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Digital twin simulation for integration of blockchain and internet of things for optimal smart management of PV-based connected microgrids

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SOLAR ENERGY 2023年第1期251卷 306-314页

作者： Li, Yihong Tao, Qi Gong, Yadong Zhaoqing Univ Sch Comp Sci & Software Guangdong 526000 Peoples R China South China Normal Univ Sch Data Sci & Engn Guangdong 516600 Peoples R China

Recent attention is focused on interlinked (networked) hybrid connected microgrids (MGs) because of their technical and economic benefits over traditional ones. A system like that would connect the DC resources, such as solar energy, to DC busses and the AC resources, such as wind turbines, to AC busses. Therefore, the system's overall performance is remarkably improved. In addition, it is cost-effective compared to traditional grids since no power electronics are required to convert DC to AC. While a system like this has numerous benefits, it also has numerous challenges. System-level energy management (EM), for example, presents greater challenges because each change in the subsystem (DC/AC sections) may potentially alter the EM for the whole grid. Therefore, the present study develops the whale optimization algorithm (WOA) for finding the optimum solution. Additionally, blockchain (BC) technology has been applied for increasing the security of power trading in hybrid MGs (HMGs). Stochastic load flow, according to a copula model, is used to create a realistic layout. According to the results, the suggested networked HMG-EM structure based on BC is demonstrated as being effective.

关键词： whale optimization algorithm Modified blockchain Hybrid connected microgrid Security enhancement Uncertainty

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Emotion recognition in speech signals using optimization based multi-SVNN classifier

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第2期34卷 384-397页

作者： Mannepalli, Kasiprasad Sastry, Panyam Narahari Suman, Maloji KL Univ KLEE Vijayawada 522502 Andhra Pradesh India CBIT Hyderabad 500075 Telangana India

Emotion recognition is an interdisciplinary area, and it achieves a significant attention of the researchers in the past few years. Automatic recognition of an emotional state intends to attain an interface among the machines and human beings. Accordingly, a speaker emotion recognition system, named Fractional Deep Belief Network (FDBN), is designed in the literature using fractional deep belief network that combines fractional theory and deep belief network. This work introduced a novel emotion recognition scheme, whale-Imperialist optimization algorithm (whale-IpCA) based Multiple Support Vector Neural Network (Multi-SVNN) classifier, for identifying the emotions in the speech signal. The newly proposed whale-IpCA algorithm hybridizes the whale optimization algorithm (WOA) and Imperialist Competitive algorithm (IpCA), and it trains the Multi-SVNN classifier for identifying the emotions. Also, the spectral feature set is extracted from the input signal and provided to the proposed whaleIpCA based Multi-SVNN for the recognition purpose. Simulation of the proposed whale-IpCA based Multi-SVNN is done with the help of the standard emotion databases, such as Berlin and Telugu. From the results, it is evident that the proposed whale-IpCA based Multi-SVNN classifier has surpassed other existing works with the values of 0.0025, 0, and 0.9987 for the FNR, FPR, and accuracy, respectively. (c) 2018 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Emotion recognition Speech signal Multi-SVNN whale optimization algorithm Imperialist Competitive algorithm

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Double Filter and Double Wrapper Feature Selection algorithm for High-Dimensional Data Analysis

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IEEE ACCESS 2025年 13卷 86185-86202页

作者： Chen, Hong Zheng, Yuefeng Jilin Normal Univ Sch Math & Comp Sci Siping 136000 Peoples R China

With the advent of the big data era, we often deal with datasets containing a large number of redundant features, and in this context, dimensionality reduction of data becomes crucial. To address this issue, this study proposes a double filter and double wrapper (DFDW) feature selection algorithm for high-dimensional data. In the double filter stage, the algorithm first evaluates all features from two perspectives using two filter algorithms: ReliefF and the Pearson correlation coefficient. It then selects the top k features and obtains a candidate feature subset F by taking the intersection. Next, the standard Cauchy distribution was used for population initialization. Subsequently, the algorithm enters the double wrapper stage, where it uses the Random Walk whale optimization algorithm (RWWOA) and the improved Adaptive Differential Evolution (ADE) to jointly optimize and obtain the optimal feature subset. Among them, in order to overcome the problem of single algorithm falling into the local optimum, the algorithm Iteration Mechanism is proposed, which selectively runs two wrapper algorithms to make the algorithm jump out of local optimum and explore a broader optimization space. Finally, we verified the effectiveness of the algorithm through three sets of comparative experiments. The experimental results show that the DFDW algorithm performed well in obtaining the optimal feature subsets on 10 high-dimensional datasets, with an average classification accuracy of more than 95.1% on 8 datasets, a dimensionality reduction rate of less than 0.64% on all datasets, and the lowest dimensionality reduction rate of 0.19%.

关键词： Feature extraction Classification algorithms whale optimization algorithms Filtering algorithms Correlation coefficient Correlation Dimensionality reduction Computational efficiency Vectors High dimensional data Feature selection whale optimization algorithm differential evolution ReliefF Pearson correlation coefficient

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Energy-efficiency optimization and the comparative performance analysis for Wireless Body Area Networks (WBANs)

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2024年 42卷

作者： Arora, Neha Gupta, Sindhu Hak Kumar, Basant Amity Univ Amity Inst Def Technol Noida Uttar Pradesh India Amity Univ Dept Elect & Commun Engn Noida Uttar Pradesh India MNNIT Dept Elect & Commun Engn Allahabad Uttar Pradesh India

This paper examines the energy efficiency of a non-cooperative and cooperative On-body Wireless Body Area Network (WBAN) and link reliability in a cooperative WBAN using IEEE 802.15.6 based CM3A channel model. The proposed energy optimization framework is based on whale optimization algorithm (WOA) and Particle Swarm optimization (PSO) techniques. For the optimized code rate, obtained results illustrate that WOA gives 44.7%. and 43.4% better results for the non-cooperative and cooperative cases respectively than the PSO. The link reliability has been investigated by observing the effect of the fading parameter (m) on the outage probability over the Nakagami m fading channel. The obtained results reveal how the fading parameter (m) affected the likelihood of an outage.

关键词： communication Energy optimization Particle swarm optimization whale optimization algorithm Wireless body area networks

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Fault diagnosis and prediction of wind turbine gearbox based on a new hybrid model

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第9期30卷 24506-24520页

作者： Wang, Haifeng Zhao, Xingyu Wang, Weijun North China Elect Power Univ Dept Econ & Management 689 Huadian Rd Baoding 071003 Peoples R China

Gearbox is an important part of wind turbine. Diagnosing and forecasting gearbox faults of wind turbines can effectively reduce the costs of operation and maintenance and improve the reliability of gearbox operation. Due to high dimensionality and nonlinearity of system parameters, the paper uses the grey relation analysis to select features related to gearbox oil temperature. Features with a relational degree above 0.7 are selected as input data related to oil temperature, including wind speed, ambient temperature, power, and gearbox shaft temperature. Then, a new extreme learning machine with kernel improved by the whale optimization algorithm is established to forecast gearbox oil temperature. Through the residuals between gearbox oil temperature predicted by the proposed model and monitored by the SCADA, whether the gearbox exists faults can be diagnosed. In the case study, the test data was divided into two groups (the test data with and without faults). In the data test without faults, compared with three other models, the proposed model has the smallest false-negative rate (0.211%) and mean absolute percentage error (2.812%). In the data test with faults, the proposed model can diagnose gearbox faults earlier (160 min in advance) than the other three benchmark models. The results show that the proposed hybrid model performs well in the fault diagnosis and prediction of wind turbine gearbox.

关键词： Fault diagnosis and prediction Extreme learning machine with kernel whale optimization algorithm Wind turbine gearbox Statistical process control

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A new demand response management strategy considering renewable energy prediction and filtering technology

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RENEWABLE ENERGY 2023年第1期211卷 656-668页

作者： Zheng, Xidong Bai, Feifei Zhuang, Zhiyuan Chen, Zixing Jin, Tao Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia Griffith Univ Sch Engn & Built Environm Gold Coast Qld 4222 Australia State Grid Fuzhou Elect Power Supply Co Fuzhou 350009 Peoples R China

Accurate prediction of renewable energy generation acts as a critical role which not only provides short-term power generation in the future, but also facilitates scheduling and pre-configuration of energy storage sys-tems. More importantly, the power generation prediction is of great significance to the demand response man-agement (DRM) of renewable energy to participate in the electricity spot market. Therefore, DRM helps improve the stability and reliability of renewable energy systems. This paper presents a novel prediction-smoothing based methodology to reduce and eliminate the influence caused by the uncertainty of renewable energy output. Firstly, the whale optimization algorithm (WOA) is combined with Long Short-Term Memory (LSTM) to predict short-term wind power output. Then, the Hampel-Butterworth-SG filtering strategy with outlier regression and specific risk band elimination is introduced. After that, according to the short-term output forecast results, the scheduling and pre-configuration scheme of peak-filling type energy storage is developed. Finally, based on the predicted renewable energy output and electricity price, the dynamic changes of demand response (DR) are calculated based on Logistics function, optimistic response and pessimistic response factors. Through extensive case studies, it is demonstrated that the assessment deviations in different scenarios is less than 5%, which are 0.64% for Scenario 1 and 3.64% for Scenario 2, and no additional penalty is required at this time. In addition, the proposed Demand Response Load Adjustment Rate (DRLAR) help compare the differences between predicted and actual DR, which are DRLAR = 0.03% in Scenario 1 and DRLAR = 0.01% in Scenario 2. Users are able to adjust the DR dynamically according to the electricity price to realize the optimal scheduling of their renewable re-sources. The proposed methodology creates a special connection between DRM and renewable energy prediction, which provides a reliable r

关键词： Demand response management Logistics function Short-term wind power prediction whale optimization algorithm Renewable energy smoothing strategy Energy storage system

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A hybrid deep feature selection framework for emotion recognition from human speeches

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第8期82卷 11461-11487页

作者： Marik, Aritra Chattopadhyay, Soumitri Singh, Pawan Kumar Jadavpur Univ Dept Informat Technol Jadavpur Univ Second CampusPlot 8LB Block Kolkata 700106 W Bengal India

Speech Emotion Recognition (SER) is an active area of signal processing research that aims at identifying emotional states from audio speech signals. Applications of SER range from psychological diagnosis to human-computer interaction and as such, a robust framework is needed for accurate classification. To this end, we propose a two-stage hybrid deep feature selection (HDFS) framework that combines deep learning with automated feature engineering for emotion recognition from human speeches, which shines both in terms of accuracy and computational efficiency. Our pipeline extracts self-learned features using a customized Wide-ResNet-50-2 deep learning model from mel-pectrograms of raw audio signals, whose dimensionality is reduced using a hybrid deep feature selection algorithm that comprises a fuzzy entropy and similarity-based feature ranking method, followed by whale optimization algorithm, which is a popular meta-heuristic optimization algorithm in literature. A k-nearest neighbor classifier is used to classify the optimized feature subset into the respective emotion classes. The proposed pipeline is evaluated on three publicly available SER datasets using a 5-fold cross-validation scheme, where it is found to outperform several state-of-the-art existing works in literature by significant margins thus, justifying the superiority and reliability of the proposed research. The source codes of the proposed method can be found at: .

关键词： Speech emotion recognition Deep learning Feature selection Fuzzy entropy & similarity measures whale optimization algorithm

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A Data-driven method for adaptive resource requirement allocation via probabilistic solar load and market forecasting utilizing digital twin

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SOLAR ENERGY 2023年第1期250卷 368-376页

作者： Yao, Chuantao Wang, Jian Sun, Hao Chu, Haiyang Jin, Tao Xiang, Quanzhou China Southern Power Grid EHV Power Transmiss Co Tianshengqiao Bur Xingyi 562400 Guizhou Peoples R China

Fog computing is used to handle applications that require a lot of calculations at the network's edges for wireless Internet of Things (IoT) networks that have source-limited equipment. In addition to reducing computational time and fronthaul traffic information, fog computing still poses challenges in allocating the existing computing and communication sources, while maintaining the strict quality of service (QoS) standards. The current study examines the issue regarding task management and heterogeneous resource allocation among equipment for wireless IoT networks. As a means of transferring the information to the fog computing nodes (FNs), IoT devices collecting large amounts of information require appropriate offloading decisions. This paper investigates how non-orthogonal multiple access (NOMA) can be deployed for IoT networks in order to enable many devices for transmitting information of the load and the market to a similar FN simultaneously, in a similar frequency range, and in the same code domain. Several IoT devices are optimized together based on their resource allocation and transmission power in this paper while meeting the QoS standards. Additionally, the optimization problem can be expressed in the form of a mixed-integer nonlinear programming problem with the goal of minimizing the system's power usage. The problem would be NP-hard so a whale optimization algorithm (WOA) has been proposed for solving. According to simulation outcomes, the suggested architecture performs well in terms of throughput, delays, blackout likelihood, and power usage.

关键词： Energy consumption Resource allocation whale optimization algorithm Internet of things Fog computing

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Temperature Compensation Strategy of Fiber Bragg Grating Sensor Based on Improved Neural Network optimization algorithm 3

Temperature Compensation Strategy of Fiber Bragg Grating Sen...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Liu, Zhong Lv, Qiancheng Li, Peng Tan, Zejie Tian, Bing Luo, Baifeng China Southern Power Grid Digital Grid Research Institute Guangzhou China

ISBN: (纸本)9798350341546

Fiber Bragg grating (FBG) sensors are widely used in the field of transmission line ice monitoring, but the cross-sensitivity of temperature and stress seriously affects their measurement accuracy. To address this problem, this paper proposed a temperature compensation strategy based on the improved back propagation (BP) neural network algorithm. A whale optimization algorithm is used to optimize the BP neural network to improve its performance for eliminating the influence of cross-sensitivity of temperature and stress on FBG sensors. To address the issue of a small sample size, a K-fold cross validation method is adopted to improve the reliability of network prediction. The simulation verifies that the proposed strategy could effectively achieve higher accuracy in monitoring the icing thickness of transmission lines. © 2023 IEEE.

关键词： Fiber optic sensor ice covering monitoring K-fold cross validation neural network whale optimization algorithm

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Test scheduling of network-on-chip using hybrid WOA-GWO algorithm

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INTELLIGENT DATA ANALYSIS 2025年第3期29卷 769-786页

作者： Sadesh, S. Chandrasekaran, Gokul Thangaraj, Rajasekaran Kumar, Neelam Sanjeev Velalar Coll Engn & Technol Dept Artificial Intelligence & Data Sci Erode India Karpagam Inst Technol Dept Elect & Commun Engn Coimbatore Tamil Nadu India Nandha Engn Coll Dept Comp Sci & Engn Erode India SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

The promising Network-on-Chip (NoC) model replaces the existing system-on-chip (SoC) model for complex VLSI circuits. Testing the embedded cores using NoC incurs additional costs in these SoC models. NoC models consist of network interface controllers, Internet Protocol (IP) data centers, routers, and network connections. Technological advancements enable the production of more complex chips, but longer testing times pose a potential problem. NoC packet switching networks provide high-performance interconnection, a significant benefit for IP cores. A multi-objective approach is created by integrating the benefits of the whale optimization algorithm (WOA) and Grey Wolf optimization (GWO). In order to minimize the duration of testing, the approach implements optimization algorithms that are predicated on the behavior of grey wolves and whales. The P22810 and D695 benchmark circuits are under consideration. We compare the test time with existing optimization techniques. We assess the effectiveness of the suggested hybrid WOA-GWO algorithm using fourteen established benchmark functions and an NP-hard problem. This proposed method minimizes the time needed to test the P22810 benchmark circuit by 69%, 46%, 60%, 19%, and 21% compared to the Modified Ant Colony optimization, Modified Artificial Bee Colony, WOA, and GWO algorithms. In the same vein, the proposed method reduces the testing time for the d695 benchmark circuit by 72%, 49%, 63%, 21%, and 25% in comparison to the same algorithms. We experimented to determine the time savings achieved by adhering to the suggested procedure throughout the testing process.

关键词： Test time whale optimization algorithm test access mechanism grey wolf optimization test scheduling network-on-chip

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