- 标题
- 摘要
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- 实验方案
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[IEEE 2019 Chinese Control And Decision Conference (CCDC) - Nanchang, China (2019.6.3-2019.6.5)] 2019 Chinese Control And Decision Conference (CCDC) - A Fault Classification Method of Photovoltaic Array Based on Probabilistic Neural Network
摘要： The energy crisis has promoted the development of solar photovoltaic power generation systems, but during the operation of solar panels, there will be hidden troubles such as ground fault, line-to-line fault, open-circuit fault, short-circuits fault and the hot spots. This will cause serious obstacles to the power generation of photovoltaic systems. Therefore, the immediate diagnosis and elimination of the fault of the photovoltaic system is the guarantee for the stable operation of the photovoltaic system. To address these issues, this paper makes contribution in the following Three aspects: (1) Building a 4 3× PV array model based on the key points and model parameters extracted from PV array by using Matlab, an efficient feature vector of five dimensions is proposed as the input of the fault diagnosis model; (2) The probabilistic neural network (PNN) is proposed as the fault classification tools, and achieving a good classification effect by using the simulated data after normalization to classify; (3) Performing the field test and inputting the experimental data into PNN for classification, with an accuracy of 97%. Both the simulation and experimental results show that the PNN can achieve high accuracy classification, provide a more favorable premise basis for the intelligent classification of faults in photovoltaic arrays.
关键词： PV array,Fault Diagnosis,PNN,Fault classification
更新于2025-09-11 14:15:04
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Optical Cavity-Less 40-GHz Picosecond Pulse Generator in the Visible Wavelength Range
摘要： A method combined ensemble empirical mode decomposition, Volterra model and decision acyclic graph support vector machine was proposed to improve adaptability, feature resolution, and identification accuracy when diagnosing mechanical faults in an on-load tap changer of a transformer. In detail, the ensemble empirical mode decomposition algorithm was applied to decompose the multi-channel vibration signals in the switchover process of the on-load tap changer. Then, a Volterra model for the mechanical state of the on-load tap changer was established based on time-frequency characteristics obtained through the use of the ensemble empirical mode decomposition algorithm. Moreover, a matrix of coefficient vectors was also used in the Volterra model. This method will not only reduce the aliasing effect of empirical mode decomposition but also obtain high-resolution characteristics of nonstationary vibration signals. Furthermore, taking the singular values of the Volterra coefficient matrix as the fault characteristic, the data states of the model for diagnosing the on-load tap changer were automatically classified and identified by establishing a rapid, multi-classification decision acyclic graph support vector machine model with a low misjudgment rate. Finally, based on a certain on-load tap changer, the test platform for simulating mechanical faults was built. On this basis, by using the proposed method, the vibration signals generated due to typical mechanical faults, such as loosening of moving contacts, lessening of transition contact, and motor jam were acquired and analyzed, thus validating the effectiveness of the method through case studies. Compared with other methods, the new method could overcome many defects in existing methods and it has higher fault identification accuracy.
关键词： signal processing algorithms,Mechanical variables measurement,power transformers,fault diagnosis,electromechanical devices,time series analysis,support vector machines,switches
更新于2025-09-11 14:15:04
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Fault Diagnosis Method of Photovoltaic Array Based on BP Neural Network
摘要： Photovoltaic arrays are prone to various failures due to long-term work. In order to quickly and accurately diagnose the type of failure of the PV array and implement online monitoring of the PV array, this paper proposes the BP neural network for PV array fault diagnosis, and proposes a network search method when training BP neural network. And the K-cross-validation method is used to select the number of hidden layer nodes. The BP neural network fault diagnosis model designed and trained by this method is proved to have high precision.
关键词： BP neural network,hidden layer nodes,fault diagnosis,K-cross-validation,Photovoltaic array
更新于2025-09-11 14:15:04
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PV module fault diagnosis based on micro-converters and day-ahead forecast
摘要： The employment of solar micro-converter allows a more detailed monitoring of the PV output power at the single module level; thus, machine learning techniques are capable to track the peculiarities of modules in the PV plants such as regular shadings. In this way it is possible to compare in real-time the day-ahead forecast power with the actual one in order to better evaluate faults or anomalous trends which might have occurred in the PV plant. This paper presents a method for an effective fault diagnosis; this method is based on the day-ahead forecast of the output power from an existing PV module, linked to a micro-converter, and on the outcome of the neighbor PV modules. Finally, this paper proposes also the analysis of the most common error definitions with new mathematical formulations, by comparing their effectiveness and immediate comprehension, in view of increasing power forecasting accuracy and performing both real-time and offline analysis of PV modules performance and possible faults.
关键词： PV system,day-ahead forecast,micro-inverter,Fault diagnosis
更新于2025-09-11 14:15:04
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A real-time impact detection and diagnosis system of catenary using measured strains by fibre Bragg grating sensors
摘要： This paper describes an impact detection system using strain signals based on fibre optic sensors(FBG) for the real-time monitoring of the catenary system. The proposed detection system consists of three subsystems: a measuring system, a data processing and analysis system, and a status display and data access system. Because the strain signals obey the normal distribution, to monitor the catenary system in real time, a novel method that combines mobile standard deviation with the mobile Pauta criterion is proposed to distinguish real impact from the strain signal background. The use of this adaptive judging method reduces the misjudgment rate of impacts and improves the impact recognition accuracy. These impacts can be identified by the data analysis system, which provides impact location and their causes using the features of the catenary system. This method can simplify the detection system compared with the traditional location method. An application to a commercial metro line system indicated that the impacts on the catenary system were main caused by overlaps, expansion joints or steady arms, and were verified by correspondence with the floor plan of the catenary and manual inspection results. These results verified the reliability and effectiveness of the proposed impact detection system.
关键词： catenary,real-time monitoring,fibre Bragg grating,Impact detection,fault diagnosis
更新于2025-09-09 09:28:46
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Recent industrial applications of infrared thermography: a review
摘要： Infrared thermography is a non-invasive technique that is drawing an increasing attention in industry. The spectacular advance in the features of the infrared cameras that has come together with their progressive cost reduction have expanded the use of this technique to many industrial applications that were unfeasible just a few years ago. This paper compiles and comments the most recent scientific contributions related to the application of this technique in the industrial context. The paper classifies the analyzed references into three main groups: electrical, mechanical and other applications. Especial emphasis is made on induction motor-related applications of the infrared thermography due to the extensive participation of these machines in the industrial context. The paper provides a critical review of most of the analyzed references, emphasizes the way in which the infrared technique is applied to the specific application and presents the limitations and pending issues as well as the future challenges regarding the application of the technique.
关键词： industry,fault diagnosis,thermography,infrared,mechanical faults,induction motors,transformers
更新于2025-09-09 09:28:46