Performance of a Wave-Energy-Converter Array Operating Under Model-Predictive Control Based on a Convex Formulation

2018 ◽  
Author(s):  
Qian Zhong ◽  
Ronald W. Yeung
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Reactive Power ◽  
Optimal Solution ◽  
Energy Output ◽  
Control Input ◽  
Energy Extraction ◽  
Power Performance ◽  
Point Absorbers

Economics decision drives the operation of ocean-wave energy converters (WEC) to be in a “farm mode”. Control strategy developed for a WEC array will be of high importance for improving the aggregate energy extraction efficiency of the whole system. Model-predictive control (MPC) has shown its strong potential in maximizing the energy output in devices with hard constraints on operation states and machinery inputs (See Ref. [1–3]). Computational demands for using MPC to control an array in real time can be prohibitive. In this paper, we formulate the MPC to control an array of heaving point absorbers, by recasting the optimization problem for energy extraction into a convex Quadratic Programming (QP) problem, the solution of which can be carried out very efficiently. Large slew rates are to be penalized, which can also guarantee the convexity of the QP and improve the computational efficiency for achieving the optimal solution. Constraints on both the states and the control input can be accommodated in this MPC method. Full hydro-dynamic interference effects among the WEC array components are taken into account using the theory developed in [4]. Demonstrative results of the application are presented for arrays of two, three, and four point absorbers operating at different incident-wave angles. Effects of the interacting waves on power performance of the array under the new MPC control are investigated, with simulations conducted in both regular and irregular seas. Heaving motions of individual devices at their optimal conditions are shown. Also presented is the reactive power required by the power takeoff (PTO) system of the array to achieve optimality. We are pleased to contribute this article in celebration of our collegiality with Professor Bernard Molin on the occasion of his honoring symposium.

An Efficient Convex Formulation for Model-Predictive Control on Wave-Energy Converters

2017 ◽  
Author(s):  
Qian Zhong ◽  
Ronald W. Yeung
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Optimization Problem ◽  
Reactive Power ◽  
Tuning Parameter ◽  
Primary Concern ◽  
Penalty Term ◽  
Wave Energy Converters ◽  
Energy Converters

Model-Predictive Control (MPC) has shown its strong potential in maximizing energy extraction for Wave-Energy Converters (WECs) while handling hard constraints. As MPC can solve the optimization problem on-line, it can better account for state changes and reject disturbances from the harsh sea environment. Interests have arisen in applying MPC to an array of WECs, since researchers found that multiple small-size WECs are more economically viable than a single large-size WEC. However, the computational demand is known to be a primary concern for applying MPC in real-time, which can determine the feasibility of such a controller, particularly when it comes to controlling an array of absorbers. In this paper, we construct a cost function and cast the problem into a Quadratic Programming (QP) with the machinery force being the “optimizer,” for which the convexity can be guaranteed by introducing a penalty term on the slew rate of the machinery force. The optimization problem can then be solved efficiently, and a feasible solution will be assured as the global optima. Constraints on the motion of the WEC and the machinery force will be taken into account. The current MPC will be compared to others existing in literature, including a nonlinear MPC [1] which has been applied in wave-tank tests. The effects of constraints on the control law and the absorbed power are investigated. Performances of the WEC are shown for both regular and irregular wave conditions. The current MPC is found to have good energy-capture capability and is able to broaden the band-width for capturing wave energy. The reactive power required by the PTO system is presented. The additional penalty term provides a tuning parameter, of which the effects on the MPC performance and the reactive power requirement are discussed.

scholarly journals Convergence Guaranteed Nonlinear Constraint Model Predictive Control via I/O Linearization

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Xiaobing Kong ◽  
Xiangjie Liu ◽  
Xiuming Yao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Output Feedback ◽  
Feedback Linearization ◽  
Optimal Solution ◽  
Quadratic Program ◽  
Control Input ◽  
Input Output ◽  
Nonlinear Constraint ◽  
Constraint Model

Constituting reliable optimal solution is a key issue for the nonlinear constrained model predictive control. Input-output feedback linearization is a popular method in nonlinear control. By using an input-output feedback linearizing controller, the original linear input constraints will change to nonlinear constraints and sometimes the constraints are state dependent. This paper presents an iterative quadratic program (IQP) routine on the continuous-time system. To guarantee its convergence, another iterative approach is incorporated. The proposed algorithm can reach a feasible solution over the entire prediction horizon. Simulation results on both a numerical example and the continuous stirred tank reactors (CSTR) demonstrate the effectiveness of the proposed method.

An Efficient Convex Formulation for Model-Predictive Control on Wave-Energy Converters1

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Qian Zhong ◽  
Ronald W. Yeung
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Predictive Control ◽  
Wave Energy ◽  
Reactive Power ◽  
Primary Concern ◽  
Penalty Term ◽  
Irregular Wave ◽  
Wave Energy Converters ◽  
Hard Constraints

Model-predictive control (MPC) has shown its strong potential in maximizing energy extraction for wave-energy converters (WECs) while handling hard constraints. However, the computational demand is known to be a primary concern for applying MPC in real time. In this work, we develop a cost function in which a penalty term on the slew rate of the machinery force is introduced and used to ensure the convexity of the cost function. Constraints on states and the input are incorporated. Such a constrained optimization problem is cast into a Quadratic Programming (QP) form and efficiently solved by a standard QP solver. The current MPC is found to have good energy-capture capability in both regular and irregular wave conditions, and is able to broaden favorably the bandwidth for capturing wave energy compared to other controllers in the literature. Reactive power required by the power-take-off (PTO) system is presented. The effects of the additional penalty term are discussed.

scholarly journals Passive Model Predictive Control on a Two-Body Self-Referenced Point Absorber Wave Energy Converter

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1731
Author(s):  
Dan Montoya ◽  
Elisabetta Tedeschi ◽  
Luca Castellini ◽  
Tiago Martins
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Power Flow ◽  
Linear Constraints ◽  
Wave Energy Converter ◽  
Control Technique ◽  
Computational Time ◽  
Energy Converter ◽  
Point Absorber

Wave energy is nowadays one of the most promising renewable energy sources; however, wave energy technology has not reached the fully-commercial stage, yet. One key aspect to achieve this goal is to identify an effective control strategy for each selected Wave Energy Converter (WEC), in order to extract the maximum energy from the waves, while respecting the physical constraints of the device. Model Predictive Control (MPC) can inherently satisfy these requirements. Generally, MPC is formulated as a quadratic programming problem with linear constraints (e.g., on position, speed and Power Take-Off (PTO) force). Since, in the most general case, this control technique requires bidirectional power flow between the PTO system and the grid, it has similar characteristics as reactive control. This means that, under some operating conditions, the energy losses may be equivalent, or even larger, than the energy yielded. As many WECs are designed to only allow unidirectional power flow, it is necessary to set nonlinear constraints. This makes the optimization problem significantly more expensive in terms of computational time. This work proposes two MPC control strategies applied to a two-body point absorber that address this issue from two different perspectives: (a) adapting the MPC formulation to passive loading strategy; and (b) adapting linear constraints in the MPC in order to only allow an unidirectional power flow. The results show that the two alternative proposals have similar performance in terms of computational time compared to the regular MPC and obtain considerably more power than the linear passive control, thus proving to be a good option for unidirectional PTO systems.

scholarly journals Model Predictive Control for Wave Energy Converters: A Moving Window Blocking Approach

2020 ◽  
Vol 53 (2) ◽  
pp. 12815-12821
Author(s):  
Juan Guerrero-Fernández ◽  
Oscar J. González-Villarreal ◽  
John Anthony Rossiter ◽  
Bryn Jones
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Moving Window ◽  
Wave Energy Converters ◽  
Energy Converters

scholarly journals Hardware-in-the-Loop Validation of Model Predictive Control of a Discrete Fluid Power Power Take-Off System for Wave Energy Converters

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3668
Author(s):  
Anders H. Hansen ◽  
Magnus F. Asmussen ◽  
Michael M. Bech
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Fluid Power ◽  
Wave Power ◽  
Reactive Control ◽  
Power Extraction ◽  
Wave Energy Converters ◽  
Extraction Algorithm ◽  
Energy Converters

Model predictive control based wave power extraction algorithms have been developed and found promising for wave energy converters. Although mostly proven by simulation studies, model predictive control based algorithms have shown to outperform classical wave power extraction algorithms such as linear damping and reactive control. Prediction models and objective functions have, however, often been simplified a lot by for example, excluding power take-off system losses. Furthermore, discrete fluid power forces systems has never been validated experimentally in published research. In this paper a model predictive control based wave power extraction algorithm is designed for a discrete fluid power power take-off system. The loss models included in the objective function are based on physical models of the losses associated with discrete force shifts and throttling. The developed wave power extraction algorithm directly includes the quantized force output and the losses models of the discrete fluid power system. The experimental validation of the wave power extraction algorithm developed in the paper shown an increase of 14.6% in yearly harvested energy when compared to a reactive control algorithm.

Finite Control Set Model Predictive Control for Dynamic Reactive Power Compensation With Hybrid Active Power Filters

2018 ◽  
Vol 65 (3) ◽  
pp. 2608-2617 ◽  
Author(s):  
Silvia Costa Ferreira ◽  
Robson Bauwlez Gonzatti ◽  
Rondineli Rodrigues Pereira ◽  
Carlos Henrique da Silva ◽  
L. E. Borges da Silva ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Reactive Power ◽  
Reactive Power Compensation ◽  
Active Power ◽  
Active Power Filters ◽  
Power Filters ◽  
Finite Control ◽  
Control Set

ON MODEL PREDICTIVE CONTROL OF QUASI-RESONANT CONVERTERS

2009 ◽  
Vol 18 (07) ◽  
pp. 1167-1183 ◽  
Author(s):  
FARZAD TAHAMI ◽  
MEHDI EBAD
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Models ◽  
Control Strategies ◽  
Sampling Interval ◽  
Control Synthesis ◽  
Resonant Converters ◽  
Control Input ◽  
Model Approximation ◽  
Dynamic Matrix

In this paper, different model predictive control synthesis frameworks are examined for DC–DC quasi-resonant converters in order to achieve stability and desired performance. The performances of model predictive control strategies which make use of different forms of linearized models are compared. These linear models are ranging from a simple fixed model, linearized about a reference steady state to a weighted sum of different local models called multi model predictive control. A more complicated choice is represented by the extended dynamic matrix control in which the control input is determined based on the local linear model approximation of the system that is updated during each sampling interval, by making use of a nonlinear model. In this paper, by using and comparing these methods, a new control scheme for quasi-resonant converters is described. The proposed control strategy is applied to a typical half-wave zero-current switching QRC. Simulation results show an excellent transient response and a good tracking for a wide operating range and uncertainties in modeling.

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