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Two-step method for identifying photovoltaic grid-connected inverter controller parameters based on the adaptive differential evolution algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第17期11卷 4282-4290页

作者： Liu, Zhongqian Wu, Hongbin Jin, Wei Xu, Bin Ji, Yu Wu, Ming Hefei Univ Technol Sch Elect Engn & Automat 193 Tunxi Rd Hefei 230009 Anhui Peoples R China State Grid Anhui Elect Power Co Hefei 230061 Anhui Peoples R China State Grid Anhui Elect Power Res Inst Hefei 230601 Anhui Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China

Photovoltaic (PV) grid-connected inverter is the core component of PV generation system;quickly and accurately obtaining the parameters of inverter controller has great significance in analysis of transient characteristics of PV generation system. Parameter identification relies on disturbance signals and measurements selection, and the trajectory sensitivities of inverter controller parameters are calculated with different system outputs as the measurements, the disturbance signals include AC three-phase short-circuit fault and DC voltage reference jump, accordingly, a two-step identification method is proposed, the first step use a three-phase fault to identify all voltage loop parameters and the proportional coefficient of current loop, and the second step consider a DC voltage reference jump disturbance to identify the integral coefficient of current loop and the setting inductance value. The adaptive differential evolution is taken as the identification algorithm, which adopts adaptive strategies for algorithm parameters in order to speed up the convergence and enhances the balance between the global search and local mining. The simulation analysis validates the effectiveness of this method. In addition, the impact of the inductance error on the identification is discussed.

关键词： photovoltaic power systems power grids invertors evolutionary computation power system parameter estimation transient analysis power system transients power generation control photovoltaic grid-connected inverter controller parameters adaptive differential evolution algorithm PV grid-connected inverter PV generation system transient characteristic analysis voltage loop parameter identification disturbance signals measurement selection trajectory sensitivities AC three-phase short-circuit fault two-step identification method proportional current loop coefficient DC voltage reference jump disturbance local mining global search

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Optimal sub-band adaptive thresholding based edge preserved satellite image denoising using adaptive differential evolution algorithm

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NEUROCOMPUTING 2016年第PartB期174卷 698-721页

作者： Bhandari, A. K. Kumar, D. Kumar, A. Singh, G. K. PDPM Indian Inst Informat Technol Design & Mfg Jabalpur 482005 Madhya Pradesh India Natl Inst Technol Dept Elect & Commun Engn Patna 800005 Bihar India Indian Inst Technol Roorkee Dept Elect Engn Roorkee 247667 Uttarakhand India

An image is often corrupted by different kinds of noise during its acquisition and transmission. Conventional denoising methods can suppress the Gaussian noise effectively, but fail to maintain the quality of denoised images and may blur edges in an image. To address these short comings, this paper aims to develop an optimized adaptive thresholding function based framework for edge preserved satellite image denoising using different nature inspired algorithms which is capable of effectively removing the Gaussian noise from images without over smoothing edge details. Image denoising using adaptive thresholding functions selects the suitable threshold values to separate noise from the actual image without affecting the actual features of the image. In this approach, most widely used nature inspired optimization algorithms are exploited for learning the parameters of adaptive thresholding function required for optimum performance. It was found that the proposed adaptive differential evolution algorithm (JADE) algorithm based denoising approach has superior features and give better performance in terms of PSNR, MSE, SSIM and FSIM as compared to other methods. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Image denoising DWT Optimization algorithms adaptive thresholding function adaptive differential evolution algorithm

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Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm

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RENEWABLE ENERGY 2019年第Mar.期132卷 425-438页

作者： Biswas, Partha P. Suganthan, P. N. Wu, Guohua Amaratunga, Gehan A. J. Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Cent S Univ Sch Traff & Transportat Engn Changsha Hunan Peoples R China Univ Cambridge Dept Engn Cambridge England

A solar cell or photovoltaic (PV) module is electrically represented by an appropriate circuit model with certain defined parameters. The parameters are required to be correctly computed from solar cell characteristic and/or a set of experimental data for simulation and control of the PV system. However, experimental data or accurate characteristic data (i.e. current-voltage or I-V curve) of a PV module may not be readily available. The manufacturer of a PV system usually provides relevant information on open circuit, short circuit and maximum power points. Therefore, an alternate approach is to estimate the PV system parameters by utilizing the I-V characteristic data at these three major points. The process involves formulation and solution of complex non-linear equations from an adopted solar cell model. This paper proposes an application of an advanced adaptive differential evolution algorithm on the problem of PV module parameter estimation using minimum available information from the manufacturer datasheet by implementing single-diode and double-diode models. Linear population size reduction technique of success history based adaptive differential evolution (L-SHADE) algorithm is implemented to minimize the error of current-voltage relationships at the above-mentioned three important points defining the I-V characteristic. The algorithm facilitates evolution of solutions that result in almost zero error (< 10(-12)) at these three major points. All relevant parameters of the PV cell are optimized by the algorithm without any assumption or predetermination of parameters. It is observed that a set of feasible solutions (parameters) is obtained for the PV module from multiple runs of the algorithm. The fact of attaining several probable solutions from datasheet information using few other metaheuristics is also discussed in this work. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Photovoltaic module Parameter estimation Single-diode model Double-diode model Current-voltage characteristic adaptive differential evolution algorithm

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An effective multivariate time series classification approach using echo state network and adaptive differential evolution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 43卷 237-249页

作者： Wang, Lin Wang, Zhigang Liu, Shan Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China

The multivariate time series (MIS) classification is a very difficult process because of the complexity of the MIS data type. Among all the methods to resolve this problem, the attribute-value representation classification approaches are the most popular. Despite their proven effectiveness of these however, these approaches are time consuming, sensitive to noise, or prone to damage of inner data properties as well as capable of producing undesirable accuracy. In this paper, we propose a new approach (CADS) for MIS classification that utilizes recurrent neural network (RNN) and adaptive differential evolution (ADE) algorithm. The approach can effectively overcome specific shortcomings of the attribute-value representation approaches. The principle of this approach adheres to three steps. First, an RNN is used to project the training MIS samples into different state clouds (samples in the same class are projected into a state cloud). Second, classifiers from these state clouds are induced for different classes. Third, the final MIS classifiers are obtained using ADE for parameter optimization. This approach makes full use of the network state space of a given RNN to induce classifiers rather than to train the network. Experimental results performed on 18 data sets demonstrate the accuracy and robustness of the proposed approach for MIS classification. As a new and universal approach, CADS can be very effective and stable for handling a variety of complex classification problems. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Multivariate time series classification Recurrent neural network adaptive differential evolution algorithm

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Train operation optimization with adaptive differential evolution algorithm based on decomposition

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2019年第12期14卷 1772-1779页

作者： Liu, Di Zhu, Songqing Xu, Youxiong Liu, Kun Nanjing Inst Technol Sch Automat 1 Hongjing RdJiangning Sci Pk Nanjing Jiangsu Peoples R China

The train operation is a multi-objective optimization process including several operating indexes, such as safety, punctuality, precision stop, comfort, and low-energy consumption. The speed profile and timetable for trains should be adjusted correspondingly to satisfy the passengers' demands while minimizing energy consumption. To solve this problem, we propose a multi-objective optimization approach for train operation. First, we develop a single-particle model of the train and a multi-objective optimization model for the train operation, which is subject to the constraints such as safety requirement, passenger comfort, and low energy consumption. Second, to obtain the Pareto frontier of train operation, a uniform design multi-objective adaptive differential evolution algorithm based on decomposition (UMADE/D) is studied and applied to solve the multi-objective optimization model for the train operation. Finally, we present numerical examples based on the real-life operation data from the Nanjing Metro Line 1 in Nanjing, China. Three searching methods, namely, the proposed UMADE/D, Non-Dominated Sorting Genetic algorithm, and Multi-Objective evolutionary algorithm based on Decomposition, are implemented to find the Pareto frontier of train operation quickly and efficiently. The simulation results show the efficiency of the proposed UMADE/D algorithm, which can provide corresponding operation strategies and satisfy the multi-objective demands when the train is in different operation state. (c) 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： subway train multi-objective optimization adaptive differential evolution algorithm decomposition

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Piezoelectric performance optimization of the PMN-PT based on self-adaptive differential evolution algorithm

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COMPUTATIONAL MATERIALS SCIENCE 2017年 127卷 96-102页

作者： Liu, Tundong He, Miao Ye, Zengruan Lou, Duanjun Zhang, Sa Sun, Qiao Xiamen Univ Dept Automat Xiamen 361005 Fujian Peoples R China Xiamen Univ Dept Mat Sci & Engn Coll Mat Xiamen 361005 Fujian Peoples R China Xiamen Univ Fujian Key Lab Adv Mat Xiamen 361005 Fujian Peoples R China Univ Calgary Dept Mech & Mfg Engn Calgary AB T2N 1N4 Canada

In order to improve the piezoelectric properties of ferroelectric materials, an optimization based method is developed to identify the topology of the piezoelectric material in the unit cell. A mathematical model is first established to identify the relationship between the piezoelectricity and some observable or measurable parameters in the unit cell. As for single crystal, the noncollinear polarization angles are chosen as the optimization variables. While for polycrystalline materials, the orientations of the grain are chosen as the optimization variables, which falling in a Gaussian distribution after poling. Since it is impossible to analyze all possible combinations of the distribution parameters or the angles, the differential evolution (DE) is applied to solve the proposed optimization model as a widely used global optimization method. Numerical experiments are carried out in Pb(MB1/3Nb2/3)O-3-PbTiO3(PMN-PT) for both single crystals and polycrystalline materials. A good agreement is achieved with the published data in single crystals, and the distribution parameters with enhanced piezoelectric properties are obtained for crystals. This study provides a fundamental perspective to enhance the piezoelectricity by designing of microscopic crystal morphology. (C) 2016 Elsevier B.V. All rights reserved.

关键词： C. Ferroelectric properties PMN-PT adaptive differential evolution algorithm Homogenization theory Finite element analysis

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Iterative Learning Control of Batch Processes Using adaptive differential evolution algorithm

Iterative Learning Control of Batch Processes Using Adaptive...

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Asia Simulation Conference/International Conference on System Simulation and Scientific Computing (AsiaSim and ICSC 2012)

作者： Zhang, Guohua Yuan, Kai Jia, Li Shanghai Univ Shanghai Key Lab Power Stn Automat Technol Dept Automat Coll Mechatron Engn & Automat Shanghai 200072 Peoples R China

ISBN: (纸本)9783642343803

Considering the potentials of iterative learning control as framework for industrial batch process control and optimization, an iterative learning control based on adaptive differential evolution algorithm is proposed in this paper. At first, quadratic criterion-iterative learning control with adaptive differential evolution algorithm is used to improve the performance of iterative learning control. In addition, the strategy of eliminating error using iterative algorithm is employed to drive the solution to the optimal point. As a result, the proposed method can avoid the problem of falling into local extreme points when solving the objective function with multiple local extreme points, which usually exists in traditional gradient-based iterative learning control. Lastly, example is used to illustrate the performance and applicability of the proposed method.

关键词： batch process adaptive differential evolution algorithm iterating learning control

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Integer Ambiguity Search algorithm Based on adaptive differential evolution algorithm 11

Integer Ambiguity Search Algorithm Based on Adaptive Differe...

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11th International Conference on Graphics and Image Processing (ICGIP)

作者： Dou, Zheng Wu, Yang Harbin Engn Univ Coll Informat & Commun Harbin Peoples R China

ISBN: (纸本)9781510635241

A new integer ambiguity estimation method which can improve computational speed and accuracy of ambiguity estimation significantly is proposed in this paper. Firstly, the carrier-phase double-difference equation is used to solve the float solution of the double-difference integer ambiguity. Then, the float solution is decorrelated by the inverse integer Cholesky algorithm. Finally, the adaptive differential evolution algorithm (ADE) is applied to search for the optimum solution of integer ambiguity. Simulation results show that ADE has a faster convergence speed and is more robust than other intelligent algorithms, and ADE has a higher success rate of estimation than those traditional integer ambiguity estimation algorithms.

关键词： GNSS carrier phase differential positioning integer ambiguity intelligent algorithm adaptive differential evolution algorithm

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The Intelligent Search Method of Ambiguity Function Main Ridge Based on the adaptive differential evolution algorithm 4

The Intelligent Search Method of Ambiguity Function Main Rid...

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IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Pu, Yunwei Ma, Lanyu Hou, Wentai Guo, Yuanpu Kunming Univ Sci & Tech Fac Informat Engn & Automat Kunming Yunnan Peoples R China Kunming Univ Sci & Tech Comp Ctr Kunming Yunnan Peoples R China

ISBN: (纸本)9781538653739

The ambiguity function is an effective tool to solve the problem of radar emitter signals sorting in complex systems. To improve the real-time and accuracy of the ambiguity function main ridge (AFMR), this paper proposes an intelligent method to search AFMR based on adaptive differential evolution algorithm. The signal is pre-identified by the instantaneous frequency (IF) features to reduce the AFMR search range and the complexity of the problem. In this way, the differential evolution algorithm parameters can be adaptively adjusted according to the pre-recognition results to accelerate the convergence of the algorithm. Then the statistical hypothesis testing theory is used to analyze and test the search results. The experiment results show that the proposed method reduces the time of searching AFMR to about 4% compared with the exhaustive method with higher accuracy and stability.

关键词： radar emitter signal adaptive differential evolution algorithm ambiguity function main ridge signal sorting and recognition

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Research on Optimizing Software Test Data by Applying adaptive differential evolution algorithm 3

Research on Optimizing Software Test Data by Applying Adapti...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ying, Li Lan, Xue Jiangxi Vocational & Technical College of Information Application Jiangxi Nanchang330043 China

ISBN: (纸本)9798350319125

With the continuous development of computer technology, the scale and complexity of software systems are increasing, and the importance of software testing has become increasingly prominent. Traditional software testing methods are often unable to meet the needs of modern software systems, so finding a more efficient and reliable testing method has become a hot issue in the industry. adaptive differential evolution (ADE) is an excellent optimization algorithm, and its application in software testing is of great significance. This paper will discuss the function and enlightenment of adaptive differential evolution algorithm in software testing and its influence on the field of software testing. © 2023 IEEE.

关键词： adaptive differential evolution algorithm computer Software testing Test data

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