CN-122001087-A - Electricity consumption abnormality early warning system based on big data analysis

CN122001087ACN 122001087 ACN122001087 ACN 122001087ACN-122001087-A

Abstract

The application relates to the technical field of electricity consumption detection, and discloses an electricity consumption abnormality early warning system based on big data analysis, which comprises a plurality of grading monitoring acquisition modules, an early warning module and an early warning module, wherein the grading monitoring acquisition modules are used for carrying out grading monitoring on an electricity consumption detection area and acquiring electricity consumption state data in a grading monitoring subarea in real time, the electricity consumption state data comprises current data, voltage data and electricity consumption data, the big data analysis module is used for carrying out analysis and judgment on whether an abnormal electricity consumption scene exists or not and judging the type and the reason of the abnormal electricity consumption scene according to the electricity consumption state data of the grading monitoring subarea, the abnormality signal positioning module is used for carrying out abnormality range positioning on the abnormal electricity consumption scene and outputting abnormality positioning information, and the early warning module is used for judging whether to trigger early warning action or not according to the type of the abnormal electricity consumption scene and the reason.

Inventors

ZHANG JIAN
MA TING
AI JIAQIU
MA SHAOWEI
HE FAHAI

Assignees

国网宁夏电力有限公司石嘴山供电公司

Dates

Publication Date: 20260508
Application Date: 20260127

Claims (9)

1. An electricity consumption anomaly early warning system based on big data analysis, which is characterized by comprising: The power utilization detection areas are subjected to classified monitoring, and power utilization state data in the classified monitoring subareas are collected in real time, wherein the power utilization state data comprise current data, voltage data and power consumption data; The big data analysis module is used for analyzing and judging whether an abnormal electricity utilization scene exists or not according to the electricity utilization state data of the hierarchical monitoring subarea and judging the type and the reason of the abnormal electricity utilization scene; The abnormal signal positioning module is used for positioning the abnormal range of the abnormal power utilization scene and outputting abnormal positioning information; And the early warning module is used for judging whether to trigger early warning actions according to the abnormal electricity utilization scene type and the abnormal reasons.
2. The electricity consumption abnormality early warning system based on big data analysis according to claim 1, wherein the process of hierarchically monitoring the electricity consumption detection area includes: The power utilization detection area is divided into a plurality of hierarchical monitoring subareas from high to low according to power utilization requirements, and each hierarchical monitoring subarea is provided with a corresponding hierarchical monitoring acquisition module; For each hierarchical monitoring subarea, sorting the power utilization state data monitored by the hierarchical monitoring module within a certain fixed time period into component data sets; The hierarchical data set includes current data, voltage data, and power consumption data for the hierarchical monitoring sub-region.
3. The electricity consumption abnormality early warning system based on big data analysis according to claim 1, wherein the hierarchical monitoring acquisition module comprises a plurality of distributed acquisition units; a timing module is arranged in the distributed acquisition unit and used for acquiring the power utilization state data containing the unique time mark; the timing module orderly arranges the power utilization state data contained in the hierarchical data set according to the sequence of the time stamps to form an orderly hierarchical data set with timeliness; and judging the relevance of each abnormal electricity utilization scene according to the ordered hierarchical data set.
4. The electricity consumption abnormality early warning system based on big data analysis according to claim 2, wherein the big data analysis module comprises an anti-false-report processing unit and an electricity consumption habit analysis unit; the false alarm prevention processing unit forms a dynamic threshold management system according to the electricity demand of each hierarchical monitoring subarea, and the dynamic threshold management system comprises a core parameter set divided according to the corresponding electricity demand; The core parameter set comprises a current threshold value, a voltage threshold value and average power consumption of the specified power consumption of the hierarchical monitoring subarea; The electricity consumption habit analysis unit generates a personalized electricity consumption behavior model according to the ordered hierarchical data set of the hierarchical monitoring subarea in a fixed time period; The false alarm prevention processing unit adjusts an abnormal judgment threshold value of the hierarchical monitoring subarea in real time according to the core parameter set and the personalized electricity behavior model; if the ordered hierarchical data set of a certain hierarchical monitoring subarea is detected to exceed a standard safety threshold, comparing the ordered hierarchical data set with the core parameter set and the abnormality judgment threshold; And if the ordered hierarchical data set accords with the core parameter set and does not exceed the abnormality judgment threshold, judging that no abnormal electricity utilization scene exists.
5. The electricity consumption abnormality early warning system based on big data analysis according to claim 1, wherein the abnormality signal positioning module comprises a positioning result visualization unit and a coordinate analysis unit; The coordinate analysis unit converts the abnormal positioning information into physical position information in an actual state according to the abnormal positioning information and combining the electronic position information and the peripheral position information of the hierarchical monitoring area; the positioning result visualization unit is used for outputting a visual report containing the physical position information, the related range and the abnormal electricity utilization scene type and synchronously pushing the visual report to the early warning module.
6. The electricity consumption abnormality early warning system based on big data analysis according to claim 1, wherein the big data analysis module further comprises a cross-region correlation analysis unit; When abnormal electricity utilization scenes occur in the plurality of hierarchical monitoring subareas, the cross-regional association analysis unit is used for judging whether association exists among the hierarchical monitoring subareas; The method for judging whether the relevance exists comprises the following steps: intercepting the contemporaneous electricity utilization state data of each hierarchical monitoring subarea with abnormality, calculating the electricity utilization state data difference among each hierarchical monitoring subarea and grouping the subareas Taking the first abnormal hierarchical monitoring subarea as a grouping base point according to the ordered hierarchical data set, taking the subsequent hierarchical monitoring subareas with the power utilization state data difference smaller than or equal to a preset grouping threshold value into the area grouping, and dividing the hierarchical monitoring subareas with the power utilization state data difference larger than the preset grouping threshold value into a new area grouping; each hierarchical monitoring subarea in the same area group is preliminarily judged to have suspected relevance.
7. The electricity consumption abnormality early warning system based on big data analysis according to claim 6, wherein the method for judging whether there is a correlation between the hierarchical monitoring sub-areas further comprises performing similarity judgment on abnormal electricity consumption scenes of the hierarchical monitoring sub-areas in the area group; taking an ordered hierarchical data set of the hierarchical monitoring subareas serving as the grouping base point as a judging reference; Performing similarity matching on the ordered hierarchical data sets of each hierarchical monitoring subarea, and judging that the abnormal electricity utilization scenes of each hierarchical monitoring subarea are the same if matching conditions are met; the matching conditions include: the peak value deviation of the power load in each ordered hierarchical data set is smaller than or equal to a preset deviation threshold value, and the coincidence ratio of abnormal occurrence time periods is larger than or equal to a preset coincidence threshold value; And defining the hierarchical monitoring subarea which is the same as the abnormal electricity utilization scene as a homologous area, and predicting a propagation path according to the homologous area.
8. The electricity consumption abnormality pre-warning system based on big data analysis according to claim 7, wherein the process of performing propagation path prediction includes: the earliest hierarchical monitoring subarea of the abnormal power utilization scene of the homologous region is taken as an initial propagation source, and the starting moment of the ordered hierarchical data set is taken as a propagation time sequence starting point; Taking each hierarchical monitoring subarea in the homologous region as a distribution node, and connecting the distribution nodes from the starting point of the propagation time sequence according to the time sequence to form a homologous relation network comprising at least one distribution path; Judging whether a transmission risk exists between a last distribution node corresponding to each distribution path and a similar hierarchical monitoring subarea of the abnormal electricity scene which does not occur or not based on the homology relation network; dividing the similar hierarchical monitoring subareas with the propagation risk into the homologous relation network to serve as new distribution nodes until no new distribution nodes are generated; all of the new distribution nodes are connected to form the predicted propagation path.
9. The electricity consumption abnormality early warning system based on big data analysis according to claim 8, wherein the method for judging whether there is a propagation risk includes: If the distance between the distribution node and the similar hierarchical monitoring subarea is smaller than or equal to a preset safety distance threshold value, the potential transmission risk of the similar hierarchical monitoring subarea is indicated; and further judging the similar hierarchical monitoring subarea with the potential transmission risk, if the distribution node and the power supply line connection relation of the similar hierarchical monitoring subarea have a direct connection relation, indicating that the similar hierarchical monitoring subarea has a substantial transmission risk, and the similar hierarchical monitoring subarea and the homologous relation network have a transmission risk.

Description

Electricity consumption abnormality early warning system based on big data analysis Technical Field The application relates to the technical field of electricity consumption detection, in particular to an electricity consumption abnormality early warning system based on big data analysis. Background With the rapid development of economy and society, the scale of the urban power grid is continuously enlarged, the power load is continuously increased, and the power field is increasingly complex. The coverage area of the power system is continuously enlarged, so that the traditional power grid monitoring and early warning system cannot meet the requirement on stable operation of the power system. At present, the prior art relies on single fixed threshold detection to judge whether abnormal conditions exist, but under a complex electricity utilization environment, high-frequency false alarm conditions are easy to occur due to seasonal factors and special activities, and different electricity utilization rules of different areas cannot be adapted. The traditional monitoring and early warning method only carries out early warning and tracking on the area where the abnormal electricity scene appears, can not predict and intervene in the initial stage of enlarging the abnormal range, lacks cross-area association analysis capability when a plurality of areas appear abnormal conditions, can not judge propagation risks and paths, and easily causes delay of the optimal time for fault treatment, thereby causing chain reaction to cause larger loss. Therefore, the invention provides the electricity consumption abnormality monitoring system which can adapt to complex electricity consumption environments and realize intelligent analysis. Disclosure of Invention The invention aims to provide an electricity consumption abnormality early warning system based on big data analysis, which solves at least one of the problems. The invention provides an electricity consumption abnormality early warning system based on big data analysis, which comprises: The power utilization detection areas are subjected to classified monitoring, and power utilization state data in the classified monitoring subareas are collected in real time, wherein the power utilization state data comprise current data, voltage data and power consumption data; The big data analysis module is used for analyzing and judging whether an abnormal electricity utilization scene exists or not according to the electricity utilization state data of the hierarchical monitoring subarea and judging the type and the reason of the abnormal electricity utilization scene; The abnormal signal positioning module is used for positioning the abnormal range of the abnormal power utilization scene and outputting abnormal positioning information; And the early warning module is used for judging whether to trigger early warning actions according to the abnormal electricity utilization scene type and the abnormal reasons. As a further technical solution, the process of hierarchical monitoring of the electricity utilization detection area includes: The power utilization detection area is divided into a plurality of hierarchical monitoring subareas from high to low according to power utilization requirements, and each hierarchical monitoring subarea is provided with a corresponding hierarchical monitoring acquisition module; For each hierarchical monitoring subarea, sorting the power utilization state data monitored by the hierarchical monitoring module within a certain fixed time period into component data sets; The hierarchical data set includes current data, voltage data, and power consumption data for the hierarchical monitoring sub-region. As a further technical scheme, the hierarchical monitoring acquisition module comprises a plurality of distributed acquisition units; a timing module is arranged in the distributed acquisition unit and used for acquiring the power utilization state data containing the unique time mark; the timing module orderly arranges the power utilization state data contained in the hierarchical data set according to the sequence of the time stamps to form an orderly hierarchical data set with timeliness; and judging the relevance of each abnormal electricity utilization scene according to the ordered hierarchical data set. As a further technical scheme, the big data analysis module comprises an anti-false-report processing unit and an electricity habit analysis unit; the false alarm prevention processing unit forms a dynamic threshold management system according to the electricity demand of each hierarchical monitoring subarea, and the dynamic threshold management system comprises a core parameter set divided according to the corresponding electricity demand; The core parameter set comprises a current threshold value, a voltage threshold value and average power consumption of the specified power consumption of the hierarchical monitoring subarea; The electricity