PS4003 Computational Intelligence Techniques to Power Systems Syllabus:

PS4003 Computational Intelligence Techniques to Power Systems Syllabus – Anna University PG Syllabus Regulation 2021

UNIT I INTRODUCTION

Application of genetic algorithm to power system load forecasting, participle swam optimization for reactive power optimization, Optimization Techniques for emission dispatch of power plant, Differential Evolution Algorithm, Optimization Techniques for pole placement and state feed back algorithms, – Problem formulation and forms of optimal Control– Selection of performance measures. Necessary conditions for optimal control – State inequality constraints – Minimum time problem.

UNIT II LINEAR QUADRATIC TRACKING PROBLEMS ANDNUMERICAL TECHNIQUES FOR OPTIMAL CONTROL

Linear tracking problem – LQG problem – Computational procedure for solving optimal control problems – Characteristics of dynamic programming solution – Dynamic programming application to discrete and continuous systems – Hamilton Jacobi Bellman equation. Numerical solution of 2-point boundary value problem by steepest descent and Fletcher Powell method – solution of Ricatti equation by negative exponential and interactive Methods.

UNIT III MODEL DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORK

CNN Classification, CNN Algorithm ,model decomposition techniques, application of model decomposition and CNN based techniques for various power system fault digonesis problems, model predictive controllers for power system for power system stabilizers

UNIT IV FILTERING AND ESTIMATION

Filtering – Linear system and estimation – System noise smoothing and prediction – Gauss Markov discrete time model – Estimation criteria – Minimum variance estimation Least square estimation – Recursive estimation

UNIT V KALMAN FILTER

Filter problem and properties – Linear estimator property of Kalman Filter – Time invariance and asymptotic stability of filters – Time filtered estimates and signal to noise ratio improvement – Extended Kalman filter,. Application of Kalman filter for power system protection applications

TOTAL : 45 PERIODS

COURSE OUTCOMES:

Ability to:
CO1: Understand the concept of Optim Optimization Techique for power system.
CO2: Identify, Formulate and measure the performance of Optimal Controllers for power system.
CO3: Understand the Linear Quadratic Tracking Problems and implement dynamic programming application for discrete and continuous systems.
CO4: Apply Filtering and Estimation techniques for power system applications.
CO5: Design Kalman filter for power system protection application

REFERENCES:

1. Ajith Abraham and Swagatham Das.,”Computaional Intelligence in Power Engineering”, 2010 Springer Verlag.
2. Yong Hua Song, Johns Allen, Aggarwal Raj, ‘Computational Intelligence Application to Power System’, Springer Netherlands., 1997.