A Markovian jump system approach for the estimation and adaptive diagnosis of decreased power generation in wind farms

In this study, a Markovian jump model of the power generation system of a wind turbine is proposed and the authors present a closed-loop model-based observer to estimate the faults related to energy losses. The observer is designed through an H∞-based optimisation problem that optimally fixes the trade-off between the observer fault sensitivity and robustness. The fault estimates are then used in data-based decision mechanisms for achieving fault detection and isolation. The performance of the strategy is then ameliorated in a wind farm (WF) level scheme that uses a bank of the aforementioned observers and decision mechanisms. Finally, the proposed approach is tested using a well-known benchmark in the context of WF fault diagnosis

A simple procedure for fault detectors design in SISO systems

In this work, we present a novel approach for fault detectors design and implementation in the case of actuator faults. Both the design and the implementation are focused on simplicity. The fault detector is based in an output observer that estimates the fault signal followed by a decision mechanism that detects the presence of a fault from the estimation. The observer consists of two transfer functions fed by the process manipulated variable and the sensor measurement. For the synthesis of the fault detector, we just need an input–output model of the process and two tuning parameters; one used in the observer, and the other in the decision mechanism. We present simple rules for the design considering the trade-off between the detection time, the minimum detectable fault and the false alarm rate. Our implementation method uses standard tools available in industrial control systems and we have applied it to a real two-tank system setup. The main contribution of this work is the simplicity of the design and implementation of the fault detector, making it suitable for process industry and for being managed by not experts in control systems. Another contribution is the a priori design based in intuitive engineering performance indices

Sensores virtuales para procesos con medidas escasas y retardos temporales

En este trabajo se aborda el problema de controlar un proceso cuya salida se muestrea de forma irregular. Para ello se propone utilizar un predictor que estima las salidas del proceso en instantes regulares de tiempo más un controlador convencional que calcula la acción de control a partir de las estimaciones del predictor (técnica conocida como control inferencial). La predicción consiste en estimar las variables de salida que se desean controlar a partir de las mediciones realizadas con diversos sensores utilizando para ello un modelo matemático del proceso. El filtro de Kalman permite hacer la predicción de forma óptima si las perturbaciones tienen una distribución gaussiana de media cero, pero con el inconveniente de requerir un elevado coste computacional cuando se utilizan diferentes sensores con retardos temporales variantes. En este trabajo se propone una estrategia de predicción alternativa de bajo coste computacional cuyo diseño se basa en el conocimiento de la disponibilidad de mediciones y de los retardos (del proceso, del sistema de medición o del sistema de transmisión de datos) y de la naturaleza de las perturbaciones. Los predictores propuestos minimizan el error de predicción frente al muestreo aleatorio con retardos variantes, perturbaciones, ruido de medida, error de modelado, retardos en la acción de control e incertidumbre en los tiempos de medición. Las diferentes estrategias de diseño que se proponen se clasifican según el tipo de información que se dispone de las perturbaciones y del coste computacional requerido. Se han planteado los diseños para sistemas monovariables, multivariables, lineales y no lineales. Asimismo, también se ha elaborado una forma más eficiente de incluir mediciones escasas con retardo en el filtro de Kalman, con el objetivo de reducir el coste computacional de la predicción. En este trabajo se demuestra que los sistemas de control inferencial que utilizan los predictores propuestos cumplen con el principio de sepPeñarrocha Alós, I. (2006). Sensores virtuales para procesos con medidas escasas y retardos temporales [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/3882Palanci

Trade-offs on fault estimation via proportional multiple-integral and multiple-resonant observers for discrete-time systems

The authors develop a fault estimation strategy which is based on a novel proportional multiple-integral (PMI) and multiple-resonant observer. This observer is an extension of the well-known PMI observer and it is able to estimate from low to high-frequency fault signals. The proposed estimation strategy is applied to discrete-time systems which are affected by faults and stochastic noises. We present a multi-objective design strategy of the observer that fixes the trade-offs between practical engineering parameters regarding the noise attenuation and the ability to track each kind of fault dynamics considered by the augmented observer. They study the influence of the order of the observer on the steady-state and transient performance of the estimation of different types of faults. Finally, a numerical example is given to illustrate the effectiveness of the proposed observer, design and characterisation

Multiobjective performance-based designs in fault estimation and isolation for discrete-time systems and its application to wind turbines

In this work, we develop a performance-based design of model-based observes and statistical-based decision mechanisms for achieving fault estimation and fault isolation in systems affected by unknown inputs and stochastic noises. First, through semidefinite programming, we design the observers considering different estimation performance indices as the covariance of the estimation errors, the fault tracking delays and the degree of decoupling from unknown inputs and from faults in other channels. Second, we perform a co-design of the observers and decision mechanisms for satisfying certain trade-off between different isolation performance indices: the false isolation rates, the isolation times and the minimum size of the isolable faults. Finally, we extend these results to a scheme based on a bank of observers for the case where multiple faults affect the system and isolability conditions are not verified. To show the effectiveness of the results, we apply these design strategies to a well-known benchmark of wind turbines which considers multiple faults and has explicit requirements over isolation times and false isolation rates

A new method for experimental tuning of PI controllers based on the step response

In this paper we present a new method for tuning Proportional Integral (PI) controllers from experimental data obtained through an open loop step test over the process to be controlled. The tuning procedure requires first the measurement of the process gain, and the times taken to reach the 5%, 35.3% and 85.3% of the final output and then applying a set of tuning equations. The tuning equations approximate the controller that minimizes the Integral of Absolute Error (IAE) of the disturbance response for a model with three real poles and time delay and are very accurate for a wide range of non oscillatory stable systems. The user can select the desired robustness (through the required maximum of the Sensitivity function ()), as a difference with usual methods that allow only to choose among two or three predefined robustness. The PI controller that minimizes the disturbance IAE is defined by default, but the user can also select a detuning factor to define slower controllers with the same robustness, allowing to find the desired compromise between performance and actuator activity due to sensor measurement noise. An application for Android, that can be downloaded for free, and a web based application, have been developed to implement the tuning procedure.Funding for open access charge: CRUE-Universitat Jaume

Banks of estimators and decision mechanisms for pitch actuator and sensor FE in wind turbines

Comunicació presentada a SAFEPROCESS 2018. 10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (Warsaw, Poland, 29–31 August 2018)Wind turbines are prone to multiple different faults and input observability conditions are not always guaranteed for these faults. In such cases, it is not possible to build estimators which provide appropriate fault estimates for its further use in active FTC schemes such as fault tolerant MPC. Provided that these faults are generally non-simultaneous, we make use of this property for building banks of model-based estimators and statistical-based decision mechanisms that provide appropriate fault estimates for enhancing active FTC capabilities. We apply these strategies to a well-known wind turbine FDI and FTC benchmark and we show the effectiveness of the bank of estimators and decision mechanisms for estimating the faults occurring in the pitch system of a wind turbine

Robust estimation and diagnosis of wind turbine pitch misalignments at a wind farm level

Wind turbine pitch misalignments provoke aerodynamic asymmetries which cause severe damage to the turbine. Hence, it is of interest to develop fault tolerant strategies to cope with pitch misalignments. Fault tolerant strategies require the information regarding the diagnosis and the estimation of the faults. However, most existing works focus only on open-loop misalignment diagnosis and do not provide robust fault estimates. In this work, we present a novel strategy to both estimate and diagnose pitch misalignments. The proposed strategy is developed at a wind farm level and it exploits altogether the information provided by the temporal and spatial relations of the turbines in the farm. Fault estimation is first addressed with a closed-loop switched observer. This observer is robust against disturbances and it adapts to the varying conditions along the wind turbine operation range. Fault diagnosis is then achieved via statistical-based decision mechanisms with adaptive thresholds. Both the observer and the decision mechanisms are designed to guarantee the desired performance. Introducing some restrictions over the number of simultaneous faulty turbines in the farm, the proposed approach is ameliorated via a bank of the aforementioned observers and decision mechanisms. Finally, the strategies are tested using a well-known wind farm benchmark

Model-based observer proposal for surface roughness monitoring

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)In the literature, many different machining monitoring systems for surface roughness and tool condition have been proposed and validated experimentally. However, these approaches commonly require costly equipment and experimentation. In this paper, we propose an alternative monitoring system for surface roughness based on a model-based observer considering simple relationships between tool wear, power consumption and surface roughness. The system estimates the surface roughness according to simple models and updates the estimation fusing the information from quality inspection and power consumption. This monitoring strategy is aligned with the industry 4.0 practices and promotes the fusion of data at different shop-floor levels

Networked gain-scheduled fault diagnosis under control input dropouts without data delivery acknowledgement

This paper investigates the fault diagnosis problem for discrete‐time networked control systems under dropouts in both control and measurement channel with no delivery acknowledgment. We propose to use a proportional integral observer‐based fault diagnoser collocated with the controller. The observer estimates the faults and computes a residual signal whose comparison with a threshold alarms the fault appearance. We employ the expected value of the arriving control input for the open‐loop estimation and the measurement reception scenario for the correction with a jump observer. The jumping gains are scheduled in real time with rational functions depending on a statistic of the difference between the control command being applied in the plant and the one being used in the observer. We design the observer, the residual, and the threshold to maximize the sensitivity under faults while guaranteeing some minimum detectable faults under a predefined false alarm rate. Exploiting sum‐of‐squares decomposition techniques, the design procedure becomes an optimization problem over polynomials