Abstract: The present invention relates to a method of determining the wind speed in the plane of a rotor (PR) of a wind turbine (1), by measuring (MES2) the rotational speed of the rotor, the angle of the blades and the generated power. The method according to the invention uses a wind turbine model (MOD) constructed from wind speed measurements (LID), and by use of measurement clustering (GRO) and regressions (REG).

Abstract: The present invention is a method of controlling a wind turbine by determining the wind speed in the plane of a rotor (PR) of a wind turbine (1), by measuring the rotational speed of the rotor, the angle of the blades and the generated power. The method according to the invention implements a dynamic wind turbine model, a dynamic wind model and an unscented Kalman filter.

Abstract: The present invention is a method of determining the average wind speed in a vertical plane by use of a LiDAR sensor (2), comprising performing measurements (MES), constructing a measurement model (MOD M) and a wind model (MOD V). Then an adaptive Kalman filter (KAL) is used to determine the wind speed (v), and determining the average wind speed in the vertical plane under consideration (RAWS).

Abstract: The present invention is a method of determining an induction factor of the wind for a wind turbine (1) equipped with a LiDAR sensor (2). For this method, wind speed measurements are performed in measurement planes (PM) by use of LiDAR sensor (2), then induction factors between measurement planes (PM) are determined by use of the measurements and of a first linear Kalman filter, and the induction factor between a measurement plane (PM) and the rotor plane (PR) of wind turbine (1) is determined by a second linear Kalman filter.

Abstract: The present invention relates to a method of determining an induction factor between the rotor plane (PR) and a measurement plane (PM), involving measuring the wind speed in at least two measurement planes (PM), determining the wind speed in rotor plane (PR) by use of a Kalman filter from the measurements, and measuring the induction factor by use of an adaptive Kalman filter from the measurements and the wind speed in rotor plane (PR).

Abstract: The present invention relates to a method of determining the magnetic flux ? of an electric machine, based on measurements (MES) of currents and voltages in the phases of the electric machine, on a dynamic model (MOD) of the magnetic flux and on an adaptive Kalman filter (KAL).

Abstract: The invention is a method for detecting aberrant values of an incident wind field in a space located upstream of a lidar sensor. The method comprises acquiring and modelling a measurement rws(k) with the lidar sensor of an incident wind field, by estimating a median mr(k) and a mean absolute deviation dr(k) in real time of measurements of the incident wind field and detecting aberrant values in real time using the estimated median mr(k) and the mean absolute deviation dr(k).

Abstract: The invention relates to a method of determining the vertical profile of the wind speed upstream from a wind turbine (1), wherein wind speed measurements are performed by a LiDAR sensor (2), then the exponent ? of the power law is determined by an unscented Kalman filter and measurements, and the exponent ? is applied to the power law in order to determine the vertical wind speed profile.

Abstract: The present invention relates to a method of determining the direction of the wind by a LiDAR sensor (2). This method comprises performing measurements by the LiDAR sensor (2), deducing a Gaussian distribution of the longitudinal (u) and transverse (v) components of the wind speed, and determining wind direction (?) by a spherical cubature approximation method and of the Gaussian distribution of the longitudinal and transverse components of the wind speed.

Abstract: The present invention is a method for real-time determination of the forces exerted by incident waves on a mobile part of a wave energy system. Models are constructed of the radiation force exerted on the mobile part and of the drag force exerted on the mobile part and a non-linear model of the wave energy system dynamics. The invention uses only measurements of the float kinematics (position, velocity and possibly acceleration) and of the force applied by a converter machine, which measurements are normally available on a wave energy system since they are used for control and supervision thereof. Determination of the excitation force exerted by incident waves on the mobile part uses the models, the measurements and an unscented Kalman filter.

Abstract: The present invention relates to a method of determining the wind speed in the plane of a rotor (PR) of a wind turbine (1), by measuring (MES2) the rotational speed of the rotor, the angle of the blades and the generated power. The method according to the invention uses a wind turbine model (MOD) constructed from wind speed measurements (LID), and by use of measurement clustering (GRO) and regressions (REG).

Abstract: The present invention relates to a method of determining an induction factor between the rotor plane (PR) and a measurement plane (PM), involving measuring the wind speed in at least two measurement planes (PM), determining the wind speed in rotor plane (PR) by use of a Kalman filter from the measurements, and a step of measuring the induction factor by use of an adaptive Kalman filter from the measurements and the wind speed in rotor plane (PR).

Abstract: The present invention relates to a method of determining the average wind speed in a vertical plane by use of a LiDAR sensor (2), comprising performing measurements (MES), constructing a measurement model (MOD M) and a wind model (MOD V), then using an adaptive Kalman filter (KAL) to determine the wind speed (v), and determining the average wind speed in the vertical plane being considered (RAWS).

Abstract: Provided is a manpower management system comprising a data communication unit configured to receive motion signals from a wearable unit worn by a specific employee to sense motions of the specific employee; a data processing unit configured to label the motion signals with behavior data to generate a labeled training dataset for the specific employee; a deep learning model for the specific employee configured to be trained through machine learning using the labeled training dataset, a recognition unit configured to recognize behavior of the specific employee in response to an input of a motion signal of the specific employee received by the data communication unit, using the trained deep learning model, and output a behavior data of the specific employee; and a simulation unit configured to evaluate employee's work efficiency using the output behavior data.

Abstract: The invention relates to a method for acquisition and modelling of an incident wind field by a LiDAR sensor. Acquisition and modelling include a step of estimating the wind amplitudes and directions for a set of discretized points, and a step of incident wind field reconstruction in three dimensions and in real time. The invention also relates to a method of controlling and/or monitoring a wind turbine equipped with such a LiDAR sensor from the incident wind field reconstructed in three dimensions and in real time.

Abstract: The present invention relates to a method of determining the magnetic flux ? of an electric machine, based on measurements (MES) of currents and voltages in the phases of the electric machine, on a dynamic model (MOD) of the magnetic flux and on an adaptive Kalman filter (KAL).

Abstract: The present invention is a method for controlling a wave-energy-converter system, wherein the gains of a control law having a proportional component are optimally determined in terms of the harvest of energy, and are robust with respect to uncertainties in the dynamics of the wave-energy-converter system. The harvest of energy is maximized for the worst case described by the uncertainties, by solving a semidefinite-positive optimization problem.

Abstract: The present invention relates to a method of determining the direction of the wind by a LiDAR sensor (2). This method comprises performing measurements by the LiDAR sensor (2), deducing a Gaussian distribution of the longitudinal (u) and transverse (v) components of the wind speed, and determining wind direction (?) by a spherical cubature approximation method and of the Gaussian distribution of the longitudinal and transverse components of the wind speed.

Abstract: The present invention is a method of determining the wind speed in the plane of a rotor (PR) of a wind turbine (1), by measuring the rotational speed of the rotor, the angle of the blades and the generated power. The method according to the invention implements a dynamic wind turbine model, a dynamic wind model and an unscented Kalman filter.

Abstract: The present invention provides improvement of the operation of a wave energy system by use of a method for predictive control (COM) of the converter machine that maximizes the energy generated by considering the energy conversion efficiency (MOD ENE) and a wave prediction (PRED). Furthermore, the method according to the invention determines the optimal control by minimizing an objective function weighted and discretized by the trapezoidal rule.