility scale with different performance indicators. This paper introduces a multifaceted comparative analysis including various aspects like energy generation, reliability, environmental effect, economic viability, and footprint area. Real measured data, including ambient temperature, solar irradiance, and a utility-scale load, were used for studying both systems in the City of Detroit. The optimal system sizing and energy management strategy are attained using the whale optimization algorithm. Minimizing the loss of power supply probability and sizing the number of photovoltaic panels (N-PV) are carried out for both cases. Results revealed that the bifacial solar system generates more power with a lower N-PV, a smaller installation area, and hence a lower levelized cost of energy for the entire project lifetime compared to the monofacial system. Accordingly, the bifacial system outlined in this paper is recommended and can be implemented in various locations to establish a sustainable solar energy system that is economically feasible with clean energy production for the entire project's lifespan.

关键词： Bifacial PV system Cleaner Production Loss of Power Supply Probability Solar energy whale optimization algorithm

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Short-term Load Forecasting of Power System Based on Weighted Improved Adaptive Neuro-Fuzzy Inference System and Random Forest 2

Short-term Load Forecasting of Power System Based on Weighte...

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IEEE 2nd International Conference on Power Science and Technology (ICPST)

作者： Yu, Zhongming Du, Hongze Lei, Yuan Shan, Menghao Kunming Univ Sci & Technol Fac Elect Power Engn Kunming Yunnan Peoples R China

ISBN: (纸本)9798350349047;9798350349030

Load forecasting of power system has become one of the important symbols to measure the modernization of power system operation and management, and it is very important for the safe and stable operation and economic dispatching of power system. Aiming at the problem that the accuracy of short term load forecasting of power system is reduced due to external interference, this paper first introduces the basic principle of related algorithms. Then, a short-term load forecasting method based on weighted adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF) is proposed. A whale optimization algorithm (WOA) is employed to fine-tune the parameters within the membership function of the adaptive neuro-fuzzy inference system, while a random forest algorithm is utilized to identify the most important features. In conclusion, a comparison is made on the load data predictions using different algorithms. The adaptive neuro-fuzzy inference system is employed for short-term load forecasting in the power system, yielding experimental results with a maximum relative error of 1.6659%, a minimum relative error of 0.00768%, and an average absolute error of 64.352MW. By optimizing the membership function parameters of the adaptive neuro-fuzzy inference system using the whale optimization algorithm and weighting the results with random forest predictions, the experimental results showed a maximum relative error of 0.991%, a minimum relative error of 0.00008%, and an average absolute error of 24.84MW, further enhancing the accuracy of the predictions. Compared with the relevant algorithms, the proposed WOA-ANFIS-RF algorithm can further increase the nonlinear processing ability, reduce the error of load prediction and improve the accuracy of load prediction. In addition, the load prediction results obtained by the proposed algorithm meet the practical application requirements and have certain reference value.

关键词： neuro-fuzzy whale optimization algorithm random forest load forecasting

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An Efficient Geometric Parameter optimization Method for Microstrip Bandpass Filter Based on One-Dimensional Convolutional Neural Network

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2024年第1期2024卷

作者： Gao, Yang Wang, Danyang Yu, Huilong Hua, Tao Hou, Ning Lu, Yapeng China Three Gorges Univ Dept Elect Engn & New Energy Yichang 443002 Peoples R China Henan Univ Urban Construct Sch Elect & Control Engn Pingdingshan 467000 Peoples R China Nanjing Inst Technol Sch Informat & Commun Engn Nanjing 211167 Peoples R China

Machine learning-assisted electromagnetic simulation has become an effective acceleration tool for designing microwave components by introducing high-precision models and optimization algorithms, featuring fast design and high efficiency. However, enormous amount of data generated from the blind preliminary and computationally expensive simulation is required to predict the accuracy response. An efficient geometric parameter optimization method for microstrip bandpass filter (BPF) based on a one-dimensional convolutional neural network is proposed. Nonlinear convergence factor, adaptive weight, and Gaussian difference mutation strategies are integrated using the whale optimization algorithm to avoid the local optimum and improve optimization accuracy. Computational efficiency is improved significantly with small-scale training data. The validity and efficiency of the proposed method are confirmed by fifth-order microstrip BPFs, and the performance of the predicted structure parameters is significantly improved, which shows great promise for application in optimization and performance improvement in microwave electromagnetic simulation.

关键词： microstrip bandpass filter one-dimensional convolutional neural network whale optimization algorithm

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Optimizing constrained engineering problem nH-WDEOA: using hybrid nature-inspired algorithm

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1899-1907页

作者： Mishra, Pawan Pooja Tripathi, Shashi Prakash Department of Electronics and Communication University of Allahabad UP Prayagraj India Department of Applied Mathematics and Computer Science Liverpool John Moores University Liverpool United Kingdom

Handling optimization problems can be a challenging task. In order to achieve optimality, several variants of nature-inspired algorithms viz. swarming based, population-based and a few based on physical laws have been proposed with different working mechanisms. Although, nature-inspired algorithms perform mathematical operations mimicking certain unique feature of a natural specie to obtain the best optimal solutions, nevertheless, they can be inefficient in term of easily falling in local optimal region, providing the best convergence quality, premature convergence, slow convergence rate etc. The present work provides a different hybrid approach for optimization, named as novel hybridized whale-differential evolution optimization algorithm (nH-WDEOA). © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Constraint optimisation problem Differential evolution algorithm Nature inspired algorithms whale optimization algorithm

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Research on fault diagnosis of KPCA-WOA-BP transformer 5

Research on fault diagnosis of KPCA-WOA-BP transformer

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Peng, Hong Zhao, Chonghao Liaoning Polytech Univ Sch Elect & Control Engn Huludao Peoples R China Liaoning Polytech Univ Sch Elect & Informat Engn Huludao Peoples R China

ISBN: (纸本)9798350386783;9798350386776

In order to quickly identify transformer faults and process them, a transformer fault diagnosis model based on Kernel PCA (KPCA) and whale optimization algorithm (WOA) optimized BP neural network is provided. Firstly, the traditional data of various miscellaneous gases used to solve the nonlinear fault of transformer are reduced by kernel principal component analysis (KPCA). According to the uncertainty of a single BP (Back Backpropagation) neural network, which is easy to fall into local optimization and has poor accuracy, the input data is normalized first, and then whale algorithm is used to solve its various weights and thresholds for each work, reducing the workload, The overall mean square error of the training set and the test set is used as the fitness function to optimize the parameters. The performance comparison with Grey Wolf Optimizer (GWO)and Particle Swarm optimization (PSO) proves its superiority. KPCA-WOA-BP transformer fault diagnosis model is built. The accuracy is 90%, Compared with KPCA-GWO-BP model, KPCA-PSO-BP model and KPCA-BP model, it is improved by 8%, 12% and 22% respectively.

关键词： whale optimization algorithm BP neural network Kernel principal component analysis Mean square erro

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Dynamic Evaluation of Distribution Network Reliability Based on Improved DNN with Large-scale Distributed Generations Access 6

Dynamic Evaluation of Distribution Network Reliability Based...

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6th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Yi, Chenying Zhou, Yuanchao Zhou, Yangjun Huang, Weixiang Zhang, Wei Li, Shan Guangxi Power Grid Co Ltd Guangxi Key Lab Intelligent Control & Maintenance Elect Power Res Inst Nanning Peoples R China Guangxi Power Grid Co Ltd Nanning Power Supply Bur Nanning Peoples R China

ISBN: (纸本)9798350373486;9798350373479

With the access of large-scale distributed generations, its uncertainty brings great risk to the reliable operation of distribution network. Therefore, it is of great significance to consider the dynamic reliability evaluation of distribution network with large-scale distributed generations access. However, the traditional reliability evaluation method of distribution network is not suitable for evaluating the reliability index of distribution network with large-scale distributed generations access, which often leads to the difficulty of modeling and the sharp increase of calculation. Therefore, this paper proposes a large-scale distributed power generations access dynamic evaluation method based on improved depth neural network (IDNN), and uses whale optimization algorithm (WOA) to optimize the relevant parameters of DNN. In addition, this paper uses the grey correlation analysis to preprocess the input variables of the depth neural network, and then selects the factors that have great influence on the reliability of the distribution network as its input variables, so as to improve the calculation speed of the model as much as possible under the premise of ensuring the accuracy. Finally, an urban distribution network is selected for analysis and evaluation to verify the effectiveness of the proposed method.

关键词： Improved depth neural network Large-Scale Access of Distributed Generations whale optimization algorithm Grey correlation analysis Dynamic Reliability Evaluation Method

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Efficient resource allocation using whale optimization for cell-free massive MIMO networks in 6G HetNet

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PHYSICAL COMMUNICATION 2024年 66卷

作者： Mishra, Saurabh Kumar Singh, Moirangthem Biken Sharma, Deewanshu Pratap, Ajay Banaras Hindu Univ Indian Inst Technol Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India

The increase in data rate demands has driven the development of Cell-Free massive Multiple-Input Multiple- Output (CFmMIMO) technology in 6G Heterogeneous Networks (HetNets). This paper proposes an integration of Unmanned Aerial Vehicles (UAVs) and Device-to-Device (D2D) communication in CFmMIMO, aiming to enhance network capacity, coverage, and service delivery in next-generation wireless networks. The paper investigates uplink transmission in a network where Access Points (APs) have imperfect channel state information. It considers Cell-free User Equipment (CUE), UAVs, and D2D pairs, deriving closed-form uplink achievable rates for each user type. Moreover, two optimization problems are formulated to enhance user data rates: one focuses on maximizing the sum data rate through pilot assignment, while the other aims to achieve weighted max-min power control through power allocation. Furthermore, to address these problem, we propose a Modified whale optimization algorithm (MWOA) and a novel Pilot Assignment based on whale optimization algorithm (PAWOA). Performance studies demonstrate significant improvements over state-of-the-art algorithms.

关键词： Cell-free massive MIMO 6G Pilot assignment Power allocation whale optimization algorithm

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whale optimization-orchestrated Federated Learning-based resource allocation scheme for D2D communication

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AD HOC NETWORKS 2024年 163卷

作者： Jadav, Nilesh Kumar Tanwar, Sudeep Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad 382481 Gujarat India

Device -to -Device (D2D) communication plays a prominent role in mobile data offloading from the cellular infrastructure (e.g., base station). This paradigm empowers user equipment to communicate with each other directly, offering an efficient resort for data communication that eliminates the need for the base station. However, significant challenges, such as interference, resource allocation, and energy efficiency, impede the performance of D2D communication. In the context of resource allocation, most of the existing work primarily focuses on game and graph theoretical models, which raises the computational complexity as the number of D2D users increases. In this article, we formulated a sum rate maximization problem, which is solved using a combinatorial scheme comprised of whale optimization algorithm (WOA) and Federated Learning (FL). First, we discover the optimal CUs-D2D Groups (D2DGs) pairs by utilizing the social behavior of whales in the WOA. Only these optimal links are permitted to participate in the FL -based resource allocation, ensuring a physical layer access control. Next, we generated a dataset from the WOA-based optimal CU-D2DG links, which is employed by the Convolutional Neural Network (CNN) model for decentralized learning. FL offers a proactive decision for resource assignment, i.e., whose CU resources will be used by the D2DG. The proposed scheme is evaluated by considering different performance parameters, such as convergence rate, statistical measure (accuracy, loss), fairness (0.72), and overall sum rate ( approximate to 25 Mbps).

关键词： Resource allocation Physical layer access control D2D communication Federated Learning whale optimization algorithm Bio-inspired algorithm

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Fractional whale driving training-based optimization enabled transfer learning for detecting autism spectrum disorder

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2024年 113卷 108200页

作者： Sriramakrishnan, G., V Paul, P. Mano Gudimindla, Hemachandra Rachapudi, Venubabu Saveetha Univ Saveetha Inst Med & Tech Sci SIMATS Dept Comp Sci & Engn Chennai 602105 Tamil Nadu India Alliance Univ Dept IT ACED Anekal India M S Ramaiah Inst Technol Elect & Elect Engn Bengaluru Karnataka India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India

Autism Spectrum Disorder (ASD) is a neurological illness that degrades communication and interaction among others. Autism can be detected at any stage. Early detection of ASD is important in preventing the communication, interaction and behavioral outcomes of individuals. Hence, this research introduced the Fractional whale-driving Driving Training-based Based optimization with Convolutional Neural Network-based Transfer learning (FWDTBO-CNN_TL) for identifying ASD. Here, the FWDTBO is modelled by the incorporation of Fractional calculus (FC), whale optimization algorithm (WOA) and Driving Training-based optimization (DTBO) that trains the hyperparameters of CNN-TL. Moreover, the Convolutional Neural Networks (CNN) utilize the hyperparameters from trained models, like Alex Net and Shuffle Net in such a way that the CNN-TL is designed. To improve the detection efficiency, the nub region was extracted and carried out with the functional connectivity-based whale Driving Training optimization (WDTBO) algorithm. Moreover, the TL is tuned by the FWDTBO algorithm. The result reveals that the ASD detection technique, FWDTBO-CNN-TL acquired 90.7 % accuracy, 95.4% sensitivity, 93.7% specificity and 93%f-measure with the ABIDE-II dataset.

关键词： Driving Training optimization Fractional calculus whale optimization algorithm Alex Net ShuffleNet

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