CN112290546B - 一种基于人机共融的变电站一次设备故障判别方法 - Google Patents
一种基于人机共融的变电站一次设备故障判别方法 Download PDFInfo
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Abstract
本发明提供了一种基于人机共融的变电站一次设备故障的判别方法,根据以往设备运行发生故障的数据以及相应的故障大类、具体故障类型、严重程度和故障处理方案,构建出故障数据库、神经网络模型以及综合智能决策***,在实际应用中,通过巡检机器人获取一次设备运行发生故障的数据,诊断专家基于设备运行的故障数据对故障大类做出判断,从而对故障判别空间进行降维,降低模型复杂度以提高判别准确性,并将故障数据输入神经网络模型,预测出具体故障类型,以及相应的严重程度,所述综合智能决策***根据神经网络模型的输出结果获得故障的处理方案,可以为维修人员提供建议参考和指导,提高检修效率。
Description
技术领域
本发明涉及变电站一次设备故障判断技术领域,更具体地,涉及一种基于人机共融的变电站一次设备故障判别方法。
背景技术
变电站的日常巡检和维保是重要工作之一。目前通过人工巡检、传感器报警、巡检***或者巡检机器人报警等方式发现故障后需要通知维修人员前往维护。从发出故障警报到维修人员到达现场这段时间难以有效利用,这段时间更是事故趋于严重化的高发时间段,紧急情况下原本需连续运行的故障设备由于保护设置可能会自动切断,因此这段时间需要密切监视故障设备状态。另一方面,维修人员到达现场后需要先了解情况再针对性的处置,这无疑更加拖慢了故障处置的速度。
发明内容
本发明要解决的技术问题是提供一种基于人机共融的变电站一次设备故障判别方法,针对故障的变电站一次设备,在维修人员检修前,由巡检机器人前往巡查,采集设备运行的故障数据,通过智能算法和诊断专家意见相结合判别故障状况,给出检修处理方案做参考,提高检修效率。
为解决上述技术问题,本发明采用的技术方案是:一种基于人机共融的变电站一次设备故障判别方法,包括训练阶段和应用阶段,所述训练阶段包括:
步骤S1:构建故障数据库,通过巡检机器人收集变电站一次设备运行的故障数据,获取故障大类、具体故障类型和严重程度;
步骤S2:基于机器人平台对一次设备运行的故障数据进行预处理,同时对故障大类、具体故障类型和严重程度进行编码,构建神经网络模型,以预处理后的故障数据为输入,以具体故障类型和严重程度的编码作为输出对神经网络模型进行训练;
步骤S3:构建包括知识库的综合智能决策***,所述综合智能决策***用于根据神经网络模型的输出结果获得故障的处理方案;
所述应用阶段包括:
步骤S4:机器人平台接收报警信息获取报警事故点,指挥巡检机器人前往故障现场,并将报警信息通过通信终端报至维修人员;
步骤S5:巡检机器人获取故障数据传送至机器人平台,诊断专家基于故障数据对故障大类做出判断,机器人平台将故障数据输入神经网络模型,预测出具体故障类型,以及相应的严重程度;
步骤S6:将设备的具体故障类型和严重程度输入综合智能决策***,获取相应的各种处理方案,并将所有具体故障类型、严重程度和处理方案传送至维修人员的通信终端;
优选的,所述步骤S1中,所述巡检机器人上搭载有红外相机、高清相机、温湿度传感器、臭氧浓度传感器、甲烷浓度传感器、氧气浓度传感器中的一种或多种。
优选的,所述步骤S2中,所述故障数据的预处理以及所述故障大类、具体故障类型和严重程度的编码包括:
步骤S21:对红外图像进行图像增强和伪色彩变换,获取图像的R通道值,将R通道值超过233的像素点标记为1,否则标记为0;
步骤S22:对于RGB图像,将采集到的图像和设备正常情况下的图像求像素差值,再二值化,获取二值化图像,分别求取两类图像的像素面积,对于一条数据中两类像素面积,分别剔除离群点;
步骤S23:对于上述处理后的像素面积、温湿度、臭氧浓度、甲烷浓度、氧气浓度分别各自进行Z-score数据归一化。
步骤S24:使用One-hot编码方法对故障类型进行编码,将故障大类共分为5类,则编码分别为10000、01000、00100、00010、00001,相应的对具体故障类型和严重程度也采用One-hot方式进行编码。
优选的,所述步骤S2中,以故障大类的编码作为神经网络模型的模型约束。
优选的,所述步骤S2中,所述神经网络模型由全连接输入层、embedding层、LSTM网络层、CNN卷积神经网络层、Softmax输出层构成。
优选的,所述步骤S3中,所述综合智能决策***的输出结果可被人为进行干预或修正。
优选的,所述步骤S4中,所述巡检机器人为距离报警事故点最近的巡检机器人。
优选的,所述步骤S5中,由诊断专家对故障大类做出判断,并将判断结果输入神经网络模型作为模型约束。
优选的,在故障处理完成前,不断循环步骤S5,若神经网络模型预测的具体故障类型和严重程度出现新的情况,进入步骤S6。
优选的,故障处理完成后,上传最终的具体故障类型、严重程度和处理方案至综合智能决策***,将故障运行数据和故障大类、具体故障类型和严重程度储存至故障数据库。
与现有技术相比,本发明达到的有益效果是:
本发明提供一种人机共融的变电站一次设备故障判别方法,根据以往的设备运行故障数据以及相应的故障大类、具体故障类型、严重程度和故障处理方案,构建出故障数据库、神经网络模型以及综合智能决策***,在实际应用中,通过巡检机器人获取设备运行的故障数据,诊断专家基于故障数据对故障大类做出判断,通过诊断专家的约束,从而对故障判别空间进行降维,降低模型复杂度以提高判别准确性,并将故障数据输入神经网络模型,预测出具体故障类型,以及相应的严重程度,所述综合智能决策***根据神经网络模型的输出结果获得故障的处理方案,可以为维修人员提供建议参考和指导,提高检修效率。
附图说明
为了更清楚地说明本发明实施例中的技术方案,下面将对实施例描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的优选实施例,对于本领域普通技术人员来讲,在不付出创造性劳动性的前提下,还可以根据这些附图获得其他的附图。
图1是本发明的故障数据库构建流程图;
图2是本发明的神经网络模型训练流程图;
图3是本发明的综合智能决策***构建流程图;
图4是本发明的应用阶段整体流程图;
具体实施方式
为了更好理解本发明技术内容,下面提供具体实施例,并结合附图对本发明做进一步的说明:
参见图1-图4,本发明提供一种基于人机共融的变电站一次设备故障判别方法,包括训练阶段和应用阶段,所述训练阶段包括:
步骤S1:构建故障数据库,通过巡检机器人收集变电站一次设备运行的故障数据,获取故障大类、具体故障类型和严重程度;
步骤S2:基于机器人平台对一次设备运行的故障数据进行预处理,同时对故障大类、具体故障类型和严重程度进行编码,构建神经网络模型,以预处理后的故障数据为输入,以具体故障类型和严重程度的编码作为输出对神经网络模型进行训练;
步骤S3:构建包括知识库的综合智能决策***,所述综合智能决策***用于根据神经网络模型的输出结果获得故障的处理方案;
所述应用阶段包括:
步骤S4:机器人平台接收报警信息获取报警事故点,指挥巡检机器人前往故障现场,并将报警信息通过通信终端报至维修人员;
步骤S5:巡检机器人获取设备运行的故障数据传送至机器人平台,诊断专家基于设备运行的故障数据对故障大类做出判断,机器人平台将设备运行的故障数据输入神经网络模型,预测出具体故障类型,以及相应的严重程度;
步骤S6:将设备的具体故障类型和严重程度输入综合智能决策***,获取相应的各种处理方案,并将所有具体故障类型、严重程度和处理方案传送至维修人员的通信终端;
在训练阶段,通过巡检机器人采集设备运行的故障数据,根据采集时间打上时间标签以便构成时间序列上连续的设备运行的故障数据。人工查询维保工作日志或者由维修人员提供相应的故障类型(闪络、接地、短路等)以及故障严重程度(轻微、一般、严重)。将故障时间内的各类设备运行的故障数据和相应的故障大类、具体故障类型及其严重程度组合,作为一条数据并保存,在本实施例中初步采集并保存500条数据,形成故障数据库;通过机器人平台对设备运行的故障数据进行预处理,同时基于机器学习非线性回归或者深度神经网络构建神经网络模型,以预处理后的设备运行的故障数据为输入,以具体故障类型和严重程度的编码作为输出对神经网络模型进行训练至收敛;针对故障数据库500条数据中的具体故障类型和严重程度,人工收集专家和维修人员的处理经验和方案,形成500条故障类型和严重程度与处理方案的数据,根据决策树算法构建综合智能决策***,以具体故障类型和严重程度作为输入,处理方案作为输出,训练至收敛;
在应用阶段,机器人平台根据巡检机器人异常信息报警、各类设备传感器报警或者接驳人工巡检接口收到的报警信息获取报警事故点,通过通信***发送故障告警和报警事故点至各个层级的通信终端,尤其是主要负责人和维修人员,通知维修人员前往处置,并指挥巡检机器人前往故障现场;巡检机器人获取现场设备运行的故障数据传送至机器人平台,机器人平台使用与步骤S2中相同的数据预处理方法处理设备运行的故障数据,使得其符合神经网络模型的输入标准。处理后的数据输入训练收敛的神经网络模型,同时后台诊断专家根据机器人平台上各类信息的展示依据专业知识对故障大类做出判断,并作为约束输入神经网络模型中,得到初步判定的几类具体故障类型和严重程度;并将其送入综合智能决策***,最终得到故障处理方案,将所有可能故障、故障严重程度和处理方案传送至维修人员的通信终端,可以为维修人员提供建议参考和指导,提高检修效率。
具体的,所述巡检机器人上搭载有红外相机、高清相机、温湿度传感器、臭氧浓度传感器、甲烷浓度传感器、氧气浓度传感器中的一种或多种。所述红外相机用于采集故障现场的热红外图像,所述高清相机用于采集故障现场的实际彩色RGB图像,所述温湿度传感器用于采集故障现场的温湿度,所述臭氧浓度传感器用于采集故障现场的臭氧浓度、所述甲烷浓度传感器用于采集故障现场的甲烷浓度、所述氧气浓度传感器用于采集故障现场的氧气浓度。
具体的,各类设备运行的故障数据在输入神经网络模型前需要经过机器人平台进行预处理,最终得到无量纲的各类特征数据,使得其符合人机协作的神经网络模型的输入标准,所述设备运行的故障数据的预处理以及所述故障大类、具体故障类型和严重程度的编码包括:
步骤S21:对红外图像进行图像增强和伪色彩变换,获取图像的R通道值,将R通道值超过233的像素点标记为1,否则标记为0;
步骤S22:对于RGB图像,将采集到的图像和设备正常情况下的图像求像素差值,再二值化,获取二值化图像,分别求取两类图像的像素面积,对于一条数据中两类像素面积,分别剔除离群点;
步骤S23:对于上述处理后的像素面积、温湿度、臭氧浓度、甲烷浓度、氧气浓度分别各自进行Z-score数据归一化。
步骤S24:使用One-hot编码方法对故障类型进行编码,将故障大类共分为5类,则编码分别为10000、01000、00100、00010、00001,相应的对具体故障类型和严重程度也采用One-hot方式进行编码。
需要说明的是,对于设备运行的故障数据的收集和预处理,可以有多种方式,收集数量只要满足后续使用即可,预处理方式也可以有多种,只要满足后续的模型训练、模型使用或者映射关系的构建及使用即可。
具体的,在训练阶段中,选取故障数据库中的故障大类的编码作为神经网络模型的模型约束。而在应用阶段中,由后台诊断专家根据机器人平台上展示的各类信息,依据专业知识对故障大类做出判断,并将判断结果并作为约束输入神经网络模型中。通过这种诊断专家的约束,对故障判别空间进行降维,降低模型复杂度并提高判别准确性。
具体的,所述神经网络模型由全连接输入层、embedding层、LSTM网络层、CNN卷积神经网络层、Softmax输出层构成。所述全连接输入层用于故障运行数据得人输入,输入维度等于数据维度5;所述embedding层用于故障大类的输入;所述LSTM网络层和CNN卷积神经网络层用于训练学习,所述Softmax输出层的输出维度为6,分别对应三种最可能的具体故障类型和相应的严重程度;将故障数据库中500条数据随机分为480条的训练集和20条的测试集,使用Adam优化器训练至收敛,测试集结果表明,有19条数据的结果预测正确,1条预测错误,并且为故障严重程度预测错误,符合预期。在实际应用阶段,通过所述神经网络模型可得到初步判定的具体故障类型和严重程度共3条(如:严重短路故障、严重断路故障、一般接地故障),并将其送入综合智能决策***中,最终得到三个对应的故障处理方案。
具体的,所述综合智能决策***的输出结果可被人为进行干预或修正。在500条故障类型和严重程度与处理方案的数据中,随机取490条作为训练集、10条作为测试集,通过训练、剪枝得到训练收敛的综合智能决策***,输入测试集上的故障类型和严重程度,通过诊断专家的正常干预,均可以得到正确的处理方案,符合预期。在应用阶段中,诊断专家可实时监测综合智能决策***的运行流程和过程输出,若有不同意见,可直接对运行流程进行或对过程变化进行建议、干预或者修正,待***做出最终决策后,将所有可能故障、故障严重程度和处理方案传送至维修人员的通信终端。通过这种方式可以将诊断专家经验与机器决策进行有效结合,更快得到更为精准的决策结果。
具体的,所述巡检机器人为距离报警事故点最近的巡检机器人。机器人平台收到故障告警后,调取当前各个巡检机器人的位置信息和变电站巡检机器人可行进电子地图,找到距离故障点最近的巡检机器人,下发前往故障点指令,巡检机器人停止当前巡检任务,保存巡检节点,前往报警事故点。到达后,开启自身所有相机和传感设备,通过通信***实时返回设备运行的故障数据到机器人平台。
具体的,在维修人员将故障处理完成前,不断循环步骤S5,依靠巡检机器人不间断采集设备运行的故障数据,并持续通过神经网络模型对具体故障类型和严重程度进行预测,当神经网络模型预测出的新的具体故障类型(如一般断路器拒绝合闸故障)或者故障严重程度提高,进入步骤S6,即通过综合智能决策***获取新处理方案,并从新发送新的具体故障类型、严重程度以及处理方案至维修人员终端,使得维修人员能实时获取最新情况。
具体的,维修人员将故障处理完成后,通过通信终端上传最终判定的具体故障类型、严重程度和处理方案,机器人平台确认故障处置,接受最终具体故障类型、严重程度和处理方案,存储至综合智能决策***的知识库;调取故障发生至处置完成时间段内所有巡检机器人返回的设备运行的故障数据,并与最终确定的故障大类、具体故障类型和严重程度储存至故障数据库。定期(如一个月)调用新数据对现有神经网络模型和综合智能决策***自动进行增量训练,另外人工也可以分别对神经网络模型和综合智能决策***下达新数据增量训练的指令。
显然,本发明的上述实施例仅仅是为清楚地说明本发明所作的举例,而并非是对本发明的实施方式的限定。对于所属领域的普通技术人员来说,在上述说明的基础上还可以做出其它不同形式的变化或变动。这里无需也无法对所有的实施方式予以穷举。凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等,均应包含在本发明权利要求的保护范围之内。
Claims (10)
1.一种基于人机共融的变电站一次设备故障判别方法,其特征在于,包括训练阶段和应用阶段,所述训练阶段包括:
步骤S1:构建故障数据库,通过巡检机器人收集变电站一次设备运行的故障数据,获取故障大类、具体故障类型和严重程度;
步骤S2:基于机器人平台对一次设备运行的故障数据进行预处理,同时对故障大类、具体故障类型和严重程度进行编码,构建神经网络模型,以预处理后的故障数据为输入,以具体故障类型和严重程度的编码作为输出对神经网络模型进行训练;
步骤S3:构建包括知识库的综合智能决策***,所述综合智能决策***用于根据神经网络模型的输出结果获得故障的处理方案;
所述应用阶段包括:
步骤S4:机器人平台接收报警信息获取报警事故点,指挥巡检机器人前往故障现场,并将报警信息通过通信终端报至维修人员;
步骤S5:巡检机器人获取故障数据传送至机器人平台,诊断专家基于故障数据对故障大类做出判断,机器人平台将故障数据输入神经网络模型,预测出具体故障类型,以及相应的严重程度;
步骤S6:将设备的具体故障类型和严重程度输入综合智能决策***,获取相应的各种处理方案,并将所有具体故障类型、严重程度和处理方案传送至维修人员的通信终端。
2.根据权利要求1所述的一种基于人机共融的变电站一次设备故障判别方法,其特征在于,所述步骤S1中,所述巡检机器人上搭载有红外相机、高清相机、温湿度传感器、臭氧浓度传感器、甲烷浓度传感器、氧气浓度传感器中的一种或多种。
3.根据权利要求1所述的一种基于人机共融的变电站一次设备故障判别方法,其特征在于,所述步骤S2中,所述故障数据的预处理以及所述故障大类、具体故障类型和严重程度的编码包括:
步骤S21:对红外图像进行图像增强和伪色彩变换,获取图像的R通道值,将R通道值超过233的像素点标记为1,否则标记为0;
步骤S22:对于RGB图像,将采集到的图像和设备正常情况下的图像求像素差值,再二值化,获取二值化图像,分别求取两类图像的像素面积,对于一条数据中两类像素面积,分别剔除离群点;
步骤S23:对于上述处理后的像素面积、温湿度、臭氧浓度、甲烷浓度、氧气浓度分别各自进行Z-score数据归一化;
步骤S24:使用One-hot编码方法对故障类型进行编码,将故障大类共分为5类,则编码分别为10000、01000、00100、00010、00001,相应的对具体故障类型和严重程度也采用One-hot方式进行编码。
4.根据权利要求1所述的一种基于人机共融的变电站一次设备故障判别方法,其特征在于,所述步骤S2中,以故障大类的编码作为神经网络模型的模型约束。
5.根据权利要求1所述的一种基于人机共融的变电站一次设备故障判别方法,其特征在于,所述步骤S2中,所述神经网络模型由全连接输入层、embedding层、LSTM网络层、CNN卷积神经网络层、Softmax输出层构成,所述全连接输入层用于故障运行数据的输入,输入维度等于数据维度5,所述embedding层用于故障大类的输入,所述LSTM网络层和CNN卷积神经网络层用于训练学习,所述Softmax输出层的输出维度为6。
6.根据权利要求1所述的一种基于人机共融的变电站一次设备故障判别方法,其特征在于,所述步骤S3中,所述综合智能决策***的输出结果可被人为进行干预或修正。
7.根据权利要求1所述的一种基于人机共融的变电站一次设备故障判别方法,其特征在于,所述步骤S4中,所述巡检机器人为距离报警事故点最近的巡检机器人。
8.根据权利要求1所述的一种基于人机共融的变电站一次设备故障判别方法,其特征在于,所述步骤S5中,由诊断专家对故障大类做出判断,并将判断结果输入神经网络模型作为模型约束。
9.权利要求1所述的一种基于人机共融的变电站一次设备故障判别方法,其特征在于,在故障处理完成前,不断循环步骤S5,若神经网络模型预测的具体故障类型和严重程度出现新的情况,进入步骤S6。
10.根据权利要求1所述的一种基于人机共融的变电站一次设备故障判别方法,其特征在于,故障处理完成后,上传最终的具体故障类型、严重程度和处理方案至综合智能决策***,将故障运行数据和故障大类、具体故障类型和严重程度储存至故障数据库。
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