Power Grid Maintenance Plan Checking Using Load Clustering
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Solution Overview
Problem
The current manual methods for checking medium-term and long-term maintenance plans in power grids are inefficient, leading to low accuracy and difficulty in ensuring safe and stable operation due to lack of quantitative safety checks and boundary data loss.
Innovation Solution
A method and system that utilize clustering algorithms to acquire and process load data, update power grid operation models, and calculate ground state power flow data to automatically check maintenance plans, reducing human intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual experience-based methods are used for safety check, then ease of operation is improved, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent replaces manual experience-based safety check methods with an automated computerized system that uses clustering algorithms and power flow calculations. This substitution eliminates reliance on human expertise while providing quantitative, objective assessment of maintenance plan safety, thereby improving measurement precision without compromising ease of operation.
Solution Approach 2:
The system enables automatic safety verification by computing power flow data and comparing it against safety thresholds without requiring manual intervention. The automated clustering algorithm independently identifies boundary conditions and generates safety assessments, making the system self-sufficient and eliminating the trade-off between manual operation simplicity and check accuracy.
2Measurement precision
If automated clustering algorithms are used to process load data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent employs universal clustering algorithms that can handle various types of load data and boundary conditions through a single unified framework. This multi-functional approach allows the system to accurately identify diverse operational scenarios without requiring multiple specialized systems, thereby improving measurement precision while controlling device complexity through algorithmic versatility.
3Reliability
If comprehensive power flow calculation is performed for safety check, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary clustering of historical load data to identify representative boundary conditions before executing comprehensive power flow calculations. By pre-processing data to extract critical scenarios, the system reduces the number of full power flow calculations needed while maintaining reliability, thus decreasing checking time without compromising safety verification thoroughness.
Data Source
AI summary
A method for checking a medium-term and long-term maintenance plan of a power grid. A predicted load value and one or more historical load values of a power grid are acquired and clustered through a clustering algorithm, and a historical moment at which a historical load value is in a same cluster as the predicted load value and has a largest load value is selected as a target historical moment. A power grid operation mode model at the target historical moment is acquired, and the power grid operation mode model is updated according to the maintenance plan and open loop point information by using a full wiring approach to obtain a future-state power grid operation mode model. Ground state power flow data of the power grid are calculated based on the operation mode model, and safety check is carried out according to a preset ground state power flow limit.