WO2022041264A1 - 一种大数据支持轨交电力系统运营的方法 - Google Patents

一种大数据支持轨交电力系统运营的方法 Download PDF

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WO2022041264A1
WO2022041264A1 PCT/CN2020/112710 CN2020112710W WO2022041264A1 WO 2022041264 A1 WO2022041264 A1 WO 2022041264A1 CN 2020112710 W CN2020112710 W CN 2020112710W WO 2022041264 A1 WO2022041264 A1 WO 2022041264A1
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big data
rail transit
power
power system
power grid
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夏泽宇
方芳
夏钢
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苏州大成电子科技有限公司
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  • the invention relates to a method for supporting rail transit power system operation by big data, and belongs to the field of big data analysis and computer technology.
  • the smart grid of rail transit is the key to the rail transit operation network.
  • the smart rail transit power grid utilizes advanced information and communication technology, computer technology, control technology and other advanced technologies to realize the coordination of the rail transit power grid operation and terminal power demand and functions, improve the high-efficiency operation of each part of the system, reduce costs and At the same time of environmental impact, improve the reliability, self-healing ability and stability of the system as much as possible.
  • Big data analysis is a technical system or technical framework that extracts its value in an economical way by capturing, discovering and analyzing a large amount of data with complex types and sources at a high speed. Therefore, how to combine big data with power transmission, substation, distribution, power consumption and dispatching is an urgent problem that the rail transit industry needs to solve.
  • the present invention provides a method for supporting rail transit power system operation with big data.
  • a method for supporting rail transit power system operation with big data comprising the following steps:
  • Step 1 Power system transient stability analysis and control
  • Step 2 Fault diagnosis and condition maintenance based on online monitoring data of power grid equipment
  • Step 3 Short-term/ultra-short-term load forecasting
  • Step 4 Distribution network fault location
  • Step 5 Anti-theft electricity management
  • Step 6 Grid equipment asset management.
  • the step 1 includes: based on the power system transient stability criterion and control strategy decision-making based on WAMS data, under the guidance of big data theory and technology, combining the existing analysis method with the data processing technology, in the Considering the calculation speed parameters, the analysis results are displayed in an intuitive way, which effectively guides operators to make scientific decisions.
  • the step 2 includes: realizing the fusion of various historical data and real-time data of GIS, PMS, and online monitoring systems, applying big data technology for fault diagnosis, and providing decision-making for condition maintenance, and realizing dynamic monitoring of key performance of power grid equipment. Evaluation and fault diagnosis based on complex correlation identification provides technical support for solving existing condition maintenance problems.
  • step 3 includes: load forecasting and power generation forecasting need to consider distributed energy resources and microgrids, and must also take into account the impact of weather and energy trading conditions, including market-led demand response; applying big data technology to establish The quantitative relationship between various influencing factors and load forecasting can be used to build a load forecasting model in a targeted manner, which can more accurately predict short-term/ultra-short-term load.
  • the step 4 includes: using big data technology, cooperating with the fault complaint system, integrating the data in SCADA, EMS, DMS, and D-SCADA systems to make optimal judgments, establishing a new distribution network fault management system, and quickly locating faults, deal with power outages and improve the reliability of power supply.
  • the step 5 includes: the power intelligent network establishes a power theft behavior analysis model through comprehensive analysis of the power differential exceeding the limit, the phase failure, the line loss rate exceeding the standard, the abnormal alarm information, and the event data of the meter opening.
  • the power intelligent network establishes a power theft behavior analysis model through comprehensive analysis of the power differential exceeding the limit, the phase failure, the line loss rate exceeding the standard, the abnormal alarm information, and the event data of the meter opening.
  • Early warning of electricity theft through the data fusion of the operation and distribution system, compare the user load curve, meter current, voltage and power factor data and transformer load, and combine the power grid operation data to realize the daily settlement of line losses for specific lines, which can be known through the line loss management function.
  • the step 6 includes: based on power grid equipment information, operation information, environmental information and historical fault and defect information, obtaining the minimum value of the life cycle cost; according to external information of traffic, road administration, and municipal administration, associating power grid equipment and lines GPS coordinates for early warning and analysis of power grid damage caused by external force.
  • a method for big data supporting rail transit power system operation uses universal domain knowledge mining technology to mine the smart grid big data of rail transit to obtain universal knowledge hidden in habit or experience, It can help operators and planners to improve the operation capability of the entire rail transit smart grid.
  • a method for supporting rail transit power system operation with big data comprising the following steps:
  • Power system transient stability analysis and control On-line transient stability analysis and control has always been the goal pursued by power operators.
  • the transient stability analysis and control mode of "real-time matching" can no longer meet the requirements of safe and stable operation of large power grids, so it gradually develops in the direction of "real-time decision-making and real-time control”.
  • WAMS data the power system transient stability criterion and control strategy decision, under the guidance of big data theory and technology, combine the existing analysis methods with data processing technology, consider the calculation speed parameter, use the analysis results to The intuitive method is displayed to effectively guide operators to make scientific decisions.
  • Fault diagnosis and condition maintenance based on online monitoring data of power grid equipment realize the integration of various historical data and real-time data of GIS, PMS, and online monitoring systems, apply big data technology for fault diagnosis, and provide decision-making for condition maintenance, and realize the power grid.
  • Dynamic evaluation of equipment key performance and fault diagnosis based on complex correlation identification provide technical support for solving existing condition maintenance problems.
  • Short-term/ultra-short-term load forecasting load forecasting and power generation forecasting need to consider distributed energy and microgrids, and must also take into account the impact of weather and energy trading conditions, including market-led demand response; traditional forecasting methods cannot reflect certain conditions. The influence of these factors on the load fundamentally limits its application scope and prediction accuracy. Applying big data technology to establish quantitative correlations between various influencing factors and load forecasting, and construct load forecasting models in a targeted manner, short-term/ultra-short-term loads can be predicted more accurately.
  • Distribution network fault location use big data technology, cooperate with fault complaint system, integrate data in SCADA, EMS, DMS, D-SCADA and other systems to make optimal judgments, and establish a new distribution network fault management system, which can quickly locate fault, deal with the problem of power failure and improve the reliability of power supply. In addition, with the gradual increase of the proportion of distributed power in the system, its access will affect the system protection settings and positioning criteria. For the fault location of the distribution network with distributed power generation, an appropriate location strategy should also be selected according to different grid-connected requirements.
  • Anti-electricity theft management The power network establishes an analysis model of electricity theft behavior through comprehensive analysis of data such as power differential exceeding limit, phase failure, line loss rate exceeding the standard, abnormal alarm information, and meter opening events to establish an analysis model for electricity theft behavior of users.
  • Early warning through the data fusion of the operation and distribution system, the user load curve, meter current, voltage and power factor data can be compared with the transformer load, combined with the power grid operation data, the daily settlement of line losses of specific lines can be realized, and the line loss management function can not only know Implement the specific line where the electricity stealing user is located, and can locate a specific user, overcoming the problem of wide inspection scope and difficult investigation and punishment.
  • Grid equipment asset management Based on grid equipment information, operation information, environmental information (meteorology, climate, etc.) and historical fault and defect information, starting from the long-term interests of equipment or projects, comprehensively consider the planning of equipment of different types and operating years , the whole process of design, manufacture, purchase, installation, commissioning, operation, maintenance, transformation, updating and scrapping, to find the minimum life cycle cost.

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Abstract

一种大数据支持轨交电力系统运营的方法，步骤1：轨道交通的电力系统暂态稳定性分析和控制；步骤2：基于电网设备在线监测数据的故障诊断与状态检修；步骤3：短期/超短期负荷预测；步骤4：配电网故障定位；步骤5：防窃电管理；步骤6：电网设备资产管理。一种大数据支持轨交电力系统运营的方法，用普适领域知识挖掘技术对轨交系统的智能电网大数据进行挖掘，获取隐藏于习惯或经验中的普适知识，可以帮助运行及规划人员提高对整个轨交系统智能电网的运营能力。

Description

一种大数据支持轨交电力系统运营的方法 技术领域
本发明涉及一种大数据支持轨交电力系统运营的方法，属于大数据分析及计算机技术领域。
背景技术
目前，轨道交通的智能电网是轨交运营网络中的关键。智能轨交电网利用先进的信息通信技术、计算机技术、控制技术及其他先进技术，实现对轨交电网运行、终端用电的需求和功能的协调，提高系统各部分的高效率运行、降低成本和环境影响的同时，尽可能提高系统的可靠性、自愈能力和稳定性。
大数据分析通过对大量的、种类和来源复杂的数据进行高速地捕捉、发现和分析，用经济的方法提取其价值的技术体系或技术架构。所以，如何将大数据与输电、变电、配电、用电及调度各环节相结合，是轨道交通行业急需要解决的问题。
发明内容
目的：为了克服现有技术中存在的不足，本发明提供一种大数据支持轨交电力系统运营的方法。
技术方案：为解决上述技术问题，本发明采用的技术方案为：
一种大数据支持轨交电力系统运营的方法，包括如下步骤：
步骤1：电力系统暂态稳定性分析和控制；
步骤2：基于电网设备在线监测数据的故障诊断与状态检修；
步骤3：短期/超短期负荷预测；
步骤4：配电网故障定位；
步骤5：防窃电管理；
步骤6：电网设备资产管理。
作为优选方案，所述步骤1包括：基于WAMS数据的电力系统暂态稳定判据和控制策略决策，在大数据理论和技术指导下，将现有的分析方法与数据的处理技术相结合，在考虑计算速度参数下，将分析结果用直观的方法展示出来，有效指导运行人员做出科学的决策。
作为优选方案，所述步骤2包括：实现GIS、PMS、在线监测系统各类历史数据和实时数据融合，应用大数据技术进行故障诊断，并为状态检修提供决策，实现对电网设备关键性能的动态评估与基于复杂相关关系识别的故障诊断，为解决现有状态维修问题提供技术支撑。
作为优选方案，所述步骤3包括：负荷预测和发电预测需要考虑分布式能源和微网，还必须考虑到天气的影响以及能源交易状况，包括市场引导下的需求响应；应用大数据技术，建立各类影响因素与负荷预测之间的量化关联关系，有针对性地构建负荷预测模型，可更加精确地预测短期/超短期负荷。
作为优选方案，所述步骤4包括：利用大数据技术，配合故障投诉系统，融合SCADA、EMS、DMS、D-SCADA系统中的数据作出最优判断，建立新型配电网故障管理系统，快速定位故障，应对故障停电问题，提高供电可靠性。
作为优选方案，所述步骤5包括：电力智能网络通过电量差动越限、断相、线损率超标、异常告警信息、电表开盖事件数据的综合分析，建立窃电行为分析模型，对用户窃电行为进行预警；通过营配系统数据融合，比较用户负荷曲线、电表电流、电压和功率因数数据和变压器负载，结合电网运行数据，实现具体线路的线损日结算，通过线损管理功能知道实施窃电用户所在的具体线路，并且定位至某一具体用户，克服目前检查范围广，查处难度大的问题。
作为优选方案，所述步骤6包括：基于电网设备信息、运行信息、环境信息以及历史故障和缺陷信息，求取寿命周期成本最小值；依据交通、路政、市政的外部信息，关联电网设备及线路GPS坐标，对电网外力破坏故障进行预警分析。
有益效果：本发明提供的一种大数据支持轨交电力系统运营的方法，用普适领域知识挖掘技术对轨道交通的智能电网大数据进行挖掘，获取隐藏于习惯或经验中的普适知识，可以帮助运行及规划人员提高对个整个轨道交通智能电网的运营能力。
具体实施方式
一种大数据支持轨交电力系统运营的方法，包括步骤如下：
1)电力系统暂态稳定性分析和控制：在线暂态稳定分析与控制一直是电力运行人员追求的目标，随着互联电网规模越来越大，“离线决策，在线匹配”和“在线决策，实时匹配”的暂态稳定分析与控制模式已不能满足大电网安全稳定运行要求，因而逐渐向“实时决策，实时控制”的方向 发展。基于WAMS数据的电力系统暂态稳定判据和控制策略决策，在大数据理论和技术指导下，将现有的分析方法与数据的处理技术相结合，在考虑计算速度参数下，将分析结果用直观的方法展示出来，有效指导运行人员做出科学的决策。
2)基于电网设备在线监测数据的故障诊断与状态检修：实现GIS、PMS、在线监测系统各类历史数据和实时数据融合，应用大数据技术进行故障诊断，并为状态检修提供决策，实现对电网设备关键性能的动态评估与基于复杂相关关系识别的故障诊断，为解决现有状态维修问题提供技术支撑。
3)短期/超短期负荷预测：负荷预测和发电预测需要考虑分布式能源和微网，还必须考虑到天气的影响以及能源交易状况，包括市场引导下的需求响应；传统的预测方法无法体现某些因素对负荷的影响，从根本上限制了其应用范围和预测精度。应用大数据技术，建立各类影响因素与负荷预测之间的量化关联关系，有针对性地构建负荷预测模型，可更加精确地预测短期/超短期负荷。
4)配电网故障定位：利用大数据技术，配合故障投诉系统，融合SCADA、EMS、DMS、D-SCADA等系统中的数据作出最优判断，建立新型配电网故障管理系统，可以快速定位故障，应对故障停电问题，提高供电可靠性。此外，随着分布式电源在系统中比重的逐渐增加，其接入会影响到系统保护的定值及定位判据。对于带分布式电源的配电网故障定位也要根据不同的并网要求选择合适的定位策略。
5)防窃电管理：电力网络通过电量差动越限、断相、线损率超标、异 常告警信息、电表开盖事件等数据的综合分析，建立窃电行为分析模型，对用户窃电行为进行预警；通过营配系统数据融合，可比较用户负荷曲线、电表电流、电压和功率因数数据和变压器负载，结合电网运行数据，实现具体线路的线损日结算，通过线损管理功能不仅可以知道实施窃电用户所在的具体线路，并且可以定位至某一具体用户，克服目前检查范围广，查处难度大的问题。
6)电网设备资产管理：基于电网设备信息、运行信息、环境信息(气象、气候等)以及历史故障和缺陷信息，从设备或项目的长期利益出发，全面考虑不同种类、不同运行年限设备的规划、设计、制造、购置、安装、调试、运行、维护、改造、更新直至报废的全过程，求取寿命周期成本最小值。依据交通、路政、市政等可能具备的外部信息，如工程施工、季节特点、树木生长、工程车GPS等外部信息，关联电网设备及线路GPS坐标，对电网外力破坏故障进行预警分析。
以上所述仅是本发明的优选实施方式，应当指出：对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。

Claims (7)

  1. 一种大数据支持轨交电力系统运营的方法，其特征在于：包括如下步骤：
    步骤1：电力系统暂态稳定性分析和控制；
    步骤2：基于电网设备在线监测数据的故障诊断与状态检修；
    步骤3：短期/超短期负荷预测；
    步骤4：配电网故障定位；
    步骤5：防窃电管理；
    步骤6：电网设备资产管理。
  2. 根据权利要求1所述的一种大数据支持轨交电力系统运营的方法，其特征在于：所述步骤1包括：基于WAMS数据的电力系统暂态稳定判据和控制策略决策，在大数据理论和技术指导下，将现有的分析方法与数据的处理技术相结合，在考虑计算速度参数下，将分析结果用直观的方法展示出来，有效指导运行人员做出科学的决策。
  3. 根据权利要求1所述的一种大数据支持轨交电力系统运营的方法，其特征在于：所述步骤2包括：实现GIS、PMS、在线监测系统各类历史数据和实时数据融合，应用大数据技术进行故障诊断，并为状态检修提供决策，实现对电网设备关键性能的动态评估与基于复杂相关关系识别的故障诊断，为解决现有状态维修问题提供技术支撑。
  4. 根据权利要求1所述的一种大数据支持轨交电力系统运营的方法，其特征在于：所述步骤3包括：负荷预测和发电预测需要考虑分布式能源和微网，还必须考虑到天气的影响以及能源交易状况，包括市场引导下的需 求响应；应用大数据技术，建立各类影响因素与负荷预测之间的量化关联关系，有针对性地构建负荷预测模型，可更加精确地预测短期/超短期负荷。
  5. 根据权利要求1所述的一种大数据支持轨交电力系统运营的方法，其特征在于：所述步骤4包括：利用大数据技术，配合故障投诉系统，融合SCADA、EMS、DMS、D-SCADA系统中的数据作出最优判断，建立新型配电网故障管理系统，快速定位故障，应对故障停电问题，提高供电可靠性。
  6. 根据权利要求1所述的一种大数据支持轨交电力系统运营的方法，其特征在于：所述步骤5包括：电力网络通过电量差动越限、断相、线损率超标、异常告警信息、电表开盖事件数据的综合分析，建立窃电行为分析模型，对用户窃电行为进行预警；通过营配系统数据融合，比较用户负荷曲线、电表电流、电压和功率因数数据和变压器负载，结合电网运行数据，实现具体线路的线损日结算，通过线损管理功能知道实施窃电用户所在的具体线路，并且定位至某一具体用户，克服目前检查范围广，查处难度大的问题。
  7. 根据权利要求1所述的一种大数据支持轨交电力系统运营的方法，其特征在于：所述步骤6包括：基于电网设备信息、运行信息、环境信息以及历史故障和缺陷信息，求取寿命周期成本最小值；依据交通、路政、市政的外部信息，关联电网设备及线路GPS坐标，对电网外力破坏故障进行预警分析。
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CN115267323B (zh) * 2022-08-01 2023-11-03 合肥顺帆信息科技有限公司 一种线损分析管理系统
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CN116660621A (zh) * 2023-07-27 2023-08-29 江西琰圭技术服务有限公司 一种本地采样分析的防窃电智能管理系统
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CN117097027B (zh) * 2023-09-05 2024-03-22 深圳市思凯迈科技有限公司 一种基于数据分析的电源智能控制管理系统及方法
CN117097027A (zh) * 2023-09-05 2023-11-21 深圳市思凯迈科技有限公司 一种基于数据分析的电源智能控制管理系统及方法
CN117031211B (zh) * 2023-09-25 2024-01-12 国网安徽省电力有限公司合肥供电公司 一种台区电网故障诊断方法
CN117031211A (zh) * 2023-09-25 2023-11-10 国网安徽省电力有限公司合肥供电公司 一种台区电网故障诊断方法
CN117347791A (zh) * 2023-11-01 2024-01-05 国网河南省电力公司濮阳供电公司 基于大数据的电力电网故障在线识别系统及方法
CN117347791B (zh) * 2023-11-01 2024-05-03 国网河南省电力公司濮阳供电公司 基于大数据的电力电网故障在线识别系统及方法
CN117767299A (zh) * 2023-12-26 2024-03-26 国网吉林省电力有限公司经济技术研究院 基于电网系统的数字化转型体系框架的构建方法
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