SK
S. Kronemeijer
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Optimal Control Strategy for the DOT500PRO Wind Turbine System
Using Data-Driven Wind Prediction and Comparing Control Strategies to Maximise Revenue
This thesis aims to develop an optimal control strategy for the DOT 500kW Pilot Reverse Osmosis (DOT500PRO) turbine system. The system integrates a 500 kW wind turbine with a reverse osmosis (RO) module to produce freshwater. The primary goal is to maximise revenue generation by optimising the turbine’s state transitions based on wind predictions.
The thesis begins with an analysis of the DOT500PRO and its state machine, identifying operational states, transitions, and constraints. A Markov model is used to model and predict wind speeds, which fits nicely with the Markov Decision Process (MDP) framework. The problem is formulated as an MDP, and multiple control strategies, including Threshold Control, Model Predictive Control (MPC), Stochastic Dynamic Programming (SDP), and Approximate Dynamic Programming (ADP), are evaluated.
MPC is found to be computationally intensive, making it less feasible for real-time control. SDP shows promising results, but is limited by the curse of dimensionality, restricting the use to higher order models. ADP, which approximates SDP solutions, can offer a potential controller for higher order models but requires further tuning and optimisation.
Simulations are conducted to compare the performance of these control strategies in several scenarios. While SDP demonstrates slight improvements over threshold control on the training dataset, its performance on different wind patterns is less consistent. The study concludes that while proactive control strategies such as SDP and ADP can offer improvements over reactive methods, their performance is dependent on the accuracy of wind predictions and the specific operational conditions.
Future work suggestions include refining the turbine and wind models, exploring adaptive control methods, and conducting real-life experiments to validate the control strategies, which are crucial for practical implementation and optimisation. ...
The thesis begins with an analysis of the DOT500PRO and its state machine, identifying operational states, transitions, and constraints. A Markov model is used to model and predict wind speeds, which fits nicely with the Markov Decision Process (MDP) framework. The problem is formulated as an MDP, and multiple control strategies, including Threshold Control, Model Predictive Control (MPC), Stochastic Dynamic Programming (SDP), and Approximate Dynamic Programming (ADP), are evaluated.
MPC is found to be computationally intensive, making it less feasible for real-time control. SDP shows promising results, but is limited by the curse of dimensionality, restricting the use to higher order models. ADP, which approximates SDP solutions, can offer a potential controller for higher order models but requires further tuning and optimisation.
Simulations are conducted to compare the performance of these control strategies in several scenarios. While SDP demonstrates slight improvements over threshold control on the training dataset, its performance on different wind patterns is less consistent. The study concludes that while proactive control strategies such as SDP and ADP can offer improvements over reactive methods, their performance is dependent on the accuracy of wind predictions and the specific operational conditions.
Future work suggestions include refining the turbine and wind models, exploring adaptive control methods, and conducting real-life experiments to validate the control strategies, which are crucial for practical implementation and optimisation. ...
This thesis aims to develop an optimal control strategy for the DOT 500kW Pilot Reverse Osmosis (DOT500PRO) turbine system. The system integrates a 500 kW wind turbine with a reverse osmosis (RO) module to produce freshwater. The primary goal is to maximise revenue generation by optimising the turbine’s state transitions based on wind predictions.
The thesis begins with an analysis of the DOT500PRO and its state machine, identifying operational states, transitions, and constraints. A Markov model is used to model and predict wind speeds, which fits nicely with the Markov Decision Process (MDP) framework. The problem is formulated as an MDP, and multiple control strategies, including Threshold Control, Model Predictive Control (MPC), Stochastic Dynamic Programming (SDP), and Approximate Dynamic Programming (ADP), are evaluated.
MPC is found to be computationally intensive, making it less feasible for real-time control. SDP shows promising results, but is limited by the curse of dimensionality, restricting the use to higher order models. ADP, which approximates SDP solutions, can offer a potential controller for higher order models but requires further tuning and optimisation.
Simulations are conducted to compare the performance of these control strategies in several scenarios. While SDP demonstrates slight improvements over threshold control on the training dataset, its performance on different wind patterns is less consistent. The study concludes that while proactive control strategies such as SDP and ADP can offer improvements over reactive methods, their performance is dependent on the accuracy of wind predictions and the specific operational conditions.
Future work suggestions include refining the turbine and wind models, exploring adaptive control methods, and conducting real-life experiments to validate the control strategies, which are crucial for practical implementation and optimisation.
The thesis begins with an analysis of the DOT500PRO and its state machine, identifying operational states, transitions, and constraints. A Markov model is used to model and predict wind speeds, which fits nicely with the Markov Decision Process (MDP) framework. The problem is formulated as an MDP, and multiple control strategies, including Threshold Control, Model Predictive Control (MPC), Stochastic Dynamic Programming (SDP), and Approximate Dynamic Programming (ADP), are evaluated.
MPC is found to be computationally intensive, making it less feasible for real-time control. SDP shows promising results, but is limited by the curse of dimensionality, restricting the use to higher order models. ADP, which approximates SDP solutions, can offer a potential controller for higher order models but requires further tuning and optimisation.
Simulations are conducted to compare the performance of these control strategies in several scenarios. While SDP demonstrates slight improvements over threshold control on the training dataset, its performance on different wind patterns is less consistent. The study concludes that while proactive control strategies such as SDP and ADP can offer improvements over reactive methods, their performance is dependent on the accuracy of wind predictions and the specific operational conditions.
Future work suggestions include refining the turbine and wind models, exploring adaptive control methods, and conducting real-life experiments to validate the control strategies, which are crucial for practical implementation and optimisation.
The world’s reliability on the Global Position System (GPS) is experiencing more and more vulnerabilities. Not just environmental factors are responsible for GPS inaccuracies, but blocking or spoofing location signals has become more commonly available worldwide. This raises interest in other methods to navigate without 3rd party connections. Using an Inertial Navigation System (INS) is such a method. In this paper an algorithm is created based on the sensor fusion input of acceleration and orientation data. In order to correct itself from integration drift the algorithm makes use of Kalman filtering. Multiple simulations and real-world experiments have been done with the use of a tablet. This has promising results in the computer simulations but showcases some real difficulties when used in practice. This report gives a basis of research in this field, and many more recommendations of extensions are made.
...
The world’s reliability on the Global Position System (GPS) is experiencing more and more vulnerabilities. Not just environmental factors are responsible for GPS inaccuracies, but blocking or spoofing location signals has become more commonly available worldwide. This raises interest in other methods to navigate without 3rd party connections. Using an Inertial Navigation System (INS) is such a method. In this paper an algorithm is created based on the sensor fusion input of acceleration and orientation data. In order to correct itself from integration drift the algorithm makes use of Kalman filtering. Multiple simulations and real-world experiments have been done with the use of a tablet. This has promising results in the computer simulations but showcases some real difficulties when used in practice. This report gives a basis of research in this field, and many more recommendations of extensions are made.