Photovoltaic output prediction remote monitoring system based on cloud platform
The cloud-based photovoltaic power output prediction and remote monitoring system solves the problems of high management and maintenance costs and difficulty in data unification for distributed photovoltaic power plants, achieving efficient and reliable photovoltaic power output prediction and monitoring, and reducing system complexity and cost.
Patent Information
- Application Number
- CN202420270255.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Utility models(China)
- Current Assignee / Owner
- Filing Date
- 2024-02-04
- Publication Date
- 2025-01-10
- Estimated Expiration
- 2034-02-04
AI Technical Summary
Distributed photovoltaic power stations have high management and maintenance costs, and existing monitoring systems are difficult to achieve centralized management and data unification, especially in complex geographical environments where photovoltaic output prediction is very difficult.
A cloud-based photovoltaic power output prediction and remote monitoring system is constructed, including a power module, a data acquisition terminal, a gateway, and a server. It adopts LoRa and NB-IoT communication modules, integrates multiple sensors for data acquisition and uploading, and utilizes the OneNET cloud platform for data aggregation and processing.
It enables cross-platform, distributed photovoltaic power output prediction and monitoring, reduces management costs, improves data transmission efficiency and system stability, simplifies system architecture, and supports flexible configuration and efficient data processing.
Smart Images
Figure CN222339079U_ABST
Abstract
Description
Technical Field
[0001] The utility model relates to a photovoltaic output prediction remote monitoring system based on a cloud platform, belonging to the field of photovoltaic power station monitoring. Background Art
[0002] As photovoltaic power stations have the characteristics of scattered layout, numerous access points, and difficulty in centralized management, and the existing photovoltaic monitoring system has relatively low flexibility and scalability for distributed photovoltaic power stations of different sizes, the level of informatization of the monitoring system is not high, making the management and maintenance costs of distributed photovoltaic power stations high. As the locations of distributed photovoltaic power stations are relatively scattered, it is difficult to transmit, monitor and unify the data of photovoltaic power stations.
[0003] Qujing is located in the Yunnan-Guizhou Plateau, a special geographical environment with low latitude and high altitude. The terrain and latitude will cause complex weather conditions. The overall climate presents a three-dimensional form, and there are large differences between regions. Large-scale contiguous distributed photovoltaics have different operating states, different working states, and inconsistent output characteristics. It is of great significance to remotely monitor the output forecast of contiguous distributed photovoltaics in the counties of Qujing. The construction of a cloud-based photovoltaic output forecast remote monitoring system requires consideration of the system's composition and connection. Summary of the invention
[0004] The utility model provides a photovoltaic output prediction remote monitoring system based on a cloud platform. The photovoltaic output prediction remote monitoring system based on a cloud platform is constructed based on the ingenious layout of a data acquisition terminal, a gateway, and a server terminal. It can be deployed across platforms and in a distributed manner, and further provides hardware support for photovoltaic output prediction remote monitoring.
[0005] The technical solution of the utility model is: a photovoltaic output prediction remote monitoring system based on a cloud platform, including a power module, a data acquisition terminal, a gateway and a server; the power module is used for power supply, the data acquisition terminal includes a sensor module, a first LoRa communication module, a level conversion circuit module, and a first SMT32 single-chip minimum system; the gateway includes an NB-IoT communication module, a drive circuit, an alarm device, an LED display, a second SMT32 single-chip minimum system, a storage module, and a second LoRa communication module; the server includes a OneNET cloud platform and a Tomact server; the sensor module collects the operation data of the photovoltaic power station and communicates with the level conversion circuit module through RS485 The first STM32 single-chip microcomputer minimum system is connected, the first STM32 single-chip microcomputer minimum system sends data to the second LoRa communication module of the gateway through the first LoRa communication module, and is connected to the second STM32 single-chip microcomputer minimum system through the second LoRa communication module. The driving circuit connected to the second STM32 single-chip microcomputer minimum system is used to connect the alarm device and the LED display. The storage module in the gateway stores the collected data, and the gateway uploads the data to the OneNET cloud platform on the server side through the NB-IoT communication module connected to the second STM32 single-chip microcomputer minimum system. The OneNET cloud platform collects the data uploaded by the gateway and pushes the data to the Tomact server through the API interface.
[0006] The sensor module includes a DHT22 digital temperature and humidity sensor, a BRW200-1001 anemometer, a HSTL-FX02 wind vane, a STBQSV12OV radiation sensor, a DS18B20 digital temperature sensor and a FR-DCMG-MMPU DC monitor; wherein the DHT22 digital temperature and humidity sensor is used to measure the temperature and humidity of the environment in the photovoltaic power station; the BRW200-1001 anemometer and the HSTL-FX02 wind vane provide wind speed and wind direction data respectively; the STBQSV12OV radiation sensor measures the solar irradiance in the photovoltaic power station; the DS18B20 digital temperature sensor is used to measure the temperature of the photovoltaic modules in the photovoltaic power station; and the FR-DCMG-MMPU DC monitor is responsible for monitoring the status of the DC system.
[0007] The first LoRa communication module adopts an A39-T400A30D1a radio frequency module; the second LoRa communication module is composed of two LoRa communication modules of the same model as an expanded gateway channel, and their chip models are both A39-T400A30D1a radio frequency modules.
[0008] The beneficial effects of the utility model are as follows: the photovoltaic output prediction remote monitoring system based on the cloud platform constructed by the utility model through reasonable composition and connection provides hardware support for photovoltaic output prediction remote monitoring, each module is independent of each other, and can be deployed across platforms and in a distributed manner to achieve flexible configuration; at the same time, the introduction of the cloud platform further ensures efficient, convenient and reliable data transmission; and the introduction of LoRa can avoid the problem of a large amount of wiring, and at the same time, the module is small in size, low in power consumption and easy to install. BRIEF DESCRIPTION OF THE DRAWINGS
[0009] Figure 1 This is a structural principle diagram of the utility model's photovoltaic output prediction remote monitoring system based on a cloud platform;
[0010] Figure 2 This is the circuit schematic diagram of the STM32 minimum system of the utility model;
[0011] Figure 3 The utility model is a circuit diagram of automatic power switching. DETAILED DESCRIPTION
[0012] The utility model is further described below in conjunction with the accompanying drawings and embodiments, but the content of the utility model is not limited to the described scope.
[0013] Example 1: Figure 1-3As shown, a photovoltaic output prediction remote monitoring system based on a cloud platform includes a power module, a data acquisition terminal, a gateway and a server; the power module is used for power supply, the data acquisition terminal includes a sensor module, a first LoRa communication module, a level conversion circuit module, and a first SMT32 single-chip microcomputer minimum system; the gateway includes an NB-IoT communication module, a drive circuit, an alarm device, an LED display, a second SMT32 single-chip microcomputer minimum system, a storage module, and a second LoRa communication module; the server includes a OneNET cloud platform and a Tomact server; the sensor module collects the operation data of the photovoltaic power station and is connected to the first STM32 single-chip microcomputer minimum system through the level conversion circuit module through RS485 communication, and the first ST The M32 single-chip microcomputer minimum system sends data to the second LoRa communication module of the gateway through the first LoRa communication module, and is connected to the second STM32 single-chip microcomputer minimum system through the second LoRa communication module. The drive circuit connected to the second STM32 single-chip microcomputer minimum system is used to connect the alarm device and the LED display. The storage module in the gateway stores the collected data. The gateway uploads the data to the OneNET cloud platform on the server side through the NB-IoT communication module connected to the second STM32 single-chip microcomputer minimum system. The OneNET cloud platform collects the data uploaded by the gateway and pushes the data to the Tomact server through the API interface for further photovoltaic output prediction processing. Web users query the output of the photovoltaic power station by accessing the Web terminal.
[0014] Furthermore, the power module adopts a dual power supply mode and uses an automatic switching circuit to manage the two power supplies to ensure the power supply stability of the monitoring system. Figure 3 This is the automatic power switching circuit diagram of the utility model. The power module adopts a dual power supply solution to meet the power supply needs of the equipment. When the main power supply is powered, the relay coil is energized, the internal related contacts are attracted, and the main power supply supplies power to the load; when the main power supply is powered off, the relay coil is powered off, the contacts return to the initial state, and the backup power supply supplies power to the load.
[0015] Furthermore, the sensor module includes a DHT22 digital temperature and humidity sensor, a BRW200-1001 anemometer, a HSTL-FX02 wind vane, a STBQSV12OV radiation sensor, a DS18B20 digital temperature sensor and a FR-DCMG-MMPU DC monitor; wherein the DHT22 digital temperature and humidity sensor is used to measure the temperature and humidity of the environment in the photovoltaic power station, and can monitor whether it exceeds the safety range and generate a fault signal; the BRW200-1001 anemometer and the HSTL-FX02 wind vane provide wind speed and wind direction data respectively, and can generate an alarm signal under extreme weather conditions; the STBQSV12OV radiation sensor measures the solar irradiance in the photovoltaic power station; the DS18B20 digital temperature sensor is used to measure the temperature of the photovoltaic modules in the photovoltaic power station, and generates an alarm signal when the temperature of the photovoltaic modules is too high; the FR-DCMG-MMPU DC monitor is responsible for monitoring the status of the DC system, and generates a fault signal when the system exceeds the threshold voltage and current.
[0016] Furthermore, the second LoRa communication module is composed of two LoRa communication modules of the same model as an expansion gateway channel, and their chip models are both A39-T400A30D1a radio frequency modules.
[0017] Furthermore, the chip model of the level conversion circuit module is SP3485; the main control chip model of the first and second STM32 minimum systems is STM32F103RCT6; the chip model of the power module is LM2596; the chip model of the NB-IoT communication module is M5310-A; the chip model of the drive circuit is SRD-05VDC-SL-C relay. The chip model of the power circuit is AMS1177, and the chip model of the debug interface circuit is SWD&USART.
[0018] Furthermore, the NB-IoT communication module also includes a power supply and a SIM card circuit.
[0019] Furthermore, if Figure 2 The following is the circuit schematic diagram of the minimum STM32 system. The main control chip is STM32F103RCT6, which has an operating voltage of 2.0 to 3.6 V and 51 GPIO ports. It is suitable for a variety of scenarios. Its main features are as follows:
[0020] (1) It has 256KB of flash program and 48KB of storage capacity, with a maximum frequency of 72MHz and a running speed of
[0021] Fast.
[0022] (2) 23-channel 12-bit ADC, lμs transfer time, up to 16 input channels.
[0023] (3) Up to 13 communication interfaces, including 2 I 2 C interface, 5 USART interfaces, 3 SPI interfaces, 1 CAN interface, 1 USB interface, and 1 SDIO interface.
[0024] The power supply circuit, clock circuit, reset circuit, debug interface circuit and startup selection circuit are deployed around the STM32 control chip to form the minimum system. The power conversion circuit adopts the AMS1177 model chip, which reduces the 5V power module voltage to the working voltage of the STM32 and provides a power supply with a suitable voltage for the single-chip microcomputer; the clock circuit is composed of a crystal oscillator and a capacitor to provide the internal clock signal required by the STM32. The STM32 is equipped with two high-speed and low-speed clocks, with frequencies of 8MHz and 32.768kHz respectively; the debug interface circuit uses JTAG and SWD to perform program download debugging; the reset circuit forces the single-chip microcomputer to return to the initial state, the STM32 reset port is valid at a low level, VCC is connected to the single-chip microcomputer REST end via a resistor, and a grounding capacitor connected in series with the reset button is sent down. When the reset button is pressed, the REST end is in a low level state, and the STM32 single-chip microcomputer is reset; the startup selection circuit is used to select the startup mode of the single-chip microcomputer, and the startup mode is switched by configuring the level states of the two pins BOOT0 and BOOT1. Since the first two startup modes are often used, BOOT1 is grounded and the level state of BOOT0 is changed by line cap jumper to select the startup mode.
[0025] By applying the above technical solution, it can be known that the sensor module of the utility model integrates DHT22 digital temperature and humidity sensor, BRW200-1001 anemometer, HSTL-FX02 wind vane, STBQSV12OV radiation sensor, DS18B20 digital temperature sensor and FR-DCMG-MMPU DC monitor to collect key environment and equipment operation data in real time. The data types collected by each sensor at the data acquisition end are mainly divided into telesignal data and telemetry data. The telesignal data are mostly fault and alarm signals. The telesignal data sent by the data acquisition end represent that the sensor has failed; the telemetry data are mostly analog and integral quantities, mainly including temperature and humidity, light intensity and cumulative irradiance data measured by the sensor. First, the data acquisition end is initialized; then, the data acquisition end directly uploads the telesignal data to the gateway through the LoRa communication module, packages the collected telemetry data, and uploads it to the gateway periodically according to a period of time. When uploading data, the data acquisition end scans the gateway channel and selects an idle channel for data upload. The gateway channel is the second LoRa communication module. The gateway has two LoRa communication modules, channel 1 and channel 2. If both channels are in a non-idle state, it will delay 20ms to scan again, and then use the idle channel to upload data. If the upload fails three times, the upload process ends, and the gateway will mark the data acquisition end node as a node that has not uploaded data; finally, after the data upload is completed, it is determined whether the received message contains instructions. If so, the relevant instructions are executed, otherwise the process ends. It should be noted that the above data collection and upload processes all require the STM32 minimum system to summarize, save and upload the data. Among them, the data uploaded from the data acquisition end includes telesignaling data and telemetry data. When the gateway receives the telesignaling data, it must be uploaded immediately, and the received telemetry data is uploaded to the cloud platform periodically. First, the gateway initializes the LoRa module and storage module, and enables the NB-IoT module to access the network; secondly, within a certain period of time, the gateway will continuously receive the telemetry data uploaded by the data acquisition end and save the data to the storage module. When the timing cycle is up, the gateway will package other data together with the address number of the acquisition end that has not uploaded data and upload it to the cloud platform; finally, after the data is successfully uploaded, a confirmation value will be returned, so that the gateway knows that the data upload is complete. The data uploaded by the gateway to the server cloud platform is uploaded to the server through the APT interface after analyzing the output and stored in their respective databases. The server identifies the fault of the uploaded remote signal data, determines the fault type, installs rules for graded alarms, and finally stores the alarm records in the alarm database; and the telemetry data will be stored in the detailed database after calculation and processing. Web users can view the relevant parameters and indicator status by using the curve tool; through the real-time calculation of the photovoltaic output forecast, real-time indicators and historical indicators will be obtained for real-time monitoring by users.
[0026] The utility model provides a photovoltaic output prediction remote monitoring system based on a cloud platform, which can realize remote monitoring of meteorological data of photovoltaic power stations and photovoltaic output prediction, and has the following characteristics: the single-chip minimum system mainly adopts a microcontroller chip whose main control chip model is STM32F103RCT6, which has the characteristics of high performance, real-time performance, low power consumption, etc.; the gateway radio frequency transceiver module adopts two LoRa modules of the same model to expand the gateway channel to ensure stable data reception; the gateway is equipped with an audible and visual alarm device, a drive circuit auxiliary drive alarm device and an LED display, and the drive circuit auxiliary drive alarm device contains a fault detection module, which will trigger the alarm device and display the fault type when a fault occurs in the station or the operation is abnormal; the power supply module adopts a dual-power The power supply scheme uses an automatic switching circuit to manage the two power supplies; the power module mainly supplies power to the data acquisition terminal and the gateway as a whole. The maximum switching power of its switching circuit is 150W, and the maximum voltage level is 12V. The working voltage of the microcontroller STM32 is 2-3.6V, and the voltage of the sensor and communication module is mostly 5V. Therefore, a level conversion circuit is needed to convert the power supply voltage to provide the normal working voltage of each internal module; introducing the cloud platform into the distributed photovoltaic power station monitoring system can efficiently, conveniently and reliably transmit and process data; it can simplify the overall architecture of the system, thereby providing system stability; it can be deployed across platforms and in a distributed manner, reducing storage costs while ensuring stability and reliability.
[0027] The specific implementation modes of the present invention have been described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above implementation modes, and various changes can be made within the knowledge scope of ordinary technicians in the field without departing from the purpose of the present invention.
Claims
1. A photovoltaic output prediction remote monitoring system based on a cloud platform, characterized in that: It includes a power module, a data acquisition terminal, a gateway and a server; the power module is used for power supply, the data acquisition terminal includes a sensor module, a first LoRa communication module, a level conversion circuit module, and a first SMT32 single-chip microcomputer minimum system; the gateway includes an NB-IoT communication module, a drive circuit, an alarm device, an LED display, a second SMT32 single-chip microcomputer minimum system, a storage module, and a second LoRa communication module; the server includes a OneNET cloud platform and a Tomact server; the sensor module collects the operation data of the photovoltaic power station and is connected to the first STM32 single-chip microcomputer minimum system through the level conversion circuit module through RS485 communication Then, the first STM32 single-chip microcomputer minimum system sends data to the second LoRa communication module of the gateway through the first LoRa communication module, is connected to the second STM32 single-chip microcomputer minimum system through the second LoRa communication module, and the driving circuit connected to the second STM32 single-chip microcomputer minimum system is used to connect the alarm device and the LED display. The storage module in the gateway stores the collected data, and the gateway uploads the data to the OneNET cloud platform on the server side through the NB-IoT communication module connected to the second STM32 single-chip microcomputer minimum system. The OneNET cloud platform collects the data uploaded by the gateway and pushes the data to the Tomact server through the API interface.
2. The photovoltaic output prediction remote monitoring system based on cloud platform according to claim 1 is characterized in that: The sensor module includes a DHT22 digital temperature and humidity sensor, a BRW200-1001 anemometer, a HSTL-FX02 wind vane, a STBQSV12OV radiation sensor, a DS18B20 digital temperature sensor and a FR-DCMG-MMPU DC monitor; wherein the DHT22 digital temperature and humidity sensor is used to measure the temperature and humidity of the environment in the photovoltaic power station; the BRW200-1001 anemometer and the HSTL-FX02 wind vane provide wind speed and wind direction data respectively; the STBQSV12OV radiation sensor measures the solar irradiance in the photovoltaic power station; the DS18B20 digital temperature sensor is used to measure the temperature of the photovoltaic modules in the photovoltaic power station; and the FR-DCMG-MMPU DC monitor is responsible for monitoring the status of the DC system.
3. The photovoltaic output prediction remote monitoring system based on cloud platform according to claim 1 is characterized in that: The first LoRa communication module adopts an A39-T400A30D1a radio frequency module; the second LoRa communication module is composed of two LoRa communication modules of the same model as an expanded gateway channel, and their chip models are both A39-T400A30D1a radio frequency modules.