A novel switched model predictive control of wind turbines using artificial neural network-Markov chains prediction with load mitigation

Pervez, Mahum; Kamal, Tariq; Fernández‐Ramírez, Luis M.

doi:10.1016/j.asej.2021.09.004

Cited by 14 publications

(4 citation statements)

References 54 publications

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“…To solve these problems and create hybrid control systems for MG management, there is growing interest in merging rule-based control methodologies with deep learning techniques [10]. In order to enable more adaptive and clever control decisions, deep learning models with the ability to comprehend intricate patterns and relationships from historical data include Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNNs) [11]. Hybrid control approaches can potentially improve machine learning systems' efficiency, stability, and resilience by fusing the interpretability of rule-based systems with the learning power of deep neural networks [12,13].…”

Section: Of 23mentioning

confidence: 99%

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Akbulut,

Cavus,

Cengiz

et al. 2024

Preprint

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Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimising MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based-control and deep learning techniques, including gated recurrent units (GRU), basic recurrent neural network (RNN) and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimises MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyper-parameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values.

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Section: Of 23mentioning

confidence: 99%

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Akbulut,

Cavus,

Cengiz

et al. 2024

Preprint

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“…The final steps in the S-MPC are to solve the cost function and obtain "optimal decision variables", as shown in Algorithm 1. After that, the hybrid AR-LSTM method is initiated by configuring the controller model F. The current state X is found using Equation (20), before training the "optimal control decisions". Finally, the control variable U and ŷ are solved by utilizing updated reference R and Equations ( 21) and (22).…”

Section: Formal Definitionmentioning

confidence: 99%

“…The study showcased the viability and efficacy of S-MPC in attaining control objectives for wind turbine systems in real-time, utilizing brief control periods. In addition, the study in [20] presented a novel technique for enhancing wind turbine control by introducing a S-MPC framework. The proposed approach aimed to solve the limitations of the conventional continuous control-based MPC algorithm.…”

Section: Introductionmentioning

confidence: 99%

Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids

Cavus,

Ugurluoglu,

Ayan

et al. 2023

Applied Sciences

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Switched model predictive control (S-MPC) and recurrent neural networks with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for the energy management of microgrids (MGs). These methods ease constraint satisfaction, computational demands, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method dynamically selects optimal models and control strategies based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) control with these powerful control methods improves the prediction accuracy and the adaptability of the system conditions. This paper compares the two control approaches and proposes a novel algorithm called switched auto-regressive neural control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC, while enabling continuous learning. The results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model’s computational time decreased by nearly 47.2%. In addition, this study evaluated our predictive model’s accuracy: (i) the R-squared error was 0.951, indicating a strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions, with minimal deviations from the actual values.

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“…To solve these problems and create hybrid control systems for MG management, there is growing interest in merging rule-based control methodologies with deep learning techniques [11]. In order to enable more adaptive and clever control decisions, deep learning models with the ability to comprehend intricate patterns and relationships from historical data include gated recurrent units (GRUs), long short-term memory (LSTM), and recurrent neural networks (RNNs) [12]. Hybrid control approaches can potentially improve machine learning systems' efficiency, stability, and resilience by fusing the interpretability of rule-based systems with the learning power of deep neural networks [13].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Akbulut,

Cavus,

Cengiz

et al. 2024

Energies

View full text Add to dashboard Cite

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.

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A novel switched model predictive control of wind turbines using artificial neural network-Markov chains prediction with load mitigation

Cited by 14 publications

References 54 publications

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

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