Wind energy is an environmentally friendly and renewable source of power that is key to realizing the climate control targets. Wind turbines can harness the power of artificial intelligence (AI) to adapt independently to variations in load and environmental conditions. This article deliberates on the growing role of AI in wind energy systems (WESs).
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Role of AI in WES
In recent years, wind energy has gained significant popularity due to its potential as an alternative to conventional energy sources like oil and coal. In WESs, wind turbines installed in onshore and offshore locations convert the energy from wind into electricity.
Offshore wind farms are more advantageous than onshore farms due to their marginal impact on the ecological environment and landscape and access to higher wind speeds. However, monitoring WESs is often challenging and requires high maintenance costs.
Thus, fault detection and condition monitoring systems are essential in WESs to prevent catastrophic disasters and achieve satisfactory performance. Rapid technological developments in recent years have led to the growing use of AI in intelligent condition monitoring systems that improve the operational effectiveness and efficiency of WES.
In addition to condition monitoring and fault detection, the integration of AI can optimize the design of wind turbine components and predict the changes in climatic conditions to enhance the overall system performance.
AI Techniques Used in WES
Artificial Neural Networks (ANN): The most common application of ANN is fault detection and prediction for all wind turbine components, such as blades, gearboxes, and bearings. ANN is also used to optimize operation and maintenance costs, assess maintenance needs, and forecast power using environmental and economic variables.
Other major applications include false alarm detection and data analysis for state classification and fatigue estimation. Gearboxes are the most investigated components in wind turbines owing to their likelihood of failure. Thus, the use of ANN is increasing to diagnose and detect faults in this component.
Icing and structural damage are the most common faults in wind turbines. ANN is often combined with other techniques like decision trees, genetic algorithms (GAs), and particle swarm optimization (PSO) to improve the overall performance.
ANN-based maintenance management utilizes supervisory control and data acquisition data for cost and maintenance optimization. Power forecasting using different variables is a less studied application of ANN. For instance, temperature, power output, wind speed, and previous data stored in the system are the variables used by ANN for short-term predictions.
GA and PSO: These techniques are primarily applied as optimization tools. Studies using these techniques have mostly focused on assessing the design of various aspects or components of wind farms and turbines and optimization of microgrid configuration and size.
The most studied components were the generators, blades, and foundations, while the aspects were wind farm size, layout, location, and power dispatch. Design optimization before wind farm construction is done using a combination of GA and other AI techniques.
Maintenance scheduling and optimization are also primarily done using GA by considering the optimization problems as cost-based functions. Other variables that are considered include power production, environmental conditions, or system reliability and faults.
In programming and maintenance, key applications of GA are feasibility analysis, daily operations and maintenance scheduling, and large-scale maintenance planning. PSO is frequently combined with other AI techniques to optimize various wind turbine design aspects and maintenance management, such as gearbox condition monitoring and maintenance planning.
AI Applications in WES
In wind energy optimization, the integration of AI has facilitated the development of novel solutions that improve the performance and efficiency of WESs. For instance, an innovative approach has been developed to optimize the cable layout design in offshore wind farms.
The approach is based on an algorithm that combines minimum spanning tree and firefly algorithm to reduce the total cable length during the offshore wind farms' design phase. This development has huge significance due to the complex construction processes and high investment costs associated with offshore wind energy projects.
Specifically, the algorithm's ability to shorten cable lengths and reduce power losses represents a major step forward in increasing the economic viability of offshore wind farms and making them competitive with onshore farms. The approach will be crucial for countries with offshore wind energy development potential and extensive seafronts, such as Morocco.
The application of generative design for the mass optimization of WESs was investigated in a study focused on a Magnus effect-based experimental wind generator. Despite the need for complex manufacturing techniques like additive manufacturing, the generative design's potential as a tool to optimize electromechanical applications was validated in the study.
Specifically, the substantial mass reduction realized in the wind turbine components underscored the AI-driven design approaches' potential to improve the performance and efficiency of WESs. Thus, this development offered crucial insights into the application of AI for developing sustainable and more efficient wind energy technologies.
The impact of integrating wind farms into electrical power generation systems has been analyzed using block diagrams and Markov chain techniques to evaluate the reliability of generation systems with wind farms.
The connection of wind farms increased system reliability, resulting in improved voltage stability and reduced congestion. In this context, AI has a critical role as it optimizes the maintenance and operation of wind farms, thereby improving the overall power system reliability. AI-driven real-time monitoring and predictive maintenance significantly reduce downtime and improve the efficiency of WESs.
The intermittent nature of wind energy leads to issues like power quality concerns and low fault ride-through capability. These issues can be mitigated by enhancing predictive maintenance and optimizing grid operations using AI.
Although AI offers transformative potential in wind energy, several challenges exist with its integration, including the need for robust data analytics capabilities and sophisticated algorithms, grid integration complexities, and economic constraints.
A multi-faceted approach involving capacity building, policy support, and technological innovation is required to effectively address these challenges. Overcoming these challenges is crucial to unlock the full potential of WESs.
New Developments
A paper published in IEEE Access proposed an AI-integrated fractional order robust control for a doubly fed induction generator-based wind energy conversion system. A fuzzy system was employed for adaptive discontinuous control gain adjustment while preserving the closed-loop system's robustness to reduce the chattering phenomena within the excitation signal.
A fractional order Lyapunov system ensured the stability of the closed-loop system with fuzzy fractional order robust control. The system performance under the proposed fuzzy fractional order robust control scheme was compared with classical sliding mode control.
Experimental results displayed the superiority of the proposed control scheme under all test conditions. For instance, under ideal conditions and with the proposed control scheme, the speed tracking error was approximately zero, while the slide mode control method showed a 0.4 radian/s peak tracking error.
Additionally, the reactive and active power tracking was smooth with the proposed fuzzy fractional order robust control scheme, while the reactive power of the slide mode control method oscillated on both sides of the reference and reached -0.01 kVAR on the negative side and 0.01 kVAR on the positive side of the plot.
The system with the proposed scheme displayed a 0.01 radian/s minimum steady-state error, while a peak value of 0.6 radian/s was recorded in the case of slide mode control with a saturation function under parameter variation. Moreover, a 0.1 radian/s speed tracking error was recorded using slide mode control with a sign function. The proposed fuzzy fractional order robust control scheme also exhibited minimal chattering.
A recent paper published in Energy conducted a case study for predictive maintenance of wind farms, where endoscopic images were utilized for bearing fault detection. The objective was to investigate whether AI assistance can improve technical inspectors' time efficiency and specificity.
Overall, the experiment involved 2,301 images collected over 138 wind turbines and involved 54 technical inspectors. Researchers showed the images to each inspector and asked them to identify bearing faults in the presence and absence of AI-assistance. Results displayed that AI-assistance had a statistically significant impact on enhancing the time efficiency and specificity of the technical inspector.
Interestingly, the benefit of AI assistance depended on the inspectors' experience. General inspectors showed a more significant improvement in both speed and accuracy compared to specialists. Both groups responded positively to the usefulness and reuse intention of AI-assistance, and the cognitive load changes were not statistically significant.
In conclusion, AI is revolutionizing wind energy by optimizing designs, predicting climate impacts, and improving efficiency through real-time monitoring and control. Overcoming data, grid integration, and economic challenges will unlock AI's full potential for a sustainable future.
References and Further Reading
Ullah, N., Sami, I., Chowdhury, M. S., Techato, K., & Alkhammash, H. I. (2020). Artificial intelligence integrated fractional order control of doubly fed induction generator-based wind energy system. IEEE Access, 9, 5734-5748. https://doi.org/10.1109/ACCESS.2020.3048420
Ohalete, N. C., Aderibigbe, A. O., Ani, E. C., Ohenhen, P. E., Daraojimba, D. O., Odulaja, B. A. (2023). AI-driven solutions in renewable energy: A review of data science applications in solar and wind energy optimization. World Journal of Advanced Research and Reviews, 20(3), 401-417. https://www.researchgate.net/publication/376389932_AI-driven_solutions_in_renewable_energy_A_review_of_data_science_applications_in_solar_and_wind_energy_optimization
Garcia Marquez, F. P., Peinado Gonzalo, A. (2022). A comprehensive review of artificial intelligence and wind energy. Archives of Computational Methods in Engineering, 29(5), 2935-2958. https://doi.org/10.1007/s11831-021-09678-4
AI in Wind Energy [Online] Available at https://www.zsw-bw.de/en/research/wind-energy/topics/ai-in-wind-energy.html (Accessed on 03 June 2024)
Shin, W., Han, J., Rhee, W. (2021). AI-assistance for predictive maintenance of renewable energy systems. Energy, 221, 119775. https://doi.org/10.1016/j.energy.2021.119775