Power plant performance can decrease along with its life span,and move away from the design and commissioning targets.Maintenance issues,operational practices,market restrictions,and financial objectives may lead to t...Power plant performance can decrease along with its life span,and move away from the design and commissioning targets.Maintenance issues,operational practices,market restrictions,and financial objectives may lead to that behavior,and the knowledge of appropriate actions could support the system to retake its original operational performance.This paper applies unsupervised machine learning techniques to identify operating patterns based on the power plant’s historical data which leads to the identification of appropriate steam generator efficiency conditions.The selected operational variables are evaluated in respect to their impact on the system performance,quantified by the Variable Importance Index.That metric is proposed to identify the variables among a much wide set of monitored data whose variation impacts the overall power plant operation,and should be controlled with more attention.Principal Component Analysis(PCA)and k-means++clustering techniques are used to identify suitable operational conditions from a one-year-long data set with 27 recorded variables from a steam generator of a 360MW thermal power plant.The adequate number of clusters is identified by the average Silhouette coefficient and the Variable Importance Index sorts nine variables as the most relevant ones,to finally group recommended settings to achieve the target conditions.Results show performance gains in respect to the average historical values of 73.5%and the lowest efficiency condition records of 68%,to the target steam generator efficiency of 76%.展开更多
The operation of complex systems can drift away from the initial design conditions,due to environmental condi-tions,equipment wear or specific restrictions.Steam generators are complex equipment and their proper opera...The operation of complex systems can drift away from the initial design conditions,due to environmental condi-tions,equipment wear or specific restrictions.Steam generators are complex equipment and their proper opera-tion relies on the identification of their most relevant parameters.An approach to rank the operational parameters of a subcritical steam generator of an actual 360 MW power plant is presented.An Artificial Neural Network-ANN delivers a model to estimate the steam generator efficiency,electric power generation and flue gas outlet temperature as a function of seven input parameters.The ANN is trained with a two-year long database,with training errors of 0.2015 and 0.2741(mean absolute and square error)and validation errors of 0.32%and 2.350(mean percent and square error).That ANN model is explored by means of a combination of situations proposed by a Design of Experiment-DoE approach.All seven controlled parameters showed to be relevant to express both steam generator efficiency and electric power generation,while primary air flow rate and speed of the dynamic classifier can be neglected to calculate flue gas temperature as they are not statistically significant.DoE also shows the prominence of the primary air pressure in respect to the steam generator efficiency,electric power generation and the coal mass flow rate for the calculation of the flue gas outlet temperature.The ANN and DoE combined methodology shows to be promising to enhance complex system efficiency and helpful whenever a biased behavior must be brought back to stable operation.展开更多
基金Authors acknowledge Energy of Portugal EDP for the financial and technical support to this projectJ.Duarte acknowledges the financial support from CNPq 154147/2020-6 for her undergraduate scholarship+2 种基金L.W.Vieira acknowledges the INCT-GD and the financial support from CAPES 23038.000776/2017-54 for her Ph.D.grantA.D.Marques ac-knowledges the financial support from CNPq 132422/2020-4 for his MSc grantP.S.Schneider acknowledges CNPq for his research grant(PQ 301619/2019-0).T.S.Prass acknowledges the support of FAPERGS(ARD 01/2017,Processo 17/2551-0000826-0).
文摘Power plant performance can decrease along with its life span,and move away from the design and commissioning targets.Maintenance issues,operational practices,market restrictions,and financial objectives may lead to that behavior,and the knowledge of appropriate actions could support the system to retake its original operational performance.This paper applies unsupervised machine learning techniques to identify operating patterns based on the power plant’s historical data which leads to the identification of appropriate steam generator efficiency conditions.The selected operational variables are evaluated in respect to their impact on the system performance,quantified by the Variable Importance Index.That metric is proposed to identify the variables among a much wide set of monitored data whose variation impacts the overall power plant operation,and should be controlled with more attention.Principal Component Analysis(PCA)and k-means++clustering techniques are used to identify suitable operational conditions from a one-year-long data set with 27 recorded variables from a steam generator of a 360MW thermal power plant.The adequate number of clusters is identified by the average Silhouette coefficient and the Variable Importance Index sorts nine variables as the most relevant ones,to finally group recommended settings to achieve the target conditions.Results show performance gains in respect to the average historical values of 73.5%and the lowest efficiency condition records of 68%,to the target steam generator efficiency of 76%.
文摘The operation of complex systems can drift away from the initial design conditions,due to environmental condi-tions,equipment wear or specific restrictions.Steam generators are complex equipment and their proper opera-tion relies on the identification of their most relevant parameters.An approach to rank the operational parameters of a subcritical steam generator of an actual 360 MW power plant is presented.An Artificial Neural Network-ANN delivers a model to estimate the steam generator efficiency,electric power generation and flue gas outlet temperature as a function of seven input parameters.The ANN is trained with a two-year long database,with training errors of 0.2015 and 0.2741(mean absolute and square error)and validation errors of 0.32%and 2.350(mean percent and square error).That ANN model is explored by means of a combination of situations proposed by a Design of Experiment-DoE approach.All seven controlled parameters showed to be relevant to express both steam generator efficiency and electric power generation,while primary air flow rate and speed of the dynamic classifier can be neglected to calculate flue gas temperature as they are not statistically significant.DoE also shows the prominence of the primary air pressure in respect to the steam generator efficiency,electric power generation and the coal mass flow rate for the calculation of the flue gas outlet temperature.The ANN and DoE combined methodology shows to be promising to enhance complex system efficiency and helpful whenever a biased behavior must be brought back to stable operation.
