CN114157038A - 一种配电网络智能安全全方位预警与控制系统 - Google Patents
一种配电网络智能安全全方位预警与控制系统 Download PDFInfo
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Abstract
本发明公开了一种配电网络智能安全全方位预警与控制系统,包括:运维大数据平台,用于感知运维数据和处理运维操作,对运维事件进行分析、处理并做出决策,运维操作包括采集、存储和展示各种运维数据;智能运维组件,用于根据具体的运维场景、业务规则或专家经验采用人工智能算法构建的组件,包括运维知识图谱和动态决策;自动化工具,用于确定逻辑的运维工具,对技术系统实施运行控制、监控、重启、回滚、版本变更或流量控制操作,以维护技术系统的安全、稳定和可靠运行。根据电网运行关联匹配,为电网故障预测模型提供更加充分合理的数据支撑,进而有效规避电力系统网络运行风险。
Description
技术领域
本发明属于电力系统技术领域,尤其涉及一种配电网络智能安全全方位预警与控制系统。
背景技术
随着配电网络建设的增强,配电线路越来越复杂,传统的运维模式已经不能完全发现运行过程中的隐患并提出解决方法,这期间造成事故的可能性就大大提升,考虑这种传统运维模式带来的问题,一些专家学者提出建立智能电网运维系统,通过智能运维系统随时随地全面了解各电气设备的实时状态,及时准确的掌握故障发生的详细信息,缩短故障的处理时间和降低故障影响。
目前,智能运维系统的出现,一定高程度上解决了运维隐患,但在智能运维的告警系统上,还存在很多弊端,故障定位完全依赖于人的经验,预警是靠人的经验去设定一个阈值,而设定阈值是一个耗时耗力的工作,需要运维人员充分了解业务的前提下,考虑业务的平稳发展状态从而进行设定,这样的工作将会消耗运维人员大量的时间。
发明内容
有鉴于此,本发明提供了一种配电网络智能安全全方位预警与控制系统,可以减少预警错误,引入深度学习方法,避免人为经验造成的损失,通过对高纬度、高离散性的气象致灾因子进行初步处理,为后续电网气象灾害故障预警提供重要依据和合理完整的数据支撑,具体采用以下技术方案来实现。
本发明提供了一种配电网络智能安全全方位预警与控制系统,包括:
运维大数据平台,用于感知运维数据和处理运维操作,对运维事件进行分析、处理并做出决策,运维操作包括采集、存储和展示各种运维数据;
智能运维组件,用于根据具体的运维场景、业务规则或专家经验采用人工智能算法构建的组件,包括运维知识图谱和动态决策;
自动化工具,用于确定逻辑的运维工具,对技术系统实施运行控制、监控、重启、回滚、版本变更或流量控制操作,以维护技术系统的安全、稳定和可靠运行;
其中,自动化工具包括通信单元、计算单元和控制单元,通信单元用于将终端设备感知与采集的数据信息上传至配电子站或主站中心,并将主站优化计算后的决策控制指令下发至各个子站或终端节点;计算单元用于将终端数据采集上来的数据信息进行预处理、分析和深度挖掘,以满足优化系统运行、辨别系统汇总存在的故障节点;控制单元用于实现配电网CPS系统的控制功能,通过二次控制终端设备和一次开关执行器完成;
基于配电网CPS模型中的系统信息与物理侧的交互流程为:配电网CPS的物理系统状态经由FTU、TTU二次设备进行状态感知并采集,转换为适合在通信链路中传输的数字、模拟信号;该状态信息通过信息通信网络上传至配电主站控制中心进行决策分析或状态优化,生成控制指令;该控制指令经由通信网络下发至配电子站或二次终端设备,并导致断路器、分段开关电力一次设备动作以改变物理系统状态,配电网CPS形成一个完整的闭环控制过程。
作为上述技术方案的进一步改进,运维大数据平台包括接口模块、计算中心、模型库和标识模块,接口模块为所有接口包括应用程序开发人员和第三方应用程序接入的接口;计算中心由通用的分布式计算引擎组成,其包含一个通用分布式计算模块与分布式控制模块组成;标识模块为建筑群或机电设备的特殊标签;模型库是利用标识模块中的电子标签识别所属的建筑群或机电设备的网络拓扑连接的基础信息。
作为上述技术方案的进一步改进,智能运维组件包括设备模型库、模型管理引擎和运行管理库;
设备模型库,用于存储全部建筑模型,采用统一动态链接库封装,刻画各类机电设备和建筑空间的相关参数、物理量、运行方式和状态;
模型管理引擎,用于管理、调度、监控设备模型,其包括模型调度运行模块和模型数据定时采集模块,模型调度运行模块用于对设备模型接口和内存进行管理,并通过接口实现不同模型参数的相互调用;模型数据定时采集模块用于存储和管理不同模型的触发周期,并通过接口实现对不同模型的定时触发,完成对模型数据的定时读取和内存参数的定时更新;
运行管理库,用于提供基本数据层支撑,包括内存库和数据库,实现数据的存储、处理、传递和集成。
作为上述技术方案的进一步改进,智能运维组件还包括线程调度中心和CPN网络系统;
线程调度中心,用于提供多线程的调度支撑,保证系统稳定高效运行;
CPN网络系统,用于模拟群智能建筑空间或机电设备的物理架构,分布式群智能应用程序的运行环境为CPN网络系统中的各个节点设备,CPN网络系统接口用于连接CPN系统与设备模型,使CPN系统与设备模型建立数据交换;
其中,内存库提供动态的数据支持,存储模型和CPN网络系统的临时数据并支撑其运行数据读写,数据库提供静态数据支持,实现对数据的集成管理,给显示模块提供数据支撑。
作为上述技术方案的进一步改进,运维大数据平台包括特征提取模块,特征提取模块的执行过程为:
对深度收缩自编码网络进行逐层贪婪预训练,即逐个对收缩自编码器分别进行训练,每个自编码器通过对损失函数最小化得到该层的初始化参数,训练完成后的收缩自编码器的输出作为下一个即将进行训练的自编码器的输入,直到所有收缩自编码器都训练完成,得到致灾因子的初始特征;
在最后一层的收缩自编码器后构建一个场景识别分类器,利用电网气象灾害场景对深度收缩自编码网络进行有监督的微调,使整个深度自编码网络更加适用于在电网气象灾害下对致灾因子的特征提取。
作为上述技术方案的进一步改进,改进收缩自编码网络利用自编码器的编码、解码过程,通过逐层贪婪的无监督训练方式实现对致灾因子的初始特征提取,无监督的预训练利用收缩自编码网络的自学习能力,用无标签训练集对网络的连接权重和偏置量进行调整和初步确定的过程,每次训练一层收缩自编码器,通过每一层自编码器输出对输入的复现进行网络的训练学习,最后得到整个网络的初始化参数,实现对输入因子的初始特征提取。
作为上述技术方案的进一步改进,电网气象灾害致灾因子的特征提取过程如下:
基于专家经验和其历史数据确定气象致灾因子组合权重,并根据气象致灾因子实时状态对组合权重,并根据气象致灾因子实时状态对组合权重进行修正,利用获得的动态权重对气象因子进行初步预处理;
利用预处理后的无标签数据样本,对改进深度收缩自编码网络进行预训练,通过逐层贪婪的方式,使网络无监督的初步挖掘致灾因子内部的有效特征;
利用场景识别分类器通过监督学习的方式对网络参数进行针对性微调,优化整个网络的连接权重和偏置量,建立各类因子之间的耦合关联关系,完成网络训练;
利用模型对未知场景的致灾因子进行特征提取,得到致灾因子抽象特征,并由场景识别分类器获取三类因子与气象灾害场景关联度。
本发明提供了一种配电网络智能安全全方位预警与控制系统,相对于现有技术,具有以下的有益效果:
通过运维大数据平台、智能运维组件和自动化工具组成智能运维系统,智能运维能够自动执行脚本来实现系统的整体运维,分析海量的数据,实现大规模运维的系统,以大数据和机器学习作为基础,从多种数据源中采集海量数据进行实时或离线分析,提高运维的主动性,实现人性化和动态可视化,增强传统运维的能力。配电网CPS系统利用状态感知、分布式计算、实时通信、智能化控制等互补协作与深层次融合,实现了配电网物理空间与信息空间的深层次耦合,从而提升系统泛在感知、协同自治、智能交互的能力,依次完成对系统资源配置的按需调配,快速响应、动态控制与优化运行。根据电网运行关联匹配,为电网故障预测模型提供更加充分合理的数据支撑,进而有效规避电力系统网络运行风险。
附图说明
为了更清楚地说明本发明实施例的技术方案,下面将对实施例中所需要使用的附图作简单地介绍,应当理解,以下附图仅示出了本发明的某些实施例,因此不应被看作是对范围的限定,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他相关的附图。
图1为本发明的配电网络智能安全全方位预警与控制系统的结构框图;
图2为本发明的配电网的交互流程图:
图3为本发明的特征提取模块的执行过程图;
图4为本发明的电网气象灾害致灾因子的特征提取过程图。
具体实施方式
下面详细描述本发明的实施例,所述实施例的示例在附图中示出,其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的,仅用于解释本发明,而不能理解为对本发明的限制。
参阅图1,本发明提供了一种配电网络智能安全全方位预警与控制系统,包括:
运维大数据平台,用于感知运维数据和处理运维操作,对运维事件进行分析、处理并做出决策,运维操作包括采集、存储和展示各种运维数据;
智能运维组件,用于根据具体的运维场景、业务规则或专家经验采用人工智能算法构建的组件,包括运维知识图谱和动态决策;
自动化工具,用于确定逻辑的运维工具,对技术系统实施运行控制、监控、重启、回滚、版本变更或流量控制操作,以维护技术系统的安全、稳定和可靠运行;
其中,自动化工具包括通信单元、计算单元和控制单元,通信单元用于将终端设备感知与采集的数据信息上传至配电子站或主站中心,并将主站优化计算后的决策控制指令下发至各个子站或终端节点;计算单元用于将终端数据采集上来的数据信息进行预处理、分析和深度挖掘,以满足优化系统运行、辨别系统汇总存在的故障节点;控制单元用于实现配电网CPS系统的控制功能,通过二次控制终端设备和一次开关执行器完成;
参阅图2,基于配电网CPS模型中的系统信息与物理侧的交互流程为:
S1:配电网CPS的物理系统状态经由FTU、TTU二次设备进行状态感知并采集,转换为适合在通信链路中传输的数字、模拟信号;
S2:该状态信息通过信息通信网络上传至配电主站控制中心进行决策分析或状态优化,生成控制指令;
S3:该控制指令经由通信网络下发至配电子站或二次终端设备,并导致断路器、分段开关电力一次设备动作以改变物理系统状态,配电网CPS形成一个完整的闭环控制过程。
本实施例中,运维大数据平台包括接口模块、计算中心、模型库和标识模块,接口模块为所有接口包括应用程序开发人员和第三方应用程序接入的接口;计算中心由通用的分布式计算引擎组成,其包含一个通用分布式计算模块与分布式控制模块组成;标识模块为建筑群或机电设备的特殊标签;模型库是利用标识模块中的电子标签识别所属的建筑群或机电设备的网络拓扑连接的基础信息。智能运维系统以智能感知和智能控制为核心,通过物联网技术对配电设备进行全天的智能监控,完成配电站电力、环境、视频、门禁、通风等系统的数据采集和监控,信息集中在监控中心处理,实现配电设备的安全运行,为变电站各方面运维提供可靠的保障,系统实现遥测如电流、电压、功率、有功、无功、谐波,通信如开关状态监测、遥控如远程控制和遥视功能。根据收集到的信息提供设备运行状况的整体评价,自动提供故障解决方案。
需要说明的是,通过高压综合保护测控装置用于测量、控制、保护、通讯一体化的一种经济型保护,实现高压进出线的保护和测控,通过温控器采集交配电站变压器铁芯温度,并可以联动风机,当温度高于设定值时启动风机,温度降下来之后自动停止风机,采用CT和智能多功能电表,实时采集配电房低压母线、馈线回路的电流、电压、电能、有功/无功功率等数据,通过开关分合闸辅助接点/中间继电器和智能多功能电表,实现开关分合闸和次数在线监测与采集。开关每次分合闸动作,终端设备自动统计开关和动作次数,并上传到配电主站,采用电气火灾监控探测器配合剩余电流互感器、温度传感器,实时在线监测馈线回路的漏电流和各相电缆温度。当被保护电路中的被探测参数超过报警设定值时,能发出报警信号、控制信号并能指示报警部位,通过直流屏监控模块获取到合闸母线、控制母线输出的电压、电流,电池电压、电池温度、充电器运行状态等信息,并通过RS485通讯接口将直流屏运行状态数据上传。
可选地,智能运维组件包括设备模型库、模型管理引擎和运行管理库;
设备模型库,用于存储全部建筑模型,采用统一动态链接库封装,刻画各类机电设备和建筑空间的相关参数、物理量、运行方式和状态;
模型管理引擎,用于管理、调度、监控设备模型,其包括模型调度运行模块和模型数据定时采集模块,模型调度运行模块用于对设备模型接口和内存进行管理,并通过接口实现不同模型参数的相互调用;模型数据定时采集模块用于存储和管理不同模型的触发周期,并通过接口实现对不同模型的定时触发,完成对模型数据的定时读取和内存参数的定时更新;
运行管理库,用于提供基本数据层支撑,包括内存库和数据库,实现数据的存储、处理、传递和集成。
本实施例中,智能运维组件还包括线程调度中心和CPN网络系统;线程调度中心,用于提供多线程的调度支撑,保证系统稳定高效运行;CPN网络系统,用于模拟群智能建筑空间或机电设备的物理架构,分布式群智能应用程序的运行环境为CPN网络系统中的各个节点设备,CPN网络系统接口用于连接CPN系统与设备模型,使CPN系统与设备模型建立数据交换;内存库提供动态的数据支持,存储模型和CPN网络系统的临时数据并支撑其运行数据读写,数据库提供静态数据支持,实现对数据的集成管理,给显示模块提供数据支撑。
需要说明的是,功率流动方向通常是单向流动的,即从供电系统电源端经过各级配电线路流向用户端,当配电系统中某个线路区段发生故障时,该故障线路区段及其上游线路区段均会受到故障所带来的影响,此时配电系统中的配电保护依据“选择性”、“速动性”、“可靠性”和“灵敏度”的基本要求,针对不同故障做出响应动作来切除故障,确保配电系统其它部分的正常工作。
参阅图3,可选地,运维大数据平台包括特征提取模块,特征提取模块的执行过程为:
S10:对深度收缩自编码网络进行逐层贪婪预训练,即逐个对收缩自编码器分别进行训练,每个自编码器通过对损失函数最小化得到该层的初始化参数,训练完成后的收缩自编码器的输出作为下一个即将进行训练的自编码器的输入,直到所有收缩自编码器都训练完成,得到致灾因子的初始特征;
S11:在最后一层的收缩自编码器后构建一个场景识别分类器,利用电网气象灾害场景对深度收缩自编码网络进行有监督的微调,使整个深度自编码网络更加适用于在电网气象灾害下对致灾因子的特征提取。
本实施例中,改进收缩自编码网络利用自编码器的编码、解码过程,通过逐层贪婪的无监督训练方式实现对致灾因子的初始特征提取,无监督的预训练利用收缩自编码网络的自学习能力,用无标签训练集对网络的连接权重和偏置量进行调整和初步确定的过程,每次训练一层收缩自编码器,通过每一层自编码器输出对输入的复现进行网络的训练学习,最后得到整个网络的初始化参数,实现对输入因子的初始特征提取。
参阅图4,可选地,电网气象灾害致灾因子的特征提取过程如下:
S20:基于专家经验和其历史数据确定气象致灾因子组合权重,并根据气象致灾因子实时状态对组合权重,并根据气象致灾因子实时状态对组合权重进行修正,利用获得的动态权重对气象因子进行初步预处理;
S21:利用预处理后的无标签数据样本,对改进深度收缩自编码网络进行预训练,通过逐层贪婪的方式,使网络无监督的初步挖掘致灾因子内部的有效特征;
S22:利用场景识别分类器通过监督学习的方式对网络参数进行针对性微调,优化整个网络的连接权重和偏置量,建立各类因子之间的耦合关联关系,完成网络训练;
S23:利用模型对未知场景的致灾因子进行特征提取,得到致灾因子抽象特征,并由场景识别分类器获取三类因子与气象灾害场景关联度。
本实施例中,通过对电网气象灾害致灾因子的耦合复杂性和环境差异性进行分析,结合电网实际运行经验和灾害系统将电网气象灾害相关要素分为气象因子、设备因子和环境因子三类,并根据收集统计到的三类要素的信息整理获得相对应的细致化致灾因子。利用主、客观赋权得到致灾因子的主观权重和客观权重,然后根据致灾因子状态对致灾因子进行分级得到致灾因子状态权重,利用状态权重对致灾因子进行动态修正,最后通过算例分析表明,动态权重确定既可以保留主客观组合赋权的优点,又融合了致灾因子的实时性状态,可以相应提高异常状态因子的权重,降低正常状态因子的权重,使在不同灾害场景下起主导作用的致灾因子不被中和。
在这里示出和描述的所有示例中,任何具体值应被解释为仅仅是示例性的,而不是作为限制,因此,示例性实施例的其他示例可以具有不同的值。
应注意到:相似的标号和字母在下面的附图中表示类似项,因此,一旦某一项在一个附图中被定义,则在随后的附图中不需要对其进行进一步定义和解释。
以上所述实施例仅表达了本发明的几种实施方式,其描述较为具体和详细,但并不能因此而理解为对本发明范围的限制。应当指出的是,对于本领域的普通技术人员来说,在不脱离本发明构思的前提下,还可以做出若干变形和改进,这些都属于本发明的保护范围。
Claims (7)
1.一种配电网络智能安全全方位预警与控制系统,其特征在于,包括:
运维大数据平台,用于感知运维数据和处理运维操作,对运维事件进行分析、处理并做出决策,运维操作包括采集、存储和展示各种运维数据;
智能运维组件,用于根据具体的运维场景、业务规则或专家经验采用人工智能算法构建的组件,包括运维知识图谱和动态决策;
自动化工具,用于确定逻辑的运维工具,对技术系统实施运行控制、监控、重启、回滚、版本变更或流量控制操作,以维护技术系统的安全、稳定和可靠运行;
其中,自动化工具包括通信单元、计算单元和控制单元,通信单元用于将终端设备感知与采集的数据信息上传至配电子站或主站中心,并将主站优化计算后的决策控制指令下发至各个子站或终端节点;计算单元用于将终端数据采集上来的数据信息进行预处理、分析和深度挖掘,以满足优化系统运行、辨别系统汇总存在的故障节点;控制单元用于实现配电网CPS系统的控制功能,通过二次控制终端设备和一次开关执行器完成;
基于配电网CPS模型中的系统信息与物理侧的交互流程为:配电网CPS的物理系统状态经由FTU、TTU二次设备进行状态感知并采集,转换为适合在通信链路中传输的数字、模拟信号;该状态信息通过信息通信网络上传至配电主站控制中心进行决策分析或状态优化,生成控制指令;该控制指令经由通信网络下发至配电子站或二次终端设备,并导致断路器、分段开关电力一次设备动作以改变物理系统状态,配电网CPS形成一个完整的闭环控制过程。
2.根据权利要求1所述的配电网络智能安全全方位预警与控制系统,其特征在于,运维大数据平台包括接口模块、计算中心、模型库和标识模块,接口模块为所有接口包括应用程序开发人员和第三方应用程序接入的接口;计算中心由通用的分布式计算引擎组成,其包含一个通用分布式计算模块与分布式控制模块组成;标识模块为建筑群或机电设备的特殊标签;模型库是利用标识模块中的电子标签识别所属的建筑群或机电设备的网络拓扑连接的基础信息。
3.根据权利要求1所述的配电网络智能安全全方位预警与控制系统,其特征在于,智能运维组件包括设备模型库、模型管理引擎和运行管理库;
设备模型库,用于存储全部建筑模型,采用统一动态链接库封装,刻画各类机电设备和建筑空间的相关参数、物理量、运行方式和状态;
模型管理引擎,用于管理、调度、监控设备模型,其包括模型调度运行模块和模型数据定时采集模块,模型调度运行模块用于对设备模型接口和内存进行管理,并通过接口实现不同模型参数的相互调用;模型数据定时采集模块用于存储和管理不同模型的触发周期,并通过接口实现对不同模型的定时触发,完成对模型数据的定时读取和内存参数的定时更新;
运行管理库,用于提供基本数据层支撑,包括内存库和数据库,实现数据的存储、处理、传递和集成。
4.根据权利要求3所述的配电网络智能安全全方位预警与控制系统,其特征在于,智能运维组件还包括线程调度中心和CPN网络系统;
线程调度中心,用于提供多线程的调度支撑,保证系统稳定高效运行;
CPN网络系统,用于模拟群智能建筑空间或机电设备的物理架构,分布式群智能应用程序的运行环境为CPN网络系统中的各个节点设备,CPN网络系统接口用于连接CPN系统与设备模型,使CPN系统与设备模型建立数据交换;
其中,内存库提供动态的数据支持,存储模型和CPN网络系统的临时数据并支撑其运行数据读写,数据库提供静态数据支持,实现对数据的集成管理,给显示模块提供数据支撑。
5.根据权利要求1所述的配电网络智能安全全方位预警与控制系统,其特征在于,运维大数据平台包括特征提取模块,特征提取模块的执行过程为:
对深度收缩自编码网络进行逐层贪婪预训练,即逐个对收缩自编码器分别进行训练,每个自编码器通过对损失函数最小化得到该层的初始化参数,训练完成后的收缩自编码器的输出作为下一个即将进行训练的自编码器的输入,直到所有收缩自编码器都训练完成,得到致灾因子的初始特征;
在最后一层的收缩自编码器后构建一个场景识别分类器,利用电网气象灾害场景对深度收缩自编码网络进行有监督的微调,使整个深度自编码网络更加适用于在电网气象灾害下对致灾因子的特征提取。
6.根据权利要求5所述的配电网络智能安全全方位预警与控制系统,其特征在于,改进收缩自编码网络利用自编码器的编码、解码过程,通过逐层贪婪的无监督训练方式实现对致灾因子的初始特征提取,无监督的预训练利用收缩自编码网络的自学习能力,用无标签训练集对网络的连接权重和偏置量进行调整和初步确定的过程,每次训练一层收缩自编码器,通过每一层自编码器输出对输入的复现进行网络的训练学习,最后得到整个网络的初始化参数,实现对输入因子的初始特征提取。
7.根据权利要求5所述的配电网络智能安全全方位预警与控制系统,其特征在于,电网气象灾害致灾因子的特征提取过程如下:
基于专家经验和其历史数据确定气象致灾因子组合权重,并根据气象致灾因子实时状态对组合权重,并根据气象致灾因子实时状态对组合权重进行修正,利用获得的动态权重对气象因子进行初步预处理;
利用预处理后的无标签数据样本,对改进深度收缩自编码网络进行预训练,通过逐层贪婪的方式,使网络无监督的初步挖掘致灾因子内部的有效特征;
利用场景识别分类器通过监督学习的方式对网络参数进行针对性微调,优化整个网络的连接权重和偏置量,建立各类因子之间的耦合关联关系,完成网络训练;
利用模型对未知场景的致灾因子进行特征提取,得到致灾因子抽象特征,并由场景识别分类器获取三类因子与气象灾害场景关联度。
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