Wind Power Fault Prediction and Maintenance Scheduling
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Solution Overview
Problem
Current wind power plant management systems lack effective fault prediction and maintenance planning, which can lead to unexpected failures and reduced energy generation reliability.
Innovation Solution
A wind power plant management system that collects work environment data, compares it to a predefined normal state model to predict faults, estimates the time until a fault occurs, and establishes a maintenance plan, including automatic control and resource allocation to prevent or address faults before they happen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fault prediction and maintenance planning systems are implemented in wind power plants, then reliability of power generation is improved, but device complexity increases
Solution Approach 1:
The management system is divided into distinct functional modules: a data collection unit that gathers work environment data from sensors, a fault occurrence prediction unit that analyzes the collected data, and a maintenance plan establishment unit that generates maintenance schedules. This segmentation allows each module to perform its specific function independently, improving reliability through specialized processing while managing complexity through modular design.
Solution Approach 2:
The system performs preliminary fault prediction by continuously analyzing work environment data before actual faults occur. The fault occurrence prediction unit processes sensor data to identify potential issues in advance, and the maintenance plan establishment unit prepares maintenance schedules proactively. This preliminary action prevents unexpected failures, thereby improving power generation reliability.
2Reliability
If continuous monitoring and fault prediction are performed, then power generation suspension is reduced, but loss of time for data processing increases
Solution Approach 1:
The fault occurrence prediction unit focuses on analyzing only the critical work environment data parameters that are most indicative of potential faults, rather than processing all possible data equally. This partial action approach identifies the most relevant data points for prediction, reducing overall data processing time while maintaining the ability to detect faults that would cause power generation suspensions.
3Productivity
If maintenance plans are established based on predicted fault occurrence time, then productivity is improved by preventing failures, but loss of time for planning and scheduling increases
Solution Approach 1:
The maintenance plan establishment unit creates maintenance schedules in advance based on predicted fault occurrence times from the fault occurrence prediction unit. By performing this planning action preliminarily, the system prevents unexpected failures that would disrupt power generation and reduce productivity. The automated scheduling process minimizes the time required for manual planning while ensuring maintenance is performed at optimal intervals.
Data Source
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AI summary
Provided is a wind power generation management apparatus including: a collection unit(130) configured to collect first data generated in a wind power plant; a fault occurrence prediction unit configured to compare the collected first data with a predefined normal state model, to create a second data indicating a state of the wind power plant based on a result of the comparison, and to predict occurrence of a fault in the wind power plant according to the second data; and a maintenance plan establishment unit configured to estimate an operating time corresponding to a time from a start of the wind power plant until the fault occurs, and to generate a maintenance plan which can be performed for the fault during the estimated operating time.