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An Improved immune clone selection algorithm for Parameters Optimization of Marine Electric Power System Stabilizer

Energy Engineering 2022年第3期119卷 907-928页

作者： Zong Bi Weifeng Shi Tiewei Song Department of Electrical Engineering Shanghai Maritime UniversityShanghai201306China

In themarine electric power system,the marine generators will be disturbed by the large change of loads or the fault of the power *** marine generators usually installed power system stabilizers to damp power system oscillations through the excitation *** paper proposes a novel method to obtain optimal parameter values for Power System Stabilizer(PSS)to suppress low-frequency oscillations in the marine electric power *** this paper,a newly developed immune clone selection algorithm was improved from the three aspects of the adaptive incentive degree,vaccination,and adaptive mutation ***,the typical PSS implementation type of leader-lag structure was adopted and the objective function was set in the optimization *** performance of PSS tuned by improved immune clone selection algorithm was compared with PSS tuned by basic immune clone selection algorithm(ICSA)under various operating conditions and ***,an improved immune clone selection algorithm(IICSA)optimization technique was implemented on two test systems for test *** on the simulations,it is found that an improved immune clone selection algorithm demonstrates superiority over the basic immune clone selection algorithm in getting a smaller number of iterations and fast convergence rates to achieve the optimal parameters of the power system ***,the proposed approach improves the stability and dynamic performance under various loads conditions and disturbances of the marine electric power system.

关键词： Marine electric power system excitation system immune clone selection algorithm low frequency oscillations power system stability

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immune clone selection algorithm for Fuzzy Logic Controller Design

Immune Clone Selection Algorithm for Fuzzy Logic Controller ...

引用

2009中国控制与决策会议

作者： WenJie Tian~1,Lan Ai~2,Yu Geng~3,JiCheng Liu 1.Automation Institute,BEIJING Union University,BEIJING,China,100101 2.Automation Institute,BEIJING Union University,BEIJING,China,100101 3.Automation Institute,BEIJING Union University,BEIJING,China,100101 4.Automation Institute,BEIJING Union University,BEIJING,China,100101

＜正＞This paper provides an overview on immune clone selection algorithm for the automated design and optimization of fuzzy logic controller.A new optimization method for fuzzy logic controller design is *** membership functions of input and output variables are defined by six parameters,which are adjusted to maximize the performance index of the controller by using immune clone selection *** method can shorten coding length, incarnate the characteristic of mutation and improve the capability of search and convergence of *** experiment on water level controller is discussed by using above *** simulation results show that the fuzzy logic controller based on immune clone selection algorithm avoids premature effectively and prove its feasibility.

关键词： immune clone selection algorithm Fuzzy logic controller Mutation Membership function convergence

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A New Fuzzy Identification Approach Using Support Vector Regression and immune clone selection algorithm

A New Fuzzy Identification Approach Using Support Vector Reg...

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2009中国控制与决策会议

作者： WenJie Tian~1,Lan Ai~2,Yu Geng~3,JiCheng Liu~4 1.Automation Institute,BEIJING Union University,BEIJING,China,100101 2.Automation Institute,BEIJING Union University,BEIJING,China,100101 3.Automation Institute,BEIJING Union University,BEIJING,China,100101 4.Automation Institute,BEIJING Union University,BEIJING,China,100101

＜正＞A new fuzzy identification approach using support vector regression（SVR） and immune clone selection algorithm（ICSA） is presented in this *** positive definite reference function is utilized to construct a qualified Mercer kernel for *** an improved ICSA is developed for parameters selection of SVR,in which the number of support vectors and regression accuracy are regarded simultaneously to guarantee the conciseness of the constructed fuzzy ***,a set of TS fuzzy rules can be extracted from the SVR *** results show that the resulting fuzzy model not only costs less fuzzy rules,but also possesses good generalization ability.

关键词： immune clone selection algorithm Fuzzy system identification Positive defmite reference function TS fuzzy rule Support vector regression.

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