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Interval prediction algorithm and optimal scenario making model for wind power producers bidding strategy

optimization AND ENGINEERING 2021年第3期22卷 1807-1829页

作者： Heydari, Azim Memarzadeh, Gholamreza Astiaso Garcia, Davide Keynia, Farshid De Santoli, Livio Sapienza Univ Dept Astronaut Elect & Energet Engn DIAEE Via Eudossiana 18 I-00184 Rome RM Italy Grad Univ Adv Technol Dept Power & Control Engn Kerman Iran Sapienza Univ Rome Dept Planning Design & Technol Architecture Rome Italy Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran

Nowadays, renewable energies are important sources for supplying electric power demand and a key entity of future energy markets. Therefore, wind power producers (WPPs) in most of the power systems in the world have a key role. On the other hand, the wind speed uncertainty makes WPPs deferent power generators, which in turn causes adequate bidding strategies, that leads to market rules, and the functional abilities of the turbines to penetrate the market. In this paper, a new bidding strategy has been proposed based on optimal scenario making for WPPs in a competitive power market. As known, the WPP generation is uncertain, and different scenarios must be created for wind power production. Therefore, a prediction intervals method has been improved in making scenarios and increase the accuracy of the presence of WPPs in the balancing market. Besides, a new optimization algorithm has been proposed called the grasshopper optimization algorithm to simulate the optimal bidding problem of WPPs. A set of numerical examples, as well as a case-study based on real-world data, allows illustrating and discussing the properties of the proposed method.

关键词： Wind power producers Prediction intervals Decision-making under uncertainty Optimal bidding strategies grasshopper optimization algorithm

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Modeling and Design of High Performance Converters for Optimal Utilization of Interconnected Renewable Energy Resources to Micro Grid with GOLRS Controller

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2021年第1期19卷 63-75页

作者： Vadivel, Surendar Ragupathy, Uthandipalayam Subramaniyam Kongu Engn Coll Dept Elect & Elect Engn Perundurai India

This paper proposes a bidirectional Z-source dc-dc converter topology for the optimal utilization of renewable energy sources to the microgrid with the proposed control technique. Compared to the existing dc-dc converter circuits, they can reduce in-rush and harmonic current, provide larger range of output dc voltage and improve reliability. It can operate in voltage-fed and current-fed when the place of the source and load is exchanged each other, and it can be perform buck-boost function in these two conditions. Its power flow can be bidirectional. The bidirectional Z-source dc-dc converter is revealed with the consideration of enhanced converter efficiency, effective utilization of renewable energy sources and reduced switching losses. The proposed control technique is the combination of both the grasshopper optimization algorithm (GOA) and local random search (LRS) and hence it is known as GOLRS controller. Here, the searching behaviour of the GOA is enhanced by LRS with the help of two operators named as crossover and mutation. In the proposed technique, the GOLRS is used to generate the optimal gain dataset based on the minimum error objective function and select the exact gain parameter of the PI controller. Batteries are used as an energy source to balance out and allow the renewable power system units to continue running at a steady and stable output power. The proposed technique is executed in the MATLAB/Simulink working platform and compared with various existing techniques.

关键词： grasshopper optimization algorithm high performance converters interconnected renewable energy resources local random search (LRS) micro grid

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grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

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Computers, Materials & Continua 2022年第8期72卷 3985-3997页

作者： Rabab Hamed M.Aly Aziza I.Hussein Kamel H.Rahouma The Higher Institute for Management and Information Technology Minya61768Egypt Department of Electrical and Computer Engineering Effat UniversityJeddahKSA Electrical Engineering Department Faculty of EngineeringMinia UniversityMinia6111Egypt

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’*** algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best *** work of this paper consists of two *** first part is based on collecting data of employees to calculate and illustrate the performance of each *** second part is based on the classification and prediction techniques of the employee *** model is designed to help companies in their decisions about the employees’*** classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the *** of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming *** also show that the grasshopper optimization,followed by“KF”with the Gradient Boosting Tree as classifier and predictor,is characterized by a high *** proposed algorithm is compared with other known techniques where our results are fund to be superior.

关键词： Metaheuristic algorithm KUWAHARA filter grasshopper optimization algorithm and Gradient boosting tree

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The Sensitivity Design of Piezoresistive Acceleration Sensor in Industrial IoT

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IEEE ACCESS 2019年 7卷 16952-16963页

作者： Dong, Chen Ye, Yin Liu, Ximeng Yang, Yang Guo, Wenzhong Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Fujian Peoples R China Fuzhou Univ Fujian Prov Key Lab Informat Secur Network Syst Fuzhou 350116 Fujian Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou 350116 Fujian Peoples R China

Piezoresistive acceleration sensors are widely used in various fields of the industrial Internet of Things because of their lightweight, fast response, and small size. The structural sensitivity of the sensor affects the accuracy of the measurement. And the sensitivity that the traditional method designs are only a feasible solution, not an optimal solution. Due to the differences in factory processes, the optimization of structural sensitivity is an NP-hard problem. To solve the design problem of structural sensitivity, we adopt the swarm intelligence algorithm in this paper, and we design a model for the structural sensitivity of the piezoresistive acceleration sensor. In addition, an improved grasshopper optimization algorithm (CC-GOA) that combines chaos strategy and Cauchy mutation is proposed to optimize the structural sensitivity of the piezoresistive acceleration sensor, and the structure of the sensor is composed of four beams and mass block. The experiments are compared with six well-known algorithms on 16 benchmark functions to verify the algorithm performance of CC-GOA, and then, the structural sensitivity of the piezoresistive acceleration sensor is optimized by CC-GOA. The results indicate that the piezoresistive acceleration sensor is designed with high sensitivity and superiority.

关键词： Industrial Internet of Things piezoresistive acceleration sensor structural sensitivity optimization grasshopper optimization algorithm

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Compressive strength prediction of admixed HPC concrete by hybrid deep learning approaches

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期44卷 8711-8724页

作者： Weng, Peng Xie, JingJing Zou, Yang Changzhou Univ Huaide Coll JingJiang Peoples R China Shanghai Construct 2 Grp Co Ltd Shanghai Peoples R China

The estimation of compressive strength includes time-consuming, finance-wasting, and laboring approaches to undertaking High-performance concrete (HPC) production. On the other side, a vast volume of concrete consumption in industrial construction requires an optimal mix design with different percentages to reach the highest compressive strength. The present study considered two deep learning approaches to handle compressive strength prediction. The robustness of the deep model was put high through two novel optimization algorithms as a novelty in the research world that played their precise roles in charge of model structure optimization. Also, a dataset containing cement, silica fume, fly ash, the total aggregate amount, the coarse aggregate amount, superplasticizer, water, curing time, and high-performance concrete compressive strength was used to develop models. The results indicate that the AMLP-I and GMLP-I models served the highest prediction accuracy. R-2 and RMSE of AMLP-I stood at 0.9895 and 1.7341, respectively, which declared that the AMLP-I model could be presented as the robust model for estimating compressive strength. Generally, using optimization algorithms to boost the capabilities of prediction models by tuning the internal characteristics has increased the reliability of artificial intelligent approaches to substitute the more experimental practices.

关键词： HPC concrete compressive strength deep learning arithmetic optimization algorithm grasshopper optimization algorithm

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OPTIMIZING A BI-OBJECTIVE LOCATION-ALLOCATION-INVENTORY PROBLEM IN A DUAL-CHANNEL SUPPLY CHAIN NETWORK WITH STOCHASTIC DEMANDS

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RAIRO-OPERATIONS RESEARCH 2021年第5期55卷 3245-3279页

作者： Rezaei, Aida Shahedi, Tina Aghsami, Amir Jolai, Fariborz Feili, Hamidreza Univ Tehran Coll Engn Sch Ind & Syst Engn Tehran Iran KN Toosi Univ Technol KNTU Sch Ind Engn Tehran Iran Islamic Azad Univ Dept Ind Engn Karaj Branch Karaj Iran

Integrating strategic and tactical decisions to location-allocation and green inventory planning by considering e-commerce features will pave the way for supply chain managers. Therefore, this study provides an effective framework for making decisions related to different levels of the dual-channel supply chain. We provide a bi-objective location-allocation-inventory optimization model to design a dual-channel, multi-level supply chain network. The main objectives of this study are to minimize total cost and environmental impacts while tactical and strategic decisions are integrated. Demand uncertainty is also addressed using stochastic modeling, and inventory procedure is the periodic review (S, R). We consider many features in inventory modeling that play a very important role, such as lead time, shortage, inflation, and quality of raw materials, to adapt the model to the real conditions. Since a dual-channel supply chain is becoming more important for sustainable economic development and resource recovery, we combine online and traditional sales channels to design a network. We generate five test problems and solve them by using the augmented epsilon-constraint method. Also, the grasshopper optimization algorithm was applied to solve the model in a reasonable time for a large size problem. In order to provide managerial insights and investigate the sensitivity of variables and problem objectives with respect to parameters, sensitivity analysis was performed.

关键词： Inventory-location allocation problem dual-channel supply chain periodic review policy stochastic demands augmented epsilon-constraint grasshopper optimization algorithm

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A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

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Frontiers of Environmental Science & Engineering 2023年第7期17卷 133-149页

作者： Zhaocai Wang Qingyu Wang Tunhua Wu College of Information Shanghai Ocean UniversityShanghai 201306China School of Information Engineering Wenzhou Business CollegeWenzhou 325035China

Water quality prediction is vital for solving water pollution and protecting the water *** terms of the characteristics of nonlinearity,instability,and randomness of water quality parameters,a short-term water quality prediction model was proposed based on variational mode decomposition(VMD)and improved grasshopper optimization algorithm(IGOA),so as to optimize long short-term memory neural network(LSTM).First,VMD was adopted to decompose the water quality data into a series of relatively stable components,with the aim to reduce the instability of the original data and increase the predictability,then each component was input into the iGOA-LSTM model for ***,each component was added to obtain the predicted *** this study,the monitoring data from Dayangzhou Station and Shengmi Station of the Ganjiang River was used for training and *** experimental results showed that the prediction accuracy of the VMDIGOA-LSTM model proposed was higher than that of the integrated model of Ensemble Empirical Mode Decomposition(EEMD),the integrated model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),Nonlinear Autoregressive Network with Exogenous Inputs(NARX),Recurrent Neural Network(RNN),as well as other models,showing better performance in short-term *** current study will provide a reliable solution for water quality prediction studies in other areas.

关键词： Waterquality prediction grasshopper optimization algorithm Variational mode decomposition Long short-term memory neural network

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Enhancement of power quality issues for a hybrid AC/DC microgrid based on optimization methods

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IET RENEWABLE POWER GENERATION 2022年第8期16卷 1773-1791页

作者： Khosravi, Nima Echalih, Salwa Baghbanzadeh, Rasoul Hekss, Zineb Hassani, Rahman Messaoudi, Mustapha R&D Management NPC Dept Elect & Instrumentat Engn Tehran Iran Hassan II Univ Casablanca ENSEM Casablanca ESE Lab Casablanca Morocco Islamic Azad Univ Dept Elect Engn Tabriz Branch Tabriz Iran R&D Management PGPIC Dept Elect & Instrumentat Engn Tehran Iran Islamic Azad Univ Dept Elect Engn Khorramabad Branch Khorramabad Iran Univ Gabes Natl Engn Sch Gabes Proc Energy Environm & Elect Syst Gabes Tunisia

The purpose of this paper is to study the harmonic behavior of hybrid microgrids (MGs). To achieve the desired goal, a modified active power filter (MAPF) and a power filter compensator kit (PFCK) modules have been used to improve the harmonics of the AC part of the system. The main challenge and feature of this research is the optimization of MAPF and PFCK controller gain coefficients hierarchical, using Harris hawk optimization (HHO), grasshopper optimization algorithm (GOA), artificial bee colony (ABC), and differential evolution (DE), respectively. Following, this technique was applied in the three control loops including, voltage and current harmonics, and system controller error to reduce the range of the mentioned harmonics to the permissible range. The method will be interesting considering that the PI and PID tuning is based on parallel feed-forward. The programmed objective functions include the three components current THD, voltage THD and controller error hierarchically. From the point of view of the MGs hierarchical control, the method presented in this study will be implemented in the second level of hierarchical control. In order to improve the operation of the DC part of the grid, a boost converter module (BCM) has been introduced in the form of a new synchronization scheme. The output of the simulation shows the reduction of the harmonic amplitude of the system by the mentioned modules.

关键词： AC-DC power convertors artificial bee colony current harmonics BCM power filter compensator kit modules three-term control MAPF power generation control distributed power generation modified active power filter hybrid AC-DC microgrids synchronization scheme power harmonic filters grasshopper optimization algorithm differential evolution ABC Harris hawk optimization boost converter module voltage harmonics current THD PFCK controller gain coefficients GOA electric current control parallel feed-forward programmed objective functions compensation power quality issues system controller error voltage THD simulation evolutionary computation MG hierarchical control optimization methods harmonic amplitude power supply quality harmonic distortion HHO control loops artificial bee colony algorithm voltage control PID tuning synchronisation

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Health condition identification for rolling bearing using a multi-domain indicator-based optimized stacked denoising autoencoder

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2020年第5期19卷 1602-1626页

作者： Yan, Xiaoan Liu, Ying Jia, Minping Nanjing Forestry Univ Sch Mechatron Engn Nanjing 210037 Jiangsu Peoples R China Southeast Univ Sch Mech Engn Nanjing Jiangsu Peoples R China

Stacked denoising autoencoder is one of the most classic models of deep learning. However, there are two problems in the traditional stacked denoising autoencoder: (1) the parameter selection of stacked denoising autoencoder mainly depends on expert experience and (2) stacked denoising autoencoder is mainly restricted to learn automatically single-domain features from raw vibration signals while identifying the fault type, which implies that no linear mapping relationship located in other domains of vibration data is neglected, which may lead to the imperfect diagnostic results. Consequently, to address these issues, learn the well-rounded feature representation, and improve recognition accuracy, this article presents a novel approach called multi-domain indicator-based optimized stacked denoising autoencoder for automatic fault identification of rolling bearing. First, multi-domain indicator of the original vibration signal is constructed through calculating the expression of different domains (e.g. time frequency domain, and time frequency domain). Second, the constructed multi-domain indicator is regarded as the input dataset to train stacked denoising autoencoder architecture containing three hidden layers, and a recently reported nature-inspired algorithm named grasshopper optimization algorithm is employed to synchronously determine the model parameters of stacked denoising autoencoder, which is aimed at learning more robust and reliable feature representation. Finally, the feature representation learned in the testing set is fed into the trained stacked denoising autoencoder model containing softmax classifier for identifying bearing health conditions. The presented method is evaluated using two bearing vibration datasets. Experimental results indicate that our approach can provide high-accuracy recognition over 99% for bearing health condition, and it achieves more decent and precise classification results compared with some shallow learning model and standa

关键词： Stacked denoising autoencoder multi-domain indicator grasshopper optimization algorithm rolling bearing health condition identification

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Fusion of modern meta-heuristic optimization methods using arithmetic optimization algorithm for global optimization tasks

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SOFT COMPUTING 2022年第14期26卷 6749-6763页

作者： Mahajan, Shubham Abualigah, Laith Pandit, Amit Kant Al Nasar, Mohammad Rustom Alkhazaleh, Hamzah Ali Altalhi, Maryam Shri Mata Vaishno Devi Univ Sch Elect & Commun Katra 182320 Jammu & Kashmir India Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia ALDAR Univ Coll Sch Engn & Technol Dept Informat Technol Garhoud Dubai U Arab Emirates Univ Dubai IT Dept Coll Engn & IT Academic City U Arab Emirates Taif Univ Coll Business Adm Dept Management Informat Syst POB 11099 At Taif 21944 Saudi Arabia

Several population-based techniques have subsequently been proposed. Despite their broad use in a variety of applications, we are still investigating the use of proposed methods to tackle real-world challenges. As a result, researchers must considerably modify and enhance their approaches based on the primary evolutionary processes in order to achieve rapid convergence, consistent equilibrium with high-quality data, and optimization. The paper proposes a fusion method (AOA-GOA) meta-heuristic optimization methods for global optimization tasks. They can be applied to different problems, including image processing, machine learning, wireless networks, power systems, engineering design, etc. The method fusion proposed is analyzed in context with GOA and AOA. To evaluate the performance, each method is tested on the same parameters like population size and number of iteration. The proposed IAOA is evaluated by varying the dimensions. The impact of varying dimensions is a standard test used in previous studies for optimizing test functions that show the effect of varying dimensions on efficiency of IAOA. From this, it is noted that it works efficiently for both high and low dimensional problems. In high dimensional problem, the proposed method gives efficient search results.

关键词： Arithmetic optimization algorithm grasshopper optimization algorithm Metaheuristic optimization methods

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