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Regression based stochastic energy management strategy for a grid connected microgrid using Artificial Electric Field algorithm

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 8卷

作者： Patty, Sukriti Rani, Nibha Malakar, Tanmoy Electrical Engineering Department National Institute of Technology Silchar India

Recent decade has witnessed steep technological advancement in the renewable energy sector due to growing concern of climate change and emission cut target. The lack of availability of fossil fuels and the high price of the same have made the use of Renewable Energy Sources (RES) as a promising approach for generating clean and sustainable energy at local level. This persuades the implementation of Microgrid (MG) supported with RES, to cater various types of load demand. However, planning and optimizing a MG operation have become challenging due to the intermittent nature of RES and uncertain loads. To address this issue, this paper introduces a Stochastic Energy Management Strategy (SEMS) for a grid connected MG comprising Photovoltaic, Wind Turbine, Fuel Cell, Micro Turbine, Battery Energy Storage and electrical as well as heat energy demand. The volatilities of RES power output and energy demand are modeled via Gaussian Process Regression (GPR) and Monte Carlo Simulation (MCS) respectively. The problem is framed as non-linear (NL) dynamic stochastic optimization problem and solved using ‘Artificial Electric Field algorithm (AEFA)’. Here, minimization of expected value of operational and emission cost are taken as objective functions;while various practicality aspects are modeled as constraints in the problem. Further, the efficacy of the proposed approach is validated around the first central moment of the load. Various case studies are performed to estimate the day ahead optimum operating schedule of the Distributed Energy Resources (DER) and to assess the impact of load uncertainties on DER operation and total MG operation cost. Further, the efficacy of the GPR is verified through comparison with MCS based result. © 2024

关键词： Energy management metaheuristic optimization algorithm Microgrid Regression model Renewable energy source

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A Novel Hybrid GOA-XGB Model for Estimating Wheat Aboveground Biomass Using UAV-Based Multispectral Vegetation Indices

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REMOTE SENSING 2022年第14期14卷 3506-3506页

作者： Han, Yixiu Tang, Rui Liao, Zhenqi Zhai, Bingnian Fan, Junliang North West Agr & Forestry Univ Coll Resources & Environm Yangling 712100 Shaanxi Peoples R China Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China North West Agr & Forestry Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Minist Agr Key Lab Plant Nutr & Agrienvironm Northwest China Yangling 712100 Shaanxi Peoples R China

The rapid and nondestructive determination of wheat aboveground biomass (AGB) is important for accurate and efficient agricultural management. In this study, we established a novel hybrid model, known as extreme gradient boosting (XGBoost) optimization using the grasshopper optimization algorithm (GOA-XGB), which could accurately determine an ideal combination of vegetation indices (VIs) for simulating wheat AGB. Five multispectral bands of the unmanned aerial vehicle platform and 56 types of VIs obtained based on the five bands were used to drive the new model. The GOA-XGB model was compared with many state-of-the-art models, for example, multiple linear regression (MLR), multilayer perceptron (MLP), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), random forest (RF), support vector machine (SVM), XGBoost, SVM optimization by particle swarm optimization (PSO), SVM optimization by the whale optimization algorithm (WOA), SVM optimization by the GOA (GOA-SVM), XGBoost optimization by PSO, XGBoost optimization by the WOA. The results demonstrated that MLR and GOA-MLR models had poor prediction accuracy for AGB, and the accuracy did not significantly improve when input factors were more than three. Among single-factor-driven machine learning (ML) models, the GPR model had the highest accuracy, followed by the XGBoost model. When the input combinations of multispectral bands and VIs were used, the GOA-XGB model (having 37 input factors) had the highest accuracy, with RMSE = 0.232 kg m(-2), R-2 = 0.847, MAE = 0.178 kg m(-2), and NRMSE = 0.127. When the XGBoost feature selection was used to reduce the input factors to 16, the model accuracy improved further to RMSE = 0.226 kg m(-2), R-2 = 0.855, MAE = 0.172 kg m(-2), and NRMSE = 0.123. Based on the developed model, the average AGB of the plot was 1.49 +/- 0.34 kg.

关键词： metaheuristic optimization algorithm feature selection XGBoost Gaussian process regression aboveground biomass mapping

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Novel Selective Mapping with Oppositional Hosted Cuckoo optimization algorithm for PAPR Reduction in 5G UFMC Systems

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2022年第2期29卷 464-471页

作者： Jayamathi, A. Jayasankar, T. Kumar, K. Vinoth KSR Coll Engn Dept ECE Tiruchengode India Anna Univ Dept ECE Univ Coll Engn BIT Campus Tiruchirappalli Tamil Nadu India SSM Inst Engn & Technol Dept Elect & Commun Engn Dindigul 624002 Tamil Nadu India

In recent times, there is a continuous requirement of achieving high data rates owing to an increase in the number of devices and significant demand for various services with maximum reliability and minimum delay. It results in the development of fifth generation (5G) to offer better services with enhanced data rate. Recently, a major alternative to OFDM technology for 5G networks called universal filtered multi-carrier (UFMC) is presented where every individual sub-band is filtered that reduces the OOB radiation and eliminates guard band. But high peak-to-average power ratio (PAPR) is a crucial issue which arises from the utilization of several subcarriers to generate the time domain transmission signal. For resolving this issue, this paper presents a novel selective mapping with oppositional hosted cuckoo optimization (SM-OHOCO) algorithm for PAPR reduction in 5G UFMC systems. In the SM-OHOCO algorithm, rather than the generation of several random phase sequences, SM-OHOCO algorithm is performed iteratively to attain a better solution with few searching rounds, showing the novelty of the work. As the optimization of phase sequence in the SLM technique is considered as an NP hard optimization problem, the OHOCO algorithm is applied, which is derived by incorporating the concepts of the HOCO algorithm with oppositional based learning (OBL) strategy. To validate the effective performance of the proposed SM-OHOCO algorithm, an extensive experimental analysis is performed to highlight the improved performance in 5G networks. The resultant values pointed out the superior outcome of the proposed SM-OHOCO algorithm over the other existing methods in terms of distinct measures

关键词： metaheuristic optimization algorithm PAPR reduction selective mapping universal filtered multi-carrier 5G networks

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Negative Gradient Differential Biogeography-based optimization for Mobile Robot Path Planning

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2022年第5期31卷 2250030-2250030页

作者： Wang, Jiaqian Na, Xiaodong Li, Zhihui Han, Min Li, Decai Dalian Univ Technol Elect Informat & Elect Engn Dalian 116024 Liaoning Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Liaoning Peoples R China Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equip Minist Educ Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Profess Technol Innovat Ctr Distributed Control I Dalian 116024 Peoples R China

Mobile robot path planning (MRPP) plays an irreplaceable role in the process of intelligent robots and practical artificial intelligence. The traditional global path planning methods have some shortcomings, such as difficulty in digging into environmental information and finding the optimal path effectively. To solve the above problems, this paper proposes a negative gradient differential biogeography-based optimization (NG-DBBO), which has strong local search ability and global optimization ability. Firstly, we present a differential migration approach to increase the population diversity in the iterative process of NG-DBBO, which can realize the information sharing between feature solutions effectively. Then a negative gradient descent strategy based on negative gradient descent is introduced to improve the learning rate, which not only enhances initial global search ability, but also avoids premature convergence. Noteworthily, the convergence of the algorithm is analyzed for single-peak and multi-peak problems respectively. After that, NG-DBBO is combined with the cubic spline interpolation to realize MRPP by the defined coding method and fitness function. The simulation experiments are used to demonstrate the availability of our method, which consist of two parts. In the first part, we select 23 benchmark functions to verify the accuracy and convergence speed of the NG-DBBO algorithm. The practicability of path planning in different environments is demonstrated in the second part.

关键词： Mobile robot path planning biogeography-based optimization negative gradient strategy differential evolution metaheuristic optimization algorithm

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Epistocracy algorithm: A Novel Hyper-heuristic optimization Strategy for Solving Complex optimization Problems

Epistocracy Algorithm: A Novel Hyper-heuristic Optimization ...

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Computing Conference

作者： Mojab, Seyed Ziae Mousavi Shams, Seyedmohammad Soltanian-Zadeh, Hamid Fotouhi, Farshad Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Henry Ford Hlth Syst Dept Radiol Detroit MI 48202 USA

ISBN: (纸本)9783030801267;9783030801250

This paper proposes a novel evolutionary algorithm called Epistocracy which incorporates human socio-political behavior and intelligence to solve complex optimization problems. The inspiration of the Epistocracy algorithm originates from a political regime where educated people have more voting power than the uneducated or less educated. The algorithm is a self-adaptive, and multi-population optimizer in which the evolution process takes place in parallel for many populations led by a council of leaders. To avoid stagnation in poor local optima and to prevent a premature convergence, the algorithm employs multiple mechanisms such as dynamic and adaptive leadership based on gravitational force, dynamic population allocation and diversification, variance-based step-size determination, and regression-based leadership adjustment. The algorithm uses a stratified sampling method called Latin Hypercube Sampling (LHS) to distribute the initial population more evenly for exploration of the search space and exploitation of the accumulated knowledge. To investigate the performance and evaluate the reliability of the algorithm, we have used a set of multimodal benchmark functions, and then applied the algorithm to the MNIST dataset to further verify the accuracy, scalability, and robustness of the algorithm. Experimental results show that the Epistocracy algorithm outperforms the tested state-of-the-art evolutionary and swarm intelligence algorithms in terms of performance, precision, and convergence.

关键词： Epistocracy algorithm Evolutionary computation metaheuristic optimization algorithm Multi-dimensional search Swarm intelligence

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Direct Model-Reference Adaptive Control for Wheelchair Simulator Control via a Haptic Interface

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IFAC-PapersOnLine 2022年第29期55卷 49-54页

作者： Amel Ait Ghezala Chouki Sentouh Philippe Pudlo

A wheelchair locomotion simulator (WCS) is an innovative solution to assess the biomechanical cost of wheelchairs (WC) accessibility in a controlled and safe virtual environment. In this context, this paper presents a haptic feedback control architecture based on a direct model reference adaptive control (MRAC) with intelligent tuning of its adaptation gains. The control objective is to follow the reference model velocity while producing the force feedback during the push phase, in order to faithfully recreate the dynamic behavior of the WC in a virtual environment. To accomplish this, a wheelchair ergometer model with friction is used to provide realistic navigation in the virtual environment (VE), by detecting and driving the wheelchair wheels. A two-wheeled vehicle model including the rolling resistance aspect is used to describe the wheelchair dynamic behavior. Since the controller adaptation gains are operated on the tracking error between the reference model and the simulator output, the WC model is also used as a reference model to specify the desired dynamics of the adaptive control system. For an optimal solution, an intelligent metaheuristic algorithm Elephant Herding optimization (EHO) is employed to optimize the controller gain adaptation parameter to keep the tracking error as small as possible. Finally, the simulation results obtained show the effectiveness of the proposed control strategy.

关键词： Direct MRAC system People with motor disabilities Wheelchair Simulator metaheuristic optimization algorithm Haptic interface Parameter estimation

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A Solution of Implicit Model of Series-Parallel Photovoltaic Arrays by Using Deterministic and metaheuristic Global optimization algorithms

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ENERGIES 2020年第4期13卷 801-801页

作者： Perez Archila, Luis Miguel David Bastidas-Rodriguez, Juan Correa, Rodrigo Trejos Grisales, Luz Adriana Gonzalez-Montoya, Daniel Univ Ind Santander Escuela Ingn Elect Elect & Telecomunicac Bucaramanga 680003 Colombia Univ Nacl Colombia Dept Ingn Elect Elect & Comp Manizales 170003 Colombia Inst Tecnol Metropolitano Dept Electromecan & Mecatron Medellin 050013 Colombia Inst Tecnol Metropolitano Dept Elect & Telecomunicac Medellin 050013 Colombia

The implicit model of photovoltaic (PV) arrays in series-parallel (SP) configuration does not require the LambertW function, since it uses the single-diode model, to represent each submodule, and the implicit current-voltage relationship to construct systems of nonlinear equations that describe the electrical behavior of a PV generator. However, the implicit model does not analyze different solution methods to reduce computation time. This paper formulates the solution of the implicit model of SP arrays as an optimization problem with restrictions for all the variables, i.e., submodules voltages, blocking diode voltage, and strings currents. Such an optimization problem is solved by using two deterministic (Trust-Region Dogleg and Levenberg Marquard) and two metaheuristics (Weighted Differential Evolution and Symbiotic Organism Search) optimization algorithms to reproduce the current-voltage (I-V) curves of small, medium, and large generators operating under homogeneous and non-homogeneous conditions. The performance of all optimization algorithms is evaluated with simulations and experiments. Simulation results indicate that both deterministic optimization algorithms correctly reproduce I-V curves in all the cases;nevertheless, the two metaheuristic optimization methods only reproduce the I-V curves for small generators, but not for medium and large generators. Finally, experimental results confirm the simulation results for small arrays and validate the reference model used in the simulations.

关键词： implicit model solution photovoltaic array series-parallel global optimization partial shading deterministic optimization algorithm metaheuristic optimization algorithm

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A comparative analysis of global optimization algorithms for surface electromyographic signal onset detection

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Decision Analytics Journal 2023年 8卷

作者： Alam, Shafiq Zhao, Xin Niazi, Imran Khan Ayub, Muhammad Sohaib Khan, Muhammad Asad School of Management Massey University Auckland New Zealand School of Computing Whitireia Community Polytechnic Auckland New Zealand New Zealand College of Chiropractic //AUT//AAU Auckland New Zealand Department of Computer Science Lahore University of Management Sciences Lahore Pakistan Department of Telecommunication Hazara University Mansehra Pakistan

Surface Electromyography (sEMG) is a technique for measuring muscle activity by recording electrical signals from the surface of the body. It is widely used in fields such as medical diagnosis, human–computer interaction, and sports injury rehabilitation. The detection of the onset and offset of muscle activation is a longstanding challenge in sEMG analysis. This study pioneers the implementation, configuration, and evaluation of Particle Swarm optimization (PSO) against other optimization algorithms for sEMG signal detection, including Genetic algorithms (GA), Simulated Annealing (SA), Ant Colony optimization (ACO), and Tabu Search (TS). The results show that the PSO algorithm achieves the highest median accuracy and F1-Score and is the fastest among the selected algorithms but has lower stability compared to Genetic algorithms and Ant colony optimization. The design and value of the cost function had a significant impact on the results, with optimal results obtained when the cost value was between 0.1203 and 0.1384. The use of these algorithms improved detection efficiency and reduced the need for manual parameter adjustment. To the best of our knowledge, no published studies have utilized Simulated Annealing, Ant colony optimization, and Tabu search meta-heuristic algorithms to detect sEMG signal onsets. © 2023 The Authors

关键词： Ant colony optimization Genetic algorithm metaheuristic optimization algorithm Onset detection Particle swarm optimization Simulated annealing Surface electromyography Tabu search

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Optimal design of pile wall retaining system during deep excavation using swarm intelligence technique

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STRUCTURES 2020年 28卷 1991-1999页

作者： Taiyari, F. Kharghani, M. Hajihassani, M. Univ Tabriz Fac Civil Engn Tabriz *** Iran Islamic Azad Univ Fac Engn Sci & Res Branch Tehran Tehran Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

In this paper, the efficiency of various metaheuristic optimization algorithms in the optimum design of pile wall retaining systems is investigated. To achieve this aim, an on-going Tabriz Metro station project with a deep excavation pit is selected as a case study. Subsequently, the detailed finite element model is developed in the OpenSees platform to perform the static analyses. Four different optimization techniques including Genetic, Particle swarm optimization, Bee, and Biogeography-based optimization algorithms are selected for the optimization purposes. The total cost of the retaining structures is considered as an objective function, which should be minimized in the design space of the variables. The results show the efficiency of the applied techniques in achieving an optimal design of the considered retaining system. However, it can be concluded that the performance of the Bee algorithm is most effective in both obtaining the minimum objective function with lower dispersion as well as rapid convergence among other different algorithms. Finally, the pile lateral deflection during the different excavation phases as well as the soil shear stress are investigated based on the obtained optimal results.

关键词： metaheuristic optimization algorithm Deep excavation Static analysis Optimal design

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metaheuristics-optimized ensemble system for predicting mechanical strength of reinforced concrete materials

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STRUCTURAL CONTROL & HEALTH MONITORING 2021年第5期28卷 e2706-e2706页

作者： Chou, Jui-Sheng Nguyen, Ngoc-Mai Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Taipei Taiwan

This paper develops a novel artificial intelligence (AI)-based approach, called the metaheuristics-optimized ensemble system (MOES), to assist civil engineers significantly in achieving accurate estimations of the mechanical strength of reinforced concrete (RC) materials. MOES integrates the advantages of hybrid and ensemble models by combining a metaheuristic optimization algorithm and efficient AI models. The metaheuristic algorithm finds the optimal hyperparameters of individual AI techniques and simultaneously adjusts their weights to yield the best optimized-weight-ensemble model. Particularly, the developed MOES was established by integrating the forensic-based investigation optimization algorithm, the radial basis function neural network, and the least squares support vector regression. Four case studies of predicting structural mechanics of RC beams were performed to evaluate the performance of MOES and compare it to those of other single AI models, conventional ensemble models, hybrid models, and empirical methods. The analytical results of cross-validation reveal that MOES was the most reliable approach, achieving the best values of all performance evaluation indexes. The automated predictive analytics revealed the robustness, efficiency, and stability of MOES. Thus, the proposed approach is a highly promising tool for predicting the structural mechanics of RC beams. The success of MOES in estimating the mechanical strength of RC beams has redefined the way of optimizing an ensemble AI model, which is the primary contribution of this research to the relevant body of knowledge.

关键词： automated predictive analytics machine learning metaheuristic ensemble of hybrid learning metaheuristic optimization algorithm reinforced concrete structural mechanics

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