High-Voltage Device Overload Capacity Using Sensor-Based Load Forecasting
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Solution Overview
Problem
Existing methods inadequately determine the operational state of high-voltage devices, leading to suboptimal exploitation of their overload capacity.
Innovation Solution
A method that uses sensors to continuously capture and transmit measured values to a communication unit, which connects to a data processing cloud. The cloud processes these values, along with load forecasts and state parameters, to determine the maximum overload capacity of high-voltage devices based on their actual consumed lifetime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static overload curves are used to determine overload capacity, then the method is simple and based on specifications, but the overload potential for shorter time periods is not exploited and the operational state is inadequately determined
Solution Approach 1:
The system performs preliminary actions by continuously capturing measured values and determining state parameters in advance, storing them for later use in overload capacity determination. This allows the system to have accurate, real-time data ready before overload decisions are made, rather than relying on static pre-defined curves.
Solution Approach 2:
The system transitions from static overload curves to dynamic determination of overload capacity. By continuously capturing measured values, determining state parameters, and calculating overload capacity based on actual operational data, the system adapts to changing conditions and exploits the true overload potential for different time periods.
2Productivity
If continuous measurement and data processing is implemented, then the overload capacity can be fully exploited, but the device complexity and data processing requirements increase
Solution Approach 1:
The system employs a multi-functional approach where a single integrated platform performs multiple functions: capturing measured values, determining state parameters, storing data, forecasting load, and calculating overload capacity. This universal system consolidates what could be separate complex components into one coordinated platform.
Solution Approach 2:
The system introduces an intermediary data processing layer that acts as a mediator between the physical high-voltage device and the overload determination process. This intermediary continuously monitors state parameters and translates raw measured values into meaningful overload capacity information, simplifying the overall system architecture.
3Measurement precision
If actual consumed lifetime is continuously tracked and used in overload determination, then the remaining lifetime is accurately calculated, but the data processing and storage requirements increase
Solution Approach 1:
The system extracts only the essential information needed for overload determination - specifically the actual consumed lifetime - from the continuous stream of measured values. By focusing on this single critical parameter rather than storing all raw data, the system achieves accurate lifetime calculation with minimal data storage requirements.
Solution Approach 2:
The system transforms raw measured values into a simplified parameter representation - the actual consumed lifetime. This parameter change from multiple continuous measurements to a single aggregated value maintains the accuracy needed for overload determination while dramatically reducing data storage requirements.
Data Source
AI summary
A method determines an overload capacity of at least one high-voltage device. In which method, measurement values are continuously recorded by sensors located in or on the high-voltage device. The measurement values and/or values derived therefrom are transmitted via a near field communication connection from the sensors to a communication unit of the high-voltage device. The communication unit is connected to a data processing cloud. For high-voltage devices, a load forecast request is created for a predetermined time-period and is transmitted to a data processing cloud. For each high-voltage device, a state parameter is determined in part based on the measurement values. The load forecast request and each state parameter are transmitted at a request time to a load forecasting model; and the load forecasting model determines the maximum load in the predetermined time period.