Records with Keyword: Fault Detection
Showing records 1 to 25 of 96. [First] Page: 1 2 3 4 Last
Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults
Tomas A. Garcia-Calva, Daniel Morinigo-Sotelo, Oscar Duque-Perez, Arturo Garcia-Perez, Rene de J. Romero-Troncoso
March 29, 2023 (v1)
Keywords: Fault Detection, induction motors, signal processing, spectral analysis, spectrogram, stator current, time-frequency analysis, transient regime
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 stresse... [more]
A Review of Infrared Thermography for Condition-Based Monitoring in Electrical Energy: Applications and Recommendations
Ganesh Kumar Balakrishnan, Chong Tak Yaw, Siaw Paw Koh, Tarek Abedin, Avinash Ashwin Raj, Sieh Kiong Tiong, Chai Phing Chen
March 28, 2023 (v1)
Keywords: condition-based monitoring, diagnosis, Fault Detection, infrared thermography, non-destructive
Condition-based monitoring (CBM) has emerged as a critical instrument for lowering the cost of unplanned operations while also improving the efficacy, execution, and dependability of tools. Thermal abnormalities can be thoroughly examined using thermography for condition monitoring. Thanks to the advent of high-resolution infrared cameras, researchers are paying more attention to thermography as a non-contact approach for monitoring the temperature rise of objects and as a technique in great experiments to analyze processes thermally. It also allows for the early identification of weaknesses and failures in equipment while it is in use, decreasing system downtime, catastrophic failure, and maintenance expenses. In many applications, the usage of IRT as a condition monitoring approach has steadily increased during the previous three decades. Infrared cameras are steadily finding use in research and development, in addition to their routine use in condition monitoring and preventative ma... [more]
Bollinger Bands Based on Exponential Moving Average for Statistical Monitoring of Multi-Array Photovoltaic Systems
Silvano Vergura
March 28, 2023 (v1)
Keywords: bollinger bands, exponential moving average, Fault Detection, low-intensity anomaly, photovoltaic systems, statistical monitoring, upper/lower band
Monitoring the performance of a photovoltaic (PV) system when environmental parameters are not available is very difficult. Comparing the energy datasets of the arrays belonging to the same PV plant is one strategy. If the extension of a PV plant is limited, all the arrays are subjected to the same environmental conditions. Therefore, identical arrays produce the same energy amount, whatever the solar radiation and cell temperature. This is valid for small- to medium-rated power PV plants (3−50 kWp) and, moreover, this typology of PV plants sometimes is not equipped with a meteorological sensor system. This paper presents a supervision methodology based on comparing the average energy of each array and the average energy of the whole PV plant. To detect low-intensity anomalies before they become failures, the variability of the energy produced by each array is monitored by using the Bollinger Bands (BB) method. This is a statistical tool developed in the financial field to evaluate the... [more]
Islanding Fault Detection in Microgrids—A Survey
Mehdi Hosseinzadeh, Farzad Rajaei Salmasi
March 27, 2023 (v1)
Keywords: active method, Fault Detection, islanding fault, microgrid, passive method
This paper provides an overview of islanding fault detection in microgrids. Islanding fault is a condition in which the microgrid gets disconnected from the microgrid unintentionally due to any fault in the utility grid. This paper surveys the extensive literature concerning the development of islanding fault detection techniques which can be classified into remote and local techniques, where the local techniques can be further classified as passive, active, and hybrid. Various detection methods in each class are studied, and advantages and disadvantages of each method are discussed. A comprehensive list of references is used to conduct this survey, and opportunities and directions for future research are highlighted.
Fault Detection Algorithm for Wind Turbines’ Pitch Actuator Systems
Gisela Pujol-Vazquez, Leonardo Acho, José Gibergans-Báguena
March 27, 2023 (v1)
Keywords: Fault Detection, interval observer, pitch actuator, wind turbines
A fault detection innovation to wind turbines’ pitch actuators is an important subject to guarantee the efficiency wind energy conversion and long lifetime operation of these rotatory machines. Therefore, a recent and effective fault detection algorithm is conceived to detect faults on wind turbine pitch actuators. This approach is based on the interval observer framework theory that has proved to be an efficient tool to measure dynamic uncertainties in dynamical systems. It is evident that almost any fault in any actuator may affect its historical-time behavior. Hence, and properly conceptualized, a fault detection system can be successfully designed based on interval observer dynamics. This is precisely our main contribution. Additionally, we realize a numerical analysis to evaluate the performance of our approach by using a dynamic model of a pitch actuator device with faults. The numerical experiments support our main contribution.
Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems−A Review
Noor Hussain, Mashood Nasir, Juan Carlos Vasquez, Josep M. Guerrero
March 24, 2023 (v1)
Keywords: coordination techniques, Fault Detection, Microgrids, protection techniques
The protection of AC microgrids (MGs) is an issue of paramount importance to ensure their reliable and safe operation. Designing reliable protection mechanism, however, is not a trivial task, as many practical issues need to be considered. The operation mode of MGs, which can be grid-connected or islanded, employed control strategy and practical limitations of the power electronic converters that are utilized to interface renewable energy sources and the grid, are some of the practical constraints that make fault detection, classification, and coordination in MGs different from legacy grid protection. This article aims to present the state-of-the-art of the latest research and developments, including the challenges and issues in the field of AC MG protection. A broad overview of the available fault detection, fault classification, and fault location techniques for AC MG protection and coordination are presented. Moreover, the available methods are classified, and their advantages and d... [more]
An Energy Graph-Based Approach to Fault Diagnosis of a Transcritical CO2 Heat Pump
Kenneth R. Uren, George van Schoor, Martin van Eldik, Johannes J. A. de Bruin
March 24, 2023 (v1)
Keywords: energy-based representation, Fault Detection, fault diagnosis, transcritical heat pump
The objective of this paper is to describe an energy-based approach to visualize, identify, and monitor faults that may occur in a water-to-water transcritical CO 2 heat pump system. A representation using energy attributes allows the abstraction of all physical phenomena present during operation into a compact and easily interpretable form. The use of a linear graph representation, with heat pump components represented as nodes and energy interactions as links, is investigated. Node signature matrices are used to present the energy information in a compact mathematical form. The resulting node signature matrix is referred to as an attributed graph and is populated in such a way as to retain the structural information, i.e., where the attribute points to in the physical system. To generate the energy and exergy information for the compilation of the attributed graphs, a descriptive thermal−fluid model of the heat pump system is developed. The thermal−fluid model is based on the... [more]
Simulation Test of a DC Fault Current Limiter for Fault Ride-Through Problem of Low-Voltage DC Distribution
Bing Han, Yonggang Li
March 24, 2023 (v1)
Keywords: DC fault current limiter, Fault Detection, fault ride-through strategy, LVDC distribution network, short-current characteristics of faults
The low voltage direct current (LVDC) distribution networks are connected with too many kinds of loads and sources, which makes them prone to failure. Due to the small damping value in the DC lines, the fault signal propagates so fast that the impact current with the wave front of millisecond and the transient voltage pose great challenges for fault detection. Even worse, some faults with small currents are difficult to detect and the communication is out of sync, resulting in protection misoperation. These problems have severely affected the new energy utilization. In view of this, a DC fault current limiter (FCL) composed of inductance, resistance, and power electronic switch was designed in this paper. The rising speed of fault current can be decreased by the series inductance and the peak value of the fault current can be limited by series impedance, thus in this way the running time can be gained for fault detection and protection. For distributed energy access, by deducing the sh... [more]
Detection and Location of Earth Fault in MV Feeders Using Screen Earthing Current Measurements
Krzysztof Lowczowski, Jozef Lorenc, Jozef Zawodniak, Grzegorz Dombek
March 23, 2023 (v1)
Keywords: cable screen, earthing system distribution feeder, Fault Detection, fault location, mixed cable-overhead line, protection relay
The paper analyzes the utilization of cable screen currents for earth fault identification and location. Attention is paid on cable and mixed feeders—cable and overhead lines. The principle of operation is based on utilization of 3 criterion values: Ratio of cable screen earthing current and zero sequence cable core current—RF110/15, phase shift between cable screen earthing current and zero sequence cable core current—α and cable screen admittance defined as a ratio of cable screen earthing current and zero sequence voltage—Y0cs. Earth fault location is possible thanks to discovered relation between RF110/15 and α, whereas Y0cs allows for reliable detection of earth faults. Detection and identification are very important because it allows to increase the reliability of supply—reduce downtime and number of consumers affected by the fault. The article presents a phase to ground fault current flow for different power system configurations. At the end solution, which improves location cap... [more]
A Multivariate Statistics-Based Approach for Detecting Diesel Engine Faults with Weak Signatures
Jinxin Wang, Chi Zhang, Xiuzhen Ma, Zhongwei Wang, Yuandong Xu, Robert Cattley
March 22, 2023 (v1)
Keywords: condition monitoring, diesel engine, Fault Detection, Multivariate Statistics, principal component analysis
The problem of timely detecting the engine faults that make engine operating parameters exceed their control limits has been well-solved. However, in practice, a fault of a diesel engine can be present with weak signatures, with the parameters fluctuating within their control limits when the fault occurs. The weak signatures of engine faults bring considerable difficulties to the effective condition monitoring of diesel engines. In this paper, a multivariate statistics-based fault detection approach is proposed to monitor engine faults with weak signatures by taking the correlation of various parameters into consideration. This approach firstly uses principal component analysis (PCA) to project the engine observations into a principal component subspace (PCS) and a residual subspace (RS). Two statistics, i.e., Hotelling’s T 2 and Q statistics, are then introduced to detect deviations in the PCS and the RS, respectively. The Hotelling’s T 2 and Q statisti... [more]
Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection
Jing Tang, Yongheng Yang, Jie Chen, Ruichang Qiu, Zhigang Liu
March 22, 2023 (v1)
Keywords: characteristics analysis, Fault Detection, induction motor, rotor fault, stator fault, torque estimation
Inverter-fed induction motors (IMs) contain a serious of current harmonics, which become severer under stator and rotor faults. The resultant fault components in the currents affect the monitoring of the motor status. With this background, the fault components in the electromagnetic torque under stator faults considering harmonics are derived in this paper, and the fault components in current harmonics under rotor faults are analyzed. More importantly, the monitoring based on the fault characteristics (both in the torque and current) is proposed to provide reliable stator and rotor fault diagnosis. Specifically, the fault components induced by stator faults in the electromagnetic torque are discussed in this paper, and then, fault components are characterized in the torque spectrum to identify stator faults. To achieve so, a full-order flux observer is adopted to calculate the torque. On the other hand, under rotor faults, the sidebands caused by time and space harmonics in the current... [more]
Diagnosis of Static Eccentricity in 3-Phase Synchronous Machines using a Pseudo Zero-Sequence Current
Konstantinos N. Gyftakis, Carlos A. Platero, Yucheng Zhang, Santiago Bernal
March 21, 2023 (v1)
Keywords: condition monitoring, electrical machines, Fault Detection, static eccentricity, synchronous generators
Large synchronous generators are the heart of the modern world, while producing the vast majority of the electric power consumed globally. Although they are robust devices, they are prone to degradation and failure. If such failures are not detected at an early stage, then the negative impact may be catastrophic in terms of financial costs, repair times, human lives and quality of life. This is the reason for continuous research in the field of condition monitoring aiming towards the reliable operation of synchronous generators. This paper proposes a novel technique for the diagnosis of the static eccentricity in synchronous generators. The proposed approach is off-line and non-intrusive, allowing the estimation of the fault severity with stator current measurements only. The performed work has been carried out with Finite Element Simulations and extensive experimental testing.
Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors
Maciej Skowron, Marcin Wolkiewicz, Teresa Orlowska-Kowalska, Czeslaw T. Kowalski
March 21, 2023 (v1)
Keywords: axial flux, Fault Detection, Hopfield recursive network, induction motor drive, Kohonen network, MLP network, neural networks, rotor fault, stator fault
This paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars is based on the analysis of an axial flux of the induction motor. In order to automate the fault detection process, three different structures of neural networks were used: multi-layer perceptron, self-organizing Kohonen network and recursive Hopfield network. Tests were carried out for various levels of stator and rotor failures. In order to assess the sensitivity of the applied neural detectors, the tests were carried out for variable load conditions and for different values of the supply voltage frequency. Experimental results of the elaborated neural detectors are presented and discussed.
Algorithm for Fast Detection of Stator Turn Faultsin Variable-Speed Synchronous Generators
Rodolfo V. Rocha, Renato M. Monaro
March 20, 2023 (v1)
Keywords: Fault Detection, protection, stator faults, synchronous generator, three-level converter, turn faults, variable speed, voltage-source converters
Faults between stator winding turns of synchronous generators have led to specific changes in the harmonic content of currents. In this paper, these changes are used to detect faults in variable-speed synchronous generators connected to three-level converters during their operation. Currents typically measured for control purposes are used to avoid installation of additional sensors. The neutral point current of the three-level converter is also evaluated under faults in the generator. Encoder-tuned dynamic filters based on Park transformation and Fourier coefficients together with low-pass filters are used to detect the selected harmonics under variable speeds. The geometric loci of these components are proposed as a method to distinguish between healthy and faulty conditions. Simulation and experimental data are used to test sensitivity, selectivity and detection time of the proposed technique for different fault types. Generalization for a different generator is also presented and t... [more]
Prevention of Wildfires Using an AI-Based Open Conductor Fault Detection Method on Overhead Line
Junsoo Che, Taehun Kim, Suhan Pyo, Jaedeok Park, Byeonghyeon An, Taesik Park
March 17, 2023 (v1)
Keywords: deep neural network, Fault Detection, fire protection, high impedance fault, open conductor fault
Overhead lines that are exposed to the outdoors are susceptible to faults such as open conductors on weak points and disconnection caused by external factors such as typhoons. Arcs that occur during disconnection generate energy at a high heat of over 10,000 °C, requiring swift fault shut-off. However, most conventional fault detection methods to protect electrical power systems detect an overcurrent; thus, they can only detect faults after the line is disconnected and the cross-section of the line that generates the arc discharge makes contact with another line or the ground, causing a high risk of fire. Furthermore, in the case of ground faults owing to the disconnection of overhead lines, the load and the grounding impedance are not parallel. Therefore, in the case of the fault current not exceeding the threshold or a high impedance fault due to the high grounding impedance of the surrounding environment, such as grass or trees, it is difficult to determine overhead line faults with... [more]
On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data
Phong B. Dao
March 17, 2023 (v1)
Keywords: cointegration, condition monitoring, Fault Detection, SCADA data, wind turbine
Cointegration theory has been recently proposed for condition monitoring and fault detection of wind turbines. However, the existing cointegration-based methods and results presented in the literature are limited and not encouraging enough for the broader deployment of the technique. To close this research gap, this paper presents a new investigation on cointegration for wind turbine monitoring using a four-year SCADA data set acquired from a commercial wind turbine. A gearbox fault is used as a testing case to validate the analysis. A cointegration-based wind turbine monitoring model is established using five process parameters, including the wind speed, generator speed, generator temperature, gearbox temperature, and generated power. Two different sets of SCADA data were used to train the cointegration-based model and calculate the normalized cointegrating vectors. The first training data set involves 12,000 samples recorded before the occurrence of the gearbox fault, whereas the sec... [more]
The Implementation of Multiple Linear Regression for Swimming Pool Facilities: Case Study at Jøa, Norway
Ole Øiene Smedegård, Thomas Jonsson, Bjørn Aas, Jørn Stene, Laurent Georges, Salvatore Carlucci
March 10, 2023 (v1)
Keywords: energy prediction, Fault Detection, multiple linear regression analysis, swimming facilities
This paper presents a statistical model for predicting the time-averaged total power consumption of an indoor swimming facility. The model can be a powerful tool for continuous supervision of the facility’s energy performance that can quickly disclose possible operational disruptions/irregularities and thus minimize annual energy use. Multiple linear regression analysis is used to analyze data collected in a swimming facility in Norway. The resolution of the original training dataset was in 1 min time steps and during the investigation was transposed both by time-averaging the data, and by treating part of the dataset exclusively. The statistically significant independent variables were found to be the outdoor dry-bulb temperature and the relative pool usage factor. The model accurately predicted the power consumption in the validation process, and also succeeded in disclosing all the critical operational disruptions in the validation dataset correctly. The model can therefore be appli... [more]
An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique
Ruijun Guo, Guobin Zhang, Qian Zhang, Lei Zhou, Haicun Yu, Meng Lei, You Lv
March 10, 2023 (v1)
Keywords: coal-fired power plant, Fault Detection, induced draft fan, model update, multivariate state estimation technique
The induced draft (ID) fan is an important piece of auxiliary equipment in coal-fired power plants. Early fault detection of the ID fan can provide predictive maintenance and reduce unscheduled shutdowns, thus improving the reliability of the power generation. In this study, an adaptive model was developed to achieve the early fault detection of ID fans. First, a non-parametric monitoring model was constructed to describe the normal operating characteristics with the multivariate state estimation technique (MSET). A similarity index representing operation status was defined according to the prediction deviations to produce warnings of early faults. To deal with the model accuracy degradation because of variant condition operation of the ID fan, an adaptive strategy was proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the MSET model, thereby improving the fault detection results. The proposed method was applied to a 300 MW coal-fi... [more]
Applicability Analysis of Indices-Based Fault Detection Technique of Six-Phase Induction Motor
Khaled Farag, Abdullah Shawier, Ayman S. Abdel-Khalik, Mohamed M. Ahmed, Shehab Ahmed
March 9, 2023 (v1)
Keywords: Fault Detection, indices-based technique, multiphase induction motors
The multiphase induction motor is considered to be the promising alternative to the conventional three-phase induction motor, especially in safety-critical applications because of its inherent fault-tolerant feature. Therefore, the attention of many researchers has been paid to develop different techniques for detecting various fault types of multiphase induction motors, to securely switch the control mode of the multiphase drive system to its post-fault operation mode. Therefore, several fault detection methods have been researched and adapted; one of these methods is the indices-based fault detection technique. This technique was firstly introduced to detect open-phase fault of multiphase induction motors. The main advantage of this technique is that its mathematical formulation is straightforward and can easily be understood and implemented. In this paper, the study of the indices-based fault detection technique has been extended to test its applicability in detecting some other sta... [more]
A Study on the Predictive Maintenance Algorithms Considering Load Characteristics of PMSMs to Drive EGR Blowers for Smart Ships
Sung-An Kim
March 9, 2023 (v1)
Subject: Other
Keywords: exhaust gas recirculation blower, Fault Detection, life prediction, permanent magnet synchronous motor, predictive maintenance, smart ship
Exhaust gas recirculation (EGR) is a NOx reduction technology that can meet stringent environmental regulatory requirements. EGR blower systems must be used to increase the exhaust gas pressure at a lower rate than the scavenging air pressure. Electric motor drive systems are essential to rotate the EGR blowers. For the effective management of the EGR blower systems in smart ships, there is a growing need for predictive maintenance technology fused with information and communication technology (ICT). Since an electric motor accounts for about 80% of electric loads driven by the EGR, it is essential to apply the predictive maintenance technology of the electric motor to maximize the reliability and operation time of the EGR blower system. Therefore, this paper presents the predictive maintenance algorithm to prevent the stator winding turn faults, which is the most significant cause of the electrical failure of the electric motors. The proposed algorithm predicts the remaining useful li... [more]
Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects
Tarek Berghout, Mohamed Benbouzid, Toufik Bentrcia, Xiandong Ma, Siniša Djurović, Leïla-Hayet Mouss
March 9, 2023 (v1)
Keywords: condition monitoring, deep learning, Fault Detection, faults diagnosis, Machine Learning, open source datasets, photovoltaic systems
To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learnin... [more]
Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault
Jannis N. Kahlen, Michael Andres, Albert Moser
March 8, 2023 (v1)
Keywords: data augmentation, diagnostics, electrical power equipment, Fault Detection, Machine Learning, small sample size
Machine-learning diagnostic systems are widely used to detect abnormal conditions in electrical equipment. Training robust and accurate diagnostic systems is challenging because only small databases of abnormal-condition data are available. However, the performance of the diagnostic systems depends on the quantity and quality of the data. The training database can be augmented utilizing data augmentation techniques that generate synthetic data to improve diagnostic performance. However, existing data augmentation techniques are generic methods that do not include additional information in the synthetic data. In this paper, we develop a model-based data augmentation technique integrating computer-implementable electromechanical models. Synthetic normal- and abnormal-condition data are generated with an electromechanical model and a stochastic parameter value sampling method. The model-based data augmentation is showcased to detect an abnormal condition of a distribution transformer. Fir... [more]
Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems
Len Gelman, Krzysztof Soliński, Andrew Ball
March 8, 2023 (v1)
Keywords: condition monitoring, Fault Detection, vibration analysis
Higher order spectra exhibit a powerful detection capability of low-energy fault-related signal components, buried in background random noise. This paper investigates the powerful nonlinear non-stationary instantaneous wavelet bicoherence for local gear fault detection. The new methodology of selecting frequency bands that are relevant for wavelet bicoherence fault detection is proposed and investigated. The capabilities of wavelet bicoherence are proven for early-stage fault detection in a gear pinion, in which natural pitting has developed in multiple pinion teeth in the course of endurance gearbox tests. The results of the WB-based fault detection are compared with a stereo optical fault evaluation. The reliability of WB-based fault detection is quantified based on the complete probability of correct identification. This paper is the first attempt to investigate instantaneous wavelet bicoherence technology for the detection of multiple natural early-stage local gear faults, based on... [more]
Utilization of Two Sensors in Offline Diagnosis of Squirrel-Cage Rotors of Asynchronous Motors
Petr Kacor, Petr Bernat, Petr Moldrik
March 8, 2023 (v1)
Keywords: broken bar, Fault Detection, FEM simulation, offline diagnosis, oval patterns, signal processing, squirrel-cage
In the manufacture squirrel-cage rotors of asynchronous motors, a high standard of quality is required in every part of the production cycle. The die casting process usually creates porosity in the rotor bars. This most common defect in the rotor often remains hidden during the entire assembly of the machine and is usually only detected during final testing of the motor, i.e., at the end of the production process. This leads to unnecessary production costs. Therefore, the aim is to conduct a continuous control immediately after the rotor has been cast before further processing. In our paper, we are interested in selecting a suitable method of offline rotor diagnostics of an asynchronous motor that would be effective for these needs. In the first step, the selection of the method and its integration into the overall manufacturing process is carried out. The arrangement of the sensors and their calibration is then simulated on a 2D Finite Element Model of the rotor. The proposed offline... [more]
Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA
Tito G. Amaral, Vitor Fernão Pires, Armando J. Pires
March 7, 2023 (v1)
Keywords: Fault Detection, image processing, photovoltaic systems (pv), principal component analysis (PCA), tracking system, two-axis
Photovoltaic power plants nowadays play an important role in the context of energy generation based on renewable sources. With the purpose of obtaining maximum efficiency, the PV modules of these power plants are installed in trackers. However, the mobile structure of the trackers is subject to faults, which can compromise the desired perpendicular position between the PV modules and the brightest point in the sky. So, the diagnosis of a fault in the trackers is fundamental to ensure the maximum energy production. Approaches based on sensors and statistical methods have been researched but they are expensive and time consuming. To overcome these problems, a new method is proposed for the fault diagnosis in the trackers of the PV systems based on a machine learning approach. In this type of approach the developed method can be classified into two major categories: supervised and unsupervised. In accordance with this, to implement the desired fault diagnosis, an unsupervised method based... [more]
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