Power Quality Detection Using LSTM Deep Learning (AI)

Learn how to detect & identify power quality disturbances using LSTM-based AI models with real-world voltage & current
What you'll learn
✓ Understand the fundamentals of power quality and its impact
✓ Identify and classify various power quality disturbances
✓ Build and train an LSTM model for detection and prediction
✓ Analyze real-world voltage and current signals
✓ Evaluate model accuracy and interpret LSTM outputs
Requirements
● Electrical Engineer
● MATLAB / Simulink
● Office Program (Excel)
● Calculator
● PDF Reader
Description
In this course, you'll learn how to detect and identify power quality problems in electrical systems using Artificial Intelligence (AI) - specifically the Long Short-Term Memory (LSTM) deep learning technique.
Power quality issues such as voltage sag, swell, interruption, surge, flicker, and harmonic distortion can severely affect electrical equipment and system performance. This course guides you step-by-step through how to analyze, classify, and predict these problems with high accuracy using LSTM.
You'll explore both simple and complex disturbances, including cases where multiple problems occur together - such as a sag with harmonic distortion.
To help you learn practically
- MATLAB (M-file) simulation codes will be attached.
- Real-world voltage and current data (in Excel files) will be included for hands-on analysis.
The course has been explained in a practical manner, relying on simplicity in theoretical explanations and placing greater emphasis on visuals and real-life practical examples. This approach allows us to connect academic theoretical study with what actually exists in practical reality for real-world application after completing this course.
The course we have is closely related to the power systems and electrical distribution systems. In this course, we provide the following
Categorisation of Power Quality Problems Using AI Technique
or
Detection and Identification of Power Quality Problems using Artificial Intelligence Technique (LSTM Network)
1. Introduction
· Importance of power quality in modern electrical systems
· Impact of poor power quality on equipment and operations
· Overview of AI techniques for power quality detection
· Focus on Long Short-Term Memory (LSTM) deep learning model
2. Power Quality Overview
· Definition of power quality and related standards
· Typical power quality indices (RMS voltage, frequency, THD)
· Causes and effects of poor power quality
3. Types of Power Quality Problems
· Simple problems: voltage interruption, sag, swell, surge, flicker, harmonic distortion
· Complex problems: combinations like sag + harmonics, swell + harmonics
4. Data Preparation and Simulation
· Generation of voltage waveforms for various disturbances
· MATLAB simulations for normal and distorted voltage conditions
· Time-domain and frequency-domain analysis
· Use of Excel datasets for real current and voltage measurements
5. Long Short-Term Memory (LSTM) Technique
· Concept and structure of LSTM networks
· Difference between RNN and LSTM
· LSTM layers, gates, and sequence learning
· Advantages of LSTM in time-series signal analysis
6. Model Training and Testing
· Dataset splitting (training, validation, testing)
· Model parameters: epochs, learning rate, activation functions
· Loss function and accuracy evaluation
· MATLAB implementation and model outputs
7. Case Studies
· Normal voltage waveform (without noise): baseline analysis
· Voltage sag, swell, interruption, harmonic distortion: detection and classification
· Complex disturbances: sag + harmonic distortion, swell + harmonic distortion
· Visualization of LSTM outputs and classification accuracy
8. Results and Evaluation
· Comparison between actual and predicted results
· High detection accuracy (>99%) for all cases
· LSTM output interpretation
o Output 1 = 1 → no problem
o Output 2 = 0 → no harmonic distortion
· Total Harmonic Distortion (THD) values and duration accuracy
9. Practical Applications
· Implementation in real power systems and smart grids
· Role of AI in condition monitoring and predictive maintenance
· Integration with data acquisition systems
10. Course Files and Resources
· MATLAB (M-file) for simulation and prediction
· Excel files with real voltage and current data
· Figures, tables, and waveform examples for each disturbance case
· Reference materials for further reading
Who this course is for
■ Electrical engineers and power system professionals
■ Researchers working in power quality and AI applications
■ Students and postgraduate learners in electrical or power engineering
■ Anyone interested in applying deep learning to signal analysis
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