Advances in power quality analysis techniques for electrical machines and drives: a review

The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.Peer ReviewedPostprint (published version

Power disturbance monitoring through techniques for novelty detection on wind power and photovoltaic generation

Novelty detection is a statistical method that verifies new or unknown data, determines whether these data are inliers (within the norm) or outliers (outside the norm), and can be used, for example, in developing classification strategies in machine learning systems for industrial applications. To this end, two types of energy that have evolved over time are solar photovoltaic and wind power generation. Some organizations around the world have developed energy quality standards to avoid known electric disturbances; however, their detection is still a challenge. In this work, several techniques for novelty detection are implemented to detect different electric anomalies (disturbances), which are k-nearest neighbors, Gaussian mixture models, one-class support vector machines, self-organizing maps, stacked autoencoders, and isolation forests. These techniques are applied to signals from real power quality environments of renewable energy systems such as solar photovoltaic and wind power generation. The power disturbances that will be analyzed are considered in the standard IEEE-1159, such as sag, oscillatory transient, flicker, and a condition outside the standard attributed to meteorological conditions. The contribution of the work consists of the development of a methodology based on six techniques for novelty detection of power disturbances, under known and unknown conditions, over real signals in the power quality assessment. The merit of the methodology is a set of techniques that allow to obtain the best performance of each one under different conditions, which constitutes an important contribution to the renewable energy systems.Postprint (published version

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

[EN] Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.This research work was funded by the Ministry of Education and the Deanship of Scientific Research, Najran University. Kingdom of Saudi Arabia, under code number NU/ESCI/19/001.Hussain, A.; Draz, U.; Ali, T.; Tariq, S.; Glowacz, A.; Irfan, M.; Antonino Daviu, JA.... (2020). Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach. Energies. 13(15):1-22. https://doi.org/10.3390/en13153930S1221315Lionetto, M. G., Guascito, M. R., Caricato, R., Giordano, M. E., De Bartolomeo, A. R., Romano, M. P., … Contini, D. (2019). Correlation of Oxidative Potential with Ecotoxicological and Cytotoxicological Potential of PM10 at an Urban Background Site in Italy. Atmosphere, 10(12), 733. doi:10.3390/atmos10120733Wiedinmyer, C., Yokelson, R. J., & Gullett, B. K. (2014). Global Emissions of Trace Gases, Particulate Matter, and Hazardous Air Pollutants from Open Burning of Domestic Waste. Environmental Science & Technology, 48(16), 9523-9530. doi:10.1021/es502250zYan, F., Zhu, F., Wang, Q., & Xiong, Y. (2016). Preliminary Study of PM2.5 Formation During Municipal Solid Waste Incineration. Procedia Environmental Sciences, 31, 475-481. doi:10.1016/j.proenv.2016.02.054Curtis, L., Rea, W., Smith-Willis, P., Fenyves, E., & Pan, Y. (2006). Adverse health effects of outdoor air pollutants. Environment International, 32(6), 815-830. doi:10.1016/j.envint.2006.03.012Gollakota, A. R. K., Gautam, S., & Shu, C.-M. (2020). Inconsistencies of e-waste management in developing nations – Facts and plausible solutions. Journal of Environmental Management, 261, 110234. doi:10.1016/j.jenvman.2020.110234Anitha, A. (2017). Garbage monitoring system using IoT. IOP Conference Series: Materials Science and Engineering, 263, 042027. doi:10.1088/1757-899x/263/4/042027Sirsikar, S., & Karemore, P. (2015). Review Paper on Air Pollution Monitoring system. IJARCCE, 218-220. doi:10.17148/ijarcce.2015.4147Tavares Neto, R. F., & Godinho Filho, M. (2013). Literature review regarding Ant Colony Optimization applied to scheduling problems: Guidelines for implementation and directions for future research. Engineering Applications of Artificial Intelligence, 26(1), 150-161. doi:10.1016/j.engappai.2012.03.011Ali, T., Irfan, M., Alwadie, A. S., & Glowacz, A. (2020). IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System for Smart Cities. Arabian Journal for Science and Engineering, 45(12), 10185-10198. doi:10.1007/s13369-020-04637-wSilva, B. N., Khan, M., & Han, K. (2018). Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society, 38, 697-713. doi:10.1016/j.scs.2018.01.053Gutierrez, J. M., Jensen, M., Henius, M., & Riaz, T. (2015). Smart Waste Collection System Based on Location Intelligence. Procedia Computer Science, 61, 120-127. doi:10.1016/j.procs.2015.09.170Carbon Monoxide Dangers in the Boiler Room www.pmmag.com/articles/97528-carbonmonoxide-danger-in-the-boiler-roomDe Vito, S., Massera, E., Piga, M., Martinotto, L., & Di Francia, G. (2008). On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sensors and Actuators B: Chemical, 129(2), 750-757. doi:10.1016/j.snb.2007.09.060Guiry, J., van de Ven, P., & Nelson, J. (2014). Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices. Sensors, 14(3), 5687-5701. doi:10.3390/s140305687Ali, T., Draz, U., Yasin, S., Noureen, J., shaf, A., & Zardari, M. (2018). An Efficient Participant’s Selection Algorithm for Crowdsensing. International Journal of Advanced Computer Science and Applications, 9(1). doi:10.14569/ijacsa.2018.090154Ali, T., Noureen, J., Draz, U., Shaf, A., Yasin, S., & Ayaz, M. (2018). Participants Ranking Algorithm for Crowdsensing in Mobile Communication. ICST Transactions on Scalable Information Systems, 5(16), 154476. doi:10.4108/eai.13-4-2018.15447

Signal Processing and Analysis of Electrical Circuit

Glowacz, A.; Antonino-Daviu, JA. (2020). Signal Processing and Analysis of Electrical Circuit. Electronics. 9(1):1-4. https://doi.org/10.3390/electronics9010017149

Signal Processing and Analysis of Electrical Circuit

The analysis of electrical circuits is an essential task in the evaluation of electrical systems [...

Smart Sensor for Fault Detection in Induction Motors Based on the Combined Analysis of Stray-Flux and Current Signals: A Flexible, Robust Approach

[EN] The most recent trend in the electric motor condition monitoring area relies on combining the information obtained from different machine quantities in order to reach a more reliable conclusion about the motor¿s health. This knowledge is of critical importance nowadays, especially in industrial applications in which unexpected outages can lead to severe repercussions. This paper presents a new intelligent sensor that combines, in a single unit, the information obtained from the analysis of stray fluxes (both axial and radial) and currents by means of a feed-forward neural network (FFNN) for classification purposes. Unlike other solutions, the sensor is based on the application of advanced signal processing tools that are adapted to the online analysis of these quantities under transient from a single processing unit (smart sensor). The combination of these new tools with the classical steady-state analysis of such quantities enables to obtain a more reliable conclusion on the motor health. The experiments included in the paper demonstrate the reliability provided by the sensor, which is being prepared to incorporate a third input based on infrared data.This work was derived from a project supported by the Beca Leonardo a Investigadores y Creadores Culturales 2019 of Fundacion BBVA (ref: IN[19]_ ING_ING_0083). The BBVA Foundation is not responsible for the opinions, comments, or contents included in the project or the results derived from it. These are the exclusive responsibility of the authors. The authors would like to thank Consejo Nacional de Ciencia y Tecnologia (scholarship with key code 2019-000037-02NACF).Zamudio-Ramírez, I.; Osornio-Rios, RA.; Antonino-Daviu, JA. (2022). Smart Sensor for Fault Detection in Induction Motors Based on the Combined Analysis of Stray-Flux and Current Signals: A Flexible, Robust Approach. IEEE Industry Applications Magazine. 28(2):56-66. https://doi.org/10.1109/MIAS.2021.3114647566628

Signal Processing and Analysis of Electrical Circuit

This Special Issue with 35 published articles shows the significance of the topic “Signal Processing and Analysis of Electrical Circuit”. This topic has been gaining increasing attention in recent times. The presented articles can be categorized into four different areas: signal processing and analysis methods of electrical circuits; electrical measurement technology; applications of signal processing of electrical equipment; fault diagnosis of electrical circuits. It is a fact that the development of electrical systems, signal processing methods, and circuits has been accelerating. Electronics applications related to electrical circuits and signal processing methods have gained noticeable attention in recent times. The methods of signal processing and electrical circuits are widely used by engineers and scientists all over the world. The constituent papers represent a significant contribution to electronics and present applications that can be used in industry. Further improvements to the presented approaches are required for realizing their full potential

Advanced Fault Detection of Synchronous Generators using Stray Magnetic Field

Many methods used for precise fault detection in salient pole synchronous generators (SPSGs) often require a priori knowledge of the healthy case, but this requirement impedes application of the methods since an accurate analysis of the different machine quantity waveforms is not usually carried out during commissioning. The inspection and maintenance processes in SPSGs are also costly and time-consuming; therefore, reliable methods that can detect and discriminate between different faults without comparison with the healthy condition are highly desirable. This paper proposes a precise method for detection and discrimination between different fault types in SPSG. The method does not require healthy machine data and is applied to diagnose both inter-turn short circuits (ITSC) in the field winding and dynamic eccentricities (DE). The proposed non-intrusive detection algorithm is based on advanced signal analysis of stray magnetic field data and can be applied during SPSG operation. Moreover, a distinctive pattern for both ITSC and DE fault enables the discrimination between both considered failures, even if they are present at the same time. The proposed method is validated through finite element modeling and experimentally on a 100 kVA and a 22 MVA SPSG to demonstrate its applicability in real power plants

Recent Educational Experiences in Electric Machine Maintenance Teaching

Maintenance of electric machines and installations is a particularly important area; eventual faults in these devices may lead to significant losses in terms of time and money. The investment and concern in developing proper maintenance protocols have been gradually increasing over recent decades. As a consequence, there is a need to instruct future engineers in the electric machines and installations maintenance area. The subject "Maintenance of Electric Machines and Installations" has been designed under this idea. It is taught within an official master degree in Maintenance Engineering. This work describes the educational experiences reached during the initial years of the teaching of the subject. Aspects such as student profiles, subject approaches, design of the syllabus, methodology and structure of the laboratory sessions are remarked in the work. In addition, the paper discusses other educational strategies which are being introduced to increase the interest in the subject, such as integration of Information and Communication Technologies (ICT), promotion of the collaborative work, inclusion of the possibility of remote learning or development of new assessment systems

Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps

The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only a few contributions to current-based pump diagnosis. In this paper, two current-based methods for the detection of bearing defects, impeller clogging, and cracked impellers are presented. The first approach, load point-dependent fault indicator analysis (LoPoFIA), is an approach that was derived from motor current signature analysis (MCSA). Compared to MCSA, the novelty of LoPoFIA is that only amplitudes at typical fault frequencies in the current spectrum are considered as a function of the hydraulic load point. The second approach is advanced transient current signature analysis (ATCSA), which represents a time-frequency analysis of a current signal during start-up. According to the literature, ATCSA is mainly used for motor diagnosis. As a test item, a VSD-driven circulation pump was measured in a pump test bench. Compared to MCSA, both LoPoFIA and ATCSA showed improvements in terms of minimizing false alarms. However, LoPoFIA simplifies the separation of bearing defects and impeller defects, as impeller defects especially influence higher flow ranges. Compared to LoPoFIA, ATCSA represents a more efficient method in terms of minimizing measurement effort. In summary, both LoPoFIA and ATCSA provide important insights into the behavior of faulty pumps and can be advantageous compared to MCSA in terms of false alarms and fault separation