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Power quality has become one of major concerns in the power industry. It can be described as the reliability of the electric power to maintain continuity operation of end-use equipment. Power quality problems are defined as deviation of voltage or current waveforms from the ideal value. The expansion plan of wind power generation has raised concern regarding how it influences the voltage and current signals. The variability nature of wind energy and the requirements of wind power generation increase the potential problems such as frequency and harmonic distortions. In order to analyze and mitigate problems in wind power generation, it is important to monitor power quality in wind farm. Therefore, the more accurate and reliable parameter estimation methods suitable for wind power generation are needed. Three parameter estimation methods are proposed in this thesis to estimate the unknown parameters, i.e. amplitude and phase angle of fundamental and harmonic components, DC component and system frequency, during the dynamic change in wind farm. In the first method, a self-tuning procedure is introduced to least square method to increase the immunity of the algorithm to noise. In the second method, nonrecursive Newton Type Algorithm is utilised to estimate the unknown parameters by obtaining the left pseudoinverse of Jacobian matrix. In the last technique, unscented transformation is used to replace the linearization procedure to obtain mean and covariance which will be used in Kalman filter method. All of the proposed methods have been tested rigorously using computer simulated data and have shown their capability to track the unknown parameters under extreme distortions. The performances of proposed methods have also been compared using real recorded data from several wind farms in Europe and have demonstrated high correlation. This comparison has verified that UKF requires the shortest processing time and STLS requires the longest.