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Optimal control design and management of complete DC- renewable energy microgrid system

AIN SHAMS ENGINEERING JOURNAL 2023年第5期14卷

作者： Abou El-Ela, A. A. Mosalam, Hanan A. Amer, Ragab A. Menoufia Univ Fac Engn Elect Engn Dept Shibin Al Kawm Egypt Mansoura Higher Inst Engn & Technol Dept Elect Engn Mansoura Egypt

This paper presents an optimal control design for a DC-renewable Microgrid (DC-MG) system optimized by applying the arithmetic optimization algorithm(AOA). This approach is proposed in this study to get the optimal PI controller design parameters for the DC-MG when connecting and disconnecting from the grid. The suggested system comprises a solar photovoltaic system(PVS), an Energy Storage System (ESS), a DC-AC inverter, DC load, LC filter, variable AC load, and a single-phase induction motor. The ESS is used to store energy in hydrogen form and is made up of a fuel cell system (FCS), an electrolyser, and a hydrogen tank module. The results are compared with those obtained from control system design using Particle Swarm optimization(PSO), Cuckoo Search(CS), and Gray Wolf Optimizer (GWO) methodologies for a vari-ety of test situations. The simulated results using AOA are more accurate and have less settling time, rise time, and overshoot compared to other techniques. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://***/licenses/ by-nc-nd/4.0/).

关键词： DC-microgrid system Constant power generation Particle Swarm optimization Cuckoo Search technique Gray Wolf optimization arithmetic optimization algorithm

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Predicting the consumed heating energy at residential buildings using a combination of categorical boosting (CatBoost) and Meta heuristics algorithms

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JOURNAL OF BUILDING ENGINEERING 2023年 71卷

作者： Dasi, He Ying, Zhang Yang, Boyuan Zhongyuan Univ Technol Sch Energy & Environm Zhengzhou 450007 Peoples R China Xijing Univ Xian 710123 Shaanxi Peoples R China

The main purpose of this study is to investigate the amount of the daily consumed heating at residential buildings. In order to improve the predictions, the Categorical Boosting (CatBoost) method combined with six other meta-heuristics algorithms, and six different hybrid models were made. During the network training, the K-Fold cross validation algorithm has been used to pre-vent overfitting. Also, characteristics of the building as well as the temperature outside the building are considered as the main inputs of the problem. The results showed that the proposed hybrid model can improve the predictions of consumed heating with acceptable accuracy. The results confirm that optimizing the hyper-parameters of Catboost can be very useful in improving the predictions. The results showed that the Catboost model which its hyper-parameters opti-mized by Artificial Bee Colony algorithm, has the best performance among all investigated hybrid models. On the other hand, the hybrid Catboost-ABC model has the weakest performance among all models. For example, based on the test dataset, the R2 values of the hybrid Catboost-AOA model and the hybrid Catboost-ABC model are respectively equal to 0.9851 and 0.9770, which are the highest and lowest values of this index.

关键词： Consumed heating energy Residential building Categorical boosting Meta-heuristic algorithms arithmetic optimization algorithm

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Adaptive VMD-K-SVD-Based Rolling Bearing Fault Signal Enhancement Study

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SENSORS 2023年第20期23卷 8629-8629页

作者： Mao, Meijiao Zeng, Kaixin Tan, Zhifei Zeng, Zhi Hu, Zihua Chen, Xiaogao Qin, Changjiang Xiangtan Univ Sch Mech Engn & Mech Xiangtan 411105 Peoples R China Xiangtan Univ Minist Educ Engn Res Ctr Complex Trajectory Machining Proc & E Xiangtan 411105 Peoples R China

To address the challenges associated with nonlinearity, non-stationarity, susceptibility to redundant noise interference, and the difficulty in extracting fault feature signals from rolling bearing signals, this study introduces a novel combined approach. The proposed method utilizes the variational mode decomposition (VMD) and K-singular value decomposition (K-SVD) algorithms to effectively denoise and enhance the collected rolling bearing signals. Initially, the VMD method is employed to separate the overall noise into intrinsic mode functions (IMFs), reducing the noise content within each IMF. To optimize the mode component, K, and the penalty factor, alpha, in VMD, an improved arithmetic optimization algorithm (IAOA) is employed. This ensures the selection of optimal parameters and the decomposition of the signal into a set of IMFs, forming the original dictionary. Subsequently, the signals are decomposed into multiple IMFs using VMD, and an original dictionary is constructed based on these IMFs. K-SVD is then applied to the original dictionary to further reduce the noise in each IMF, resulting in a denoised and enhanced signal. To validate the efficacy of the proposed method, rolling bearing signals collected from Case Western Reserve University (CWRU) and thrust bearing test rigs were utilized. The experimental results demonstrate the feasibility and effectiveness of the proposed approach in denoising and enhancing the rolling bearing signals.

关键词： rolling bearing arithmetic optimization algorithm variational mode decomposition K-singular value decomposition

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Prediction of peak particle velocity using hybrid random forest approach

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SCIENTIFIC REPORTS 2024年第1期14卷 1-27页

作者： Yan, Yu Guo, Jiwei Bao, Shijie Fei, Honglu Liaoning Tech Univ Sch Civil Engn Fuxin 123000 Peoples R China Collaborat Innovat Ctr Mine Major Disaster Prevent Fuxin 123000 Peoples R China Liaoning Tech Univ Institue Blasting Technol Fuxin 123000 Peoples R China

Blasting excavation is widely used in mining, tunneling and construction industries, but it leads to produce ground vibration which can seriously damage the urban communities. The peak particle velocity (PPV) is one of main indicators for determining the extent of ground vibration. Owing to the complexity of blasting process, there is controversy over which parameters will be considered as the inputs for empirical equations and machine learning (ML) algorithms. According to current researches, the burden has controversial impact on the blast-induced ground vibration. To judge whether the burden affects blast-induced ground vibration, the data of ground vibration considering burden have been recorded at the Wujiata coal mine. Correlation coefficient is used to analyze the relationship between variables, the correlation between the distance from blasting center to monitored point (R) and peak particle velocity (PPV) is greatest and the value of correlation coefficient is - 0.67. This study firstly summarizes the most common empirical equations, and a new empirical equation is established by dimension analysis. The new equation shows better performance of predicting PPV than most other empirical equations by regression analysis. Secondly, the machine learning is confirmed the applicability of predicting PPV. Based on the performance assessments, regression error characteristic curve and Uncertainty analysis in the first round of predicting PPV, the random forest (RF) and K-Nearest Neighbors (KNN) show better performance than other four machine learning algorithms. Then, in the second round, based on the artithmetic optimization algorithm (AOA), the optimized random forest (AOA-RF) model as the most accurate model compared with the optimized K-Nearest Neighbors (AOA-KNN) presented in the literature. Finally, the points of predicted PPV which have been informed of danger are marked based on Chinese safety regulations for blasting.

关键词： Burden Blast-induced ground vibration Peak particle velocity Machine learning algorithms arithmetic optimization algorithm

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Application of SVR models built with AOA and Chaos mapping for predicting tunnel crown displacement induced by blasting excavation

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Li, Chuanqi Mei, Xiancheng Grenoble Alpes Univ CNRS UMR 5521 Lab 3SR F-38000 Grenoble France Chinese Acad Sci Inst Rock & Soil Mech Wuhan 430071 Peoples R China

This study utilizes the support vector regression (SVR) model to predict the tunnel crown displacement (TCD) induced by the blasting excavation. 95 blasting operations considering the tunnel shape parameters, the blasting parameters, the explosive parameters, and the rock mass properties are used to train and test models. To enhance the model prediction performance, a novel combination of the arithmetic optimization algorithm (AOA) with the chaos mapping (CM) called CMAOA, is proposed to select the optimal hyperparameters of the SVR model with advantages of the computing speed and the optimization ability. Sixteen benchmark functions are adopted to verify the authenticity of the prediction performance improvement of the CMAOA, and six statistical indices are used to evaluate the performance of the proposed hybrid models for predicting the TCD. The evaluation results show that the SVR optimized by the AOA with Circle map (CirAOA-SVR) model has the best prediction accuracy among all models. The results of parameter analysis indicate that the degree of bonding of bedding surface is the most important input parameter for predicting the TCD, but the blasting and explosive parameters with fine importance scores cannot be ignored either. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Tunnel crown displacement Blasting excavation Support vector regression arithmetic optimization algorithm Chaos mapping

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Enhanced conditional random field-long short-term memory for name entity recognition in English texts

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第9期35卷

作者： Bhumireddypalli, Veera Sekhar Reddy Koppula, Srinivas Rao Koppula, Neeraja MLR Inst Technol Dept Comp Sci & Engn Hyderabad India Geethanjali Coll Engn & Technol Dept Comp Sci & Engn Hyderabad India MLR Inst Technol Dept Comp Sci & Engn Hyderabad India

Named entity recognition (NER) is an essential topic in the real world during the advanced development of technologies. Hence, in this article, develop enhanced conditional random field-long short-term memory (ECRF-LSTM) for NER in the English language. The proposed ECRF-LSTM is the combination of conditional random field-long short-term memory (ECRF-LSTM) and chaotic arithmetic optimization algorithm (CAOA). The proposed research concentrated to perform NER for Indian names from the given input database for Indian digital database management and processing. The Chaotic AOA leads to fast convergence and helps to avoid the local optima. The proposed method is working with three phases preprocessing phase, the feature extraction phase, and the NER phase. In the initial stage, the datasets are collected from the online system. In the preprocessing phase, the removal of the URL, removal of special symbol, username removal, tokenization, and stop word removal are done. After that, the essential features such as domain weight, event weight, textual similarity, spatial similarity, temporal similarity, and relative document-term frequency difference are extracted and then applied to train the proposed model. To empower the training phase of the CRF-LSTM method, CAOA is utilized to select optimal weight parameter coefficients of CRF-LSTM for training the model parameters. The proposed method is validated by statistical measurements and compared with the conventional methods such as convolutional neural network-particle swarm optimization and convolutional neural network respectively. The proposed method achieves 98.91 accuracy, 97.36 sensitivity, 97.19 specificity, 97.54 precision, and 97.63 recall which is better than the existing methodologies.

关键词： arithmetic optimization algorithm feature extraction long short-term memory named entity recognition preprocessing

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A novel decomposition integration model for power coal price forecasting

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RESOURCES POLICY 2023年 80卷

作者： Wu, Siping Xia, Guilin Liu, Lang East China Jiaotong Univ Sch Econ & Management Nanchang 330013 Peoples R China Guizhou Univ Finance & Econ Sch Business Adm Guiyang 550000 Peoples R China

Accurate prediction of steam coal prices is important for stabilizing the coal trading market and formulating coal use strategies scientifically. In this paper, a new decomposition integration model (VADM) is proposed to predict coal prices by combining the variational modal decomposition (VMD), arithmetic optimization algorithm (AOA), deep temporal convolutional network (DeepTCN), and mean impact value algorithm (MIV). Firstly, the AOA optimization algorithm is used to improve the VMD, AOA-VMD was obtained. It is used to decompose the steam coal price series. Then, the decomposed subsequences are predicted for the prediction of steam coal prices by using DeepTCN. Finally, the MIV algorithm is applied to analyze the impact of different factors on the price of steam coal. It is found that: the steam coal price sub-series decomposed by AOA-VMD are smoother and more linear compared with the original series;the errors in forecasting steam coal prices are significantly reduced after considering newly proposed factors, interest rates, such as the overnight Shanghai interbank offered rate and the six-month treasury bond yield;the MAPE, MASE and SMAPE of the VADM model all show different degrees of decline compared with benchmark models. The forecasting effect of VADM model is better than the benchmark model in terms of stability and accuracy, and can be used for short-term forecasting of coal prices.

关键词： arithmetic optimization algorithm Variational modal decomposition Deep temporal convolutional network Mean impact value algorithm Steam coal price forecasting

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A New Fitness Function for Sparse Linear Array Evaluation Based on the Point Spread Function

A New Fitness Function for Sparse Linear Array Evaluation Ba...

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IEEE UFFC Latin America Ultrasonics Symposium (LAUS)

作者： Eduardo de Souza, Julio Cesar Prado, Vander Teixeira Martinez-Graullera, Oscar Higuti, Ricardo Tokio Univ Estadual Paulista UNESP Dept Engn Elect Ilha Solteira Brazil Univ Tecno Fed Parana UTFPR Dept Engn Elect Cornelio Procopio Brazil CSIC Inst Tecno Fis & Informac Madrid Spain

ISBN: (纸本)9781665443593

This work proposes a new fitness function for sparse linear array design using the Point Spread Function (PSF), where the energy and entropy are combined. The arithmetic optimization algorithm is used as a search mechanism, and a new strategy to find sparse linear configurations is proposed. This new strategy eliminates the necessity of penalization functions to control the number of elements in sparse arrays. The algorithm is used to find two sparse linear arrays, one using a fitness function based on the radiation pattern, and the other with the proposed fitness function. Comparing the results, the configuration found using the proposed fitness function presented better lateral resolution and contrast.

关键词： Sparse Linear Array Fitness Function Ultrasound Imaging arithmetic optimization algorithm

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Impact of maximized utility benefit based on customer willingness for economic operation of a grid connected microgrid system

Journal of Engineering Research (Kuwait)

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Journal of Engineering Research (Kuwait) 2024年

作者： Dey, Bishwajit Pal, Arnab Jadav, Raj Kadiwala, Sahil Singh, Alok Kumar Dutta, Soham Department of Electrical Engineering Technology University of Johannesburg Johannesburg South Africa Department of Electrical Engineering Adani University Gujarat Ahmedabad India Department of Electrical and Electronics Engineering Silicon Institue of Technology Odisha Bhubaneswar India Department of Electrical Engineering Adani Institute of Infrastructure Engineering Gujarat Ahmedabad India Department of Electrical and Electronics Engineering Manipal Academy of Higher Education Karnataka Manipal India

Optimal scheduling of distributed energy resources (DERs) to obtain a minimized generation cost of a low voltage (LV) microgrid (MG) system has always gravitated the power system optimization researchers. This paper proposes a dual level optimization framework which emphasizes on maximizing the DISCOM profit in the first level and later minimizes the generation cost of the MG system. The MG system considered for the study consists of renewable energy sources, fossil fueled generators, micro turbine and fuel cell and the cost components involve depreciation cost, fuel cost, penalized emission cost and operation and maintenance (O&M) cost. An incentive based demand response (IBDR) policy is implemented which utilizes customer's willingness to curtail their load demands and offers incentives for the same. When the DISCOMs share of profit is maximized using IBDR policy in level one, the modified load demand of the MG system obtained by deducting the curtailed load from the forecasted load is considered to minimize the generation cost of the MG system in the second level. A recently developed swift and facile arithmetic optimization algorithm (AOA) is used as optimization tool for the study. The maximum DISCOM benefit was found to be 211[Formula presented] when the customers curtailed 105 kWh energy as per their willingness. Literatures reported the minimum power generation cost of the MG system as 880[Formula presented], 871[Formula presented] and 807[Formula presented] for base load, load shifting based demand side management (DSM) and customer incentive-based DR policies respectively. The proposed IBDR policy outperformed the reported literatures with a minimal generation cost of 768[Formula presented]. Measures of central tendencies also corroborate to the robustness and efficiency of AOA. © 2024 The Authors

关键词： arithmetic optimization algorithm Customer willingness DISCOM Incentive Based Demand Response (IBDR) Microgrid energy management

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On the problem formulation for parameter extraction of the photovoltaic model: Novel integration of hybrid evolutionary algorithm and Levenberg Marquardt based on adaptive damping parameter formula

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ENERGY CONVERSION AND MANAGEMENT 2022年 256卷 115403-115403页

作者： Ridha, Hussein Mohammed Hizam, Hashim Q. Mirjalili, Seyedali Othman, Mohammad Lutfi Ya'acob, Mohammad Effendy Ahmadipour, Masoud Ismaeel, Nooruldeen Q. Univ Putra Malaysia Dept Elect & Elect Engn Fac Engn Serdang 43400 Malaysia Univ Putra Malaysia Adv Lightning Power & Energy Res ALPER Fac Engn Serdang 43400 Malaysia Univ Al Mustansiriyah Dept Comp Engn Baghdad 10001 Iraq Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ Putra Malaysia Dept Proc & Food Engn Fac Engn Serdang 43400 Selangor Malaysia Univ Teknol MARA Sch Elect Engn Coll Engn Shah Alam 40450 Selangor Malaysia Ibn Sina Univ Med & Pharmaceut Sci Baghdad Iraq

The estimation of the unknown parameters of the photovoltaic (PV) model is crucial for accurately verifying its real performance precisely under a wide range of climatic conditions. This paper presents an approach to determine the nine parameters of the three diode (TD) PV model based on the integration of the guaranteed convergence arithmetic optimization algorithm and Levenberg-Marquardt with adaptive damping nonlinear parameter method named as GCAOA(AdLM). The keystone of the GCAOA(AdLM) model is accomplished by efficaciously enhancing the exploiter-explorer tendency with inclusion of various powerful hybrid strategies in terms the methodology itself. In addition, the objective function is newly designed leveraging on Levenberg-Marquardt with adaptive damping parameter method to accurately determine the initial roots parameters of the TD PV model. The experimental results demonstrate that the proposed GCAOA(AdLM) can reduce the root mean square error (RMSE), mean bias error (MBE), deviation of solar radiation's levels (d(i)), test statistical (TS), and absolute error (AE) to zero and the determination coefficient (R-2) to 1 for all environmental conditions, with statistical reasons and comparisons against well-published approaches available in the literature.

关键词： Photovoltaic cell optimization arithmetic optimization algorithm Newton Raphson Levenberg-Marquardt Lambert W function

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