Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication Year
- Publication
- Publication Type
Articles 1 - 30 of 86
Full-Text Articles in Engineering
Task Allocation Method For Multi-Uav Cooperative Reconnaissance In Complex Environment, Fuzhen Zhang, Yaoqin Zhu
Task Allocation Method For Multi-Uav Cooperative Reconnaissance In Complex Environment, Fuzhen Zhang, Yaoqin Zhu
Journal of System Simulation
Abstract: The existing cooperative planning methods of multiple UAVs often carry out path planning and task allocation separatly, which causes the cooperative scheme not being the best in a complex environment. The cost matrix of multi-UAV cooperative reconnaissance on heterogeneous targets is established. Aiming at the various obstacle constraints and the characteristics of UAV motion and track in complex environment, an improved PSO-AFSA is used to solve the single UAV track planning model. Hungarian algorithm is used to complete the cooperative allocation of reconnaissance tasks of UAVs. The simulation results show that the algorithm can make the flying range of …
Electronic Solid Waste Prediction Based On Intelligent Optimization Grey Model, Xiaoan Sun, Xiaoli Luan, Fei Liu
Electronic Solid Waste Prediction Based On Intelligent Optimization Grey Model, Xiaoan Sun, Xiaoli Luan, Fei Liu
Journal of System Simulation
Abstract: Aiming at the problems of complex modeling mechanism and low modeling accuracy in the prediction of electronic solid waste production, an intelligent modeling method combining fractional order multiple gray model and neural network compensation model is proposed. Particle swarm optimization is used to optimize the accumulative order and background parameters of the gray model to maximize the performance of the gray model. BP neural network is used to compensate the error of gray modeling and improve the prediction accuracy of solid waste production. The effectiveness of the proposed method is verified by Washington state electronic solid waste data. The …
Block Size Optimization For Pow Consensus Algorithm Based Blockchainapplications By Using Whale Optimization Algorithm, Betül Aygün, Hi̇lal Arslan
Block Size Optimization For Pow Consensus Algorithm Based Blockchainapplications By Using Whale Optimization Algorithm, Betül Aygün, Hi̇lal Arslan
Turkish Journal of Electrical Engineering and Computer Sciences
Blockchain-based applications come up with cryptocurrencies, especially Bitcoin, introducing a distributed ledger technologies for peer-to-peer networks and essentially records the transactions in blocks containing hash value of the previous blocks. Block generation constitutes the basis of this technology, and the optimization of such systems is among the most crucial concerns. Determining either the block size or the number of transactions in the block brings out a remarkable problem that has been solved by the miners in recent years. First, higher block size results in higher transaction time, on the other hand, smaller block size has many disadvantages such as security, …
Prediction Of The Height Of Water Flowing Fractured Zone Based On Pso-Bp Neural Network, Lou Gaozhong, Tan Yi
Prediction Of The Height Of Water Flowing Fractured Zone Based On Pso-Bp Neural Network, Lou Gaozhong, Tan Yi
Coal Geology & Exploration
Prediction of the height of water flowing fractured zone based on PSO-BP neural network
Short-Term Power Load Forecasting Based On Lstm Neural Network Optimized By Improved Pso, Tengfei Wei, Tinglong Pan
Short-Term Power Load Forecasting Based On Lstm Neural Network Optimized By Improved Pso, Tengfei Wei, Tinglong Pan
Journal of System Simulation
Abstract: To improve the accuracy of short-term power load forecasting, a short-term power load forecasting model (ACMPSO-LSTM) based on long-short memory neural network (LSTM) optimized by adaptive Cauchy mutation particle swarm optimization (ACMPSO) is proposed. For the problem of difficult selection of LSTM model parameters, ACMPSO is used to optimize model parameters, and non-linear changing inertia weights are adopted to improve the global optimization ability and convergence speed of PSO algorithm. In the optimization process, a mutation operation based on genetic algorithm is added to reduce the risk of particles falling into local optimal solutions. The simulation results show that …
Research On Active Suspension Control Of High-Speed Train Based On Fuzzy Compound Strategy, Jianjun Meng, Zhongjun Wang, Ruxun Xu, Decang Li
Research On Active Suspension Control Of High-Speed Train Based On Fuzzy Compound Strategy, Jianjun Meng, Zhongjun Wang, Ruxun Xu, Decang Li
Journal of System Simulation
Abstract: In order to improve the running stability of high-speed train, a semi-vertical six-degree-of- freedom state space model is established based on the active suspension system. Based on fuzzy control theory, the fuzzy controllers and fuzzy PID controllers are designed. Aiming at the defects of strong subjectivity of fuzzy PID control which needs manual adjustment, particle swarm optimization is used to optimize the initial parameters of the controller to make the suspension system be more correspond with the objective reality. In order to verify the advance of this strategy, with the sky-hook damping strategy for comparison, the controller model …
A Novel Optimum Pi Controller Design Based On Stability Boundary Locussupported Particle Swarm Optimization In Avr System, Mahmut Temel Özdemi̇r
A Novel Optimum Pi Controller Design Based On Stability Boundary Locussupported Particle Swarm Optimization In Avr System, Mahmut Temel Özdemi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
This study proposes a new approach that combines stability and optimization in the design of proportional? integral (PI) controller of automatic voltage regulators (AVR) of synchronous generators with variable system parameters. Thanks to this approach, a PI controller, providing the desired performance and the stability of the AVR system, has been designed. The approach follows a method investigating the PI gain values to achieve the desired goals. In the first step of the study, a new stability boundary locus is calculated for the case in which AVR system?s parameters have changed. The stability boundary locus (SBL) method is a graphic-based …
Adaptation Of Metaheuristic Algorithms To Improve Training Performance Of Aneszsl Model, Şi̇fa Özsari, Mehmet Serdar Güzel, Gazi̇ Erkan Bostanci, Ayhan Aydin
Adaptation Of Metaheuristic Algorithms To Improve Training Performance Of Aneszsl Model, Şi̇fa Özsari, Mehmet Serdar Güzel, Gazi̇ Erkan Bostanci, Ayhan Aydin
Turkish Journal of Electrical Engineering and Computer Sciences
Zero-shot learning (ZSL) is a recent promising learning approach that is similar to human vision systems. ZSL essentially allows machines to categorize objects without requiring labeled training data. In principle, ZSL proposes a novel recognition model by specifying merely the attributes of the category. Recently, several sophisticated approaches have been introduced to address the challenges regarding this problem. Embarrassingly simple approach to zeroshot learning (ESZSL) is one of the critical of those approaches that basically proposes a simple but efficient linear code solution. However, the performance of the ESZSL model mainly depends on parameter selection. Metaheuristic algorithms are considered as …
Stereo Camera Calibration Based On Multiple Fitness Full-Parameter Autonomous Mutation Particle Swarm, Guiyang Zhang, Muyao Xue, Zijian Zhu, Huo Ju
Stereo Camera Calibration Based On Multiple Fitness Full-Parameter Autonomous Mutation Particle Swarm, Guiyang Zhang, Muyao Xue, Zijian Zhu, Huo Ju
Journal of System Simulation
Abstract: The acquisition of target parameters based on visual measurement provides reliable data support for performance analysis and evaluation of simulation system.The precision of measurement results is determined by the accuracy of camera calibration.A calibration method based on full parameter autonomous mutation particle swarm optimization is proposed.Traditional calibration method is utilized to obtain the initial internal parameters.The fast and global calibration algorithm based on particle swarm optimization is achieved by inertial coefficient contraction adjustment,global factor learning adjustment strategy based on particle distance,multi-adaptation function and the independent variation law.The experimental results show that the proposed method can improve the …
Global Optimization Method Based On Consensus Particle Swarm Optimization, Zhanwen Lu, Xingong Cheng, Yongfeng Zhang
Global Optimization Method Based On Consensus Particle Swarm Optimization, Zhanwen Lu, Xingong Cheng, Yongfeng Zhang
Journal of System Simulation
Abstract: According to the characteristics of particle swarm optimization (PSO) and efficient global optimization algorithm (EGO), a global black box optimization algorithm based on consensus particle swarm optimization and local surrogate model (CPSO-LSM) is proposed. The algorithm fixes the period of the PSO algorithm to group the particles and stops after the particles reach a consensus. The high-quality sub-regions around each group of particles are used as the modeling area of the surrogate model, and the high-quality optimal solution or global optimal solution is obtained by comparing the optimal values of each region. It can not only avoid the complex …
Operation Loss Reduction Control For Large-Scale Wind Farm Based On Hybrid Modeling Simulation, Yunqi Xiao, Wang Yi
Operation Loss Reduction Control For Large-Scale Wind Farm Based On Hybrid Modeling Simulation, Yunqi Xiao, Wang Yi
Journal of System Simulation
Abstract: Due to the large number of transformers and collection lines in large-scale wind farms, the losses of collecting system is serious in actual operation. A reactive power/voltage control strategy is proposed, which takes wind turbines as the distributed reactive power sources to optimize the power flow in wind farm and reduce the overall losses of collector system. To improve the efficiency of wind farm modeling and multi-scene loss reduction simulation, a hybrid modeling and simulation scheme based on combining object model configuration and control algorithm programming is proposed. The wind farm model consists of module configuration, and can be …
Research On Autonomous Intercept Method For Ucav Based On Lead Attack Aiming, Huaxing Wu, Huang Wei, Fengju Kang, Hongbao Mao
Research On Autonomous Intercept Method For Ucav Based On Lead Attack Aiming, Huaxing Wu, Huang Wei, Fengju Kang, Hongbao Mao
Journal of System Simulation
Abstract: For the problem of autonomous intercepting moving target with air-to-air missiles for an unmanned combat air vehicle, autonomous guiding method based on lead attack aiming errors was studied. The aiming errors were computed by building the motion model of UCAV and the model of lead attack aiming. Then to meet the goal of decreasing the aiming errors during flight control, two maneuver strategies based on model predictive control approach were presented: one used proportional navigation method and the other employed simulated annealing particle swarm optimization algorithm. The simulation result shows that both strategies can implement autonomous intercept for targets …
An Improved Particle Swarm Optimization Algorithm And Its Application In Clustering Analysis, Chuang Wang, Zhang Yong, Xuegui Li, Hongli Dong
An Improved Particle Swarm Optimization Algorithm And Its Application In Clustering Analysis, Chuang Wang, Zhang Yong, Xuegui Li, Hongli Dong
Journal of System Simulation
Abstract: In this paper, a novel artificial fish swarm particle swarm optimization algorithm (AF-PSO) is proposed corresponding to the shortcomings of the standard particle swarm algorithm including the fast convergence speed in the initial stage, the easiness to fall into premature convergence in the late, the local optimization and the poor ability to global search. This paper firstly introduces the crowding factorδ and the Markov chain, and then adds the artificial fish swarm algorithm to the particle swarm optimization algorithm. By calculating the crowding factor, the velocity model is updated to switch among four modes: foraging, clustering, following and random. …
Self-Adaptive Multi-Swarm Particle Swarm Optimization Algorithm, Xuewen Xia, Bojian Wang, Jin Chang, Guoliang He, Chengwang Xie, Wei Bo
Self-Adaptive Multi-Swarm Particle Swarm Optimization Algorithm, Xuewen Xia, Bojian Wang, Jin Chang, Guoliang He, Chengwang Xie, Wei Bo
Journal of System Simulation
Abstract: To overcome the shortcomings of poor ability to escape a local optimal, premature convergence and low precision of the traditional particle swarm optimization algorithm (PSO), a self-adaptive multi-swarm particle swarm optimization (SMPSO) was proposed. In SMPSO, the whole population was divided into many parallel-evolution multi-swarms, the aim of which was to keep diversity of the population. Furthermore, a self-adaptive regrouping operator was proposed to reinforce the information sharing and interaction between different swarms. In addition, particles’ historical information were periodic sampling and the statistics results were used to direct the best solution to carry out a detecting …
Soft Sensor Of Particle Size Of Grinding Process Based On Improved Csapso Neural Networks, Zhou Ying, Huimin Zhao, Chen Yang, Wang Long
Soft Sensor Of Particle Size Of Grinding Process Based On Improved Csapso Neural Networks, Zhou Ying, Huimin Zhao, Chen Yang, Wang Long
Journal of System Simulation
Abstract: Aiming at the problems that the particle size can’t be measured online and the offline analysis by lab sample existing in large-time delay, by combining the characteristics of the one stage grinding circuit, the soft sensor model of particle size was proposed by the combination of improved chaotic self-adaptive particle swarm optimization and BP neural network algorithm. Taking advantages of chaotic theory ergodicity and PSO global optimal searching ability, the algorithm above couldadjust the weights of BP network adaptively and avoid falling into the local optimum. As a result of MATLAB simulation, the measurement accuracy of the improved CSAPSO-BP …
Simulation And Application Of Dkipso-Svc Combined Model For Credit Risk Assessment, Zhenhai Wan, Tieying Liu, Zhang Yang, Jishuang Li
Simulation And Application Of Dkipso-Svc Combined Model For Credit Risk Assessment, Zhenhai Wan, Tieying Liu, Zhang Yang, Jishuang Li
Journal of System Simulation
Abstract: In order to improve the problem of inefficient parameter selection of the GDS-SVC model and DIPSO-SVC model, and utilize the generalization ability and robustness of support vector classification (SVC), the reduction factor of location updating was introduced based on the dynamic improvement Particle Swarm Optimization (DIPSO), and then the DKIPSO-SVC of parameters selecting in SVC was established based on DKIPSO. The method was applied to credit scoring of commercial banks. The simulation results demonstrate that the robustness of the DKIPSO-SVC model is better than DIPSO-SVC. But beyond that, the accuracy of DKIPSO-SVC model achieves 96.6049%, higher than that of …
Cqpso Algorithm Based Control System Parameter Optimization, Genyuan Wei, Xinqiang Feng, Han Pu
Cqpso Algorithm Based Control System Parameter Optimization, Genyuan Wei, Xinqiang Feng, Han Pu
Journal of System Simulation
Abstract: According to the shortcomings of optimization methods of control system parameters, and the result of PSO algorithm usually falling into the partial optimum, Chaos Search and Quantum Space Search were added to the PSO algorithm, constituting the Chaos Quantum Particle Swarm Optimization algorithm, which was applied to the typical thermal control system parameters optimization. Introducing the selection of object functions of control system parameter optimization, describing the CQPSO algorithm process, the CQPSO algorithm was tested and analyzed through multiple test functions. The result shows that, compared with PSO and CPSO algorithm, CQPSO algorithm makes the particle swarm get out …
Signal Denoising Method Based On Atom Curve Fitting Improved Dictionary Learning, Gao Bo, Wang Jun, Gege Zhang
Signal Denoising Method Based On Atom Curve Fitting Improved Dictionary Learning, Gao Bo, Wang Jun, Gege Zhang
Journal of System Simulation
Abstract: In signal denoising problems, using K-SVD and other classic dictionary learning algorithm can not effectively eliminate the noise impact. The method made some amendments for classical dictionary learning by applying nonlinear least squares curve fitting and particle swarm optimization. K-SVD algorithm was used to train the dictionary. Nonlinear least-squares approach was used to fit every atom in the dictionary. Particle swarm optimization method was used to solve the sparse representation of the signal. The reconstructed signal was obtained. The experimental results show that, the denoising effects of the proposed method apparently has increased compared with K-SVD and RLS-DLA.
Study Of Modified Particle Swarm Optimization Algorithm Based On Adaptive Mutation Probability, Huang Song, Tian Na, Zhicheng Ji
Study Of Modified Particle Swarm Optimization Algorithm Based On Adaptive Mutation Probability, Huang Song, Tian Na, Zhicheng Ji
Journal of System Simulation
Abstract: Mutation operator is an effective method to solve the premature of particle swarm optimization. According to the characteristic of population diversity, a modified particle swarm optimization based on adaptive mutation probability and hybrid mutation strategy was proposed. Aggregation degree was introduced to adjust the mutation probability of each generation, and a hybrid Gaussian and Cauchy mutation based on the global-best position and an adaptive wavelet mutation based on the worst personal-best position were carried out. The simulation of the comparisons with other particle swarm optimizations with mutation operator on matlab was proposed. The results demonstrate that the proposed algorithm …
Coral Reefs Optimization For Solving Parameter Identification In Permanent Magnet Synchronous Motor, Yawei Quan, Tian Na, Zhicheng Ji, Wang Yan
Coral Reefs Optimization For Solving Parameter Identification In Permanent Magnet Synchronous Motor, Yawei Quan, Tian Na, Zhicheng Ji, Wang Yan
Journal of System Simulation
Abstract: High accuracy identification of parameters in permanent magnet synchronous motor (PMSM) is the basis of controller design. According to the drawbacks of slow speed, big error, and small number of parameters in classical particle swarm optimization (PSO) and least square method, Coral Reefs Optimization (CRO) was proposed to solve the parameter identification problem in PMSM. In order to improve the identification accuracy, the parameter setting in CRO was adjusted. The mathematical model of PMSM in coordinate system was established, CRO, PSO and RLS were applied to identify parameters in PMSM, and were verified in Matlab/Simulink for comparison. The simulation …
Intelligent Evaluation Of Simulation Training For Aerial Ammunition Technical Support, Xu Gang, Zhang Lei, Tian Lei
Intelligent Evaluation Of Simulation Training For Aerial Ammunition Technical Support, Xu Gang, Zhang Lei, Tian Lei
Journal of System Simulation
Abstract: Quantitative evaluation is an important part of the simulation training of the Aviation Ammunition technical support. In order to realize the automatic evaluation of the simulation training, intelligent evaluation technology is introduced, and a prediction model based on Sigmoid regression is proposed. On the basis of analyzing the linear relationship of the sample data of the performance indicators, a subset of the characteristic indicators is selected as the input of the prediction mathematical model. In order to avoid the gradient descent method falling into the local solution problem, the gradient descent + PSO algorithm is presented. After testing the …
Study Of Im Parameter Identification Using Multi-Objective Particle Swarm Optimization With Proportional Guided Strategy, Huang Song, Tian Na, Wang Yan, Zhicheng Ji
Study Of Im Parameter Identification Using Multi-Objective Particle Swarm Optimization With Proportional Guided Strategy, Huang Song, Tian Na, Wang Yan, Zhicheng Ji
Journal of System Simulation
Abstract: A multi-parameter and multi-objective identification model of induction motor was established, and a multi-objective particle swarm optimization based on Pareto set and all personal-best positions guided strategy was proposed and applied to the identification model. Not considerring the weighted coefficient of each objective, Pareto set is able to avoid subjective choice of the coefficients of multi-objective identification and proportion strategy with all personal-best positions guided could balance the learning ability from personal-best positions and global-best position. Having verified the performance on Matlab/Simulink, the results show that the proposed algorithm is able to improve parameter identification accuracy, and has …
Study Of Dynamic Economic Load Dispatch Using Particle Swarm Optimization With Multi-Information Characteristics, Huang Song, Wang Yan, Zhicheng Ji
Study Of Dynamic Economic Load Dispatch Using Particle Swarm Optimization With Multi-Information Characteristics, Huang Song, Wang Yan, Zhicheng Ji
Journal of System Simulation
Abstract: According to the characteristics of power system, an model of dynamic economic load dispatch based on multiple fuel resources and 24-hour forecasting load demand was established, and a particle swarm optimizer using multi-information characteristics of all personal-best information was proposed to solve it. In the algorithm, centroid position and median position were defined, and then the personal-best position, centroid position and median position was developed to modify the velocity update formula. The algorithm could reduce the premature phenomenon for solving complex optimization problems, and it could make effectively solve dynamic economic load dispatch. The simulation was conducted to optimize …
Simulation Study Of A Coverage Enhancement Scheme In Directional Sensor Network Based On Particle Swarm Algorithm, Juwei Zhang, Wang Yu
Simulation Study Of A Coverage Enhancement Scheme In Directional Sensor Network Based On Particle Swarm Algorithm, Juwei Zhang, Wang Yu
Journal of System Simulation
Abstract: A Dezert-Smarandache theory (DSmT) data fusion scheme was proposed based on the probability sensing model of directional sensor. In order to enhance the coverage ratio of directional sensor network, a sensor node deployment strategy was presented based on particle swarm optimization algorithm. Overlap rate was reduced by iteratively adjusting the sensing direction, which was regarded as fitness function in the algorithm, and the reliability of data was enhanced. The simulation results show that this algorithm has a fast convergence speed for the random deployment of sensor network nodes with continuously adjustable sensing direction, can effectively enhance the utilization rate …
Permanent Magnet Synchronous Motor Parameter Identification Based On Improved Teaching-Learning-Based Optimization, Li Jie, Wang Yan, Zhicheng Ji
Permanent Magnet Synchronous Motor Parameter Identification Based On Improved Teaching-Learning-Based Optimization, Li Jie, Wang Yan, Zhicheng Ji
Journal of System Simulation
Abstract: High accuracy identification of parameters in permanent magnet synchronous motor (PMSM) is the basis of controller design. In order to overcome the shortages of traditional identification methods such as slow speed and low identification accuracy, an improved teaching-learning-based optimization algorithm (ITLBO) was proposed to identify the permanent magnet synchronous motor parameters. In the teaching phrase, tutorial teaching mechanism was introduced to strengthen teacher's capacity and improved the convergence rate of algorithm, in the learning phrase, the course stepwise learning was used to improve learners' learning efficiency. Besides, opposition-based-learning was introduced for small probability mutation, which enhanced the possibility out …
Ship Maneuverability Index Identification Based Ais Data, Fangxin Ning, Xiong Yong, Junmin Mou, Xingdong Huang
Ship Maneuverability Index Identification Based Ais Data, Fangxin Ning, Xiong Yong, Junmin Mou, Xingdong Huang
Journal of System Simulation
Abstract: A novel method of ship maneuverability index identification on the basis of Automatic Identification System (AIS) data was proposed. By decoding and processing the raw data of AIS, data of rate of turning (ROT) are re-constructed with application of cubic spline interpolation method. Time series spectrum analysis was introduced to select appropriate ROT data segment. Based on the ROT data segment, rudder angle information was obtained by using ship Voyage Data Recorder (VDR). To identify ship's maneuverability index, particle swarm optimization algorithm (PSO) was applied to obtain ship maneuvering parameters under the current speed. The result was compared …
Improved Particle Swarm Optimization Based On Lévy Flights, Rongyu Li, Wang Ying
Improved Particle Swarm Optimization Based On Lévy Flights, Rongyu Li, Wang Ying
Journal of System Simulation
Abstract: The particle swarm optimization (PSO) has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. The Lévy particle swarm optimization (Lévy PSO) was proposed. In the particle position updating formula, Lévy PSO eliminated the impact of speed on the convergence rate, and used Levy flight to change the direction of particle positions movement to prevent particles getting into local optimum value, and then using greedy strategy to update the evaluation and choose the best solution to obtain the global optimum. The experimental results show that Lévy PSO can effectively …
Forecasting Of Short-Term Power Load Of Secrpso-Svm Based On Data-Driven, Hairong Sun, Bixia Xie, Tian Yao, Zhuoqun Li
Forecasting Of Short-Term Power Load Of Secrpso-Svm Based On Data-Driven, Hairong Sun, Bixia Xie, Tian Yao, Zhuoqun Li
Journal of System Simulation
Abstract: For the parameter selection of support vector machine in modeling, a particle swarm optimization algorithm based on second-order oscillation and repulsion factor was proposed to optimize the parameter of SVM. The algorithm employed the nonlinear decreasing weight to balance the global and local search ability. Second-order oscillation factor could maintain the population diversity. The repulsion factor was introduced to make the swarm even distribution in search space, which could avoid local optimum. For the complex characteristics of nonlinearity, time-varying and multifactorial of electric power load, a support vector machine forecasting model based on data was proposed, and the influence …
A Hybrid Model Based On The Convolutional Neural Network Model And Artificial Bee Colony Or Particle Swarm Optimization-Based Iterative Thresholding For The Detection Of Bruised Apples, Mahmut Heki̇m, Onur Cömert, Kemal Adem
A Hybrid Model Based On The Convolutional Neural Network Model And Artificial Bee Colony Or Particle Swarm Optimization-Based Iterative Thresholding For The Detection Of Bruised Apples, Mahmut Heki̇m, Onur Cömert, Kemal Adem
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthy objects using iterative thresholding approaches based on artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms supported by a convolutional neural network (CNN) deep learning model. The proposed model includes the following stages: image acquisition, image preprocessing, the segmentation of anatomical regions (stem-calyx regions) to be discarded, the detection of bruised areas on the apple images, and their classification. For this aim, by using the image acquisition platform with a NIR camera, a total of 1200 images at 6 different angles were taken …
Robust Optimal Operation Of Smart Distribution Grids With Renewable Basedgenerators, Omid Zare, Sadjad Galvani, Murtaza Farsadi
Robust Optimal Operation Of Smart Distribution Grids With Renewable Basedgenerators, Omid Zare, Sadjad Galvani, Murtaza Farsadi
Turkish Journal of Electrical Engineering and Computer Sciences
Modern distribution systems are equipped with various distributed energy resources (DERs) because of the importance of local generation. These distribution systems encounter more and more uncertainties because of the ever-increasing use of renewable energies. Other sources of uncertainty, such as load variation and system components? failure, will intensify the unpredictable nature of modern distribution systems. Integrating energy storage systems into distribution grids can play a role as a flexible bidirectional source to accommodate issues from constantly varying loads and renewable resources. The overall functionality of these modern distribution systems is enhanced using communication and computational abilities in smart grid frameworks. …