The DARES Group is part of the Dependable Systems Cyber Laboratories at Tampere University, a dynamic research infrastructure designed to simulate real-world cyber-physical environments. This infrastructure integrates modern industrial control networks, covering machinery automation, process automation, and Smart Grids. It enables researchers to test and refine fault-tolerant, attack-resilient control, cybersecurity solutions, and condition monitoring technologies for critical sectors such as energy, transportation, and manufacturing. Our research leverages the lab’s capabilities to address emerging challenges in industrial automation, ensuring dependable systems under faults, cyber-attacks, and operational inefficiencies.
Selected Research Highlights
Below are selected examples of past and recent work, demonstrating how technologies are applied to enhance system dependability in critical sectors such as wind energy, microgrids, and industrial automation.
Fault-Tolerant Torque Load Control for Wind Turbines
This research project developed methods for diagnosing actuator faults in wind turbines and designed fault-tolerant torque control mechanisms. This ensures the safe operation of wind turbines under various fault conditions, improving overall energy production reliability.
H. Badihi, Y. M. Zhang, and H. Hong, “Wind turbine fault diagnosis and fault-tolerant torque load control against actuator faults,” IEEE Transactions on Control Systems Technology, vol. 23, no. 4, pp. 1351-1372, 2015.
Fault-Tolerant Individual Pitch Control for Dependable Load Mitigation in Wind Turbines
This project developed a fault-tolerant individual pitch control (IPC) system for wind turbines to reduce structural loads while ensuring turbine performance, even in the presence of actuator faults. By integrating fault detection and diagnosis with adaptive IPC, the control system dynamically adjusts the pitch of individual blades, mitigating unbalanced aerodynamic loads and fault effects. This contributes to improved load distribution, prolonged turbine lifespan, and enhanced overall reliability.
H. Badihi, Y. M. Zhang, P. Pillay, and S. Rakheja, “Fault-tolerant individual pitch control for load mitigation in wind turbines,” IEEE Transactions on Industrial Electronics, vol. 68, no. 1, pp. 532-543, 2021.
Fault-Tolerant Cooperative Control in Offshore Wind Farms
This project explores fault-tolerant cooperative control strategies for offshore wind farms, using both model-free and model-based fault detection and diagnosis approaches. The goal is to enhance the reliability and efficiency of wind farm operations by enabling wind turbines to work cooperatively in fault-tolerant modes. The developed solution detects faults, diagnoses their nature, and reconfigures the turbine controllers to maintain stable performance and optimize power output, even under faulty conditions. This improves the robustness and operational dependability of offshore wind farms, which are often exposed to harsh and variable environmental conditions.
H. Badihi, Y. M. Zhang, and H. Hong, “Fault-tolerant cooperative control in an offshore wind farm using model-free and model-based fault detection and diagnosis approaches,” Applied Energy, vol. 201, pp. 284-307, 2017.
Scalable Fault-Tolerant Control for Large-Scale Wind Farms and Clusters
This project focuses on the development of scalable fault-tolerant cooperative control strategies for large-scale wind farms and wind farm clusters. The control system integrates advanced fault detection and diagnosis mechanisms to allow multiple wind turbines to operate cooperatively, ensuring that the farm can maintain optimal power generation even in the presence of faults. The cooperative nature of the control system allows for better resource sharing, improved fault-tolerance, and enhanced efficiency across large, interconnected wind farm clusters. The project contributes to ensuring stable and reliable power generation from renewable energy sources on a large scale.
S. Jadidi, H. Badihi, and Y. M. Zhang, “Fault-tolerant cooperative control of large-scale wind farms and wind farm clusters,” Energies, An invited paper for the Special Issue on “Diagnosis and Fault Tolerant Control of Wind Energy Conversion Systems”, vol. 14, no. 21, 2021.
Enhanced Wind Turbine Monitoring through Power Curve Analysis
This project introduces a novel condition monitoring approach for wind turbines by developing a normal behavior model (NBM) based on power curves and stacked regressions. The NBM uses data-driven techniques to model the expected performance of wind turbines under various operational conditions. By comparing actual turbine behavior to the modeled normal behavior, this approach helps in early detection of performance deviations that may indicate faults or inefficiencies. This method enhances the ability to monitor turbine health, detect emerging issues, and prevent failures, ultimately improving the reliability and efficiency of wind energy systems.
F. Bilendo, H. Badihi, N. Lu, P. Cambron, and B. Jiang, “A normal behavior model based on power curve and stacked regressions for condition monitoring of wind turbines,” IEEE Transactions on Instrumentation & Measurement, vol. 71, pp. 1-13, 2022.
Multi-Target Condition Monitoring in Wind Turbines
This project develops a multi-target normal behavior model (NBM) for wind turbine condition monitoring using heterogeneous stacked regressions and change-point detection. The NBM is designed to handle multiple performance indicators simultaneously, offering a comprehensive view of turbine health. By detecting changes in operational behavior across multiple targets, the system identifies early signs of faults or degradation. This advanced condition monitoring approach improves the accuracy and timeliness of fault detection, enhancing turbine reliability and reducing downtime in wind energy systems.
F. Bilendo, N. Lu, H. Badihi, A. Meyer, U. Cali, and P. Cambron, “Multi-target normal behavior model based on heterogeneous stacked regressions and change-point detection for wind turbine condition monitoring,” IEEE Transactions on Industrial Informatics, vol. 20, no. 4, pp. 5171-5181, 2024.
Imaging Wind Turbine Fault Signatures for Image-Based Fault Diagnosis
This project investigated an effective approach to wind turbine fault diagnosis by imaging fault signatures using power curves and self-organizing maps (SOM). The method visualizes turbine performance data and identifies fault patterns, making it easier to diagnose faults using image-based techniques. By mapping the power curve data through SOM, the system can detect and classify different types of faults visually, offering a powerful tool for early diagnosis and maintenance planning. This approach leads to improved operational reliability and reduced maintenance costs for wind turbines.
F. Bilendo, H. Badihi, N. Lu, P. Cambron, and B. Jiang, “Imaging wind turbine fault signatures based on power curve and self-organizing map for image-based fault diagnosis,” in Proc. of the 7th International Symposium on Advanced Control of Industrial Processes, Vancouver, BC, Canada, August 7-9, 2022.
Monitoring and Diagnosis of Cyber-Physical PV Systems in Microgrids
This project designed an intelligent hybrid diagnosis scheme for cyber-physical photovoltaic (PV) systems operating at the microgrid level. The proposed scheme combines rule-based, signal-based, and model-based approaches to enhance the detection and diagnosis of faults in PV systems, ensuring reliable and efficient power generation. By integrating cyber-physical data from PV systems, the hybrid scheme improves the identification of faults and cyber-attacks in real-time, enabling timely maintenance and minimizing disruptions in energy supply. This project contributes to the dependability and sustainability of microgrids powered by renewable energy sources.
S. Jadidi, H. Badihi, and Y. M. Zhang, “Design of an intelligent hybrid diagnosis scheme for cyber-physical PV systems at the microgrid level,” International Journal of Electrical Power and Energy Systems, vol. 150, 2023.
Secure and Resilient Control Solutions for Microgrids
This project focuses on developing an active fault-tolerant and attack-resilient control system for renewable microgrids. The proposed control scheme addresses power-loss faults and data integrity attacks by detecting and mitigating these disruptions in real-time. By integrating fault-tolerant control with cybersecurity measures, the system ensures continuous and reliable operation of renewable microgrids, even in the presence of faults or cyber-attacks. This project enhances the resilience of microgrids, which are critical for the integration of renewable energy sources into the power grid, ensuring both safety and security.
S. Jadidi, H. Badihi, and Y. M. Zhang, “Active fault-tolerant and attack-resilient control for a renewable microgrid against power-loss faults and data integrity attacks,” IEEE Transactions on Cybernetics, vol. 54, no. 4, pp. 2113-2128, 2024.
Innovative Cybersecurity Measures for Wind Farm Networks
This project developed a smart cyber-attack diagnosis and mitigation system designed for wind farm network operators. The system leverages advanced machine learning and diagnostic techniques to detect, identify, and mitigate cyber-attacks in real-time. By continuously monitoring network traffic and operational data, the system can quickly diagnose security breaches and respond to prevent disruptions in wind farm operations. This approach improves the overall security and dependability of wind farm networks, ensuring continuous power generation and safeguarding against emerging cyber threats.
H. Badihi, S. Jadidi, Z. Yu, Y. M. Zhang, and N. Lu, “Smart cyber-attack diagnosis and mitigation in a wind farm network operator,” IEEE Transactions on Industrial Informatics, vol. 19, no. 9, pp. 9468-9478, 2023.