Autonomous Energy Detection for Safe Grid Maintenance
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Solution Overview
Problem
During interventions on low voltage branches of electricity distribution networks, workers are at risk due to undetected autonomous renewable energy sources, such as photovoltaic panels and wind turbines, which can continue to supply electricity even after the main network is shut down, requiring lengthy and hazardous searches to locate and deactivate these sources.
Innovation Solution
A method using consumption data from smart meters and meteorological data to identify potential autonomous energy production sites, employing detection models like tree boosting or convolutional neural networks to calculate probability scores for the presence of such sources, allowing for remote shutdown of meters to ensure a zero voltage reading before intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If workers perform manual door-to-door searches to locate autonomous energy sources, then worker safety is improved, but intervention time increases significantly
Solution Approach 1:
The system performs preliminary identification of dwellings with autonomous energy sources by analyzing historical consumption data and meteorological conditions before workers arrive. This pre-screening creates a targeted list of potential locations, so workers don't need to manually search every dwelling door-to-door, thus reducing intervention time while maintaining safety through accurate identification.
Solution Approach 2:
The manual mechanical search process (workers physically going door-to-door) is replaced by an automated information processing system that uses consumption data analysis and meteorological correlation algorithms. This substitution eliminates the time-consuming manual search while providing reliable identification of autonomous energy sources through data-driven detection.
2Reliability
If workers manually search each dwelling to deactivate autonomous means, then complete de-energization is achieved, but the process becomes lengthy and tedious
Solution Approach 1:
The system preliminarily identifies and ranks dwellings likely to contain autonomous energy sources based on consumption pattern analysis and meteorological data correlation. This pre-identification creates a prioritized list that guides workers to the most probable locations first, ensuring complete de-energization is achieved efficiently without wasting time on dwellings unlikely to have autonomous sources.
Solution Approach 2:
Instead of applying a uniform search approach to all dwellings, the system applies differentiated analysis based on local characteristics of each dwelling's consumption data and environmental conditions. This targeted approach focuses resources on specific locations where autonomous energy sources are most likely present, improving both completeness and efficiency of de-energization.
3Reliability
If the network is shut down for maintenance, then worker safety during intervention is improved, but autonomous renewable sources may continue producing electricity
Solution Approach 1:
Before the network shutdown for maintenance, the system performs preliminary identification of autonomous energy sources using consumption data and meteorological analysis. This advance detection allows the system to know which dwellings have autonomous sources before workers arrive, enabling targeted verification and deactivation efforts that ensure no unintended electricity production occurs during the intervention.
Solution Approach 2:
The system uses feedback from consumption data monitoring and meteorological conditions to continuously assess the likelihood of autonomous energy production. This feedback mechanism allows real-time updates to the identification process, ensuring that the list of potential autonomous sources remains accurate and that workers are directed to the correct locations for verification and deactivation.
Data Source
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AI summary
The invention aims to ensure the safety of personnel working on a low-voltage branch of an electricity distribution network. Dwellings connected to this branch may contain independent power generation devices (PV1, ..., PVn) that generate electrical voltage, potentially endangering personnel during their work. The invention therefore includes a step of obtaining initial data, consisting of consumption readings from the meter (C1, ..., Cn) of each dwelling, taken at regular intervals, and secondly, meteorological data (MET) for the geographical area of these dwellings, in order to identify at least some conditions conducive to energy production by independent means.A detection model is then applied, based on the first and second data points, to identify a correlation between periods of decreased consumption measured by a meter and weather conditions conducive to electricity production by autonomous systems during those periods. This information is then used to determine whether or not autonomous systems are present in the dwelling equipped with the meter. This information is stored in a database (MEM) associated with a dwelling identifier, and, prior to any intervention, the database identifies dwellings likely to contain autonomous systems.