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A PD-type Multi Input Single Output SSSC damping controller design employing hybrid improved differential evolution-pattern search approach

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APPLIED SOFT COMPUTING 2015年 32卷 532-543页

作者： Panda, Sidhartha Kiran, S. Harish Dash, S. S. Subramani, C. VSSUT Dept Elect & Elect Engn Burla 768018 Odisha India SRM Univ Dept Elect Engn Madras 603203 Tamil Nadu India

In this paper, a Proportional Derivative (PD)-type Multi Input Single Output (MISO) damping controller is designed for Static Synchronous Series Compensator (SSSC) controller. Both local and remote signals with associated time delays are chosen as the input signal to the proposed MISO controller. The design problem is formulated as an optimization problem and a hybrid improved differential evolution and Pattern Search (hIDEPS) technique is employed to optimize the controller parameters. The improvement in differential evolution (DE) algorithm is introduced by changing two of its most important control parameters i.e. Scaling Factor F and Crossover Constant CR with an objective of achieving improved performance of the algorithm. The superiority of proposed improved DE (IDE) over original DE and hIDEPS over IDE has also been demonstrated. To show the effectiveness and robustness of the proposed design approach, simulation results are presented and compared with DE and Particle Swarm Optimization (PSO) optimized Single Input Single Output (SISO) SSSC based damping controllers for both Single Machine Infinite Bus (SMIB) power system and multi-machine power system. It is noticed that the proposed approach provides superior damping performance compared to some approaches available in literature. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Power system stability Static Synchronous Series Compensator (SSSC) improved differential evolution algorithm Pattern search Multi Input Single Output controller

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Structure control classification and optimization model of hollow carbon nanosphere core polymer particle based on improved differential evolution support vector machine

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APPLIED MATHEMATICAL MODELLING 2013年第12-13期37卷 7442-7451页

作者： Yang, Zhen Yu, Qingni Dong, Wenping Gu, Xingsheng Qiao, Wenming Liang, Xiaoyi E China Univ Sci & Technol Inst Informat Sci Minist Educ State Key Lab Chem EngnKey Lab Special Funct Pol Shanghai 200237 Peoples R China China Astronaut Res & Training Ctr Natl Key Lab Human Factors Engn Beijing 100095 Peoples R China

The structure of core polymer particle is an important index of efficiency in hollow carbon nanosphere. How to control and optimize the structure of core polymer particle has been investigated using pattern recognition method in this research. A novel method of pattern recognition material design based on differential evolution support vector machine was proposed. The control model was established and software was adopted to carry out a digital simulation for the model. Using the model, we found the control criteria and optimized conditions for pore structure of composite polymer. Then, the results are compared to other classification methodologies. Experimental results show this model has higher classification accuracy in most of data sets. Experimental and dynamics results show that the properties of hollow carbon nanosphere have been greatly improved. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Hollow carbon nanosphere Structure control Support vector machine improved differential evolution algorithm Classification Optimization

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Multi-Input Single Output SSSC based damping controller design by a hybrid improved differential evolution-Pattern Search approach

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ISA TRANSACTIONS 2015年 58卷 173-185页

作者： Panda, Sidhartha Yegireddy, Narendra Kumar Veer Surendra Sai Univ Technol Dept Elect & Elect Engn Burla 768018 Odisha India

In this paper, a hybrid improved differential evolution and Pattern Search (hIDEPS) approach is proposed for the design of a PI-Type Multi-Input Single Output (MISO) Static Synchronous Series Compensator (SSSC) based damping controller. The improvement in differential evolution (DE) algorithm is introduced by a simple but effective scheme of changing two of its most important control parameters i.e. step size and crossover probability with an objective of achieving improved performance. Pattern Search (PS) is subsequently employed to fine tune the best solution provided by modified DE algorithm. The superiority of a proposed hIDEPS technique over DE and improved DE has also been demonstrated. At the outset, this concept is applied to a SSSC connected in a Single Machine Infinite Bus (SMIB) power system and then extended to a multi-machine power system. To show the effectiveness and robustness of the proposed design approach, simulation results are presented and compared with DE and Particle Swarm Optimization (PSO) optimized Single Input Single Output (SISO) SSSC based damping controllers. It is observed that the proposed approach yield superior damping performance compared to some approaches available in the literature. (C) 2015 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Power system stability Static Synchronous Series Compensator (SSSC) improved differential evolution algorithm Pattern Search Multi-Input Single Output controller

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improved differential evolution Based BP Neural Network for Prediction of Groundwater Table

Improved Differential Evolution Based BP Neural Network for ...

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2010 Third International Symposium on Knowledge Acquisition and Modeling (KAM 2010)

作者： Jihong Qu, Yuepeng Li and Juan Zhou North China University of Water Conversancy and Hydroelectric Power Zhengzhou, 450011, China

Groundwater table often shows complex nonlinear characteristic. Back Propagation (BP) neural network is increasingly used to predict groundwater table. However man-made selecting the structure of BP neural network has blindness and expends much time, so differential evolution (DE) algorithm was adopted to automatically search BP neural network weight matrix and threshold matrix. In order to improve the convergence of DE algorithm, a chaotic sequence based on logistic map was introduced to self-adaptively adjust mutation factor. Furthermore, a self-adapting crossover probability factor was presented to improve the population’s diversity and the ability of escaping from the local optimum. Study case shows that, compared with groundwater level prediction model based on traditional BP neural network, the new prediction model based on DE and BP neural network can greatly improve the convergence speed and prediction precision.

关键词： improved differential evolution algorithm Back Propagation linear crossover probability self-adaptive mutation factor groundwater table prediction model

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Optimal Energy-Efficient Operation of a Metro Train on a Long and Steep Downhill Segment

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CSEE Journal of Power and Energy Systems 2023年第6期9卷 2356-2365页

作者： Deqiang He Hanqing Jian Yanjun Chen Jian Miao Zhixiao Luo Chonghui Ren Lang Zhang the department of State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures Guangxi Key Laboratory of Manufacturing System&Advanced Manufacturing TechnologySchool of Mechanical EngineeringGuangxi UniversityNanning 530004China the department of Guangxi Police College Nanning 530028China. the department of Nanning Rail Transit Co. Ltd.Nanning 530029China

Because the standard four-stage operation and con-trol strategy cannot fully utilize the gravitational potential energy of a train operating on a long and steep downhill segment,this paper further improves the method for train operation and control *** improved operation includes three stages of acceleration,coasting-speed limit cruising,and *** the speed limit,time limit,and distance limit as the constraints,the coasting condition switching point,braking condition switching point,traction coefficient,and braking force coefficient are used as the decision ***,an improved train traction energy consumption model is constructed,and an improved differential evolution algorithm is designed to solve this *** improved method is used to simulate two long and steep downhill segments of the Nanning *** results show that the improved method can meet the requirements of speed limit,time limit,and distance *** with the standard four-stage operation,the improved train operation and control strategy can reduce train energy consumption by more than 40%on the two long and steep downhill segments;compared with other similar algorithms,the improved algorithm is more suitable for solving the model.

关键词： Energy-efficient operation improved differential evolution algorithm long and steep downhill segment metro train

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Hybrid algorithm of differential evolution and flower pollination for global optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期237卷

作者： Song, Haohao Bei, Jinling Zhang, Hongyu Wang, Jiquan Zhang, Panli Northeast Agr Univ Coll Engn Harbin 150030 Peoples R China

differential evolution (DE) and flower pollination algorithm (FPA) are efficient and novel meta-heuristic algorithms, which are effective tools for solving global optimization problems (GOPs). However, a single algorithm is always restricted to its own principle and mechanism, and even suffers from the imbalance of global and local search ability when solving large-scale GOPs. The organic combination of the two algorithms with a certain strategy can improve the performance of the hybrid algorithm. Motivated by this idea, a hybrid global optimization algorithm based on DE and FPA is proposed. Firstly, an improved initial population generation method is provided for improving the quality of the initial population. Secondly, an improved DE is given, and the parameters F and CR are adjusted in an adaptive and dynamic fashion, and a combined mutation operator is designed. Thirdly, an improved FPA is proposed, and a method for adaptively and dynamically adjusting the switch probability p is given, and formulas for updating the global and local position are introduced. Lastly, a hybrid strategy of organically combining DE and FPA is proposed to form a hybrid algorithm of differential evolution and flower pollination (HADEFP). To demonstrate the performance of the hybrid algorithm, the proposed hybrid algorithm is tested against other algorithms in the literature on CEC 2017 test functions (10-D, 50-D and 100-D) and practical optimization problem, respectively. The results show that the proposed HADEFP outperforms other comparison algorithms and has better robustness.

关键词： improved differential evolution algorithm improved flower pollination algorithm Population initialization method Hybrid strategy Operation of removing similarity

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New Stochastic Bi-Objective Optimal Cost and Chance of Operation Management Approach for Smart Microgrid

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2016年第6期12卷 2031-2040页

作者： Zare, Mohsen Niknam, Taher Azizipanah-Abarghooee, Rasoul Ostadi, Amir Univ Jahrom Dept Elect Engn Jahrom *** Iran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz 71555313 Iran Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

This paper develops a new probabilistic optimization framework based on chance constrained programming for bi-objective optimal energy management in microgrids considering intermittent characteristics of wind and photovoltaic power, and customers' load profile. The proposed approach uses a method based on jointly distributed random variables to calculate the chance of meeting the load requirements while maintaining the operation cost below a present value. The framework benefits from the new improved hybrid artificial bee colony and differential evolution algorithm as the optimization technique to solve the optimal energy management of a grid-connected microgrid. A sample average approximation approach is used to verify the results of the proposed method in comparison with the scenario-based and the Monte Carlo-based stochastic programming.

关键词： Artificial bee colony chance constrained programming (CCP) improved differential evolution algorithm jointly distributed random variables (JDRV) microgrid

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A random perturbation modified differential evolution algorithm for unconstrained optimization problems

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SOFT COMPUTING 2019年第15期23卷 6307-6321页

作者： Wei, Zhaokun Xie, Xinlian Bao, Tiantian Yu, Yue Dalian Maritime Univ Transportat Management Coll Room 3181 Linghai Rd Dalian 116026 Peoples R China

To solve unconstrained optimization problems, a random search differential evolution algorithm (RPMDE) is designed based on a modified differential evolution algorithm. The efficiency of an evolutionary algorithm usually depends on its exploration competence and development capability. Considering these characteristics, an effective difference operator called DE/M_pBest-best/1' is designed, which originates from DE/best/1/' and DE/current-pbest/1'. The operator makes use of information from the best population of individuals to generate new solutions for the development of RPMDE and guarantee swarm quality during the later evolution of the algorithm, which improves its searching ability. To prevent the solutions from falling into local optima, RPMDE also adopts random perturbation to update the current solution with a better solution after difference mutation and crossover are competed. Furthermore, a levy distribution is employed to adjust the scale factor as a control parameter. All designed operators are beneficial to improve the exploration competence and the diversity of the whole population. Last, a large number of computational experiments and comparisons are conducted by employing 15 benchmark functions. The experimental results indicate that the designed algorithm, RPMDE, is more effective than other differential evolution variants in dealing with unconstrained optimization problems.

关键词： improved differential evolution algorithm Difference strategy Random perturbation Levy distribution Parameter adjustment

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Design of multipurpose digital FIR double-band filter using hybrid firefly differential evolution algorithm

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APPLIED SOFT COMPUTING 2017年 59卷 529-545页

作者： Dash, Judhisthir Dam, Bivas Swain, Rajkishore Silicon Inst Technol Dept ECE Bhubaneswar Orissa India Jadavpur Univ Dept IEE Kolkata India Silicon Inst Technol Dept EEE Bhubaneswar Orissa India

Signal filtering can be treated as one of the basic requisite of communication networks. Design of an appropriate digital filter demands such filter coefficients that will create the desired frequency response with tolerable amount of ripples in the stop band(s) and pass band(s) along with high attenuation in the stop band(s). Now-a-days, artificial evolutionary methods are employed in the modern digital filter design due to lots of advantages over typical methods. In this paper, multipurpose digital linear phase double band filter (LPDBF) is designed proposing a hybrid meta-heuristic technique called hybrid firefly differential evolution (HFDE) algorithm. Generally these filters are required in different specific modern digital system networks for the simultaneous processing of signals present in two or three different channels. The proposed HFDE is an efficient evolutionary hybrid technique and is modelled considering both the optimization advantages of improved differential evolution (IDE) and firefly techniques. The global searching capability of IDE technique is strengthened by improved firefly movement. The performance of the proposed HFDE method of LPDBF design is contrasted with few popular optimal methods of design. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Double-band filter improved differential evolution algorithm HFDE algorithm Fitness error

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Efficient method for estimation of smooth and nonsmooth fuel cost curves for thermal power plants

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2018年第3期28卷 1-1页

作者： Sayah, Samir Hamouda, Abdellatif Ferhat Abbas Univ Dept Elect Engn QUERE Lab Setif 1 Algeria Ferhat Abbas Univ Dept Opt & Fine Mech QUERE Lab Setif 1 Algeria

For accurate economic dispatch solution, it is necessary to periodically estimate the parameters of fuel cost function. This paper proposes an improved differential evolution algorithm for computing the optimal parameters of fuel cost functions for thermal power plants. A new mutation strategy is suggested to enhance convergence rate and improve solution quality of original differential evolution. The proposed approach is examined on different test systems with several generator cost curve models involving smooth and nonsmooth/nonconvex functions. The results using the proposed approach are compared to those available in recent literature. The results show the efficiency of the proposed estimation approach for obtaining accurate fuel cost parameters without any restriction on the mathematical model of the generator cost curve.

关键词： economic dispatch fuel cost function improved differential evolution algorithm optimal power flow parameters estimation valve-point loading

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