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The State of Charge Estimation of Power Lithium Battery Based on RBF Neural Network Optimized by particle swarm optimization

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2017年第4期20卷 483-490页

作者： Hou, Zhixiang Xie, Ping Hou, Jiqiang Changsha Univ Sci & Technol Coll Automobile & Mech Engn Changsha 410004 Hunan Peoples R China

In order to ensure the safe and stable operation of electric vehicles (EV), it is necessary to accurately estimate the state of charge (SOC) of power lithium battery for electric vehicle. Because of the nonlinear relationship between SOC and its influencing factors, RBF neural network has obvious advantages in solving nonlinear problems, so in this paper, an SOC estimation method of power battery based on RBF neural network is proposed. In order to improve the accuracy of SOC estimation, we use particle swarm optimization (PSO) to optimize the RBF neural network model and identify the value of RBF network center vector and the weights through global optimal searching ability of PSO algorithm. The results simulation show that the SOC model based on PSO-RBF neural network has good estimation accuracy.

关键词： Electric Vehicle State-of-charge Estimation Radial Basis Function Neural Network particle swarm optimization algorithm

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Integrated parallel forecasting model based on modified fuzzy time series and SVM

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Journal of Systems Engineering and Electronics 2017年第4期28卷 766-775页

作者： Yong Shuai Tailiang Song Jianping Wang Technical Support Engineering Faculty Academy of Armored Forced Engineering Beijing 100072 China Unit 68207 of the PLA Jiayuguan 735100 China China Defense Science & Technology Information Center Beijing 100142 China

A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly, the input data sets are optimized and their coherence is ensured, the region scale algorithm is modified and non-isometric multi scale region fuzzy time series model is built. At the same time, the particle swarm optimization algorithm about the particle speed, location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.

关键词： fuzzy C-means clustering fuzzy time series interval partitioning support vector machine particle swarm optimization algorithm parallel forecasting

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Fast basis search for adaptive Fourier decomposition

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2018年第1期2018卷 74-74页

作者： Wang, Ze Wan, Feng Wong, Chi Man Qian, Tao Univ Macau Dept Elect & Comp Engn Fac Sci & Technol Ave Univ Taipa Macau Peoples R China Univ Macau Dept Math Fac Sci & Technol Ave Univ Taipa Macau Peoples R China

The adaptive Fourier decomposition (AFD) uses an adaptive basis instead of a fixed basis in the rational analytic function and thus achieves a fast energy convergence rate. At each decomposition level, an important step is to determine a new basis element from a dictionary to maximize the extracted energy. The existing basis searching method, however, is only the exhaustive searching method that is rather inefficient. This paper proposes four methods to accelerate the AFD algorithm based on four typical optimization techniques including the unscented Kalman filter (UKF) method, the Nelder-Mead (NM) algorithm, the genetic algorithm (GA), and the particle swarm optimization (PSO) algorithm. In the simulation of decomposing four representative signals and real ECG signals, compared with the existing exhaustive search method, the proposed schemes can achieve much higher computation speed with a fast energy convergence, that is, in particular, to make the AFD possible for real-time applications.

关键词： Adaptive Fourier decomposition Unscented Kalman filter Nelder-Mead algorithm Genetic algorithm particle swarm optimization algorithm

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Enhanced Bat algorithm for Analyzing the Reliability and Contingency Issue in Radial Distribution Power System

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2018年第19-20期46卷 2071-2083页

作者： Kumar, V. Swapnil, Shubham Ranjan, R. Singh, V. R. Maharshi Dayanand Univ Dept Elect Engn Rohtak Haryana India Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA PDM Coll Engn Bhadurgarh India MDU Dept Elect Engn PDMCE Bahadurgarh Haryana India

The paper proposes the hybrid technique to observe the contingency and improve the reliability of radial distribution system (RDS). The hybrid algorithm used is interface of Bat algorithm and particle swarm optimization that is used to examine the contingency and reliability indices in the RDS. The feeder power loss, system node voltage deviation, system average interruption frequency index, system average interruption unavailability index and the energy not supplied at every load position in a sharing scheme are used to resolve the contingencies to minimize the power quality and reliability indices. The proposed techniques are implemented on MATLAB/Simulink platform and experienced on the IEEE 33 bus RDS. To establish the effectiveness of the proposed algorithm, results are compared with the reported Bat and Firefly algorithm.

关键词： IEEE 33 bus radial distribution system reliability contingency system node voltage deviation system average interruption frequency index system average interruption unavailability index enhanced bat algorithm particle swarm optimization algorithm power loss

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Prediction of Novel High-Pressure Structures of Magnesium Niobium Dihydride

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ACS APPLIED MATERIALS & INTERFACES 2017年第31期9卷 26169-26176页

作者： Zhang, Chuanzhao Sun, Guoliang Wang, Jingjing Lu, Cheng Jin, Yuanyuan Kuang, Xiaoyu Hermann, Andreas Sichuan Univ Inst Atom & Mol Phys Chengdu 610065 Sichuan Peoples R China Nanyang Normal Univ Dept Phys Nanyang 473061 Peoples R China Yangtze Univ Dept Phys & Optoelect Engn Jingzhou 434023 Peoples R China Univ Nevada Dept Phys Las Vegas NV 89154 USA Univ Nevada High Pressure Sci & Engn Ctr Las Vegas NV 89154 USA Univ Edinburgh Sch Phys & Astron Ctr Sci Extreme Condit Edinburgh EH9 3JZ Midlothian Scotland Univ Edinburgh Sch Phys & Astron SUPA Edinburgh EH9 3JZ Midlothian Scotland

On the basis of a combination of the particle-swarm optimization technique and density functional theory (DFT), we explore the crystal structures of MgH2, NbH2, and MgNbH2 under high pressure. The enthalpy-pressure (H-P) diagrams indicate that the structural transition sequence of MgH2 isay alpha -> gamma -> delta -> epsilon -> zeta and that NbH2 transforms from the Fm (3) over barm phase to the Pnma phase at 47.80 GPa. However, MgNbH2 is unstable when the pressure is too low or too high. Two novel MgNbH2 structures, the hexagonal P (6) over bar m2 phase and the orthorhombic Cmcm phase, are discovered, which are stable in the pressure ranges of 13.24-128.27 GPa and 128.27-186.77 GPa, respectively. The P (6) over bar m2 phase of MgNbH2 consists of alternate layers of polymetric NbH6 and MgH6 triangular prisms, while the Cmcm phase contains distorted MgH6 trigonal prisms. The calculated elastic constants and phonon dispersions confirm that both phases are mechanically and dynamically stable. The analyses of density of states (DOS), electron localization function (ELF), and Bader charge demonstrate that a combination of ionic and metallic bonds exist in both P (6) over bar m2 and Cmcm phases. We hope the newly predicted magnesium niobium dihydrides with desirable electronic properties will promote future experimental and theoretical studies on mixed main group-transition metal hydrides.

关键词： magnesium niobium dihydride particle swarm optimization algorithm density functional theory structural transition electronic structures

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A machine learning technique toward generating minimum energy structures of small boron clusters

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INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY 2018年第17期118卷

作者： Mitikiri, Praveen Jana, Gourhari Sural, Shamik Chattaraj, Pratim K. Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Dept Chem Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Ctr Theoret Studies Kharagpur 721302 W Bengal India

The search for a global minimum related to molecular electronic structure and chemical bonding has received wide attention based on some theoretical calculations at various levels of theory. particle swarm optimization (PSO) algorithm and modified PSO have been used to predict the energetically stable/metastable states associated with a given chemical composition. Out of a variety of techniques such as genetic algorithm, basin hopping, simulated annealing. PSO, and so on. PSO is considered to be one of the most suitable methods due to its various advantages over others. We use a swarm-intelligence based parallel code to improve a PSO algorithm in a multidimensional search space augmented by quantum chemical calculations on gas phase structures at 0 K without any symmetry constraint to obtain an optimal solution. Our currently employed code is interfaced with Gaussian software for single point energy calculations. The code developed here is shown to be efficient. Small population size (small cluster) in the multidimensional space is actually good enough to get better results with low computational cost than the typical larger population. But for larger systems also the analysis is possible. One can try with a large number of particles as well. We have also analyzed how arbitrary and random structures and the local minimum energy structures gravitate toward the target global minimum structure. At the same time, we compare our results with that obtained from other evolutionary techniques.

关键词： B-5 and B-6 clusters global optimal structure multi-threaded code optimization particle swarm optimization algorithm

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A joint indoor positioning scheme exploiting pedestrian dead reckoning and radio frequency tomography

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2018年第18期32卷 2386-2403页

作者： Zhou, Biao Jing, Changqiang Sun, Chao Kim, Youngok Jiangnan Univ Sch Internet Things Engn Wuxi Peoples R China Linyi Univ Sch Informat Linyi Peoples R China Kwangwoon Univ Dept Elect Engn Seoul South Korea

The pedestrian dead reckoning (PDR) is a popular technique for recognizing a pedestrian's trajectory. However, its inherent disadvantage of susceptibility to error accumulation limits its performance as the travelled distance is increased. Various techniques are combined with PDR to mitigate the error accumulation by regular position resetting. The radio frequency (RF) Tomography is an emerging technology for localization by sensing the received signal strength (RSS) fluctuation on a line-of-sight wireless link (LOSL). Conventional RF Tomography based schemes need specific large-scale networks and face with the problem of harsh environments and high hardware costs. Thus in this paper, we proposed the flexible and practical GF operators for PDR resetting work, in which we introduce the displacement estimation by PDR technique and the LOSL triggering sequence to build a PDR geometrical formulation (PDR-GF) model, and then this model is optimized by particle swarm optimization (PSO) algorithm for crossing point (CP) estimation on the LOSLs. Finally, the estimated CP is in turn used for regular position resetting. Practical experiments are performed to evaluate the proposed positioning scheme. According to the results, the proposed scheme can achieve a finer accuracy of less than 1 m, outperforming the existing RSS-PDR positioning scheme.

关键词： Pedestrian dead reckoning radio frequency tomography geometrical formulation position resetting particle swarm optimization algorithm

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Energy-Storage optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method

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ENERGIES 2018年第12期11卷

作者： Yun, Pingping Ren, Yongfeng Xue, Yu Inner Mongolia Univ Technol Coll Energy & Power Engn Hohhot 010051 Inner Mongolia Peoples R China Inner Mongolia Engn Res Ctr Wind Power Technol & Hohhot 010051 Inner Mongolia Peoples R China

Wind power penetration ratios of power grids have increased in recent years;thus, deteriorating power grid stability caused by wind power fluctuation has caused widespread concern. At present, configuring an energy storage system with corresponding capacity at the grid connection point of a large-scale wind farm is an effective solution that improves wind power dispatchability, suppresses potential fluctuations, and reduces power grid operation risks. Based on the traditional energy-storage battery dispatching scheme, in this study, a multi-objective hybrid optimization model for joint wind-farm and energy-storage operation is designed. The impact of two new aspects, the energy-storage battery output and wind-power future output, on the current energy storage operation are considered. Wind-power future output assessment is performed using a wind-power-based Markov prediction model. The particle swarm optimization algorithm is used to optimize the wind-storage grid-connected power in real time, to develop an optimal operation strategy for an energy storage battery. Simulations incorporating typical daily wind power data from a several-hundred-megawatt wind farm and rolling optimization of the energy storage output reveal that the proposed method can reduce the grid-connected wind power fluctuation, the probability of overcharge and over-discharge of the stored energy, and the energy storage dead time. For the same smoothing performance, the proposed method can reduce the energy storage capacity and improve the economic efficiency of the wind-storage joint operation.

关键词： wind-power fluctuation smoothing energy storage system Markov prediction model particle swarm optimization algorithm multi-objective optimization energy-storage battery output level

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A self-adaptive intelligence gray prediction model with the optimal fractional order accumulating operator and its application

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MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2017年第18期40卷 7843-7857页

作者： Zeng, Bo Liu, Sifeng Chongqing Technol & Business Univ Coll Business Planning Chongqing 400067 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210016 Jiangsu Peoples R China

The self-adaptive intelligence gray predictive model (SAIGM) has an alterable-flexible model structure, and it can build a dynamic structure to fit different external environments by adjusting the parameter values of SAIGM. However, the order number of the raw SAIGM model is not optimal, which is an integer. For this, a new SAIGM model with the fractional order accumulating operator (SAIGM_FO) was proposed in this paper. Specifically, the final restored expression of SAIGM_FO was deduced in detail, and the parameter estimation method of SAIGM_FO was studied. After that, the particle swarm optimization algorithm was used to optimize the order number of SAIGM_FO, and some steps were provided. Finally, the SAIGM_FO model was applied to simulate China's electricity consumption from 2001 to 2008 and forecast it during 2009 to 2015, and the mean relative simulation and prediction percentage errors of the new model were only 0.860% and 2.661%, in comparison with the ones obtained from the raw SAIGM model, the GM(1, 1) model with the optimal fractional order accumulating operator and the GM(1, 1) model, which were (1.201%, 5.321%), (1.356%, 3.324%), and (2.013%, 23.944%), respectively. The findings showed both the simulation and the prediction performance of the proposed SAIGM_FO model were the best among the 4 models.

关键词： fractional order accumulating operator particle swarm optimization algorithm prediction of China's electricity consumption self-adaptive intelligence gray predictive model

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A kernel extreme learning machine algorithm based on improved particle swam optimization

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MEMETIC COMPUTING 2017年第2期9卷 121-128页

作者： Lu, Huijuan Du, Bangjun Liu, Jinyong Xia, Haixia Yeap, Wai K. China Jiliang Univ Coll Informat Engn Hangzhou 310018 Zhejiang Peoples R China Ctrip Travel Network Shanghai 200335 Peoples R China Zhejiang Sci Tech Univ Coll Informat Hangzhou 310018 Zhejiang Peoples R China Auckland Univ Technol Ctr Artificial Intelligence Res Auckland New Zealand

Kernel extreme learning machine (KELM) increases the robustness of extreme learning machine (ELM) by turning linearly non-separable data in a low dimensional space into a linearly separable one. However, the internal power parameters of ELM are initialized at random, causing the algorithm to be unstable. In this paper, we use the active operators particle swam optimization algorithm (APSO) to obtain an optimal set of initial parameters for KELM, thus creating an optimal KELM classifier named as APSO-KELM. Experiments on standard genetic datasets show that APSO-KELM has higher classification accuracy when being compared to the existing ELM, KELM, and these algorithms combining PSO/APSO with ELM/KELM, such as PSO-KELM, APSO-ELM, PSO-ELM, etc. Moreover, APSO-KELM has good stability and convergence, and is shown to be a reliable and effective classification algorithm.

关键词： Kernel extreme learning machine Active operator particle swarm optimization algorithm Gene expression data classification

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