Industrial Control Input Correction for Switching EMI
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Solution Overview
Problem
Industrial control systems, particularly those with converter- and inverter-operated motors, face significant interference issues due to electromagnetic interference (EMI) that corrupt measured values, leading to measurement errors.
Innovation Solution
A method and system that predict the temporal occurrence of activation and deactivation operations of switching components, using program code instructions to correct measured values, and employ a neural network for learning and digital filtering to minimize interference effects, allowing for real-time correction and stabilization of measured values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If switching components are activated for motor control, then motor operation is enabled, but electromagnetic interference corrupts measured values
Solution Approach 1:
The system predicts the temporal occurrence of switching component activation and deactivation operations before they happen by analyzing program code instructions. This preliminary prediction allows the system to prepare correction measures in advance, identifying when electromagnetic interference will occur and compensating for it before the interference corrupts the measured values
Solution Approach 2:
The system converts the harmful electromagnetic interference into a beneficial correction opportunity. By predicting switching operations and analyzing their impact on measured values, the system generates correction values that compensate for the interference. The harmful EMI events are used to train a neural network that learns to automatically correct future measurements, turning the interference problem into a self-improving correction mechanism
2Reliability
If digital filtering is applied to stabilize measured values, then interference suppression is improved, but response time decreases
Solution Approach 1:
The system dynamically adjusts the filtering approach based on predicted switching operations. Instead of applying continuous heavy filtering that would slow response, the system uses prediction information to apply correction only when and where interference is expected to occur. This dynamic, selective correction maintains measurement stability while preserving fast response times during non-interference periods
Solution Approach 2:
The neural network learns optimal correction parameters by analyzing the relationship between switching operations and measured value corruption. The system changes the correction parameters dynamically based on the specific interference conditions, using different correction strategies for different types of switching events. This allows effective interference suppression while maintaining appropriate response characteristics for each situation
Data Source
AI summary
A method for operating an industrial control system that has an automation controller with a sequential program, an actuator which actuates a switching component of the power electronics, and an input module, wherein activation and deactivation operations of the switching component cause electromagnetic interference that corrupts a measured value recorded via the input module, where a temporal occurrence of the activation and deactivation operations and/or an operating state is predicted for the switching component, and where the prediction is used to perform a correction of the measured value at a prediction time instant or during a prediction time range with respect to the corruption caused by the electromagnetic interference.