An intelligent SCADA-integrated deep learning framework for bird-safe offshore wind farm operation

Abstract

Abstract The rapid expansion of offshore wind energy has intensified concerns regarding avian collisions with turbine blades, particularly for migratory and high-risk bird species. Conventional mitigation approaches—including radar monitoring, manual intervention, and acoustic deterrents—are often limited by high false alarm rates, delayed response times, and the lack of species-level identification. To address these challenges, this study proposes an intelligent framework that integrates a Supervisory Control and Data Acquisition (SCADA) system with a Deep Convolutional Neural Network (DCNN)-based Bird Detection and Classification (BDC) model. The proposed system performs automated image-based bird classification and translates detection outputs into SCADA-driven turbine control actions through a multi-zone proximity assessment strategy. The model is trained and evaluated on a dataset comprising 525 avian species with over 90,000 images. Comparative analysis against conventional classifiers—including Support Vector Machines (SVM), Random Forest, K-Nearest Neighbor, and VGG16—demonstrates that the proposed BDC model achieves superior performance, with an accuracy of 99.62%, precision of 99.92%, recall of 100%, and an F1-score of 99.93%. In addition to classification performance, the system demonstrates a simulation-based system, achieving inference latency below 30 ms and SCADA response execution within 40 ms. These results highlight the potential of integrating deep learning with operational control systems to enable automated, risk-aware turbine response mechanisms for wildlife protection. It is important to note that the evaluation is conducted under controlled dataset conditions, and the dataset does not fully represent real offshore environments characterized by long-distance detection, motion blur, occlusion, and complex backgrounds. Therefore, the reported performance should be interpreted as an upper-bound estimate, and future validation using real-world offshore data is required to confirm deployment robustness. Overall, the proposed framework provides a simulation-based proof-of-concept approach for bridging AI-based avian monitoring with SCADA-enabled turbine control, contributing toward environmentally sustainable offshore wind farm operation.