0000000000162311
AUTHOR
Jagath Sri Lal Senanayaka
Sensorless small wind turbine with a sliding-mode observer for water heating applications
Water heating applications consume a considerable portion of electricity demand in most of countries. Small wind turbines are one of attractive alternatives for grid electricity based water heating systems. Wind energy can be converted to heat energy in a high efficient manner. However it is essential that wind turbine based water heating systems should be economical and reliable. Maximum power point tracking algorithm of most of available wind turbines requires information from a wind speed sensor and a rotor speed sensor which reduces the reliability of the system. In this paper, the proposed 5 kW wind turbine does not require external wind speed sensors and rotor speed sensors. The syste…
Sliding-mode observer based sensor-less control of a small wind energy conversion system
Small wind turbines are becoming an attractive solution for household applications. These micro generation units can be used as standalone applications or grid connected applications. However to get the full potential benefits of these wind turbines, systems should be low cost and reliable. Introducing the wind speed and rotor speed sensors at the generator shaft may reduce the reliability of small wind turbines. In this study, a grid connected sensor-less 5 kW small wind energy conversion system has been studied. The maximum power point tracking method of the wind turbine is totally independent from wind speed and rotor speed measurements. Optimum rotor speed and actual rotor speed are est…
Autoencoders and Recurrent Neural Networks Based Algorithm for Prognosis of Bearing Life
Bearings are one of the most critical components in electric motors, gearboxes and wind turbines. Therefore, bearing fault detection and prognosis of remaining useful life are important to prevent productivity losses. In this study, a novel method is proposed for prognosis of bearing life using an autoencoder and recurrent neural networks-based prediction algorithm. Promising results have been obtained from the experimental data. A monotonic upward trend of the produced health indicator is obtained for all test cases, being one of critical indicators of a proper prognosis. The remaining useful life estimation is moderately accurate under a limited data.
Multiple Fault Diagnosis of Electric Powertrains Under Variable Speeds Using Convolutional Neural Networks
Electric powertrains are widely used in automotive and renewable energy industries. Reliable diagnosis for defects in the critical components such as bearings, gears and stator windings, is important to prevent failures and enhance the system reliability and power availability. Most of existing fault diagnosis methods are based on specific characteristic frequencies to single faults at constant speed operations. Once multiple faults occur in the system, such a method may not detect the faults effectively and may give false alarms. Furthermore, variable speed operations render a challenge of analysing nonstationary signals. In this work, a deep learning-based fault diagnosis method is propos…
Autoencoders and Data Fusion Based Hybrid Health Indicator for Detecting Bearing and Stator Winding Faults in Electric Motors
The main objective of a condition monitoring programs is to track the health status of critical components of a machine. In this paper, a hybrid health indicator is proposed to monitor the health status of bearings and stator winding of a motor. The proposed method is based on a feature learning from deep autoencoders and data fusion. The features can be learned by autoencoders using individual current and vibration signals, and then learning features are fused to make final health indicators. The experimental data from a permanent magnet synchronous motor is used to validate the proposed method. Promising results in detecting faults and severities of the stator and bearing faults at differ…
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…
Towards online bearing fault detection using envelope analysis of vibration signal and decision tree classification algorithm
Online bearing fault detection is an important method for monitoring the health status of bearings in critical machines. This work proposes a classification algorithm, which can be extended towards an online bearing fault detection. The objective is to detect and classify the bearing faults in early stages. The overall design aspects of the online bearing fault detection and classification system are discussed. The proposed method is validated using experimental data, and a high accuracy of the fault classification was observed. Therefore, the proposed method can be applied for an online early fault detection and classification system.
Online Fault Diagnosis System for Electric Powertrains Using Advanced Signal Processing and Machine Learning
Online condition monitoring and fault diagnosis systems are necessary to prevent unexpected downtimes in critical electric powertrains. The machine learning algorithms provide a better way to diagnose faults in complex cases, such as mixed faults and/or in variable speed conditions. Most of studies focus on training phases of the machine learning algorithms, but the development of the trained machine learning algorithms for an online diagnosis system is not detailed. In this study, a complete procedure of training and implementation of an online fault diagnosis system is presented and discussed. Aspects of the development of an online fault diagnosis based on machine learning algorithms are…
CNN based Gearbox Fault Diagnosis and Interpretation of Learning Features
Machine learning based fault diagnosis schemes have been intensively proposed to deal with faults diagnosis of rotating machineries such as gearboxes, bearings, and electric motors. However, most of the machine learning algorithms used in fault diagnosis are pattern recognition tools, which can classify given data into two or more classes. The underlined physical phenomena in fault diagnosis are not directly interpretable in machine learning schemes, thus it is usually called black/gray box models. In this study, convolutional neural networks (CNN) machine learning algorithm is proposed to classify gearbox faults, and the learning features of the CNN filters are visualized to understand the…
Integration of distributed energy systems in micro-grid architecture for making a virtual power plant
This paper presents a novel concept for increasing the penetration level of distributed renewable energy systems into the main electricity grid. When increasing the renewable energy penetration, it is important to implement the frequency based power delivery in distributed generators and work as traditional synchronous generators. This can be achieved by improving the power processing unit of each renewable generation units to work as active generators. But in existing grid architecture, the grid frequency is controlled as one common variable over the electricity grid. With such a method, it is difficult to use frequency based power sharing in small distributed generators and participate in…
Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion
Safe and reliable operations of industrial machines are highly prioritized in industry. Typical industrial machines are complex systems, including electric motors, gearboxes and loads. A fault in critical industrial machines may lead to catastrophic failures, service interruptions and productivity losses, thus condition monitoring systems are necessary in such machines. The conventional condition monitoring or fault diagnosis systems using signal processing, time and frequency domain analysis of vibration or current signals are widely used in industry, requiring expensive and professional fault analysis team. Further, the traditional diagnosis methods mainly focus on single components in st…
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.
Direct Torque Control of a Small Wind Turbine with a Sliding-Mode Speed Controller
In this paper. the method of direct torque control in the presence of a sliding-mode speed controller is proposed for a small wind turbine being used in water heating applications. This concept and control system design can be expanded to grid connected or off-grid applications. Direct torque control of electrical machines has shown several advantages including very fast dynamics torque control over field-oriented control. Moreover. the torque and flux controllers in the direct torque control algorithms are based on hvsteretic controllers which are nonlinear. In the presence of a sliding-mode speed control. a nonlinear control system can be constructed which is matched for AC/DC conversion …
Power dispatching of active generators using droop control in grid connected micro-grid
Masteroppgave i fornybar energi ENE 500 Universitetet i Agder 2014 Renewable energy promises a green energy future for the world. However, many technical problems still have to be solved. Almost all renewable energy sources require power electronic converters for power processing and therefore inverter control systems and power dispatching strategies are significant in renewable energy applications. The existing electricity grid architecture is based on centralized power generations while most renewable power generations are distributed and connected to lower or medium voltage networks. Some of important renewable energy resources such as solar and wind power are intermittent in nature. Thi…
Robust Active Learning Multiple Fault Diagnosis of PMSM Drives with Sensorless Control under Dynamic Operations and Imbalanced Datasets
Authors accepted manuscript © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper proposes an active learning scheme to detect multiple faults in permanent magnet synchronous motors in dynamic operations without using historical labelled faulty training data. The proposed method combines the self-supervised anomaly detector based on a local outlier factor…
A novel soft-stall power control for a small wind turbine
In this paper, the problem of Soft-stall power control design for a small wind turbine is considered. Passive stalling and furling methods are widely used to limit the output power of small wind turbines at above-rated wind speed conditions. However, these methods have substantial limitations, for instance, related to tracking the maximum power at some wind speed levels, limited variable speed operation and introducing unbalanced forces on wind turbine blades. Soft-stall power control is a promising technique to overcome above limitations and improve the performance of small wind turbines. Small wind turbines have a comparatively low moment of inertia value, and it is possible to make fast …
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...