This paper presents an in-depth analysis of data from the Alpha Ventus offshore wind farm, emphasizing the identification and detection of anomalies in wind turbine performance. Utilizing real-world data from the RAVE (Research at Alpha Ventus) project, we explore the complexities of offshore wind energy generation, including the effects of wind speed, nacelle position, and environmental factors on turbine behaviour. In this paper, among the various machine learning techniques, we have selected k-nearest neighbours (k-NN), to identify patterns and detect anomalies indicative of potential issues. Our findings demonstrate that some turbines of the wind farm, centrally located, are subject to significant wake effects and operational irregularities. By adjusting the parameters of the k-NN model, we achieved an anomaly detection framework, enhancing the reliability of turbine operation and maintenance.