Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems
Abstract
:1. Introduction
2. Control Technology of Offshore Wind Power Systems
2.1. Advanced Control of Wind Turbines
- Fuzzy Logic is primarily used to address the uncertainties and ambiguities in MPPT (maximum power point tracking). For instance, some studies have focused on developing fuzzy fractional-order proportional–integral controllers to enhance the performance of direct-drive permanent-magnet synchronous generator wind turbines. This highlights the flexibility of fuzzy logic in adapting to rapidly changing wind speed conditions [17]. Further research includes comparative analyses of different fuzzy logic controllers in semisubmersible platform wind turbines [18]. In addition, some research also involves comparing the performance differences between fuzzy logic control and the traditional proportional integral controller [19], and combining the fuzzy logic method of sliding mode control to improve the robustness and performance of doubly-fed induction generator systems [20]. Fuzzy logic offers effective solutions to wind turbine control in complex environments.
- Intelligent Algorithms are recognized as powerful optimization tools, they are widely applied in MPPT (maximum power point tracking) control of wind turbines. Research has shown that control strategies optimized through intelligent algorithms significantly enhance the performance and efficiency of wind turbine systems [21,22]. For instance, the genetic algorithm (GA) has been used to adjust FLC (fuzzy logic control) system parameters for optimizing wind turbine MPPT strategy [10], as well as the intelligent control strategies for the offshore wind turbine MPPT zone [23]. Methods like MOPSO (multiobjective particle swarm optimization) have been utilized to optimize the control parameters of yaw control systems in horizontal-axis wind turbines, aiming to improve energy capture efficiency [11]. The YYGWO (yin–yang grey wolf optimizer) algorithm, through nonlinear model predictive control, has been employed for maximizing wind energy extraction in large wind turbines [12]. These examples highlight the advantages of intelligent optimization algorithms in parameter optimization and their potential to enhance system stability and adaptability.
- Neural Networks are primarily used for model prediction and system behavior simulation in MPPT (maximum power point tracking) applications. For instance, an unsupervised neural network-based MPPT control strategy for wind turbines has been proposed, which can adapt to different environmental conditions and optimize turbine actions to achieve maximum power [13]. Other research includes power prediction models for wind turbines using artificial neural networks, optimizing yaw angles across wind farms to reduce wake effects and enhance overall efficiency [24]. The flexibility of neural networks enables them to handle complex nonlinear systems, such as optimizing wind energy capture under variable speed conditions [25], and enhancing the performance of wind power systems with neural network controllers based on transfer function models [26]. These applications demonstrate the capabilities of neural networks in prediction and optimization, as well as their potential in real-time control and adaptive adjustments.
- Data-driven methods exhibit advantages in handling large volumes of complex data in MPPT control for wind turbines. These methods rely on historical and real-time data to enhance the accuracy and efficiency of control strategies [27]. In addition, the extended Kalman filter is used to improve MPPT control of wind turbines with the permanent magnet synchronous generator [28], and MPPT control based on wind speed estimation technology is applied to a double-fed induction generator [14]. These research efforts demonstrate the potential of data-driven approaches in enhancing the performance and adaptability of wind turbine control systems.
- Deep Learning has shown significant capabilities in data processing and feature extraction within the field of MPPT control for wind turbines. Research leveraging deep learning techniques has been successful in creating power curve models for wind turbines to predict power output under various conditions [29]. Additionally, deep learning solutions have been developed for power prediction in multiple wind turbine units within a wind farm [30]. These studies demonstrate the remarkable role of deep learning in enhancing the accuracy of performance prediction and optimization of wind turbine systems.
- Reinforcement Learning has increasingly demonstrated unique advantages in MPPT control for wind turbines, especially in managing complex dynamic systems. Research indicates that using reinforcement learning to improve the pitch control of wind turbine units effectively addresses the nonlinear characteristics and dynamic complexities of wind power equipment [15]. Additionally, a method combining data-driven and reinforcement learning approaches has been proposed for the torque and blade pitch control of wind turbines [14], showcasing the potential of reinforcement learning in optimizing complex control systems. There are also studies on blade pitch control [31] and MPPT methods for wind turbines [32] using reinforcement learning, highlighting its promising future in adapting to environmental uncertainties and optimizing complex control strategies.
2.2. Wake Control of Offshore Wind Farms
3. Layout and Integrated Design of Offshore Wind Farms
3.1. Turbine Selection for Offshore Wind Farms
3.2. Layout Optimization of Offshore Wind Farms
3.3. Power Collection System Optimization of Offshore Wind Farms
4. Conclusions
- (1)
- Wind Turbine Control: Rapid development in wind turbine advanced control techniques is evident, especially in maximum power point tracking (MPPT) and fatigue load optimization. AI methods like neural networks, fuzzy logic control, and reinforcement learning have shown substantial benefits in optimizing turbine performance and adapting to complex marine conditions.
- (2)
- Wake Control in Offshore Wind Farms: Significant progress is made in effectively managing power maximization, load–power balance optimization, and scheduling issues in wind farms, especially through heuristic intelligent algorithms and deep reinforcement learning, enhancing wake control efficiency.
- (3)
- Turbine Selection: AI’s role in this domain primarily revolves around solving algorithms, aiding in optimizing key design variables like turbine placement and rotor radius.
- (4)
- Layout Optimization: AI is applied mainly in constructing wake models and algorithmic problem-solving. ANN-based wake models speed up calculations while maintaining accuracy, enhancing solution efficiency. Metaheuristic algorithms and reinforcement learning are powerful tools for handling complex, large-scale layout optimization.
- (5)
- Power Collection System Optimization: Given the complexity, researchers often use hybrid methods. Combining clustering, heuristic, metaheuristic, and global optimization algorithms offers effective solutions for cost-optimization and system reliability.
- (1)
- Developing high-performance offshore wind power system simulation tools to enhance modeling methods and capabilities for various scenarios. For example, in the scenario of large-scale wind farm design, the data-driven model of wind farm internal wake is studied to speed up wind energy system simulation while ensuring certain accuracy.
- (2)
- Focusing on efficient data processing methods to manage and analyze vast wind farm data and enhancing existing algorithms to better handle complex, dynamic marine environments. For instance, deep learning is used to learn and process massive sensor data to predict possible environmental changes to improve the control performance of offshore wind power systems.
- (3)
- Creating standard models for case studies tailored to different regions and environments, providing benchmarks for comparing the effectiveness of various research solutions. For example, the types of offshore wind turbines suitable for marine environments with different depths may vary. Deep-sea areas typically require floating wind turbines, yet there is currently a lack of standardized case studies related to floating wind farms.
- (4)
- Ensuring technological innovations are economically viable and market-adaptable, including cost–benefit analysis, market policy research, and decision support for policymakers and investors. Deep learning technology may be helpful to fit and predict market data such as electricity price, providing reference for decision-makers in the wind energy industry.
Funding
Data Availability Statement
Conflicts of Interest
References
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Field | Language | Searched Keywords |
---|---|---|
Wind turbine control | English | “Offshore wind turbine” AND “MPPT”, “Fatigue control” |
Offshore wind farm wake control | English | “Offshore wind farm” AND “Wake control” |
Offshore wind farm turbine selection | English | “Offshore wind farm” AND “Wind turbine selection” |
Offshore wind farm layout optimization | English | “Offshore wind farm” AND “Layout optimization” |
Offshore wind farm collection system optimization | English | “Offshore wind farm” AND “Collection system optimization”, “Cable network optimization” |
Ref. | Year | Objective | Decision Variable | Framework | Method | Contribution |
---|---|---|---|---|---|---|
[10] | 2023 | MPPT | Rotor speed | FLC | GA | The proposed method is straightforward to implement, effectively minimizes steady-state oscillations, and swiftly adapts to changes in wind speed. |
[11] | 2018 | MPPT | Yaw angle | MPC | MOPSO | The proposed method adjusts control parameters based on wind direction changes and desired performance, resulting in improved power extraction efficiency. |
[12] | 2021 | Maximum wind energy extraction and minimum motor torque fluctuation | Rotor speed | MPC | YYGWO | The proposed algorithm demonstrates robustness in solving dynamic optimization problems, with a high optimization rate and rapid convergence performance. |
[13] | 2023 | MPPT | Generator speed | DSC | ANN | The control system, based on neural network online learning, can adapt to disturbances in MPPT control. |
[14] | 2023 | MPPT | Generator torque | MPC | DNN RL | In scenarios with uncertainty and unexpected actuator failures, the proposed method exhibits superior robustness and control performance. |
[15] | 2022 | MPPT | Pitch angle | PID | RL | The method enhances the efficiency of intelligent control strategies, reducing the power output error of the optimal hybrid controller by approximately 41%. |
[16] | 2023 | Minimum the asymmetric load of wind turbines | Individual pitch angle | PI | BO | The strategy introduces an actuator derating control approach, enhancing the fault tolerance of derating controls. |
Ref. | Year | Objective | Decision Variable | Method | Contribution |
---|---|---|---|---|---|
[44] | 2017 | Power and fatigue load balance | Active power setting | GA | The proposed method addresses real-time optimization issues under constraints related to the active power limitations of wind turbines and wind farms. |
[45] | 2020 | Maximum power | Axial induction factor | PSO | The proposed approach is highly efficient in solving problems for medium- and small-scale wind farms. |
[46] | 2021 | Maximum power | Axial induction factor, yaw angle | MC-BAS | The proposed approach enhances the capability of the BAS algorithm to handle high-dimensional nonlinear problems effectively. |
[47] | 2022 | Maximum power, minimum fatigue load | Axial induction factor, yaw angle | CMC-BSO | This proposed method solves multiobjective nonconvex optimization problems based on decentralized communication network topologies. |
[48] | 2023 | Maximum power | Axial induction factor, yaw angle | IEO | The proposed method combines centralized and distributed optimization strategies through iterative updates and cluster processing to improve the algorithm. |
[49] | 2020 | Maximum power | Yaw angle | Distributed RL | Considering the delay in wake propagation and the time-stepping variation of inflow conditions, this method achieves an efficiency gain of 8.2%. |
[50] | 2020 | Maximum power | Axial induction factor | KA-DDPG | Combining expert knowledge with a reinforcement learning framework while ensuring learning safety, this approach results in a gain of 10%. |
[51] | 2021 | Maximum power | Yaw angle | DDPG | The proposed control scheme demonstrates strong robustness and utilizes a sparse dataset, resulting in an efficiency gain of 15%. |
[52] | 2022 | Maximum power | Yaw angle | CER-DDPG | It has improved sampling and learning efficiency, enhancing its applicability in real wind farms, with a gain of 25%. |
[53] | 2022 | Maximum power | Thrust coefficient, yaw angle | DN-DDPG | The proposed method is able to handle incompatibilities between different control signals, ensuring a reliable training process, and achieving a gain of 33%. |
[54] | 2021 | Power tracking | Yaw angle | Deep RL | Using a model-free approach, it can solve the optimal behavior in real-time considering different environmental conditions. |
[55] | 2022 | Power tracking | Thrust coefficient, yaw angle | PR-DRL | It addresses the short-sightedness issue of traditional power-tracking methods. |
[56] | 2023 | Maximum power | Axial induction factor, yaw angle | SA-ISPSO | An intelligent optimization method based on a surrogate model is proposed, used for the first time in the power maximization problem of floating wind farms. |
[57] | 2023 | Maximum power | Axial induction factor, yaw angle | SAFDR | It proposes a dimensionality reduction-based surrogate modeling-assisted global optimization framework, further reducing the time cost of optimization. |
Ref. | Year | Objective | Decision Variable | Method | Contribution |
---|---|---|---|---|---|
[70] | 2019 | LCOE | Rotor diameter, placement | SOM | As an unsupervised learning technique, the self-organizing map (SOM) algorithm, is used for solving the turbine selection problem. |
[71] | 2019 | LCOE | Rotor diameter WTs number placement | GA | Compared to similar studies, fine modeling of the cost for wind farms is studied. |
[75] | 2021 | LCOE | Hub height, rotor diameter, rated power | GA | The algorithm and mathematical model are versatile, improving the economic output of wind farms. |
[76] | 2021 | Power output | Power, capacity | Turbine performance index | The reference introduces a turbine performance index and uses the ranking based on this index to identify the most suitable turbine type for a wind farm. |
[77] | 2021 | AEP | WT type | Weibull density distribution | The productivity of multiple types of wind turbines is analyzed to select the most efficient turbine type. |
[78] | 2023 | AEP | Hub height, rotor radius, placement | MINLP | A mixed integer nonlinear programming (MINLP) model is established, then a recursive algorithm is utilized to enhance the annual electricity generation from offshore wind farms. |
Ref. | Year | Criteria | Method | Contribution |
---|---|---|---|---|
[79] | 2020 | Hub height, wind speed, percentages of zero and rated output, annual energy production (AEP) | TOPSIS | The proposed TOPSIS-based method is versatile and has been validated using a real dataset from Saudi Arabia. |
[80] | 2020 | Hub height, wind speed, percentage of zero power, percentage of rated power, net capacity factor | Fuzzy logic | A fuzzy logic-based approach has been introduced, allowing decision-makers to develop flexible decision rules for turbine selection problems. |
[81] | 2020 | Machine characteristics, economic impact, environmental impact, technical specification | SWARA-SVNS-TOPSIS | The SWARA method is utilized for weighting process, and it has not been used in previous relevant research. |
[82] | 2021 | Technology, adaptability to wind resources, economy impact, historical achievements, supplier services | ANP-EWM | Subjective ANP is combined with objective EWM to fully leverage their respective strengths in selecting wind turbines. |
[83] | 2021 | Reliability, economic impact, supplier services | FPP-ANP | Triangular fuzzy numbers and fuzzy comparison matrices are introduced, and fuzzy preference programming (FPP) is combined with the analytic network process to construct a fuzzy analytic network process (FANP) unit selection model. |
[85] | 2022 | Technical performance, adaptability to wind farm, economic impact, historical achievements, supplier services | PCA-TOPSIS | The reference develops the D-number to address the uncertainty arising from the fuzziness of linguistic evaluations and the subjectivity of expert assessments in language evaluation. |
[86] | 2022 | Technical performance, adaptability to wind resources, economic impact, historical achievements, supplier services | SWARA-TOPSIS | The reference establishes a MCDM model based on the Dempster–Shafer evidence theory for offshore wind turbine selection. |
[87] | 2022 | Technical performance, economic impact, supplier services | SWARA II-CoCoSo | The reference combines the PWA operator, SWARA II, MEREC, CPT, and CoCoSo methods for the first time. |
Ref. | Year | Objective | Decision Variable | Method | Contribution |
---|---|---|---|---|---|
[88] | 2023 | Mixture | Continuous layout, turbine type | IPSO | For the retrofitting of old wind farms, a layout optimization framework is proposed to achieve a balance between power generation, equipment investment, and aesthetic objectives. |
[89] | 2022 | Effect on radar | Discrete layout | Mathematical programming | The reference establishes a wind turbine–radar interference analysis model and uses it to optimize the wind turbine layout appropriately, significantly improving radar tracking performance above the wind farm. |
[90] | 2023 | Power efficiency | Continuous layout | SQP | An efficient and accurate machine learning wake model is used in this reference. |
[91] | 2022 | AEP | Continuous layout | SLSQP | Simultaneously considering wave loads and wind energy output in wind farm layout optimization, the reference aims to minimize the total wave loads within the wind farm while maintaining a high annual energy production (AEP). |
[94] | 2023 | Mixture | Discrete layout | WPM | A MCDM optimization approach is innovatively proposed for wind farm layout. |
[95] | 2023 | AEP | Continuous layout | GA | The reference addresses the issue of retrofitting old wind farms by simultaneously maintaining the initial wind turbines and renovating the wind farm without the need for additional wind farm area. |
[96] | 2023 | Cost/ Power | Discrete layout | HPSOGA | A hybrid algorithm that combines particle swarm optimization (PSO) and the genetic algorithm (GA) is proposed, which effectively combines PSO’s global search capability with GA’s local search capability. |
[105] | 2022 | COE | Discrete layout, turbine number | PSO | The reference introduces a hybrid optimization approach that uses a greedy algorithm to optimize the number of turbines and employs particle swarm optimization (PSO) to optimize the turbine layout. |
[106] | 2023 | Total power output | Continuous layout | GWO | A low-complexity grey wolf optimization (GWO) algorithm with a two-dimensional encoding mechanism is proposed for solving the problem. |
[108] | 2023 | Total power output | Continuous layout | RS | The paper conducts the optimization of floating multiturbine platform layout with consideration of adaptive characteristics. |
[109] | 2023 | Cost/ Power | Discrete layout | LSHADE | A new variant of DE is developed to enhance the algorithm’s local search capability. |
[110] | 2022 | AEP | Discrete layout | SIMP | First-time attempt to apply interpolation techniques in the context of wind farm layout optimization. |
[111] | 2022 | Cost/ Power | Discrete layout | EO-PS | A hybrid algorithm EO-PS is developed for wind farm layout optimization, introducing PS (pattern search) techniques to enhance the local search capability of the EO (evolutionary optimization) algorithm. |
[112] | 2022 | Mixture | Discrete layout | VNS | A variable neighborhood search metaheuristic method is employed to optimize wind farm layout, and a novel initial solution algorithm is developed. |
[115] | 2022 | Cost/ Power | Discrete layout | GA-MCTS | The wind turbine layout is modeled as a reinforcement learning problem, and the Monte Carlo tree search algorithm is embedded in a genetic algorithm to enhance problem-solving capabilities. |
[116] | 2022 | AEP | Continuous layout, Mooring system | pyOptSparse | The mooring system design is incorporated as part of the floating offshore wind turbine (FOWT) layout design. An open-source tool is provided for this method. |
Ref. | Year | Objective | Design Variables | Method | Contribution |
---|---|---|---|---|---|
[117] | 2021 | Construction cost, reliability | WTs collection system, cable type | APSO | The cost model considers the dynamic power cables of floating wind turbines. |
[118] | 2022 | Construction cost, reliability | Location of OSSs, WTs collection system, interconnection OSSs to OCPs | AHC- SMC | A SMC method is developed to estimate the reliability of the measurement system. |
[119] | 2023 | Construction cost, power loss cost, reliability | WTs collection system, cable type | GSA- MIQP | The proposed method is applicable to large-scale topology optimization problems. The time complexity of the method is analyzed in this paper. |
[120] | 2022 | Layer 1: Impact on onshore substations and construction cost of onshore substations Layer 2: Construction cost of OSSs and cable Layer 3: Construction cost of cable | Layer 1: Location and capacity of onshore substations Layer 2 Location and capacity of OSSs Layer 3: WTs collection system, cable type | MLQP | For the first time, a detailed analysis from the perspective of grid planning is conducted on the concept of offshore grids that integrate offshore wind farm clusters. |
[121] | 2023 | Construction cost, power loss cost | Location of OSSs, WTs collection system, cable type | Prim- PSO-AO | The hybrid optimization method combines the search characteristics of both PSO and AO to enhance the search effectiveness. |
[126] | 2023 | LCOE | Location of OSSs, WTs collection system, cable type | BPSO- IMCTS | The reference establishes a lifecycle cost optimization model for floating wind farm collection systems considering environmental factors. |
[129] | 2023 | Construction cost | WTs collection system, cable type | ACO | The reference proposes a novel optimization algorithm based on ant colony heuristics, enhancing computational performance through the introduction of problem decomposition techniques. |
[131] | 2022 | LCCOE | Location of OSSs, WTs collection system, cable type | IPGA | For the first time, the reference proposes an optimization model for series–parallel collection systems that considers curtailment. |
[132] | 2021 | Construction cost, power loss cost | Location of OSSs, WTs collection system, cable type | Outer layer: FCM-GA Inner layer: Two-phase Clark and Wright’s saving algorithm | The reference combines GA with deterministic algorithms to enhance algorithm performance. |
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Song, D.; Shen, G.; Huang, C.; Huang, Q.; Yang, J.; Dong, M.; Joo, Y.H.; Duić, N. Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems. J. Mar. Sci. Eng. 2024, 12, 424. https://doi.org/10.3390/jmse12030424
Song D, Shen G, Huang C, Huang Q, Yang J, Dong M, Joo YH, Duić N. Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems. Journal of Marine Science and Engineering. 2024; 12(3):424. https://doi.org/10.3390/jmse12030424
Chicago/Turabian StyleSong, Dongran, Guoyang Shen, Chaoneng Huang, Qian Huang, Jian Yang, Mi Dong, Young Hoon Joo, and Neven Duić. 2024. "Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems" Journal of Marine Science and Engineering 12, no. 3: 424. https://doi.org/10.3390/jmse12030424