Recently, regression artificial neural networks are used to model various systems that have high dimensionality with nonlinear relations. The system under study must have enough dataset available to train the neural n...Recently, regression artificial neural networks are used to model various systems that have high dimensionality with nonlinear relations. The system under study must have enough dataset available to train the neural network. The aim of this work is to apply and experiment various options effects on feed-foreword artificial neural network (ANN) which used to obtain regression model that predicts electrical output power (EP) of combined cycle power plant based on 4 inputs. Dataset is obtained from an open online source. The work shows and explains the stochastic behavior of the regression neural, experiments the effect of number of neurons of the hidden layers. It shows also higher performance for larger training dataset size;at the other hand, it shows different effect of larger number of variables as input. In addition, two different training functions are applied and compared. Lastly, simple statistical study on the error between real values and estimated values using ANN is conducted, which shows the reliability of the model. This paper provides a quick reference to the effects of main parameters of regression neural networks.展开更多
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
The Unified Power Quality Conditioner (UPQC) plays an important role in the constrained delivery of electrical power from the source to an isolated pool of load or from a source to the grid. The proposed system can co...The Unified Power Quality Conditioner (UPQC) plays an important role in the constrained delivery of electrical power from the source to an isolated pool of load or from a source to the grid. The proposed system can compensate voltage sag/swell, reactive power compensation and harmonics in the linear and nonlinear loads. In this work, the off line drained data from conventional fuzzy logic controller. A novel control system with a Combined Neural Network (CNN) is used instead of the traditionally four fuzzy logic controllers. The performance of combined neural network controller compared with Proportional Integral (PI) controller and Fuzzy Logic Controller (FLC). The system performance is also verified experimentally.展开更多
Rural power network planning is a complicated nonlinear optimized combination problem which based on load forecasting results, and its actual load is affected by many uncertain factors, which influenced optimization r...Rural power network planning is a complicated nonlinear optimized combination problem which based on load forecasting results, and its actual load is affected by many uncertain factors, which influenced optimization results of rural power network planning. To solve the problems, the interval algorithm was used to modify the initial search method of uncertainty load mathematics model in rural network planning. Meanwhile, the genetic/tabu search combination algorithm was adopted to optimize the initialized network. The sample analysis results showed that compared with the certainty planning, the improved method was suitable for urban medium-voltage distribution network planning with consideration of uncertainty load and the planning results conformed to the reality.展开更多
Combined heat and power (CHP) plants (co-generation plants) using biomass as fuel, can be an interesting alternative to the predominant electrical heating in Canada. The biomass-fueled boiler provides heat for the ste...Combined heat and power (CHP) plants (co-generation plants) using biomass as fuel, can be an interesting alternative to the predominant electrical heating in Canada. The biomass-fueled boiler provides heat for the steam cycle which in turn generates electricity from the generator connected to the steam turbine. In addition, heat from the process is supplied to a district heating system. The heat can be extracted from the system in a number of ways, by using a back-pressure steam turbine, an extraction steam turbine or by extracting heat directly from the boiler. The objective of the paper is the design, modeling and simulation of such CHP plant. The plant should be sized for providing electric-ity and heat for the Anticosti Island community in Quebec.展开更多
The determination of source-side extracted heating parameters is of great significance to the economic operation of cogeneration systems.This paper investigated the coupling performance of a cogeneration heating and p...The determination of source-side extracted heating parameters is of great significance to the economic operation of cogeneration systems.This paper investigated the coupling performance of a cogeneration heating and power system multidimensionally based on the operating characteristics of the cogeneration units,the hydraulic and thermodynamic characteristics of the heating network,and the energy loads.Taking a steam network supported by a gas-steam combined cycle cogeneration system as the research case,the interaction effect among the source-side prime movers,the heating networks,and the terminal demand thermal parameters were investigated based on the designed values,the plant testing data,and the validated simulation.The operating maps of the gas-steam combined cycle cogeneration units were obtained using THERMOFLEX,and the minimum source-side steam parameters of the steam network were solved using an inverse solution procedure based on the hydro-thermodynamic coupling model.The cogeneration operating maps indicate that the available operating domain considerably narrows with the rise of the extraction steam pressure and flow rate.The heating network inverse solution demonstrates that the source-side steam pressure and temperature can be optimized from the originally designed 1.11 MPa and 238.8°C to 1.074 MPa and 191.15°C,respectively.Under the operating strategy with the minimum source-side heating parameters,the power peak regulation depth remarkably increases to 18.30%whereas the comprehensive thermal efficiency decreases.The operation under the minimum source-side heating steam parameters can be superior to the originally designed one in the economy at a higher price of the heating steam.At a fuel price of$0.38/kg and the power to fuel price of 0.18 kg/(kW·h),the critical price ratio of heating steam to fuel is 119.1 kg/t.The influence of the power-fuel price ratio on the economic deviation appears relatively weak.展开更多
In this paper, the design of a planar ultra-broadband sum-and-difference network is presented. This network employs a novel power divider and anti-phase balun as a building block. An equivalent 180° coupler with ...In this paper, the design of a planar ultra-broadband sum-and-difference network is presented. This network employs a novel power divider and anti-phase balun as a building block. An equivalent 180° coupler with a bandwidth of 6.2 - 14GHz is achieved by back-connecting the power divider and balun together. Four such couplers are connected to form an ultra-broadband sum-and-difference network which has a bandwidth of 91%. This network, with insertion loss less than 1.8dB in sum port and nulls less than -20dB in all delta ports, is verified to be excellent, resulting in the advantages of being compact, easy manufacturing and low cost.展开更多
现有的数据中心节能降碳优化方法没有综合考虑碳足迹涉及的能源输入、生产耗能以及废余利用等环节的耦合性,难以实现系统性节能降碳。为此,提出了一种基于深度强化学习的优化算法DeepCCHP(deep combined cooling,heating and power gene...现有的数据中心节能降碳优化方法没有综合考虑碳足迹涉及的能源输入、生产耗能以及废余利用等环节的耦合性,难以实现系统性节能降碳。为此,提出了一种基于深度强化学习的优化算法DeepCCHP(deep combined cooling,heating and power generation),针对数据中心冷热电联产系统,联合控制供电子系统和制冷子系统,优化用电成本、碳排放量和能效。DeepCCHP结合长、短期时间序列网络和深度强化学习方法对联合优化问题进行求解,实现前摄式的联合控制发电设备和制冷设备。在基于Trnsys软件的仿真环境中,通过阿里巴巴数据中心集群数据的训练和验证。实验结果表明,与基准算法相比,DeepCCHP算法可以节省最高40%的成本和28%的碳排放量,且能够在能源成本、碳排放和能效三者之间取得更好的折中与平衡。展开更多
配电网线损时间序列受高比例新能源接入的影响,呈现高度的非线性和波动性,面对此种类型的数据,使得常规的预测模型难以捕捉其变化趋势,预测值往往滞后于真实值变化,而模态分解再预测的处理方法能够较好地应对此问题。因此,该文提出了一...配电网线损时间序列受高比例新能源接入的影响,呈现高度的非线性和波动性,面对此种类型的数据,使得常规的预测模型难以捕捉其变化趋势,预测值往往滞后于真实值变化,而模态分解再预测的处理方法能够较好地应对此问题。因此,该文提出了一种基于缎蓝园丁鸟(satin bower birdoptimization algorithm,SBO)算法优化的二次模态分解和卷积双向长短期记忆神经网络的线损预测框架,以合理划分线损分量,并针对各分量设计预测模型开展预测。首先采用改进完全集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)对历史线损数据进行初次分解,得到各ICIMFn分量并计算其样本熵;对样本熵最高的ICIMF1利用经SBO优化的变分模态分解(variational mode decomposition,VMD)对其进一步分解,得到各VIMFn分量。其次,考虑分解后线损各分量受天气负荷等不同因素影响,依据最大互信息系数(maximal information coefficien,MIC),提取对各线损分量产生影响的主要因素,实现特征降维。最后,结合组合模型的各自特点,建立基于卷积双向长短期记忆神经网络(convolutional neural networks-Bidirectional long short term memory,CNN-BiLSTM)的预测模型,使用CNN对分解后的各分量进行特征提取,输入到BiLSTM中,建立时间特征关系,学习历史数据间的正、反向规律,最终输出线损预测结果。与现有方法相比较,所提方法在应对滞后效应的同时,提升了预测效率及精度,为精细化线损管理提供了数据支持。展开更多
文摘Recently, regression artificial neural networks are used to model various systems that have high dimensionality with nonlinear relations. The system under study must have enough dataset available to train the neural network. The aim of this work is to apply and experiment various options effects on feed-foreword artificial neural network (ANN) which used to obtain regression model that predicts electrical output power (EP) of combined cycle power plant based on 4 inputs. Dataset is obtained from an open online source. The work shows and explains the stochastic behavior of the regression neural, experiments the effect of number of neurons of the hidden layers. It shows also higher performance for larger training dataset size;at the other hand, it shows different effect of larger number of variables as input. In addition, two different training functions are applied and compared. Lastly, simple statistical study on the error between real values and estimated values using ANN is conducted, which shows the reliability of the model. This paper provides a quick reference to the effects of main parameters of regression neural networks.
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The Unified Power Quality Conditioner (UPQC) plays an important role in the constrained delivery of electrical power from the source to an isolated pool of load or from a source to the grid. The proposed system can compensate voltage sag/swell, reactive power compensation and harmonics in the linear and nonlinear loads. In this work, the off line drained data from conventional fuzzy logic controller. A novel control system with a Combined Neural Network (CNN) is used instead of the traditionally four fuzzy logic controllers. The performance of combined neural network controller compared with Proportional Integral (PI) controller and Fuzzy Logic Controller (FLC). The system performance is also verified experimentally.
文摘Rural power network planning is a complicated nonlinear optimized combination problem which based on load forecasting results, and its actual load is affected by many uncertain factors, which influenced optimization results of rural power network planning. To solve the problems, the interval algorithm was used to modify the initial search method of uncertainty load mathematics model in rural network planning. Meanwhile, the genetic/tabu search combination algorithm was adopted to optimize the initialized network. The sample analysis results showed that compared with the certainty planning, the improved method was suitable for urban medium-voltage distribution network planning with consideration of uncertainty load and the planning results conformed to the reality.
文摘Combined heat and power (CHP) plants (co-generation plants) using biomass as fuel, can be an interesting alternative to the predominant electrical heating in Canada. The biomass-fueled boiler provides heat for the steam cycle which in turn generates electricity from the generator connected to the steam turbine. In addition, heat from the process is supplied to a district heating system. The heat can be extracted from the system in a number of ways, by using a back-pressure steam turbine, an extraction steam turbine or by extracting heat directly from the boiler. The objective of the paper is the design, modeling and simulation of such CHP plant. The plant should be sized for providing electric-ity and heat for the Anticosti Island community in Quebec.
基金Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization(South China University of Technology)(2013A061401005)Research Fund(JMSWFW-2110-044)from Zhongshan Jiaming Electric Power Co.,Ltd.
文摘The determination of source-side extracted heating parameters is of great significance to the economic operation of cogeneration systems.This paper investigated the coupling performance of a cogeneration heating and power system multidimensionally based on the operating characteristics of the cogeneration units,the hydraulic and thermodynamic characteristics of the heating network,and the energy loads.Taking a steam network supported by a gas-steam combined cycle cogeneration system as the research case,the interaction effect among the source-side prime movers,the heating networks,and the terminal demand thermal parameters were investigated based on the designed values,the plant testing data,and the validated simulation.The operating maps of the gas-steam combined cycle cogeneration units were obtained using THERMOFLEX,and the minimum source-side steam parameters of the steam network were solved using an inverse solution procedure based on the hydro-thermodynamic coupling model.The cogeneration operating maps indicate that the available operating domain considerably narrows with the rise of the extraction steam pressure and flow rate.The heating network inverse solution demonstrates that the source-side steam pressure and temperature can be optimized from the originally designed 1.11 MPa and 238.8°C to 1.074 MPa and 191.15°C,respectively.Under the operating strategy with the minimum source-side heating parameters,the power peak regulation depth remarkably increases to 18.30%whereas the comprehensive thermal efficiency decreases.The operation under the minimum source-side heating steam parameters can be superior to the originally designed one in the economy at a higher price of the heating steam.At a fuel price of$0.38/kg and the power to fuel price of 0.18 kg/(kW·h),the critical price ratio of heating steam to fuel is 119.1 kg/t.The influence of the power-fuel price ratio on the economic deviation appears relatively weak.
文摘In this paper, the design of a planar ultra-broadband sum-and-difference network is presented. This network employs a novel power divider and anti-phase balun as a building block. An equivalent 180° coupler with a bandwidth of 6.2 - 14GHz is achieved by back-connecting the power divider and balun together. Four such couplers are connected to form an ultra-broadband sum-and-difference network which has a bandwidth of 91%. This network, with insertion loss less than 1.8dB in sum port and nulls less than -20dB in all delta ports, is verified to be excellent, resulting in the advantages of being compact, easy manufacturing and low cost.
文摘现有的数据中心节能降碳优化方法没有综合考虑碳足迹涉及的能源输入、生产耗能以及废余利用等环节的耦合性,难以实现系统性节能降碳。为此,提出了一种基于深度强化学习的优化算法DeepCCHP(deep combined cooling,heating and power generation),针对数据中心冷热电联产系统,联合控制供电子系统和制冷子系统,优化用电成本、碳排放量和能效。DeepCCHP结合长、短期时间序列网络和深度强化学习方法对联合优化问题进行求解,实现前摄式的联合控制发电设备和制冷设备。在基于Trnsys软件的仿真环境中,通过阿里巴巴数据中心集群数据的训练和验证。实验结果表明,与基准算法相比,DeepCCHP算法可以节省最高40%的成本和28%的碳排放量,且能够在能源成本、碳排放和能效三者之间取得更好的折中与平衡。
文摘配电网线损时间序列受高比例新能源接入的影响,呈现高度的非线性和波动性,面对此种类型的数据,使得常规的预测模型难以捕捉其变化趋势,预测值往往滞后于真实值变化,而模态分解再预测的处理方法能够较好地应对此问题。因此,该文提出了一种基于缎蓝园丁鸟(satin bower birdoptimization algorithm,SBO)算法优化的二次模态分解和卷积双向长短期记忆神经网络的线损预测框架,以合理划分线损分量,并针对各分量设计预测模型开展预测。首先采用改进完全集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)对历史线损数据进行初次分解,得到各ICIMFn分量并计算其样本熵;对样本熵最高的ICIMF1利用经SBO优化的变分模态分解(variational mode decomposition,VMD)对其进一步分解,得到各VIMFn分量。其次,考虑分解后线损各分量受天气负荷等不同因素影响,依据最大互信息系数(maximal information coefficien,MIC),提取对各线损分量产生影响的主要因素,实现特征降维。最后,结合组合模型的各自特点,建立基于卷积双向长短期记忆神经网络(convolutional neural networks-Bidirectional long short term memory,CNN-BiLSTM)的预测模型,使用CNN对分解后的各分量进行特征提取,输入到BiLSTM中,建立时间特征关系,学习历史数据间的正、反向规律,最终输出线损预测结果。与现有方法相比较,所提方法在应对滞后效应的同时,提升了预测效率及精度,为精细化线损管理提供了数据支持。
