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6th International Conference on Information Communication and Signal Processing, ICICSP 2023

作者： Gao, Hanlin Wu, Chenglong Zhu, Dandan Li, Yaan Zhang, Yang School of Marine Science and Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350339994

The underwater acoustic signals are rife with various non-linear and non-stationary elements. Denoising is vital in processing these signals. In this paper, we propose a denoising method for underwater acoustic signals based on an enhanced variational mode decomposition (VMD) and reverse weighted permutation entropy (RWPE). A significant challenge in the VMD algorithm is the incorrect selection of the mode component K and the penalty factor alpha. This can lead to partial decomposition and mode interference. To tackle this issue, we employed the whale optimization algorithm (WOA). This algorithm refines the penalty factor and adjusts the mode component count in the VMD. Consequently, the optimal parameter combination for K and alpha is identified. Using this optimal set, the VMD processes the acoustic signal to yield several intrinsic mode functions (IMFs). The RWPE then identifies the IMFs embodying significant signal features. These selected IMFs are utilized for reconstruction, culminating in the denoising of the underwater acoustic signal. Comparative analyses indicate that our refined VMD method outperforms other denoising techniques. © 2023 IEEE.

关键词： reverse weighted permutation entropy underwater acoustic signals variational mode decomposition whale optimization algorithm

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Use of ML algorithms in Optimisation of Electrical Automatic Industries: Empirical Analysis

Use of ML Algorithms in Optimisation of Electrical Automatic...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Mishra, Rahul Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350357769

The whale optimization algorithm (WOA) is a meta-heuristic algorithm that draws inspiration from the bubble-net hunting tactic employed by humpback whales. Widely embraced in engineering, WOA is renowned for its simplicity, minimal operator requirements, rapid convergence, and effective balance between exploration and exploitation. This research study examines the utilization, adaptations, and combinations of the whale optimization algorithm (WOA) within several engineering disciplines. Conducting a Systematic Literature Review of 80 articles from 2018 to 2022, the study uncovers WOA's prevalence in 5 fields and 17 subfields of engineering. The review identifies strengths, weaknesses, and opportunities, indicating a growing research trend in WOA. With an anticipation of continued exploration, the paper aims to provide insights into WOA-based algorithm development, inspiring both experienced researchers and novices in the field. © 2023 IEEE.

关键词： Bubble-net hunting Metaheuristic Swarm-based whale optimization algorithm

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WOA-BiLSTM-Attention Model for Short-term Power Load Projection 6

WOA-BiLSTM-Attention Model for Short-term Power Load Project...

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2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Gao, Linxin Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350313444

Aiming at the periodicity, nonlinearity and certain volatility of power load, this study employs historical power load data and the defining characteristics of its influencing factors for the purpose of multivariate short-term power load prediction. Subsequently, an integrated model (WOA-BiLSTM-Attention) is introduced, utilizing the whale optimization algorithm to synergize the attention mechanism and the bi-directional long short-term memory (BiLSTM) network. This innovative approach serves as a novel methodology for short-term power load forecasting. Initially, the utilization of BiLSTM involves power load prediction, wherein both historical and prospective power load data, along with their respective influencing factors, are harnessed to comprehensively capture the inherent temporal intricacies of the dataset. Following this, the Attention mechanism comes into play, acquiring the ability to independently discern the significance attributed to diverse features and chronological segments. By prioritizing attributes intricately associated with the target, it adeptly manages the extended interdependence intrinsic to the data, thus culminating in an augmented predictive accuracy. Finally, whale optimization algorithm (WOA) is utilized to find the optimal neural network parameters to reduce the model stochasticity and time costs. The utilization of electricity load data from Panama serves as an illustrative case for the conducted experiments, revealing that the WOA-BiLSTM-Attention model attains an elevated level of prediction accuracy compared to single neural network methods such as BP, LSTM, BiLSTM, etc., corresponding to an R2 of 0.93 and an RMSE of 29.49. © 2023 IEEE.

关键词： attention mechanism bi-directional long and short-term memory networks power load forecasting whale optimization algorithm

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Transformer fault diagnosis based on TWOA-SVM 10

Transformer fault diagnosis based on TWOA-SVM

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10th International Forum on Electrical Engineering and Automation, IFEEA 2023

作者： Yin, Yongmiao Ma, Hongwei Yan, Feichao Shanghai Dianji University Shanghai China

ISBN: (纸本)9798350309379

An improved whale optimization (T-WOA) algorithm is proposed for the problems of low accuracy of transformer fault diagnosis methods used at the present stage, low accuracy of traditional support vector machine data identification and classification, difficulty in coordinating the exploration and development ability of whale optimization algorithm, and easy to be limited to the local optimum value. Firstly, the Tent chaotic mapping method is used to initialize the population to increase the uniformity and diversity of the distribution of individuals in the initial population;secondly, the nonlinear convergence factor and nonlinear weight are introduced and the improved parameters are used to update the position of the individuals, to achieve the purpose that the algorithm's searching ability for the global optimal solution is improved. After each iteration is completed, the individuals are ranked according to their fitness, and the elite individuals with higher fitness values are selected;finally, the elite individuals are perturbed by Gaussian perturbation. The experiment verifies the high accuracy and robustness of this method for transformer fault diagnosis. © 2023 IEEE.

关键词： chaotic mapping fault diagnosis Gaussian variation transformer whale optimization algorithm

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Prediction of Vehicle Fuel Consumption Based on Combination of Improved whale optimization and XGBoost

Prediction of Vehicle Fuel Consumption Based on Combination ...

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第35届中国控制与决策会议

作者： Tonghui Jiang Ruiyou Zhang College of Information Science and Engineering Northeastern University

The highway transportation industry is the most oil-intensive industry in the ***,an accurate prediction of fuel consumption not only reveals the relationship between fuel consumption and driving characteristics,but also promotes green development of the automobile industry.A fuel consumption prediction model based on Extreme Gradient Boosting(XGBoost) is built in this research,where an improved whale optimization algorithm(IWOA) is developed to optimize the main parameters in the prediction *** the IWOA,Sobol sequence initialization is introduced to improve the diversity of the population and expand the search *** nonlinear convergence coefficient and Levy Flight are also introduced in the algorithm to balance the global exploration ability and the local exploitation *** experimental results show that the IWOA-XGBoost model performs better than some existing popular models in both accuracy and *** study provides fundamental theoretical,methodological,and technical support for automobile fuel saving and emission reduction.

关键词： Extreme Gradient Boosting whale optimization algorithm Sobol Sequence Levy Flight Fuel consumption prediction

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Adaptive Active Noise Cancellation Method of High Voltage Reactor Based on DnCNN Network 6

Adaptive Active Noise Cancellation Method of High Voltage Re...

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6th International Conference on Machine Learning and Intelligent Systems, MLIS 2024

作者： Han, Li Lu, Bin Fang, Jizhong Zhang, Qingxia Qian, Yong Ma, Rui Guo, Wei Cheng, Cheng Electric Power Research Institute of State Grid Ningxia Electric Power Company China Shanghai Shenjie Environment Protection Science and Technology Co Ltd China Ultra-high Voltage Company of State Grid Ningxia Electric Power Company China

ISBN: (纸本)9781643685533

Current noise cancellation methods can barely deal with nonlinear signals in an effective way and the overall noise reduction performance is relatively poor. Aiming at these issues, we propose an adaptive active noise cancellation (ANC) method of high voltage reactors based on the denoising convolutional neural network (DnCNN). First, a feedforward adaptive ANC system was designed by considering the noise characteristics of high voltage reactors. Then, a noise control model was constructed and the weight coefficient of the filter was optimized by using the DnCNN network;meanwhile, the inverse waveform was reconstructed by adding spectrum analysis, thereby achieving effective control of noise signals. Additionally, the whale optimization algorithm was employed to solve the problem models of minimization of total radiation power of different sound sources, and the optimized location, amplitude, and phase angle of the secondary sound sources were determined. Based on LabVIEW, an online noise monitoring and ANC system was developed and used for experimental analysis of the proposed method. The results showed that the noise reduction performance was optimized (the overall noise reduction = 9 dB) when the number of secondary sound sources was 8 and the distance from the primary sound source was 1.0 m, suggesting that the proposed method can reliably reduce the noises of high voltage reactors. © 2024 The Authors.

关键词： adaptive active noise cancellation system DnCNN network high voltage reactor parameter optimization of secondary sound sources spectrum analysis whale optimization algorithm

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Wind Power Correction Prediction Considering Similar Wind Power Climbing Events

Wind Power Correction Prediction Considering Similar Wind Po...

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International Conference on Energy Engineering, ICEE 2023

作者： Li, Kai Shi, Kaiming Ma, Ruiming Sang, Shengpeng Cao, Shitong Gou, Yuliang College of Electric Power Inner Mongolia University of Technology Inner Mongolia Hohhot010051 China Electric Engineering Technology Research Institute of Inner Mongolia Energy Power Investment Group Co. Ltd. Inner Mongolia Hohhot010090 China

ISBN: (纸本)9789819771455

In response to the issues of wind turbine ramp events affecting the safe and stable operation of power systems and the accuracy of wind power prediction, a wind power correction prediction method is proposed considering similar ramp events. The whale optimization algorithm (WOA) is utilized to optimize the parameters of the Least Squares Support Vector Machine (LSSVM) for initial wind power prediction. Extracting ramp events by using the wind power ramp identification method based on extremum point extraction from the predicted results. To cope with the variable time windows of the identified ramp events, the Dynamic Time Warping (DTW) method is employed to identify similar ramp events from historical data. Finally, a second prediction is conducted based on similar historical ramp events to obtain the corrected wind power prediction. Experimental results show that the proposed method can improve the short-term prediction accuracy of wind power effectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： dynamic time warping least squares support vector machine whale optimization algorithm wind power prediction wind power ramp events

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Power System Harmonic Estimation Using whaleoptimization algorithm and Least Square Approach

Power System Harmonic Estimation Using WhaleOptimization Alg...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Bhoi, Ashok Mallick, Ranjan Kumar Nayak, Pravati Panda, Gayadhar Mishra, Sairam Swain, Ajay kumar Department Electrical Engineering NIT Meghalay Shillong India Department of EEE Siksha 'O' Anushandhan Deemed to be University Bhubaneswar India Department Electrical Engineering Siksha 'O' Anushandhan Deemed to be University Bhubaneswar India Department Electrical Engineering Synergy Institute of Technology Phulnakhara Bhubaneswar India

ISBN: (纸本)9798350325188

The accurate estimation of harmonic components is crucial for analyzing and mitigating power system distortions, ensuring the reliable operation of electrical networks. In this paper, we present a novel approach called Optimal Harmonic Estimation (OHE) that addresses the challenges of accurately estimating harmonic content in power systems. OHE leverages advanced optimization techniques to overcome the limitations of traditional methods and provides robust and precise harmonic estimation results. In this paper we combine whale optimization algorithm (WOA) and Recursive Least Square (RLS) approach for facilitating efficient power system harmonic analysis. The proposed approach shows promising potential for harmonic estimation. © 2023 IEEE.

关键词： Harmonic estimation Power System recursive least square whale optimization algorithm

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Optimal variable structure control with sliding modes for unstable processes

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Journal of Central South University 2021年第10期28卷 3147-3158页

作者： KUMAR Satyendra AJMERI Moina Department of Electrical Engineering National Institute of TechnologyPatna 800005India

In this work,a variable structure control(VSC)technique is proposed to achieve satisfactory robustness for unstable *** values of unknown parameters of VSC are obtained using whale optimization algorithm which was recently reported in *** analysis has been done to verify the suitability of the proposed structure for industrial *** proposed control strategy is applied to three different types of unstable processes including non-minimum phase and nonlinear systems.A comparative study ensures that the proposed scheme gives superior performance over the recently reported VSC ***,the proposed method gives satisfactory results for a cart inverted pendulum system in the presence of external disturbance and noise.

关键词： variable structure control sliding mode control whale optimization algorithm robustness non-linear system

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Secure and Efficient Data Transmission for IoT Devices in Untrusted Environments

Secure and Efficient Data Transmission for IoT Devices in Un...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jain, Upma Smitha, K. Battula, R.S.S. Raju Sarkar, Arindam Martin, Natasha Natrayan, L. Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Adichunchanagiri University Bgs Institute of Technology Department of Artificial Intelligence and Machine Learning India Aditya University Department of Computer Applications India Heritage Institute of Technology Department of Applied Electronics and Instrumentation Engineering Kolkata India Department of Law Symbiosis Law Pune India School of Engineering Simats Department of Mechanical Engineering Saveetha India

ISBN: (纸本)9798350375442

One technological trend that will not go away in the modern world is the Internet of Things (IoT). With the development of the Internet of Things, the world is becoming smarter. The efficacy, flexibility, and simplicity of deployment of IoT make it the preferred choice for most real-time applications. But energy efficiency and security are the big obstacles for the Internet of Things. Presented here are safe, energy-efficient solutions for the Internet of Things (IoT), a problem whose significance this study acknowledges. Since routing is the primary energy consumer, this study presents secure routing strategies that are both energy efficient and reliable. Three distinct safe data transmission techniques for the Internet of Things (IoT) are presented in this paper, all of which aim to reduce energy usage during routing. The first part of this study lays out a clustering-and secure route selection-based architecture for safe and energy-efficient data transmission with the Internet of Things (IoT). The W-HHO technique, which is based on whale optimization, is used to identify the secure routes, and fuzzy-based secure clustering is used for that purpose. Encryption protocols that work in tandem with cypher block chaining ensure the safety of sensitive information. Using industry-standard metrics like packet delivery rate, throughput, energy efficiency, network longevity, etc., we assess how well each solution performs. When compared to the current methods, the outcomes produced by the presented works demonstrate promising performance. © 2024 IEEE.

关键词： clustering data transmission energy efficiency fuzzy clustering harris hawks optimization (HHO) internet of things (IoT) secure routing security whale optimization algorithm

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