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backtracking search algorithm with reusing differential vectors for parameter identification of photovoltaic models

ENERGY CONVERSION AND MANAGEMENT 2020年 223卷 113266-113266页

作者： Zhang, Yiying Huang, Caifeng Jin, Zhigang Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Chinese Acad Sci Inst Engn Thermophys Beijing 100190 Peoples R China

In order to simulate, control and optimize photovoltaic (PV) systems, how to accurately identify the unknown parameters of PV models is a major challenge. To overcome this challenge, this work reports a very simple but efficient optimization method called backtracking search algorithm with reusing differential vectors (BSARDVs). BSARDVs has a very simple structure and only needs the essential population size and stopping criterion for optimization. Mutation operator is employed to generate new individuals in the search process of backtracking search algorithm (BSA), which guides the search direction of population by the differential vectors between history population and current population. To enhance the global search ability of BSA, BSARDVs first archives some most promising difference vectors from history population and then reuses these differential vectors for generating next generation population. The performance of BSARDVs is investigated for parameter identification of three PV models, i.e. single diode model, double diode model and PV module model. Experimental results reveal BSARDVs can find the better solution than the compared algorithms on double diode model. In addition, for single diode model and PV module model, the solutions of BSARDVs are the same solutions with those of some compared algorithms while BSARDVs consumes less function evaluations than these algorithms. This proves the effectiveness of reusing differential vectors in BSA for parameter identification of PV models.

关键词： Photovoltaic model backtracking search algorithm Reusing differential vectors Solar energy

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backtracking search algorithm with Levy flight for estimating parameters of photovoltaic models

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ENERGY CONVERSION AND MANAGEMENT 2020年第0期208卷 112615-000页

作者： Zhang, Yiying Jin, Zhigang Zhao, Xiaofang Yang, Qiuling Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Hainan Univ Sch Comp Sci & Cyberspace Secur Haikou 570228 Hainan Peoples R China

An accurate mathematical model plays an important role for simulation, evaluation and optimization of photovoltaic (PV) models. The characteristic current equations describing the PV models are implicit, nonlinear and transcendental. Given the features of the characteristic current equations, traditional optimization algorithms are usually easy to converge to local optimal solutions. Thus using metaheuristic methods called modern optimization algorithms to estimate parameters of PV models has been a research hotspot in recent years. Although many metaheuristic methods have been employed to solve this problem, it is still necessary for researchers to propose new optimization algorithms to obtain more accuracy and reliability solutions. This paper presents a new metaheuristic algorithm called backtracking search algorithm with Levy flight (LFBSA) to estimate the parameters of PV models. Compared with the basic backtracking search algorithm (BSA), LFBSA has the following two remarkable features. Firstly, an information sharing mechanism with Levy flight is built to enhance population diversity. Secondly, mutation operator based on the hunting mechanism of grey wolves is introduced to increase the chance of LFBSA to escape from local minima. LFBSA is used to estimate parameters of three different PV models. Experimental results show the proposed LFBSA is superior to BSA and the other compared algorithms in terms of accuracy and reliability.

关键词： backtracking search algorithm Metaheuristic method Photovoltaic modeling Levy flight

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Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm

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JOURNAL OF CLEANER PRODUCTION 2016年第0期137卷 1516-1531页

作者： Lu, Chao Gao, Liang Li, Xinyu Chen, Peng Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

Energy savings have become an essential consideration in sustainable manufacturing projects due to the associated environmental impacts and constraints on carbon emissions. In the past, machining operations primarily examined technological consideration (e.g., machining quality) and neglected energy consumption. Therefore, this paper investigates an energy-efficient multi-pass turning operation problem and establishes a multi-objective multi-pass turning operations model. Energy consumption and machining quality are both considered in this problem. Although several models of this problem have considered these criteria, the objectives are usually combined into a single objective using a weighted sum approach, which results in poor non-dominated solutions. To obtain high quality trade-offs between the two challenging objectives, a novel multi-objective backtracking search algorithm is proposed to solve this multi-objective optimization problem. To verify the feasibility and validity of the proposed algorithm, it is compared with other classical multi-objective metaheuristics on multi-objective multi pass turning operations. This study's experimental results demonstrate that the proposed algorithm significantly outperforms other algorithms for this optimization problem, which is a significant result regarding practical application. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Multi-objective evolutionary algorithm Energy consumption Parameter optimization backtracking search algorithm

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A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting

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ENERGY CONVERSION AND MANAGEMENT 2017年第Jul.期143卷 360-376页

作者： Zhang, Chu Zhou, Jianzhong Li, Chaoshun Fu, Wenlong Peng, Tian Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Hubei Key Lab Digital Valley Sci & Technol Wuhan 430074 Peoples R China Three Gorges Univ Sch Hydraul & Environm Engn Yichang 443002 Peoples R China

Reliable wind speed forecasting is essential for wind power integration in wind power generation system. The purpose of paper is to develop a novel hybrid model for short-term wind speed forecasting and demonstrates its efficiency. In the proposed model, a compound structure of extreme learning machine (ELM) based on feature selection and parameter optimization using hybrid backtracking search algorithm (HBSA) is employed as the predictor. The real-valued BSA (RBSA) is exploited to search for the optimal combination of weights and bias of ELM while the binary-valued BSA (BBSA) is exploited as a feature selection method applying on the candidate inputs predefined by partial autocorrelation function (PACF) values to reconstruct the input-matrix. Due to the volatility and randomness of wind speed signal, an optimized variational mode decomposition (OVMD) is employed to eliminate the redundant noises. The parameters of the proposed OVMD are determined according to the center frequencies of the decomposed modes and the residual evaluation index (REI). The wind speed signal is decomposed into a few modes via OVMD. The aggregation of the forecasting results of these modes constructs the final forecasting result of the proposed model. The proposed hybrid model has been applied on the mean half-hour wind speed observation data from two wind farms in Inner Mongolia, China and 10-min wind speed data from the Sotavento Galicia wind farm are studied as an additional case. Parallel experiments have been designed to compare with the proposed model. Results obtained from this study indicate that the proposed hybrid model can provide an effective modeling approach to capture the nonlinear characteristics of wind speed signal and thus providing more satisfactory forecasting results. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Wind speed forecasting Extreme learning machine backtracking search algorithm Variational mode decomposition Feature selection Parameter optimization

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A New SEBAL Approach Modified with backtracking search algorithm for Actual Evapotranspiration Mapping and On-Site Application

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2018年第8期46卷 1213-1222页

作者： Atasever, Umit Haluk Ozkan, Coskun Erciyes Univ Engn Fac Dept Geomat Engn TR-38039 Kayseri Turkey

Actual evapotranspiration is one of the most important component for efficient water management and planning. Until recently, evapotranspiration computations and measurements have been performed locally. But, in recent years, actual evapotranspiration computations can be calculated regionally thanks to improvements on remote sensing discipline and satellites. Surface Energy Balance algorithm for Land (SEBAL) is one of the most commonly preferred technics for actual evapotranspiration mapping. However, this algorithm has some difficulties such as mismatch with Landsat 8 and hot-wet pixel selection which require expert knowledge. In this paper, a novel SEBAL based approach (SEBAL-BSA), which can use Landsat 8 images as data and can automatically perform hot-wet pixel selection using backtracking search algorithm (BSA) with ground control points, has been proposed. SEBAL-BSA was developed and tested using January 22-2015, April 28-2015 and July 01-2015 dated Landsat 8 images in Akarsu Irrigation Water User Association command area in the cukurova Region of Turkey and actual evapotranspiration mapping was realized without albedo measurements. Accuracy of SEBAL-BSA has been examined by comparing values of predefined ground truth points and values obtained from proposed approach. According to results of parametric matched pairs T test and nonparametric Wilcoxon sign rank test, SEBAL-BSA is highly successful. Applications also show that the SEBAL-BSA is a user-friendly approach for institutions and organizations related to water authorization and management.

关键词： Actual evapotranspiration SEBAL-BSA backtracking search algorithm Albedo

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An efficient method for parameter estimation of a nonlinear system using backtracking search algorithm

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2018年第3期21卷 338-350页

作者： Mishra, Puneet Kumar, Vineet Rana, K. P. S. Birla Inst Technol & Sci Dept Elect & Elect Engn Pilani Rajasthan India Netaji Subhas Inst Technol Div Instrumentat & Control Engn New Delhi 110078 India

Tank level systems are ubiquitous in process industries and exhibit a nonlinear nature. This nonlinear behaviour arises prominently due to nonlinear dependence of outflow rate on tank level and/or due to presence of other nonlinear elements in loop such as nonlinear actuators. It is essential to completely investigate the dynamics of system so as to generate an effective and accurate process model for obvious reasons. It may be noted that it is hard to find a unified model of a nonlinear system which can characterize the process in the entire operating range. To address this issue, this paper is a sincere effort to accurately identify a nonlinear tank level system with the help of a recently developed evolutionary algorithm i.e. backtracking search algorithm (BSA). Two different optimization problems were created and solved using BSA to estimate the tank level system parameters and are termed as Method 1 and Method 2, respectively. The optimization was performed to minimize summation of absolute error (SAE) between experimental and simulated data yielding the parameters of model. During the training phase, Method 1 used only one data set and Method 2 used a combination of five different data sets of experiments at different operating conditions. From the results obtained through the experimentation on a hardware setup, it can be easily inferred that parameter estimation using Method 2 gives better identification than Method 1 by providing lower SAE values. (C) 2018 Karabuk University. Publishing services by Elsevier B.V.

关键词： Parameter estimation backtracking search algorithm Tank level system Nonlinear system Evolutionary algorithm

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Short-Term Electricity Price Forecasting via Hybrid backtracking search algorithm and ANFIS Approach

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IEEE ACCESS 2019年 7卷 77674-77691页

作者： Pourdaryaei, Alireza Mokhlis, Hazlie Illias, Hazlee Azil Kaboli, S. Hr Aghay Ahmad, Shameem Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia Kuwait Univ Coll Engn & Petr Elect Engn Dept Safat 13060 Kuwait

In this paper, a hybrid electricity price forecasting method which is composed of two-stage feature selection method and optimized adaptive neuro-fuzzy inference system (ANFIS) technique as a forecasting engine is proposed to accurately forecast electricity price. A multi-objective feature selection approach comprises of multi-objective binary-valued backtracking search algorithm (MOBBSA) as an efficient evolutionary search algorithm and ANFIS method is developed in this paper to extract the most infiuential subsets of input variables with maximum relevancy and minimum redundancy. Through the combination of backtracking search algorithm (BSA) in learning process of ANFIS approach, a hybrid machine learning algorithm has been developed to forecast the electricity price more accurately. Real-world electricity demand and price dataset from Ontario power market;which is reported as among the most volatile market worldwide, has been used as case study to validate the performance of the proposed approach. From the simulation results, it has been seen that the proposed hybrid forecasting method was effective in accurately forecast the Ontario electricity price. In addition, to prove the superiority of the proposed hybrid forecasting method the simulation results obtained using ANN and ANFIS models optimized by other well-known optimization methods have been compared with that of proposed method.

关键词： Adaptive neuro-fuzzy inference system backtracking search algorithm electricity price forecasting feature selection

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Coordination of Directional Overcurrent Relays Using the backtracking search algorithm

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JOURNAL OF ELECTRICAL SYSTEMS 2016年第2期12卷 387-405页

作者： Bouchekara, Houssem Rafik El-Hana Zellagui, Mohamed Abido, Mohammad Ali Univ Freres Mentouri Constantine Dept Elect Engn LEC Lab Constantine Algeria Univ Batna 2 Dept Elect Engn LSP IE Lab Batna Algeria King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran Saudi Arabia

The Directional Overcurrent Relays (DOCRs) coordination problem is of paramount importance for power system protection. This paper uses a new metaheuristic method called the backtracking search algorithm (BSA) to solve the DOCRs coordination problem. The coordination problem has been formulated as an optimization problem to minimize the operating time of all the primary relays. The proposed algorithm has been implemented and applied to three test systems including the 6-bus, the 8-bus and the 15-bus test systems. Furthermore, a reliable approach has been developed to compare the results obtained using the proposed BSA with those obtained using other well-known metaheuristics. The results show the superiority of BSA over other methods in solving DOCRs coordination problems.

关键词： Power system protection overcurrent relay coordination optimization backtracking search algorithm

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Quasi-oppositional backtracking search algorithm to Solve Load Frequency Control Problem of Interconnected Power System

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2020年第2期44卷 781-804页

作者： Guha, Dipayan Roy, Provas Banerjee, Subrata Motilal Nehru Natl Inst Technol Allahabad Dept Elect Engn Prayagraj India Kalyani Govt Engn Coll Dept Elect Engn Kalyani W Bengal India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India

This study addresses a powerful optimization technique with the notion of quasi-oppositional-based learning, namely quasi-oppositional backtracking search algorithm (QOBSA), for load frequency control (LFC) of power system. Two widely used power systems have been selected to establish the efficiency of QOBSA. Supplementary controllers in LFC are designed by taking frequency and tie-line power deviations of each area as an input, and QOBSA is applied for simultaneous optimization of the controller gains. Integral error-based performance criterions are formulated to claim the tuning optimality of QOBSA. Comparisons are also made with the existing results to establish the superiority of QOBSA in terms of convergence mobility and time response measurements. The effectiveness and competence of the applied method have been confirmed after penetration of renewable energy resources, and in the presence of power system nonlinearities. The robustness of the developed controller has been appraised with system uncertainty and random perturbation.

关键词： Load frequency control backtracking search algorithm Quasi-oppositional-based learning Small-signal stability Hybrid energy power system

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Wind Integrated Multi Area Economic Dispatch using backtracking search algorithm 6

Wind Integrated Multi Area Economic Dispatch using Backtrack...

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6th IEEE International Conference on Power Systems (ICPS)

作者： Dubey, Hari Mohan Pandit, Manjaree Tyagi, Nitin Panigrahi, B. K. MITS Dept Elect Engn Gwalior India IIT Dept Elect Engn Delhi India

ISBN: (纸本)9781509001286

This paper presents a solution of multi area economic dispatch (MAED) problem with wind integration using backtracking search algorithm (BSA), a novel optimization technique. BSA is a stochastic search algorithm with only one control parameter and mutation and crossover operators for exploration and exploitation of the search space for problem under consideration. It can easily take care of various complex operating constraints, which is difficult to solve using any mathematical approaches. To show its efficiency, BSA algorithm is applied to two different categories of MAED problems. Category I has a two area network having 40 and 140 power generating units with valve point loading effects (VPL), ramp rate limits (RRL) and prohibited operating zones (POZ) and category II presents simulation study of MAED problem with wind integration. The outcome of simulation results in terms of operating cost are compared with other recently published relevant approaches and are found to be better.

关键词： backtracking search algorithm Multi area Economic dispatch wind over estimation and wind under estimation prohibited operating zones

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