Welcome to AMIEE Association
Upcoming Event
One Week FDP on Application of Artificial Intelligence and Machine Learning in Electrical Engineering
We are excited to announce a one-week online FDP scheduled from 30th June to 4th July 2025 (Monday to Friday).
Faculty Development Programme
We are excited to announce a One-Week Online Faculty Development Program (FDP) on the cutting-edge theme: “Application of Artificial Intelligence and Machine Learning in Electrical Engineering.” This FDP will be held from 30th June to 4th July 2025 (Monday to Friday). The sessions will take place in the evening, providing convenient access for working professionals and educators.
This FDP is designed to provide educators, researchers, and scholars with a comprehensive understanding of AI and ML applications in electrical engineering. Across five engaging days, participants will explore modern tools, techniques, and applications that are shaping the future of the discipline. The program emphasizes both theoretical foundations and hands-on learning, guided by expert speakers from academia and industry.
Register Now
Schedule of the Event
Faculty Development Programme
Foundation of AI/ML in Electrical Engineering
- Introduction to AI/ML: Concepts and Tools
- Overview of Artificial Intelligence and Machine Learning and their relevance in engineering disciplines, particularly electrical engineering.
- Introduction to Python/R and key libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, and Keras.
- Brief history, evolution, and real-world applications in electrical systems.
- Understanding the AI/ML pipeline: data collection → preprocessing → model building → evaluation → deployment.
-
Data Preprocessing and Feature Engineering
- Importance of data quality in ML model performanc
- Techniques for handling missing data, normalization, and encoding.
- Feature selection vs. feature extraction.
- Principal Component Analysis (PCA), Mutual Information, and domain-driven feature engineering examples from power and energy data.
Day 2: Machine Learning Algorithms
- Supervised/Unsupervised Learning for Load Forecasting
- Overview of supervised (regression, classification) and unsupervised (clustering, dimensionality reduction) methods.
- Application of models like Linear Regression, Decision Trees, SVM, K-means, and Hierarchical Clustering in electrical load prediction.
- Case studies and datasets (e.g., smart meter data or SCADA data).
- Deep Learning for Signal Processing
- Introduction to Artificial Neural Networks (ANNs), CNNs, and RNNs for electrical signal processing.
- Time-series analysis using LSTM for fault signal detection or waveform prediction.
- Use of spectrograms and frequency-domain features in classification tasks.
Day 3: Power Systems Applications
- AI in Smart Grids
- Role of AI in energy demand forecasting, real-time grid monitoring, and decentralized power management.
- Applications in dynamic pricing, demand response, and energy theft detection.
- Case examples involving IoT-enabled grid environments.
- Renewable Energy Forecasting
- Challenges in solar and wind energy prediction due to weather variability.
- Use of ML/DL models to predict power generation using meteorological and historical data.
- Model deployment for energy scheduling and grid stability.
Day 4: Condition Monitoring and Fault Diagnosis
- Predictive Maintenance of Machines
- Use of AI to analyze machine data (vibration, temperature, current) for anticipating equipment failure.
- Introduction to predictive maintenance frameworks: condition-based and reliability-centered maintenance.
- ML classifiers for remaining useful life (RUL) estimation.
- AI-based Fault Detection in Power Systems
- Data-driven approaches for fault classification and location.
- Applications in transmission line fault analysis, transformer monitoring, and circuit breaker performance.
- Comparison of rule-based systems vs. intelligent models (ANN, SVM, Random Forest).
Day 5: Control Systems and Optimization
- AI in Control Systems
- Integration of AI/ML in PID tuning, adaptive control, and real-time system regulation.
- Reinforcement Learning in automatic control applications.
- Intelligent controllers for robotics, power converters, and process control systems.
- Optimization Techniques (GA, PSO, ANN)
- Fundamentals of metaheuristic optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN) in engineering design.
- Multi-objective optimization for electrical network design, load dispatch, and capacitor placement.
- MATLAB/Python implementation examples.
