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Optimal Time of Use Electricity Pricing Model and Its Application to Electrical Distribution System

IEEE ACCESS 2019年 7卷 123558-123568页

作者： Yang, Hejun Wang, Lei Ma, Yinghao Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China

Because the time of use (TOU) strategies can directly affect the power flow distribution of electrical distribution system, this paper investigates the optimal TOU electricity pricing model and its functions for improving the power quality and reducing the power loss of electrical distribution system. Firstly, an optimal period partitioning algorithm based on a moving boundary technique is proposed for dividing an entire day to the different periods. Secondly, an optimal TOU electricity pricing model is proposed through minimizing the peak-valley difference, the voltage fluctuation, and the power loss. The particle swarm optimization (PSO) algorithm is adopted to solve the proposed optimization problem, and the multi-objective constrained optimization problem is transformed into a single objective unconstrained optimization problem. Thirdly, two novel indices of describing the voltage variation and the power loss are defined for considering the impact of TOU strategies and improving the power quality and reducing the power loss. Finally, an IEEE 14-bus system is applied to verifying the correctness and effectiveness of the proposed method. The results prove that the algorithm which proposed in this paper has a great significance in improving the power quality and the economic benefits of electrical distribution system.

关键词： Time of use optimal period partitioning moving boundary technique particle swarm optimization algorithm electrical distribution system

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A New Two-Stage Approach for a Bi-Objective Facility Layout Problem Considering Input/Output Points Under Fuzzy Environment

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IEEE ACCESS 2019年 7卷 134083-134103页

作者： Mohamadi, Arash Ebrahimnejad, Sadoullah Soltani, Roya Khalilzadeh, Mohammad Islamic Azad Univ Dept Ind Engn Sci & Res Branch Tehran *** Iran Islamic Azad Univ Dept Ind Engn Karaj Branch Karaj *** Iran KHATAM Univ Dept Ind Engn Tehran *** Iran

Facility layout problem is one of the most important problems in a huge range of industries and services organizations. Simultaneous study of some qualitative and quantitative parameters like closeness relationship between facilities, physical constraints such as input/output points and how to arrange facilities can play a key role to determine the facility layout. Considering these parameters can lead to reduce production costs, increase production capacity, and remove additional displacements. A two-stage approach is proposed to achieve these goals. In the first stage, a goal programming model is proposed to determine weights of the attributes based on the experts' opinions with different preference representation structures. Afterwards, the closeness ratings of the facilities are calculated using weights of attributes. In the second stage, an efficient layout is designed by determining facilities placement sequence, location of the next facility adjacent to the previous one, location of input/output points, and rectilinear feasible shortest path between facilities. Two meta-heuristic algorithms including particle swarm optimization and genetic algorithm are designed due to computational complexity. The objective function is to minimize the sum of products distance between facilities and closeness rating and also to minimize the dead space. A case study of an Auto Body Parts company is demonstrated to verify the efficiency of the proposed two-stage approach. Furthermore, in order to assess the performance of the proposed approach, a comparison is drawn between the proposed approach and five existing approaches in the literature to solve different problems by using the two meta-heuristic algorithms.

关键词： Facility layout problem different preference representation structures goal programming genetic algorithm particle swarm optimization algorithm fuzzy sets

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Non-contact voltage measurement of three-phase overhead transmission line based on electric field inverse calculation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第12期12卷 2952-2957页

作者： Xiao, Dongping Xie, Yutong Ma, Qichao Zheng, Qi Zhang, Zhanlong Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se 174 Shapingba Dist Chongqing Peoples R China

With the development of smart grid, the demand for voltage measurement along the overhead transmission lines is increasing. However, the installation of voltage transformers in the existing lines entails numerous difficulties. This study proposes a new non-contact measurement method by inversely calculating the voltages on AC overhead transmission lines based on the power-frequency electric field measurement data. To improve the calculation accuracy, the 3D model of transmission lines is built, and the relations between the 3D electric fields and voltages are presented. An improved algorithm that intermingles with the particle swarm algorithm and genetic algorithm is developed to ascertain optimal inverse solutions, meanwhile to improve the convergence speed and calculation stability. To further reduce the computational complexity, the constraint relations between the voltages and electric fields are derived, thereby the simplification from three decision variables to one is achieved. Then, some simulation cases with different measurement errors and voltage running states are conducted to show the good robustness and high accuracy of the proposed inversion method. Finally, a three-phase experimental system is built and the actual measured data are used to inversely calculate, which verifies the practicability of the proposed non-contact voltage measurement method.

关键词： voltage measurement power overhead lines inverse problems electric fields smart power grids potential transformers particle swarm optimisation computational complexity three-phase overhead transmission line electric field inverse calculation smart grid development voltage parameter measurement voltage transformers AC overhead transmission lines inverse calculation electric field measurement data 3D mathematical model particle swarm optimization algorithm genetic algorithm optimal inverse solutions convergence speed computational complexity optimisation algorithm multiple decision variables single decision variable selection strategy asymmetrical simulation three-phase symmetrical simulation measurement error 3D real-time electric field measurement devices noncontact voltage measurement method

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A novel blind deconvolution method and its application to fault identification

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JOURNAL OF SOUND AND VIBRATION 2019年 460卷 114900-000页

作者： Cheng, Yao Chen, Bingyan Mei, Guiming Wang, Zhiwei Zhang, Weihua Southwest Jiaotong Univ State Key Lab Tract Power 111First SectNorth Second Ring Rd Chengdu 610031 Sichuan Peoples R China

Blind deconvolution is a method for enhancing the fault feature of rolling element bearings. Based on different maximization criteria, including kurtosis, correlated kurtosis, D-norm, multi-D-norm, and cyclostationarity indicator, different blind deconvolution algorithms have been proposed as powerful tools for fault feature extraction. However, kurtosis and D-norm are susceptible to extreme values, while the other three criteria strongly rely on prior knowledge of the fault period. To overcome the shortcomings of the existing criteria, this study proposes a new criterion called impulse-norm. It is a time-domain parameter defined as the ratio of the average amplitude of the first several maximum energy points to the energy of the entire signal. As opposed to kurtosis and D-norm, the impulse-norm is not affected by strong random impulses. Unlike correlation kurtosis, multi-D-norm and cyclostationarity indicator, it is also independent from the fault period. Based on impulse-norm, we also propose a new deconvolution algorithm called particle swarm optimization-based maximum impulse-norm deconvolution. This blind deconvolution algorithm employs generalized sphere coordinate transformation and adopts the PSO algorithm to optimally solve the filter coefficients by maximizing the impulse-norm of the signal being filtered. The proposed method was validated using simulated signals and high-speed train axle-box bearing experimental signals. The simulation and experimental results indicated that the proposed PSO-MIND method can effectively identify the weak impulse fault feature of rolling element bearings. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Blind deconvolution particle swarm optimization algorithm Fault identification Railway Rolling element bearing

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Designing a New Artificial Lift Method Using Computational Simulation and Evolutionary optimization

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IEEE ACCESS 2019年 7卷 123846-123859页

作者： Moreira, Andre Pimentel Lepikson, Herman Augusto Schnitman, Leizer Bezerra Ramalho, Geraldo Luis Fed Inst Educ Sci & Technol Ceara Dept Ind BR-60040215 Fortaleza Ceara Brazil Mfg & Technol Integrated Campus SENAI Inst Innovat Automat & Logist BR-41650010 Salvador BA Brazil Univ Fed Bahia Ctr Technol Qualificat Ind Automat BR-40210630 Salvador BA Brazil

Oil exploitation has moved into deeper reservoirs with the advances in drilling techniques and thus the development of new pumping techniques has become a challenge to improve production. The positive displacement pumping system proposed in this paper is presented as an artificial lift technique. Here we present a new pumping device, its estimated operational curves through numerical simulations and a prototype of the device, denominated a Double Acting Submersible Linear Pump (SLP), which is a hydraulic system adapted for inside well operations. Design parameters used in the tests were optimized using the particle swarm optimization (PSO) algorithm to maximize oil production while optimizing the parameters (characterstics) of the pump, such as maximum pump diameters and submersible electric motor power, pressure and volumetric displacement of the hydraulic pump. Also presented are the operating curves estimated from the numerical simulation of the power and pump modules, as well as the SLP0 and SLP1 experimental test curves in a controlled environment using the optimized parameters obtained using PSO The data obtained were compared to the computational simulations performed with the Automation Studio (TM) software. The SLP0 and SLP1 optimal design tests showed the importance to delimit the range of operation within which the SLP must operate, since the pipes and hydraulic components must be suitable for the flow of hydraulic oil and the production oil. The findings presented here play an important role in the production process of a fully operational SLP prototype.

关键词： Artificial lift technique petroleum industry particle swarm optimization algorithm computational modeling and simulation

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Shape optimization of U-shaped steel dampers subjected to cyclic loading using an efficient hybrid approach

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ENGINEERING STRUCTURES 2019年 197卷 108874-000页

作者： Khatibinia, Mohsen Jalaipour, Mehdi Gharehbaghi, Sadjad Univ Birjand Dept Civil Engn Birjand Iran Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran

U-shaped steel damper (USSD), as an energy dissipation device, has been recommended in the literature for using in the isolation systems. This type of damper is capable of appropriately dissipating the input energy which a structure imparts from an earthquake. The capability is provided through a large range of plastic deformations occurred in the USSD. This paper aimed at presenting a methodology structured in the framework of a shape optimization problem to enhance the seismic energy dissipation and deformation capability of the USSD under cyclic loading. To achieve this goal, the straight part, thickness and height of the USSD were considered as the design variables of the optimization problem and optimized through maximizing the ratio of energy dissipation through plastic deformation to the maximum equivalent plastic strain. In order to find the optimum shape of the USSD under cyclic loading, a hybrid approach consisted of two phases was applied. In the first phase, as an alternative for the time-consuming finite element analysis, a support vector machine (SVM) approach was trained, tested and used to predict the inelastic responses of the USSD. In the second phase, a modified particle swarm optimization (PSO) algorithm was adopted to find the optimum shape of the USSD subjected to two critical directions of cyclic loading. After finding the optimum shape of the USSD, the energy dissipation and deformation capability of the optimum shaped-USSD were assessed. Results demonstrate that the proposed shape optimization methodology renders an optimum-shaped USSD with significantly improved energy dissipation and deformation capability compared with those of available in the literature.

关键词： U-shaped steel damper Cyclic Loading Energy dissipation Shape optimization Support vector machine particle swarm optimization algorithm

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Optimized BP neural network for Dissolved Oxygen prediction

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IFAC-PapersOnLine 2018年第17期51卷 596-601页

作者： Wu, Jing Li, Zhenbo Zhu, Ling Li, Guangyao Niu, Bingshan Peng, Fang College of Information and Electrical Engineering China Agricultural University Beijing China Key Laboratory of Agricultural Information Acquisition Department of Agriculture Beijing China Agricultural Internet of Things Engineering Technology Research Center Beijing China

To solve the low accuracy, slow convergence and poor robustness problem of traditional neural network method for water quality forecasting, a new model of dissolved oxygen content prediction is proposed based on sliding window, particle swarm optimization (PSO) and BP neural network. dissolved oxygen content prediction model in water quality is established by handling dissolved oxygen content data through sliding window, and using particle swarm optimization algorithm to obtain BP neural network parameters. This model is applied to prediction analysis of dissolved oxygen with online monitoring of regional groundwater in Xilin Gol League on July 25, 2017 to December 5, 2017. Experimental results show that the model has better prediction effect, and mean square error (MSE), root mean square error(RMSE), mean absolute error(MAE) value of PSO algorithm to optimize the BP neural network based on sliding window are 0.437% and 6.611%, 0. 251% respectively, which are better than single forecasting method by using sliding window, PSO, and BP neural network individually. The Optimized BP neural network not only has fast convergence speed and high prediction accuracy, but also provides decision-making basis for water pollution control and water management. © 2018

关键词： Neural networks Backpropagation Biochemical oxygen demand Decision making Dissolution Dissolved oxygen Errors Forecasting Groundwater Mean square error particle swarm optimization (PSO) Predictive analytics Water management Water pollution Water pollution control Water quality BP neural networks Dissolved oxygen contents Fast convergence speed Neural network method particle swarm optimization algorithm Root mean square errors Sliding Window Water quality predictions

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Knowledge modeling for root cause analysis of complex systems based on dynamic fuzzy cognitive maps

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IFAC-PapersOnLine 2018年第21期51卷 13-18页

作者： Yue, Weichao Chen, Xiaofang Huang, Keke Zeng, Zhaohui Xie, Yongfang School of Information Science and Engineering Central South University Changsha410083 China

This paper proposes a knowledge model for root cause analysis (RCA) of complex systems based on fuzzy cognitive maps (FCMs) and particle swarm optimization algorithm (PSO). The process knowledge and experience of technicians can be captured by FCMs that are characterized by briefness of knowledge modeling and execution. The traditional methods for RCA based on FCMs are restricted to fixed incidence matrix. However, the individualized features are there existing in each system of the same kind, therefore fixed weights are unreasonable. PSO is introduced to detect the weight that can reveal the individualized features of systems among concepts of FCMs. And then a dynamic knowledge model for RCA is obtained, including predictive, diagnostic, and hybrid RCA. The three types RCA can be used for forecasting future event of output, identifying root cause and presenting measures of abnormal event. The effectiveness of proposed method is validated in aluminum reduction process, and the experiments results show the proposed method is effective and application potential. © 2018

关键词： Cognitive systems Fuzzy rules Large scale systems Models Ore reduction particle swarm optimization (PSO) Aluminum reduction Fuzzy cognitive map Fuzzy cognitive maps (FCMs) Incidence matrices Knowledge particle swarm optimization algorithm Process knowledge Root cause analysis

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Adaptive Balance Factor in particle swarm optimization 1

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6th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Sharma, Siddhi Kumari Sharma, R. S. Rajasthan Tech Univ Kota India

ISBN: (数字)9789811033223

ISBN: (纸本)9789811033223;9789811033216

particle swarm optimization (PSO) is a refined optimization method, that has drawn interest of researchers in different areas because of its simplicity and efficiency. In standard PSO, particles roam over the search area with the help of two accelerating parameters. The proposed algorithm is tested over 12 benchmark test functions and compared with basic PSO and two other algorithms known as Gravitational search algorithm (GSA) and Biogeography based optimization (BBO). The result reveals that ABF-PSO will be a competitive variant of PSO.

关键词： Meta-heuristic optimization techniques particle swarm optimization algorithm swarm intelligence Acceleration coefficients Nature inspired algorithm

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Hybrid QPSO-NNIA2 algorithm for Multi-Objective optimization Problem

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2019年第8期33卷

作者： Zhang, Lan Northwestern Polytech Univ Dept Appl Math Xian Shaanxi Peoples R China Xian Aeronaut Polytech Inst Basic Dept Xian Shaanxi Peoples R China

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

关键词： Multi-objective optimization problem Pareto optimal solution vicinity distance Quantum particle swarm optimization algorithm particle swarm optimization algorithm

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