Distributed photovoltaic direct-current arc fault detecting and positioning system
The distributed photovoltaic DC arc fault detection and location system utilizes current sensors and signal processing units to achieve precise location of DC arc faults, solving the problem of inaccurate location in existing technologies and providing efficient fault detection and remote monitoring functions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Utility models(China)
- Current Assignee / Owner
- NORTH CHINA ELECTRIC POWER UNIV
- Filing Date
- 2025-05-23
- Publication Date
- 2026-05-15
AI Technical Summary
In existing technologies, the detection of DC arc faults mainly focuses on identification, while there is little research on location and a lack of mature engineering applications, making it difficult to achieve accurate location.
A distributed photovoltaic DC arc fault detection and location system is adopted, including a current sensor, a signal processing unit, a data acquisition and analysis unit, a LoRa wireless communication unit, and a host computer. The system acquires signals through a Hall current sensor, processes the data using an integral amplifier circuit, a filter circuit, and an STM32 microcontroller, and achieves fault location by combining arc fault threshold judgment and resonant frequency analysis.
It achieves high-precision DC arc fault location with a location error of less than 0.5 meters, supports real-time fault alarm and remote monitoring, and provides accurate fault location display and alarm information transmission.
Smart Images
Figure CN224247847U_ABST
Abstract
Description
Technical Field
[0001] This utility model relates to the field of arc fault detection technology, and more specifically to a distributed photovoltaic DC arc fault detection and location system. Background Technology
[0002] Current research on DC arc faults largely focuses on their detection and identification, aiming to "find" them. For example, Professor Shaporo in the US uses the time-frequency domain characteristics of current and voltage signals as a basis for detecting arc occurrence; Wu Chunhua et al. use wavelet transform to extract the frequency domain features of fault arcs, enabling arc type identification; Ding Xin et al. performed various analyses on collected arc current data, extracting 17 relevant features and using support vector machine algorithms to identify DC fault arcs. However, research on DC arc location—the question of "where is it?"—is relatively limited, and there are no mature engineering applications. Therefore, for those skilled in the art, achieving accurate DC arc fault location is a pressing issue. Utility Model Content
[0003] In view of this, the present invention provides a distributed photovoltaic DC arc fault detection and location system to solve the problems mentioned in the background art.
[0004] To achieve the above objectives, this utility model adopts the following technical solution: a distributed photovoltaic DC arc fault detection and location system, comprising a current sensor, a signal processing unit, a data acquisition and analysis unit, a LoRa wireless communication unit, and a host computer; wherein,
[0005] One end of the current sensor is connected to the DC side of the photovoltaic system, and the other end of the current sensor is connected to the signal processing unit. The output end of the signal processing unit is connected to the input end of the data acquisition and analysis unit. The output end of the data acquisition and analysis unit is connected to the LoRa wireless communication unit. The LoRa wireless communication unit is connected to the host computer through a wireless communication network. The host computer is connected to the cloud server.
[0006] Preferably, the signal processing unit includes an integrating amplifier circuit, a filtering circuit, and a central processing unit; the output terminal of the current sensor is connected to the input terminal of the integrating amplifier circuit, the output terminal of the integrating amplifier circuit is connected to the input terminal of the filtering circuit, the output terminal of the filtering circuit is connected to the input terminal of the central processing unit, and the central processing unit is composed of a digital control chip and its peripheral circuits, used to determine whether an electric arc exists.
[0007] Preferably, the integrating amplifier circuit uses the LM358 integrated operational amplifier circuit; the digital control chip is an STM32 microcontroller.
[0008] Preferably, transient voltage suppressors are installed at the input terminals of both the signal processing unit and the data acquisition and analysis unit to prevent surge damage to the circuit.
[0009] Preferably, it also includes a display module connected to the central processing unit for displaying processed data; the display module is a touch screen.
[0010] Preferably, it also includes a fault early warning module, which is connected to the signal processing unit and is used to receive the data processing results of the signal processing unit to issue a fault alarm.
[0011] Preferably, the cloud server is also wirelessly connected to the monitoring terminal for storing fault warning signals and transmitting the fault warning signals to the monitoring terminal, which includes a mobile terminal and a computer terminal.
[0012] As can be seen from the above technical solution, compared with the prior art, this utility model discloses a distributed photovoltaic DC arc fault detection and location system, which has the following beneficial technical effects: the sensing module is composed of a current sensor and a self-designed signal processing circuit, the data acquisition and analog-to-digital conversion are performed by an STM32 microcontroller, and a touch screen is provided to realize the intuitive display of fault alarms and complete efficient fault detection. The fault location is displayed by calculating the distance of the arc from the detection point, and the fault alarm information is uploaded through the communication unit. Attached Figure Description
[0013] To more clearly illustrate the technical solutions in the embodiments of this utility model or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this utility model. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0014] Figure 1 This is a schematic diagram of the system structure of this utility model;
[0015] Figure 2 This is a circuit diagram of the signal processing unit of this utility model. Detailed Implementation
[0016] The technical solutions of the present utility model will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present utility model, and not all embodiments. Based on the embodiments of the present utility model, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present utility model.
[0017] This utility model discloses a distributed photovoltaic DC arc fault detection and location system, such as Figure 1 As shown, the system includes a current sensor, a signal processing unit, a data acquisition and analysis unit, a LoRa wireless communication unit, and a host computer. One end of the current sensor is connected to the DC side of the photovoltaic system, and the other end is connected to the signal processing unit. The output of the signal processing unit is connected to the input of the data acquisition and analysis unit, and the output of the data acquisition and analysis unit is connected to the LoRa wireless communication unit. The LoRa wireless communication unit is connected to the host computer via a wireless communication network, and the host computer is connected to a cloud server.
[0018] Furthermore, the current sensor employs a Hall effect current sensor with a range covering 0-25A arc current, a ratio difference of ≤0.1%, an accuracy class of 0.2, and is wired with shielded stranded wire. Its frequency response capability is sufficient for photovoltaic DC arc monitoring, and its high accuracy is sufficient for complete waveform acquisition and detailed analysis, providing a solid foundation for highly accurate fault location.
[0019] Furthermore, such as Figure 2 As shown, the signal processing unit includes an integrating amplifier circuit, a filtering circuit, and a central processing unit (CPU). The output terminal of the current sensor is connected to the input terminal of the integrating amplifier circuit, the output terminal of the integrating amplifier circuit is connected to the input terminal of the filtering circuit, and the output terminal of the filtering circuit is connected to the input terminal of the CPU. The CPU consists of a digital control chip and its peripheral circuits and is used to determine whether an electric arc exists.
[0020] The integrating amplifier circuit uses the LM358 integrated operational amplifier; the digital control chip is an STM32 microcontroller. Since the acquisition card used in this embodiment does not have the function of receiving negative voltage input, the signal processing unit is also designed with a half-wave rectified current based on the fast recovery diode ES2B to ensure that no negative voltage enters and damages the acquisition card. Transient voltage suppressors (TVS) are also installed at the input terminals of both the signal processing unit and the data acquisition and analysis unit to prevent surge damage. In this embodiment, the acquisition card used is an STM32F407ZGT6 core board, equipped with an XM8A51216V external SRAM chip. Combined with the CPU's capacity, its SRAM reaches 1M + 192K bytes, fully meeting the requirements of the algorithm and acquired data. To prevent data loss after power failure, data is also stored in the FLASH module for a certain period. This core board has 1024K of FLASH storage space, which meets the requirements. It has three 12-bit acquisition precision analog-to-digital converters with a main frequency of up to 168MHz, which can meet the requirements for rapid detection while acquiring data.
[0021] Furthermore, it includes a display module connected to the central processing unit (CPU) for displaying processed data. The display module is a 3.5-inch resistive touchscreen with a resolution of 480*320, providing clear visuals. Its operating temperature range is -20℃ to 70℃, and its surface glass is made of durable tempered glass to withstand harsh outdoor environments. Data transmission with the microcontroller is achieved via a 32-pin FPC cable.
[0022] Furthermore, it also includes a fault early warning module, which is connected to the signal processing unit and is used to receive the data processing results from the signal processing unit to issue fault alarms.
[0023] Furthermore, the cloud server is wirelessly connected to the monitoring terminal to store fault warning signals and transmit them to the monitoring terminal. The monitoring terminal includes mobile terminals and computer terminals, which, together with the host industrial control computer, forward the fault alarm information to the cloud server, thereby enabling the mobile APP to connect to the on-site monitoring situation and receive and view alarm information in real time.
[0024] The working principle of this system is as follows: The Hall current sensor collects the DC side current signal of the photovoltaic system. The current signal enters the signal processing unit, where it is amplified and integrated by the integrating amplifier circuit. Then, the signal is filtered out of interference by the filtering circuit and input to the microcontroller for calculation. The microcontroller stores an arc fault threshold. The input signal is compared with the preset threshold. If the preset threshold is exceeded, it indicates that an arc fault exists. The system issues a fault warning and uploads the fault warning information to the cloud server through the LoRa wireless communication unit. This enables the mobile APP to connect to the site for monitoring and to receive and view the alarm information in real time.
[0025] Furthermore, by performing FFT analysis on the AC component of the acquired current signal through the signal processing unit, frequency characteristics and resonant points are obtained. The presence of a large-amplitude AC component indicates an arc fault. The closer the resonant point is to the low-frequency band, the farther the fault point is from the detection point. By using preset AC component thresholds and resonant point thresholds, the occurrence of a fault and its distance can be determined. The larger the fault distance d, the smaller the detected current resonant frequency f. Therefore, the distance between the fault point and the detection point can be inferred based on the decrease in f.
[0026] It should be noted that the arc fault threshold stored in the microcontroller is calculated based on multi-state quantities. Specifically, based on the current signals of the photovoltaic system under normal and fault conditions, the time-domain characteristics, frequency-domain characteristics, and time-frequency domain characteristics based on ensemble empirical mode decomposition of the current data before and after the photovoltaic system fault are compared and analyzed to extract the feature quantities that can be used to detect DC arc faults. The arc fault threshold is calculated by combining the feature vectors corresponding to these feature quantities.
[0027] Parasitic parameters cause signals of a specific frequency to resonate during signal transmission, which will be prominently highlighted in the spectrum. As the distance between the fault arc and the detection point increases, the signal transmission path lengthens, the inductance value involved increases, and the resonant point will gradually move to a lower frequency. By analyzing the relationship between the resonant frequency and the fault distance, the accurate location of series DC arc faults is achieved, with a location accuracy within 30m and a location error of <0.5m.
[0028] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.
[0029] The above description of the disclosed embodiments enables those skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A distributed photovoltaic DC arc fault detection and location system, characterized in that, The system includes a current sensor, a signal processing unit, a data acquisition and analysis unit, a LoRa wireless communication unit, and a host computer. One end of the current sensor is connected to the DC side of the photovoltaic system, and the other end is connected to the signal processing unit. The output of the signal processing unit is connected to the input of the data acquisition and analysis unit, and the output of the data acquisition and analysis unit is connected to the LoRa wireless communication unit. The LoRa wireless communication unit is connected to the host computer via a wireless communication network, and the host computer is connected to a cloud server.
2. The distributed photovoltaic DC arc fault detection and location system according to claim 1, characterized in that, The signal processing unit includes an integrating amplifier circuit, a filtering circuit, and a central processing unit; the output terminal of the current sensor is connected to the input terminal of the integrating amplifier circuit, the output terminal of the integrating amplifier circuit is connected to the input terminal of the filtering circuit, and the output terminal of the filtering circuit is connected to the input terminal of the central processing unit. The central processing unit is composed of a digital control chip and its peripheral circuits, and is used to determine whether an electric arc exists.
3. The distributed photovoltaic DC arc fault detection and location system according to claim 2, characterized in that, The integrating amplifier circuit uses the LM358 integrated operational amplifier circuit; the digital control chip is an STM32 microcontroller.
4. The distributed photovoltaic DC arc fault detection and location system according to claim 1, characterized in that, Transient voltage suppressors are installed at the input terminals of both the signal processing unit and the data acquisition and analysis unit to prevent surges from damaging the circuit.
5. A distributed photovoltaic DC arc fault detection and location system according to claim 2, characterized in that, It also includes a display module, which is connected to the central processing unit and is used to display the processed data. The display module is a touch screen.
6. The distributed photovoltaic DC arc fault detection and location system according to claim 1, characterized in that, It also includes a fault warning module, which is connected to the signal processing unit and is used to receive the data processing results of the signal processing unit to issue a fault alarm.
7. The distributed photovoltaic DC arc fault detection and location system according to claim 1, characterized in that, The cloud server is also wirelessly connected to the monitoring terminal for storing fault warning signals and transmitting the fault warning signals to the monitoring terminal, which includes a mobile terminal and a computer terminal.