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Multi-objective planning of micro-grid system considering renewable energy and hydrogen storage systems with demand response

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International Journal of Hydrogen Energy 2023年第41期48卷 15626-15645页

作者： Zhu, J.R. Jin, Yihua Zhu, Weihong Lee, Dong-Kun Bohlooli, Navid Interdisciplinary Program in Landscape Architecture Seoul National University South Korea College of Agriculture Yanbian University JiLin China College of Geography and Ocean Sciences Yanbian University JiLin China Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul South Korea Electrical Engineering Department Sun-Life Company Baku Seoul Azerbaijan

Recently, researchers in the power system industry have considered the intelligent parking lot idea. The intelligent parking lots (IPL) try to exchange power with the upstream by electric vehicles (EVs) charge/discharge application. The mentioned process of charge/discharge can provide a solution model of regular problems in this field, i.e., peak time problems. Additionally, such vehicles' existence can play an essential role in environmental performance enhancement in power systems. The primary purpose of this paper is to consider such enhancements of EVs in power systems and environmental performance. For this purpose, a multi-objective optimization approach is suggested for the environmental performance and economical operation of IPL by considering the time-of-use rates of a demand response program. Since such a model is associated with various practical bounds, the multi-objective grasshopper optimization algorithm is suggested to solve this problem. The outcomes indicate the effectiveness of the compared techniques utilizing fuzzy decision-making strategies. Chaos theory is employed to increase this method and progress searching operators. Additionally, the proposed multi-objective technique is a model created using fuzzy theory, variable detection, strategy selection-based memory, and non-dominated sorting theory to choose the best Pareto from a list of solutions with robust functionality for resolving the problems mentioned above. The proposed technique is tested on a sample system that includes an intelligent parking lot, local production facilities, and non-renewable and renewable production systems under the proposed uncertainty strategy of upstream grid pricing. The study showed the effectiveness of the suggested method. The simulation findings indicate that the emission rate and overall cost of intelligent parking lots are reduced by 3.87 and 2%. It indicates that economic and environmental objectives are also satisfied due to the successful deployment

关键词： Demand side management grasshopper optimization algorithm Intelligent parking lot Multi-objective optimization

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Frequency Control in an Autonomous Two-area Hybrid Microgrid using grasshopper optimization based Robust PID Controller

Frequency Control in an Autonomous Two-area Hybrid Microgrid...

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IEEE India International Conference on Power Electronics

作者： Anil Annamraju Srikanth Nandiraju Department of EE NITW Warangal India

ISBN: (纸本)9781538649978;9781538649961

The intermittency in the renewable energy sources output power, along with the system and load perturbations in the standalone multi-microgrid results in large frequency deviations. The traditional PI/PID controller is unable to provide the acceptable performance for the operating conditions as mentioned above. To address this problem, this paper suggests an efficient load frequency controller for the frequency control of a microgrid (MG). In this proposed controller, the parameters of PID controllers are fine-tuned by using grasshopper optimization algorithm (GOA). In the proposed strategy, diesel engine generators (DEGs) and redox flow batteries (RFBs) are responsible for balancing the generation and load in the hybrid microgrid system. The proposed controller is executed on an autonomous 2-area hybrid microgrid test system, under various parametric and systemic disturbances. Finally, the superiority of the proposed controller is alleviated concerning error reduction, settling time and overshoot by comparing with various techniques available in the literature.

关键词： Frequency control Robust controller Two-area microgrid RFBs grasshopper optimization algorithm

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k-Tournament grasshopper Extreme Learner for FMG-Based Gesture Recognition

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SENSORS 2023年第3期23卷 1096页

作者： Barioul, Rim Kanoun, Olfa Tech Univ Chemnitz Chair Measurement & Sensor Technol D-09126 Chemnitz Germany

The recognition of hand signs is essential for several applications. Due to the variation of possible signals and the complexity of sensor-based systems for hand gesture recognition, a new artificial neural network algorithm providing high accuracy with a reduced architecture and automatic feature selection is needed. In this paper, a novel classification method based on an extreme learning machine (ELM), supported by an improved grasshopper optimization algorithm (GOA) as a core for a weight-pruning process, is proposed. The k-tournament grasshopper optimization algorithm was implemented to select and prune the ELM weights resulting in the proposed k-tournament grasshopper extreme learner (KTGEL) classifier. Myographic methods, such as force myography (FMG), deliver interesting signals that can build the basis for hand sign recognition. FMG was investigated to limit the number of sensors at suitable positions and provide adequate signal processing algorithms for perspective implementation in wearable embedded systems. Based on the proposed KTGEL, the number of sensors and the effect of the number of subjects was investigated in the first stage. It was shown that by increasing the number of subjects participating in the data collection, eight was the minimal number of sensors needed to result in acceptable sign recognition performance. Moreover, implemented with 3000 hidden nodes, after the feature selection wrapper, the ELM had both a microaverage precision and a microaverage sensitivity of 97% for the recognition of a set of gestures, including a middle ambiguity level. The KTGEL reduced the hidden nodes to only 1000, reaching the same total sensitivity with a reduced total precision of only 1% without needing an additional feature selection method.

关键词： extreme learning machine force myography grasshopper optimization algorithm k-tournament selection

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Using modern heuristic algorithms for optimal control of a gas lifted field

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2019年 183卷 106348-000页

作者： Mahdiani, Mohammad Reza Khamehchi, Ehsan Suratgar, Amir Abolfazl Amirkabir Univ Technol Tehran Polytech Dept Petr Engn Hafez Ave Tehran Iran Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Hafez Ave Tehran Iran

In the gas lift, gas injection rate has an optimum value and increasing or decreasing its amount decreases the oil production rate. This optimum point changes in production time and creates an optimum trajectory. There has previously been much research in this area, but none have used a powerful tool in a dynamic model to find a good optimal path for maximizing the production or NPV in a gas-lifted field. In addition, gas allocation in a time step changes the production of different wells and the pattern of fluid flow and also reservoir pressure decline in the reservoir. This can affect the optimum gas allocation of the next time step, but this has not been analyzed in any of the previous works. In this paper, first, a model of reservoir and well of a gas lift well is developed and using some experimental data points the best-fitted correlations are selected. Then, two common and famous heuristic algorithms (genetic algorithm, simulated annealing), and two recently introduced optimization algorithms (Moth Swarm algorithm and grasshopper optimization algorithm) are used to find the optimal path of the injection lift gas rates of the wells. Finally, the results of these four algorithms are compared with each other and some other allocation scenarios. In addition, different aspects of the injection and production rates of the different wells and their cumulative rates and NPV paths are analyzed. The results illustrated that using MSA has an optimum point with higher production. In addition, MSA finds a specific control path with a need for lower lift gas and also a smaller compressor. It also has a really different control path with other optimization algorithm paths. In addition, in this paper, a new long term instability in flow is observed, which was explained by the drainage area of each well. This control path of each well was discussed and it was concluded that GA can find the most stable path.

关键词： Genetic algorithm grasshopper optimization algorithm Moth swarm algorithm Simulated annealing

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Clustering of transformer condition using frequency response analysis based on k-means and GOA

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期202卷 107619-107619页

作者： Bigdeli, Mehdi Abu-Siada, Ahmed Islamic Azad Univ Dept Elect Engn Zanjan Branch Zanjan Iran Curtin Univ Elect & Comp Engn Discipline Perth WA Australia

Frequency response analysis (FRA) is considered as the most popular and reliable method to detect mechanical deformations within power transformers. Despite this popularity, interpretation of FRA signatures has not yet been standardized worldwide. Correct interpretation of FRA signatures results in reliable diagnosis of the transformer mechanical integrity which facilitates timely and proper remedial action. As a further step towards the full understanding of the analysis of FRA signatures, this paper presents a k-means method to cluster power transformers under different fault types. In this regard, series of FRA measurements has been conducted on various transformer models under different fault types. Then, a feature based on interval maximum to global maximum (IMGM) is extracted from the obtained FRA measurements to facilitate data clustering. Using grasshopper optimization algorithm (GOA), the centers of the clusters are determined and by applying the data obtained from operating transformers, the performance of the proposed method is evaluated and compared under different cases.

关键词： Power transformers Frequency response analysis Condition monitoring k-means clustering grasshopper optimization algorithm

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Global solar radiation prediction: Application of novel hybrid data-driven model

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APPLIED SOFT COMPUTING 2021年 112卷 107768-107768页

作者： Alrashidi, Massoud Alrashidi, Musaed Rahman, Saifur Virginia Tech Adv Res Inst Bradley Dept Elect & Comp Engn Arlington VA 22203 USA Qassim Univ Coll Engn Dept Elect Engn Onaizah 56452 Saudi Arabia Qassim Univ Coll Engn Dept Elect Engn Buraydah 51452 Saudi Arabia

One of the significant prerequisites for harvesting solar energy is precise global solar radiation (GHI) forecasts. However, variability and uncertainty are inherent characteristics of solar radiation. It is challenging to show better generalization using current data analysis approaches. Thus, this research presents a new intelligence framework by hybridizing Support Vector Regression (SVR) with the grasshopper optimization algorithm (GOA) and the Boruta-based feature selection algorithm (BA) for forecasting GHI values at different sites of Saudi Arabia. Interestingly, the most significant distinction that differentiates this proposed prediction model (SVR-GOA-BAK) from other models is that the GOA is automatically employed to search for optimal SVR's hyperparameters. In contrast, these hyperparameters are chosen randomly and manually in conventional models. Consequently, the contribution helps save time, reduce cost, and avoid the possibility of models' overfitting or underfitting caused by random and manual selection. A diversity of statistical measures has justified the proposed model's effectiveness and superiority. In terms of mean absolute percentage error (MAPE), the proposed model outperformed the standalone SVR models by 32.15-39.69% at different study sites. In tuning the SVR's parameters, GOA outperforms popular optimization algorithms. All the simulation test results demonstrate the superiority of the proposed model. Hence, the proposed approach provides a foundation for precise solar radiation forecasting, which can aid in the growth of renewable-energy-based technologies. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Boruta algorithm Support vector regression grasshopper optimization algorithm Hyperparameter Global solar radiation prediction Feature selection

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An Optimized Fuzzy Based Control Solution for Frequency Oscillation Reduction in Electric Grids

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ENERGIES 2022年第19期15卷 6981页

作者： Maqbool, Hina Yousaf, Adnan Asif, Rao Muhammad Rehman, Ateeq Ur Eldin, Elsayed Tag Shafiq, Muhammad Hamam, Habib Super Univ Lahore Dept Elect Engn Lahore 54000 Pakistan Govt Coll Univ Dept Elect Engn Lahore 54000 Pakistan Future Univ Egypt Fac Engn & Technol New Cairo 11835 Egypt Yeungnam Univ Dept Informat & Commun Engn Gyongsan 38541 South Korea Uni Moncton Fac Engn Moncton NB E1A 3E9 Canada Spectrum Knowledge Prod & Skills Dev Sfax 3027 Tunisia Int Inst Technol & Management BP1989 Libreville Gabon Univ Johannesburg Sch Elect Engn Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa

The demand for uninterruptible electricity supply is increasing, and the power engineering sector has started researching alternative solutions. Distributed generation (DG) dissemination into the electric grid to cope with the accelerating demand for electricity is taken into consideration. However, its integration with the traditional grid is a key task as sudden changes in load and their fickle nature cause the frequency to deviate from its adjusted range and affect the grid's reliability. Moreover, the increased use of DG will significantly impact power system frequency response, posing a new challenge to the traditional power system frequency framework. Therefore, maintaining the frequency within the nominal range can improve its reliability. This deviation should be removed within a few seconds to keep the system's frequency stable so that supply and demand are balanced. In a traditional grid system, the controllers installed at the generation side help to control the system's frequency. These generators have capital installation costs that are not desirable for system operators. Therefore, this article proposed a comprehensive control framework to enable high penetration of DG while still providing adequate frequency response. This is accomplished by investigating a grasshopper optimization algorithm-based (GOA) fuzzy PD-PI controller (FPD-PI) for analyzing frequency control and optimizing the FPD-PI controller gains to minimize the frequency fluctuations. In this paper, interconnected hybrid power systems (HPS) are considered. In this study, the response of a system is analyzed, and the results validate that the oscillations in frequency are substantially reduced by the proposed controller. Moreover, our model is the best option for controlling frequency instead of conventional controllers, as it is efficient and fast to regulate frequency by switching the preferred loads on or off.

关键词： distributed generation cascaded controller fuzzy PD-PI controller grasshopper optimization algorithm

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A Novel HF Broadband Frequency-Reconfigurable Whip Antenna With Radiation Blades Loading

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IEEE ACCESS 2019年 7卷 168944-168955页

作者： Wang, Hengfeng Liu, Chao Xie, Xu Wu, Huaning Naval Univ Engn Coll Elect Engn Wuhan 430033 Peoples R China

Aiming at the problems of low gain, low efficiency in low frequency band and warping pattern in high frequency band of the existing HF broadband whip antenna, this paper introduces frequency reconfigurable antenna technology and radiation blades loading technology to redesign whip structure, loading and matching network in different bands, and designs a kind of frequency-reconfigurable whip antenna with improved gain and efficiency. The whip body structure, loading and matching network structure of the antenna in each band are optimized, and the on-off control is carried out through the radio frequency switch according to the actual needs. The results show that the standing wave ratio of the proposed antenna is all less than 3, the average value is 2.32;the gain is all greater than -2dB, the average value is 3.63dB;the total efficiency is all over 20%, the average value is 72.77%, and the pattern keeps horizontal omnidirectional in the whole short wave band without upward warping phenomenon.

关键词： Frequency-reconfigurable technology radiation blades loading broadband antenna lumped loading matching network grasshopper optimization algorithm

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Comparison of priori and posteriori approach of multi-objective optimization for WEDM on Ti6Al4V alloy

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MATERIALS RESEARCH EXPRESS 2022年第7期9卷 076504页

作者： Jain, Shubham Parashar, Vishal MANIT Dept Mech Engn Bhopal 462003 Madhya Pradesh India

Titanium alloys are known to have some excellent properties, such as good biocompatibility, good fatigue resistance and high strength to weight ratio. Due to these properties, Ti6Al4V alloy is widely used in the biomedical field, aerospace and automobile industries. In this article, pulse on-time (T-ON), pulse off time (T-OFF), and servo voltage (SV) were selected as process parameters for wire electric discharge machining (WEDM) on Ti6Al4V alloy. The material removal rate (MRR) and surface roughness (SR) were determined as responses. MRR and SR have been equated by a central composite design (CCD: a response surface method technique). Then multi-objective Artificial Bee Colony optimization (MO-ABC) with Gray relational analysis (GRA) was selected as a priori approach for multi-objective optimization. Also, a multi-objective grasshopper optimization algorithm (MO-GOA) has been chosen as a posterior approach for optimization. These two algorithms have been tested on various iterations and populations. Based on the elapsed time, it has been found that the priori approach of multi-objective optimization is better than the posterior approach of multi-objective optimization. When comparing these algorithms based on the results, it is obtained that the posterior approach gives a better combination of multiple results. The major outcome of the research is that the priori method is quick, while the posterior approach produces many promising solutions.

关键词： Ti6Al4V alloy wire electric discharge machining grey relational analysis artificial bee colony optimization grasshopper optimization algorithm priori approach posteriori approach

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New hybrid segmentation algorithm: UNet-GOA

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1499-e1499页

作者： Yousefi, Tohid Aktas, Ozlem Dokuz Eylul Univ Comp Engn Izmir Buca Turkiye

The U-Net architecture is a prominent technique for image segmentation. However, a significant challenge in utilizing this algorithm is the selection of appropriate hyperpa-rameters. In this study, we aimed to address this issue using an evolutionary approach. We conducted experiments on four different geometric datasets (triangle, kite, paral-lelogram, and square), with 1,000 training samples and 200 test samples. Initially, we performed image segmentation without the evolutionary approach, manually adjusting the U-Net hyperparameters. The average accuracy rates for the geometric images were 0.94463, 0.96289, 0.96962, and 0.93971, respectively. Subsequently, we proposed a hybrid version of the U-Net architecture, incorporating the grasshopper optimization algorithm (GOA) for an evolutionary approach. This method automatically discovered the optimal hyperparameters, resulting in improved image segmentation performance. The average accuracy rates achieved by the proposed method were 0.99418, 0.99673, 0.99143, and 0.99946, respectively, for the geometric images. Comparative analysis revealed that the proposed UNet-GOA approach outperformed the traditional U-Net architecture, yielding higher accuracy rates.

关键词： grasshopper optimization algorithm Image processing Meta-heuristic algorithms optimization U-Net

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