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Optimal Allocation and Size of FACTS Devices Using Several Optimizers: a Comprehensive Study

Optimal Allocation and Size of FACTS Devices Using Several O...

引用

International Middle-East Power System Conference, MEPCON

作者： Ayman Awad Salah Kamel Mohamed H. Hassan Abdalla A. Ibrahim Francisco Jurado Dept. of Electrical Engineering Faculty of Engineering Aswan University Aswan Egypt Dept. of Electrical Engineering Aswan University Egypt Dept. of Electrical Engineering University of Jaen Jaen Spain

ISBN: (纸本)9781665463645

FACTS devices are playing an essential role in modern power systems, as they have the ability to control the parameters of a system's transmission lines so that the transferability of the system is improved, and the overall performance is enhanced. However, it is crucial to identify the best allocation and size of the incorporated FACTS device to achieve the best results considering the objective required. In this paper, several FACTS devices are incorporated into the IEEE 30-bus standard system, where the optimal allocation and size of such devices are required regarding the cost of generation and power losses as a single objective function of each, considering the integration of wind turbines in the system. The optimization is performed using several recent optimizers including Moth Flame Optimizer (MFO), whales optimization Algorithm (WOA), Skill optimization Algorithm (SOA), and Equilibrium Optimizer (EO), investigating the best results concluded by such optimizers. Simulation results reveal that the EO algorithm is more efficient and superior for optimal allocation and size of FACTS devices solution compared with the other recent algorithms.

关键词： Performance evaluation Thyristors Costs Static VAr compensators Linear programming whale optimization algorithms Wind turbines

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Power-Efficient Cluster Head Selection in Wireless Sensor Networks using whale and Seagull algorithms

Power-Efficient Cluster Head Selection in Wireless Sensor Ne...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： P.Vinoth Kumar K. Venkatesh Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781665462761

WSN includes deployed sensors. These small sensor nodes have energy limits since their batteries can't be replaced. Collect, transmit, and transfer data to the base station. Data transmission depletes WSN energy. Clustering is an efficient WSN data transport technique. whale optimization (WSOA) combines a seagull method with a global issue to meet global optimization concerns. The suggested technique helps choose based on residual node energy and nearby nodes, energy-aware cluster heads energy into consideration. Lifetime, energy economy, throughput, and stability of the recommended algorithm are tested. The hybrid metaheuristic's performance is compared to LEACH. The suggested approach outperforms others in residual energy, network longevity, and stability period.

关键词： Wireless sensor networks Metaheuristics Clustering algorithms whales Throughput Control systems whale optimization algorithms

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An Efficient Neurocontroller Position Method for PMSM Drive System

An Efficient Neurocontroller Position Method for PMSM Drive ...

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IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)

作者： O. Aguilar-Mejia H. Minor Popocatl J.M. Garcia-Morales C. O. Castillo-Ibarra A. Valderrabano-Gonzalez UPAEP University Puebla Puebla Mexico Facultad de Ingenieria Universidad Panamericana Zapopan Jalisco Mexico

ISBN: (纸本)9781665458931

PMSM has been widely used in high-precision variable-speed applications, however, the control scheme demands normally a high dynamic performance under several operating contidions. Due to the non-linear nature of the PMSM, the use of an adaptive controller based on B-spline neural networks is proposed to determine the control signals. The proposed control technique through neural networks exhibits the best performance because it can be adapted to each operating condition, demanding low computational cost for an online operation, and considering non-linearities of the system. The performance of the proposed controller is evaluated in the presence of uncertainties. The results are compared with the conventional PI controller, optimized using whale optimization algorithm.

关键词： Neurocontrollers Uncertainty PI control Adaptive systems Neural networks whale optimization algorithms Power electronics

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An Optimized Job Scheduling Mechanism for Mapreduce Framework Using DIW-WOA in Big Data

An Optimized Job Scheduling Mechanism for Mapreduce Framewor...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： Vishal Kumar Sumit Kushwaha University Institute of Computing Chandigarh University India

For processing, storing, and managing huge amounts of data, big data has turned out to be famous. The most popular map reduce programming model is the open-source Hadoop. It executes the MapReduce (MR) programming model to process big data with MR jobs. Nevertheless, scheduling MR jobs across multiple nodes has been regarded as an optimization issue in spite of current endeavors toward ameliorating MR's performance. Hence, to carry out an MR Job scheduling centered on Completion Time (CT) and cost whilst meeting the security constraints, a new optimization algorithm like Decreasing Inertia Weight induced whale optimization Algorithm (DIW-WOA) is proposed. At first, for big data clustering, a Butterfly optimization-centered K-Means clustering Algorithm (BO-KMA) is applied. At last, for scheduling the client's tasks or jobs to the optimized scheduler. The whale optimization algorithm is hybridized with inertia weight concept and finally DIW-WOA is employed. When weighed against the other well-known algorithms, the experimental outcomes exhibit that the proposed algorithms for data clustering, DS and Task Scheduling (TS) achieve better performance in terms of make-span and throughput. It can be incorporated into the Hadoop environment.

关键词： System performance Clustering algorithms Big Data Programming Throughput Scheduling whale optimization algorithms

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Comparative Analysis of Swarm algorithms to Classification of covid19 on X-Rays

Comparative Analysis of Swarm Algorithms to Classification o...

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Data Science and Intelligent Computing (ICDSIC), International Conference on

作者： Abubakr S. Issa Yossra H. Ali Tarik A. Rashid Computer sciences department University of Technology Baghdad Iraq Computer Science & Engineering Department University of Kurdistan Hewler Iraq

The use of Chest radiograph (CXR) images in the examination and monitoring of different lung disorders like infiltration, tuberculosis, pneumonia, atelectasis, and hernia has long been known. The detection of COVID-19 can also be done with CXR images. COVID-19, a virus that results in an infection of the upper respiratory tract and lungs, was initially detected in late 2019 in China's Wuhan province and is considered to majorly damage the airway and, thus, the lungs of people afflicted. From that time, the virus has quickly spread over the world, with the number of mortalities and cases increasing daily. The COVID-19 effects on lung tissue can be monitored via CXR. As a result, This paper provides a comparison regarding k-nearest neighbors (KNN), Support-vector machine (SVM), and Extreme Gradient Boosting (XGboost) classification techniques depending on Harris Hawks optimization algorithm (HHO), Salp swarm optimization algorithm (SSA), whale optimization algorithm (WOA), and Gray wolf optimizer (GWO) utilized in this domain and utilized for feature selection in the presented work. The dataset used in this analysis consists of 9000 2D X-ray images in Poster anterior chest view, which has been categorized by using valid tests into two categories: 5500 images of Normal lungs and 4044 images of COVID-19 patients. All of the image sizes were set to $200\times 200$ pixels. this analysis used several quantitative evaluation metrics like precision, recall, and F1-score.

关键词： COVID-19 Support vector machines Tuberculosis Lung Feature extraction whale optimization algorithms Classification algorithms

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Research on online detection and identification method of vibration signal of cutting process

Research on online detection and identification method of vi...

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Science and Technology Innovation (IAECST), International Academic Exchange Conference on

作者： Youzhi Zhang Xiu Wu Qiang Shao Yu Liu Dalian Minzu University Dalian China

Chattering is one of the major performance limitations during turning, which leads to poorer surface quality, dimensional errors, and shorter tool and machine life, so tool condition monitoring is very important. In this paper, for spindle chatter monitoring in turning process, a whale optimization Algorithm is used to optimize the variational modal decom- position, and a new whale optimization Algorithm-Variational Modal Decomposition-Energy Distribution method is proposed to identify the spindle chatter, and this method is confirmed to improve the identification accuracy by this experiment. And the approximate entropy recognition method and the sample entropy recognition method are tested. The recognition accuracy and computation time of the three methods were compared. The conducted experimental study shows that the proposed whale optimization Algorithm-Variational Modal Decomposition- Energy Distribution method provides a recognition method with higher recognition accuracy, better stability and less computation time for chattering monitoring.

关键词： Vibrations Condition monitoring Technological innovation Approximation algorithms Turning whale optimization algorithms Entropy

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Optimized coordinated fractional power system stabilizer for SPV penetrated multi machine system

Optimized coordinated fractional power system stabilizer for...

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Sustainable Energy, Signal Processing and Cyber Security (iSSSC), IEEE International Symposium on

作者： Samarjeet Satapathy Narayan Nahak Renu Sharma Department of Electrical Engineering Siksha ‘O’Anusandhan Deemed to be University Bhubaneswar India

ISBN: (纸本)9781665490573

Multiple fractional power system stabilizers are (FPSS) optimally controlled by modified whale optimization (MWOA) algorithm in this work for enhancing small signal stability (SSS)of power system. This control action is implemented for intermittent variation in mechanical power to generator and solar power penetration. The MWOA optimization is based on multi-objective function considering variation in speed, variation in real power, overshoot, undershoot and settling time. The FPSS action improves the close loop performance much better in comparison to conventional PSS and also the control action being improved with optimal coordination employing the MWOA algorithm. The experimentations to justify the proposed controller performances consist of disturbances that are variation in solar and mechanical power input to generators. The performance of control action is analyzed in the time and frequency domain. Also, MWOA performance is compared with whale optimization (WOA) algorithm. System Eigen value distributions has been examined with sudden change in solar penetration with proposed controller. It is found that responses with FPSS is better than conventional PSS and damping capability of PSS can be much enhanced when they are coordinately controlled by MWOA algorithm

关键词： Damping Frequency-domain analysis Signal processing algorithms Power system stability Control systems whale optimization algorithms Generators

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Optimizing LQG Controller for a Single-Phase Power Inverter in an AC Microgrid System

Optimizing LQG Controller for a Single-Phase Power Inverter ...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Hassanul Karim Roni Yafes Emran Haque Emal Saad Solayman Surain Jannath Ritu Md. Fazla Rabby Md. Fatin Ishraque Md. Sohel Rana Dept of Electrical and Electronic Engineering Bangladesh Army University of Science & Technology Bangladesh Dept of Electrical Electronic and Communication Engineering Pabna University of Science and Technology Bangladesh Dept. of Electrical and Electronic Engineering Rajshahi University of Engineering and Technology Bangladesh

This article presents an optimization-based Linear Quadratic Gaussian (LQG) control application for controlling a single-phase power inverter in an ac micro-grid system. Several optimization techniques (e.g., genetic algorithm, particle swarm optimization, grey wolf optimization, pattern search optimization, and whale optimization) have been applied with two different objective functions (i.e., ITAE and RMSE) to prove the effectiveness of different optimizers for enhancing control performance. A comparative analysis between the different optimizers has been presented based on their optimization performance and controller performances. From the comparison presented grey wolf optimization outperforms the other studied optimizers with only 2.89% & 2.04% overshoots and 0.07 & 0.30% steady-state error with ITAE and RMSE objective functions respectively. The simulations and optimizations are conducted in MATLAB/ Simulink environment.

关键词： Software packages Linear programming Search problems Inverters whale optimization algorithms Power industry Steady-state

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Cryptanalysis of Lightweight Block Ciphers using Metaheuristic algorithms in Cloud of Things (CoT)

Cryptanalysis of Lightweight Block Ciphers using Metaheurist...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Md Saquib Jawed Mohammad Sajid Department of Computer Science Aligarh Muslim University Aligarh India

ISBN: (纸本)9781665490597

The Cloud of Things (CoT) has transformed communication and network technologies by expanding the Iimitation of IoT devices. The Cloud of Things is a fusion of Cloud Computing with IoT that provides endless storage, networking, and compute capabilities to IoT devices. Cloud of Things (CoT) has substantially boosted IoT applications in healthcare, automotive, agriculture, education, welfare, and businesses. Even though Cloud of Things (CoT) is scalable, faster, cost-effective, and remotely accessible,it also faces many challenges like interoperability, compatibility, performance, longevity, and security. Security is the biggest hurdle in adopting Cloud of Things (CoT) because it directly involves users’ private data. Researchers and academicians are equally concerned and proposed various lightweight block ciphers (LWBC) to secure the user’s data. In this paper, the relative cryptographic strength of three lightweight block ciphers (LWBC) is measured by performing Cryptanalysis with three metaheuristic algorithms using the proposed framework. The chosen three lightweight block ciphers (LWBC) are SIMON, SPECK, and PRESENT ciphers, which belong to different categories of Lightweight Block Ciphers (LWBC), and three metaheuristic algorithms are Cuckoo Search (CS), whale optimization Algorithm (WOA), and Salp Swarm Algorithm (SSA). The result of the experiments shows that SIMON and SPECK are equally strong while PRESENT is a relatively weak cipher. Also, the Cuckoo Search (CS) is found to be better than both WOA and SSA. However, for the weak keys, WOA is able to break all three ciphers completely.X

关键词： Performance evaluation Ciphers Cloud computing Metaheuristics Medical services Immunity testing whale optimization algorithms

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Indoor RFID Localization Algorithm Based on WOA

Indoor RFID Localization Algorithm Based on WOA

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Yuyang Li Liangbo Xie Yong Wang Mu Zhou Wei Nie Chenhui Xia School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

With the advantage of low cost, RFID has become a hot topic in indoor localization research. This paper presents an indoor location algorithm based on the whale optimization algorithm, which transforms the location problem into an optimization problem. Combined with esprit angle measurement algorithm and the frequency hopping ranging principle, an evaluation function integrating angle error function and distance error function is designed to constrain the search direction of whale optimization algorithm, then the positioning is completed through continuous iteration. Through experimental verification, the median error of the proposed positioning algorithm is 8.81cm.

关键词： Location awareness Antenna measurements Phased arrays Measurement uncertainty Transforms Position measurement whale optimization algorithms

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