CN112907095A - 基于改进bp神经网络的火电机组智能泄漏监测方法 - Google Patents
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Abstract
本发明公开了基于改进BP神经网络的火电机组智能泄漏监测方法。该方法首先针对经典BP算法作出改进,提出加动量算法的三层BP算法神经网络,其次收集火电机组目标系统的运行历史大数据,主要包括相关系统的热力学参数、环境温度及机组负荷,作为改进BP神经网络的训练样本,训练完成后根据火电机组目标系统当前运行数据,判别一个或多个高低能级系统之间是否发生泄漏,当泄露发生时发出警报提示。本发明可以自动实时在线监测一个或多个高低能级系统之间是否内漏,从而消除火电机组运行中由于泄漏而长期存在的能级耗损,确保机组安全经济运行。
Description
技术领域
本发明涉及火电机组自动监测领域,尤其涉及基于改进BP神经网络的火电机组智能泄漏监测技术,属于热工控制领域。
背景技术
国内在管系泄漏监测方面主要采用声发射、质量平衡、压力分析等阀门检漏技术,实际应用效果不理想。国内外对热力系统泄漏监测技术的研究主要集中在阀门检漏,很少考虑管道及其连接件的泄漏和影响,也很少考虑加装各种测点及装置获取泄漏状态的特征量。热力系统泄漏是两个或多个不同能级系统之间的工质及能量交换过程,由于泄漏的存在,相关热力系统之间的热力参数会发生变化,热力参数的变化是时间的函数。现有的泄漏技术在系统性方面考虑不足,在泄漏特征信号的研究上也忽视了时空的连续性,导致现有泄漏技术难于取得理想的实际效果。
本发明对火电机组复杂热力系统中不同能级系统之间的泄漏监测技术开展研究,提出基于改进BP神经网络的火电机组智能泄漏监测技术。研究在火电机组现有热力系统及设备的边界下,一个或多个高低能级系统之间发生泄漏时,系统相关热力参数之间数学关系及随时间的变化规律,在此基础上,设计开发基于火电机组大数据分析和热力平衡的复杂热力系统泄漏智能监测系统,实现对火电机组多能级系统的泄漏进行有效监测。
发明内容
技术问题:本发明的目的是提供一种基于改进BP神经网络的火电机组智能泄漏监测方法,有效监测火电机组复杂热力系统的能级流失状况,消除火电机组运行中由于泄漏而长期存在的能级耗损,确保机组安全经济运行。
技术方案:为实现上述技术目标,本发明提出基于改进BP神经网络的火电机组智能泄漏监测方法,使得自动监测准确率提高。
本发明的基于改进BP神经网络的火电机组智能泄漏监测方法如下:
步骤一:基于经典BP算法的最速下降法学习规则,引入动量项,加速BP算法收敛,从而改进BP神经网络的模式识别能力;
步骤二:构建x个输入2输出的三层BP网络,收集火电机组目标系统过去一年的运行历史大数据,剔除机组停机及采集异常的数据,作为三层BP神经网络的学习样本,在火电机组现有热力系统及设备的边界下,在一个或多个高低能级系统之间发生泄漏时,训练BP神经网络对系统相关热力参数之间数学关系及随时间变化规律的感知能力;
步骤三:根据火电机组目标系统当前运行数据,判别一个或多个高低能级系统之间是否发生泄漏。
其中:
步骤一所述的引入动量项,加速BP算法收敛,即:
w(n0+1)=w(n0)+η(n0)d(n0)+αΔw(n0),
Aw(n0)=w(n0)-w(n0-1)=η(n0-1)d(n0-1),这时权值修正量加上了有关上一时刻权值修改方向的记忆。
步骤二所述构建x个输入2输出的三层BP网络,网络的隐节点数选为10,网络输入层的输入个数x,对应目标系统的所需热力学参数温度、压力、环境温度以及机组负荷,神经网络隐层采用双极性Sigmoid激活函数,输出层采用单极性Sigmoid激活函数,隐节点数取10,学习率η=0.1,目标误差ε=0.01,最大学习次数10000,初始权值和偏移取[-0.1,0.1]内随机值,令系统发生泄漏的样本输出为[0 1],系统未发生泄漏的样本输出为[1 0]。
步骤三所述判别一个或多个高低能级系统之间是否发生泄漏,是将火电机组目标系统当前运行数据送入训练完成后的三层BP神经网络,判别一个或多个高低能级系统之间是否发生泄漏,当BP网络输出为[1 0]时,判定泄露未发生;当BP网络输出为[0 1]时,判定泄露发生,当泄露发生时发出警报提示。
有益效果:本发明利用改进的BP神经网络,基于机组目标系统的历史运行大数据,可以自动实时在线监测一个或多个高低能级系统之间是否内漏,从而消除火电机组运行中由于泄漏而长期存在的能级耗损,确保机组安全经济运行。
附图说明
图1为多能级热力系统分割示意图。
图中有:P1为系统1的压力,P2为系统2的压力,Pn为系统n的压力,t1为系统1的温度,t2为系统2的温度,tn为系统n的温度。
具体实施方式
首先根据火电机组的典型设计边界,结合泄漏监测要求及目的,确定泄漏监测技术所研究的热力系统的范围。监测系统主要涵盖:主、再蒸汽及其疏放水系统、高低旁系统、清洁疏水系统、真空系统、高排通风系统、再热器安全门等。对确定范围内的高低能级系统布置方式、隔离方式、测点布置及外部环境等系统边界进行分析,充分考虑在线数据的完备性及相关性,将所研究复杂热力系统分割成不同特征的子块。图1为上述多能级热力系统分割示意图。以图1中的系统1为例,所需热力学参数为温度和压力。
该智能泄漏监测方法如下:
步骤一:基于经典BP算法的最速下降法学习规则,引入动量项,加速BP算法收敛,从而改进BP神经网络的模式识别能力;
步骤二:构建x个输入2输出的三层BP网络,收集火电机组目标系统过去一年的运行历史大数据,剔除机组停机及采集异常的数据,作为三层BP神经网络的学习样本,在火电机组现有热力系统及设备的边界下,在一个或多个高低能级系统之间发生泄漏时,训练BP神经网络对系统相关热力参数之间数学关系及随时间变化规律的感知能力;
步骤三:根据火电机组目标系统当前运行数据,判别一个或多个高低能级系统之间是否发生泄漏。
考虑引入动量项,加速BP算法收敛,即:
w(n0+1)=w(n0)+η(n0)d(n0)+αΔw(n0),
Δw(n0)=w(n0)-w(n0-1)=η(n0-1)d(n0-1),这时权值修正量加上了有关上一时刻权值修改方向的记忆。
基于改进的BP算法,构建4输入2输出的三层BP网络,网络的隐节点数选为10,网络输入层的输入为目标系统的所需热力学参数(温度、压力)、环境温度以及机组负荷,神经网络隐层采用双极性Sigmoid激活函数,输出层采用单极性Sigmoid激活函数,隐节点数取10,学习率η=0.1,目标误差ε=0.01,最大学习次数10000,初始权值和偏移取[-0.1,0.1]内随机值,令系统发生泄漏的样本输出为[0 1],系统未发生泄漏的样本输出为[1 0]。收集火电机组目标系统过去一年的运行历史大数据,剔除机组停机及采集异常的数据,作为三层BP神经网络的学习样本。在火电机组现有热力系统及设备的边界下,在一个或多个高低能级系统之间发生泄漏时,训练BP神经网络对系统相关热力参数之间数学关系及随时间变化规律的感知能力。
将火电机组目标系统当前运行数据送入训练完成后的三层BP神经网络,当BP网络输出为[1 0]时,判定系统1泄露未发生;当BP网络输出为[01]时,判定系统1泄露发生,并发出警报提示。
Claims (4)
1.一种基于改进BP神经网络的火电机组智能泄漏监测方法,其特征在于该智能泄漏监测方法如下:
步骤一:基于经典BP算法的最速下降法学习规则,引入动量项,加速BP算法收敛,从而改进BP神经网络的模式识别能力;
步骤二:构建x个输入2输出的三层BP网络,收集火电机组目标系统过去一年的运行历史大数据,剔除机组停机及采集异常的数据,作为三层BP神经网络的学习样本,在火电机组现有热力系统及设备的边界下,在一个或多个高低能级系统之间发生泄漏时,训练BP神经网络对系统相关热力参数之间数学关系及随时间变化规律的感知能力;
步骤三:根据火电机组目标系统当前运行数据,判别一个或多个高低能级系统之间是否发生泄漏。
3.根据权利要求1所述的基于改进BP神经网络的火电机组智能泄漏监测方法,其特征在于:步骤二所述构建x个输入2输出的三层BP网络,网络的隐节点数选为10,网络输入层的输入个数x,对应目标系统的所需热力学参数温度、压力、环境温度以及机组负荷,神经网络隐层采用双极性Sigmoid激活函数,输出层采用单极性Sigmoid激活函数,隐节点数取10,学习率η=0.1,目标误差ε=0.01,最大学习次数10000,初始权值和偏移取[-0.1,0.1]内随机值,令系统发生泄漏的样本输出为[0 1],系统未发生泄漏的样本输出为[1 0]。
4.根据权利要求1所述的基于改进BP神经网络的火电机组智能泄漏监测方法,其特征在于:步骤三所述判别一个或多个高低能级系统之间是否发生泄漏,是将火电机组目标系统当前运行数据送入训练完成后的三层BP神经网络,判别一个或多个高低能级系统之间是否发生泄漏,当BP网络输出为[1 0]时,判定泄露未发生;当BP网络输出为[0 1]时,判定泄露发生,当泄露发生时发出警报提示。
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