Generator Incipient Failure Detection via Particle Swarm Optimization
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Solution Overview
Problem
Existing power grid systems face unplanned generator outages due to incipient failures, which can lead to rolling blackouts, and current standards lack preventive maintenance and fault forecasting capabilities.
Innovation Solution
A system and method using particle swarm optimization (PSO) to detect incipient failures in generators by estimating performance parameters through real-time measurement and comparison with baseline values, enabling operators to plan maintenance and prevent outages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and parameter estimation systems are implemented to detect incipient failures, then generator reliability and power grid stability are improved, but device complexity and measurement requirements increase
Solution Approach 1:
The system uses a multi-functional approach where the same monitoring infrastructure measures multiple parameters (terminal voltages, currents, power) that serve both operational control and failure detection purposes. The particle swarm optimization technique simultaneously estimates multiple generator parameters (synchronous reactances, transient reactances, time constants) from these measurements, eliminating the need for separate dedicated testing equipment and reducing overall device complexity.
Solution Approach 2:
The system enables self-diagnosis of the generator by using its own operational data (voltages, currents, power measurements during normal operation) to estimate its internal parameters and detect incipient failures. The particle swarm optimization algorithm processes the generator's self-generated measurement data to identify parameter deviations, allowing the system to monitor its own health without external intervention or additional sensors.
2Measurement precision
If comprehensive parameter measurement and estimation is performed to detect turn-to-turn shorts and other faults, then measurement precision and fault detection capability are improved, but loss of time for data processing and computation increases
Solution Approach 1:
The system performs preliminary estimation of generator parameters during normal operation using particle swarm optimization, establishing baseline values before failures occur. By continuously updating parameter estimates during routine operation rather than waiting for faults or scheduling separate tests, the system prepares detection thresholds in advance, reducing the time needed when actual failures need to be identified.
Solution Approach 2:
The system implements continuous feedback by repeatedly estimating generator parameters from ongoing operational measurements and comparing them against baseline values. The particle swarm optimization algorithm processes new measurement data iteratively, providing real-time parameter updates that immediately reflect changing generator conditions, enabling rapid detection without batch processing delays.
Data Source
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AI summary
A method, system and computer software for detecting an incipient failure of a generator in a power system including the steps of ascertaining one or more generator reference parameter of the generator for use as a baseline reference; measuring one or more operating parameter values of the generator; using the one or more operating parameter values to solve for an estimated present value of the one or more of the generator's current performance parameters using particle swarm optimization technique; and determining whether the estimated present values of the one or more of the generator's current performance parameters are outside of an acceptable limit.