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Experimental prototype of a novel feed forward compensation for DC-link voltage stabilization in grid-tied PV system

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第1期30卷

作者： Kumar, Ajay Gupta, Nitin Gupta, Vikas MNIT Dept Elect Engn Jaipur Rajasthan India

DC-link voltage control is an eminent attribute of grid-tied photovoltaic (PV) systems, and it is maintained at 10% to 15% higher than the peak of supply voltage to control the flow of active power. However, large variations in amplitude of supply voltage, compensating current, and PV current affect the recital of DC-link voltage controller, which further deteriorates the performance of control algorithm. This paper presents a small signal analysis using instantaneous power balance on both sides of inverter for DC-link voltage stabilization. A novel feed forward compensation (FFC) technique is proposed for mitigating the effects of external disturbances. Active current detection technique is also proposed for load current decomposition and compensating current detection which is needed for FFC technique design. An integral time absolute error criterion is used for collecting the error generated by the floating nature of DC-link voltage reference. This generated error is minimized by optimal tuning of the proportional controller using gravitational search algorithm. The effectiveness of the proposed controller is validated using sensitivity analysis. The dynamic performance of the proposed control technique is tested and verified using MATLAB/Simulink. Hardware prototype using a cost-effective Arm Cortex M4 microcontroller (STM32F407VGT6) is also presented to validate the simulation results.

关键词： active current detection Arm Cortex M-4 microcontroller feed forward compensation gravitational search algorithm solar photovoltaic

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Multiplanar reconstruction with incomplete data via enhanced fuzzy radial basis function neural networks

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2020年第0期57卷 101766-101766页

作者： Chao, Zhen Kim, Dohyeon Kim, Hee-Joung Yonsei Univ Coll Hlth Sci Dept Radiat Convergence Engn 1 Yonseidae Gil Wonju 220710 Gangwon South Korea Yonsei Univ Coll Hlth Sci Dept Radiol Sci 1 Yonseidae Gil Wonju 220710 Gangwon South Korea

Slice images based on a single slicing direction often contain incomplete data and cannot be used by clinicians for diagnosis or observation. It is thus necessary to reconstruct the slices using multiplanar reconstruction technology. In the case of complete data, it is not difficult to obtain a series of clear images from other slicing directions. In the case of incomplete data, interpolation methods are commonly employed on reconstructed images to compensate for the missing information. However, such results are often not ideal. In this study, we propose a new method based on an enhanced fuzzy radial basis function neural network. First, a series of incomplete transverse section images that have been accurately registered are adopted. Then, we superpose the sequence images to obtain the three-dimensional data volume. Thereafter, we can acquire the coronal or sagittal images by reformatting this data volume. For a reconstructed image, the proposed system was applied to compensate for the lost data. We used 15 sets of proposed neural networks to obtain 15 sets of output data, with the final output data acquired via the inverse distance-weighted algorithm. We trained the system via a gravitational search algorithm. Finally, we repaired all the interpolated data. In this experiment, we used two types of datasets, i.e., images obtained via brain magnetic resonance imaging and abdominal computed tomography. Subjective observations and objective evaluations confirm the superiority and effectiveness of the proposed method compared to other state-of-the-art methods. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Multiplanar reconstruction technology Incomplete transverse section images Interpolation algorithm Enhanced radial basis function neural network gravitational search algorithm

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Fractional PSOGSA algorithm Approach to Solve Optimal Reactive Power Dispatch Problems With Uncertainty of Renewable Energy Resources

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IEEE ACCESS 2020年 8卷 215399-215413页

作者： Khan, Noor Habib Wang, Yong Tian, De Jamal, Raheela Ebeed, Mohamed Deng, Qirong North China Elect Power Univ Sch New Energy Beijing 102206 Peoples R China Tarim Univ Coll Mech & Elect Engn Alar 843300 Xinjiang Peoples R China Sohag Univ Fac Engn Dept Elect Engn Sohag 82524 Egypt

The optimal reactive power dispatch (ORPD) is a major tool, and it plays a vital role for enhancement of the power system performance. ORPD is one multimodal, non-convex, and non-linear problem. Many elegant benefits can be obtained by using the renewable energy resources (RERs), but many technical issues related to the RERs including the stochastic characteristics of these resources due to continuous variations of solar irradiance and the wind speed lead to increasing the uncertainties of system. Thus, solving the ORPD problem with RERs is a crucial task. The contribution of the paper includes application a modified hybrid algorithm for solving the ORPD considering the uncertainties of the RERs and the load demand. The proposed algorithm is based on Fractional Calculus with Particle Swarm Optimization gravitational search algorithm (FPSOGSA) which aims to enhance the searching capabilities of the conventional PSOGSA algorithm and overcome its tendency to stagnation. The proposed algorithm is tested on IEEE 30-bus system for reducing power losses and voltage deviation as well as enhancing voltage stability. The scenario-based method is employed to produce a set of scenarios from the uncertainties of load, wind speed and solar irradiance. The simulation results verify the effectiveness of the proposed algorithm for solving the ORPD problem with and without considering the uncertainties in the system. Furthermore, the proposed algorithm is superior compared with the state-of-the-art techniques in terms of the reduction of power losses and voltage deviations as well as the stability enhancement.

关键词： Uncertainty Power system stability Stability criteria Reactive power Optimization Voltage control Standards Optimal reactive power dispatch renewable energy resources optimization fractional calculus particle swarm optimization gravitational search algorithm uncertainty

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Forward Clearing of Energy Market for Wind Incorporated Power System 8

Forward Clearing of Energy Market for Wind Incorporated Powe...

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8th IEEE International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC)

作者： Sharma, Akanksha Jain, Sanjay K. Thapar Inst Engn & Technol Patiala Punjab India

ISBN: (纸本)9781728108377

In this paper the forward market clearing of wind integrated power system considering the randomness in wind power generation is presented. The intermittent nature of wind power is characterized by Weibull probability density function (PDF). The aim is to minimize the cost of energy supplied by thermal and wind generators. The cost of energy also incorporates overestimation and underestimation cost of available wind power. gravitational search algorithm (GSA) based scheduling is utilized for the market clearing purpose. The results have been obtained on IEEE 30-bus and IEEE 57-bus test systems to investigate the effectiveness of the presented algorithm for optimal scheduling.

关键词： gravitational search algorithm market clearing weibull probability distribution function wind energy

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Augmented Lagrangian combined to evolutionary heuristic for energy efficiency in OCDMA networks

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OPTICAL SWITCHING AND NETWORKING 2020年 36卷 100542-100542页

作者： Pendeza Martinez, Cristiane A. Durand, Fabio Renan Martinez, Andre Luis M. Abrao, Taufik Univ Tecnol Fed Parana UTFPR Dept Math DAMAT BR-86300000 Cornelio Procopio Brazil Univ Tecnol Fed Parana UTFPR Dept Elect Engn BR-86300000 Cornelio Procopio Brazil Londrina State Univ UEL Dept Elect Engn DEEL PB 10-011 BR-86057970 Londrina PR Brazil

This paper proposes combining the augmented Lagrangian method (ALM) with evolutionary heuristic methods, as well as quasi-Newton optimization methods applied to the energy efficiency (EE) maximization in the optical code division multiple access (OCDMA) communication network. The particle swarm optimization (PSO) and a hybridization between the PSO and the gravitational search algorithm (GSA) called PSOGSA have been deployed. The ALM structure replaces the objective function and allows a best fit to the problem, and ultimately provide more information about the solution. Numerical results demonstrate the robustness and low-complexity of hybrid ALM-PSO, while the ALM associated with PSOGSA attains robustness at cost of high-complexity. In turn, the usually ALM combined with Broyden-Fletcher-Goldfarb-Shanno (BFGS) method presents convergence for a restrict scenarios, failing to perform suitably for networks with large numbers of users.

关键词： Augmented Lagrangian method BFGS method Particle swarm optimization gravitational search algorithm OCDMA networks Energy efficiency

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Analysis of antlion optimizer-based ABT for automatic generation control of an interconnected power system

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SOFT COMPUTING 2019年第18期23卷 8563-8577页

作者： Priya, G. Soorya Sivakumar, Ponnurangam Anna Univ Chennai Tamil Nadu India Rajalakshmi Engn Coll Chennai Tamil Nadu India

In this paper, a novel ant lion optimization algorithm is proposed for the recent availability-based tariff (ABT) pricing scheme for the automatic generation control of a three-area interconnected power system (IPS). The innovative ant lion optimizer (ALO) process is performed in conjunction with ABT. The real frequency and load variation are realistic to the input of ALO and minimize the objective function area control error, generation costs and marginal cost of the system. Based on the output of the ALO, the ABT is resolute and assumed as the input of the proposed controller. The dynamic performance of the three-area IPS values including hydrothermal, wind turbine and photovoltaic power systems are evaluated on the basis of limits such as frequency deviation, generation power and tie-line power. Based on these limits, the frequency deviation is controlled and the parameters of the multi-resolution PID (MRPID) controller are regulated. The proposed MRPID controller is modified with the proposed ABT-based ALO to increase the optimal resolution. A three-area IPS is modeled under numerous loads to improve the efficiency of the proposed algorithm. The performance of the this method is demonstrated and compared with existing methods including particle swarm optimization and gravitational search algorithm.

关键词： Availability-based tariff Particle swarm optimization gravitational search algorithm Automatic generation control Ant lion optimizer Frequency deviation

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Job Allocation on Cloud: A Comparative Study 3rd

Job Allocation on Cloud: A Comparative Study

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3rd International Conference on Microelectronics, Computing and Communication Systems (MCCS)

作者： Manasa, M. Priyadarshini, J. VIT SCSE Chennai Tamil Nadu India

ISBN: (纸本)9789811370915;9789811370908

The rapid growth in the field of cloud computing leads to migration of people to cloud, which makes them feel more tensile and adaptive in the environment. Cloud makes the users give less importance to maintain their hardware and other resources because all these works are done by a service provider. This would restrict the users from spending more money on capital expenditure and so the people are ready to invest more in the cloud. The virtual machine (VM) is a buzz which replaced a traditional physical machine through the method called virtualization. Virtualization is themain objective for establishing cloud services. The core idea of virtualization is to create an instance or virtual machine according to user demands, and the number of servers needed is proportional to the amount need for the resource pool. This paper presents a heuristic algorithm-gravitational search algorithm (GSA)-which formulate an optimal solution for allocating task on the cloud. To analysis the efficiency of the proposed algorithm, a comparative study has been done with other heuristic algorithms like the ant colony and particle swarm optimization which is also used for job allocation.

关键词： gravitational search algorithm Job allocation Particle swarm optimization Task scheduling Virtual machine

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Meta-Heuristic Optimization Based Two-stage Residential Load Pattern Clustering Approach Considering Intra-cluster Compactness and Inter-cluster Separation

Meta-Heuristic Optimization Based Two-stage Residential Load...

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54th Annual Meeting of the IEEE-Industry-Applications-Society (IEEE IAS)

作者： Li, Kangping Ge, Xinxin Lu, Xiaoxing Wang, Fei Mi, Zengqiang North China Elect Power Univ Dept Elect Engn Baoding 071003 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding 071003 Peoples R China North China Elect Power Univ Hebei Key Lab Distributed Energy Storage & Microg Baoding 071003 Peoples R China

ISBN: (纸本)9781538645390

This paper proposes a meta-heuristic optimization based two-stage residential load pattern clustering (LPC) approach to address two main issues that exist in the most current LPC methods: 1) unreasonable typical load pattern (TLP) extraction, 2) a good clustering should achieve reasonable balance between the intra-cluster compactness and inter-cluster separation of the formed clusters. However, most of the current clustering algorithms usually only take one of the aspects into consideration. In the first stage, an adaptive DBSCAN is proposed to automatically detect the uncommon load curves and obtain the TLP of each individual customer. In the second stage, LPC is formulated as an optimization problem in which clustering validity index (CVI) considering both compactness and separation is used as the objective function. gravitational search algorithm (GSA) is adopted to solve this optimization problem. Four different CVIs are investigated to find the most appropriate one for LPC. A comparative case study using the real load data from 208 households from the U.K. verified the effectiveness of the proposed approach.

关键词： Load pattern clustering Typical load pattern gravitational search algorithm Intra-cluster compactness Inter-cluster separation

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Improved Particle Swarm Optimization for the Mutual Inductance of an Implantable Biomedical Application 5

Improved Particle Swarm Optimization for the Mutual Inductan...

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International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS)

作者： Houcine, Marouani Amin, Sallem Mondher, Chaoui Nouri, Masmoudi Univ Sfax LETI Sfax Tunisia

ISBN: (纸本)9781538678503

Particle Swarm Optimization (PSO) and Differential Evolution (DE) are two dominant metaheuristic optimization techniques with different special features. These specialties are combined together in an improved particle swarm optimization (DPSO) algorithm is estimated. The proposed idea consists of incorporating the concept of the DE algorithm when updating velocity equation of the PSO algorithm in sort to guarantee the global optimal. Also, the objective is to bias the solution, the difference between the selected randomly and the target particle, with an adjust parameter. The improved algorithm is simulated on several test functions and compared between three references metaheuristics and one variant most used in the literature. Then, DPSO has been used to optimize the mutual inductance of an implantable biomedical problem.

关键词： Improved DPSO Particle Swarm Optimization Differential Evolution Backtracking search algorithm gravitational search algorithm Mutual inductance Archimedes coil Solenoid coil

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Dynamic Parameter Adaptation Based on Using Interval Type-2 Fuzzy Logic in Bio-inspired Optimization Methods 1

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9th International Conference on Innovations in Bio- Inspired Computing and Applications (IBICA) / 7th World Congress on Information and Communication Technologies (WICT)

作者： Castillo, Oscar Tijuana Inst Technol Calzada Tecnol S-N Tomas Aquino 22379 Tijuana Mexico

ISBN: (数字)9783030166816

ISBN: (纸本)9783030166816;9783030166809

In this paper we perform a comparison of the use of type-2 fuzzy logic in two bio-inspired methods: Ant Colony Optimization (ACO) and gravitational search algorithm (GSA). Each of these methods is enhanced with a methodology for parameter adaptation using interval type-2 fuzzy logic, where based on some metrics about the algorithm, like the percentage of iterations elapsed or the diversity of the population, we aim at controlling their behavior and therefore control their abilities to perform a global or a local search. To test these methods two benchmark control problems were used in which a fuzzy controller is optimized to minimize the error in the simulation with nonlinear complex plants.

关键词： Interval type-2 fuzzy logic Ant Colony Optimization gravitational search algorithm Dynamic parameter adaptation

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