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Prof. Rene Romero-Troncoso
Facultad de Ingeniería, Universidad Autónoma de Querétaro, 76010 Querétaro, México

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Research Keywords & Expertise

0 Power Quality
0 Signal Processing
0 Smart Grids
0 Induction Motors
0 Monitoring and diagnosis

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Induction Motors
Signal Processing
Power Quality
Monitoring and diagnosis

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Journal article
Published: 05 June 2021 in Sensors
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The study of power quality (PQ) has gained relevance over the years due to the increase in non-linear loads connected to the grid. Therefore, it is important to study the propagation of power quality disturbances (PQDs) to determine the propagation points in the grid, and their source of generation. Some papers in the state of the art perform the analysis of punctual measurements of a limited number of PQDs, some of them using high-cost commercial equipment. The proposed method is based upon a developed proprietary system, composed of a data logger FPGA with GPS, that allows the performance of synchronized measurements merged with the full parameterized PQD model, allowing the detection and tracking of disturbances propagating through the grid using wavelet transform (WT), fast Fourier transform (FFT), Hilbert–Huang transform (HHT), genetic algorithms (GAs), and particle swarm optimization (PSO). Measurements have been performed in an industrial installation, detecting the propagation of three PQDs: impulsive transients propagated at two locations in the grid, voltage fluctuation, and harmonic content propagated to all the locations. The results obtained show that the low-cost system and the developed methodology allow the detection of several PQDs, and track their propagation within a grid with 100% accuracy.

ACS Style

Oscar Pardo-Zamora; Rene Romero-Troncoso; Jesus Millan-Almaraz; Daniel Morinigo-Sotelo; Roque Osornio-Rios; Jose Antonino-Daviu. Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization. Sensors 2021, 21, 3910 .

AMA Style

Oscar Pardo-Zamora, Rene Romero-Troncoso, Jesus Millan-Almaraz, Daniel Morinigo-Sotelo, Roque Osornio-Rios, Jose Antonino-Daviu. Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization. Sensors. 2021; 21 (11):3910.

Chicago/Turabian Style

Oscar Pardo-Zamora; Rene Romero-Troncoso; Jesus Millan-Almaraz; Daniel Morinigo-Sotelo; Roque Osornio-Rios; Jose Antonino-Daviu. 2021. "Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization." Sensors 21, no. 11: 3910.

Journal article
Published: 14 May 2021 in Energies
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Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization.

ACS Style

Artvin-Darien Gonzalez-Abreu; Miguel Delgado-Prieto; Roque-Alfredo Osornio-Rios; Juan-Jose Saucedo-Dorantes; Rene-De-Jesus Romero-Troncoso. A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances. Energies 2021, 14, 2839 .

AMA Style

Artvin-Darien Gonzalez-Abreu, Miguel Delgado-Prieto, Roque-Alfredo Osornio-Rios, Juan-Jose Saucedo-Dorantes, Rene-De-Jesus Romero-Troncoso. A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances. Energies. 2021; 14 (10):2839.

Chicago/Turabian Style

Artvin-Darien Gonzalez-Abreu; Miguel Delgado-Prieto; Roque-Alfredo Osornio-Rios; Juan-Jose Saucedo-Dorantes; Rene-De-Jesus Romero-Troncoso. 2021. "A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances." Energies 14, no. 10: 2839.

Journal article
Published: 08 March 2021 in Energies
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The fault diagnosis of electrical machines during startup transients has received increasing attention regarding the possibility of detecting faults early. Induction motors are no exception, and motor current signature analysis has become one of the most popular techniques for determining the condition of various motor components. However, in the case of inverter powered systems, the condition of a motor is difficult to determine from the stator current because fault signatures could overlap with other signatures produced by the inverter, low-slip operation, load oscillations, and other non-stationary conditions. This paper presents a speed signature analysis methodology for a reliable broken rotor bar diagnosis in inverter-fed induction motors. The proposed fault detection is based on tracking the speed fault signature in the time-frequency domain. As a result, different fault severity levels and load oscillations can be identified. The promising results show that this technique can be a good complement to the classic analysis of current signature analysis and reveals a high potential to overcome some of its drawbacks.

ACS Style

Tomas Garcia-Calva; Daniel Morinigo-Sotelo; Vanessa Fernandez-Cavero; Arturo Garcia-Perez; Rene Romero-Troncoso. Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients. Energies 2021, 14, 1469 .

AMA Style

Tomas Garcia-Calva, Daniel Morinigo-Sotelo, Vanessa Fernandez-Cavero, Arturo Garcia-Perez, Rene Romero-Troncoso. Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients. Energies. 2021; 14 (5):1469.

Chicago/Turabian Style

Tomas Garcia-Calva; Daniel Morinigo-Sotelo; Vanessa Fernandez-Cavero; Arturo Garcia-Perez; Rene Romero-Troncoso. 2021. "Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients." Energies 14, no. 5: 1469.

Journal article
Published: 09 November 2020 in IEEE Transactions on Instrumentation and Measurement
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Power quality disturbances (PQD) are generated by non-linear loads and they propagate through the electrical grid to other sensitive devices causing malfunction. To study the propagation of PQD, it is important to perform synchronized and simultaneous measurements at multiple locations in the electrical network. This paper proposes a methodology to synchronize measurements of electrical variables at multiple locations in the electrical grid based on the synchronization of the internal time reference of several data loggers with the pulse per second (PPS) reference provided by a global positioning system (GPS) receiver module. As the PPS signal is synchronized to the atomic clocks of the satellites in the GPS, a precise time reference in multiple sites can be obtained simultaneously. The proposed methodology has been tested in two stages. The first stage is a validation experiment that compares the number of samples obtained between three GPS synchronized data loggers and two data loggers with internal synchronization only. The PPS signal has been interrupted in one GPS synchronized data logger to simulate a signal loss and test the resynchronization capability of the methodology. A very low synchronization error has been obtained for the GPS synchronized data loggers whereas the data loggers with the internal synchronization show greater synchronization errors. The second stage consists in experimental case of study that measuring electrical variables in multiple locations of a real electrical grid where the propagation of multiple disturbances are tracked with the GPS synchronized data loggers.

ACS Style

Oscar N. Pardo-Zamora; Rene De J. Romero-Troncoso; Jesus R. Millan-Almaraz; Daniel Morinigo-Sotelo; Roque A. Osornio-Rios. Methodology for Power Quality Measurement Synchronization Based on GPS Pulse-Per-Second Algorithm. IEEE Transactions on Instrumentation and Measurement 2020, 70, 1 -9.

AMA Style

Oscar N. Pardo-Zamora, Rene De J. Romero-Troncoso, Jesus R. Millan-Almaraz, Daniel Morinigo-Sotelo, Roque A. Osornio-Rios. Methodology for Power Quality Measurement Synchronization Based on GPS Pulse-Per-Second Algorithm. IEEE Transactions on Instrumentation and Measurement. 2020; 70 (99):1-9.

Chicago/Turabian Style

Oscar N. Pardo-Zamora; Rene De J. Romero-Troncoso; Jesus R. Millan-Almaraz; Daniel Morinigo-Sotelo; Roque A. Osornio-Rios. 2020. "Methodology for Power Quality Measurement Synchronization Based on GPS Pulse-Per-Second Algorithm." IEEE Transactions on Instrumentation and Measurement 70, no. 99: 1-9.

Research article
Published: 04 September 2020 in IET Generation, Transmission & Distribution
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The present work proposes a methodology for the detection and classification of some transient power quality disturbances. It uses a genetic algorithm for estimating the amplitude, frequency, and phase of the fundamental component in an optimum way to suppress it from an electric signal. By using this pre-processing strategy, it is possible to remove a single frequency component instead of removing a whole frequency band as other methodologies do. Once the fundamental component is suppressed, it is possible to perform a better identification of an anomalous condition in a signal because its high energy does not hide the presence of a disturbance. After the suppression, the proposed methodology computes three higher-order statistics (variance, kurtosis, and sixth-order cumulant) from the resulting signal and uses them as inputs for a fuzzy-based classifier. Higher-order statistics are selected because they are insensible to the presence of Gaussian noise adding robustness to the proposed methodology. Experimentation is performed using both synthetic and real signals. Real signals come from a photovoltaic generation plant and a hospital facility, both located in Spain. Results prove that the proposed methodology allows enhancing the results delivered by other methodologies up to 30%.

ACS Style

Luis Alejandro Romero‐Ramirez; David Alejandro Elvira‐Ortiz; Arturo Y. Jaen‐Cuellar; Daniel Morinigo‐Sotelo; Roque A. Osornio‐Rios; Rene De J. Romero‐Troncoso. Methodology based on higher‐order statistics and genetic algorithms for the classification of power quality disturbances. IET Generation, Transmission & Distribution 2020, 14, 4580 -4592.

AMA Style

Luis Alejandro Romero‐Ramirez, David Alejandro Elvira‐Ortiz, Arturo Y. Jaen‐Cuellar, Daniel Morinigo‐Sotelo, Roque A. Osornio‐Rios, Rene De J. Romero‐Troncoso. Methodology based on higher‐order statistics and genetic algorithms for the classification of power quality disturbances. IET Generation, Transmission & Distribution. 2020; 14 (20):4580-4592.

Chicago/Turabian Style

Luis Alejandro Romero‐Ramirez; David Alejandro Elvira‐Ortiz; Arturo Y. Jaen‐Cuellar; Daniel Morinigo‐Sotelo; Roque A. Osornio‐Rios; Rene De J. Romero‐Troncoso. 2020. "Methodology based on higher‐order statistics and genetic algorithms for the classification of power quality disturbances." IET Generation, Transmission & Distribution 14, no. 20: 4580-4592.

Journal article
Published: 01 September 2020 in IEEE Transactions on Industrial Informatics
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ACS Style

Juan Jose Saucedo-Dorantes; Miguel Delgado-Prieto; Roque Alfredo Osornio-Rios; Rene De Jesus Romero-Troncoso. Industrial Data-Driven Monitoring Based on Incremental Learning Applied to the Detection of Novel Faults. IEEE Transactions on Industrial Informatics 2020, 16, 5985 -5995.

AMA Style

Juan Jose Saucedo-Dorantes, Miguel Delgado-Prieto, Roque Alfredo Osornio-Rios, Rene De Jesus Romero-Troncoso. Industrial Data-Driven Monitoring Based on Incremental Learning Applied to the Detection of Novel Faults. IEEE Transactions on Industrial Informatics. 2020; 16 (9):5985-5995.

Chicago/Turabian Style

Juan Jose Saucedo-Dorantes; Miguel Delgado-Prieto; Roque Alfredo Osornio-Rios; Rene De Jesus Romero-Troncoso. 2020. "Industrial Data-Driven Monitoring Based on Incremental Learning Applied to the Detection of Novel Faults." IEEE Transactions on Industrial Informatics 16, no. 9: 5985-5995.

Journal article
Published: 07 August 2020 in Energies
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In this work, a new time-frequency tool based on minimum-norm spectral estimation is introduced for multiple fault detection in induction motors. Several diagnostic techniques are available to identify certain faults in induction machines; however, they generally give acceptable results only for machines operating under stationary conditions. Induction motors rarely operate under stationary conditions as they are constantly affected by load oscillations, speed waves, unbalanced voltages, and other external conditions. To overcome this issue, different time-frequency analysis techniques have been proposed for fault detection in induction motors under non-stationary regimes. However, most of them have low-resolution, low-accuracy or both. The proposed method employs the minimum-norm spectral estimation to provide high frequency resolution and accuracy in the time-frequency domain. This technique exploits the advantages of non-stationary conditions, where mechanical and electrical stresses in the machine are higher than in stationary conditions, improving the detectability of fault components. Numerical simulation and experimental results are provided to validate the effectiveness of the method in starting current analysis of induction motors.

ACS Style

Tomas A. Garcia-Calva; Daniel Morinigo-Sotelo; Oscar Duque-Perez; Arturo Garcia-Perez; Rene De J. Romero-Troncoso. Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults. Energies 2020, 13, 4102 .

AMA Style

Tomas A. Garcia-Calva, Daniel Morinigo-Sotelo, Oscar Duque-Perez, Arturo Garcia-Perez, Rene De J. Romero-Troncoso. Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults. Energies. 2020; 13 (16):4102.

Chicago/Turabian Style

Tomas A. Garcia-Calva; Daniel Morinigo-Sotelo; Oscar Duque-Perez; Arturo Garcia-Perez; Rene De J. Romero-Troncoso. 2020. "Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults." Energies 13, no. 16: 4102.

Journal article
Published: 17 June 2020 in Applied Sciences
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Broken rotor bar (BRB) is one of the most common failures in induction motors (IMs) these days; however, its identification is complicated since the frequencies associated with the fault condition appear near the fundamental frequency component (FFC). This situation gets worse when the IM slip or the operation frequency is low. In these circumstances, the common techniques for condition monitoring may experience troubles in the identification of a faulty condition. By suppressing the FFC, the fault detection is enhanced, allowing the identification of BRB even at low slip conditions. The main contribution of this work consists of the development of a preprocessing technique that estimates the FFC from an optimization point of view. This way, it is possible to remove a single frequency component instead of removing a complete frequency band from the current signals of an IM. Experimentation is performed on an IM operating at two different frequencies and at three different load levels. The proposed methodology is compared with two different approaches and the results show that the use of the proposed methodology allows to enhance the performance delivered by the common methodologies for the detection of BRB in steady state.

ACS Style

Daivd A. Elvira-Ortiz; Daniel Morinigo-Sotelo; Angel L. Zorita-Lamadrid; Roque A. Osornio-Rios; Rene De J. Romero-Troncoso. Fundamental Frequency Suppression for the Detection of Broken Bar in Induction Motors at Low Slip and Frequency. Applied Sciences 2020, 10, 4160 .

AMA Style

Daivd A. Elvira-Ortiz, Daniel Morinigo-Sotelo, Angel L. Zorita-Lamadrid, Roque A. Osornio-Rios, Rene De J. Romero-Troncoso. Fundamental Frequency Suppression for the Detection of Broken Bar in Induction Motors at Low Slip and Frequency. Applied Sciences. 2020; 10 (12):4160.

Chicago/Turabian Style

Daivd A. Elvira-Ortiz; Daniel Morinigo-Sotelo; Angel L. Zorita-Lamadrid; Roque A. Osornio-Rios; Rene De J. Romero-Troncoso. 2020. "Fundamental Frequency Suppression for the Detection of Broken Bar in Induction Motors at Low Slip and Frequency." Applied Sciences 10, no. 12: 4160.

Journal article
Published: 07 June 2020 in Mathematics
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A new multiple signal classification (MUSIC)-based methodology is presented for detecting and locating multiple damage types in a truss-type structure subjected to dynamic excitations. The methodology is based mainly on two steps: in step 1, the MUSIC method is employed to obtain the pseudo-spectra of vibration signatures, healthy and damaged, to be used for damage detection. In step 2, a new damage index, based on the obtained pseudo-spectra, is proposed to measure the structure condition. Furthermore, the damage location is estimated according to the variation in the amplitudes of the estimated pseudo-spectra. The presented results show that the proposed methodology can make an accurate and reliable estimation of the condition and location of three specific damage conditions, i.e., loosened bolts, internal corrosion, and external corrosion.

ACS Style

Carlos A. Perez-Ramirez; Jose M. Machorro-Lopez; Martin Valtierra-Rodriguez; Juan P. Amezquita-Sanchez; Arturo Garcia-Perez; David Camarena-Martinez; Rene De J. Romero-Troncoso. Location of Multiple Damage Types in a Truss-Type Structure Using Multiple Signal Classification Method and Vibration Signals. Mathematics 2020, 8, 932 .

AMA Style

Carlos A. Perez-Ramirez, Jose M. Machorro-Lopez, Martin Valtierra-Rodriguez, Juan P. Amezquita-Sanchez, Arturo Garcia-Perez, David Camarena-Martinez, Rene De J. Romero-Troncoso. Location of Multiple Damage Types in a Truss-Type Structure Using Multiple Signal Classification Method and Vibration Signals. Mathematics. 2020; 8 (6):932.

Chicago/Turabian Style

Carlos A. Perez-Ramirez; Jose M. Machorro-Lopez; Martin Valtierra-Rodriguez; Juan P. Amezquita-Sanchez; Arturo Garcia-Perez; David Camarena-Martinez; Rene De J. Romero-Troncoso. 2020. "Location of Multiple Damage Types in a Truss-Type Structure Using Multiple Signal Classification Method and Vibration Signals." Mathematics 8, no. 6: 932.

Journal article
Published: 11 January 2020 in Applied Sciences
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Renewable generation sources like photovoltaic plants are weather dependent and it is hard to predict their behavior. This work proposes a methodology for obtaining a parameterized model that estimates the generated power in a photovoltaic generation system. The proposed methodology uses a genetic algorithm to obtain the mathematical model that best fits the behavior of the generated power through the day. Additionally, using the same methodology, a mathematical model is developed for harmonic distortion estimation that allows one to predict the produced power and its quality. Experimentation is performed using real signals from a photovoltaic system. Eight days from different seasons of the year are selected considering different irradiance conditions to assess the performance of the methodology under different environmental and electrical conditions. The proposed methodology is compared with an artificial neural network, with the results showing an improved performance when using the genetic algorithm methodology.

ACS Style

David A. Elvira-Ortiz; Arturo Y. Jaen-Cuellar; Daniel Morinigo-Sotelo; Luis Morales-Velazquez; Roque A. Osornio-Rios; Rene De J. Romero-Troncoso. Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation. Applied Sciences 2020, 10, 542 .

AMA Style

David A. Elvira-Ortiz, Arturo Y. Jaen-Cuellar, Daniel Morinigo-Sotelo, Luis Morales-Velazquez, Roque A. Osornio-Rios, Rene De J. Romero-Troncoso. Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation. Applied Sciences. 2020; 10 (2):542.

Chicago/Turabian Style

David A. Elvira-Ortiz; Arturo Y. Jaen-Cuellar; Daniel Morinigo-Sotelo; Luis Morales-Velazquez; Roque A. Osornio-Rios; Rene De J. Romero-Troncoso. 2020. "Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation." Applied Sciences 10, no. 2: 542.

Research article
Published: 09 September 2019 in IET Generation, Transmission & Distribution
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Electric transient events are recurrent phenomena in electrical installations and distribution systems or grids; hence, the identification of these kinds of phenomena has become an important topic for researchers. This work presents a methodology to identify and accurately delimit transients in current signals of non-residential buildings. The proposed method firstly analyses the signal using the wavelet transform to pre-visualise the transients, and then opening and closing morphological operators are applied to the signal to find the beginning and ending of the transient; furthermore, the highest point of the transient and its location is obtained. The experimentation is performed with real current signals measured in a non-residential building. The results indicate that the proposed method can precisely identify and delimit transients, even when the transients are very close to each other.

ACS Style

Emmanuel Guillén‐García; Luis Morales‐Velazquez; Angel Luis Zorita‐Lamadrid; Oscar Duque‐Perez; Roque Alfredo Osornio‐Rios; Rene De Jesus Romero‐Troncoso. Accurate identification and characterisation of transient phenomena using wavelet transform and mathematical morphology. IET Generation, Transmission & Distribution 2019, 13, 4021 -4028.

AMA Style

Emmanuel Guillén‐García, Luis Morales‐Velazquez, Angel Luis Zorita‐Lamadrid, Oscar Duque‐Perez, Roque Alfredo Osornio‐Rios, Rene De Jesus Romero‐Troncoso. Accurate identification and characterisation of transient phenomena using wavelet transform and mathematical morphology. IET Generation, Transmission & Distribution. 2019; 13 (18):4021-4028.

Chicago/Turabian Style

Emmanuel Guillén‐García; Luis Morales‐Velazquez; Angel Luis Zorita‐Lamadrid; Oscar Duque‐Perez; Roque Alfredo Osornio‐Rios; Rene De Jesus Romero‐Troncoso. 2019. "Accurate identification and characterisation of transient phenomena using wavelet transform and mathematical morphology." IET Generation, Transmission & Distribution 13, no. 18: 4021-4028.

Journal article
Published: 31 July 2019 in IEEE Transactions on Industrial Electronics
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In recent years wound-rotor induction motors (WRIM) have received special attention because of their broad use as generators in wind turbine units as well as in some large power applications in industrial plants. Some classical approaches perform the detection of certain faults based on the FFT analysis of the steady state current (MCSA); they have been lately complemented with new transient time-frequency-based techniques to avoid false alarms. Nonetheless, there is still a need to improve the already existing methods to overcome their remaining drawbacks and increase the reliability of the diagnostic. In this regard, emergent technologies are being explored, such as the analysis of stray flux at the vicinity of the motor. Recently, this technique has been applied to detect broken rotor bar failures and misalignments in cage motors, avoiding some drawbacks of well-established tools. However, the application of these techniques to WRIM has not been studied. This work explores the analysis of the external magnetic field under the starting to detect rotor winding asymmetry defects in WRIM by using advanced signal processing techniques. Moreover, a new fault indicator based on this quantity is introduced, demonstrating the potential of this technique to quantify and monitor rotor winding asymmetries in WRIM.

ACS Style

Israel Zamudio-Ramirez; Jose Alfonso Antonino-Daviu; Roque A. Osornio-Rios; Rene De Jesus Romero-Troncoso; Hubert Razik. Detection of Winding Asymmetries in Wound-Rotor Induction Motors via Transient Analysis of the External Magnetic Field. IEEE Transactions on Industrial Electronics 2019, 67, 5050 -5059.

AMA Style

Israel Zamudio-Ramirez, Jose Alfonso Antonino-Daviu, Roque A. Osornio-Rios, Rene De Jesus Romero-Troncoso, Hubert Razik. Detection of Winding Asymmetries in Wound-Rotor Induction Motors via Transient Analysis of the External Magnetic Field. IEEE Transactions on Industrial Electronics. 2019; 67 (6):5050-5059.

Chicago/Turabian Style

Israel Zamudio-Ramirez; Jose Alfonso Antonino-Daviu; Roque A. Osornio-Rios; Rene De Jesus Romero-Troncoso; Hubert Razik. 2019. "Detection of Winding Asymmetries in Wound-Rotor Induction Motors via Transient Analysis of the External Magnetic Field." IEEE Transactions on Industrial Electronics 67, no. 6: 5050-5059.

Journal article
Published: 04 July 2019 in Electric Power Systems Research
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Energy from the fundamental frequency component (FFC) in electrical signals is usually much higher than the energy from the rest of spectral components. This situation makes it difficult to obtain a proper analysis of the whole frequencies involved in a particular signal. To improve the spectral analysis of electrical signals, some works have proposed the use of filters and digital signal processing techniques for suppressing the influence of the FFC. However, these methodologies suppress a frequency band, introducing undesired effects in the frequencies close to the FFC. Thus, this paper proposes the use of non-linear least squares to identify the amplitude, frequency and phase that describe the FFC to suppress only one frequency component instead of a frequency band. The methodology is tested using synthetic signals and experimentation is performed with real electric signals from three different scenarios: current signals from two induction motors operating at two different frequencies (60 Hz and 31 Hz), and a voltage signal from a photovoltaic generation plant. Results show that the methodology can adequately recognize and subtract the FFC. This methodology aims to be a tool to enhance the results delivered by methodologies for condition monitoring of induction motors and power quality assessment.

ACS Style

David Alejandro Elvira-Ortiz; Daniel Morinigo-Sotelo; Luis Morales-Velazquez; Roque A. Osornio-Rios; Rene J. Romero-Troncoso. Non-linear least squares methodology for suppressing the fundamental frequency in the analysis of electric signals. Electric Power Systems Research 2019, 175, 105924 .

AMA Style

David Alejandro Elvira-Ortiz, Daniel Morinigo-Sotelo, Luis Morales-Velazquez, Roque A. Osornio-Rios, Rene J. Romero-Troncoso. Non-linear least squares methodology for suppressing the fundamental frequency in the analysis of electric signals. Electric Power Systems Research. 2019; 175 ():105924.

Chicago/Turabian Style

David Alejandro Elvira-Ortiz; Daniel Morinigo-Sotelo; Luis Morales-Velazquez; Roque A. Osornio-Rios; Rene J. Romero-Troncoso. 2019. "Non-linear least squares methodology for suppressing the fundamental frequency in the analysis of electric signals." Electric Power Systems Research 175, no. : 105924.

Journal article
Published: 01 July 2019 in Journal of Renewable and Sustainable Energy
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ACS Style

David A. Elvira-Ortiz; Daniel Morinigo-Sotelo; Oscar Duque-Perez; Roque A. Osornio-Rios; Rene J. Romero-Troncoso. Study of the harmonic and interharmonic content in electrical signals from photovoltaic generation and their relationship with environmental factors. Journal of Renewable and Sustainable Energy 2019, 11, 043502 .

AMA Style

David A. Elvira-Ortiz, Daniel Morinigo-Sotelo, Oscar Duque-Perez, Roque A. Osornio-Rios, Rene J. Romero-Troncoso. Study of the harmonic and interharmonic content in electrical signals from photovoltaic generation and their relationship with environmental factors. Journal of Renewable and Sustainable Energy. 2019; 11 (4):043502.

Chicago/Turabian Style

David A. Elvira-Ortiz; Daniel Morinigo-Sotelo; Oscar Duque-Perez; Roque A. Osornio-Rios; Rene J. Romero-Troncoso. 2019. "Study of the harmonic and interharmonic content in electrical signals from photovoltaic generation and their relationship with environmental factors." Journal of Renewable and Sustainable Energy 11, no. 4: 043502.

Journal article
Published: 17 May 2019 in Applied Soft Computing
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Strategies of condition monitoring applied to electric motors play an important role in the competitiveness of multiple industrial sectors. However, the risk of faults coexistence in an electric motor and the overlapping of their effects in the considered physical magnitudes represent, currently, a critical limitation to provide reliable diagnosis outcomes. In this regard, additional investigation efforts are required towards high-dimensional data fusion schemes, particularly over the features calculation and features reduction, which represent two decisive stages in such data-driven approaches. In this study, a novel multiple-fault detection and identification methodology supported by a feature-level fusion strategy and a Self-Organizing Maps (SOM) hierarchical structure is proposed. The condition diagnosis as well as the corresponding estimated probability are obtained. Moreover, the proposed method allows the visualization of the results while preserving the underlying physical phenomenon of the system under monitoring. The proposed scheme is performed sequentially; first, a set of statistical-time based features is estimated from different physical magnitudes. Second, a hybrid feature reduction method is proposed, composed by an initial soft feature reduction, based on sequential floating forward selection to remove the less informative features, and followed by a hierarchical SOM structure which reveals directly the diagnosis and probability assessment. The effectiveness of the proposed detection and identification scheme is validated with a complete set of experimental data including healthy and five faulty conditions. The accuracy’s results are compared with classical condition monitoring approaches in order to validate the competency of the proposed method.

ACS Style

Juan Jose Saucedo-Dorantes; Miguel Delgado-Prieto; René De Jesús Romero-Troncoso; Roque Alfredo Osornio-Rios. Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine. Applied Soft Computing 2019, 81, 105497 .

AMA Style

Juan Jose Saucedo-Dorantes, Miguel Delgado-Prieto, René De Jesús Romero-Troncoso, Roque Alfredo Osornio-Rios. Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine. Applied Soft Computing. 2019; 81 ():105497.

Chicago/Turabian Style

Juan Jose Saucedo-Dorantes; Miguel Delgado-Prieto; René De Jesús Romero-Troncoso; Roque Alfredo Osornio-Rios. 2019. "Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine." Applied Soft Computing 81, no. : 105497.

Journal article
Published: 16 May 2019 in IEEE Transactions on Energy Conversion
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Transient motor current signature analysis has become a mature technique for fault detection in induction motors. By using start-up transients, the whole range of slip in the machine is exploited to generate well-defined fault frequency patterns. However, in the inverter-fed motor case, the fault-patterns are always close to the supply frequency and often of low amplitude. Therefore, it is difficult to distinguish and localize the fault-patterns. In this paper, a novel method is proposed to create a new fault-pattern; the proposed technique can concentrate the fault-harmonic in a specific frequency bandwidth and avoid the spectral leakage by reducing the supply frequency amplitude. The methodology has been validated through experimental tests carried out to detect broken rotor bar in an induction motor started through a voltage source inverter.

ACS Style

Tomas Alberto Garcia-Calva; Daniel Morinigo-Sotelo; Arturo Garcia-Perez; David Camarena-Martinez; Rene De Jesus Romero-Troncoso. Demodulation Technique for Broken Rotor Bar Detection in Inverter-Fed Induction Motor Under Non-Stationary Conditions. IEEE Transactions on Energy Conversion 2019, 34, 1496 -1503.

AMA Style

Tomas Alberto Garcia-Calva, Daniel Morinigo-Sotelo, Arturo Garcia-Perez, David Camarena-Martinez, Rene De Jesus Romero-Troncoso. Demodulation Technique for Broken Rotor Bar Detection in Inverter-Fed Induction Motor Under Non-Stationary Conditions. IEEE Transactions on Energy Conversion. 2019; 34 (3):1496-1503.

Chicago/Turabian Style

Tomas Alberto Garcia-Calva; Daniel Morinigo-Sotelo; Arturo Garcia-Perez; David Camarena-Martinez; Rene De Jesus Romero-Troncoso. 2019. "Demodulation Technique for Broken Rotor Bar Detection in Inverter-Fed Induction Motor Under Non-Stationary Conditions." IEEE Transactions on Energy Conversion 34, no. 3: 1496-1503.

Journal article
Published: 01 May 2019 in Energies
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Induction motors (IMs) are essential components in industrial applications. These motors have to perform numerous tasks under a wide variety of conditions, which affects performance and reliability and gradually brings faults and efficiency losses over time. Nowadays, the industrial sector demands the necessary integration of smart-sensors to effectively diagnose faults in these kinds of motors before faults can occur. One of the most frequent causes of failure in IMs is the degradation of turn insulation in windings. If this anomaly is present, an electric motor can keep working with apparent normality, but factors such as the efficiency of energy consumption and mechanical reliability may be reduced considerably. Furthermore, if not detected at an early stage, this degradation could lead to the breakdown of the insulation system, which could in turn cause catastrophic and irreversible failure to the electrical machine. This paper proposes a novel methodology and its application in a smart-sensor to detect and estimate the healthiness of the winding insulation in IMs. This methodology relies on the analysis of the external magnetic field captured by a coil sensor by applying suitable time-frequency decomposition (TFD) tools. The discrete wavelet transform (DWT) is used to decompose the signal into different approximation and detail coefficients as a pre-processing stage to isolate the studied fault. Then, due to the importance of diagnosing stator winding insulation faults during motor operation at an early stage, this proposal introduces an indicator based on wavelet entropy (WE), a single parameter capable of performing an efficient diagnosis. A smart-sensor is able to estimate winding insulation degradation in IMs using two inexpensive, reliable, and noninvasive primary sensors: a coil sensor and an E-type thermocouple sensor. The utility of these sensors is demonstrated through the results obtained from analyzing six similar IMs with differently induced severity faults.

ACS Style

Israel Zamudio Ramirez; Roque Alfredo Osornio-Rios; Miguel Trejo-Hernandez; Rene De Jesus Romero-Troncoso; Jose Alfonso Antonino-Daviu. Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux. Energies 2019, 12, 1658 .

AMA Style

Israel Zamudio Ramirez, Roque Alfredo Osornio-Rios, Miguel Trejo-Hernandez, Rene De Jesus Romero-Troncoso, Jose Alfonso Antonino-Daviu. Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux. Energies. 2019; 12 (9):1658.

Chicago/Turabian Style

Israel Zamudio Ramirez; Roque Alfredo Osornio-Rios; Miguel Trejo-Hernandez; Rene De Jesus Romero-Troncoso; Jose Alfonso Antonino-Daviu. 2019. "Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux." Energies 12, no. 9: 1658.

Journal article
Published: 01 March 2019 in Electric Power Systems Research
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Impulsive transients can stimulate power system resonance and generate oscillatory transients and interferences in the action of power electronic devices connected to an electrical installation. These transients can be propagated to different points in the installation and provoke interferences in the equipment. This paper presents an algorithm based on the wavelet transform to identify transient phenomena at different points in the electrical installation and to track the transient to estimate if it is propagated to other points in the installation. The transient propagation is established firstly according with the time difference (Δt) between transients registered at different points, secondly some IEEE std. 1159-209 suggested indices like energy, amplitude and decay time are computed to see the differences between the transients identified as propagated and the rest of them. The experimentation is done in a non-residential building at five different points. The results show several differences in the indices between the group of transients classified as propagated and the rest of them.

ACS Style

Emmanuel Guillén-García; Luis Morales-Velazquez; Angel L. Zorita-Lamadrid; Oscar Duque-Perez; Roque A. Osornio-Rios; René J. Romero-Troncoso. Short-time transient tracking algorithm for a non-residential facility based on characteristic indices. Electric Power Systems Research 2019, 171, 185 -193.

AMA Style

Emmanuel Guillén-García, Luis Morales-Velazquez, Angel L. Zorita-Lamadrid, Oscar Duque-Perez, Roque A. Osornio-Rios, René J. Romero-Troncoso. Short-time transient tracking algorithm for a non-residential facility based on characteristic indices. Electric Power Systems Research. 2019; 171 ():185-193.

Chicago/Turabian Style

Emmanuel Guillén-García; Luis Morales-Velazquez; Angel L. Zorita-Lamadrid; Oscar Duque-Perez; Roque A. Osornio-Rios; René J. Romero-Troncoso. 2019. "Short-time transient tracking algorithm for a non-residential facility based on characteristic indices." Electric Power Systems Research 171, no. : 185-193.

Journal article
Published: 14 January 2019 in IEEE Transactions on Industrial Electronics
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Induction motor fault identification is essential to improve efficiency in industrial processes improving costs, production line and maintenance time. This paper presents a novel motor fault detection methodology based on Quaternion Signal Analysis (QSA). The proposed method establishes the quaternion coefficients as the value of motor current measurement and the variables x, y and z are the measurements from a triaxial-accelerometer mounted on the induction motor chassis. The method obtains the rotation of quaternions and applies quaternion rotation statistics such as mean, cluster shades and cluster prominence in order to get their features, and these are used to classify the motor state using the tree classification algorithm. This methodology is validated experimentally and compared to other methods to determine the efficiency of this method for feature detection and motor fault identification and classification.

ACS Style

Jose Luis Contreras Hernandez; Dora Luz Almanza-Ojeda; Sergio Ledesma-Orozco; Arturo Garcia-Perez; Rene De Jesus Romero-Troncoso; Mario-Alberto Ibarra-Manzano. Quaternion Signal Analysis Algorithm for Induction Motor Fault Detection. IEEE Transactions on Industrial Electronics 2019, 66, 8843 -8850.

AMA Style

Jose Luis Contreras Hernandez, Dora Luz Almanza-Ojeda, Sergio Ledesma-Orozco, Arturo Garcia-Perez, Rene De Jesus Romero-Troncoso, Mario-Alberto Ibarra-Manzano. Quaternion Signal Analysis Algorithm for Induction Motor Fault Detection. IEEE Transactions on Industrial Electronics. 2019; 66 (11):8843-8850.

Chicago/Turabian Style

Jose Luis Contreras Hernandez; Dora Luz Almanza-Ojeda; Sergio Ledesma-Orozco; Arturo Garcia-Perez; Rene De Jesus Romero-Troncoso; Mario-Alberto Ibarra-Manzano. 2019. "Quaternion Signal Analysis Algorithm for Induction Motor Fault Detection." IEEE Transactions on Industrial Electronics 66, no. 11: 8843-8850.

Review
Published: 05 December 2018 in IEEE Transactions on Industrial Informatics
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ACS Style

Roque Alfredo Osornio-Rios; Jose Alfonso Antonino-Daviu; Rene De Jesus Romero-Troncoso. Recent Industrial Applications of Infrared Thermography: A Review. IEEE Transactions on Industrial Informatics 2018, 15, 615 -625.

AMA Style

Roque Alfredo Osornio-Rios, Jose Alfonso Antonino-Daviu, Rene De Jesus Romero-Troncoso. Recent Industrial Applications of Infrared Thermography: A Review. IEEE Transactions on Industrial Informatics. 2018; 15 (2):615-625.

Chicago/Turabian Style

Roque Alfredo Osornio-Rios; Jose Alfonso Antonino-Daviu; Rene De Jesus Romero-Troncoso. 2018. "Recent Industrial Applications of Infrared Thermography: A Review." IEEE Transactions on Industrial Informatics 15, no. 2: 615-625.