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PSO-Tuned ANN-Based Prediction Technique for Penetration of Wind Power in Grid

  • Vijay KumarEmail author
  • Yash Pal
  • M. M. Tripathi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 597)

Abstract

Today, world is paying more attention on those types of energy sources that create minimum pollution and fulfill the gap between demand and supply. Continuous increase in the power demand of consumers from various fields such as residential, industrial, and commercial, it is difficult to procure the additional supply from conventional sources with maintaining the pollution standard. So power-producing companies/agencies invest lot of fund in the development of such sources of energy which is nearly pollution-free and available from nature. In this regards, the alternative of conventional sources may be renewable energy sources. Out of different available renewable sources such as solar, wind, biomass, and small hydro, wind can be considered one of the good sources for the generation of power. Today, living standard of any country can be recognized by per capita energy consumption by its people. As power production from renewable sources may lead to minimum possible pollution, operating cost, and mostly freely and abundant availability, it will work as a major driving factor for some countries of the world to spend maximum available energy fund in the development of such mechanism/technique that will able to generate energy from renewable sources. Although power generation from wind has many advantages, major drawbacks are its intermittent nature, frequency instability, and continuous availability with certain threshold speed that is capable for power generation at all places. This paper describes the combined technique of PSO and ANN for forecasting of speed and power of wind to penetrate it in grid. The proposed method is applied on Indian wind power sector, and its results are compared with simple ANN and ANN-SVM methods.

Keywords

Prediction technique Wind power prediction Particle swarm optimization (PSO) Artificial neural network (ANN) Maximum absolute percentage error (MAPE) 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.IMS Engineering CollegeGhaziabadIndia
  2. 2.Department of Electrical EngineeringNIT KurukshetraKurukshetraIndia
  3. 3.Department of Electrical EngineeringDTUNew DelhiIndia

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