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
Background： There has been no external validation of survival prediction models for severe adult respiratory distress syndrome （ARDS） with extracorporeal membrane oxygenation （ECMO） therapy in China. The aim of s...Background： There has been no external validation of survival prediction models for severe adult respiratory distress syndrome （ARDS） with extracorporeal membrane oxygenation （ECMO） therapy in China. The aim of study was to compare the performance of multiple models recently developed for patients with ARDS undergoing ECMO based on Chinese single-center data. Methods： A retrospective case study was performed, including twenty-three severe ARDS patients who received ECMO from January 2009 to July 2015. The PRESERVE （Predicting death for severe ARDS on VV-ECMO）, ECMOnet, Respiratory Extracorporeal Membrane Oxygenation Survival Prediction （RESP） score, a center-specific model developed lbr inter-hospital transfers receiving ECMO, and the classical risk-prediction scores of Acute Physiology and Chronic Health Evaluation （APACHE） II and Sequential Organ Failure Assessment （SOFA） were calculated. In-hospital and six-month mortality were regarded as the endpoints and model performance was evaluated by comparing the area under the receiver operating characteristic curve （AUC）. Results： The RESP and APACHE II scores showed excellent discriminate performance in predicting survival with AUC of 0.835 （95% confidence interval [CI], 0.659-1 .010, P = 0.007） and 0.762 （95% CI, 0.558-0.965, P = 0.035）, respectively. The optimal cutoff values were risk class 3.5 for RESP and 35.5 for APACHE II score, and both showed 70.0% sensitivity and 84.6% specificity. The excellent performance of these models was also evident for the pneumonia etiological subgroup, for which the SOFA score was also shown to be predictive, with an AUC of 0.790 （95% CI, 0.571-1.009, P = 0.038）. However, the ECMOnet and the score developed for externally retrieved ECMO patients failed to demonstrate significant discriminate power for the overall cohort. The PRESERVE model was unable to be evaluated fully since Conclusions： The RESP, APCHAE 11, and SOFA scorings only one patient died six months postdischarge. systems show good predictive value for intra-hospital survival of ARDS patients treated with ECMO in our single-center evaluation. Future validation should include a larger study with either more patients＇ data at single-center or by integration of domestic multi-center data. Development of a scoring system with national characteristics might be warranted.展开更多
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.
文摘Background： There has been no external validation of survival prediction models for severe adult respiratory distress syndrome （ARDS） with extracorporeal membrane oxygenation （ECMO） therapy in China. The aim of study was to compare the performance of multiple models recently developed for patients with ARDS undergoing ECMO based on Chinese single-center data. Methods： A retrospective case study was performed, including twenty-three severe ARDS patients who received ECMO from January 2009 to July 2015. The PRESERVE （Predicting death for severe ARDS on VV-ECMO）, ECMOnet, Respiratory Extracorporeal Membrane Oxygenation Survival Prediction （RESP） score, a center-specific model developed lbr inter-hospital transfers receiving ECMO, and the classical risk-prediction scores of Acute Physiology and Chronic Health Evaluation （APACHE） II and Sequential Organ Failure Assessment （SOFA） were calculated. In-hospital and six-month mortality were regarded as the endpoints and model performance was evaluated by comparing the area under the receiver operating characteristic curve （AUC）. Results： The RESP and APACHE II scores showed excellent discriminate performance in predicting survival with AUC of 0.835 （95% confidence interval [CI], 0.659-1 .010, P = 0.007） and 0.762 （95% CI, 0.558-0.965, P = 0.035）, respectively. The optimal cutoff values were risk class 3.5 for RESP and 35.5 for APACHE II score, and both showed 70.0% sensitivity and 84.6% specificity. The excellent performance of these models was also evident for the pneumonia etiological subgroup, for which the SOFA score was also shown to be predictive, with an AUC of 0.790 （95% CI, 0.571-1.009, P = 0.038）. However, the ECMOnet and the score developed for externally retrieved ECMO patients failed to demonstrate significant discriminate power for the overall cohort. The PRESERVE model was unable to be evaluated fully since Conclusions： The RESP, APCHAE 11, and SOFA scorings only one patient died six months postdischarge. systems show good predictive value for intra-hospital survival of ARDS patients treated with ECMO in our single-center evaluation. Future validation should include a larger study with either more patients＇ data at single-center or by integration of domestic multi-center data. Development of a scoring system with national characteristics might be warranted.
