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
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
