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Non-equidistant non-homogenous grey prediction model with fractional accumulation and its application

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期40卷 11861-11874页

作者： Zhu, Jia-Nian Liu, Xu-Chong Liu, Chong Xiangtan Univ Sch Publ Management Xiangtan Peoples R China Hunan Police Acad Hunan Publ Secur Sci & Technol Res Inst Changsha Peoples R China Inner Mongolia Agr Univ Sch Sci Hohhot Peoples R China

Non-equidistant non-homogenous grey model (abbreviated as NENGM (1,1, k) model) is a grey prediction model suitable for predicting time series with non-equal intervals. It is widely used in various fields of society due to its high prediction accuracy and strong adaptability. In order to further improve the prediction accuracy of the NENGM (1,1, k) model, the NENGM (1,1, k) model is optimized in terms of the cumulative order and background value of the NENGM (1,1, k) model, and a NENGM (1,1, k) model based on double optimization is established (abbreviated as FBNENGM (1,1, k) model), and the whale optimization algorithm is used to solve the best parameters of the model. In order to verify the feasibility and validity of the FBNENGM (1,1, k) model, the FBNENGM (1,1, k) model and other four prediction models are applied to three cases respectively, and three indexes commonly used to evaluate the performance of prediction models are used to distinguish. The results show that the prediction accuracy of the FBNENGM (1,1, k) model based on double optimization is better than other prediction models.

关键词： Grey system theory fractional-order accumulation grey prediction model FBNENGM (1, 1, k) model whale optimization algorithm

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Near-Optimal PI Controllers of STATCOM for Efficient Hybrid Renewable Power System

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IEEE ACCESS 2021年 9卷 34119-34130页

作者： Mosaad, Mohamed I. Ramadan, Haitham Saad Mohamed Aljohani, Mansour El-Naggar, Mohamed F. Ghoneim, Sherif S. M. Yanbu Ind Coll YIC Yanbu 46452 Saudi Arabia ISTHY Inst Int Stockage Hydrogene F-90400 Meroux Moval France Zagazig Univ Elect Power & Machines Dept Fac Engn Zagazig 44519 Egypt Prince Sattam Bin Abdulaziz Univ Dept Elect Engn Coll Engn Al Kharj 16273 Saudi Arabia Helwan Univ Dept Elect Power & Machines Engn Fac Engn Helwan 11795 Egypt Taif Univ Elect Engn Dept Coll Engn At Taif 21944 Saudi Arabia

Connecting different renewable energy sources (RESs) to the electrical grids is presently being urged to fulfill the enormous need for electric power and to decrease traditional sources' ecological related issues, the so-called hybrid systems. Unfortunately, these hybrid systems suffer from the possible negative environmental impacts of the wind gusts in wind energy conversion systems (WECSs) that may degrade the overall system performance. Additionally, various severe faults may disconnect some RESs from the hybrid system, like three-phase faults. In this paper, the static synchronous compensator (STATCOM) is considered for both improving the performance of a hybrid system, contains WECS and photovoltaics (PVs) against wind gusts and maintaining the continuous operations of RESs during three-phase fault occur at the point of common coupling (PCC) between the RESs and the grid. The STATCOM is stimulated by two PI controllers regulating the reactive power flow between the STATCOM and the hybrid system at PCC and, consequently, regulating the voltage at PCC. A metaheuristic optimizer optimally schedules these two PI controllers based on whale optimization algorithm (WOA). The impartial comparison between the WOA dynamic performance and the particle swarm optimization as another optimization algorithm verifies the efficiency of the WOA for the near-optimal gain scheduling of the PI controller gains.

关键词： Automatic voltage control PI control Reactive power optimization Hybrid power systems Voltage fluctuations Couplings Renewable energy hybrid power systems wind energy PV STATCOM PI controller reactive power regulation whale optimization algorithm

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Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19

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Computers, Materials & Continua 2021年第5期67卷 2353-2371页

作者： Sally M.Elghamrawy Aboul Ella Hassnien Vaclav Snasel MISR Higher Institute for Engineering and Technology MansouraEgypt Faculty of Computers and Information Cairo UniversityCairoEgypt VSB-Technical University of Ostrava Czech Republic

Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected *** study aimed to develop a COVID-19 diagnosis and prediction(AIMDP)model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography(CT)*** proposed system uses convolutional neural networks(CNNs)as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its *** employed the whale optimization algorithm(WOA)to select the most relevant patient signs.A set of experiments validated AIMDP *** demonstrated the superiority of AIMDP in terms of the area under the curve-receiver operating characteristic(AUC-ROC)curve,positive predictive value(PPV),negative predictive rate(NPR)and negative predictive value(NPV).AIMDP was applied to a dataset of hundreds of real data and CT images,and it was found to achieve 96%AUC for diagnosing COVID-19 and 98%for overall *** results showed the promising performance of AIMDP for diagnosing COVID-19 when compared to other recent diagnosing and predicting models.

关键词： Convolutional neural networks coronavirus disease 2019(COVID-19) CT chest scan imaging deep learning technique feature selection whale optimization algorithm

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Improving convergence in swarm algorithms by controlling range of random movement

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NATURAL COMPUTING 2021年第3期20卷 513-560页

作者： Chaudhary, Reshu Banati, Hema Univ Delhi Dept Comp Sci New Delhi India Univ Delhi Dyal Singh Coll New Delhi India

Swarm intelligence algorithms are stochastic algorithms, i.e. they perform some random movement. This random movement imparts the algorithms with exploration capabilities and allows them to escape local optima. Exploration at the start of execution helps with thorough inspection of the search/solution space. However, as the algorithm progresses, the focus should ideally shift from exploration to exploitation. This shift would help the algorithm to enhance existing solutions and improve its convergence capabilities. Hence if the range of random movement is not kept in check, it may limit an algorithm's convergence capabilities and overall efficiency. To ensure that the convergence of an algorithm is not compromised, an improved search technique to reduce range of uniform random movement was recently proposed for bat algorithm. Uniform distribution and levy distribution are the most commonly used random distributions in swarm algorithms. In this paper, the applicability of the improved search technique over different swarm algorithms employing uniform and levy distributions, as well as Cauchy distribution has been studied. The selected algorithms are firefly algorithm, cuckoo search algorithm, moth search algorithm, whale optimization algorithm, earthworm optimization algorithm and elephant herding optimization algorithm. The resultant variants of each of these algorithms show improvement upon inclusion of the improved search technique. Hence results establish that the improved search technique has positive influence over swarm algorithms employing different random distributions.

关键词： Exploitation Improved search Moth search algorithm Cuckoo search algorithm Firefly algorithm whale optimization algorithm Earthworm optimization algorithm Elephant herding optimization algorithm

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Category-Based Multiobjective Approach for Optimal Integration of Distributed Generation and Energy Storage Systems in Distribution Networks

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IEEE ACCESS 2021年 9卷 28237-28250页

作者： Adetunji, Kayode E. Hofsajer, Ivan W. Abu-Mahfouz, Adnan M. Cheng, Ling Univ Witwatersrand Johannesburg Sch Elect & Informat Engn ZA-2000 Johannesburg South Africa CSIR ZA-0184 Pretoria South Africa

Distributed generation (DG) units are power generating plants that are very important to the architecture of present power system networks. The primary benefits of the addition of these units are to increase the power supply and improve the power quality of a power grid while considering the investment cost and carbon emission cost. Most studies have simultaneously optimized these objectives in a direct way where the objectives are directly infused into the multiobjective framework to produce final values. However, this method may have an unintentional bias towards a particular objective;hence this paper implements a multi-stage framework to handle multiple objectives in a categorical manner to simultaneously integrate DG units and Battery Energy Storage System (BESS) in a distribution network. A new hybrid metaheuristic technique is developed and combined with the Technique Order for Preference by Similarity to Ideal Solution (TOPSIS) approach and the crowding distance technique to produce Pareto optimal solutions from the multiple collective objectives, namely technical, economic, and environmental. Compared to the conventional direct way approach in multiobjective handling, the proposed categorical approach reduces bias towards a set of objective(s) and efficiently handles more objectives. Results also show that the whale optimization algorithm and Genetic algorithm (WOAGA) produces the smallest power loss of 101.6 kW compared to whale optimization algorithm (WOA) and Genetic algorithm (GA), which produces 105.1 kW and 105.8 kW respectively. The algorithm, although does not have a faster convergence than the WOA, has a better computational time than the WOA and GA. The multiobjective WOAGA also performs better than the Non-dominating Sorted Genetic algorithm (NSGA-II) and the multiobjective WOA in terms of the quality of Pareto optimal solutions.

关键词： optimization Distribution networks Pareto optimization Genetic algorithms Renewable energy sources Minimization Linear programming Battery energy storage system (BESS) hybrid metaheuristic algorithm distribution networks multiobjective optimization renewable energy whale optimization algorithm genetic algorithm pareto optimal solutions

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Optimal design of a BLDC motor using African vulture optimization algorithm

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 7卷

作者： Tripathy, Surya Narayan Kundu, Shubhasri Pradhan, Arjyadhara Samal, Padarbinda School of Electrical Engineering KIIT Deemed to be University India

BLDC motors are extensively favoured in robots, electric vehicles, and many industrial applications due to their superior torque, efficiency, and speed control features as compared to traditional motors. In this paper, collective intelligence-based optimization approaches are used to improve the cost-effectiveness, volume, and total loss of a BLDC motor. The geometric dimensions of the motor are considered the optimization parameters. The study employs several optimization algorithms, such as the whale optimization algorithm (WOA), Grey Wolf optimization (GWO), Cuckoo Search algorithm (COA), Genetic algorithm (GA), Particle Swarm optimization (PSO), and the African Vulture optimization algorithm (AVOA), used for the optimal BLDC motor design. The investigation can provide data on the convergence rates and optimal design parameters of a BLDC motor through optimization approaches. The standard deviation has also been calculated for each optimization technique to assess the stability and consistency of finding the optimum solution. The paper also includes a sensitivity analysis of the AVOA regulating parameter to obtain the best solutions. In comparison to GA, PSO, COA, GWO, and WOA, the AVOA technique achieves faster convergence. Additionally, the ANOVA test is employed as a statistical test to validate the efficacy of the AVOA. According to the results, AVOA outperforms all other optimization algorithms for the design of the BLDC motor. © 2024 The Author(s)

关键词： Brush-less DC motors Cuckoo search algorithm Particle swarm optimization algorithm whale optimization algorithm

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A novel dynamic grey multivariate prediction model for multiple cumulative time-delay shock effects and its application in energy emission forecasting

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 251卷

作者： Li, Xuemei Zhang, Beijia Zhao, Yufeng Zhang, Yi Zhou, Shiwei Ocean Univ China Sch Econ Qingdao 266100 Peoples R China Ocean Univ China Inst Marine Dev Qingdao 266100 Peoples R China Ocean Univ China Sch Management Qingdao 266100 Peoples R China Ocean Univ China Sch Int Affairs & Publ Adm Qingdao 266100 Peoples R China 238 Songling Rd Qingdao City Shandong Prov Peoples R China

Energy emission systems are often influenced by external information and policy shock. However, conventional grey models ignore the existence of the impact of multiple shock events and cumulative time -delay effects, which are crucial for achieving accurate forecasts. From a dynamic perspective, we aim to establish a multivariable grey prediction model considering the impact of multiple shock events, namely MSGTDM(1,N) model. Specifically, to accurately describe the impact of different types of external shocks on the system, three kinds of nonlinear dynamic shock functions are designed, including growth dynamic shock function, decline dynamic shock function, and slope dynamic shock function. Based on this, multi -dimensional instantaneous shock utility terms and cumulative time -delay utility terms are constructed to accurately measure shock effects. And whale optimization algorithm is employed to determine the optimal parameters of the shock functions through comprehensive comparative analysis. The findings affirm the MSGTDM(1,N) model ' s higher predictive accuracy and validity. Consequently, the forecast results of China ' s carbon emissions from coal and natural gas consumption in 2022 - 2025 provide a reliable basis for adjusting the energy structure and implementing the " dual -carbon " policy effectively.

关键词： Grey multivariable model Multiple shock effects Cumulative time -delay effect Dynamic shock function whale optimization algorithm Energy carbon emission

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Failure-aware resource provisioning for hybrid computation offloading in cloud-assisted edge computing using gravity reference approach

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： Khaleel, Mustafa Ibrahim Univ Sulaimani Coll Sci Dept Comp Sulaymaniyah 46001 Iraq

This paper tackles the challenges of computation offloading in the cloud-edge paradigm. Although many solutions exist for enhancing the server's computational and communication efficiency, they mainly focus on reducing latency and often neglect the impact of overlapping multi-request processing on scheduling reliability. Additionally, these approaches do not account for the preemptive characteristics of applications running in the VMs that lead to higher energy consumption. We propose a novel hybrid integer multi-objective dynamic decision-making approach enhanced with the gravity reference point method. This method determines the proportion of computations executed on cloud servers versus those handled locally on edge servers. Our hybrid approach leverages the gravitational potential reference point and crowding degrees to improve the characteristics of whale populations, addressing the limitations of the traditional whale algorithm, which depends on individual whales' varying foraging behaviors influenced by a random probability number. By evaluating the crowding level around the prey, the foraging behavior of individual whales is adjusted to enhance the algorithm's convergence speed and optimization accuracy, thereby increasing its reliability. The results show that our hybrid computation offloading model significantly improves time latency by 76.45%, energy efficiency by 63.12%, reliability by 82%, quality of service by 83.78%, distributor throughput by 87.31%, asset availability by 73.05%, and guarantee ratio by 89.72% compared to traditional offloading methods.

关键词： whale optimization algorithm DAG-structured workflow Cloud computing Task placement Gravity reference model Offloading computation approach

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Multi-step ahead forecasting of daily urban gas load in Chengdu using a Tanimoto kernel-based NAR model and whale optimization

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ENERGY 2022年第0期260卷

作者： Deng, Yanqiao Ma, Xin Zhang, Peng Cai, Yubin Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Peoples R China Southwest Univ Sci & Technol Sch Econ & Management Mianyang 621010 Peoples R China Southwest Univ Sci & Technol Ctr Informat Management & Serv Studies Sichuan Mianyang 621010 Peoples R China Univ Elect Sci & Technol China Sch Math Sci Chengdu 611731 Peoples R China

Coal-to-gas switching in urban areas plays an important role in accelerating the pace of carbon neutrality. Accurate urban gas load forecasting is beneficial in balancing the peak-valley difference and achieving high-efficiency gas utilization. This work aims to develop a new method based on Tanimoto kernel-based nonlinear autoregressive (NAR) model for dynamical modelling. The Tanimoto kernel is extended to be available for regression modelling for the first time, and of which some important properties are analyzed. Besides, a new optimization scheme based on holdout validation and whale optimization algorithm is introduced for hyperparameter optimization. Then, the proposed Tanimoto kernel-based NAR model is applied for 5-step ahead forecasting with four regular lags 6, 9, 12, and 24 of short-(2015/1/1-2015/12/31), medium-(2014/1/1-2015/12/31), and long-(2013/1/1-2015/12/31) term daily urban gas load (UGL) in Chengdu. Results show that the proposed Tanimoto kernel-based model always produces higher precision in 80% of sub-cases than the other 11 kernel models and 8 machine learning models, with the one-step ahead forecasting mean absolute percentage error (MAPE) ranging from 2.3375% to 3.8765%, less than the other models ranging from 0.2335% to 34.5432%, and the proposed optimization scheme is efficient in improving the model's generalization ability and robustness.

关键词： Daily urban gas load forecasting Least squares support vector regression The Tanimoto kernel Nonlinear autoregressive model whale optimization algorithm

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Noise reduction method of plastic pipe leakage signal based on WOA-VMD algorithm 9

Noise reduction method of plastic pipe leakage signal based ...

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9th International Forum on Electrical Engineering and Automation (IFEEA)

作者： Chen, Mingyu Zhang, Xiaoxiao Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264026 Peoples R China

ISBN: (纸本)9781665464215

Aiming at the problem that it is difficult to select the appropriate number of decompositions K and penalty factor alpha for the denoising of plastic pipe leakage signal by variational modal decomposition (VMD) algorithm. In this paper, a noise reduction method based on whale optimization algorithm (WOA) combined with VMD algorithm was proposed. First, the permutation entropy can reflect the complexity of the one-dimensional time series, and the decomposition result with the smallest permutation entropy is taken as the optimization objective;then the permutation entropy is used as a fitness function combined with WOA to optimize the parameters of the VMD algorithm to realize the adaptive decomposition of the leakage signal;Finally, calculate the correlation coefficient of each intrinsic mode function (IMF) component after decomposition to judge its noise level, remove the high frequency component which contains much noise, so as to realize the noise reduction of pipeline leakage signal. The simulate experimental results show that the method has a good denoising effect on the leakage signal of plastic pipeline, and can effectively improve the signal-to-noise ratio (SNR) of the leakage signal, which proves its feasibility in the actual leakage detection of plastic pipeline.

关键词： variational modal decomposition whale optimization algorithm correlation coefficient leak detection

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