Dynamic Measuring Point Selection in Low-Voltage Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The transition to decentralized power generation in low-voltage networks requires efficient selection of measuring points to manage data volume and transmission capacity, as existing methods are costly and inefficient, especially with narrow-band transmission channels.
Innovation Solution
An automated method using a self-learning algorithm based on regression and neural networks to select measuring points, optimizing their number and placement based on historical and current data, load scenarios, and network conditions, reducing the need for additional measuring points and data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of measuring points is increased to cover a wide range of network conditions, then the reliability of network monitoring is improved, but the data transmission volume and costs increase
Solution Approach 1:
The system dynamically changes the selection of measuring points based on varying network conditions and load scenarios. Instead of using a fixed large set of measuring points, the system adapts the measuring point configuration to match current network states, thereby reducing data transmission volume while maintaining monitoring reliability.
Solution Approach 2:
The measuring point selection is made dynamic rather than static. The system automatically determines optimal measuring points based on real-time or near-real-time network conditions, load scenarios, and historical data, allowing the configuration to adapt to changing requirements without permanently deploying excessive measuring infrastructure.
2Measurement precision
If more measuring points are deployed to accurately record network behavior, then the measurement precision is improved, but the device complexity and installation costs increase
Solution Approach 1:
The system changes the parameters of measuring point selection based on network conditions and load scenarios. By adjusting which measuring points are active according to specific conditions, the system achieves accurate network behavior recording with a reduced set of measuring devices, avoiding the need for comprehensive permanent deployment.
Solution Approach 2:
The system performs preliminary analysis of network conditions, load scenarios, and historical data to pre-determine optimal measuring point configurations. This preliminary action allows the system to select only the necessary measuring points in advance, achieving measurement precision without deploying excessive devices.
3Productivity
If the number of measuring points is reduced to lower data transmission volume, then the loss of information may increase, but the transmission efficiency is improved
Solution Approach 1:
The system dynamically changes which measuring points are active based on network conditions and information priority. By adjusting the measuring point configuration to match current network states and information requirements, the system transmits only the most relevant data, maintaining information completeness while improving transmission efficiency.
Solution Approach 2:
The system extracts and selects only the essential measuring points needed for accurate network monitoring under specific conditions. By taking out only the necessary measurements from the full set of possible measuring points, the system reduces data transmission volume while preserving the critical information needed for network management.
4Ease of operation
If existing smart meters are used as measuring devices to avoid installation costs, then the ease of operation is improved, but the data volume management becomes more challenging
Solution Approach 1:
The system changes the operational parameters of existing smart meters by dynamically selecting which ones function as active measuring points based on network conditions. This allows the system to utilize readily available smart meters without installation costs while managing data volume through intelligent selection rather than utilizing all available devices.
Data Source
Figure 1
AI summary
The invention relates to a method for determining measuring points in low-voltage networks, having the following method steps which are repeated at periodic intervals: - measured values from measuring devices (MG 1, MG 2,... MG n) which are provided in the low-voltage network are collected in a database together with temporal information on said measured values, - load scenarios are ascertained from the collected data using self-learning algorithms, and the measuring devices with particularly meaningful measured values with respect to the load scenarios are determined and defined as measuring points (MS1, MS2,... MSk). The invention allows the automated selection of measuring points, whereby an optimized number of measuring points can be used in order to monitor networks.