为了提高半导体器件小信号建模精度并解决优化算法易陷入局部最优解的问题,提出了一种基于改进斑马优化算法(Improved Zebra Optimization Algorithm,IZOA)的氮化镓高电子迁移率晶体管(Gallium Nitride High Electron Mobility Transist...为了提高半导体器件小信号建模精度并解决优化算法易陷入局部最优解的问题,提出了一种基于改进斑马优化算法(Improved Zebra Optimization Algorithm,IZOA)的氮化镓高电子迁移率晶体管(Gallium Nitride High Electron Mobility Transistor,GaN HEMT)混合小信号建模方法。采用数学修正法和直接提取法提取小信号参数,建立初步模型,再使用改进的斑马优化算法进一步提高建模的精度。对斑马优化算法(Zebra Optimization Algorithm,ZOA)的改进主要集中在三个方面:采用混沌映射提高初始种群多样性;使用反向学习策略扩大搜索范围;使用动态概率值替代固定值平衡搜索与收敛能力。实验结果表明,IZOA将直接提取法的平均误差从3.47%降至0.19%,相比灰狼优化(Grey Wolf Optimizer,GWO)算法(平均误差0.95%)降低0.76%,较标准ZOA(平均误差0.52%)降低0.33%,验证了算法的有效性和准确性。展开更多
The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent ...The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.展开更多
文摘为了提高半导体器件小信号建模精度并解决优化算法易陷入局部最优解的问题,提出了一种基于改进斑马优化算法(Improved Zebra Optimization Algorithm,IZOA)的氮化镓高电子迁移率晶体管(Gallium Nitride High Electron Mobility Transistor,GaN HEMT)混合小信号建模方法。采用数学修正法和直接提取法提取小信号参数,建立初步模型,再使用改进的斑马优化算法进一步提高建模的精度。对斑马优化算法(Zebra Optimization Algorithm,ZOA)的改进主要集中在三个方面:采用混沌映射提高初始种群多样性;使用反向学习策略扩大搜索范围;使用动态概率值替代固定值平衡搜索与收敛能力。实验结果表明,IZOA将直接提取法的平均误差从3.47%降至0.19%,相比灰狼优化(Grey Wolf Optimizer,GWO)算法(平均误差0.95%)降低0.76%,较标准ZOA(平均误差0.52%)降低0.33%,验证了算法的有效性和准确性。
基金The research project,“Research on Power Safety Assisted Decision System Based on Large Language Models”(Project Number:JSDL24051414020001)acknowledges with gratitude the financial and logistical support it has received.
文摘The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.
