: With the increasing installed capacity of photovoltaic (PV) power generation, it has become a signiﬁcant challenge to detect abnormalities and faults of PV modules in a timely manner. Consid-ering that all the fault information of the PV module is contained in the current-voltage ( I - V ) curve, this pioneering study takes the I - V curve as the input and proposes a PV-fault identiﬁcation method based on improved deep residual shrinkage networks (DRSN). This method can not only identify single faults (e.g., short-circuit, partial-shading, and abnormal aging), but also effectively identify the simultaneous existence of hybrid faults. Moreover, it can achieve end-to-end fault diagnosis. The diagnostic accuracy of the proposed method on the measured data reaches 97.73%, is better than the convolutional neural network (CNN), the support vector machine (SVM), the deep residual network (ResNet), and the stage-wise additive modeling using multi-class exponential loss function based on the classiﬁcation and regression tree (SAMME-CART). In addition, the possibility of the aforementioned method running on the Raspberry Pi has been veriﬁed in this study, which is of great signiﬁcance for realizing the edge diagnosis of PV fault.

A Photovoltaic System Fault Identiﬁcation Method Based on Improved Deep Residual Shrinkage Networks

The paper was aimed at ensuring the stable operation of the photovoltaic power generation system (PVPGS) and improving the accuracy of automatic mismatch detection. Consequently, this paper presents a PVPGS-oriented mismatch detection system based on wireless sensing technology (WSN). Firstly, the photovoltaic array (PVA) is constructed using a microcontroller, power management chip, nRF24L01, temperature sensor, voltage, and current sensor. Then, a fault detection and localization (FDL) scheme based on the Hampel algorithm is optimized, and Matlab/Simulink implements the PVA simulation model. Finally, several typical mismatch faults are simulated to verify the feasibility of the proposed FDL scheme using the measured voltage and current data. The empirical findings corroborate that the proposed FDL scheme can automatically and regularly collect photovoltaic (PV) electrical characteristic data and quickly and accurately identify and position a mismatch. In the case of a PVA open-circuit fault, the output current loss of the PVA is equal to the sum of the current of the open-circuit fault string in the array during normal operation. When the PVA is short-circuited, the PVA output voltage loss equals the sum of the output voltages of the faulty components in the most serious fault string under normal operation. Overall, the classification accuracy of the proposed FDL scheme is 97.556%. Lastly, the experiment reveals that the classification accuracy of the proposed FDL scheme is 100% for array aging, shadow, and the open circuit. Therefore, the research proposal has a good application prospect.

/pdf/application-of-wireless-sensor-network-technology-using-1wanhjbs.pdf

Application of Wireless Sensor Network Technology Using Intelligent Algorithm in Mismatch Detection of Photovoltaic Power Generation

The invention relates to the technical field of power distribution networks, in particular to a distributed power supply scheduling control method based on edge calculation. The method comprises the following steps: A) obtaining a topological structure and scheduling data of a target distribution network, and listing a transformer, energy storage equipment and a wind-solar power station in the target distribution network; B) reading an active power prediction value and a reactive power prediction value of the load in the next time period, and predicting the output of the wind-solar power station; C) calculating a characteristic value, and if the characteristic value is smaller than a set threshold value, entering a step E); D) only outputting active power by the wind-solar power station, and returning to the step B); E) calculating the ratio of active power to reactive power output by the wind-solar power station; and F) respectively outputting the real-time output of the wind-solar power station as active power and reactive power according to the ratio, and returning to the step B. The method has the substantive effects of reducing the influence on the power distribution network when the renewable energy power station with unstable output is accessed to the power distribution network, being beneficial to expanding the access of renewable energy, and being suitable for the power distribution network with high permeability.

Distributed power supply scheduling control method based on edge calculation

Fault Diagnosis and Detection Based on Efficiency Loss Test of Photovoltaic Power Station

The application provides a distributed photovoltaic power station fault monitoring method and device based on edge computing, which are applied to an edge computing device. The method comprises the steps of: obtaining meteorological and environmental parameters of an area where a target distributed photovoltaic power station is located, cloud block image data and field equipment image data above the target distributed photovoltaic power station; predicting the power generation power of the target distributed photovoltaic power station in a set time period by using the meteorological and environmental parameters, the cloud block image data above and the field equipment image data to serve as predicted power generation power; acquiring actual power generation power of the target distributedphotovoltaic power station within the set time period; and monitoring whether the target distributed photovoltaic power station has abnormity or faults or not by comparing the predicted power generation power with the actual power generation power. In the application, the abnormity or fault monitoring of the target distributed photovoltaic power station can be achieved through the mode, and the monitoring reliability is improved.

Distributed photovoltaic power station fault monitoring method and device based on edge computing

The invention provides an abnormal data detection method and device for a photovoltaic power station and electronic equipment. The method comprises the steps of after obtaining photovoltaic residual data corresponding to a power station, determining a clustering center point of the photovoltaic residual data based on the photovoltaic residual data; clustering the photovoltaic residual error data on the basis of the clustering center point to obtain a clustering result; determining the photovoltaic residual data which does not belong to any clustering cluster as abnormal data. According to themethod and the device, the abnormal data can be determined, so that when the abnormal data occurs, the fault handling is carried out in time, and the stability and the safety of an electric power system are ensured. Furthermore, the clustering center point is determined through the density value dimension data and the distance value dimension data, so that the determined clustering center point ismore accurate, the clustering result obtained by using the clustering center point is more accurate, and the determined abnormal data is more accurate.

Abnormal data detection method and device for photovoltaic power station and electronic equipment

The invention provides a photovoltaic system power generation state monitoring method and device, and the method comprises the steps: firstly determining a benchmarking power station in all photovoltaic power stations which are located in the same region grid as a target power station and achieve the remote monitoring; taking the power generation parameter of the benchmarking power station as a reference, estimating to obtain a power generation state theoretical value of the target power station through a preset algorithm, and comparing the power generation state theoretical value with a powergeneration state actual value obtained by relying on the state grid new energy cloud, so that on the premise of not additionally adding any hardware equipment, the power generation state of the target power station is effectively evaluated, the problem that the power generation management level of the power station is low due to the fact that most small and medium-sized household photovoltaic power generation systems lack remote monitoring is solved, and the implementation cost is low.

Photovoltaic system power generation state monitoring method and device

The embodiment of the invention discloses an inspection behavior authenticity judgment and early warning method. The method includes the steps that first physiological data generated by the autonomicnerve of an inspector in the inspection process is acquired; whether the first physiological data meets a preset first comparison condition or not is judged; if yes, early warning information is sent.The embodiment of the invention further provides an intelligent wearable device and a storage medium. The embodiment provides an effective technical support for improving the working efficiency and personal safety of the inspector and reducing risks caused by unsafe human behavior factors in the enterprise production process, and has the advantages of reasonable design, intelligent control, safety and reliability, and the like.

Meng Fanteng

Papers

Fault Diagnosis and Detection Based on Efficiency Loss Test of Photovoltaic Power Station

Distributed photovoltaic power station fault monitoring method and device based on edge computing

Abnormal data detection method and device for photovoltaic power station and electronic equipment

Photovoltaic system power generation state monitoring method and device

Inspection behavior authenticity judgment and early warning method, wearable device and storage medium