A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorith...A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.展开更多
针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进IN...针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进INFO-Bi-LSTM模型)。采用Circle混沌映射和反向学习产生高质量初始化种群,引入自适应t分布提升INFO算法跳出局部最优解和全局搜索的能力。选取改进INFO-Bi-LSTM模型和多种预测模型对炉内外联合脱硫过程中4种典型工况下的SO_(2)排放质量浓度进行预测,将预测结果进行验证对比。结果表明:改进INFO算法的寻优能力得到提升,并且改进INFO-Bi-LSTM模型精度更高,更加适用于SO_(2)排放质量浓度的预测,可为变工况下的脱硫控制提供控制理论支撑。展开更多
Microbial activity and regrowth in drinking water distribution systems is a major concern for water service companies.However,previous studies have focused on the microbial composition and diversity of the drinkingwat...Microbial activity and regrowth in drinking water distribution systems is a major concern for water service companies.However,previous studies have focused on the microbial composition and diversity of the drinkingwater distribution systems(DWDSs),with little discussion on microbial molecular ecological networks(MENs)in different water supply networks.MEN analysis explores the potentialmicrobial interaction and the impact of environmental stress,to explain the characteristics of microbial community structures.In this study,the random matrix theory-based network analysis was employed to investigate the impact of seasonal variation including water source switching on the networks of three DWDSs that used different disinfection methods.The results showed that microbial interaction varied slightly with the seasons but was significantly influenced by different DWDSs.Proteobacteria,identified as key species,play an important role in the network.Combined UV-chlorine disinfection can effectively reduce the size and complexity of the network compared to chlorine disinfection alone,ignoring seasonal variations,which may affect microbial activity or control microbial regrowth in DWDSs.This study provides new insights for analyzing the dynamics of microbial interactions in DWDSs.展开更多
文摘A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.
文摘针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进INFO-Bi-LSTM模型)。采用Circle混沌映射和反向学习产生高质量初始化种群,引入自适应t分布提升INFO算法跳出局部最优解和全局搜索的能力。选取改进INFO-Bi-LSTM模型和多种预测模型对炉内外联合脱硫过程中4种典型工况下的SO_(2)排放质量浓度进行预测,将预测结果进行验证对比。结果表明:改进INFO算法的寻优能力得到提升,并且改进INFO-Bi-LSTM模型精度更高,更加适用于SO_(2)排放质量浓度的预测,可为变工况下的脱硫控制提供控制理论支撑。
基金supported by the National Key R&D Program of China (No. 2019YFC0408700)the National Science and Technology Major Projects of China (Nos. 2017ZX07108-002 and 2017ZX07502003)the funds from the National Natural Science Foundation of China (No. 51778323)
文摘Microbial activity and regrowth in drinking water distribution systems is a major concern for water service companies.However,previous studies have focused on the microbial composition and diversity of the drinkingwater distribution systems(DWDSs),with little discussion on microbial molecular ecological networks(MENs)in different water supply networks.MEN analysis explores the potentialmicrobial interaction and the impact of environmental stress,to explain the characteristics of microbial community structures.In this study,the random matrix theory-based network analysis was employed to investigate the impact of seasonal variation including water source switching on the networks of three DWDSs that used different disinfection methods.The results showed that microbial interaction varied slightly with the seasons but was significantly influenced by different DWDSs.Proteobacteria,identified as key species,play an important role in the network.Combined UV-chlorine disinfection can effectively reduce the size and complexity of the network compared to chlorine disinfection alone,ignoring seasonal variations,which may affect microbial activity or control microbial regrowth in DWDSs.This study provides new insights for analyzing the dynamics of microbial interactions in DWDSs.
