The working of Canonical switching cell(CSC)converter was studied and its equivalent circuit during ON and OFF states were obtained.State space model of CSC converter in ON and OFF states were developed using the Kirc...The working of Canonical switching cell(CSC)converter was studied and its equivalent circuit during ON and OFF states were obtained.State space model of CSC converter in ON and OFF states were developed using the Kirchhoff laws.The state space matrices were used to construct the transfer functions of ON&OFF states.The step response of the converter was simulated using MATLAB.The step response curve was obtained using different values of circuit components(L,C1,C2 and RL)and optimized.The characteristic parameters such as rise time,overshoot,settling time,steady state error and stability were determined using the step response curve.The response curve shows that there is no overshoot;the rise time and settling time are very low as expected for a converter and its stability is very high but the amplitude is very.The circuit was tuned to attain the expected amplitude using PID controller with the help of Genetic algorithm.The excellent results of circuits’characteristic parameters are very useful guideline for constructing such CSC converters for DC-DC conversions.The circuit characteristic parameters are useful in constructing such CSC converters for DCDC conversions in driving solar energy using solar panel.展开更多
基于变量预测模型的分类识别(Variable predictive model-based class discriminate,VPMCD)方法是一种新的分类识别方法,但模型类型的选择存在主观性。为了解决VPMCD方法应用于机械故障诊断过程中的模型选择问题,结合遗传算法的全局优...基于变量预测模型的分类识别(Variable predictive model-based class discriminate,VPMCD)方法是一种新的分类识别方法,但模型类型的选择存在主观性。为了解决VPMCD方法应用于机械故障诊断过程中的模型选择问题,结合遗传算法的全局优化能力,提出了基于GA-VPMCD(Genetic algorithm and variable predictive model based class discriminate)智能诊断方法。首先通过样本训练建立多个弱VPM(Variable predictive model),然后采用遗传算法优化各个弱VPM的权值,得到最优权值矩阵,最后用最优权值矩阵加权融合测试样本的弱VPM特征变量预测值,得到最佳特征变量预测值,并以误差平方和最小为辨别函数分类识别故障类型。通过GA-VPMCD方法在滚动轴承故障智能诊断中的应用实验验证了基于GA-VPMCD的故障智能诊断方法能有效地提高诊断精度和诊断系统的鲁棒性。展开更多
文摘The working of Canonical switching cell(CSC)converter was studied and its equivalent circuit during ON and OFF states were obtained.State space model of CSC converter in ON and OFF states were developed using the Kirchhoff laws.The state space matrices were used to construct the transfer functions of ON&OFF states.The step response of the converter was simulated using MATLAB.The step response curve was obtained using different values of circuit components(L,C1,C2 and RL)and optimized.The characteristic parameters such as rise time,overshoot,settling time,steady state error and stability were determined using the step response curve.The response curve shows that there is no overshoot;the rise time and settling time are very low as expected for a converter and its stability is very high but the amplitude is very.The circuit was tuned to attain the expected amplitude using PID controller with the help of Genetic algorithm.The excellent results of circuits’characteristic parameters are very useful guideline for constructing such CSC converters for DC-DC conversions.The circuit characteristic parameters are useful in constructing such CSC converters for DCDC conversions in driving solar energy using solar panel.
文摘基于变量预测模型的分类识别(Variable predictive model-based class discriminate,VPMCD)方法是一种新的分类识别方法,但模型类型的选择存在主观性。为了解决VPMCD方法应用于机械故障诊断过程中的模型选择问题,结合遗传算法的全局优化能力,提出了基于GA-VPMCD(Genetic algorithm and variable predictive model based class discriminate)智能诊断方法。首先通过样本训练建立多个弱VPM(Variable predictive model),然后采用遗传算法优化各个弱VPM的权值,得到最优权值矩阵,最后用最优权值矩阵加权融合测试样本的弱VPM特征变量预测值,得到最佳特征变量预测值,并以误差平方和最小为辨别函数分类识别故障类型。通过GA-VPMCD方法在滚动轴承故障智能诊断中的应用实验验证了基于GA-VPMCD的故障智能诊断方法能有效地提高诊断精度和诊断系统的鲁棒性。
