Records with Keyword: Fault Detection
Showing records 1 to 25 of 136. [First] Page: 1 2 3 4 5 Last
Fault Detection and Location of 35 kV Single-Ended Radial Distribution Network Based on Traveling Wave Detection Method
Xiaowei Xu, Fangrong Zhou, Yongjie Nie, Wenhua Xu, Ke Wang, Jian OuYang, Kaihong Zhou, Shan Chen, Yiming Han
January 5, 2024 (v1)
Keywords: 35 kV, distribution network, Fault Detection, traveling wave method, wavelet conversion method
With the progress of society and the iterative improvement of infrastructure construction, the power grid transmission lines have also entered an era of intelligence. The national distribution system has made ensuring the regular operation of the distribution network as well as prompting troubleshooting and detection its top priority. Research on fault diagnosis for 35 kV single-ended radial distribution networks is still in its infancy compared to other hot topics in the industry, such as short-circuit fault detection and fault node localization. This study adopts the 35 kV single-ended radial distribution network as a model, detects fault lines via the traveling wave method, and accurately locates fault nodes using the wavelet conversion method, hoping to quickly identify and locate fault nodes in distribution networks. The experimental results demonstrate that the research method can quickly identify the faulty line and carry out further fault node location detection. The final obta... [more]
A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment
Ahmad Abubakar, Mahmud M. Jibril, Carlos F. M. Almeida, Matheus Gemignani, Mukhtar N. Yahya, Sani I. Abba
November 30, 2023 (v1)
Keywords: Artificial Intelligence, boosted tree algorithms, Elman neural network, Fault Detection, Gaussian processes regression, multi-layer perceptron, sustainable development
Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman neural network (ENN), boosted tree algorithms (BTA), multi-layer perceptron (MLP), and Gaussian processes regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages its strengths for the accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction. The fault detection and diagnosis were assessed using several statistical criteria includi... [more]
A Fault Warning Approach Using an Enhanced Sand Cat Swarm Optimization Algorithm and a Generalized Neural Network
Youchun Pi, Yun Tan, Amir-Mohammad Golmohammadi, Yujing Guo, Yanfeng Xiao, Yan Chen
November 30, 2023 (v1)
Keywords: Fault Detection, generalized neural network, industrial systems, Machine Learning, sand cat swarm optimization
With the continuous development and complexity of industrial systems, various types of industrial equipment and systems face increasing risks of failure during operation. Important to these systems is fault warning technology, which can timely detect anomalies before failures and take corresponding preventive measures, thereby reducing production interruptions and maintenance costs, improving production efficiency, and enhancing equipment reliability. Machine learning techniques have proven highly effective for fault detection in modern production processes. Among numerous machine learning algorithms, the generalized neural network stands out due to its simplicity, effectiveness, and applicability to various fault warning scenarios. However, the increasing complexity of systems and equipment presents significant challenges to the generalized neural network. In real-world scenarios, it suffers from drawbacks such as difficulties in determining parameters and getting trapped in local opt... [more]
A Review of Pump Cavitation Fault Detection Methods Based on Different Signals
Xiaohui Liu, Jiegang Mou, Xin Xu, Zhi Qiu, Buyu Dong
August 3, 2023 (v1)
Keywords: artificial intelligent, cavitation state recognition, Fault Detection, feature extraction, sensors, signal processing
As one of the research hotspots in the field of pumps, cavitation detection plays an important role in equipment maintenance and cost-saving. Based on this, this paper analyzes detection methods of cavitation faults based on different signals, including vibration signals, acoustic emission signals, noise signals, and pressure pulsation signals. First, the principle of each detection method is introduced. Then, the research status of the four detection methods is summarized from the aspects of cavitation-induced signal characteristics, signal processing methods, feature extraction, intelligent algorithm identification of cavitation state, detection efficiency, and measurement point distribution position. Among these methods, we focus on the most widely used one, the vibration method. The advantages and disadvantages of various detection methods are analyzed and proposed: acoustic methods including noise and acoustic emission can detect early cavitation very well; the vibration method is... [more]
Supervisory Control and Data Acquisition for Fault Diagnosis of Wind Turbines via Deep Transfer Learning
Silvio Simani, Saverio Farsoni, Paolo Castaldi
May 23, 2023 (v1)
Keywords: condition monitoring, convolutional neural networks, diagnostics, Fault Detection, supervisory control and data acquisition, transfer learning, wind turbines
The installed wind power capacity is growing worldwide. Remote condition monitoring of wind turbines is employed to achieve higher up-times and lower maintenance costs. Machine learning approaches can be used for detecting developing faults in wind turbines in their earlier occurrence. However, training fault detection models may require large amounts of past and present data. These data are often not available or not representative of the current operation behaviour. These data can be acquired with supervisory control and data acquisition systems. Note also that newly commissioned wind farms lack data from previous operation, whilst older installations may also lack representative working condition data as a result of control software updates or component replacements. After such events, a turbine’s operation behaviour can change significantly so its data are no longer representative of its current behaviour. Therefore, this paper shows that cross−turbine transfer learning can improve... [more]
Fault Detection of Induction Motors with Combined Modeling- and Machine-Learning-Based Framework
Moritz Benninger, Marcus Liebschner, Christian Kreischer
April 28, 2023 (v1)
Keywords: Fault Detection, induction motors, Machine Learning, multiple coupled circuit model, parameter identification, supervised learning
This paper deals with the early detection of fault conditions in induction motors using a combined model- and machine-learning-based approach with flexible adaptation to individual motors. The method is based on analytical modeling in the form of a multiple coupled circuit model and a feedforward neural network. In addition, the differential evolution algorithm independently identifies the parameters of the motor for the multiple coupled circuit model based on easily obtained measurement data from a healthy state. With the identified parameters, the multiple coupled circuit model is used to perform dynamic simulations of the various fault cases of the specific induction motor. The simulation data set of the stator currents is used to train the neural network for classification of different stator, rotor, mechanical, and voltage supply faults. Finally, the combined method is successfully validated with measured data of faults in an induction motor, proving the transferability of the sim... [more]
Fault Detection and Identification in MMCs Based on DSCNNs
Guanyuan Cheng, Shaojian Song
April 28, 2023 (v1)
Keywords: DSC, Fault Detection, fault location, MMC
Fault detection and location is one of the critical issues in engineering applications of modular multilevel converters (MMCs). At present, MMC fault diagnosis based on neural networks can only locate the open-circuit fault of a single submodule. To solve this problem, this paper proposes a fault detection and localization strategy based on a depthwise separable convolutional (DSC) neural network. By inputting the bridge arm circulating current and the submodule capacitor voltage into two serially connected neural networks, not only can this method achieve the classification of submodule open-circuit faults, submodule block short-circuit faults, and bridge arm inductance faults in MMCs, but it can also locate the switch where open-circuit faults occur. The simulation experimental results show that the proposed method achieves fault classification and locates multiple submodule open-circuit faults in the same bridge arm. This method achieves accuracies of ≥99% and 87.7% for the single-p... [more]
Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current
Yuriy Zhukovskiy, Aleksandra Buldysko, Ilia Revin
April 28, 2023 (v1)
Keywords: digital technologies, Fault Detection, induction motor, Machine Learning, reliability, singular decomposition, singular spectrum analysis, SSA, SVD, time series analysis
Among the most widespread systems in industrial plants are automated drive systems, the key and most common element of which is the induction motor. In view of challenging operating conditions of equipment, the task of fault detection based on the analysis of electrical parameters is relevant. The authors propose the identification of patterns characterizing the occurrence and development of the bearing defect by the singular analysis method as applied to the stator current signature. As a result of the decomposition, the time series of the three-phase current are represented by singular triples ordered by decreasing contribution, which are reconstructed into the form of time series for subsequent analysis using a Hankelization of matrices. Experimental studies with bearing damage imitation made it possible to establish the relationship between the changes in the contribution of the reconstructed time series and the presence of different levels of bearing defects. By using the contribu... [more]
A Hilbert−Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids
Yijin Li, Jianhua Lin, Geng Niu, Ming Wu, Xuteng Wei
April 24, 2023 (v1)
Keywords: Fault Detection, Hilbert–Huang Transform (HHT), instantaneous phase difference of current high-frequency component (IPDCHC), microgrid, self-adaptive threshold
Fault detection in microgrids is of great significance for power systems’ safety and stability. Due to the high penetration of distributed generations, fault characteristics become different from those of traditional fault detection. Thus, we propose a new fault detection and classification method for microgrids. Only current information is needed for the method. Hilbert−Huang Transform and sliding window strategy are used in fault characteristic extraction. The instantaneous phase difference of current high-frequency component is obtained as the fault characteristic. A self-adaptive threshold is set to increase the detection sensitivity. A fault can be detected by comparing the fault characteristic and the threshold. Furthermore, the fault type is identified by the utilization of zero-sequence current. Simulations for both section and system have been completed. The instantaneous phase difference of the current high-frequency component is an effective fault characteristic for detectin... [more]
Machine Learning-Based Fault Detection and Diagnosis of Faulty Power Connections of Induction Machines
David Gonzalez-Jimenez, Jon del-Olmo, Javier Poza, Fernando Garramiola, Izaskun Sarasola
April 24, 2023 (v1)
Subject: Other
Keywords: data-driven, electric machine, Fault Detection, fault diagnosis, induction motor, Machine Learning, power connection failures, supervised learning
Induction machines have been key components in the industrial sector for decades, owing to different characteristics such as their simplicity, robustness, high energy efficiency and reliability. However, due to the stress and harsh working conditions they are subjected to in many applications, they are prone to suffering different breakdowns. Among the most common failure modes, bearing failures and stator winding failures can be found. To a lesser extent, High Resistance Connections (HRC) have also been investigated. Motor power connection failure mechanisms may be due to human errors while assembling the different parts of the system. Moreover, they are not only limited to HRC, there may also be cases of opposite wiring connections or open-phase faults in motor power terminals. Because of that, companies in industry are interested in diagnosing these failure modes in order to overcome human errors. This article presents a machine learning (ML) based fault diagnosis strategy to help m... [more]
Asymmetric Multilevel Inverter Topology and Its Fault Management Strategy for High-Reliability Applications
Mohammad Fahad, Mohd Tariq, Adil Sarwar, Mohammad Modabbir, Mohd Aman Zaid, Kuntal Satpathi, MD Reyaz Hussan, Mohammad Tayyab, Basem Alamri, Ahmad Alahmadi
April 24, 2023 (v1)
Keywords: Fault Detection, fault tolerance, multilevel inverters, power electronics
As the applications of power electronic converters increase across multiple domains, so do the associated challenges. With multilevel inverters (MLIs) being one of the key technologies used in renewable systems and electrification, their reliability and fault ride-through capabilities are highly desirable. While using a large number of semiconductor components that are the leading cause of failures in power electronics systems, fault tolerance against switch open-circuit faults is necessary, especially in remote applications with substantial maintenance penalties or safety-critical operation. In this paper, a fault-tolerant asymmetric reduced device count multilevel inverter topology producing an 11-level output under healthy conditions and capable of operating after open-circuit fault in any switch is presented. Nearest-level control (NLC) based Pulse width modulation is implemented and is updated post-fault to continue operation at an acceptable power quality. Reliability analysis of... [more]
A Novel Condition Monitoring Procedure for Early Detection of Copper Corrosion Problems in Oil-Filled Electrical Transformers
Ramsey Jadim, Mirka Kans, Mohammed Alhattab, May Alhendi
April 21, 2023 (v1)
Keywords: CBM strategy, condition monitoring, copper corrosion, Fault Detection, transformer failures
The negative impacts of catastrophic fire and explosion accidents due to copper corrosion problems of oil-filled electrical transformers are still in the spotlight due to a lack of effective methods for early fault detection. To address this gap, a condition monitoring (CM) procedure that can detect such problems in the initial stage is proposed in this paper. The suggested CM procedure is based on identified measurable variables, which are the relevant by-products of the corrosion reaction, and utilizes an Early Fault Diagnosis (EFD) model to detect and solve the copper corrosion problems. The EFD model includes a fault trend chart that can track a fault progression during the useful life of transformers. The purpose of this paper is to verify and validate the effectiveness of the suggested CM procedure by an empirical study in a power plant. The result of applying this procedure was early detection of copper corrosion problems in two transformers with suspected copper corrosion propa... [more]
Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model
Weiguo He, Deyang Yin, Kaifeng Zhang, Xiangwen Zhang, Jianyong Zheng
April 21, 2023 (v1)
Keywords: Fault Detection, fault diagnosis, fine-tuning Naive Bayesian model, PV array
With the widespread attention and research of distributed photovoltaic (PV) systems, the fault detection and diagnosis problems of distributed PV systems has become increasingly prominent. To this end, a distributed PV array fault diagnosis method based on fine-tuning Naive Bayes model for the fault conditions of PV array such as open-circuit, short-circuit, shading, abnormal degradation, and abnormal bypass diode is proposed. First, in view of the problem of less distributed PV fault data, a fine-tuning Naive Bayes model (FTNB) is proposed to improve the diagnosis accuracy. Second, the failure sample set is used to train the model. Then, the maximum power point data of the PV inverter and the meteorological data are collected for fault diagnosis. Finally, the effectiveness and accuracy of the proposed method are verified by the analysis of simulation. In addition, this method requires only a small number of fault sample sets and no additional measurement equipment is required, which i... [more]
Fault Detection Algorithm for Multiple-Simultaneous Refrigerant Charge and Secondary Fluid Flow Rate Faults in Heat Pumps
Samuel Boahen, Kwesi Mensah, Selorm Kwaku Anka, Kwang Ho Lee, Jong Min Choi
April 21, 2023 (v1)
Keywords: brine flow rate fault, cooling capacity, COP, Fault Detection, fault diagnosis, heat pump, refrigerant charge fault
The detection and diagnosis of faults is becoming necessary in ensuring energy savings in heat pump units. Faults can exist independently or simultaneously in heat pumps at the refrigerant side and secondary fluid flow loops. In this work, we discuss the effects that simultaneous refrigerant charge faults and faults associated with the flow rate of secondary fluids have on the performance of a heat pump operating in summer season and we developed a correlation to detect and diagnose these faults using multiple linear regression. The faults considered include simultaneous refrigerant charge and indoor heat exchanger secondary fluid flow rate faults (IFRFs), simultaneous refrigerant charge and outdoor heat exchanger secondary fluid flow rate faults (OFRFs) and simultaneous refrigerant charge, IFRF and OFRF. The occurrence of simultaneous refrigerant charge fault, IFRF and OFRF caused up to a 5.7% and 8% decrease in cooling capacity compared to simultaneous refrigerant charge and indoor h... [more]
Improving Performance of Seismic Fault Detection by Fine-Tuning the Convolutional Neural Network Pre-Trained with Synthetic Samples
Zhe Yan, Zheng Zhang, Shaoyong Liu
April 20, 2023 (v1)
Keywords: deep learning, Fault Detection, transfer learning, U-net
Fault interpretation is an important part of seismic structural interpretation and reservoir characterization. In the conventional approach, faults are detected as reflection discontinuity or abruption and are manually tracked in post-stack seismic data, which is time-consuming. In order to improve efficiency, a variety of automatic fault detection methods have been proposed, among which widespread attention has been given to deep learning-based methods. However, deep learning techniques require a large amount of marked seismic samples as a training dataset. Although the amount of synthetic seismic data can be guaranteed and the labels are accurate, the difference between synthetic data and real data still exists. To overcome this drawback, we apply a transfer learning strategy to improve the performance of automatic fault detection by deep learning methods. We first pre-train a deep neural network with synthetic seismic data. Then we retrain the network with real seismic samples. We u... [more]
A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines
Phong B. Dao
April 20, 2023 (v1)
Keywords: condition monitoring, CUSUM test, Fault Detection, multiple linear regression, SCADA data, structural change, wind turbine
This paper presents a cumulative sum (CUSUM)-based approach for condition monitoring and fault diagnosis of wind turbines (WTs) using SCADA data. The main ideas are to first form a multiple linear regression model using data collected in normal operation state, then monitor the stability of regression coefficients of the model on new observations, and detect a structural change in the form of coefficient instability using CUSUM tests. The method is applied for on-line condition monitoring of a WT using temperature-related SCADA data. A sequence of CUSUM test statistics is used as a damage-sensitive feature in a control chart scheme. If the sequence crosses either upper or lower critical line after some recursive regression iterations, then it indicates the occurrence of a fault in the WT. The method is validated using two case studies with known faults. The results show that the method can effectively monitor the WT and reliably detect abnormal problems.
A Fault Handling Process for Faults in District Heating Customer Installations
Sara Månsson, Marcus Thern, Per-Olof Johansson Kallioniemi, Kerstin Sernhed
April 20, 2023 (v1)
Keywords: district heating, Fault Detection, fault handling processes
Faults in district heating (DH) customer installations cause high return temperatures, which have a negative impact on both current and future district heating systems. Thus, there is a need to detect and correct these faults soon after they occur to minimize their impact on the system. This paper, therefore, suggests a fault handling process for the detection and elimination of faults in DH customer installations. The fault handling process is based on customer data analysis since many faults manifest in customer data. The fault handling process was based on an analysis of the results from the previous fault handling studies, as well as conducting a workshop with experts from the DH industry. During the workshop, different organizational and technical challenges related to fault handling were discussed. The results include a presentation of how the utilities are currently working with fault handling. The results also present an analysis of different organizational aspects that would h... [more]
Transient Fault Detection and Location in Power Distribution Network: A Review of Current Practices and Challenges in Malaysia
Saidatul Habsah Asman, Nur Fadilah Ab Aziz, Ungku Anisa Ungku Amirulddin, Mohd Zainal Abidin Ab Kadir
April 20, 2023 (v1)
Keywords: Fault Detection, fault location, fault-monitoring system, power distribution system, transient fault
An auto-restoration tool to minimize the impact of faults is one of the critical requirements in a power distribution system. A fault-monitoring system is needed for practical remote supervision to identify faults and reduce their impacts, and thus reduce economic losses. An effective fault-monitoring system is beneficial to improve the reliability of a protection system when faults evolve. Therefore, fault monitoring could play an important role in enhancing the safety standards of systems. Among the various fault occurrences, the transient fault is a prominent cause in Malaysia power systems but gains less attention due to its ability of self-clearance, although sometimes it unnecessarily triggers the operation of protection systems. However, the transient fault is an issue that must be addressed based on its effect that can lead to outages and short-circuits if prolonged. In this study, the authors summarize the guidelines and related standards of fault interaction associated with a... [more]
A New Bearing Fault Detection Strategy Based on Combined Modes Ensemble Empirical Mode Decomposition, KMAD, and an Enhanced Deconvolution Process
Yasser Damine, Noureddine Bessous, Remus Pusca, Ahmed Chaouki Megherbi, Raphaël Romary, Salim Sbaa
April 18, 2023 (v1)
Keywords: combined modes ensemble empirical mode decomposition, enhanced minimum entropy deconvolution, Fault Detection, KMAD indicator, rolling element bearing faults, three-sigma rule
In bearing fault diagnosis, ensemble empirical mode decomposition (EEMD) is a reliable technique for treating rolling bearing vibration signals by dividing them into intrinsic mode functions (IMFs). Traditional methods used in EEMD consist of identifying IMFs containing the fault information and reconstructing them. However, an incorrect selection can result in the loss of useful IMFs or the addition of unnecessary ones. To overcome this drawback, this paper presents a novel method called combined modes ensemble empirical mode decomposition (CMEEMD) to directly obtain a combination of useful IMFs containing fault information. This is without needing to pass through the processes of IMF selection and reconstruction, as well as guaranteeing that no defect information is lost. Owing to the small signal-to-noise ratio, this makes it difficult to determine the fault information of a rolling bearing at the early stage. Therefore, improving noise reduction is an essential procedure for detect... [more]
Failure Detection Techniques on the Demand Side of Smart and Sustainable Compressed Air Systems: A Systematic Review
Massimo Borg, Paul Refalo, Emmanuel Francalanza
April 17, 2023 (v1)
Keywords: compressed air, Energy Efficiency, Fault Detection, smart and sustainable systems
The industrial sector is a crucial economic pillar, seeing annual increases in the production output. In the last few years, a greater emphasis has been placed on the efficient and sustainable use of resources within industry. The use of compressed air in this field is hence gaining interest. These systems have numerous benefits, such as relative low investment costs and reliability; however, they suffer from low-energy efficiency and are highly susceptible to faults. Conventional detection systems, such as ultrasonic leak detection, can be used to identify faults. However, these methods are time consuming, meaning that leakages are often left unattended, contributing to additional energy wastage. Studies published in this area often focus on the supply side rather than the demand side of pneumatic systems. This paper offers a novel review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology of fault detection methods on the demand side o... [more]
Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems
Xianglun Nie, Jing Zhang, Yu He, Wenjian Luo, Tingyun Gu, Bowen Li, Xiangxie Hu
April 17, 2023 (v1)
Keywords: convolutional neural network, fault data stitching, Fault Detection, feature characterization capability, feature extraction, image generation
Fast and accurate fault detection is important for the long term, stable operation of the distribution network. For the resonant grounding system, the fault signal features extraction difficulties, and the existing detection method’s accuracy is not high. A ground fault detection method based on fault data stitching and image generation of resonant grounding distribution systems is proposed. Firstly, considering the correlation between the transient zero-sequence current (TZSC) of faulty and healthy feeders under the same operating conditions, a fault data stitching method is proposed, which splices the transient zero-sequence current signals of each feeder into system fault data, and then converts the system fault data into grayscale images by combining the signal-to-image conversion method. Then, an improved convolutional neural network (CNN) is used to train the grayscale images and then implement fault detection. The simulation results show that the proposed method has high accurac... [more]
A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks
Annalisa Santolamazza, Daniele Dadi, Vito Introna
April 14, 2023 (v1)
Keywords: artificial neural networks, condition monitoring, Fault Detection, gearbox, generator, predictive maintenance, wind turbine
Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20−30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some o... [more]
Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data
Cristian Velandia-Cardenas, Yolanda Vidal, Francesc Pozo
April 14, 2023 (v1)
Keywords: Fault Detection, imbalanced data, k nearest neighbors, Machine Learning, principal component analysis, SCADA, structural health monitoring, support vector machines, wind turbine
Wind power is cleaner and less expensive compared to other alternative sources, and it has therefore become one of the most important energy sources worldwide. However, challenges related to the operation and maintenance of wind farms significantly contribute to the increase in their overall costs, and, therefore, it is necessary to monitor the condition of each wind turbine on the farm and identify the different states of alarm. Common alarms are raised based on data acquired by a supervisory control and data acquisition (SCADA) system; however, this system generates a large number of false positive alerts, which must be handled to minimize inspection costs and perform preventive maintenance before actual critical or catastrophic failures occur. To this end, a fault detection methodology is proposed in this paper; in the proposed method, different data analysis and data processing techniques are applied to real SCADA data (imbalanced data) for improving the detection of alarms related... [more]
Higher Order Sliding Mode Observer-Based Sensor Fault Detection in DC Microgrid’s Buck Converter
Daijiry Narzary, Kalyana C. Veluvolu
April 14, 2023 (v1)
Keywords: DC microgrid, distribution generation units, Fault Detection, higher order sliding mode observer, Lyapunov’s stability, multi sensor faults
Fault detection in a Direct Current (DC) microgrid with multiple interconnections of distributed generation units (DGUs) is an interesting topic of research. The occurrence of any sensor fault in the DC microgrid should be detected immediately by the fault detection network to achieve an overall stable performance of the system. This work focuses on sensor fault diagnosis of voltage and current sensors in interconnected DGUs of the microgrid. Two separate higher order sliding mode observers (HOSM) based on model dynamics are designed to estimate the voltage and current and generate the residuals for detecting the faulty sensors in DGUs. Multiplicative single and multiple sensor faults are considered in voltage and current sensors. By appropriate selection of threshold, single and multiple sensor fault detection strategies are formulated. A hierarchical controller is designed to ensure equal sharing of current among the DGUs of the DC microgrid and stabilize the system. Simulations are... [more]
Using EMPHASIS for the Thermography-Based Fault Detection in Photovoltaic Plants
Antonio Pio Catalano, Ciro Scognamillo, Pierluigi Guerriero, Santolo Daliento, Vincenzo d’Alessandro
April 14, 2023 (v1)
Keywords: analytical method, cell-level diagnosis, Fault Detection, photovoltaic (PV) plants, power assessment, thermography
In this paper, an Efficient Method for PHotovoltaic Arrays Study through Infrared Scanning (EMPHASIS) is presented; it is a fast, simple, and trustworthy cell-level diagnosis method for commercial photovoltaic (PV) panels. EMPHASIS processes temperature maps experimentally obtained through IR cameras and is based on a power balance equation. Along with the identification of malfunction events, EMPHASIS offers an innovative feature, i.e., it estimates the electrical powers generated (or dissipated) by the individual cells. A procedure to evaluate the accuracy of the EMPHASIS predictions is proposed, which relies on detailed three-dimensional (3-D) numerical simulations to emulate realistic temperature maps of PV panels under any working condition. Malfunctioning panels were replicated in the numerical environment and the corresponding temperature maps were fed to EMPHASIS. Excellent results were achieved in both the cell- and panel-level power predictions. More specifically, the estimat... [more]
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