Power Generation Equipment Defect Prediction via Cross-Facility Analysis
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Solution Overview
Problem
Existing equipment management systems for power generation facilities connected to a power grid struggle to predict defects in equipment that do not provide defect information, and they lack efficiency in aligning maintenance schedules with power control periods, leading to suboptimal operation and maintenance practices.
Innovation Solution
An equipment management system that includes a manager and controller to predict defects in power generation equipment based on the cause of defects in other facilities, even if defect information is not available, and adjusts maintenance periods to align with power control periods, ensuring efficient operation and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If defect information is acquired only from facilities that provide it, then measurement precision is limited, but device complexity and information loss increase
Solution Approach 1:
The system creates a virtual copy of defect information by predicting defects in second facilities based on defect patterns from first facilities. The prediction unit generates defect information for facilities that do not directly report defects, effectively copying the defect detection capability across the entire fleet without requiring each facility to independently report
Solution Approach 2:
The prediction unit acts as an intermediary that bridges the gap between facilities that report defects and those that don't. It uses defect information from reporting facilities as input and generates predictive defect information for non-reporting facilities, mediating the information flow across the entire system
2Ease of operation
If maintenance schedules are independently set without considering power control periods, then ease of operation is maintained, but productivity and grid stability deteriorate
Solution Approach 1:
The determination unit implements feedback by continuously monitoring power control periods and using this information to adjust maintenance schedules. The system receives feedback about grid power control requirements and modifies maintenance timing accordingly, creating a closed-loop system that balances operational flexibility with grid stability
Solution Approach 2:
The maintenance schedule transitions from a static, predetermined plan to a dynamic schedule that adapts to changing power control period conditions. The system dynamically adjusts maintenance timing based on real-time or near-real-time power control requirements, allowing the schedule to flex between ease of operation and productivity optimization
3Ease of operation
If maintenance is performed without aligning to power control periods, then ease of operation is maintained, but reliability of power grid stability deteriorates
Solution Approach 1:
The determination unit uses feedback from power control period information to ensure maintenance activities are timed appropriately for grid stability. The system monitors grid control requirements and provides feedback to adjust maintenance scheduling, ensuring that maintenance operations do not compromise grid reliability
Solution Approach 2:
The system performs preliminary analysis of power control periods to determine optimal maintenance timing before executing maintenance activities. By预先 analyzing grid control requirements, the system can schedule maintenance in advance while ensuring it aligns with periods that minimize impact on grid stability
Data Source
AI summary
An equipment management system 100 comprises a manager 210 configured to manage information on a plurality of power generation facilities 300 having a power generation equipment 310 connected to a power grid configured by a power transmission network; and a controller 230 configured to, when defect information on defect of a power generation equipment 310 provided in a first power generation facility, which is one of the plurality of power generation facilities, is acquired, predict defect of a power generation equipment 310 provided in a second power generation facility, which is different from the first power generation facility. The controller 230 is configured to predict the defect of the power generation equipment 310 provided in the second power generation facility based on the cause of the defect of the power generation equipment 310 provided in the first power generation facility.