Parameter Identification of a Winding Function Based Model for Fault Detection of Induction Machines

Prediction of machines' faulty parts is important in industrial applications in order to reduce productivity losses. As far as electrical machines are considered, a model-based fault diagnosis approach is usually used for this purpose. The model is derived from the modified winding function theory and hence, it requires a considerable amount of parameters at various operating conditions in order to be successfully used. However, the complete set of parameters is difficult to be obtained, as manufacturers of electric machines normally provide only the parameters that describe simple motor models (e.g. T-equivalent circuit at rated conditions). Therefore, the current work presents a method th…

Assessment of synthetic winds through spectralmodeling and validation using FAST

- In this paper, we analyse the simulated and measured wind data with respect to their spectral characteristics and their effect on wind turbine loads. The synthetic data is generated from a stochastic full-field turbulent wind simulator - TurbSim for neutral stability conditions. We first investigate a model for velocity spectra and, a coherence model, by comparing the model results with the measurements. In the second part we analyse the synthetic data via spectra and coherence for two cases; without and with adding coherent events. Finally, we compare wind turbine loads calculated by using FAST simulation of 5 MW reference wind turbine on the basis of simulated and measured data for the …

Diagnostics of stator winding failures in wind turbine pitch motors using Vold-Kalman filter

Pitch systems are among the most failure-prone components in wind turbines. Winding failures in pitch motors are common due to high start-up loads and poor ventilation. This article presents a diagnostics scheme that can detect the stator winding failures in the pitch motors under time-varying speed and load conditions. The proposed approach based on three-phase motor currents can be directly integrated into the motor drive and can be used for induction as well as permanent magnet synchronous machines. The extended Park's vector calculated on the motor currents is order tracked based on the supply frequency from the drive using Vold-Kalman filter. The approach is shown to be robust under ar…

Towards farm-level health management of offshore wind farms for maintenance improvements

This paper studies a conceptual architecture for health management of offshore wind farms. To this aim, various necessary enablers of a health management sys- tem are presented to improve reliability and availability while optimizing maintenance costs. The main focus lies on improving existing condition monitoring systems based on concepts of condition-based maintenance and relia- bility centered maintenance. A brief review of the rel- evant state-of-the-art is presented and gaps to be filled towards realization of such health management system are discussed.

Sensorless control of induction motors using an extended Kalman filter and linear quadratic tracking

Induction motors are the most commonly used prime-movers in industrial applications. Many induction motors supplied by frequency converters are coupled with a physical angular rotor position/velocity sensor which makes the drive complex and require maintenance. This paper presents a sensorless control structure to avoid using a physical angular rotor position/velocity sensor. The proposed method estimates and control the angular rotor velocity using optimal control theory. The optimal controller used in this paper is based on linear quadratic tracking and the states of the machine are estimated using an extended Kalman filter. Both the controller and the estimator utilize the same internal …

Toward farm-level health management of wind turbine systems: status and scope for improvements

An outline of health management for OWFs has been detailed in this chapter with description of various important elements. The need for such farm level management is explained and benefits are discussed. Key gaps to be filled in order to realize such a system are identified. The proposed health management system is mainly based on the existing knowledge of fleet-level management in the aerospace sector. Health management is much broader than CM; there are a number of aspects beyond the prognostics capabilities that are to be designed in order to arrive at a comprehensive maintenance management scheme. A comprehensive maintenance program that is sensitive to the health of the assets and adap…

Estimation of Wind Turbine Performance Degradation with Deep Neural Networks

In this paper, we estimate the age-related performance degradation of a wind turbine working under Norwegian environment, based on a deep neural network model. Ten years of high-resolution operational data from a 2 MW wind turbine were used for the analysis. Operational data of the turbine, between cut-in and rated wind velocities, were considered, which were pre-processed to eliminate outliers and noises. Based on the SHapley Additive exPlanations of a preliminary performance model, a benchmark performance model for the turbine was developed with deep neural networks. An efficiency index is proposed to gauge the agerelated performance degradation of the turbine, which compares measured per…

Early detection and classification of bearing faults using support vector machine algorithm

Bearings are one of the most critical elements in rotating machinery systems. Bearing faults are the main reason for failures in electrical motors and generators. Therefore, early bearing fault detection is very important to prevent critical system failures in the industry. In this paper, the support vector machine algorithm is used for early detection and classification of bearing faults. Both time and frequency domain features are used for training the support vector machine learning algorithm. The trained classier can be employed for real-time bearing fault detection and classification. By using the proposed method, the bearing faults can be detected at early stages, and the machine oper…

A Two-Stage Fault Detection and Classification Scheme for Electrical Pitch Drives in Offshore Wind Farms Using Support Vector Machine

Pitch systems are one of the components with the most frequent failure in wind turbines. This paper presents a two-stage fault detection and classification scheme for electric motor drives in wind turbine pitch systems. The presented approach is suitable for application in offshore wind farms with electric pitch systems driven by induction motors as well as permanent magnet synchronous motors. The adopted strategy utilizes three-phase motor current sensing at the pitch drives for fault detection and only when a fault condition is detected at this stage, features extracted from the current signals are transmitted to a support vector machine classifier located centrally to the wind farm. The …

Automated and Rapid Seal Wear Classification Based on Acoustic Emission and Support Vector Machine

Seal wear in hydraulic cylinders results in fluid leakage, and instability of the piston rod movement. Therefore, regular inspection of seals is required using automated approaches to improve productivity and to reduce unscheduled maintenance. In literature, successful attempts have been made using acoustic emission-based condition monitoring to classify the seal wear. However, limited attempts have been made to implement automated approaches to classify seal wear using acoustic emission features. Therefore, this article presents an automated approach for rapid and computationally economical diagnosis of seal wear using acoustic emission. The experiments were performed at varying pressure c…

Multi-Component Fault Detection in Wind Turbine Pitch Systems Using Extended Park's Vector and Deep Autoencoder Feature Learning

Pitch systems are among the wind turbine components with most frequent failures. This article presents a multicomponent fault detection for induction motors and planetary gearboxes of the electric pitch drives using only the three-phase motor line currents. A deep autoencoder is used to extract features from the extended Park's vector modulus of the motor three-phase currents and a support vector machine to classify faults. The methodology is validated in a laboratory setup of a scaled pitch drive, with four commonly occurring faults, namely, the motor stator turns fault, broken rotor bars fault, planetary gearbox bearing fault and planet gear faults, under varying load and speed conditions.

Assessment of synthetic winds through spectral modeling and validation using FAST

In this paper, we analyse the simulated and measured wind data with respect to their spectral characteristics and their effect on wind turbine loads. The synthetic data is generated from a stochastic full-field turbulent wind simulator - TurbSim for neutral stability conditions. We first investigate a model for velocity spectra and, a coherence model, by comparing the model results with the measurements. In the second part we analyse the synthetic data via spectra and coherence for two cases; without and with adding coherent events. Finally, we compare wind turbine loads calculated by using FAST simulation of 5 MW reference wind turbine on the basis of simulated and measured data for the gi…

A two-stage fault detection and classification for electric pitch drives in offshore wind farms using support vector machine

This article presents a two-stage fault detection and classification scheme, for induction motor drives in wind turbine pitch systems. The presented approach is suitable for application in offshore wind farms. The adopted strategy utilizes three phase motor current sensing at the pitch drives for fault detection and only when a fault is detected at this stage, features extracted from the current signals are transmitted to a central support vector machine classifier. The proposed method is validated in a laboratory setup of the pitch drive.

Rapid Diagnosis of Induction Motor Electrical Faults using Convolutional Autoencoder Feature Extraction

EKF-based estimation and control of electric drivetrain in offshore pipe racking machine

A typical challenge for electric drivetrains is to reduce the number of sensors required for control action or system monitoring. This is particularly important for electric motors operating in offshore conditions, since they work in hostile environment which often damages data acquisition systems. Therefore, this paper deals with verification and validation of the extended Kalman filter (EKF) for sensorless indirect field-oriented control (IFOC) of an induction motor operating in offshore conditions. The EKF is employed to identify the speed of the induction motor based on the measured stator currents and voltages. The estimated speed is used in the motor speed control mode instead of a ph…

A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management

Abstract Large wind farms are gaining prominence due to increasing dependence on renewable energy. In order to operate these wind farms reliably and efficiently, advanced maintenance strategies such as condition based maintenance are necessary. However, wind turbines pose unique challenges in terms of irregular load patterns, intermittent operation and harsh weather conditions, which have deterring effects on life of rotating machinery. This paper reviews the state-of-the-art in the area of diagnostics and prognostics pertaining to two critical failure prone components of wind turbines, namely, low-speed bearings and planetary gearboxes. The survey evaluates those methods that are applicabl…

Diagnosis of inverter-fed induction motors in short time windows using physics-assisted deep learning framework

This article presents a framework for accurate fault diagnostics in inverter-fed induction machinery operating under variable speed and load conditions within very short time windows. Condition indicators based on fault characteristic frequencies observed over the extended Park's vector modulus are fused with deep features extracted using stacked autoencoders to generate a multidimensional feature space for fault classification using support vector machine. The proposed approach is demonstrated in a laboratory setup to detect the most commonly occurring faults, namely, the stator turns fault, broken rotor bars fault and bearing fault with an accuracy > 98% within a short time window of 2–3 …

Current signature based fault diagnosis of field-oriented and direct torque-controlled induction motor drives

In this article, the operation of three-phase squirrel-cage induction motors is analysed under faulty conditions in closed loop with state-of-the-art controllers, namely, the field-oriented control...

A Novel Fault Indicator for Local Demagnetization in Fractional-Slot Permanent Magnet Synchronous Motor using Winding Function Theory

Local demagnetization is an irreparable failure mode in permanent magnet synchronous motors (PMSMs), resulting in reduced motor efficiency and high cogging torque. These effects are particularly disadvantageous to fractional-slot wound PMSMs used in industrial applications, such as robotics and automations, that require high power density and precise operation. In this article, a diagnostic indicator is developed for detecting local demagnetization fault in fractional-slot wound PMSMs based on winding function theory, utilizing back-electromotive force (back-EMF) as a medium for fault detection. Further, the developed indicator is validated in a commercial fractional-slot wound PMSM of an i…

Fault Diagnostics for Electrically Operated Pitch Systems in Offshore Wind Turbines

This paper investigates the electrically operated pitch systems of offshore wind turbines for online condition monitoring and health assessment. The current signature based fault diagnostics is developed for electrically operated pitch systems using model-based approach. The electrical motor faults are firstly modelled based on modified winding function theory and then, current signature analysis is performed to detect the faults. Further, in order to verify the fault diagnostics capabilities in realistic conditions, the operating profiles are obtained from FAST simulation of offshore wind turbines in various wind conditions. In this way, the applicability of current signature analysis for …