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[IEEE 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) - Delhi, India (2018.10.22-2018.10.24)] 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) - Condition Monitoring of Photovoltaic Systems Using Machine Leaming Techniques
摘要： Condition monitoring of any system is essential to maintain its healthy operation as it results in getting maximum revenue with minimum maintenance and operation costs. The main objective of this paper is to develop a fault detection algorithm capable of classifying different faults that can be occur in a Photovoltaic (PV) systems. Output characteristics of the PV system are used as valuable information to observe various types of faults and their locations. Wavelet transforms and neural network systems were adapted to filter the non-significant anomalies and make it easier to detect faults that are to be taken care of in a timely manner. The neural network (NN) classification adapts Multilayer perceptron (MLP) to identify the type and location of occurring faults. Wavelet transform (WT) based signal processing technique is utilized in the feature extraction process to provide inputs to the NN. The developed detection algorithm is adapted for 24/7 automated surveillance. The developed algorithm achieved 98.2% accuracy when tested on a predetermined fault data set.
关键词： Neural Network,Fault Detection,Wavelet Transform,Machine Learning,Photovoltaic Systems
更新于2025-09-12 10:27:22
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Location for fault string of photovoltaic array based on current time series change detection
摘要： Various faults inevitably occur in photovoltaic (PV) array due to the harsh external working environment. Therefore, detecting the faults and theirs locations is essential for the PV array. In this paper we propose a method for detecting the faults and their locations based on time series of PV string current. A time series sliding window (TSSW) is adopted. The local outlier factor (LOF) of each current point in the TSSW is calculated. Once a number of LOFs are continuously detected to exceed the threshold value, the PV string can be judged as fault. The experiment results show that the proposed method can detect short circuit fault, open circuit fault and shadow fault for PV string under different irradiance.
关键词： Photovoltaic array,local outlier factor,fault detection,time series
更新于2025-09-12 10:27:22
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[IEEE 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) - Coimbatore, India (2019.2.20-2019.2.22)] 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) - Improved Fault Detection and Location Scheme for Photovoltaic Systems
摘要： Photovoltaic is encountering a quick innovation development since a decade ago. Yet, strange conditions, for example, shortcomings, low irradiance and so forth it influence the yield of PV framework. To enhance the execution of and productivity of PV framework, it is important to create enhanced blame location procedures. This paper for the most part centres around recognition conspire for LL and LG blames in the PV cluster. Such blames stay undetected under irradiance conditions, especially, when a most extreme power point following calculation is in administration. In the event that these shortcomings are undetected, there is extensively loss of yield of PV framework, in the event that these issues are not recognized for longer time, it might harm the board and conceivably cause fire dangers. The exhibited blame identification conspire utilizes Multi-Resolution Signal Decomposition (MSD) procedure and two machine learning calculations to be specific Fuzzy Logic and K-Nearest Neighbor (KNN) to group the blame and decide its area. Reenactment results confirm the exactness, unwavering quality and versatility of the exhibited plan.
关键词： K-Nearest Neighbour (KNN) algorithm,Fuzzy logic,MSD,Machine Learning algorithm,Fault detection
更新于2025-09-11 14:15:04
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Switch fault diagnosis for boost DC–DC converters in photovoltaic MPPT systems by using high-gain observers
摘要： Open- and short-circuit faults (OSCFs) in boost dc–dc converters for photovoltaic (PV) maximum power point trackers (MPPTs) imply an inefficiency after fault is triggered, which affect the security and profitability of PV projects. Hence, fault detection and isolation (FDI) techniques have become an important issue for PV technology. In this study, a model-based FDI technique is proposed to boost dc–dc converters in PV MPPT systems. As is well-known, major issues of model-based FDI techniques have always been parametric uncertainty and no-modelled dynamics. This study focuses on how to mitigate these shortcomings by applying a high-gain observer (HGO) as a residual generator. A striking feature of HGO's is that exponential stability is still guaranteed for bounded disturbances (or faults). As demonstrated in this study, under an integral control action in the closed-loop control system, OSCFs are characterised for ever-growing signals, enabling the suggested FDI scheme. Also, the FDI proposal is decoupled from PV current (irradiance changes) and load variations, thereby avoiding false alarms. Moreover, the output-injection gain and thresholds are selected such that the fault diagnosis is achieved in eight switching cycles, enabling a fast and reliable diagnosis. Experimental results are illustrated to validate the FDI scheme proposed in this study.
关键词： photovoltaic MPPT systems,boost dc–dc converters,parametric uncertainty,fault detection and isolation,no-modelled dynamics,high-gain observers
更新于2025-09-11 14:15:04
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Fault detection in trackers for PV systems based on a pattern recognition approach
摘要： In many photovoltaic (PV) power plants, the PV modules are installed in trackers. In these systems, the PV modules are fixed in a mobile structure to always maintain a perpendicular position to the brightest point in the sky, obtaining in this way the maximum power from the sun, during the all day. Nevertheless, these systems are subject to problems that reduce their efficiency. Thus, visual inspection or complex methods can be used to detect this problem. However, these systems normally result in delays or are expensive. To overcome these problems, this paper proposes a new method for that detection. This, method is based on the pattern recognition analysis. Thus, through the analysis of the images of the several solar panels, the PV module that presents a problem in the tracker will be detected. The orientation of the PV modules is determined using the centroid of the PV cells after applying an image pre‐processing stage. The angle is calculated using the statistical moments or by the slope of the line joining two centroids of the PV cells that are located at the vertices of the PV module. Several test cases are presented to verify the efficiency of the proposed approach.
关键词： pattern recognition,image processing,tracker,PV power plant,fault detection,PV module
更新于2025-09-10 09:29:36
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[IEEE 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) - Coimbatore, India (2018.3.1-2018.3.3)] 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) - Smart Solar Street Light
摘要： Street Lighting is one of the most important and biggest energy consuming departments of the nation. On top of that, the most difficult thing is maintaining them. Consider a situation wherein the streetlight of a locality has stopped working. Now, the people of that area have to call and complain about the same and wait for days to get it repaired, leaving that area dark and socially unsafe. The proposed system consists of a Fault Detection Circuit using two photo sensors in addition with Arduino Uno microcontroller and GSM module [1], which automatically detects the fault in the streetlight. This eliminates manual complaining of the faulty streetlight and initiates sudden repair of the streetlight by the technician, which is being informed by Fault Detection Circuit via text message. Automatic turn ON and turn OFF feature has also been included using two photo sensors which turns ON the streetlight during dark hours and automatically turns OFF the streetlight during light hours. In addition to that, solar panels have been used as source for the streetlight and LED lights have been used for the emission of light, reducing the cost of electricity by 80%.
关键词： Solar Streetlight,Constant Current Charger,Arduino UNO,GSM Module,Automatic Fault Detection
更新于2025-09-04 15:30:14
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The Design and Implementation of Photoelectric Sensor for Yarn Color Fault Detection
摘要： The paper mainly aimed at solving the problem of yarn color fault detection. Yarn with different color is hard to detect in yarn production, a special photoelectric sensor is designed in this paper. First, this paper analyzed the requirement of light source and photoelectric receiver in the photoelectric sensor, and designs the light path and driver circuit. Then this paper analyzed the amplifier circuit and noise in the photoelectric sensor, with an amplifier circuit of minimal noise proposed at last. Finally, this paper tested the yarn color fault detection system with virtual instrument, and the test results showed a great application prospect of the photoelectric sensor. Photoelectric yarn clearer was the first type of electronic yarn clearer, but due to the under development of the optical technology and measurement technology, the photoelectric yarn cleaner can't meet the requirements of textile production, gradually replaced by capacitive yarn cleaner. Though photoelectric yarn cleaner had a good visual conformity degree, it’s still a unreplaceable method in colored yarn faults.
关键词： Fault Detection,Yarn,Photoelectric Sensor,Color
更新于2025-09-04 15:30:14