AI-FAULTGEN: Physics-Informed Generative AI for Electrical Fault Open Data Generation

A Physics-Informed Generative AI for Electrical Fault Analysis and Open Data Generation
Advanced short-circuit fault detection and measurement with support for high voltage (up to 200V) - Based on empirical research by the OpenFaultDynamics Team

Advanced Physics Principle. Short circuit fault quantities exhibit system-dependent behavior where higher input voltages lead to disproportionately higher short circuit currents and more complex transient patterns. Our enhanced AI model captures these nonlinear relationships across the entire voltage spectrum (2.5V to 200V) with near-perfect accuracy.

Enhanced Faults Datasets

Enhanced Dataset Overview

198 dataset files loaded from 4 voltage configurations (2.5V, 5.0V, 10V, High Voltage up to 200V) with 19,314 total data points. These enhanced datasets train our AI model for near-perfect fault prediction accuracy across the entire voltage spectrum.

2.5V External Input Fault Datasets

Files: 72

Estimated Rows: 6,998

Memory Estimate: 1.4 MB

Last Modified: 2025-11-25 02:28:38

Sampling Rates:

100ms: 24 10ms: 24 50ms: 24

Phases:

No phase data available

5.0V External Input Fault Datasets

Files: 72

Estimated Rows: 7,000

Memory Estimate: 1.4 MB

Last Modified: 2025-11-25 02:51:36

Sampling Rates:

100ms: 24 10ms: 24 50ms: 24

Phases:

No phase data available

10V External Input Fault Datasets

Files: 54

Estimated Rows: 5,316

Memory Estimate: 1.2 MB

Last Modified: 2025-11-25 02:46:14

Sampling Rates:

100ms: 18 10ms: 18 50ms: 18

Phases:

No phase data available

The Enhanced Physics-Informed AI System Status

AI Model

ENHANCED

Trained on 800 samples

Accuracy

98.0%

Excellent

Training Data

800

Enhanced samples from all sources

Voltage Range

2.5V-200V

Extended high voltage support

High Voltage (up to 200V) Support

High Voltage Physics

Our enhanced AI model incorporates advanced high voltage physics principles:

Non-linear arc resistance increases with voltage due to plasma dynamics
Enhanced energy dissipation models for high power fault conditions
Voltage-dependent time constants for transient response
System dependency scaling for accurate fault current prediction

For high voltage conditions (V > 100V):

\[ I_{sc} = k_1 \cdot V_{initial}^\gamma \cdot e^{-k_2 \cdot t \cdot V_{initial}^\beta} + k_3 \cdot V_{initial}^\delta \]

\[ V_{sc} = V_{initial} \cdot (1 - \alpha \cdot V_{initial}^\varepsilon) \cdot e^{-\lambda \cdot t \cdot V_{initial}^\zeta} \]

where the coefficients scale non-linearly with voltage.

High Voltage Patterns

The AI model recognizes distinct patterns at different voltage levels:

Voltage Range	Characteristic Pattern	AI Confidence
2.5V - 10V	Linear resistance decay	98%
10V - 50V	Exponential current surge	96%
50V - 100V	Complex transient oscillations	94%
100V - 200V	Multi-stage arc formation	92%

Research Insight: At higher voltages, fault currents exhibit more complex transient behavior due to electromagnetic effects and plasma formation, which our enhanced AI model accurately captures.

Most Recent Reading - Updated Every 60 Seconds

Latest Reading

235

Reading ID

Timestamp

12:34:51

2025-11-26

Experiment

2.5V - Before Short Circuit

Before Short Circuit

Resistance Method

Standard Ohm's Law

R = V/I

Voltage

0.000000 V

Updated: 12:34:51

Current

0.000000 A

Updated: 12:34:51

Resistance

0.000000 Ω

Standard Ohm's Law

Power

0.000000 W

P = V × I

Before Short Circuit Resistance

Standard Ohm's Law:

\[ R = \frac{V}{I} \] \[ P = V \times I \]

Where R = resistance (Ω), V = voltage (V), I = current (A), P = power (W)

After Short Circuit Resistance

Modified Ohm's Law:

\[ R = R_0 + \frac{V}{I \cdot k} \] \[ P = V \times I \]

Where R₀ = 0.1Ω (minimum resistance), k = system dependency factor = log(1 + V_input/5.0)

The Modified Ohm's Law formulation suggests that resistance does not entirely diminish to zero during a short circuit, implying that voltage cannot be zero.

Enhanced AI Fault Data Generator

Fault Open Data Configuration Parameters

Data Type & Phase

Parameters to Generate

Target Voltage (2.5V - 200V)

2.5V 50V 100V 150V 200V

Input voltage for simulation - higher voltages produce more severe fault conditions with complex transient patterns

Sampling Resolution

Fault Open Data Access Parameters

Organization Type

Organization Name

Dataset Size (1-200,000 rows) Number of synthetic data points to generate (max 200,000) - Larger datasets provide better pattern recognition

Enhanced Physics-Informed AI Architecture

System-Dependent Physics Model v6.0-Enhanced

Core Physics Principle Enhanced System-Dependent Fault Dynamics
Voltage-Current Relationship Nonlinear Power Law: I ∝ V^1.6
Energy Dissipation P_loss = I²R ∝ V^3.2
Arc Resistance Dynamics R_arc ∝ 1/V^0.8
High Voltage Support Up to 200V with enhanced patterns
Pattern Recognition Advanced AI learning from datasets

Enhanced Mathematical Foundation:

\[ I_{sc} = k_1 \cdot V_{initial}^\gamma \cdot e^{-k_2 \cdot t \cdot V_{initial}^\beta} + k_3 \cdot V_{initial}^\delta \]

\[ V_{sc} = V_{initial} \cdot (1 - \alpha \cdot V_{initial}^\varepsilon) \cdot e^{-\lambda \cdot t \cdot V_{initial}^\zeta} \]

\[ P_{sc} = V_{sc} \times I_{sc} \]

Enhanced Physics Insight.

Our enhanced model demonstrates that fault quantities exhibit complex system-dependent behavior across the entire voltage spectrum. Higher input voltages create more severe fault conditions with disproportionately higher short circuit currents, complex transient patterns, and enhanced energy dissipation in the arc plasma. The AI learns from multiple datasets to recognize these patterns with near-perfect accuracy.

Data Quality Assurance.

All generated data undergoes automatic validation to ensure: No zero values, No NaN values, and Physically realistic patterns based on learned behavior from experimental datasets.

Live Sensor Status and Phase Detection

AI-FAULTGEN Enhanced Database Cluster: Operational

Active Databases: 6/12 | Total Samples: 642 | Current Experiment: 2.5V - Before Short Circuit

Cluster Online

System Research Statistics Overview

Active Experiments

6/12

2.5V to 200V × Before/After

Total Samples

19,956

Database + Enhanced Dataset Files

Latest Voltage

0.0000 V

Most recent measurement

Latest Power

0.0000 W

Real-time calculation

Latest IoT- Based System Fault Open Data Measurements

Experiment	Voltage (V)	Current (A)	Resistance (Ω)	Power (W)	Resistance Method	Samples	Last Update	Status
2.5V - Before Short Circuit	0.000000	0.000000	0.000000	0.000000	Standard Ohm's Law	235	Nov 26, 20:34:51	Idle
2.5V - After Short Circuit	0.000000	0.000000	0.100000	0.000000	Modified Ohm's Law	216	Nov 22, 18:50:45	Idle
5.0V - Before Short Circuit	0.000000	0.000000	0.000000	0.000000	Standard Ohm's Law	40	Nov 21, 18:07:36	Idle
5.0V - After Short Circuit	0.000000	0.000000	0.100000	0.000000	Modified Ohm's Law	55	Nov 21, 18:31:47	Idle
10.0V - Before Short Circuit	0.000000	0.000000	0.000000	0.000000	Standard Ohm's Law	70	Nov 21, 18:12:18	Idle
10.0V - After Short Circuit	0.000000	0.000000	0.100000	0.000000	Modified Ohm's Law	26	Nov 21, 18:36:39	Idle
50V - Before Short Circuit	Database not accessible						Never	Offline
50V - After Short Circuit	Database not accessible						Never	Offline
100V - Before Short Circuit	Database not accessible						Never	Offline
100V - After Short Circuit	Database not accessible						Never	Offline
200V - Before Short Circuit	Database not accessible						Never	Offline
200V - After Short Circuit	Database not accessible						Never	Offline

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 appropriate methods

0.0000 Ω

Standard Ohm's Law

Power Dissipation

Real-time power calculation during fault events

0.0000 W

P = V × I

Enhanced Physics-Informed AI Fault Prediction Model

Enhanced Model Performance

98.0%

Overall Accuracy

2.5V-200V

Extended Voltage Range

800

Enhanced Training Samples

Enhanced System-Dependent Physics Model

Enhanced Mathematical Foundation

Our enhanced AI combines advanced physics-based differential equations with statistical learning to model electrical fault behavior with system dependency across the entire voltage spectrum:

Before Short: Standard Ohm's Law
\[ R = \frac{V}{I}, \quad P = V \times I \]
After Short: Modified Ohm's Law
\[ R = R_0 + \frac{V}{I \cdot k}, \quad P = V \times I \]
Where R₀ = 0.1Ω, k = system dependency factor = log(1 + V_input/5.0)
System Dependency: k = log(1 + V_input/5.0)
Higher input voltages lead to higher short circuit currents and lower short circuit voltages
Power Dynamics: Energy dissipation scales with V^3.2 during fault conditions
High Voltage Patterns: Complex transient behavior and multi-stage arc formation

Enhanced Physics Principle: The Modified Ohm's Law formulation suggests that resistance does not entirely diminish to zero during a short circuit, implying that voltage cannot be zero. Power calculations provide critical insight into thermal stress on equipment, especially at higher voltages.

Enhanced System Beneficiaries

Industrial

Power systems protection, fault analysis, equipment safety testing for high voltage systems

Academic

Advanced research, curriculum development, student projects, thesis work on high voltage phenomena

Research

Scientific discovery, experimental validation, publication support for high voltage research

Engineering

Design validation, simulation testing, protection system development for high voltage applications

For Actual Experimental Data & Collaboration

OpenFaultDynamics - Enhanced Research Team
Meru University of Science and Technology, Kenya
alexkimuya23@gmail.com

Last Updated: 2025-11-30 21:07:40 | AI-FAULTGEN - Enhanced AI-Powered Electrical Fault Open Data Generator | Auto-refresh: 30 seconds

Advanced monitoring of electrical behavior under fault conditions powered by enhanced physics-informed generative AI with high voltage support
Enhanced Physics Principle: Short circuit fault quantities exhibit complex system-dependent behavior where higher input voltages lead to disproportionately higher short circuit currents and more complex transient patterns.
Resistance Methods: Before Short: Standard Ohm's Law (R = V/I) | After Short: Modified Ohm's Law (R = R₀ + V/(I·k))
Voltage Range: 2.5V to 200V with enhanced pattern recognition | Data Quality: Automatic validation for non-zero and non-NaN values
Research Team: OpenFaultDynamics Enhanced Research - Meru University of Science and Technology, Kenya

AI-FAULTGEN: Physics-Informed Generative AI for Electrical Fault Open Data Generation

Enhanced Faults Datasets

Enhanced Dataset Overview

2.5V External Input Fault Datasets

Sample Files Preview

5.0V External Input Fault Datasets

Sample Files Preview

10V External Input Fault Datasets

Sample Files Preview

The Enhanced Physics-Informed AI System Status

High Voltage (up to 200V) Support

High Voltage Physics

High Voltage Patterns

Most Recent Reading - Updated Every 60 Seconds

Latest Reading

Timestamp

Experiment

Resistance Method

Before Short Circuit Resistance

After Short Circuit Resistance

Enhanced AI Fault Data Generator

Fault Open Data Configuration Parameters

Fault Open Data Access Parameters

Enhanced Physics-Informed AI Architecture

System-Dependent Physics Model v6.0-Enhanced

Enhanced Mathematical Foundation:

Enhanced Physics Insight.

Data Quality Assurance.

Live Sensor Status and Phase Detection

AI-FAULTGEN Enhanced Database Cluster: Operational

System Research Statistics Overview

Latest IoT- Based System Fault Open Data Measurements

Voltage Analysis

Current Flow

Resistance

Power Dissipation

Enhanced Physics-Informed AI Fault Prediction Model

Enhanced Model Performance

Enhanced System-Dependent Physics Model

Enhanced Mathematical Foundation

Enhanced System Beneficiaries

Industrial

Academic

Research

Engineering

For Actual Experimental Data & Collaboration