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Comparison of the Effectivity of Wind Speed Forecasting Methods

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Whole thesis(3.72 MB)

Date

2016

Authors

Supervisor

Lie, Tek Tjing
Kirtley Jr, James
Nair, Nirmal
Valles, Brice

Item type

Thesis

Degree name

Doctor of Philosophy

Journal Title

Journal ISSN

Volume Title

Publisher

Auckland University of Technology

Abstract

Environmental considerations and reducing carbon emission has accelerated the use of various renewable resources for electricity generation. Wind generation, in this context has seen sustained increases globally. Wind intermittency, its independent nature of direction, and varying speed are the best-known challenges and major barrier for accommodating very larger wind power penetration. There are several factors that can address this and help improve the attractiveness of the wind power to a utility. These may include improvements in model accuracy to decrease the forecast error, changes in the conventional plant, better storage or better load management to name a few. Managing the wind energy intermittency for existing power system operation and control therefore becomes crucial. The issues posed in the wind speed prediction include reduction in time delay, improvement in speed prediction for short time, error reduction, model improvement for effective conversion of wind energy. However there is a lot of research being done in this field in which individual as well as hybrid forecasting techniques are being worked upon. One effective solution is to predict the future values of wind power production, which is usually dependent on the wind speed. Precise forecasting of wind speed is vital to the effective harvesting of wind power. So, if an accurate forecasting of the wind speed from a few minutes to several hours ahead is obtained, the effective integration of larger penetration of the wind power generation can be achieved efficiently, safely and economically. The main objective of this research is to compare the effectiveness of different techniques and to come out with a few unique hybrids that help reduce the forecasting error. The main focus lies on error reduction and improvement of model by hybridizing different techniques. It focuses on the improvement of the present forecasting methods and reduction of the forecasting error. This thesis presents a critical literature review and an up-to-date bibliography on the wind forecasting technologies. One of the objectives of this research is to develop a few novel wind speed forecasting techniques, which produce more accurate prediction. Initially a hybrid technique, Artificial Neural Networks (ANN) along with the statistical method Ensemble Kalman Filter (EnKF) is proposed. The proposed method is used for a short-term prediction of wind speed. The well-established MATLAB software computing environment is utilized to simulate and show the effectiveness of the proposed hybrid technique. By help of past observations of wind speed, the EnKF is found to correct the output of ANN to find the best estimate of wind speed. The simulation results in MATLAB show that combination of ANN with EnKF acts as an output correction scheme. For the next hybrid technique, the Wavelet Transform (WT) along with the Auto Regressive Moving Average (ARMA) is proposed. In the preliminary stage of the investigation, this combination is expected to give minimum Mean Absolute Percentage Error (MAPE). A simple simulation study has been conducted by comparing the forecasting results using the Wavelet-ARMA with the ANN-EnKF hybrid technique to verify the effectiveness of this new proposed hybrid method. The simulation results of the proposed WT-ARMA hybrid technique show significant improvements in the forecasting error. The thesis has also investigated how to fully utilize Auto Regressive Moving Average (ARMA) to predict wind speed. However, the order estimation of ARMA is a very critical issue. Therefore, ANN has been used for parameter estimation, which is then combined with the Akaike Information Criteria (AIC) for order estimation. A simulation study has been conducted by comparing the proposed hybrid results with the Genetic Algorithm (GA) for parameter estimation and an exhaustive search for order estimation. Another part of this research focuses on the Economic Dispatch (ED) problem. The stochastic nature of the wind and the highly nonlinear transformation from wind speed to electrical energy makes it more difficult to determine how to dispatch its power in order to guarantee both operational cost reduction and power system security. From a network constraint perspective in the economic dispatch problem, one of the factors to be accounted for is voltage instability, which impacts both active and/or reactive power dispatch. As a solution, an Optimal Reactive Power Dispatch (ORPD) based on Particle Swarm Optimization (PSO) using Graph Theory (GT) has been proposed to overcome the aforementioned problem. The Graph Theory has been proposed since it is very useful in cases of fault detection and isolation or to shed unbalanced nodes in case of excessive or insufficient supply. Simulation studies on the IEEE-14 Bus System have been conducted to show the effectiveness of the proposed method. The research utilizes real time data from a few minutes to several days ahead to show the effectiveness of the proposed methods. Both synthetic data and real information from New Zealand wind resources have been used. Since the forecasting errors may vary within the time frame under consideration, different modifications have been added to the proposed hybrid techniques to get robust results from the practical data. In addition, the correlation between the forecasting error indices and the economic factors has been investigated. The Economic Dispatch problem has been identified as a major one and forecasting has been used as a solution in Graph Theory. This research may not only benefit forecasting of wind but also several other applications as well such as load forecasting or price forecasting in the future.

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Keywords

Wind speed, Forecasting, Hybrid techniques, Graph theory

Source

DOI

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Permanent link

https://hdl.handle.net/10292/9941

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Doctoral Theses