A novel MPPT technology based on dung beetle optimization algorithm for PV systems under complex partial shade conditions

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Март 18, 2024

Abstract Solar power is a renewable energy source, and its efficient development utilization are important for achieving global carbon neutrality. However, partial shading conditions cause the output of PV systems to exhibit nonlinear multipeak characteristics, resulting in loss power. In this paper, we propose novel Maximum Power Point Tracking (MPPT) technique based on Dung Beetle Optimization Algorithm (DBO) maximize under various weather conditions. We performed performance comparison analysis DBO with existing renowned MPPT techniques such as Squirrel Search Algorithm, Cuckoo search Optimization, Horse Herd Particle Swarm Adaptive Factorized Gray Wolf Hybrid Nelder-mead. The experimental validation carried out HIL + RCP physical platform, which fully demonstrates advantages terms tracking speed accuracy. results show that proposed achieves 99.99% maximum point (GMPP) efficiency, well improvement 80% convergence rate stabilization rate, 8% average A faster, more robust GMPP significant contribution controller.

Язык: Английский

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Port berth allocation and microgrid cluster joint optimization scheduling based on master-slave game

Journal of Cleaner Production, Год журнала: 2024, Номер 452, С. 142220 - 142220

Опубликована: Апрель 11, 2024

Язык: Английский

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Improving frequency regulation ability for a wind-thermal power system by multi-objective optimized sliding mode control design

Energy, Год журнала: 2024, Номер 300, С. 131535 - 131535

Опубликована: Май 6, 2024

Язык: Английский

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Dung Beetle Optimization Algorithm Based on Improved Multi-Strategy Fusion

Electronics, Год журнала: 2025, Номер 14(1), С. 197 - 197

Опубликована: Янв. 5, 2025

The Dung Beetle Optimization Algorithm (DBO) is characterized by its great convergence accuracy and quick speed. However, like other swarm intelligent optimization algorithms, it also has the disadvantages of having an unbalanced ability to explore world use local resources, as well being prone settling into optimal search in latter stages optimization. In order address these issues, this research suggests a multi-strategy fusion dung beetle method (MSFDBO). To enhance quality first solution, refractive reverse learning technique expands algorithm space stage. algorithm’s increased adding adaptive curve control population size prevent from reaching optimum. improve balance exploitation global exploration, respectively, triangle wandering strategy subtractive averaging optimizer were later added Rolling Breeding Beetle. Individual beetles will congregate at current position, which near value, during last stage MSFDBO; however, value could not be value. Thus, variationally perturb solution (so that leaps out final MSFDBO) algorithmic performance (generally specifically, effect optimizing search), Gaussian–Cauchy hybrid variational perturbation factor introduced. Using CEC2017 benchmark function, MSFDBO’s verified comparing seven different intelligence algorithms. MSFDBO ranks terms average performance. can lower labor production expenses associated with welding beam reducer design after testing two engineering application challenges. When comes lowering manufacturing costs overall weight, outperforms methods.

Язык: Английский

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Multi-orthogonal-oppositional enhanced African vultures optimization for combined heat and power economic dispatch under uncertainty

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Янв. 9, 2025

Язык: Английский

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Time series modeling and forecasting with feature decomposition and interaction for prognostics and health management in nuclear power plant

Energy, Год журнала: 2025, Номер unknown, С. 135784 - 135784

Опубликована: Апрель 1, 2025

Язык: Английский

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A reconstruction-based secondary decomposition-ensemble framework for wind power forecasting

Energy, Год журнала: 2024, Номер 308, С. 132895 - 132895

Опубликована: Авг. 19, 2024

Язык: Английский

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Short-term wind power prediction method based on multivariate signal decomposition and RIME optimization algorithm

Expert Systems with Applications, Год журнала: 2024, Номер 259, С. 125376 - 125376

Опубликована: Сен. 11, 2024

Язык: Английский

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Research on Indoor Environment Prediction of Pig House Based on OTDBO–TCN–GRU Algorithm

Animals, Год журнала: 2024, Номер 14(6), С. 863 - 863

Опубликована: Март 11, 2024

Temperature and humidity, along with concentrations of ammonia hydrogen sulfide, are critical environmental factors that significantly influence the growth health pigs within porcine habitats. The ability to accurately predict these variables in pig houses is pivotal, as it provides crucial decision-making support for precise targeted regulation internal conditions. This approach ensures an optimal living environment, essential well-being healthy development pigs. existing methodologies forecasting currently hampered by issues low predictive accuracy significant fluctuations To address challenges this study, a hybrid model incorporating improved dung beetle algorithm (DBO), temporal convolutional networks (TCNs), gated recurrent units (GRUs) proposed prediction optimization barns. enhances global search capability DBO introducing Osprey Eagle (OOA). uses initially fit time-series data factors, subsequently combines long-term dependence capture TCNs non-linear sequence processing GRUs residuals fit. In concentration, OTDBO–TCN–GRU shows excellent performance mean absolute error (MAE), square (MSE), coefficient determination (R2) 0.0474, 0.0039, 0.9871, respectively. Compared DBO–TCN–GRU model, achieves reductions 37.2% 66.7% MAE MSE, respectively, while R2 value 2.5%. OOA achieved 48.7% 74.2% MSE metrics, 3.6%. addition, has less than 0.3 mg/m3 gases compared other algorithms, on sudden changes, which robustness adaptability prediction. Therefore, optimizes factor time series offers substantial decision control houses.

Язык: Английский

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Dynamic constitutive identification of concrete based on improved dung beetle algorithm to optimize long short-term memory model

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Март 15, 2024

In order to improve the accuracy of concrete dynamic principal identification, a identification model based on Improved Dung Beetle Algorithm (IDBO) optimized Long Short-Term Memory (LSTM) network is proposed. Firstly, apparent stress-strain curves containing damage evolution were measured by Split Hopkinson Pressure Bar (SHPB) test decouple and separate rheology, this system was modeled using LSTM network. Secondly, for problem low convergence easy fall into local optimum (DBO), greedy lens imaging reverse learning initialization population strategy, embedded curve adaptive weighting factor PID control optimal solution perturbation strategy are introduced, superiority IDBO algorithm proved through comparison optimization with DBO, Harris Hawk Optimization Algorithm, Gray Wolf Fruit Fly combination built construct IDBO-LSTM homeostasis model. The final results show that can recognize material without considering damage; in case damage, prediction basically match SHPB curves, which proves feasibility excellence proposed method.

Язык: Английский

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