(PDF) Ghost-RetinaNet: Fast Shadow Detection
PDF | On Jan 1, 2023, Jun Wu and others published Ghost-RetinaNet: Fast Shadow Detection Method for Photovoltaic Panels Based on Improved RetinaNet | Find, read and cite all the research you need
PV-YOLO: Lightweight YOLO for Photovoltaic Panel
Comparison of detection effects between the proposed model and the YOLOX and DAB-DETR models Fig. 12 shows the detection performance of different models when only foreign objects are detected.
PA-YOLO-Based Multifault Defect Detection Algorithm
"Battery string" and "PV panel string" in Table 1 represent the same meaning. As can be seen from Table 1, under the same experimental conditions, the algorithm PA-YOLO proposed in this paper has a significantly
CN221379298U
The utility model discloses a detection mechanism of a photovoltaic cell string repairing machine, which comprises a frame, wherein the frame is fixedly connected with a workbench, the workbench is provided with a transparent detection part for placing a photovoltaic cell string, a sucker is movably connected above the detection part, a camera is arranged under the
Defect Detection Algorithm of Photovoltaic Module EL Image
Electroluminescence imaging can obtain high-resolution images of photovoltaic modules, and it is of great significance to obtain EL images of photovoltaic modules through drones for intelligent and refined defect detection. The EL images have a complex texture background with high resolution and non-uniformity, and at the same time, defects such as
Overview of Fault Detection Approaches for Grid
Over the past few years, the power electronic converters have gained significant attraction among researchers, especially as an interface between distributed generation (DG) systems and the grid.
Explainable artificial intelligence for photovoltaic fault
Faults in photovoltaic arrays are known to cause severe energy losses. Data-driven models based on machine learning have been developed to automatically detect and diagnose such faults.
(PDF) DA-DCGAN: An Effective Methodology for DC
DC arc faults, especially series arcing, can occur in photovoltaic (PV) systems and pose a challenging detection and protection problem. Machine learning based methods are increasingly being used
Fault diagnosis of photovoltaic strings by using machine
The paper proposes a machine learning‐based stacking classifier (MLSC) for accurate fault diagnosis of PV strings. Specifically, for the operating state of PV modules, the
Deep learning for photovoltaic defect detection using
Monitoring systems are essential to maintain optimal performance of photovoltaic (PV) systems. A critical aspect in such monitoring systems is the fault diagnosis technique being used.
Improved Gaussian Distance Loss for Photovoltaic String Rotation
In response to the problems of low detection accuracy and inability to accurately locate photovoltaic strings caused by complex backgrounds, dense target distri
PA-YOLO-Based Multifault Defect Detection Algorithm
In contrast to these image-based approaches, some studies have adopted data-driven methods for PV fault detection. For instance, Madeti and Singh [8] proposed a k-nearest neighbors (kNN) rule-based photovoltaic (PV)
ORIGINAL RESEARCH PAPER
Received: 7 July 2020-Revised: 18 January 2021-Accepted: 4 February 2021-IET Circuits, Devices & Systems DOI: 10.1049/cds2.12060 ORIGINAL RESEARCH PAPER Improved voltage transfer method for lithium batter y string management chip
Collaborative fault detection for large-scale photovoltaic systems
T1 - Collaborative fault detection for large-scale photovoltaic systems. AU - Zhao, Yingying. AU - Li, Dongsheng. AU - Lu, Tun. AU - Lv, Qin. AU - Gu, Ning. AU - Shang, Li. PY - 2020/10. Y1 - 2020/10. N2 - Data-driven approaches have gained increasing interests in fault detection of photovoltaic systems due to the availability of sensor data.
(PDF) YOLOv3-MSSA based hot spot defect detection for photovoltaic
YOLOv3 model is utilized accordingly for PV stri ng target detection in PV plants. In many In many instances, the infra-red video of the PV string is influenced by multiple factors, leading to an
Defect detection of photovoltaic modules based on
This section briefly overviews the detection method of photovoltaic module defects based on deep learning. Deep learning is considered a promising machine learning technique and has been adopted
Ground Fault Detection of Photovoltaic and Energy Storage DC
The control module of the photovoltaic array monitors the state of the lithium-ion battery and controls its charging and discharging processes, ensuring the safety and longevity of the battery [23,24,25]. The control module of the photovoltaic array uses the maximum power point tracking (MPPT) algorithm, which can detect the generated voltage of the solar panels in
CN115639214A
The embodiment of the application relates to the field of photovoltaic cells, and provides a cell string detection method, which comprises the following steps: providing a battery string to be detected, wherein the battery string comprises a plurality of battery pieces, a plurality of welding spots are arranged on the battery pieces, and the adjacent battery pieces are electrically
Current indicator based fault detection algorithm for
detection is at the array level, they are quite about the loca-tion of string fault and these schemes require more number of sensors. The proposed method is to detect the fault in PV array and locate the faulty string in PV systems. The fault detection is based on the current indicator signals that are calculated using the string current
CN108206670B
The invention mainly relates to a monitoring system and a monitoring method for detecting the insulation state of a photovoltaic battery pack string. The system can detect the insulation state of each single photovoltaic cell in the photovoltaic cell group string to the maximum extent in real time, and provides a basis for the safe operation of the photovoltaic power generation system.
Fault diagnosis of photovoltaic strings by using machine
In literature, PV fault diagnosis methods include physical detection methods, threshold methods, and artificial intelligent methods [6–21]. Physical detection method is to find the fault type and
Precise performance diagnosis of photovoltaic string by operation
Quick detection, preferably real-time detection, of the changes in the performance is desired in order to confirm the sound operation of the PV system, and to detect
Failure analysis of photovoltaic strings by constructing a digital
The failure analysis and diagnosis of PV strings in PV systems initially focused on studies with specific threshold settings. These methods primarily rely on expert knowledge,
Photovoltaic Cell Defect Detection Model based-on Extracted
The derived features from solar panel images provide a significant source of information for photovoltaic applications such as fault detection assessment. In this work, a method for classifying between the normal and a defective solar cell was implemented using EL imaging with selected digital image processing techniques through the Support Vector Machine (SVM) classifier.
YOLOv7-GX-based defect detection for PV panels
In this paper, an optimization algorithm based on YOLOv7-GX for PV panel defect detection is proposed for the problem of multi-fault identification of PV panel images.
Photovoltaic Failure Detection Based on String-Inverter Voltage
Zuñiga-Reyes et al.: Photovoltaic Failure Detection Based on String-Inverter Voltage and Current Signals Vmp Im iripple Iscs Isc istr KPV nd P Pm T V Vg Vhf Vlf Imp Vm Vocs Voc vripple vstr AC AI DC DFT DWT KNN MPPT PS PVA PVG PVI PVM PVS SC Maximum-power point voltage Maximum current Ripple current Short-circuit string current Short-circuit current String current
Photovoltaic DC arc fault detection method based on deep
developing an arc fault detection method based on articial intelligence becomes a crucial step in ensuring the robust-ness and eciency of arc fault detection within photovoltaic systems. A novel detection method based on a neural network is proposed through the creation of a series arc fault test platform for photovoltaic systems.
Photovoltaic Failure Detection Based on String
The novelty of this proposal is the processing of voltage and current signals generated (ripple signals) by the electrical interaction between the photovoltaic string, the photovoltaic inverter
Anomaly detection using K-Means and long-short term memory
Data gathered from the selected S12-N1 includes the string modules output current for 7-combiner box denoted as S12-N1-D1 to S12-N1-D7 making a total of 420 electrical current of string modules and a total of 8400 PV modules are being monitored.
Detection and Localization of Disconnections in a Large-Scale String
The proposed PV ground fault detection technique has been tested in a real-world PV system, and it can confidently detect PV ground faults for different configurations of PV arrays (single and
Improved voltage transfer method for lithium battery string
The circuit reduces the leakage current to nanoampere scale and is integrated into the lithium battery string management chip, which is helpful for battery voltage balance and low cost. REFERENCES 1 Singh, M., et al.: Smartphone battery state-of-charge (SoC) estimation and battery lifetime prediction: State-of-art review .
PV Doctor
*2 If battery level is decreased, measurement will stop because the inrush current causes the instant voltage drop. *3 Battery can be NiMH rechargeable battery or alkaline battery. *4 I-V curve measurement (individual mode) takes 3.1 sec to measure a string: 1sec for probe contact check + 100ms for I-V measurement + 2 sec for
Arc Detection of Photovoltaic DC Faults
The detection of series arc fault in photovoltaic systems based on the arc current entropy. IEEE Trans. Power Electron. 2015, 31, 5917–5930. [Google Scholar] Qian, H.;
CN105743439A
The invention relates to the technical field of photovoltaic batteries, and discloses a method for detecting operating parameters of photovoltaic battery pack strings and a related device. Various inverters are controlled to collect the operating parameters of the photovoltaic battery pack strings which are connected with the inverters by the same detection parameter at the same time by
Photovoltaic panel string detection based on prior knowledge and
In order to solve this problem, this paper proposes a photovoltaic panel string detection method based on prior knowledge and feature learning. First, the infrared inspection image is
A data-driven photovoltaic string current mismatch fault diagnosis
This paper investigates and collects the data of mismatched PV strings in an actual PV plant, and further the fault characteristics of mismatched PV strings are extracted
CN102109578A
The invention relates to a system for monitoring a photovoltaic battery pack string and belongs to the field of running and management of a photovoltaic power generation system. In a photovoltaic power station, photovoltaic battery packs are connected in series and then are connected with a header box according to specific conditions, photovoltaic battery pack strings are connected in
Arc Fault Detection and Localization in Photovoltaic Systems
The positive poles of the PV array, battery, and the dc load are connected within the converter. The rated output power of each solar panel is 1091 120 W with rated voltage and current of 17.0 V and 7.1 A, respectively. The PV modules were installed on the roof of a laboratory building and the angle between the module and the ground was 45°.
6 FAQs about [Original photovoltaic battery string detection]
What is failure analysis & diagnosis of PV strings?
The failure analysis and diagnosis of PV strings in PV systems initially focused on studies with specific threshold settings.
What data is used to analyze a PV string?
The analysis is carried out by using only the real-time data of V mp1, I mp1, T m, and G, and do not require the performance history of the string, or intercomparison with the results of other PV strings or climatic data.
Why do we need a real-time detection of a PV system?
Quick detection, preferably real-time detection, of the changes in the performance is desired in order to confirm the sound operation of the PV system, and to detect the symptom of those degradation before the output power significantly deteriorates.
How do feature twins and visual twins address string failures in photovoltaic systems?
Based on the concept of multi-twins, we propose the notions and methods of feature twins and visual twins to address the phenomenon of string failures in photovoltaic systems. By describing the three types of failure processes through features and visuals, these methods are applied to subsequent classification and diagnosis tasks.
How accurate is photovoltaic failure analysis based on deep neural networks?
Timely and accurate failure analysis of photovoltaic (PV) systems is crucial forensuring the stable operation of power grids. However, existing failure analysis and diagnosis algorithms based on deep neural networks excessively rely on high-quality failure state data collected by sensors.
Why do we need a failure analysis and diagnosis of PV systems?
These issues can lead to fluctuations in system power generation efficiency and may even adversely affect the stability of the power grid. Therefore, conducting timely and accurate failure analysis and diagnosis of PV systems based on measurement data from various electrical sensors holds significant research value and practical importance .