WO2021214715A1 - Générateur d'énergie houlomotrice doté d'un dispositif de commande artificiellement intelligent - Google Patents

Générateur d'énergie houlomotrice doté d'un dispositif de commande artificiellement intelligent Download PDF

Info

Publication number
WO2021214715A1
WO2021214715A1 PCT/IB2021/053346 IB2021053346W WO2021214715A1 WO 2021214715 A1 WO2021214715 A1 WO 2021214715A1 IB 2021053346 W IB2021053346 W IB 2021053346W WO 2021214715 A1 WO2021214715 A1 WO 2021214715A1
Authority
WO
WIPO (PCT)
Prior art keywords
mass
speed
generator
support surface
wave power
Prior art date
Application number
PCT/IB2021/053346
Other languages
English (en)
Inventor
Murad AL-SHIBLI
Original Assignee
Abu Dhabi Polytechnic
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US16/857,153 external-priority patent/US10815961B2/en
Application filed by Abu Dhabi Polytechnic filed Critical Abu Dhabi Polytechnic
Publication of WO2021214715A1 publication Critical patent/WO2021214715A1/fr

Links

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02K—DYNAMO-ELECTRIC MACHINES
    • H02K7/00—Arrangements for handling mechanical energy structurally associated with dynamo-electric machines, e.g. structural association with mechanical driving motors or auxiliary dynamo-electric machines
    • H02K7/18—Structural association of electric generators with mechanical driving motors, e.g. with turbines
    • H02K7/1869—Linear generators; sectional generators
    • H02K7/1876—Linear generators; sectional generators with reciprocating, linearly oscillating or vibrating parts
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03B—MACHINES OR ENGINES FOR LIQUIDS
    • F03B13/00—Adaptations of machines or engines for special use; Combinations of machines or engines with driving or driven apparatus; Power stations or aggregates
    • F03B13/12—Adaptations of machines or engines for special use; Combinations of machines or engines with driving or driven apparatus; Power stations or aggregates characterised by using wave or tide energy
    • F03B13/14—Adaptations of machines or engines for special use; Combinations of machines or engines with driving or driven apparatus; Power stations or aggregates characterised by using wave or tide energy using wave energy
    • F03B13/16—Adaptations of machines or engines for special use; Combinations of machines or engines with driving or driven apparatus; Power stations or aggregates characterised by using wave or tide energy using wave energy using the relative movement between a wave-operated member, i.e. a "wom" and another member, i.e. a reaction member or "rem"
    • F03B13/20—Adaptations of machines or engines for special use; Combinations of machines or engines with driving or driven apparatus; Power stations or aggregates characterised by using wave or tide energy using wave energy using the relative movement between a wave-operated member, i.e. a "wom" and another member, i.e. a reaction member or "rem" wherein both members, i.e. wom and rem are movable relative to the sea bed or shore
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03B—MACHINES OR ENGINES FOR LIQUIDS
    • F03B15/00—Controlling
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H02P23/0004—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P23/0018—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using neural networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P9/00—Arrangements for controlling electric generators for the purpose of obtaining a desired output
    • H02P9/02—Details of the control
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2220/00—Application
    • F05B2220/70—Application in combination with
    • F05B2220/706—Application in combination with an electrical generator
    • F05B2220/707—Application in combination with an electrical generator of the linear type
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/10—Purpose of the control system
    • F05B2270/20—Purpose of the control system to optimise the performance of a machine
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/30—Energy from the sea, e.g. using wave energy or salinity gradient

Definitions

  • the disclosure of the present patent application relates to wave-based power production, and particularly to an ocean wave power generator with artificially intelligent controller that is based on a two-body mass-spring-damper system, the artificially intelligent controller optimizing power output.
  • Fig. 2 illustrates a conventional wave power generator 100 based on a two- body mass-spring-damper system.
  • the wave power generator 100 includes a first mass 112, having a mass m 1 , and a second mass 114, having a mass m 2 .
  • a first spring 116 having a spring constant k 1 , resiliently couples the first mass 112 to the second mass 114.
  • a first damper 118 having a damping constant b 1 , joins the first mass 112 and the second mass 114 for damping relative oscillation between the two masses.
  • a second spring 120 having a spring constant k 2 , resiliently couples the second mass 114 to a support surface S, such as the ground or a floor.
  • a second damper 122 having a damping constant b 2 Joins the second mass 114 and the support surface S for damping relative oscillation between the second mass 114 and the support surface S.
  • a linear generator 124 is mounted on the support surface S and is coupled to the second mass 114, such that the relative oscillation between the second mass 114 and the support surface S drives the linear generator 124 to generate power.
  • the modelling of equations (1) and (2) represents a coupled second order dynamical system with an external wave input force acting on the upper mass 112. Since the linear generator 124 is attached to the lower mass 114, the motion of the lower mass 114 is of interest with regard to the desired output.
  • the overall dynamical model of the lower platform can be formulated in the Laplace domain by equation (3) below:
  • T 2 (s) represents the transformed vertical displacement of the second mass 114
  • ,v is the transformation parameter
  • the ocean wave power generator with an artificially intelligent controller is a wave power generator based on a two-body mass-spring-damper system, including a first mass, a second mass, a first spring resiliently coupling the first mass to the second mass, and a first damper joining the first mass and the second mass for damping relative oscillation between the two masses.
  • the ocean wave power generator with an artificially intelligent controller includes a second spring resiliently coupling the second mass to a support surface, such as the ground or a floor, a second damper joining the second mass and the support surface for damping relative oscillation between the second mass and the support surface, and a linear generator mounted on the support surface and coupled to the second mass, such that relative oscillation between the second mass and the support surface drives the linear generator to generate power.
  • the ocean wave power generator with an artificially intelligent controller also includes a linear actuator coupled to the second mass, a first motion sensor for detecting the position and speed of the first mass, and a second motion sensor for detecting the position and speed of the second mass.
  • the maximum power output of the linear generator is determined based on the position and the speed of the first mass, and an ideal position and an ideal speed of the second mass, corresponding to the maximum power output of the linear generator and the position and the speed of the first mass, is determined.
  • the position and speed of the second mass are adjusted with the linear actuator to match the ideal position and ideal speed of the second mass.
  • the maximum power output of the linear generator and the ideal position and ideal speed of the second mass are determined from a lookup table, which is generated using an artificial intelligence model of the ocean wave power generator, which may be modeled using a nonlinear autoregressive exogenous neural network (NARX-NN), for example.
  • NARX-NN nonlinear autoregressive exogenous neural network
  • Fig. 1 is a block diagram of an ocean wave power generator with an artificially intelligent controller.
  • Fig. 2 is a diagram of a conventional prior art wave power generator based on a two-body mass-spring-damper system.
  • Fig. 3 is a waveform diagram showing the training results of a nonlinear autoregressive exogenous neural network (NARX-NN) of the ocean wave power generator with an artificially intelligent controller, particularly showing an output voltage response (top) and mean square error (MSE) performance (bottom).
  • NARX-NN nonlinear autoregressive exogenous neural network
  • MSE mean square error
  • Fig. 4 is a plot of voltage vs. frequency showing electromotive voltage results produced by a linear generator similar to that used in the ocean wave power generator with an artificially intelligent controller.
  • Figs. 5 A, 5B, 5C, and 5D show the NARX-NN regression maximum using a log scale.
  • Fig. 6 is a graph comparing the MSE of training, validation and testing data sets of the NARX-NN.
  • Fig. 7 is an error histogram of the training, validation and testing data sets at maximum epoch 1000.
  • Fig. 8 is waveform diagrams showing the NARX-NN training state reaching maximum epoch 1000.
  • Fig. 9 is a chart showing training auto-correlation with a time varying lag at maximum epoch 1000 for the NARX-NN.
  • Fig. 10 is a diagram showing training input-error cross-correlation with a time varying lag at maximum epoch 1000 for the NARX-NN.
  • Fig. 11 is a plot showing the training results of a closed-loop NARX-NN model of the ocean wave power generator with an artificially intelligent controller, including the output voltage response (top) and the MSE performance (bottom).
  • the ocean wave power generator with an artificially intelligent controller is similar to the conventional wave power generator 100 of Fig. 2, including a first mass 12 having a mass m 1 , a second mass 14 having a mass m 2 , a first spring 16 having a spring constant k 1 resiliently coupling the first mass 12 to the second mass 14, and a first damper 18 having a damping constant b 1 joining the first mass 12 and the second mass 14 for damping relative oscillation between the two masses.
  • the ocean wave power generator 10 of Fig. 1 also includes a linear actuator 26 coupled to the second mass 14, a first motion sensor 28 for detecting the position and speed of the first mass 12, and a second motion sensor 30 for detecting the position and speed of the second mass 14.
  • a controller 32 which may be a personal computer, programmable logic controller, microprocessor or the like, receives the position and the speed of the first mass 12 from the first motion sensor 28 and the position and the speed of the second mass 14 from the second motion sensor 30.
  • the controller 32 is configured to output a driving signal to linear actuator 26 to drive oscillatory motion of the second mass 14 to optimize the power output of the linear generator 24 based on the position and the speed of the first mass 12 and the position and the speed of the second mass 14.
  • the linear generator 24 may be any suitable type of generator for converting oscillatory motion into usable electrical power.
  • the linear generator 24 may comprise or consist of a conductive coil mounted on the support surface S with a magnetic rod secured to the second mass 14 traveling through the coil in an oscillatory manner as the second mass 14 oscillates.
  • V —NBvAL
  • N the number of coil loops
  • B the magnetic field strength
  • v the instantaneous velocity of the magnetic rod (which would be equal, in this case, to the instantaneous velocity of the second mass 14, as measured by the motion sensor 30)
  • AL the distance traveled by the magnetic rod within the coil (which would be equal, in this case, to the vertical displacement of the second mass 14, also measured by the motion sensor 30).
  • equations (1) and (2) above as well as Faraday’s law, it is possible to model the power output of the linear generator 24.
  • an artificial intelligence such as a neural network
  • a neural network may be used to produce a lookup table for all possible values of position and speed (or, equivalently, amplitude and frequency) of the first mass 12 and the second mass 14.
  • NARX-NN nonlinear autoregressive exogenous neural network
  • the NARX-NN 34 produces a lookup table of modeled power outputs of the linear generator 24 for each possible value of position and speed of the first mass 12 and the second mass 14.
  • the first motion sensor 28 measures the real-time position and speed of the first mass 28
  • the second motion sensor 30 measures the real-time position and speed of the second mass 30. These values are fed to the controller 32, which receives the lookup table from NARX-NN 34, and for the measured position and speed of first mass 12, the ideal position and speed of the second mass 14, which would produce the maximum power output, is determined.
  • the controller 32 sends a driving signal to the linear actuator 26 to either augment or dampen the motion of the second mass 14 (i.e., to either add or subtract from the present position and speed of the second mass 14) to match the ideal position and speed of the second mass 14 from the lookup table.
  • This process is continuous, continuously measuring the position and speed of the first and second masses 12, 14 to provide continuous optimizing augmentation or dampening of the second mass 14 to maximize the power output of the linear generator 24.
  • the linear actuator 26 may be any suitable type of linear actuator capable of instantaneously controlling the position and speed (or, equivalently, the amplitude and frequency) of the second mass 14.
  • the neural network s performance for three sets of data (training, validation and testing) is shown in Figure 6.
  • the results show high accuracy training, with the best validation performance of l.lxlO 8 at epoch 1000.
  • the NN training state and error histogram are shown reaching the maximum epoch at 1000 in Figs. 7 and 8.
  • the correlation of error of the trained NN of the ocean wave power generator 10 as a function of time, with errors over varying lags are displayed in Fig. 9.
  • the error correlation results with respect to the NN inputs and varying lag fall within the confidence limits, as illustrated in Fig. 10.
  • Training of the NARX-NN in closed loop form may be performed given initial voltage outputs, so that the NN uses its own predicted voltages recursively to predict new values.
  • the results shows a good fit between the predicted and actual responses, but with non perfect errors, as shown in Fig. 11. As shown, it took the system more than 120 seconds before the good match starts to separate.

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Other Liquid Machine Or Engine Such As Wave Power Use (AREA)

Abstract

Le générateur d'énergie houlomotrice doté d'un dispositif de commande artificiellement intelligent (10) est un générateur d'énergie houlomotrice fondé sur un système amortisseur à ressort de masse à deux corps, comprenant une première masse (12), une seconde masse (14), et un générateur linéaire (24) accouplé à la seconde masse (14). Un actionneur linéaire (26) est accouplé à la seconde masse (14), et des premier et second capteurs de mouvement (28, 30) sont positionnés pour détecter la position et la vitesse des première et seconde masses (12, 14). La sortie de puissance maximale du générateur linéaire (24) est déterminée, et une position idéale et une vitesse idéale de la seconde masse (14), correspondant à la sortie de puissance maximale du générateur linéaire (24) et à la position et la vitesse de la première masse (12), sont déterminées. La position et la vitesse de la seconde masse (14) sont réglées à l'aide d'un actionneur linéaire (26) en conséquence.
PCT/IB2021/053346 2020-04-23 2021-04-22 Générateur d'énergie houlomotrice doté d'un dispositif de commande artificiellement intelligent WO2021214715A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US16/857,153 2020-04-23
US16/857,153 US10815961B2 (en) 2018-10-01 2020-04-23 Ocean wave power generator with artificially intelligent controller

Publications (1)

Publication Number Publication Date
WO2021214715A1 true WO2021214715A1 (fr) 2021-10-28

Family

ID=78270340

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2021/053346 WO2021214715A1 (fr) 2020-04-23 2021-04-22 Générateur d'énergie houlomotrice doté d'un dispositif de commande artificiellement intelligent

Country Status (1)

Country Link
WO (1) WO2021214715A1 (fr)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1691072A1 (fr) * 2003-10-23 2006-08-16 Sumitomo Electric Industries, Ltd. Convertisseur d'ondes
JP2012215120A (ja) * 2011-03-31 2012-11-08 Mitsubishi Heavy Ind Ltd 波力発電装置
US20150152835A1 (en) * 2012-06-05 2015-06-04 Ddnt Consultants Austalia Pty Ltd Wave power generation system and method
US10415537B2 (en) * 2016-12-09 2019-09-17 National Technology & Engineering Solutions Of Sandia, Llc Model predictive control of parametric excited pitch-surge modes in wave energy converters
US10423126B2 (en) * 2016-12-09 2019-09-24 National Technology & Engineering Solutions Of Sandia, Llc Multi-resonant feedback control of a single degree-of-freedom wave energy converter
US10815961B2 (en) * 2018-10-01 2020-10-27 Abu Dhabi Polytechnic Ocean wave power generator with artificially intelligent controller

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1691072A1 (fr) * 2003-10-23 2006-08-16 Sumitomo Electric Industries, Ltd. Convertisseur d'ondes
JP2012215120A (ja) * 2011-03-31 2012-11-08 Mitsubishi Heavy Ind Ltd 波力発電装置
US20150152835A1 (en) * 2012-06-05 2015-06-04 Ddnt Consultants Austalia Pty Ltd Wave power generation system and method
US10415537B2 (en) * 2016-12-09 2019-09-17 National Technology & Engineering Solutions Of Sandia, Llc Model predictive control of parametric excited pitch-surge modes in wave energy converters
US10423126B2 (en) * 2016-12-09 2019-09-24 National Technology & Engineering Solutions Of Sandia, Llc Multi-resonant feedback control of a single degree-of-freedom wave energy converter
US10815961B2 (en) * 2018-10-01 2020-10-27 Abu Dhabi Polytechnic Ocean wave power generator with artificially intelligent controller

Similar Documents

Publication Publication Date Title
Iwasaki et al. Sensory feedback mechanism underlying entrainment of central pattern generator to mechanical resonance
Ho et al. Nonlinear dynamics of a new electro-vibro-impact system
Vyhlídal et al. Time-delay algorithms for damping oscillations of suspended payload by adjusting the cable length
Youcef-Toumi et al. Impact and force control: Modeling and experiments
CN110492479B (zh) 一种分布式并网设备的转动惯量和阻尼辨识方法
US10815961B2 (en) Ocean wave power generator with artificially intelligent controller
Shen et al. Vibration control of flexible structures with PZT sensors and actuators
CN106292550A (zh) 具有在线优化控制增益的功能的伺服控制装置
Khalid et al. Output feedback stabilization of an inertia wheel pendulum using sliding mode control
Chertovskikh et al. An adaptive PID controller with an online auto-tuning by a pretrained neural network
CN109343467B (zh) 一种非共振evc装置的椭圆轨迹误差控制系统
Pirabakaran et al. PID autotuning using neural networks and model reference adaptive control
WO2021214715A1 (fr) Générateur d'énergie houlomotrice doté d'un dispositif de commande artificiellement intelligent
CN112965383B (zh) 单向耦合分数阶自持机电地震仪系统的自适应神经网络最优定时同步控制方法
US8855826B2 (en) Controller for reducing vibrations in mechanical systems
Kang et al. A disturbance observer design for backlash compensation
Singh et al. Performance analysis of improving stability using tuned PID and Hybrid controllers
Magzoub et al. Power system stabiliser for single machine in infinite bus based on optimal control methods
JP7306926B2 (ja) 振動試験装置
Ma Research on parameter self-tuning PID control algorithm based on BP neural network
Fei et al. Adaptive fuzzy backstepping sliding mode control for MEMS gyroscope
CN101162884A (zh) 发电机励磁系统的逆模型控制方法
Lee et al. Model identification of a fluttering aerofoil using control-based continuation and normal form analysis
Fei et al. Adaptive neural compensation scheme for robust tracking of MEMS gyroscope
Ma et al. Interval eigenvalue analysis of closed-loop control for power system oscillation with interval parameters

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21792977

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21792977

Country of ref document: EP

Kind code of ref document: A1