AI Application in Power Systems
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Follow on Instagram for UpdatesAI Application in Power Systems Syllabus — Unit-wise Content
The official GTU curriculum for AI Application in Power Systems (subject code ME02000771) as prescribed for E(SE · Semester 2 divides the course into 5 units, covering 64 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (ME02000771)Unit 1 Introduction to Artificial Intelligence
- What is AI, The Foundations of Artificial Intelligence
- The History of Artificial Intelligence
- The State of the Art, Risks and Benefits of AI [1]
Unit 2 Fuzzy Logic Applications in Power Systems
- History and background
- Pros and Cons of fuzzy logic
- Linguistic approach
- Set theory
- Fuzzy set theory
- Classical set theory versus fuzzy set theory
- Example based on fuzzy logic
- Fuzzy system design
- Fuzzy Inference
- Mamdani method
- Takagi- Sugeno model
- Application example
- Load balancing using fuzzy logic
- Voltage stability analysis using fuzzy logic
- Load profiling and energy consumption modeling using fuzzy logic [2]
Unit 3 Artificial Neural Networks (ANN) in Power Systems
- History and background
- Pros and Cons of ANN, Basic structure of ANN, Structure of a neuron
- Transfer functions
- Architecture of the ANN, Steps to construct a neural network
- Learning Algorithm
- The delta rule
- Gradient descent
- Energy equivalence
- The back propagation algorithm
- The Hebb rule
- Different networks
- Multi-layer perceptrons
- Back-propagation network
- Radial basis function network
- Special Issues in NN training
- Number of hidden layer units
- weight initialization
- Scaling of inputs and outputs
- Application of NN in Simple ground fault classifier
- Advanced ground fault classifier
- Advanced load forecasting
- Stability analysis [2]
Unit 4 Genetic Algorithms and Evolutionary Techniques
- Fundamentals of Genetic Algorithms
- History of GA, Basic Concepts
- Biological background
- Creation of offsprings
- Search space
- Working principle
- Binary Encoding
- Octal encoding
- Hexadecimal encoding
- Fitness function
- Roulette Wheel selection
- Tournament selection
- Rank Selection
- Single point crossover
- Multi-point crossover
- Uniform crossover
- Mutation operator
- Mutation rate
- Application of classic economic dispatch by Genetic Algorithm
- GA based Economic dispatch solution [3]
Unit 5 Particle Swarm Optimization and its application in Power systems
- Computational implementation of PSO, Improvement to the Particle Swarm Optimization method
- Solution of the constrained Program Name: Master of Engineering Level: PG Subject Code: ME02000771 Subject Name: AI Application in Power Systems optimization problem
- Finding maximum/minimum of the function using PSO [5]
- Implementation of PSO for Unit Commitment[4]
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