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Akash Saxena
Swami Keshvanand Institute of Technology

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A Minimax Polynomial Approximation Objective Function Approach for Optimal Design of Power System Stabilizer by Embedding Particle Swarm Optimization Bhanu Pratap Soni; Akash Saxena; Vikas Gupta
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 2: May 2015
Publisher : Institute of Advanced Engineering and Science

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Abstract

The paper presents a novel approach based on Minimax approximation and evolutionary tool Particle Swarm Optimization (PSO) to fabricate the parameters of Power System Stabilizers (PSSs) for multi machine power systems. The proposed approach employs PSO algorithm for find the setting of PSS parameters. The worth mentioning feature of this work is the formulation of objective function with the help of swing curves interpolation. A novel transformation known as minimax approximation is used for converting the objective into the polynomials of degree one, two and three. To construct the objective function based on interpolation second order sensitivity analysis is performed. The performance of the PSSs is tested under different topological changes, operating conditions and system configurations. Nonlinear simulation reveals that proposed PSSs are effectively deal with local and interarea modes of oscillations. PSS design obtained through lower order polynomial expression of objective function is able to deal with the oscillatory modes efficiently. DOI: http://dx.doi.org/10.11591/telkomnika.v14i2.7602
Layer Recurrent Neural Network based Power System Load Forecasting Nikita Mittal; Akash Saxena
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp423-430

Abstract

This paper presents a straight forward application of Layer Recurrent Neural Network (LRNN) to predict the load of a large distribution network. Short term load forecasting provides important information about the system’s load pattern, which is a premier requirement in planning periodical operations and facility expansion. Approximation of data patterns for forecasting is not an easy task to perform. In past, various approaches have been applied for forecasting. In this work application of LRNN is explored. The results of proposed architecture are compared with other conventional topologies of neural networks on the basis of Root Mean Square of Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). It is observed that the results obtained from LRNN are comparatively more significant.
Co-Authors Bhanu Pratap Soni Nikita Mittal Vikas Gupta