Feed Network Model Adaptation for Decentralized Power Control
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Solution Overview
Problem
Existing methods for identifying and adapting feed network models in decentralized energy systems require active introduction of pilot signals, which disrupt normal operation and fail to account for unknown consumers, leading to limited applicability and lack of adaptability.
Innovation Solution
A method that measures feed and network variables at a power converter connection point, uses a model of the feed network to determine target values, and adapts the model automatically based on deviations without requiring pilot signals, allowing integration into operating strategies and accounting for unknown consumers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If active pilot signals are introduced to identify feed network models, then model identification accuracy is improved, but normal operation is disrupted and applicability is limited
Solution Approach 1:
The system performs model identification using only naturally occurring measurement data from feed variables and network variables during normal operation. The control unit automatically adapts the feed network model without requiring external pilot signals or manual intervention, allowing the system to serve itself for model identification while maintaining continuous normal operation.
2Measurement precision
If traditional model identification methods are used, then model accuracy is improved, but adaptability to network changes and unknown consumers is reduced
Solution Approach 1:
The control unit continuously monitors measurement data from feed variables and network variables, compares actual network behavior with model predictions, and automatically adapts the feed network model based on detected deviations. This feedback mechanism enables the system to adapt to network changes and unknown consumers while maintaining model accuracy through continuous learning from operational data.
3Measurement precision
If detailed network topology and consumer information are incorporated, then control accuracy is improved, but device complexity and data requirements increase
Solution Approach 1:
The system automatically extracts and utilizes relevant network topology and consumer information from naturally available measurement data during normal operation. The control unit identifies and adapts to the actual network configuration and consumer patterns without requiring manual input or complex pre-configured system parameters, thereby maintaining control accuracy while minimizing system complexity and data requirements.
Data Source
AI summary
A method which is improved relative to the prior art for controlling electrical network variables in a feed network, in which the correlation between at least one feed variable measured at an input of the feed network and at least one network variable measured at an output of the feed network is described in a model-like manner using a model of the feed network. In the event of an incorrect description of the correlation, an adaptation of the model of the feed network is carried out, a feed variable target value for the at least one feed variable is determined from a predefined network variable target value for the at least one network variable using the model of the feed network, and the at least one feed variable is set to the determined feed variable target value by of a current converter.