AIFaultGen - AI-Powered Electrical Fault Open Data Generator

Physics-Informed Generative AI for Electrical Fault Analysis & Open Data Generation
Based on empirical research by the OpenFaultDynamics Team - Meru University of Science and Technology, Kenya

Physics Principle: Short circuit fault quantities are fundamentally dependent on initial system power conditions. Higher input voltages lead to higher short circuit currents and lower short circuit voltages due to increased energy dissipation in the fault arc. This system-dependent behavior is accurately modeled by our physics-informed AI.

Physics-Informed AI System Status

AI Model

ONLINE

Trained on 283 samples

Accuracy

65.0%

Fair

Users Served

1,247

This month

AI Fault Data Generator

Configuration Parameters

Data Type & Phase

Parameters to Generate

Target Voltage (2.5V - 200V) Input voltage for simulation - higher voltages produce more severe fault conditions

Sampling Resolution

Organization Type

Organization Name

Dataset Size (1-100,000 rows) Number of synthetic data points to generate

Physics-Informed AI Architecture

System-Dependent Physics Model

Core Physics Principle System-Dependent Fault Dynamics
Voltage-Current Relationship Nonlinear Power Law: I ∝ V^1.3
Energy Dissipation P_loss = I²R ∝ V^2.6
Arc Resistance Dynamics R_arc ∝ 1/V^0.7
Temporal Evolution Coupled Differential Equations

I_sc = k₁·V_initial^γ · e^(-k₂·t·V_initial^β) + k₃·V_initial^δ
V_sc = V_initial · (1 - α·V_initial^ε) · e^(-λ·t·V_initial^ζ)

Key Physics Insight:

Our model demonstrates that fault quantities are fundamentally dependent on initial system conditions. Higher input voltages create more severe fault conditions with disproportionately higher short circuit currents and lower fault voltages due to increased energy dissipation in the arc plasma.

Live Sensor Status & Phase Detection

Current Electrical Measurements

Voltage

0.000000 V

Updated: 10:36:39

Current

0.000000 A

Updated: 10:36:39

Resistance

0.000000 Ω

Updated: 10:36:39

AIFaultGen Database Cluster: Operational

Active Databases: 6/6 | Total Samples: 283 | Current Experiment: 10.0V - After Short Circuit

Cluster Online

Research Statistics Overview

Active Experiments

6/6

2.5V, 5V, 10V × Before/After

Total Samples

283

Across all experiments

Latest Voltage

0.0000 V

Most recent measurement

Latest Current

0.0000 A

Most recent measurement

Latest Measurements Across All Experiments

Experiment	Samples	Last Update	Status
2.5V - Before Short Circuit	46	Nov 21, 17:53:02	Idle
2.5V - After Short Circuit	46	Nov 21, 18:18:25	Idle
5.0V - Before Short Circuit	40	Nov 21, 18:07:36	Idle
5.0V - After Short Circuit	55	Nov 21, 18:31:47	Idle
10.0V - Before Short Circuit	70	Nov 21, 18:12:18	Idle
10.0V - After Short Circuit	26	Nov 21, 18:36:39	Idle

Voltage Analysis

Monitoring voltage stability across different circuit conditions

0.0000 V

Latest Reading

Current Flow

Tracking current variations during fault conditions

0.0000 A

Latest Reading

Resistance

Calculating resistance changes using exponential decay model

0.0000 Ω

Latest Reading

Physics-Informed AI Fault Prediction Model

Model Performance

65.0%

Overall Accuracy

2.5V-200V

Voltage Range

283

Training Samples

System-Dependent Physics Model

Core Mathematical Foundation

Our AI combines advanced physics-based differential equations with statistical learning to model electrical fault behavior with system dependency:

Before Short: Temperature-dependent Ohm's Law R(T) = R₀[1 + α(T-T₀)], T = T₀ + k·I²
After Short: Nonlinear system-dependent arc model V(t) = V₀·(1-β·V₀^γ)·exp(-λ·t·V₀^δ) I(t) = k₁·V₀^ε·exp(-k₂·t·V₀^ζ) + k₃·V₀^η
Energy Balance: Power dissipation relationship P_diss = I²·R_arc = k·V₀^θ·exp(-μ·t)

Physics Principle: Fault quantities are fundamentally dependent on initial system power. Higher input voltages lead to higher short circuit currents and lower fault voltages due to increased energy dissipation in the fault arc plasma.

System Beneficiaries

Industrial

Power systems protection, fault analysis, equipment safety testing

Academic

Research, curriculum development, student projects, thesis work

Research

Scientific discovery, experimental validation, publication support

Engineering

Design validation, simulation testing, protection system development

For Actual Experimental Data & Collaboration

OpenFaultDynamics
Meru University of Science and Technology, Kenya
alexkimuya23@gmail.com

Detailed Experiment Data

2.5V - Before Short Circuit

2.5VBefore Short CircuitOffline

46 Records

ID	Sample Time	Timestamp
#46	58,781ms	Nov 21, 17:53:02
#45	55,405ms	Nov 21, 17:52:59
#44	51,728ms	Nov 21, 17:52:56
#43	49,239ms	Nov 21, 17:52:52
#42	46,182ms	Nov 21, 17:52:49
#41	43,744ms	Nov 21, 17:52:46
#40	40,027ms	Nov 21, 17:52:44
#39	37,867ms	Nov 21, 17:52:40

2.5V - After Short Circuit

2.5VAfter Short CircuitOffline

46 Records

ID	Sample Time	Scaling (a)	Decay (k)	Timestamp
#46	55,491ms	0.00824400	0.04073200	Nov 21, 18:18:25
#45	52,073ms	0.00000000	0.04073200	Nov 21, 18:18:20
#44	49,614ms	0.02018600	0.04073200	Nov 21, 18:18:16
#43	41,633ms	0.02012500	0.04446200	Nov 21, 18:18:14
#42	37,327ms	0.00063700	0.10225300	Nov 21, 18:18:06
#41	34,869ms	0.01757700	0.10225300	Nov 21, 18:18:02
#40	32,107ms	0.00452000	0.10225300	Nov 21, 18:17:59
#39	29,036ms	0.02044400	0.10225300	Nov 21, 18:17:56

5.0V - Before Short Circuit

5.0VBefore Short CircuitOffline

40 Records

ID	Sample Time	Timestamp
#40	55,203ms	Nov 21, 18:07:36
#39	52,096ms	Nov 21, 18:07:31
#38	43,153ms	Nov 21, 18:07:27
#37	26,646ms	Nov 21, 18:07:09
#36	21,676ms	Nov 21, 18:07:02
#35	16,465ms	Nov 21, 18:06:58
#34	13,472ms	Nov 21, 18:06:52
#33	6,032ms	Nov 21, 18:06:50

5.0V - After Short Circuit

5.0VAfter Short CircuitOffline

55 Records

ID	Sample Time	Scaling (a)	Decay (k)	Timestamp
#55	57,643ms	0.01450300	0.02588100	Nov 21, 18:31:47
#54	55,180ms	0.04265100	0.02999500	Nov 21, 18:31:45
#53	52,724ms	0.00000000	0.05132400	Nov 21, 18:31:42
#52	49,342ms	0.04130400	0.05132400	Nov 21, 18:31:39
#51	46,576ms	0.03956500	0.05532900	Nov 21, 18:31:36
#50	43,821ms	0.00000000	0.00763400	Nov 21, 18:31:33
#49	41,053ms	0.00899100	0.00763400	Nov 21, 18:31:30
#48	38,597ms	0.00840300	0.00763400	Nov 21, 18:31:28

10.0V - Before Short Circuit

10.0VBefore Short CircuitOffline

70 Records

ID	Sample Time	Timestamp
#70	57,114ms	Nov 21, 18:12:18
#69	54,654ms	Nov 21, 18:12:15
#68	51,583ms	Nov 21, 18:12:12
#67	49,131ms	Nov 21, 18:12:09
#66	45,139ms	Nov 21, 18:12:07
#65	42,691ms	Nov 21, 18:12:03
#64	40,228ms	Nov 21, 18:12:01
#63	37,461ms	Nov 21, 18:11:58

10.0V - After Short Circuit

10.0VAfter Short CircuitOffline

26 Records

ID	Sample Time	Scaling (a)	Decay (k)	Timestamp
#26	58,209ms	0.04654000	0.00664000	Nov 21, 18:36:39
#25	55,434ms	0.02187800	0.02997300	Nov 21, 18:36:34
#24	45,306ms	0.09019900	0.03545900	Nov 21, 18:36:30
#23	37,993ms	0.00000000	0.02064800	Nov 21, 18:36:22
#22	32,755ms	0.05752800	0.02064800	Nov 21, 18:36:13
#21	28,028ms	0.03994700	0.02064800	Nov 21, 18:36:08
#20	23,584ms	0.00527400	0.02064800	Nov 21, 18:36:03
#19	17,791ms	0.01953400	0.02064800	Nov 21, 18:35:59

Electrical Parameters Analysis

System-Dependent Statistical Summary

2.5V Experiments

Min

0.000V

Avg

0.000V

Max

0.000V

5.0V Experiments

Min

0.000V

Avg

0.000V

Max

0.000V

10.0V Experiments

Min

0.000V

Avg

0.000V

Max

0.000V

Experimental Setup & Methodology

Configuration Parameters

Input Voltage Levels 2.5V, 5.0V, 10.0V
Sampling Rates 10ms, 50ms, 100ms
Experiment Phases Before & After Short Circuit
Reference Resistance R₀ = 50Ω
Database Cluster 6 separate databases

Measurement Methodology

Before Short Circuit:

Temperature-dependent Ohm's Law: R = R₀[1 + α(T-T₀)]

After Short Circuit:

System-dependent arc model: V = V₀·(1-β·V₀^γ)·exp(-λ·t·V₀^δ)

Where: V₀ = initial voltage, β,γ,λ,δ = system-dependent constants, t = time

Data Processing:

• Real-time computation of system-dependent scaling constants

• Continuous gap-free data collection

• Multi-database architecture for scalability

System Information

Database Cluster

Project: AIFaultGen

Databases: 6 separate DBs

Total Records: 283

Hardware

Controller: ESP32

Voltage Pins: Analog input

Current Sensing: Dedicated pins

Software

Backend: PHP 7.4+

Database: MySQL Cluster

Frontend: Bootstrap 5 + Chart.js

Database Cluster Status

Database	Status	Records	Last Update	Connection
2.5V - Before Short Circuit	Online	46	Nov 21, 17:53:02	Idle
2.5V - After Short Circuit	Online	46	Nov 21, 18:18:25	Idle
5.0V - Before Short Circuit	Online	40	Nov 21, 18:07:36	Idle
5.0V - After Short Circuit	Online	55	Nov 21, 18:31:47	Idle
10.0V - Before Short Circuit	Online	70	Nov 21, 18:12:18	Idle
10.0V - After Short Circuit	Online	26	Nov 21, 18:36:39	Idle

Last Updated: 2025-11-22 05:24:19 | AIFaultGen - AI-Powered Electrical Fault Open Data Generator | Auto-refresh: 30 seconds

Advanced monitoring of electrical behavior under fault conditions powered by physics-informed generative AI
Physics Principle: Short circuit fault quantities are fundamentally dependent on initial system power conditions. Higher input voltages lead to higher short circuit currents and lower short circuit voltages due to increased energy dissipation.
Research Team: OpenFaultDynamics - Meru University of Science and Technology, Kenya