2018 7th International Conference on Systems and Control (ICSC), 2018
Wind profile variations and disturbances are the main cause for stress and fatigue for wind turbi... more Wind profile variations and disturbances are the main cause for stress and fatigue for wind turbines. These disturbances propagate along the drive train, through the gearbox and into the generator resulting in current and voltage output fluctuations. The wind profile is a non-stationary random process, thus the resulting vibrations and disturbances throughout the system are non-stationary. Classical traditional frequency-domain analysis techniques fall short when dealing with this type of signals. Modern analysis and control requirements in wind turbines justify the need for advanced techniques to cope with the non-stationary nature of measured signals. Compensating these disturbances to protect different wind turbine components, while detecting harmonics caused by these disturbances, render the turbine system operation smoother while increasing reliability, efficiency and robustness. This paper applies a Kalman filter based method for signal reconstruction through harmonic estimation for the turbine side angular velocity. In addition, a new modified Empirical Mode Decomposition (EMD) approach is introduced capable of separating the continuous component of a non-stationary signal from its added high and low frequency waves. The modified EMD intends to reduce time consumption for signal processing and isolate harmonics from the carrier wave in the angular velocity signal for analysis. Then the EMD and the Kalman filter are combined in order to improve individual harmonic component estimation while allowing the use of conventional signal processing techniques. The method can be used either to reject wind profile disturbances, or detect added fault signatures by a single component.
System diagnostic and disturbance rejection based control is vital nowadays for protecting system... more System diagnostic and disturbance rejection based control is vital nowadays for protecting systems against unwanted perturbations while reducing the fault occurrence rate. Diagnostic and control strategies require knowledge of the disturbance frequency signature in order to detect or cancel out its effect. This paper studies the estimation of stationary and nonstationary system disturbance by the means of an Extended Kalman Filter (EKF) or Unknown Input Observer (UIO) in order to isolate or estimate the disturbance in a system. An application to a wind tubine drivetrain with Permanent Magnet Synchronous Generator (PMSG) is presented in order to compare the results of the classic EKF apporach with the UIO.
2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2015
The evolution of battery management systems has imposed the necessity of evaluating batteries equ... more The evolution of battery management systems has imposed the necessity of evaluating batteries equivalent circuit models. The advantage of this type of modeling is to accurately estimate the dynamic aspects of a battery. Circuit parameters are identified for the purpose of forecasting different battery states. This paper presents multiple circuit models described in the literature. Four types of batteries are studied: Nickel-Metal Hybrid, Lithium-Ion, Lead-acid and Lithium Polymer.
2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)
This article deals with the monitoring of mechanical defects in wind turbine transmissions. The p... more This article deals with the monitoring of mechanical defects in wind turbine transmissions. The presented algorithms make possible to extract a particular harmonic relating to a defect in different speed conditions (fixed speed and variable speed). The method used here is performed without mechanical sensor. An observer is used to estimate the rotation speed measured from voltages and currents. This observer ensures amplification of the defect. Then, a Kalman Filter estimator coupled with a modified Sliding Window Empirical Mode Decomposition (SWEMD) extraction allows to extract the harmonic corresponding to the defect and follow it in real time. The results are provided in the case of a wind turbine installation.
2018 7th International Conference on Systems and Control (ICSC), 2018
Wind profile variations and disturbances are the main cause for stress and fatigue for wind turbi... more Wind profile variations and disturbances are the main cause for stress and fatigue for wind turbines. These disturbances propagate along the drive train, through the gearbox and into the generator resulting in current and voltage output fluctuations. The wind profile is a non-stationary random process, thus the resulting vibrations and disturbances throughout the system are non-stationary. Classical traditional frequency-domain analysis techniques fall short when dealing with this type of signals. Modern analysis and control requirements in wind turbines justify the need for advanced techniques to cope with the non-stationary nature of measured signals. Compensating these disturbances to protect different wind turbine components, while detecting harmonics caused by these disturbances, render the turbine system operation smoother while increasing reliability, efficiency and robustness. This paper applies a Kalman filter based method for signal reconstruction through harmonic estimation for the turbine side angular velocity. In addition, a new modified Empirical Mode Decomposition (EMD) approach is introduced capable of separating the continuous component of a non-stationary signal from its added high and low frequency waves. The modified EMD intends to reduce time consumption for signal processing and isolate harmonics from the carrier wave in the angular velocity signal for analysis. Then the EMD and the Kalman filter are combined in order to improve individual harmonic component estimation while allowing the use of conventional signal processing techniques. The method can be used either to reject wind profile disturbances, or detect added fault signatures by a single component.
System diagnostic and disturbance rejection based control is vital nowadays for protecting system... more System diagnostic and disturbance rejection based control is vital nowadays for protecting systems against unwanted perturbations while reducing the fault occurrence rate. Diagnostic and control strategies require knowledge of the disturbance frequency signature in order to detect or cancel out its effect. This paper studies the estimation of stationary and nonstationary system disturbance by the means of an Extended Kalman Filter (EKF) or Unknown Input Observer (UIO) in order to isolate or estimate the disturbance in a system. An application to a wind tubine drivetrain with Permanent Magnet Synchronous Generator (PMSG) is presented in order to compare the results of the classic EKF apporach with the UIO.
2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2015
The evolution of battery management systems has imposed the necessity of evaluating batteries equ... more The evolution of battery management systems has imposed the necessity of evaluating batteries equivalent circuit models. The advantage of this type of modeling is to accurately estimate the dynamic aspects of a battery. Circuit parameters are identified for the purpose of forecasting different battery states. This paper presents multiple circuit models described in the literature. Four types of batteries are studied: Nickel-Metal Hybrid, Lithium-Ion, Lead-acid and Lithium Polymer.
2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)
This article deals with the monitoring of mechanical defects in wind turbine transmissions. The p... more This article deals with the monitoring of mechanical defects in wind turbine transmissions. The presented algorithms make possible to extract a particular harmonic relating to a defect in different speed conditions (fixed speed and variable speed). The method used here is performed without mechanical sensor. An observer is used to estimate the rotation speed measured from voltages and currents. This observer ensures amplification of the defect. Then, a Kalman Filter estimator coupled with a modified Sliding Window Empirical Mode Decomposition (SWEMD) extraction allows to extract the harmonic corresponding to the defect and follow it in real time. The results are provided in the case of a wind turbine installation.
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Papers by Jack Salameh