- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
PV shading fault detection and classification based on I-V curve using principal component analysis: Application to isolated PV system
摘要： Health monitoring and diagnosis of photovoltaic (PV) systems is becoming crucial to maximise the power production, increase the reliability and life service of PV power plants. Operating under faulty conditions, in particular under shading, PV plants have remarkable shape of current-voltage (I-V) characteristics in comparison to reference condition (healthy operation). Based on real electrical measurements (I-V), the present work aims to provide a very simple, robust and low cost Fault Detection and Classi?cation (FDC) method for PV shading faults. At ?rst, we extract the features for di?erent experimental tests under healthy and shading conditions to build the database. The features are then analysed using Principal Component Analysis (PCA). The accuracy of the data classi?cation into the PCA space is evaluated using the confusion matrix as a metric of class separability. The results using experimental data of a 250 Wp PV module are very promising with a successful classi?cation rate higher than 97% with four di?erent con?gurations. The method is also cost e?ective as it uses only electrical measurements that are already available. No additional sensors are required.
关键词： Fault classi?cation,Principal component analysis,Fault detection,I-V curves,PV shading faults
更新于2025-09-23 15:23:52
-
Analysis of Optical Plasma Monitoring in Plasma-Enhanced Atomic Layer Deposition Process of Al <sub/>2</sub> O <sub/>3</sub>
摘要： A noninvasive, optical plasma monitoring method in plasma-enhanced atomic layer deposition (PEALD) process for nanoscale water vapor barrier film is presented. Any equipment malfunction, as well as a deviation in the condition of individual components can easily jeopardize the process result. Al2O3 deposition process was employed in this research as a test vehicle, and high-speed optical plasma monitoring was demonstrated. It is shown that optical plasma monitoring is useful for not only measuring plasma pulses in real time, but also for the detection of any variation in plasma condition which enables inferring plasma dynamics for advanced process control in nanoscale thin film deposition process.
关键词： PEALD,Process Monitoring,Plasma Diagnostics,Fault Detection
更新于2025-09-23 15:22:29
-
[IEEE 2018 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace) - Rome, Italy (2018.6.20-2018.6.22)] 2018 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace) - Active Fault Tolerant Gyromoment Control of Information Satellites and Free-flying Robots
摘要： The methods for modeling and detection of anomalous in the automatic control systems, and also practical methods for active analysis of anomalous situations, are presented. We develop algorithms for consecutive classification of onboard equipment failures and for reconfiguration of control loop. The simulation results are presented for the control systems of the information satellites and free-flying robots.
关键词： free-flying robots,gyromoment control,automatic control systems,fault detection and isolation (FDI),dynamic reliability,fault-tolerance,information satellites
更新于2025-09-23 15:21:21
-
Enhancing the Reliability of Protection Scheme for PV Integrated Microgrid by Discriminating between Array Faults and Symmetrical Line Faults using Sparse Auto Encoder
摘要： The ever increasing power demand and the stress on reducing carbon footprint have paved the way for widespread use of PV integrated microgrid. However, the development of a reliable protection scheme for PV integrated microgrid is challenging because of the similar voltage-current profile of PV array faults and symmetrical line faults. Conventional protection schemes based on pre-defined threshold setting are not able to distinguish between PV array and symmetrical faults, and hence fail to provide separate controlling actions for the two cases. In this regard, a protection scheme based on sparse auto-encoder and deep neural network (SAE-DNN) approach has been proposed to discriminate between array faults and symmetrical line faults in addition to performing the tasks of mode detection, fault detection, classification and section identification. The voltage and current signals retrieved from relaying buses are converted into grayscale image dataset, which is fed as input to the SAE to perform the unsupervised feature learning. The performance of proposed scheme has been evaluated through reliability analysis and compared with ANN, SVM and DT based techniques under both islanding and grid-connected mode of the microgrid. The scheme has been also validated for field applications by performing real-time simulations on OPAL-RT digital simulator.
关键词： sparse auto-encoder,classification,deep neural network,PV integrated microgrid,section identification,protection scheme,fault detection,OPAL-RT digital simulator
更新于2025-09-23 15:21:01
-
Performance to Peers (P2P): A benchmark approach to fault detections applied to photovoltaic system fleets
摘要： The fault detection applied to a large amount of small distributed PV systems needs to be simple, cost-effective, and reliable. This work presents a fault detection procedure applied to distributed PV system fleets, based on a novel performance indicator, designated as Performance to Peers (P2P), that can be constructed on the sole basis of the comparison of the energy production data of several neighboring PV systems. This article explains how to construct this performance indicator and how to use it to carry out automatic fault detections. This fault detection procedure has been developed in the context of the performance analysis carried out on approximately 6000 PV installations located in Europe, and it is illustrated and discussed through real application cases. The P2P has been shown to be more stable than the Performance Ratio (PR), in particular in the presence of sub-par metadata on the PV systems, and it thus constitutes a more robust performance indicator for fault detection. The stability of P2P is characterized by an Absolute Median Deviation (MAD) that is typically of 10% for hourly data and 5% for daily data. The application of P2P to fault diagnosis is illustrated on four categories of faults that are among the most frequently observed on PV systems. The main limitations of this novel methodology are discussed, and several future lines of research are suggested.
关键词： Performance,P2P,PV systems,Photovoltaic,Fault detection,Monitoring
更新于2025-09-23 15:19:57
-
Novel Open-Circuit Photovoltaic Bypass Diode Fault Detection Algorithm
摘要： In this article, a novel photovoltaic (PV) bypass diode fault detection algorithm is presented. The algorithm consists of three main steps. First, the threshold voltage of the current–voltage (I–V) curve is obtained using different failure bypass diode scenarios. Second, the theoretical prediction for the faulty regions of bypass diodes is calculated using the analysis of voltage drop in the I–V curve as well as the voltage at maximum power point. Finally, the actual I–V curve under any environmental condition is measured and compared with theoretical predictions. The proposed algorithm has been experimentally evaluated using a PV string that comprises three series-connected PV modules, and subtotal of nine bypass diodes. Various experiments have been conducted under diverse bypass diodes failure conditions. The achieved detection accuracy is always greater than 99.39% and 99.74% under slow and fast solar irradiance transition, respectively.
关键词： current–voltage (I–V) curve,Bypass diodes,power loss,fault detection,photovoltaics (PV),solar irradiance
更新于2025-09-19 17:13:59
-
[IEEE 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Xiamen, China (2019.12.17-2019.12.20)] 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Multi-layered Parallel Plate Waveguide with Electrically and Magnetically Biased Graphene Walls
摘要： This paper focuses on the fault detection problem of 2-D systems described by the Roesser model. To detect faults effectively in the presence of disturbances, a fault detection ?lter is designed to satisfy a ?nite-frequency H? index and a ?nite-frequency H∞ index simultaneously. The corresponding ?nite-frequency performance analysis conditions are obtained by the aid of the generalized Kalman–Yakubovich–Popov lemma. Then, convex ?lter design conditions are derived by constructing a hyperplane tangent combined with linear matrix inequality techniques. An algorithm is proposed to construct a desired fault detection ?lter. Finally, a numerical example is given to show the effectiveness of the proposed method.
关键词： Fault detection,two-dimensional systems,?nite frequency,Roesser model
更新于2025-09-19 17:13:59
-
[IEEE 2019 22nd International Multitopic Conference (INMIC) - Islamabad, Pakistan (2019.11.29-2019.11.30)] 2019 22nd International Multitopic Conference (INMIC) - Neuro based Integral Terminal Sliding Mode Nonlinear MPPT Control Paradigms for Stand-Alone Photovoltaic System
摘要： This paper focuses on the fault detection problem of 2-D systems described by the Roesser model. To detect faults effectively in the presence of disturbances, a fault detection ?lter is designed to satisfy a ?nite-frequency H? index and a ?nite-frequency H∞ index simultaneously. The corresponding ?nite-frequency performance analysis conditions are obtained by the aid of the generalized Kalman–Yakubovich–Popov lemma. Then, convex ?lter design conditions are derived by constructing a hyperplane tangent combined with linear matrix inequality techniques. An algorithm is proposed to construct a desired fault detection ?lter. Finally, a numerical example is given to show the effectiveness of the proposed method.
关键词： Fault detection,two-dimensional systems,?nite frequency,Roesser model
更新于2025-09-19 17:13:59
-
New monitoring method to characterize individual modules in large photovoltaic systems
摘要： Photovoltaic (PV) systems power losses are approximately 15–20% of the performance ratio for current PV systems. There are several reasons that explain PV modules failures, and since they are connected in series to the rest of the string, a failure in one module may result in losses in the entire string. In addition, some of these failures, if are not fixed in time may become permanent and may reduce the lifespan of the PV modules. Periodic monitoring is the only way to detect these failures. Monitoring techniques oriented to groups of modules are unable to detect faults in individual modules. I-V curve tracers, which are oriented to module level and use power electronics components and large capacitors, require to disconnect the PV module from the rest of the system and long measurement times. This works proposes a methodology, that is able to take partial measurements of individual PV modules and recompose their characteristics with only small capacitors in the range of tens of microfarads and without power electronics components. The monitoring methodology is able to measure individual PV modules without modifying the electrical interconnection circuit and to deviate the operating point to 0.3 A and 5 V in less than 5 ms. From this deviation, the system recomposes the PV module I-V characteristics with accuracies that are between 1 and 3% for the region close to maximum power.
关键词： Monitoring,Fault detection,Photovoltaic systems,Capacitive load,Maximum power point
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America) - Gramado, Brazil (2019.9.15-2019.9.18)] 2019 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America) - A Comparison of Machine Learning-Based Methods for Fault Classification in Photovoltaic Systems
摘要： Photovoltaic (PV) energy use has been increasing lately and, being highly dependent on environmental variables, its efficiency becomes a major factor for concern. Additionally, these systems can be affected by several kinds of faults, which can lead to a severe energy loss. In this sense, this work compares different machine-learning-based methods, such as K-Nearest Neighbors (k-NN), Decision Trees (DT), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), for detecting the following faults that can occur in Photovoltaic (PV) systems: Module short circuit, MPPT fault, Open Circuit, Partial Shading, and Degradation. The accuracy and computational time taken for training each classifier were compared. ANN achieved the best accuracy, with 99.65%, while being the slowest to train. The SVM achieved a similar result, with significant less training time. There is lack of discussion on the analysis and comparison of PV fault classification methods in the literature, specially with the focus on further practical applications and computational complexity. This way, those points are the main contributions of this work, along with making all simulations and codes publicly available.
关键词： Fault Detection,Fault Diagnostic,PV Systems,Fault Classification,Machine Learning
更新于2025-09-16 10:30:52