Resistive Loss Modelling for Inverter-fed Induction Motors

The aim of this research is to model resistive losses in induction motors. The resistive losses in the form-wound stator windings of induction motors were modelled by using time-discretized finite element analysis (FEA) and circuit models. Loss modelling with a high level of accuracy by means of FEA can be used in the demanding design of electrical machines, typically for high-power and high-speed induction motors in spite of its high computational cost. Alternatively, the equivalent circuits served as a cheap computational tool for the rapid estimation of the resistive losses of 37-kW and 1250-kW machines for motor drives without using the machine data, typically the machine structure and materials. Electromagnetic losses lead to a temperature rise in electrical machines. As a result, temperature rise analysis is required to check whether the induction motors that are designed fulfill the IEC standard or design constraints. Thermal analysis employs FEA or a thermal network depending on the specific problems being studied. In this study, Finite Element Method Magnetics - a public domain code - was used to analyse the temperature rise of the form-wound stator windings of a 1250-kW induction motor. The thermal network was used in the thermal analysis of a 300-kW high-speed motor using form-wound stator windings. After the loss and thermal information have been collected, the losses in the stator form-wound windings of the induction motors are minimized in collaboration with temperature rise checking in the design stage. In addition, the loss and temperature rise analysis may offer numerical data to evaluate the possibility of using the form-wound windings for high-speed induction motors

Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network

To monitor and handle big data obtained from electrical, electronic, electro-mechanical, and other equipment linked to the power grid effectively and efficiently, it is important to monitor them continually to gather information on power line integrity. We propose that data transmission analysis and data collection from tools like digital power meters may be used to undertake predictive maintenance on power lines without the need for specialized hardware like power line modems and synthetic data streams. Neural network models such as deep learning may be used for power line integrity analysis systems effectively, safely, and reliably. We adopt Q-learning based data analysis network for analyzing and monitoring power line integrity. The results of experiments performed over 32 km long power line under different scenarios are presented. The proposed framework may be useful for monitoring traditional power lines as well as alternative energy source parks and large users like industries. We discovered that the quantity of data transferred changes based on the problem and the size of the planned data packet. When all phases were absent from all meters, we noted a significant decrease in the amount of data collected from the power line of interest. This implies that there is a power outage during the monitoring. When even one phase is reconnected, we only obtain a portion of the information and a solution to interpret this was necessary. Our Q-network was able to identify and classify simulated 190 entire power outages and 700 single phase outages. The mean square error (MSE) did not exceed 0.10% of the total number of instances, and the MSE of the smart meters for a complete disturbance was only 0.20%, resulting in an average number of conceivable cases of errors and disturbances of 0.12% for the whole operation.publishedVersio

Novel Isolated Multiport DC Converter with Natural Bipolar Symmetry for Renewable Energy Source Integration to DC Grids

Due to their greater reliability, efficiency, and resilience as compared to unipolar dc grid systems, bipolar dc grid systems are swiftly gaining popularity for the integration of renewable energy sources. However, development of multiport converters for bipolar microgrid systems is still progressing slowly in terms of reducing costs or improving power density and compact designs. This paper proposes a multiport isolated dc-dc converter with naturally symmetric bipolar outputs (MIBDC). With respect to the number of input ports, voltage gain, and output symmetry that the proposed converter naturally possesses, it outperforms its few competitors. Additionally, the proposed MIBDC significantly reduces component count and control complexity by employing a fixed transformer with only one primary and secondary winding for any number of inputs. The suggested converter’s performance in both open and closed loops is evaluated quantitatively in simulation and experimentally using OPAL-OP5700 RT’s hardware-in-the-loop (HIL) platform under various situations.publishedVersio

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 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

Hybrid Three-Phase Transformer-Based Multilevel Inverter With Reduced Component Count

The topology of the static synchronous compensator of reactive power for a low-voltage three-phase utility grid capable of asymmetric reactive power compensation in grid phases has been proposed and analysed. It is implemented using separate, independent cascaded H-bridge multilevel inverters for each phase. Every inverter includes two H-bridge cascades. The first cascade operating at grid frequency is implemented using thyristors, and the second one—operating at high frequency is based on the high-speed MOSFET transistors. The investigation shows that the proposed compensator is able to compensate the reactive power in a low-voltage three-phase grid when phases are loaded by highly asymmetrical reactive loads and provides up to three times lower power losses in the compensator as compared with the situation when the compensator is based on the conventional three-level inverters implemented using IGBT transistors.publishedVersio

Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines

Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from scheduled maintenance towards predictive maintenance, there is a significant lack of algorithms related to fault prediction of electrical machines. There is quite a lot of research going on in this area, but it is still underdeveloped and needs a lot more work. This paper presents a signal spectrum-based machine learning approach toward the fault prediction of electrical machines. The proposed method is a new approach to the predictive maintenance of electrical machines. This paper presents the details regarding the algorithm and then validates the accuracy against data collected from working electrical machines for both cases. A comparison is also presented at the end of multiple machine learning algorithms used for training based on this approach.Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical MachinespublishedVersio

Cascaded Multilevel Inverter-Based Asymmetric Static Synchronous Compensator of Reactive Power

The topology of the static synchronous compensator of reactive power for a low-voltage three-phase utility grid capable of asymmetric reactive power compensation in grid phases has been proposed and analysed. It is implemented using separate, independent cascaded H-bridge multilevel inverters for each phase. Every inverter includes two H-bridge cascades. The first cascade operating at grid frequency is implemented using thyristors, and the second one—operating at high frequency is based on the high-speed MOSFET transistors. The investigation shows that the proposed compensator is able to compensate the reactive power in a low-voltage three-phase grid when phases are loaded by highly asymmetrical reactive loads and provides up to three times lower power losses in the compensator as compared with the situation when the compensator is based on the conventional three-level inverters implemented using IGBT transistors.publishedVersio

Successful Psoriasis Treatment Using NB-UVB with Methotrexate: The Vietnamese Experience

AIM: To compare the effectiveness of narrowband ultraviolet B (NBUVB) and oral methotrexate (MTX) to oral MTX alone in Vietnamese psoriasis patients, from May 2016 to May 2018. METHODS: We conducted a non-randomized trial on 70 patients with plaque-type psoriasis of moderate to severe. Thirty-five patients apply NBUVB once/day in 5 days/week for 4 weeks plus oral MTX 7.5 mg/week and 35 patients oral MTX 7.5 mg/week and both two groups treatment for 3 months. The extent of the lesion was assessed by the Psoriasis Area and Severity Index (PASI). RESULTS: The proportion of decreasing PASI was comparable (68.49% in NBUVB and MTX versus 57.62% in MTX alone); p < 0.05. Inside, good 28.58%, moderate 68.57% and poor 2.85% in NBUVB and MTX better than good 2.85%, moderate 71.4% and poor 25.72% in MTX alone; p < 0.05. The recurrence rate after 24 months of the NBUVB and MTX group (42.9%) was lower than the MTX alone group (71.4%); p < 0.05. CONCLUSION: NBUVB and oral MTX have affected treatment with chronic plaque psoriasis better than oral MTX alone