Power Grid Measurement Data Checking for Long-Scale Anomaly Detection
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Solution Overview
Problem
Current power grid dispatching systems lack comprehensive evaluation of long-time scale massive power grid regulation measurement data, leading to limited detection of abnormal data and hidden problems, due to focusing on single-section data quality and lacking big data analysis and distributed storage processing.
Innovation Solution
A method and apparatus that integrate time-space relationships, topological structures, and electrical relationships of massive measurement data using a Spark distributed computing engine, building a configurable rule base for abnormal problem classification and providing multi-dimensional dynamic analysis to improve data quality and support deep mining of power grid data value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If single-section data quality check method is used, then detection simplicity is maintained, but detection completeness deteriorates leading to limited abnormal data detection
Solution Approach 1:
The patent segments the power grid measurement data into multiple sections (e.g., different time periods, different grid regions, different device types) and applies targeted detection rules to each segment. This allows comprehensive coverage of various abnormal data patterns while maintaining manageable detection complexity through structured classification.
Solution Approach 2:
The patent introduces multiple detection dimensions beyond single-section analysis, including temporal dimensions (long-time scale trends), spatial dimensions (topological relationships), and hierarchical dimensions (different grid levels). This multi-dimensional approach comprehensively detects abnormal data that single-section methods would miss.
2Reliability
If long-time scale massive data analysis is implemented, then data quality evaluation completeness is improved, but processing complexity increases
Solution Approach 1:
The patent divides long-time scale massive data into manageable segments organized by time periods, grid regions, and device categories. This segmentation enables systematic processing of large datasets while maintaining evaluation completeness through structured analysis of each segment according to specific detection rules.
Solution Approach 2:
The patent introduces intermediary computational layers including feature extraction modules that preprocess raw data into meaningful characteristics, and rule engines that apply detection logic to extracted features. These intermediaries simplify the processing of massive data by transforming it into structured information that can be efficiently analyzed.
3Productivity
If distributed cluster storage and calculation framework is adopted, then data processing speed is improved, but system complexity increases
Solution Approach 1:
The patent implements distributed cluster architecture that segments data storage and computation across multiple nodes. Each cluster node processes specific data segments independently, enabling parallel processing that dramatically improves speed while distributing system complexity across manageable individual nodes rather than concentrating it in a single complex system.
Solution Approach 2:
The patent designs a universal distributed processing framework that handles multiple types of detection rules (power balance checks, topological analysis, temporal pattern recognition) through a common architecture. This multi-functional framework improves processing speed for various data types while reducing overall system complexity by avoiding separate specialized systems for each detection type.
Data Source
AI summary
A method and apparatus for checking power grid measurement data, a device, a storage medium and a program product. The method includes: a feature factor of power grid measurement data is extracted; power balance of a set time scale is checked based on the measurement data and the feature factor to obtain a check result; a classification rule base of abnormal problems of measurement data is built based on the check result; and an abnormal problem in target measurement data is checked based on the classification rule base of abnormal problems of measurement data, to obtain a measurement data quality report.