Capacitive Winding Voltage Sensing for Transformer Transient Detection
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Solution Overview
Problem
Transformers face challenges in reliably predicting and mitigating transient phenomena such as overvoltages, which can lead to degradation and failure, making it difficult to predict and prevent damage or failure, increasing operating costs and reducing reliability and safety.
Innovation Solution
A system with a voltage sensor configured for capacitive coupling to monitor voltages in transformer windings, using artificial intelligence and machine learning to detect transient phenomena, generate warning signals, and transmit data wirelessly or via hard connections to enable proactive countermeasures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional transformer operation without advanced monitoring is used, then device complexity is low, but reliability deteriorates due to inability to predict transient phenomena
Solution Approach 1:
The system performs preliminary monitoring and detection of transient phenomena before they cause damage. Sensors continuously monitor voltage, current, and temperature parameters, and the AI model predicts potential failures in advance, allowing preventive maintenance actions to be taken before actual damage occurs.
Solution Approach 2:
The system implements continuous feedback through sensors that monitor transformer parameters and feed data to the AI model. The model processes this feedback in real-time, adjusts predictions, and provides continuous assessment of transformer health status, enabling dynamic response to changing conditions.
2Reliability
If advanced monitoring and AI prediction systems are implemented, then reliability improves through early detection, but device complexity increases
Solution Approach 1:
The transformer system performs self-diagnosis and self-monitoring through integrated sensors and AI analysis. The system automatically detects anomalies, predicts failures, and generates maintenance alerts without requiring external monitoring equipment or manual inspection, making the complexity inherent to the transformer itself rather than adding separate monitoring infrastructure.
3Loss of time
If continuous monitoring and AI analysis are used, then loss of time is reduced through early warning, but use of energy increases
Solution Approach 1:
The system employs periodic sampling of transformer parameters rather than continuous monitoring. Sensors take measurements at predetermined intervals, and the AI model analyzes these periodic data points to detect trends and predict failures, reducing energy consumption while maintaining effective monitoring capability.
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 system effectively predicts and mitigates transient phenomena, reducing the risk of transformer damage or failure, improving reliability and safety while minimizing operating costs by allowing for timely interventions.
Implementation Method 1
at least one voltage sensor configured and arranged to sense a voltage from the at least one second winding by capacitive coupling
Implementation Method 2
A transformer generally achieves such a voltage conversion by employing at least one primary winding and at least one secondary winding
Data Source
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AI summary
The present disclosure relates to a system (10) comprising a transformer (12) which comprises at least one first winding (18) wound around at least one core (14) and at least one second (19) winding wound around the at least one first winding (18). The system (10) further comprises at least one voltage sensor (34) configured and arranged to sense a voltage through the at least one second winding (19) by capacitive coupling.