Electrical Network Topology Detection Using Entropy Correlation
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Solution Overview
Problem
The high cost and labor-intensive manual processes in commissioning data-driven services, such as energy management systems, predictive maintenance systems, and fault detection systems, particularly in identifying network topologies of energy systems, pose a barrier to their widespread adoption.
Innovation Solution
A method using electrical entropies received at detection devices over time steps, followed by pre-processing to exclude constants, analyzing electrical measurements for entropy, correlating devices based on their entropies, and determining network topology automatically without manual implementation, employing a deterministic algorithm to iteratively construct a graph representing the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to identify network topology, then accuracy can be maintained through expert knowledge, but the process becomes labor-intensive and costly
Solution Approach 1:
The patent replaces manual expert analysis with an automated electronic computing device that uses entropy calculations and correlation algorithms to identify network topology. The system automatically processes measurement data from detection devices, calculates entropies, determines correlations, and identifies topology without human intervention, thereby eliminating labor-intensive manual processes while maintaining accuracy through deterministic algorithms.
Solution Approach 2:
The system enables the network to self-diagnose its topology by automatically analyzing its own measurement data. The electronic computing device processes data from detection devices already installed in the network, calculating entropies and correlations to autonomously identify connections without requiring external expert intervention or manual commissioning efforts.
2Productivity
If automated methods are used to determine network topology, then commissioning time is reduced, but handling faulty and incomplete data becomes more challenging
Solution Approach 1:
The patent performs preliminary filtering and preprocessing of measurement data before main analysis. The system calculates entropies for all detection devices first, then uses these entropy values to weight and filter correlations in subsequent steps. This preliminary entropy-based filtering prepares the data by identifying reliable measurements, enabling the system to handle faulty and incomplete data effectively during automated topology determination.
Solution Approach 2:
The system incorporates feedback mechanisms where the calculated entropies and correlation strengths continuously inform the topology identification process. The electronic computing device uses entropy values as feedback to adjust the weighting of different detection devices and correlations, allowing it to adapt to data quality variations and reliably identify topology even when some measurements are faulty or incomplete.
3Measurement precision
If detailed entropy analysis is performed on all detection devices, then measurement precision improves, but computational complexity increases
Solution Approach 1:
The patent applies different levels of analysis to different detection devices based on their local characteristics. The system calculates entropies for all devices but uses these values to selectively weight correlations, focusing computational effort on devices with higher entropy (more informative measurements). This local quality approach ensures high precision for critical devices while reducing unnecessary computations for less informative ones, balancing accuracy with computational efficiency.
Solution Approach 2:
The system dynamically changes the weighting parameter for each detection device based on its calculated entropy value. Devices with higher entropy receive higher weights in the correlation analysis, while those with lower entropy receive lower weights. This parameter change strategy allows the system to maintain high measurement precision by focusing computational resources on the most informative devices rather than uniformly processing all devices at maximum detail.
Data Source
Figure 1~2
AI summary
The invention relates to a method for determining a network topology (12) for electrical components (14, 16, 18, 20, 22, 40, 42) in an electrical network (24) by means of an electronic computing device (10), comprising the steps of receiving a respective electrical entropy at respective detection devices (26, 28, 30, 32, 34, 36, 38) in the electrical network (24) over a plurality of time steps; correlating the respective detection devices (26, 28, 30, 32, 34, 36, 38) as a function of the entropies over the plurality of time steps; determining dependencies of the detection devices (26, 28, 30, 32, 34, 36, 38) to each other as a function of the correlation; and determining at least one network topology (12) as a function of the determined dependencies. Furthermore, the invention relates to a computer program product, a computer-readable storage medium and an electronic computing device (10).