Manuel Gálvez-Carrillo

Combined Signal and Model-Based Sensor Fault Diagnosis for a Doubly Fed Induction Generator

The problem of multiplicative and/or additive fault detection and isolation (FDI) in the current sensors of a doubly fed induction generator (DFIG) is considered in the presence of model uncertainty. A residual generator based on the DFIG model is proposed using the structure of the classical generalized observer scheme. However, each observer in this scheme is replaced by a robust H_/H∞ fault detection filter followed by a Kalman-like observer. The latter further attenuates the effect of the modeling uncertainties on the residuals. It exploits the specific pattern induced by the balanced three-phase nature of all the electric signals. It turns out that the FDI problem then amounts to detecting an abrupt change in the mean of the residual vector in the additive fault case, or the appearance of sine waves superimposed on a white noise vector in the multiplicative fault case. A decision algorithm made of a combination of generalized likelihood ratio algorithms allows us to detect and isolate the additive and multiplicative sensor faults. The complete FDI system is tested through simulations on a controlled DFIG.

Fault Detection and Isolation in Current and Voltage Sensors of Doubly-Fed Induction Generators

Abstract A fault detection and isolation (FDI) system for monitoring the stator current and voltage sensors of a doubly-fed induction generator (DFIG) for wind energy applications is presented. The FDI system is based on the model of the balanced three-phase signals, that is used to design a residual generator resting on the so-called Generalized Observer Scheme (GOS). Next, a decision system made of a combination of vector CUSUM (Cumulative Sum) algorithms is used in order to process the residual vector and achieve detection and isolation of incipient additive faults. The approach is validated via simulations of the DFIG controlled by a Linear Quadratic Gaussian (LQG) regulator.

APPLICATION of a SMITH PREDICTOR based NONLINEAR PREDICTIVE CONTROLLER to a SOLAR POWER PLANT

Abstract Renewable energies are gaining space in the energy generation panorama, thanks to technological advances and policy support. To take profit of these energies in an optimal and sustained way, research of new control strategies becomes imperative. This work presents the study and application of a nonlinear control strategy, where a Smith Predictor is added to the Nonlinear Extended Prediction Self-Adaptive Control (NEPSAC), for the control of a Solar Power Plant. Different simulations are performed to study the effect of the design parameters in the dynamic behavior of the system.

Robust sensor fault detection and isolation for a doubly-fed induction generator

The problem of multiplicative fault detection and isolation (FDI) in the current sensors of a doubly-fed induction generator (DFIG) is considered in the presence of model uncertainty. A residual generator based on the DFIG model is proposed using the structure of the classical generalized observer scheme (GOS). However, each observer in this scheme is replaced by a robust ℋ−/ℋ∞ fault detection filter followed by a Kalman-like observer. It turns out that the fault detection/isolation problem then amounts to detecting the appearance of sine waves superimposed to specific components of a white noise vector. A Generalized Likelihood Ratio Test (GLRT) is developed to solve the problem. The complete FDI system is validated through simulations on a controlled DFIG.

Model-based sensor fault detection and isolation in balanced three-phase signals

Abstract The inherent redundancy of balanced three-phase currents (voltages) is exploited to design a fault detection and isolation system for current (voltage) sensors. To this end, a model for balanced three-phase signals (either sinusoidal or non sinusoidal) is presented. It is used to design a residual generator based on the so-called Generalized Observer Scheme (GOS). Next, a decision system made of a combination of vector CUSUM (Cumulative Sum) algorithms is used in order to process the residual vector and achieve detection and isolation of incipient additive faults. The approach is validated on experimental data. Fault detection and isolation (FDI) is achieved for a large range of fault magnitudes and an accurate estimation of the fault occurrence time is also determined.

Robust fault detection and isolation in current sensors of doubly-fed induction generators

Abstract The problem of fault detection and isolation (FDI) in the current sensors of a doubly-fed induction generator (DFIG) in the presence of model uncertainty is discussed in this paper. The proposed residual generator is inspired by the structure of the classical generalized observer scheme (GOS). However, each observer in this scheme is replaced by a robust ℋ _ /ℋ ∞ fault detection filter (FDF) followed by a Kalman-like observer. The latter filters a specific frequency in the output generated by the ℋ _ /ℋ ∞ FDF. A multi-CUSUM (Cumulative Sum) algorithm is used as decision system. The approach is validated through simulations of a controlled DFIG.

Sensor Fault Diagnosis in Wind Turbines

This chapter addresses the early detection and isolation of sensor faults in a systematic and unified way and illustrates the approach on wind turbine simulation data. Three problems are successively considered: individual signal monitoring, fault detection and isolation (FDI) in redundant sensors, and FDI based on analytical redundancy. In all three cases, a specific approach to generate fault indicators, also called residuals, is presented and combined with an online statistical change detection/isolation algorithm. The considered case studies consist of wind turbine generator speed monitoring, as well as FDI in the stator current and voltages of a wind-driven doubly fed induction generator. For the latter problem, the fact that the three-phase signals are balanced allows one to determine a simple signal model from which a multiobserver scheme is designed for residual generation.

Additive and multiplicative fault diagnosis for a doubly-fed induction generator

The problem of additive and multiplicative fault detection and isolation (FDI) in the current sensors of a doubly-fed induction generator (DFIG) is considered in the presence of model uncertainty. A residual generator based on the DFIG model is proposed using the structure of the classical generalized observer scheme (GOS). However, each observer in this scheme is replaced by a robust H−/H∞ fault detection filter followed by a Kalman-like observer. The latter further attenuates the effect of the modelling uncertainties on the residuals. It exploits the specific pattern induced by the balanced three phase nature of all the electric signals. It turns out that the fault detection/isolation problem then amounts to detecting an abrupt change in the mean of the residual vector in the additive fault case, or the appearance of sine waves superimposed to a white noise vector in the multiplicative fault case. A decision algorithm made of a combination of generalized likelihood ratio (GLR) algorithms allows us to detect and isolate the additive and multiplicative sensor faults. The complete FDI system is tested through simulations on a controlled DFIG.