Power Equipment Cooling Control via Load Cycle Thermal Correction
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Solution Overview
Problem
Existing cooling systems for power equipment face challenges in optimizing operation efficiency across multiple time intervals, leading to suboptimal performance and accelerated aging due to temperature variations, which are not adequately addressed by current predictive models that focus on individual time intervals rather than entire load cycles.
Innovation Solution
A method that utilizes historical data to establish optimal cooling capacity parameters by analyzing the similarity, difference, and continuity between load cycles, allowing for computational correction and precise control of the cooling system across time intervals to optimize operational costs and extend equipment lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If separate optimization design is performed for each time interval, then the optimization algorithm can achieve the best result at every time interval, but the accumulated temperature difference will gradually increase power loss and life loss, making it not necessarily optimal for the entire load cycle
Solution Approach 1:
The patent applies preliminary action by using historical data from previous load cycles to pre-establish the temperature field distribution and thermal state before the current load cycle begins. This allows the system to account for accumulated temperature effects and initialize the optimization with accurate baseline conditions, preventing temperature variation accumulation from degrading performance
Solution Approach 2:
The patent implements feedback by continuously monitoring the actual temperature field distribution during operation and comparing it with the predicted thermal state. The system uses this feedback to adjust cooling control strategies in real-time, ensuring that temperature variations remain within optimal ranges throughout the entire load cycle rather than optimizing each interval independently
2Loss of energy
If cooling optimization is performed to reduce copper loss by lowering winding temperature, then power equipment efficiency is improved, but the power consumption of the cooling system increases, reducing overall efficiency
Solution Approach 1:
The patent applies local quality by using three-dimensional temperature field distribution data to identify specific regions within the power equipment that require cooling. Instead of uniformly cooling the entire system, the control strategy targets only the local areas with excessive temperature, thereby reducing copper loss in critical zones while minimizing unnecessary power consumption in already-cooled regions
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting cooling control parameters based on the predicted and actual temperature field distribution. The system modifies cooling intensity, duration, and distribution according to real-time thermal conditions, achieving optimal balance between reducing copper loss and minimizing cooling system power consumption
3Productivity
If frequent temperature variation is used to optimize power equipment efficiency, then operational efficiency is improved, but the power equipment aging accelerates, shortening its lifetime
Solution Approach 1:
The patent applies preliminary action by predicting the temperature field distribution and thermal stress patterns before they occur during each load cycle. By using historical data and thermal models, the system pre-identifies potential temperature variation patterns that could accelerate aging, and adjusts cooling strategies in advance to smooth out extreme temperature fluctuations while maintaining operational efficiency
Solution Approach 2:
The patent implements feedback by continuously monitoring temperature variations and their rate of change throughout the load cycle. The system uses this feedback to detect patterns that indicate accelerated aging and dynamically adjusts cooling control to reduce temperature variation frequency and amplitude, thereby protecting equipment lifecycle while maintaining acceptable operational efficiency
Data Source
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AI summary
It provides a method for controlling cooling system of a power equipment and a system using the same. The method includes steps of: obtaining a first data set representing operational cost related parameters specific to the power equipment and its cooling system at a series of time intervals of a first load cycle in a history profile; obtaining a second data set representing operational cost related parameters specific to the power equipment and its cooling system at a series of time intervals of a second load cycle in the history profile, where the second load cycle follows the first load cycle; in consideration of the parameters represented by the first data set, through knowledge-based predetermined numerical and/or logical linkages, establishing a third data set representing optimal cooling capacity parameters for the cooling system at the series of time intervals of the first load cycle according to criteria for operational cost optimization of the power equipment; in consideration of the parameters represented by the second data set, through knowledge-based predetermined numerical and/or logical linkages, establishing a fourth data set representing optimal cooling capacity parameters for the cooling system at the series of time intervals of the second load cycle according to criteria for operational cost optimization of the power equipment; establishing a fifth data set representing a cooling capacity parameter difference between the established cooling capacity parameters concerning the first load cycle and the second load cycle; establishing a sixth data set representing cooling capacity parameters for the cooling system at a series of time intervals of a present load cycle by computationally correcting the established cooling capacity parameter concerning the time intervals of the second cycle load with use of the cooling capacity parameter difference; and controlling the cooling system to operate at a series of time intervals of the present load cycle at the established cooling capacity parameters concerning the present load cycle represented by the sixth data set.