Methods of condition monitoring and fault detection for electrical machines

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Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of machine learning (ML) and deep learning algorithms (DL) have found applications in the enhancement of fault identification and classification within power transmission networks. Yet, the efficacy of these ML architectures is profoundly dependent upon the abundance and quality of the training data. This intellectual explanation introduces an innovative strategy for the classification and pinpointing of faults within power transmission networks. This is achieved through the utilization of variational autoencoders (VAEs) to generate synthetic data, which in turn is harnessed in conjunction with ML algorithms. This approach encompasses the augmentation of the available dataset by infusing it with synthetically generated instances, contributing to a more robust and proficient fault recognition and categorization system. Specifically, we train the VAE on a set of real-world power transmission data and generate synthetic fault data that capture the statistical properties of real-world data. To overcome the difficulty of fault diagnosis methodology in three-phase high voltage transmission networks, a categorical boosting (Cat-Boost) algorithm is proposed in this work. The other standard machine learning algorithms recommended for this study, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing the customized version of forward feature selection (FFS), were trained using synthetic data generated by a VAE. The results indicate exceptional performance, surpassing current state-of-the-art techniques, in the tasks of fault classification and localization. Notably, our approach achieves a remarkable 99% accuracy in fault classification and an extremely low mean absolute error (MAE) of 0.2 in fault localization. These outcomes represent a notable advancement compared to the most effective existing baseline methods.publishedVersio

BASIC ALGORITHM FOR INDUCTION MOTORS ROTOR FAULTS PRE-DETERMINATION

Due to importance of squirrel cage induction motor in today’s industry, the fault detection on that type of motors has become a highly developed area of interest for researchers. The electrical machine is designed for stable operations with minimum noise and vibrations under the normal conditions. When the fault emerges, some additional distortions appear. The necessity to detect the fault in an early stage, to prevent further damage of the equipment due to fault propagation, is one of the most important features of any condition monitoring or diagnostic techniques for electrical machines nowadays. In this paper possible induction motors faults classified and basic algorithm for rotor faults pre-determination is presented

Optimal Control of Automatic Manipulator for Elimination of Galvanic Line Load Oscillation

This paper provides an analysis of the current state-of-the-art technologies in the field of auto-operators used in production during the electroplating process. General schemes of operations are presented, benefits and drawbacks of each scheme are discussed. The paper discusses an increase in the operating efficiency of the auto-operator in transient conditions (braking and acceleration) by reducing suspension oscillations and provides an example of a similar problem from other industries. In addition to the classification of the auto-operators, three main ways and control methods of the auto-operator of a galvanic line are presented. The main ways of eliminating oscillations during the movement of the auto-operator, as well as the rationale for the choice of adaptive (optimal) control, based on and comparing the basic control algorithms of the robot manipulator, are discussed. The comparative analysis of algorithms used to determine the optimal control has been carried out. Application field of each optimal control method described, moreover advantages and disadvantages as well as implementation methods described. Bellman dynamic programming method was chosen to eliminate oscillations of the suspension with details during the auto-operator transient conditions, the chosen method takes into account all necessary conditions to achieve the desired result

Digital Twin as a Virtual Sensor for Wind Turbine Applications

Digital twins (DTs) have been implemented in various applications, including wind turbine generators (WTGs). They are used to create virtual replicas of physical turbines, which can be used to monitor and optimize their performance. By simulating the behavior of physical turbines in real time, DTs enable operators to predict potential failures and optimize maintenance schedules, resulting in increased reliability, safety, and efficiency. WTGs rely on accurate wind speed measurements for safe and efficient operation. However, physical wind speed sensors are prone to inaccuracies and failures due to environmental factors or inherent issues, resulting in partial or missing measurements that can affect the turbine’s performance. This paper proposes a DT-based sensing methodology to overcome these limitations by augmenting the physical sensor platform with virtual sensor arrays. A test bench of a direct drive WTG based on a permanent magnet synchronous generator (PMSG) was prepared, and its mathematical model was derived. MATLAB/Simulink was used to develop the WTG virtual model based on its mathematical model. A data acquisition system (DAS) equipped with an ActiveX server was used to facilitate real-time data exchange between the virtual and physical models. The virtual sensor was then validated and tuned using real sensory data from the physical turbine model. The results from the developed DT model showed the power of the DT as a virtual sensor in estimating wind speed according to the generated power.publishedVersio

Fault Detection and Predictive Maintenance of Electrical Machines

Nowadays, most domestic and industrial fields are moving toward Industry 4.0 standards and integration with information technology. To decrease shutdown costs and minimize downtime, manufacturers switch their production to predictive maintenance. Algorithms based on machine learning can be used to make predictions and detect timely potential faults in modern energy systems. For this, trained models with the usage of data analysis, cloud, and edge computing are implemented. The main challenge is the amount and quality of the data used for model training. This chapter discusses a specific version of a condition monitoring system, including maintenance approaches and machine learning algorithms and their general application issues

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

Design and Prototyping of Directly Driven Outer Rotor Permanent Magnet Generator for Small Scale Wind Turbines

The paper is about the design and prototyping of directly driven outer rotor permanent magnet generator for small scale wind turbine. In the paper, the initial design of the generator is given. Main issues and phenomena affecting the generator design, such as cogging torque and its reduction possibilities, selection and demagnetization risk assessment of permanent magnets, machine losses and thermal analysis, are described. Test results of prototype generator construction and final parameters are also presented. The necessity of further study is pointed out

Exploring the Limits of Early Predictive Maintenance in Wind Turbines Applying an Anomaly Detection Technique

The aim of the presented investigation is to explore the time gap between an anomaly appearance in continuously measured parameters of the device and a failure, related to the end of the remaining resource of the device-critical component. In this investigation, we propose a recurrent neural network to model the time series of the parameters of the healthy device to detect anomalies by comparing the predicted values with the ones actually measured. An experimental investigation was performed on SCADA estimates received from different wind turbines with failures. A recurrent neural network was used to predict the temperature of the gearbox. The comparison of the predicted temperature values and the actual measured ones showed that anomalies in the gearbox temperature could be detected up to 37 days before the failure of the device-critical component. The performed investigation compared different models that can be used for temperature time-series modeling and the influence of selected input features on the performance of temperature anomaly detection.publishedVersio

Optimal Control of Automatic Manipulator for Elimination of Galvanic Line Load Oscillation

This paper provides an analysis of the current state-of-the-art technologies in the field of auto-operators used in production during the electroplating process. General schemes of operations are presented, benefits and drawbacks of each scheme are discussed. The paper discusses an increase in the operating efficiency of the auto-operator in transient conditions (braking and acceleration) by reducing suspension oscillations and provides an example of a similar problem from other industries. In addition to the classification of the autooperators, three main ways and control methods of the auto-operator of a galvanic line are presented. The main ways of eliminating oscillations during the movement of the auto-operator, as well as the rationale for the choice of adaptive (optimal) control, based on and comparing the basic control algorithms of the robot manipulator, are discussed. The comparative analysis of algorithms used to determine the optimal control has been carried out. Application field of each optimal control method described, moreover advantages and disadvantages as well as implementation methods described. Bellman dynamic programming method was chosen to eliminate oscillations of the suspension with details during the auto-operator transient conditions, the chosen method takes into account all necessary conditions to achieve the desired result