High-Voltage Overload Forecasting Based on Consumed Lifetime
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Solution Overview
Problem
Existing methods inadequately determine the operational state of high-voltage devices, leading to underexploitation of their overload capacity, as they rely on static overload curves that do not account for varying load conditions and aging rates, particularly neglecting the impact of cooling power and weather conditions.
Innovation Solution
A method that continuously determines the consumed lifetime of high-voltage devices using sensors and a data processing cloud, incorporating parameters like cooling power and weather conditions to accurately calculate the maximum overload capacity, allowing for more precise exploitation of transformer potential.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static overload curves are used to determine overload capacity, then the method is simple and based on device specification, but the overload potential is not fully exploited and does not account for varying load conditions and aging rates
Solution Approach 1:
The patent transforms the static overload determination into a dynamic process by continuously monitoring actual load profiles, cooling power, and weather conditions. The system updates the consumed lifetime and remaining service life in real-time, allowing the overload capacity to be dynamically adjusted based on current device state rather than relying on fixed static curves.
Solution Approach 2:
The system implements feedback by continuously measuring actual operating parameters (load, temperature, cooling power) and using this information to update the consumed lifetime. This feedback loop enables more accurate determination of remaining service life, which in turn allows for optimized overload capacity exploitation.
2Measurement precision
If continuous monitoring of consumed lifetime and real-time parameters is implemented, then the overload capacity determination becomes accurate and dynamic, but the system complexity increases
Solution Approach 1:
The system achieves multi-functionality by using a single integrated approach that simultaneously monitors multiple parameters (load, temperature, cooling power, weather conditions) and performs multiple functions (calculating consumed lifetime, determining remaining service life, assessing overload capacity). This reduces the need for separate specialized systems for each measurement.
Solution Approach 2:
The system performs self-service by automatically collecting data from sensors, processing the information through the load-forecasting model, and generating the overload capacity determination without requiring manual intervention. The continuous monitoring and calculation processes are automated, reducing operational complexity.
3Productivity
If static overload curves define fixed overload time periods, then the determination method is straightforward, but shorter time periods with higher overload potential are not recognized
Solution Approach 1:
The system dynamically determines the maximum overload capacity for any requested time period rather than relying on fixed time periods from static curves. By continuously updating the consumed lifetime and remaining service life, the system can adapt to varying load conditions and provide accurate overload capacity assessment for both short-term and long-term periods.
Solution Approach 2:
The system changes the parameter of time period flexibility by allowing overload capacity determination for any requested duration. The load-forecasting model calculates the maximum overload capacity specifically for the requested time period based on current device state, enabling adaptation to different operational requirements rather than being constrained to fixed time periods.
Data Source
AI summary
A method determines the overload capacity of a high-voltage device. The method includes creating a load forecast request for a predefined time period, determining an operational state of the high-voltage device by obtaining state parameters, transmitting the load forecast request and the state parameters at a request time to a load-forecasting model, and determining the maximum utilization in the predefined time period by the load-forecasting model, with which the overload capacity of a high-voltage device can be fully exploited. A lifetime consumption of the high-voltage device before the request time is derived from measured values by obtaining an actually consumed lifetime and the actually consumed lifetime is fed to the load-forecasting model as a state parameter. The load-forecasting model then determines the maximum overload capacity depending on the actually consumed lifetime.