Abstract

The massive integration of wind parks (WPs) in the modern power grid resulted in a significant concern regarding the security of the entire grid. These concerns are more important in the presence of WPs with inherent stability issues, e.g., when doubly-fed induction generators (DFIGs) are connected to series-compensated transmission systems. This thesis presents a novel real-time, data-driven security monitoring system to detect false data injection (FDI) and denial of service (DoS) cyberattacks targeting the subsynchronous damping controller (SSDC) in DFIG-based WPs.

A detailed and realistic electromagnetic transient (EMT) model of a DFIG-based WP is developed, along with the design of an SSDC to mitigate the subsynchronous control interaction (SSCI) phenomenon. The cyber vulnerabilities within the WP system, based on IEC 61400 standards, are analyzed to identify potential attack vectors in its cyber layer. It is demonstrated that such attacks can render the performance of SSDC ineffective, resulting in instability and sustained oscillations. To counter these issues, a real-time security monitoring system leveraging a customized recurrent neural network (RNN)-long short-term memory (LSTM) networks model is proposed to identify FDI and DoS attacks against the SSDC. The performance of the developed RNN-LSTM model is benchmarked against well-known classifiers, including random forest (RF), k-nearest neighbors (KNN), and multilayer perceptron (MLP), demonstrating superior detection accuracy. The effectiveness of the proposed model is further validated using unseen data, ensuring its effectiveness and generalization capability. Additionally, the proposed model exhibits low latency, making it suitable for near real-time operations in WPs.