Switchgear Heating Element Temperature Rise Prediction Model
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Solution Overview
Problem
Conventional methods for detecting anomaly temperature rises in switchgears are not always accurate and inefficient, and existing online temperature monitoring systems primarily focus on temperature collection without effective anomaly detection.
Innovation Solution
A real-time regression model is proposed for predicting temperature rises of heating elements in switchgears, which includes training a model using physical quantities such as current, actual temperature rise, and ambient temperature, and employing this model to detect anomaly temperature rises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional portable infrared devices are used to detect temperature, then temperature detection can be performed, but the detection accuracy and efficiency are insufficient
Solution Approach 1:
The patent replaces conventional mechanical infrared detection with an online temperature monitoring system that uses embedded sensors and digital communication. The system substitutes manual portable devices with automated electronic measurement and data transmission mechanisms, enabling continuous monitoring without manual intervention and significantly improving both accuracy and efficiency.
Solution Approach 2:
The patent implements continuous temperature monitoring through online sensors that operate 24/7 without interruption. The system maintains constant detection of temperature parameters, eliminating the periodic manual inspection approach, thereby improving detection efficiency and providing continuous data for real-time analysis.
2Duration of action of stationary object
If online temperature monitoring systems are applied, then continuous temperature collection is achieved, but effective anomaly temperature rise detection is not provided
Solution Approach 1:
The patent implements a feedback mechanism where temperature data collected by sensors is continuously compared against reference values and historical patterns. The system provides real-time feedback analysis that identifies anomalies when temperature deviations exceed predetermined thresholds or display abnormal trends, thereby enabling effective anomaly detection while maintaining continuous monitoring.
Solution Approach 2:
The patent introduces an intermediary processing layer between temperature sensors and final detection output. This intermediary system includes data processing units that analyze temperature patterns, compare them with reference data, and identify anomalies through sophisticated algorithms, thereby transforming raw continuous temperature data into reliable anomaly detection results.
3Measurement precision
If model parameters are increased to improve prediction accuracy, then prediction precision improves, but model complexity and over-fitting risk increase
Solution Approach 1:
The patent dynamically adjusts model parameters based on operating conditions and data availability. The system changes parameters such as time constants, thermal coefficients, and prediction horizons adaptively, allowing the model to maintain high accuracy without requiring a fixed large number of parameters. This adaptive parameter adjustment prevents over-fitting while preserving prediction precision.
Solution Approach 2:
The patent implements a dynamic model where parameters evolve over time based on learned patterns from operational data. The system continuously refines parameter values through online learning and validation, allowing the model to adapt to changing conditions without requiring complex static parameter sets. This dynamic approach reduces model complexity while maintaining or improving prediction accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The proposed solution reduces outage time and improves the reliability of switchgears by providing accurate and timely detection of anomaly temperature rises, adapting to different operating conditions and setups.
Implementation Method 1
the conductive connection (such as contacts) in a main circuit of the switchgear may increase its resistance due to mechanical vibration, wear and manufacturing process defects, thereby causing the temperature of the contacts to rise
Data Source
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AI summary
Embodiments of the present disclosure provide a method and an apparatus for training a model for predicting a temperature rise of a heating element in a switchgear. The method comprises obtaining a model for predicting the temperature rise, the model comprising a plurality of inputs, an output, and several parameters to be determined; obtaining n+1 sets of physical quantities in relation to the heating element, each set of physical quantities being collected at a corresponding one of n+1 time points spaced apart from each other by a time step in a normal operation state of the heating element, each set of physical quantities comprising a current and an actual temperature of the heating element and an ambient temperature; converting the actual temperature in each set of physical quantities into an actual temperature rise based on the corresponding ambient temperature; and training the model with the current, the actual temperature rise and the ambient temperature to determine the several parameters.