Wave Energy Converter Predictive Control Maximizing Power Output
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Solution Overview
Problem
Existing wave energy conversion systems do not optimally account for energy losses during conversion and wave motion prediction, leading to suboptimal energy recovery.
Innovation Solution
A predictive control method is employed that constructs dynamic and energy models to maximize power output by predicting wave forces and accounting for energy conversion efficiency, using sensors and Kalman filters to determine optimal control values for the energy conversion machine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional control methods are used without wave motion prediction and energy loss accounting, then the control system is simpler, but the energy recovery is suboptimal
Solution Approach 1:
The control system performs preliminary wave motion prediction using a prediction algorithm to forecast future wave conditions. This allows the system to prepare optimal control actions in advance, maximizing energy recovery before the waves arrive, rather than reacting to past or present conditions alone.
Solution Approach 2:
The system implements a closed-loop feedback control where the actual wave measurements and system response are continuously fed back to the controller. This feedback is combined with the prediction algorithm to adjust control actions in real-time, optimizing energy recovery while accounting for actual system performance and energy losses.
2Power
If energy conversion losses are not accounted for in the control method, then the control algorithm is simpler, but the power output is not maximized
Solution Approach 1:
The control system incorporates energy conversion efficiency as a variable parameter in the optimization function. By expressing power output as a function of control forces and efficiency parameters, the system dynamically adjusts control actions to account for energy losses, maximizing actual power output rather than theoretical maximum.
Solution Approach 2:
An energy model acts as an intermediary between the physical wave energy conversion process and the control algorithm. This model translates physical parameters (forces, velocities, efficiency) into a form that the controller can use for optimization, bridging the gap between physical reality and control decisions.
3Productivity
If wave motion prediction is not used, then the control system is less complex, but the energy conversion is not optimized
Solution Approach 1:
The control system performs preliminary wave motion prediction using a prediction algorithm to forecast future wave conditions. This allows the system to prepare optimal control actions in advance, maximizing energy recovery before the waves arrive, rather than reacting to past or present conditions alone.
Solution Approach 2:
The system implements a closed-loop feedback control where the actual wave measurements and system response are continuously fed back to the controller. This feedback is combined with the prediction algorithm to adjust control actions in real-time, optimizing energy recovery while accounting for actual system performance and energy losses.
Data Source
AI summary
The invention is an improved wave energy conversion system (1, 2) including a model predictive control method for an energy conversion machine (1) that maximizes the power output by accounting for the energy conversion efficiency and prediction of a wave (3).