Control of a Relocatable Energy-Harvesting Autonomous Underwater Vehicle in a Spatiotemporally-Varying Gulf Stream Resource

Bin-Karim, Shamir; Muglia, Mike; Mazzoleni, Andre P.; Vermillion, Chris

doi:10.23919/acc.2018.8431318

“…MPC is a powerful method for optimizing the objective function by predicting the future actions. The MPC-based algorithm has been used for maximizing the output power of an airborne system [15], maximizing the output power of an autonomous underwater vehicle [37], and minimizing the operational cost of wind turbine considering the fault-tolerant method [38]. In this paper, MPC method is used in order to maximize the output power of the OCT system.…”

Section: B Mpc-based Optimization Designmentioning

confidence: 99%

“…Further, a permanent magnet synchronous generator is controlled through an adaptive supertwisting sliding mode algorithm to assure stable operation of the OCT under stochastic ocean velocity [23]. An estimator is designed to predict the spatially dependent velocity of ocean, and a MPC-based controller is used to optimize the location of an underwater vehicle used as an energy harvesting system [24]. A Bayesian optimization is used to specify the configuration and location to harvest maximum energy from an OCT array [25].…”

Section: Introductionmentioning

confidence: 99%

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Tang¹

2020

Preprint

0

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This paper presents a novel spatiotemporal optimization approach for maximizing the output power of an ocean current turbine (OCT) under uncertain ocean velocities. In order to determine output power, ocean velocities and the power consumed and generated by an OCT system are modeled. The stochastic behavior of ocean velocities is a function of time and location, which is modeled as a Gaussian process. The power of the OCT system is composed of three parts, including generated power, power for maintaining the system at an operating depth, and power consumed for changing the water depth to reach the maximum power. Two different algorithms, including model predictive control (MPC) as a model-based method and reinforcement learning (RL) as a learning-based method, are proposed to design the optimization structure, and comparative studies are presented. On one hand, the MPC based controller is faster in finding the optimal water depth, while the RL is also computationally feasible considering the required time for changing operating depth. On the other hand, the cumulative energy production of the RL algorithm is higher than the MPC method, which verifies that the learning-based RL algorithm can provide a better solution to address the uncertainties in renewable energy systems. Results verify the efficiency of both presented methods in maximizing the total power of an OCT system, where the total harnessed energy after 200 hours shows an over 18% increase compared to the baseline.

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“…The applications of the spatiotemporal optimization for maximizing output power of the OCT system are verified through three comparative approaches as follows: Case without Spatiotemporal Optimization: In this case, the OCT system is located in a constant depth, so there is no change in operating depth, i.e., the output power of the system is determined only through variations in the ocean velocity at the operating depth. Spatiotemporal Optimization by MPC based Algorithm: In this algorithm, the optimal water depth z is determined using objective function J(z(p)) in (24), which aims to minimize the difference between the maximum power (i.e., rated power) and the harvested power at each time step. Further, predicting the ocean velocity results in uncertainties, which is addressed through the exploration term J exploration of the objective function.…”

Section: A Simulation Setupmentioning

confidence: 99%

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Tang¹

2020

Preprint

1

0

View full text Add to dashboard Cite

This paper presents a novel spatiotemporal optimization approach for maximizing the output power of an ocean current turbine (OCT) under uncertain ocean velocities. In order to determine output power, ocean velocities and the power consumed and generated by an OCT system are modeled. The stochastic behavior of ocean velocities is a function of time and location, which is modeled as a Gaussian process. The power of the OCT system is composed of three parts, including generated power, power for maintaining the system at an operating depth, and power consumed for changing the water depth to reach the maximum power. Two different algorithms, including model predictive control (MPC) as a model-based method and reinforcement learning (RL) as a learning-based method, are proposed to design the optimization structure, and comparative studies are presented. On one hand, the MPC based controller is faster in finding the optimal water depth, while the RL is also computationally feasible considering the required time for changing operating depth. On the other hand, the cumulative energy production of the RL algorithm is higher than the MPC method, which verifies that the learning-based RL algorithm can provide a better solution to address the uncertainties in renewable energy systems. Results verify the efficiency of both presented methods in maximizing the total power of an OCT system, where the total harnessed energy after 200 hours shows an over 18% increase compared to the baseline.

show abstract

“…MPC is a powerful method for optimizing the objective function by predicting the future actions. The MPC-based algorithm was used to maximize the output power of an airborne system [12], maximize the output power of an autonomous underwater vehicle [21], and minimize the operational cost of wind turbine considering the fault-tolerant method [11]. In this paper, MPC is used in order to maximize the output power of the OCT system.…”

Section: B Mpc-based Optimization Designmentioning

confidence: 99%

“…Further, a permanent magnet synchronous generator has been controlled through an adaptive super-twisting sliding mode algorithm to assure stable operation of the OCT under stochastic ocean velocity [20]. An estimator has been designed to predict the spatially dependent velocity of ocean, and a MPC-based controller has been used to optimize the location of an underwater vehicle used as an energy harvesting system [21]. A Bayesian optimization has been used to specify the configuration and location to harvest maximum energy from an OCT array [22]; however, the model has been focused on the economic side; a more realistic dynamic model is desired.…”

Section: Introductionmentioning

confidence: 99%

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Tang¹

2020

Preprint

1

0

View full text Add to dashboard Cite

This paper presents a novel spatiotemporal optimization approach for maximizing the output power of an ocean current turbine (OCT) under uncertain ocean velocities. In order to determine output power, ocean velocities and the power consumed and generated by an OCT system are modeled. The stochastic behavior of ocean velocities is a function of time and location, which is modeled as a Gaussian process. The power of the OCT system is composed of three parts, including generated power, power for maintaining the system at an operating depth, and power consumed for changing the water depth to reach the maximum power. Two different algorithms, including model predictive control (MPC) as a model-based method and reinforcement learning (RL) as a learning-based method, are proposed to design the optimization structure, and comparative studies are presented. On one hand, the MPC based controller is faster in finding the optimal water depth, while the RL is also computationally feasible considering the required time for changing operating depth. On the other hand, the cumulative energy production of the RL algorithm is higher than the MPC method, which verifies that the learning-based RL algorithm can provide a better solution to address the uncertainties in renewable energy systems. Results verify the efficiency of both presented methods in maximizing the total power of an OCT system, where the total harnessed energy after 200 hours shows an over 18% increase compared to the baseline.

show abstract

Control of a Relocatable Energy-Harvesting Autonomous Underwater Vehicle in a Spatiotemporally-Varying Gulf Stream Resource

Cited by 10 publications

References 14 publications

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

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