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6th Global Power, Energy and Communication Conference (GPECOM)

作者： Ayrir, Wiam Helmi, Ahmed. M. Ramadan, Haitham S. Ibn Tofail Univ Natl Sch Appl Sci Elect Engn Networks & Telecommun Syst Dept Kenitra Morocco Buraydah Private Coll Coll Engn & Informat Technol Comp Engn Dept Buraydah Saudi Arabia Zagazig Univ Fac Engn Comp & Syst Engn Zagazig Egypt Zagazig Univ Elect Power & Machines Dept Zagazig Egypt Inst Int Stockage Hydrogene ISTHY F-90400 Belfort France

ISBN: (纸本)9798350351088;9798350351095

This paper presents an in-depth study on the reconfiguration of power distribution networks (PDN) in two interconnected microgrids using a recent bio-based optimization algorithm, the artificial hummingbird algorithm, to minimize power losses and improve voltage profile within interconnected microgrids while keeping the radial topology in each microgrid and within the two. The proposed approach is validated by simulation on two interconnected 20-bus systems, demonstrating its effectiveness in achieving a balance between minimizing losses and improving voltage profile while reducing the cost function. Additionally, the results provide valuable insights into the field of PDN reconfiguration, providing a practical framework to optimize network performance and promote sustainable energy management.

关键词： Power distribution network reconfiguration interconnected microgrids artificial hummingbird algorithm optimization power losses

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Multi-objective Optimization Based Bidding Strategy for DER Aggregators in Ancillary Services Market Considering Environmental Benefits

Multi-objective Optimization Based Bidding Strategy for DER ...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Sun, Qinfei Wang, Liyong Zhu, Xinyu Cao, Xin Ge, Xinxin Dai, Haonan Wang, Ge Wang, Fei State Grid Beijing Electric Power Research Institute Beijing100075 China North China Electric Power University Department of Electrical Engineering Baoding071003 China

ISBN: (纸本)9781665471640

Considering the features of distributed energy resources (DER) (i.e., small capacity, scatter, and uncertainty), DERs are supposed to be managed by distributed energy resources aggregators (DERA). On the basis of the complementarity of the various DERs, DERA can dig out the flexibility of the DER to support the power system operation via bidding in the ancillary services market. It's essential for DERA to get maximum benefit through making optimal bidding strategies. To this end, this paper proposes an optimal bidding strategy based on a multi-objective planning approach. Firstly, DERA aggregates multiple resources (distributed generation, demand response, and energy storage systems) to participate in the ancillary services market in order to maximize benefits. In addition, the impact of the energy market on its bidding program is considered. Secondly, the environmental benefits (consumption of renewable energy and pollutant emissions) of DER are considered. The multi-objective bidding strategy is solved using an improved artificial hummingbird algorithm (MOAHA). In MOAHA, a dynamic elimination-based crowding distance (DECD) method is developed to refine Pareto optimal solutions for improving the convergence of the algorithm. Case studies verified the effectiveness of the proposed model using the real-world data of the PJM market. © 2023 IEEE.

关键词： Ancillary Services Market artificial hummingbird algorithm Bidding Strategy Carbon emission DER Aggregator Distributed Energy Resources

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Overhead crane bridge structure design optimization based on safety assessment

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2024年

作者： He, Bin Zhao, Zhangyan Qiu, Faju Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan Peoples R China Ningbo Special Equipment Inspection & Res Inst Ningbo Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Peoples R China

In order to achieve safe and economical design results for crane bridge structures, an optimization method with safety assessment constraints is proposed. In this approach, the limit state method is used to test the structure design. Safety assessment is conducted based on the fuzzy analytic network process, allowing the safety score of designs to be quantified. Aiming to minimize the self-weight of the bridge, the dimensions of critical sections are regarded as design variables and constraints related to the process size, strength, stiffness, stability, and safety score are imposed, utilizing the artificial hummingbird algorithm for the structure optimal design. After validating through an engineering example, the results demonstrated that under the settled safety constraints, the proposed optimization method successfully reduced the structural self-weight by 19.709%, while maintaining a safety score proximate to the original design. In comparison to optimization without safety assessment constraints, this method resulted in a 10.441% increase in weight, but its safety was significantly improved by 21.740%. This validates the effectiveness and practicality of incorporating safety assessment into structure optimization, thereby ensuring a balanced trade-off between safety and material economy in the design of crane bridge structures.

关键词： Overhead crane bridge structure safety assessment artificial hummingbird algorithm structure design

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Parameter estimation of PEM fuel cells using metaheuristic algorithms

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MEASUREMENT 2024年 237卷

作者： Li, Xuebin Jin, Zhao Yu, Daiwei Zhang, Jun Zhang, Wenjin Wuhan 2nd Ship Design & Res Inst Wuhan 430205 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

Developing an accurate model for precise parameter estimation is crucial for the design of polymer electrolyte membrane fuel cells (PEMFC). While many studies can meet the evaluation criteria for parameter estimation, there is a significant physical inconsistency when applying the Generalized Steady State of Electrochemical Model (GSSEM) prediction in the optimization framework. This study aims to address this inconsistency by adding necessary constraints to the optimization model. It employs the artificial hummingbird algorithm (AHA) and Levy flight improvement to find unknown parameters by minimizing the sum of squared errors (SSE). The proposed method has been rigorously tested with seven PEMFC cases, demonstrating its robustness and superior accuracy compared to alternative approaches. Notably, the cathode charge transfer coefficient exhibits zero relative percent difference (RPD) between the two sources, confirming its physical consistency. Furthermore, the proposed constrained optimization model lays the groundwork for achieving consistent parameter estimation results for PEMFC when utilizing GSSEM.

关键词： PEMFC Parameter estimation Constrained optimization artificial hummingbird algorithm Levy flights

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A novel hybrid model based on multiple influencing factors and temporal convolutional network coupling ReOSELM for wind power prediction

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ENERGY CONVERSION AND MANAGEMENT 2024年 313卷

作者： Ge, Yida Zhang, Chu Wang, Yiwei Chen, Jie Wang, Zheng Nazir, Muhammad Shahzad Peng, Tian Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Huaiyin Inst Technol Jiangsu Permanent Magnet Motor Engn Res Ctr Huaian 223003 Peoples R China

Highly accurate wind power prediction is important for the operation and management of wind farms, energy utilization and power supply. In recent years, in order to further improve prediction accuracy, some researchers have considered utilizing factors such as wind speed and wind direction to establish prediction models. However, this often results in input redundancy due to a lack of technical means. In this paper, a multivariate prediction model for wind power based on variational mode decomposition (VMD), fuzzy entropy (FE), partial autocorrelation function and cross -correlation function (PACF &CCF, abbreviated as PC), improved artificial hummingbird algorithm (IAHA), temporal convolutional network (TCN), and regularized online sequential extreme learning machine (ReOSELM) is proposed. To address the nonlinearity and non-stationarity of the original wind power time series and decrease computational costs, the VMD combined with FE data preprocessing technique is employed. Considering the information redundancy in the multivariate data, the PC technique is utilized to conduct correlation analysis on the input data and select highly correlated features as inputs. When the input is multivariate and high -dimensional, the ReOSELM model struggles to achieve satisfactory prediction performance. Therefore, a new model based on TCN-ReOSELM is constructed. Additionally, to overcome the drawbacks of manual parameter adjustment, an IAHA algorithm is proposed, which integrates Latin hypercube sampling and chaotic local search strategies, to optimize the crucial parameters of TCN-ReOSELM. The experimental results demonstrate that the multivariate method proposed in this paper reduces the RMSE by 31% compared to the univariate method. Specifically, the proposed VMD-FE-PC-TCN-ReOSELM model reduces the RMSE, MAE, and SMAPE by 60 - 64%, 55 - 58%, and 10 - 36%, respectively, compared to the PC-TCN-ReOSELM model.

关键词： VMD artificial hummingbird algorithm TCN ReOSELM Wind power prediction

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A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment

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SENSORS 2023年第14期23卷 6614-6614页

作者： Li, Zhen Feng, Feng Ningxia Univ Sch Informat Engn Yinchuan 750021 Peoples R China

Coal is an important resource that is closely related to people's lives and plays an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. Therefore, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and a simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model is established using the improved algorithm to classify and predict the safety of the coal mine environment as one of four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved and that the performance of the IAHA-SVM model is significantly improved.

关键词： coal mine safety artificial hummingbird algorithm Tent chaotic mapping Levy flight simplex method support vector machine

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Review, design, stabilization and synchronization of fractional-order energy resources demand-supply hyperchaotic systems using fractional-order PD-based feedback control scheme

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ARCHIVES OF CONTROL SCIENCES 2023年第3期33卷 539-563页

作者： Soukkou, Ammar Soukkou, Yassine Haddad, Sofiane Benghanem, Mohamed Rabhi, Abdelhamid Univ MSBY Jijel Fac Sci & Technol Dept Elect Renewable Energy Lab BP 98Ouled Aissa Jijel Algeria Res Ctr Ind Technol CRTI POB 64 Algiers 16014 Algeria Islamic Univ Madinah Fac Sci Phys Dept Madinah Saudi Arabia Univ Picardie Jules Verne Informat & Syst Lab Amiens France

This paper introduces a fractional-order PD approach (F-oPD) designed to control a large class of dynamical systems known as fractional-order chaotic systems (F-oCSs). The design process involves formulating an optimization problem to determine the parameters of the developed controller while satisfying the desired performance criteria. The stability of the control loop is initially assessed using the Lyapunov's direct method and the latest stability assumptions for fractional-order systems. Additionally, an optimization algorithm inspired by the flight skills and foraging behavior of hummingbirds, known as the artificial hummingbird algorithm (AHA), is employed as a tool for optimization. To evaluate the effectiveness of the proposed design approach, the fractional-order energy resources demand-supply (Fo-ERDS) hyperchaotic system is utilized as an illustrative example.

关键词： fractional-order systems fractional-order control Lyapunov stability theory multiobjective optimization artificial hummingbird algorithm stabilization and synchronization energy resources demand-dupply systems

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On the facile and accurate determination of the highly accurate recent methods to optimize the parameters of different fuel cells: Simulations and analysis

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ENERGY 2023年第1期272卷

作者： Abdel-Basset, Mohamed Mohamed, Reda Abouhawwash, Mohamed Zagazig Univ Fac Comp & Informat Dept Comp Sci Zagazig 44519 Egypt Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt Michigan State Univ Coll Engn Dept Computat Math Sci & Engn CMSE East Lansing MA USA

The proton exchange membrane fuel cell (PEMFC) is a potential source of renewable energy that offers a dual benefit of reducing environmental pollution and enabling easy electricity savings. The mathematical model of PEMFC involves several unknown parameters that need to be precisely estimated for developing an accurate model. This process of estimating parameters is known as the parameter estimation of PEMFC and is considered an optimization problem. Although the problem of parameter estimation for PEMFC belongs to the category of optimization problems, it cannot be solved by all optimization techniques as it is a complex and nonlinear problem. Therefore, this paper presents a new parameter estimation technique based on adopting a recently published metaheuristic algorithm known as the artificial hummingbird algorithm (AHA). AHA is simple and easy to implement as its main advantages encourage us to adopt it for tackling this problem. However, unfor-tunately, AHA suffers from slow convergence speed and hence will consume a huge number of function evalu-ations even reaching the desired outcomes. Therefore, two improvements have been applied to the classical AHA for proposing a new variant , namely IAHA, for overcoming the parameter estimation of PEMFC stacks. IAHA was applied to estimate the unknown parameters of six different PEMFC stacks and compared with 11 well-known competing optimizers in terms of accuracy of outcomes, convergence speed, stability, and CPU time. Based on the experimental results, IAHA outperforms all other algorithms across all performance parameters except for CPU time, which is on par with the other methods.

关键词： Fuel cells PEMFC artificial hummingbird algorithm Modeling

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Effects of emission reduction and rework policy in a production system of green products: An interval valued optimal control theoretic approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期179卷

作者： Das, Subhajit Mandal, Goutam Manna, Amalesh Kumar Shaikh, Ali Akbar Bhunia, Asoke Kumar Univ Burdwan Dept Math Burdwan 713104 India CV Raman Global Univ Dept Math Bhubaneswar 752054 India Univ Engn & Management Kolkata Dept Basic Sci & Humanities Kolkata 700160 W Bengal India

Over the last few decades, the implementation of emission regulation by the government due to the impact of emissions from the industrial sector on the environment has included a rough demotivation among the small and moderate manufacturing industries. In the recent market situation, it is observed that, consumers much prefer to purchase eco-friendly products. Further, in the highly imprecise competitive market, it is rather a complicated task for a competent authority/operator of every manufacturing company to make an appropriate decision regarding marketing policy of the firm. Taking these three factors into account, this work aims to formulate the model of a production system that can benefit such manufacturers who are dealing with these three issues. This model is developed using interval modelling technique, and it is solved by using optimal control in interval uncertainty. Regarding emission tax estimation, two distinct cases appear to the manufacturer, which are explained separately. After the numerical analysis of the proposed model, it contemporarily observed that for a small/moderate manufacturing sector, adoption of emission reduction technology appears to be more beneficiary rather than paying emission tax free of reduction technology. Further, greenness index for the manufactured items imposes considerably-sensitive impact on the revenue of the system. Therefore the major contribution of the model is to investigate an emergent business policy for a manufacturing firm of green products under emission taxation regulation so that it may influence small and moderate manufacturers to set up their best -found marketing policies.

关键词： Interval environment Optimal control Carbon emission Green production Improved c-r optimization artificial hummingbird algorithm

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Hybrid energy storage power allocation strategy based on parameter-optimized VMD algorithm for marine micro gas turbine power system

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JOURNAL OF ENERGY STORAGE 2023年 73卷

作者： Li, Yueming Ding, Zemin Yu, Youhong Liu, Yongbao Naval Univ Engn Coll Power Engn Wuhan 430033 Peoples R China

The power system onboard ships is typically a low-inertia, small-capacity isolated grid that is highly susceptible to system disturbances and instability, especially when connected to high power pulse loads. To mitigate power fluctuations and ensure stable operation, a hybrid energy storage system (HESS), which comprises the battery system and flywheel energy storage devices, has emerged as a promising solution. This paper explores the control strategy and coordinated operation of marine gas turbine power generation systems operating under pulse load conditions and presents a novel hybrid energy storage power allocation strategy based on variational mode decomposition (VMD). Specifically, we propose to implement parameter optimization of VMD using an artificial hummingbird algorithm (AHA), which enables effective primary allocation of hybrid energy storage power. To achieve secondary power allocation, we design two fuzzy controllers to optimize the state of charge (SOC) of battery system and speed of flywheel device. To evaluate the feasibility and effectiveness of the proposed control strategy, we establish a theoretical model of a shipboard micro gas turbine (MGT) direct current (DC) power system, which includes an MGT, generator, HESS, and pulse power load. Our simulation results demonstrate that the artificial hummingbird algorithm outperforms the particle swarm algorithm concerning search speed and calculation accuracy for parameter optimization of variational mode decomposition. Moreover, the proposed parameter-optimized VMD method can adaptively decompose pulse load power fluctuations and achieve optimal allocation of the flywheel and battery power in the HESS. Finally, the parallel fuzzy controller design effectively optimizes the operation ranges of the flywheel device and battery system, prevents over-charging and over-discharging of energy storage equipment, ensures that the SOC of energy storage equipment remains within a fixed interval, and reduces freq

关键词： Micro gas turbine power generation system Flywheel energy storage system Hybrid energy storage system Variational mode decomposition artificial hummingbird algorithm Pulse power load Fuzzy control

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