AI Smart Socket for Real-Time Electrical Anomaly Detection
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Solution Overview
Problem
Existing systems for monitoring and analyzing electrical operating parameters in smart grids are limited by high costs, lack of remote control capabilities, and complexity, and they fail to provide intelligent analysis for network and load diagnostics.
Innovation Solution
A system comprising a smart socket with a microcontroller equipped with artificial intelligence, such as an artificial neural network, that monitors and analyzes electrical parameters in real-time, enabling advanced diagnostics and continuous training for improved anomaly recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network analyzers and oscilloscopes are used for advanced electrical system analysis, then measurement precision and diagnostic capability are improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a neural network to create a virtual model (copy) of the electrical network's normal behavior. This software-based copy replaces the need for complex physical measurement instruments, enabling advanced diagnostics through data processing rather than sophisticated hardware
Solution Approach 2:
The patent substitutes complex mechanical/electrical measurement instruments (network analyzers, oscilloscopes) with an information-processing system based on neural networks. The diagnostic capability is achieved through algorithmic analysis of electrical parameters rather than through complex measurement hardware
2Ease of operation
If traditional monitoring systems are used, then basic voltage and current measurement is achieved, but intelligent analysis and anomaly recognition capability is lost
Solution Approach 1:
The patent implements a feedback mechanism where the neural network continuously learns from detected anomalies and improves its diagnostic capability. The system compares measured parameters against the learned normal behavior model, providing intelligent feedback about system health status and anomaly identification
Solution Approach 2:
The neural network performs self-learning and self-improvement by continuously training on detected patterns. The system automatically adapts to normal network variations and improves anomaly recognition without external intervention, maintaining ease of operation while gaining intelligent analysis capability
3Device complexity
If simplified threshold-based anomaly detection is used, then device complexity is reduced, but diagnostic precision and anomaly recognition accuracy deteriorates
Solution Approach 1:
The patent transforms the approach from fixed threshold parameters to dynamic, learned parameters through neural network training. The system learns optimal detection thresholds and patterns from normal operation data, enabling accurate anomaly detection without requiring complex manual parameter setting or simplified fixed thresholds
Data Source
AI summary
A system for monitoring and analyzing electrical operating parameters of a load in an electric network includes a smart socket placed in series between the load and the electric network. The smart socket includes a voltage detection module that measures a voltage value in the electric network between the ends of the load, a current detection module in the electric network that measures a current value adsorbed by the load, and a control unit connected to the voltage detection module and to the current detection module. In particular, the control unit comprises a neural network arranged to carry out a training comprising the steps of definition of a number n of events association, to each event Ei of a number mi, of patterns pij of predetermined current and/or voltage trends, extrapolation of characteristic parameters cik distinguishing the pattern pij associated with the classified event Ei.