Power Plant Control Using Neural Network Efficiency
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Solution Overview
Problem
Existing power plant control systems rely on fixed efficiency values, which can lead to incorrect fuel pre-control and deviations in main controlled variables during load changes, as they fail to account for varying parameters beyond design data.
Innovation Solution
Integration of an artificial neural network into the power plant control system to determine efficiency based on multiple influencing variables, allowing for precise regulation and control of fuel supply during startup and shutdown, eliminating the need for human intervention and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed efficiency value is used for fuel pre-control, then the control system is simple to operate, but the precision of electrical output control deteriorates during load changes
Solution Approach 1:
The patent applies dynamics by transitioning from a static fixed efficiency value to a dynamic efficiency determination system using artificial neural networks. The neural network continuously adapts efficiency calculations based on real-time operating conditions, allowing the control system to respond dynamically to load changes while maintaining operational simplicity through automated adjustments.
Solution Approach 2:
The patent implements parameter changes by allowing the efficiency parameter to vary based on multiple influencing variables such as load conditions, ambient temperature, and operational history. The neural network processes these variable parameters to determine optimal efficiency values, thereby improving electrical output control precision without complicating the user interface or operation procedures.
2Device complexity
If a fixed efficiency value is used, then the control system structure is simple, but deviations in main controlled variables occur during load changes
Solution Approach 1:
The patent replaces the traditional mechanical/mathematical calculation system for efficiency determination with an artificial neural network-based intelligent system. This substitution enables the control system to automatically adapt to varying conditions without increasing structural complexity, as the neural network is integrated into the existing control architecture and processes data through software-based learning rather than additional hardware components.
Solution Approach 2:
The neural network implements self-service by autonomously determining efficiency values based on input parameters without requiring manual intervention or complex configuration. The system learns from historical data and automatically adjusts its calculations, thereby maintaining control accuracy during load changes while keeping the overall system structure simple and self-regulating.
3Productivity
If efficiency is precalculated using design data only, then the calculation is simple and fast, but incorrect fuel pre-control occurs when parameters deviate from design data
Solution Approach 1:
The patent ensures continuity of useful action by implementing real-time efficiency calculations that continuously adapt to current operating conditions rather than relying on static design data. The neural network processes incoming data streams continuously, updating efficiency determinations without interruption, which maintains both calculation speed and fuel pre-control accuracy across varying operational parameters.
Solution Approach 2:
The system implements feedback mechanisms where the neural network uses actual operating data and outcomes to refine its efficiency calculations. By continuously comparing predicted versus actual performance and adjusting its model accordingly, the system maintains high fuel pre-control accuracy even when parameters deviate from original design data, while preserving fast calculation speeds through optimized neural network processing.
Data Source
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AI summary
The invention relates to a method for operating a power station (100) and a process control technique (10) for a power station (100). The aim of the invention is to improve the operation of a power station (100). To this end, a method is provided for the operation of the power station (100), characterised in that, for the purpose of determining at least one desired operating parameter for a future moment in time, when the power station is running, an artificial neuronal network (12) integrated into the process control technique of the power station (100) determines a characteristic which is valid for said future moment and dependent on a plurality of influencing variables, or a characteristic derived therefrom. The process control technique (10) automatically uses said characteristic to carry out a regulating and/or controlling intervention in the operation of the power station in order to achieve the desired operating parameter.