Abstract
The growth in sustainable generation technology such as fuel cell, wind energy conversion system, photovoltaic system, increase in fuel cost, energy necessity and the reduction in the fossil fuel reserve, for better power quality and reliability, is obliging the power sector to use the renewable based energy sources. In India, wind energy is gradually becoming an important and significant energy resource. Keeping in opinion the aforementioned wind energy prediction is becoming an essential study for harnessing the wind energy prospective. This paper proposes an effective technique based on intelligent approach for predicting wind power in different areas. This technique is based on using an intelligent model concerning the predicted gap to its similar one and two year old data. There are many intelligent and conventional models existed in literature for the wind power prediction like support vector machines (SVM), back propagation (BP) prediction etc. In this paper an effective fuzzy logic and model predictive control based models have been developed and offered for the wind power prediction for microgrid application by using air density and wind speed as the input parameters for fuzzy system. The outcomes are compared with the computed data and existing models and it can be observed that the different errors are found within the permissible limits. The outcomes obtained from fuzzy based technique are very close to calculated values if compared with model predictive based technique. Hence, the proposed models can be employed for the prediction of wind speed and wind power generation in the selected stations. The existing models results are compared with Kolkata city outcomes. The Error RMSE with Support vector machine, Back propagation, Model of forecast error correction +SVM and Model of forecast errors correction +BP, Neural Network method, model predictive based system, and proposed fuzzy logic based system are 30.48%, 32.83%, 26.81%, 28.58%, 1.1431%, 1.38% and 1.12% respectively. Therefore, the proposed techniques provide the best results and even these are observed within the suitable limits. Additionally, the achieved outcomes can be used for Microgrid/SmartGrid applications.
Highlights
The growth in sustainable generation technology such as photovoltaic system, wind energy conversion system, fuel cell, increase in fuel cost, power necessity and the reduction in the fossil fuel reserves, improved reliability and power quality is agreeable the power sector to employ the renewable energy resources
The foundation of large scale wind energy use is the precise prediction of wind speed forecasting
Numerical weather prediction is based on a meteorological formula set and can generally get trustworthy weather prediction
Summary
The growth in sustainable generation technology such as photovoltaic system, wind energy conversion system, fuel cell, increase in fuel cost, power necessity and the reduction in the fossil fuel reserves, improved reliability and power quality is agreeable the power sector to employ the renewable energy resources. The neural network based technique has been given to overcome the problem of long term forecasting of wind power In this method, outcomes are compared with the actual values and found to be more accurate because of the criterion [35]. Keeping in view of aforementioned aspects, fuzzy logic based model for the prediction of wind power is presented considering air density, wind speed, temperature and relative humidity for the particular location. Wind power varies along with the changes in wind speed and the weather conditions it is relatively hard to evaluate the accurate wind power by using regression techniques and mathematical formulas at a specific time These difficulties are escaped by fuzzy based method.
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