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 steady-state operations. Under dynamic operating conditions, the measured quantities are non-stationary, thus those methods cannot provide reliable diagnosis results for complex gearbox based powertrains, especially in multiple fault contexts. In this dissertation, four main research topics or problems in condition monitoring of gearboxes and powertrains have been identified, and novel solutions are provided based on data-driven approach. The first research problem focuses on bearing fault diagnosis at early stages and dynamic working conditions. The second problem is to increase the robustness of gearbox mixed fault diagnosis under noise conditions. Mixed fault diagnosis in variable speeds and loads has been considered as third problem. Finally, the limitation of labelled training or historical failure data in industry is identified as the main challenge for implementing data-driven algorithms. To address mentioned problems, this study aims to propose data-driven fault diagnosis schemes based on order tracking, unsupervised and supervised machine learning, and data fusion. All the proposed fault diagnosis schemes are tested with experimental data, and key features of the proposed solutions are highlighted with comparative studies.publishedVersio

Power dispatching of active generators using droop control in grid connected micro-grid

Masteroppgave i fornybar energi ENE 500 Universitetet i Agder 2014Renewable 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. This is a challenging issue for grid integration of renewable energy into the grid and raises difficulties for power system operational control and maintaining power quality. In practical integration of renewable energy systems, these problems must be addressed and solved. In this research, grid integration methods and inverter control methods for the renewable resources have been studied. First task of the study was development of a Voltage source Converter (VSC) system. Active and reactive power control systems for power dispatching have been implemented in the VSC and so that it can work as an active generator. The power dispatching policy of the active generator is based on combination of droop and PI control method. A micro-grid model has been proposed in order to achieve high penetration of renewable energy. In the proposed 500 kW micro-grid system, it is considered that 250 kW of the power is come from the central grid and the remaining power is provided by local distributed generations. Micro-grid has been connected to the main grid via back-to-back converter topology. Frequency droop is originated from the back-to-back converter, where any power deviation from base power level (250 kW), causes ± 0.002 Hz/kW deviation from base 50 Hz frequency. In this micro-grid, there is a combination of dispatchable generations and an intermittent power generation. Dispatchable generation include a solid oxide fuel cell (SOFC) and a central energy storage system where both systems can work as active generators. Active power flow of each DG unit is based on frequency variation originated by the back-to-back converter and reactive power flow is based on the voltage variation of coupling points of each active generators. In the propose control system, power flow of local micro-grid renewable power sources are controlled for maximum utilization of available power within the micro-grid and minimize the power flow from the main-grid. In this implementation, the main-grid and micro-grid can work as two different AC power system areas where frequency and voltage are isolated through an intermediate DC path. The model has been simulated in Matlab Simulink and stable operations have been observed where micro-grid frequency, voltage and power quality were within acceptable ranges. This shows the usability of the proposed model for achieving high penetration of renewable power into micro-grids. Key words: Distributed generation (DG), Droop control, Active generators, Voltage source converters (VSC), Micro-grid, Renewable energy, Grid integratio

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 (LOF) and a deep Q-network (DQN) supervised reinforcement learner to classify interturn short-circuit, local demagnetisation and mixed faults. The first fault, which is detected by LOF and verified by an expert during maintenance, is used as training data for the DQN classifier. From that point onward, the LOF anomaly detector and DQN fault classifiers are working in tandem in the identification of new faults, which require expert intervention when either of them identifies a fault. The robustness of the scheme against dynamic operations, mixed fault and imbalanced training datasets is validated via a comparative study using stray flux data from an inhouse test setup.acceptedVersio

Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors

This paper aims to classify local demagnetisation and inter-turn short-circuit (ITSC) on position sensorless permanent magnet synchronous motors (PMSM) in transient states based on external stray flux and learning classifier. Within the framework, four supervised machine learning tools were tested: ensemble decision tree (EDT), k-nearest neighbours (KNN), support vector machine (SVM), and feedforward neural network (FNN). All algorithms are trained on datasets from one operational profile but tested on other different operation profiles. Their input features or spectrograms are computed from resampled time-series data based on the estimated position of the rotor from one stray flux sensor through an optimisation problem. This eliminates the need for the position sensors, allowing for the fault classification of sensorless PMSM drives using only two external stray flux sensors alone. Both SVM and FNN algorithms could identify a single fault of the magnet defect with an accuracy higher than 95% in transient states. For mixed faults, the FNN-based algorithm could identify ITSC in parallel-strands stator winding and local partial demagnetisation with an accuracy of 87.1%.publishedVersio

Robust Multiple-Fault Diagnosis of PMSM Drives Under Variant Operations and Noisy Conditions

publishedVersio

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 steady-state operations. Under dynamic operating conditions, the measured quantities are non-stationary, thus those methods cannot provide reliable diagnosis results for complex gearbox based powertrains, especially in multiple fault contexts. In this dissertation, four main research topics or problems in condition monitoring of gearboxes and powertrains have been identified, and novel solutions are provided based on data-driven approach. The first research problem focuses on bearing fault diagnosis at early stages and dynamic working conditions. The second problem is to increase the robustness of gearbox mixed fault diagnosis under noise conditions. Mixed fault diagnosis in variable speeds and loads has been considered as third problem. Finally, the limitation of labelled training or historical failure data in industry is identified as the main challenge for implementing data-driven algorithms. To address mentioned problems, this study aims to propose data-driven fault diagnosis schemes based on order tracking, unsupervised and supervised machine learning, and data fusion. All the proposed fault diagnosis schemes are tested with experimental data, and key features of the proposed solutions are highlighted with comparative studies

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 speed changes by generator torque control which is essential to the successful implementation of proposed control method. A sliding-mode controller is developed as the wind turbine speed controller. Furthermore, two sliding-mode current controllers are utilized in the field oriented control system of the generator. A simulation study is illustrated to validate the proposed soft-stall power control technique and results are compared with two other speed control strategies which confirm the applicability of the proposed control technique for small wind turbines

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 of the converter that gives fast responses with low overshoots. The main control objectives of the proposed small wind turbine can be maximum power point tracking and soft-stall power control. This small wind turbine consists of permanent magnet synchronous generator and external wind speed. and rotor speed measurements are not required for the system. However. a sensor is needed to detect the rated wind speed overpass events to activate proper speed references for the wind turbine. Based on the low-cost design requirement of small wind turbines. an available wind speed sensor can be modified. or a new sensor can be designed to get the required measurement. The simulation results will be provided to illustrate the excellent performance of the closed-loop control system in entire wind speed range (4-25 m/s)

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 system is consistent with sensorless maximum power point tracking algorithm, which eliminates the need for both wind speed and rotor speed sensors and gives a highly reliable solution for water heating applications. Internal voltage and current sensors are used to measure the output voltage, the output current and the power of the generator. Using those measurements, the sliding-mode observer can accurately estimate the rotor speed and position and which is used in the maximum power point tracking algorithm. To calculate the optimum rotor speed, the generator output power measurements are used with power signal feedback method. The effectiveness of the proposed system is verified using a simulation model by comparing the performances with a sensor based model