Non-contact Voltage Measurement via Capacitive Coupling and AI
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Solution Overview
Problem
Conventional contact-type voltage measurement methods pose a risk of electric shock due to improper use or high voltage in electrical systems, necessitating a safer and more accurate non-contact measurement technique.
Innovation Solution
A non-contact voltage measuring method and system utilizing a capacitive coupling structure and a trained artificial intelligence model to analyze measurement signals, allowing for safe and accurate voltage measurement without direct contact, where the capacitive coupling structure generates a measurement signal that is processed and analyzed by a signal processing circuit and AI model to produce a recovery signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contact-type voltage sensors are used to measure voltage, then measurement accuracy is improved, but the risk of electric shock increases
Solution Approach 1:
The patent introduces an intermediary capacitive coupling structure between the voltage source and the measurement circuit. This capacitor-based intermediary allows voltage signals to be transferred without direct electrical contact, thereby maintaining measurement accuracy while eliminating the electric shock risk associated with contact-type sensors.
Solution Approach 2:
The patent replaces the mechanical contact-based measurement system with an electromagnetic field-based non-contact measurement system. By using capacitive coupling and AI-driven signal processing, the system substitutes physical contact with field-based interaction, achieving both safety and accuracy.
2Object-affected harmful factors
If non-contact measurement method is used, then safety is improved, but measurement accuracy may deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the AI model continuously processes the measurement signals and adjusts its predictions based on the actual voltage conditions. This feedback loop ensures that the non-contact measurement maintains high accuracy by adapting to varying electrical conditions in real-time.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting the capacitive coupling configuration and signal processing parameters based on the measured voltage characteristics. This allows the system to optimize measurement accuracy for different voltage levels and conditions while maintaining non-contact operation.
3Measurement precision
If AI model is introduced for signal processing, then measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The patent designs the AI model to serve multiple functions: signal processing, voltage reconstruction, and accuracy enhancement. By making the AI component multi-functional, the system achieves high measurement accuracy without proportionally increasing complexity, as a single integrated component performs multiple tasks.
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
Enables safe and accurate non-contact voltage measurement, reducing the risk of electric shock and providing reliable measurement results through capacitive coupling and AI-driven signal processing.
Implementation Method 1
A voltage signal source to be measured is measured through a capacitive coupling structure to generate a measurement signal
Data Source
AI summary
A non-contact voltage measuring method and a non-contact voltage measuring system are provided. The non-contact voltage measuring method includes the following steps: measuring a voltage signal source to be measured through a capacitive coupling structure to generate a measurement signal; generating an output signal based on the measurement signal through a signal processing circuit; analyzing the output signal through a sampling unit to generate a sampled signal; and outputting the sampled signal to the trained artificial intelligence model, so that the trained artificial intelligence model outputs a recovery signal.