Transformer Cooling Control Across Variable Load Cycles
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Solution Overview
Problem
Existing cooling control systems for power equipment, such as transformers, optimize efficiency at individual time intervals but fail to consider long-term load cycles, leading to increased transformer power and life losses due to temperature variations, and do not adequately account for noise reduction, especially in urban areas.
Innovation Solution
A method and system that obtain and process data sets representing operational cost and condition parameters across a series of time intervals to establish cooling capacity parameters, optimizing cooling system operation across the entire load cycle, considering correlations between intervals to minimize power loss, extend transformer life, and reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If cooling system operates at high capacity to maintain low winding temperature and reduce copper loss, then power loss is reduced, but power consumption of cooling system increases
Solution Approach 1:
The cooling system dynamically adjusts its operation mode based on real-time temperature measurements and predictive forecasts. The controller switches between different cooling modes (e.g., natural cooling, forced cooling with different fan speeds) to match the actual cooling demand, avoiding excessive power consumption while maintaining winding temperature within optimal ranges.
Solution Approach 2:
The system uses predictive forecasting of load and ambient temperature to anticipate future cooling demands. By preparing cooling capacity in advance based on forecasts, the system can prevent temperature rises that would require high-power cooling interventions later, thus reducing overall power consumption while maintaining temperature control.
2Loss of energy
If cooling system operates frequently to maintain optimal temperature, then efficiency is optimized, but transformer aging accelerates due to temperature variation
Solution Approach 1:
The system uses predictive forecasting to anticipate temperature rises and activates cooling preemptively, maintaining more stable temperatures by preventing extreme fluctuations. This approach reduces the frequency and magnitude of temperature variations that cause thermal stress and aging, while still maintaining operational efficiency.
Solution Approach 2:
The system continuously monitors actual temperature deviations from optimal ranges and uses this feedback to adjust cooling intensity. By maintaining temperatures within a narrow optimal band through feedback control, the system achieves efficient operation while minimizing temperature variations that accelerate aging.
3Object-affected harmful factors
If cooling system operates at high capacity to reduce noise impact on residents, then noise reduction is achieved, but power consumption increases
Solution Approach 1:
The cooling system dynamically adjusts fan speeds based on predictive forecasts of when noise-sensitive periods occur (e.g., nighttime hours). During these periods, the system operates at higher speeds to reduce noise, while during daytime or less sensitive periods, it operates at lower speeds, optimizing the balance between noise reduction and power consumption.
Solution Approach 2:
The system implements periodic noise reduction modes that align with typical human activity patterns and noise sensitivity cycles. By scheduling higher cooling capacity operation during nighttime or residential hours when noise impact is most concerning, and reducing capacity during daytime, the system achieves noise reduction goals while minimizing overall power consumption.
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
This approach optimizes cooling system performance across the entire load cycle, reducing operational costs, extending transformer lifespan, and minimizing noise emissions by systematically controlling cooling capacity parameters, thereby improving energy efficiency and reducing maintenance needs.
Implementation Method 1
the power equipment needs a cooling system which, for example, can be an arrangement of at least one fan or blower pumping cooling-air or liquid coolants as oil and water
Data Source
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AI summary
It is therefore an objective of the invention to provide a method for controlling cooling system of a power equipment and a system using the same. The method includes obtaining a first data set representing operational cost related parameters specific to the power equipment and its cooling system forecasted for a series of time intervals of present load cycle in consideration of a second data set representing operational condition related parameters for the power equipment forecasted for a series of time intervals of present 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 cooling capacity parameters for the cooling system at the series of time intervals of the present load cycle according to criteria for operational cost optimization of the power equipment and its cooling system for the present load cycle; and in the present load cycle, controlling the cooling system to operate at the cooling capacity parameters at the series of time intervals represented by the established third data set. By considering the correlations between different time intervals into and making the cooling optimization valid not only at the specific time interval but also in an entire load cycle, the cooling capacities in the next at least one load cycle is optimized.