Photovoltaic power station alarm filtering system and method based on Internet of Things modeling

Abstract

The invention provides a photovoltaic power station alarm filtering system and method based on Internet of Things modeling, and belongs to the technical field of photovoltaic power station fault detection. The Internet of Things platform carries out modeling on a meteorological station in the meteorological acquisition module, an inverter in the fault detection module, a power generation amount acquisition module and a grid-connected metering ammeter in the electric energy acquisition module. The Internet of Things platform judges day and night through solar radiation data collected by the weather station, and judges whether the photovoltaic power station breaks down or not through fault signals collected by the inverter when judging that the current time is daytime. And the Internet of Things platform collects the generating capacity data of the photovoltaic power station through the inverter, and calculates the daily generating efficiency of the photovoltaic power station in combination with the radiation daily accumulated value collected by the weather station. And the Internet of Things platform collects the electric energy output data of the photovoltaic power station through the grid-connected metering ammeter and calculates the power factor of the photovoltaic power station. The configuration efficiency of the alarm rule of the photovoltaic power generation system is improved.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power plant fault detection technology. Specifically, this invention relates to a photovoltaic power plant alarm filtering system and method based on Internet of Things (IoT) modeling. Background Technology

[0002] As a core pillar of the global energy transition, photovoltaic power generation has transcended the scope of a single power generation method and deeply penetrated into various dimensions of energy security, climate governance, economic structure, and even social sustainable development. The need for fault detection and alarm of photovoltaic power generation systems is an important part of photovoltaic power generation.

[0003] Chinese Patent 105007040B relates to a method and system for fault identification and location in an intelligent photovoltaic power generation system. The method includes: step S1, obtaining the correlation coefficient of a photovoltaic power generation unit relative to the other photovoltaic power generation units; step S2, establishing fault judgment criteria for the photovoltaic power generation unit; and step S3, identifying and locating the faulty photovoltaic power generation unit. The method and system for fault identification and location in an intelligent photovoltaic power generation system provided by this invention, based on the distance between units of the photovoltaic power generation system and the real-time comprehensive power generation efficiency, performs data analysis to achieve accurate identification and location of faults in the intelligent photovoltaic power generation system.

[0004] Existing technologies develop and configure alarm rules for photovoltaic power generation systems independently, resulting in a large amount of duplication in the development and configuration of alarm rules, high system resource consumption, and a large workload for development and maintenance. Summary of the Invention

[0005] The present invention aims to provide a photovoltaic power plant alarm filtering system and method based on Internet of Things modeling, so as to improve the technical efficiency of alarm rule configuration in photovoltaic power generation system.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] This invention provides a photovoltaic power station alarm filtering system based on Internet of Things (IoT) modeling. The system comprises a meteorological data acquisition module, a fault detection module, a power generation data acquisition module, an electrical energy data acquisition module, and an IoT platform. The meteorological data acquisition module collects solar radiation data received by the photovoltaic power station; the fault detection module collects fault signals from the photovoltaic power station; the power generation data acquisition module collects power generation data from the photovoltaic power station; and the electrical energy data acquisition module collects electrical energy output data from the photovoltaic power station. The output of the meteorological data acquisition module is connected to the input of the IoT platform; the output of the fault detection module is connected to the input of the IoT platform; the output of the power generation data acquisition module is connected to the input of the IoT platform; and the output of the electrical energy data acquisition module is connected to the input of the IoT platform.

[0008] The meteorological acquisition module outputs solar radiation data to the Internet of Things (IoT) platform, the fault detection module outputs fault signals of the photovoltaic power station to the IoT platform, the power generation acquisition module outputs power generation data of the photovoltaic power station to the IoT platform, and the power energy acquisition module outputs power output data of the photovoltaic power station to the IoT platform.

[0009] The meteorological data acquisition module includes a meteorological station and an edge data acquisition device. The meteorological station is connected to the edge data acquisition device, and the edge data acquisition device is connected to the Internet of Things platform via a wireless network.

[0010] The fault detection module includes an inverter and an edge collector. The inverter is connected to the edge collector, and the edge collector is connected to the Internet of Things platform via a wireless network.

[0011] The power generation acquisition module includes an inverter and an edge acquisition unit. The inverter is connected to the edge acquisition unit, and the edge acquisition unit is connected to the Internet of Things platform via a wireless network.

[0012] The power acquisition module includes a grid-connected meter and an edge collector. The grid-connected meter is connected to the edge collector, and the edge collector is connected to an Internet of Things (IoT) platform via a wireless network.

[0013] This invention provides a method for a photovoltaic power plant alarm filtering system based on Internet of Things (IoT) modeling:

[0014] Step 1: The IoT platform models the weather station in the meteorological acquisition module, the fault detection module, the inverter in the power generation acquisition module, and the grid-connected meter in the power acquisition module.

[0015] Step 2: The IoT platform determines day and night based on solar radiation data collected by weather stations;

[0016] Step 3: The IoT platform determines that the current time is daytime and uses the fault signals collected by the inverter to determine whether a fault has occurred in the photovoltaic power station;

[0017] The IoT platform collects power generation data from photovoltaic power plants through inverters and calculates the daily power generation efficiency of photovoltaic power plants by combining the daily cumulative radiation value collected by meteorological stations. The daily power generation efficiency of photovoltaic power plants is compared with the daily power generation efficiency threshold. When the daily power generation efficiency of photovoltaic power plants is less than the daily power generation efficiency threshold, an alarm is triggered.

[0018] The IoT platform collects power output data from photovoltaic power plants through grid-connected metering meters and calculates the power factor of the photovoltaic power plants. It then compares the power factor of the photovoltaic power plants with the alarm threshold for the power factor of the photovoltaic power plants. When the power factor of the photovoltaic power plants is less than the alarm threshold for the power factor of the photovoltaic power plants, an alarm is triggered.

[0019] In step one, the IoT platform converts and configures data points for the data collected by the weather station, inverter, and grid-connected meter through the edge collectors corresponding to the weather station, inverter, and grid-connected meter.

[0020] In step two, the IoT platform collects instantaneous radiation values ​​through the meteorological data acquisition module. If the instantaneous radiation value remains in a daytime state within a set time, it is judged as daytime; otherwise, it is judged as nighttime.

[0021] In step three, the IoT platform sets a filtering time. If the fault signal collected by the IoT through the inverter remains unchanged within the filtering time, the fault signal is considered valid.

[0022] The technical effects of this invention are as follows:

[0023] (1) After data is connected to the IoT platform, discrete data is uniformly classified through device modeling, enabling a unified description of attribute parameters in alarm operation logic. Therefore, alarm configuration for each power station no longer needs to be implemented independently; only a single alarm rule needs to be configured once in the IoT platform. If the rules change later, only one modification is needed to adapt to the alarm operation of all power stations. This greatly improves the workload and efficiency of system implementation and subsequent operation and maintenance, and also effectively reduces the system resource load pressure.

[0024] (2) This invention integrates the "daytime start, nighttime stop" operation mechanism of photovoltaic power plants into the equipment-type switch alarm, achieving nighttime filtering. That is, equipment-type alarms avoid nighttime operation and only generate alarm messages during the day, greatly reducing the occurrence of invalid alarms. In addition, this solution uses the instantaneous radiation value attribute of the meteorological station as the basis for determining day and night, rather than simply using a fixed time interval for day and night determination. This avoids the day and night differences caused by various external factors such as different seasons, different regions, and different altitudes.

[0025] (3) This invention optimizes the traditional single-parameter threshold alarm by using calculation formulas such as equivalent hours and power generation efficiency. It calculates the alarm by combining various data such as the actual power generation of the inverter and radiation value, and then compares the thresholds. It filters environmental interference factors and uses day and night judgment as a prerequisite to accurately realize the alarm judgment of power station operation analysis efficiency and promptly prompts the site to clean the component boards and perform electrical maintenance.

[0026] (4) This invention utilizes the daily power generation efficiency calculation formula, performs joint calculations on the phased differences between the two attributes of the grid-connected meter's total positive active power and total negative active power, and then performs threshold comparisons to realize the abnormal perception of the power factor of the power station and promptly remind the power station site to adjust its operation strategy. Attached Figure Description

[0027] This manual includes the following figures, which illustrate the following:

[0028] Figure 1 This is a logical structure block diagram of a photovoltaic power station alarm filtering system and method based on Internet of Things modeling according to the present invention;

[0029] Figure 2 This is a two-day historical curve of the instantaneous total radiation value of a photovoltaic power station at a certain photovoltaic power station, which is the basis for the present invention of an alarm filtering system and method for photovoltaic power stations based on Internet of Things modeling.

[0030] Figure 3 This is the calculation function for day / night judgment in a photovoltaic power station alarm filtering system and method based on Internet of Things modeling according to the present invention;

[0031] Figure 4 This is a fault shutdown data curve of an inverter in a photovoltaic power plant alarm filtering system and method based on Internet of Things modeling, according to the present invention.

[0032] Figure 5 This invention provides an alarm trigger calculation function for a photovoltaic power plant alarm filtering system and method based on Internet of Things (IoT) modeling.

[0033] Figure 6 This invention relates to a photovoltaic power plant alarm filtering system and method based on Internet of Things modeling, and presents a historical curve of two days' power generation data collected by an inverter at a power plant site.

[0034] Figure 7 This invention provides a calculation function for triggering daily power generation efficiency alarms in a photovoltaic power plant based on Internet of Things (IoT) modeling alarm filtering system and method.

[0035] Figure 8 This invention provides a calculation function for triggering power factor alarms in a photovoltaic power plant based on Internet of Things (IoT) modeling alarm filtering system and method.

[0036] Figure 1 The modules are labeled as follows: 1. Meteorological data acquisition module; 2. Fault detection module; 3. Power generation data acquisition module; 4. Electricity data acquisition module; 5. Internet of Things platform. Detailed Implementation

[0037] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, in order to help those skilled in the art to have a more complete, accurate and in-depth understanding of the inventive concept and technical solution of the present invention, and to facilitate its implementation.

[0038] This invention provides a photovoltaic power station alarm filtering system based on Internet of Things (IoT) modeling. The system comprises a meteorological data acquisition module, a fault detection module, a power generation data acquisition module, an electrical energy data acquisition module, and an IoT platform. The meteorological data acquisition module collects solar radiation data received by the photovoltaic power station; the fault detection module collects fault signals from the photovoltaic power station; the power generation data acquisition module collects power generation data from the photovoltaic power station; and the electrical energy data acquisition module collects electrical energy output data from the photovoltaic power station. The output of the meteorological data acquisition module is connected to the input of the IoT platform; the output of the fault detection module is connected to the input of the IoT platform; the output of the power generation data acquisition module is connected to the input of the IoT platform; and the output of the electrical energy data acquisition module is connected to the input of the IoT platform.

[0039] The meteorological acquisition module outputs solar radiation data to the IoT platform, the fault detection module outputs fault signals of the photovoltaic power station to the IoT platform, the power generation acquisition module outputs power generation data of the photovoltaic power station to the IoT platform, and the power energy acquisition module outputs power output data of the photovoltaic power station to the IoT platform.

[0040] The meteorological data acquisition module includes a weather station and an edge data acquisition unit. The weather station is connected to the edge data acquisition unit, and the edge data acquisition unit is connected to the Internet of Things platform via a wireless network.

[0041] The fault detection module includes an inverter and an edge data collector. The inverter is connected to the edge data collector, which is connected to the IoT platform via a wireless network.

[0042] The power generation acquisition module includes an inverter and an edge acquisition unit. The inverter is connected to the edge acquisition unit, and the edge acquisition unit is connected to the Internet of Things platform via a wireless network.

[0043] The power acquisition module includes a grid-connected meter and an edge collector. The grid-connected meter is connected to the edge collector, and the edge collector is connected to the Internet of Things platform via a wireless network.

[0044] This invention provides a method for a photovoltaic power plant alarm filtering system based on Internet of Things (IoT) modeling:

[0045] Step 1: The IoT platform models the weather station in the meteorological acquisition module, the fault detection module, the inverter in the power generation acquisition module, and the grid-connected meter in the power acquisition module.

[0046] Step 2: The IoT platform determines day and night based on solar radiation data collected by weather stations;

[0047] Step 3: The IoT platform determines that the current time is daytime and uses the fault signals collected by the inverter to determine whether a fault has occurred in the photovoltaic power station;

[0048] The IoT platform collects power generation data from photovoltaic power plants through inverters and calculates the daily power generation efficiency of photovoltaic power plants by combining the daily cumulative radiation value collected by meteorological stations. The daily power generation efficiency of photovoltaic power plants is compared with the daily power generation efficiency threshold. When the daily power generation efficiency of photovoltaic power plants is less than the daily power generation efficiency threshold, an alarm is triggered.

[0049] The IoT platform collects power output data from photovoltaic power plants through grid-connected metering meters and calculates the power factor of the photovoltaic power plants. It then compares the power factor of the photovoltaic power plants with the alarm threshold for the power factor of the photovoltaic power plants. When the power factor of the photovoltaic power plants is less than the alarm threshold for the power factor of the photovoltaic power plants, an alarm is triggered.

[0050] In step one, the IoT platform converts and configures data points for the data collected by the weather station, inverter, and grid-connected meter through the edge collectors corresponding to the weather station, inverter, and grid-connected meter.

[0051] In step two, the IoT platform collects instantaneous radiation values ​​through the meteorological data acquisition module. If the instantaneous radiation value remains in a daytime state within a set time, it is judged as daytime; otherwise, it is judged as nighttime.

[0052] In step three, the IoT platform sets a filtering time. If the fault signal collected by the IoT through the inverter remains unchanged within the filtering time, the fault signal is considered valid.

[0053] The following is a detailed description of an alarm filtering system for a photovoltaic power plant based on Internet of Things (IoT) modeling, according to the present invention.

[0054] The meteorological data acquisition module is used to collect solar radiation data received by the photovoltaic power station; the fault detection module is used to collect fault signals from the photovoltaic power station; the power generation data acquisition module is used to collect power generation data from the photovoltaic power station; and the power output data acquisition module is used to collect power output data from the photovoltaic power station.

[0055] This invention discloses a photovoltaic power plant alarm filtering system based on Internet of Things (IoT) modeling, comprising a meteorological data acquisition module, a fault detection module, a power generation data acquisition module, an energy data acquisition module, and an IoT platform. The meteorological data acquisition module utilizes a weather station to collect solar radiation data for the area where the photovoltaic power plant is located, including daily cumulative radiation values ​​and instantaneous radiation values. The daily cumulative radiation values ​​are used to calculate the power generation efficiency of the photovoltaic power plant, while the instantaneous radiation values ​​are used to determine day / night cycles. In photovoltaic production scenarios, the operating status of photovoltaic power plants is highly correlated with solar radiation; therefore, judging the energy production status of photovoltaic power plants requires analysis in conjunction with the current environmental solar radiation levels.

[0056] The fault detection module uses an inverter to detect faults in photovoltaic power plants. The fault detection of photovoltaic power plants in this invention includes fault shutdown, bus overvoltage, grid abnormality, positive-to-ground insulation fault, negative-to-ground insulation fault, and step-up side circuit open circuit.

[0057] The power generation acquisition module uses an inverter to collect power generation data from the photovoltaic power plant. This invention uses the collected power generation data from the photovoltaic power plant to calculate its power generation efficiency.

[0058] The power acquisition module uses a grid-connected meter to collect power output data from the photovoltaic power plant. The power output data collection includes the collection of total forward active power and total reverse active power data. The total forward active power and total reverse active power data are used to calculate the power factor of the photovoltaic power plant. This invention uses the power factor of the photovoltaic power plant to measure the power output quality of the photovoltaic power plant.

[0059] The IoT platform uses cloud servers to model weather stations, inverters, and grid-connected meters. It collects solar radiation data, fault signals, power generation data, and power output data of the photovoltaic power station's location through edge collectors corresponding to the weather stations, inverters, and grid-connected meters. The edge collectors convert the collected data and associate the data from external devices with internal data points by configuring data points, thus enabling the IoT platform to collect output data from different models of weather stations, inverters, and grid-connected meters in a standardized manner.

[0060] The connection relationships of a photovoltaic power plant alarm filtering system based on Internet of Things modeling according to the present invention are described in detail below.

[0061] The weather station is connected to the edge data collector via RS485 / RS232 serial port, the inverter is connected to the edge data collector via RS-485 interface, the grid-connected meter is connected to the edge data collector via RS-485 interface, and the edge data collectors corresponding to the weather station, the inverter, and the grid-connected meter are connected to the IoT platform via wireless network.

[0062] The following describes in detail a method for alarm filtering in photovoltaic power plants based on Internet of Things (IoT) modeling.

[0063] The IoT platform models the weather stations in the meteorological data acquisition module, the fault detection module, the inverters in the power generation data acquisition module, and the grid-connected meters in the power acquisition module. The IoT platform uses edge data collectors corresponding to the weather stations, inverters, and grid-connected meters to convert and configure data points. Specifically, the IoT platform collects radiation data, fault signals, power generation data, and power output data of the photovoltaic power plant's location from the edge data collectors corresponding to the weather stations, inverters, and grid-connected meters. The purpose of modeling the weather stations, inverters, and grid-connected meters is to abstract and summarize the functions of products from different brands and categories. The model is based on functional definitions, classifying equipment according to its attributes and functions, and designing a unique model for each category of equipment based on the classification results. This invention creates a Thing Model based on the DTML specification standard developed by the Industrial Internet Industry Consortium (AII) Model Ad Hoc Group. DTML consists of a set of core metadata, including three categories of information: devices, components, and attributes. The hierarchical relationship is that a device encompasses a component, and a component encompasses attributes. Specifically, a device (Thing_model) describes a single physical entity, representing independent individuals of different types, brands, and models; a component is a part of a device; when a single device as a whole does not require branches, a component does not need to be defined; and attributes are specific measurement point data describing the device or component.

[0064] Based on the actual needs of the devices and data points participating in the alarm rule judgment according to this invention, weather stations, inverters, and grid-connected meters are modeled. Since each type of device has many attributes, this invention only models some of the attributes participating in the alarm rules. According to the IoT modeling principles stated in the preceding rules, the three types of devices are modeled separately.

[0065] The modeling of the weather station is as follows:

[0066]

[0067] XNYGF stands for "New Energy Photovoltaic" (capitalized), WB is the abbreviation for "weather station," and "01-00:1" represent the "brand-model-model version number" of a certain type of weather station. Since weather stations only have analog signals and their composition is relatively simple, there is no need to divide them into components.

[0068] The inverter is modeled as follows:

[0069]

[0070]

[0071] XNYGF stands for "New Energy Photovoltaics" (capitalized), Inverter is the English annotation for inverter, and "01-00:1" represent the "brand-model-model version number" of this type of inverter, respectively. Based on signal type, inverter data is divided into two categories: "Teleindication" and "Remote Measurement".

[0072] The modeling of grid-connected electricity meters is as follows:

[0073]

[0074] XNYGF stands for "New Energy Photovoltaic," EM is the abbreviation for electricity meter, and "01-00:1" represent the brand, model, and model version number of a certain type of meter. Because grid-connected metering meters only have analog signals and their composition is relatively simple, there is no need to divide them into components.

[0075] The IoT platform determines day and night by using solar radiation data collected from weather stations. The platform collects instantaneous radiation values ​​through a weather acquisition module; if the instantaneous radiation value remains at a daytime level within a set time, it is considered daytime; otherwise, it is considered nighttime. Figure 2 The image shows a two-day historical curve of the total radiation instantaneous value (WB_TotalRadiat1Instant) of a weather station (dtml:XNYGF-WS-01-00:1) at a photovoltaic power station. The instantaneous radiation value gradually increases around 7:12 AM (sunrise), peaks at noon, and gradually approaches 0 at sunset (6:24 PM), consistent with the natural variation of solar radiation with the sun's eastward and westward movement. To avoid sampling errors, in this invention, a weather station's instantaneous radiation value is considered daytime if it is greater than 0 for 5 consecutive minutes; otherwise, it is considered nighttime. A common calculation function is configured based on the model on the IoT platform, such as... Figure 3 As shown.

[0076] The IoT platform determines that the current time is daytime and uses fault signals collected by the inverter to judge whether a fault has occurred in the photovoltaic power station. The IoT platform sets a filtering time; if the fault signal collected by the IoT through the inverter remains unchanged within the filtering time, the fault signal is considered valid. Specifically, when the IoT platform receives a fault signal, i.e., an alarm signal, it checks the continuity of the alarm signal. Since the alarm signal has only two states, "0" and "1", if the signal is always 1 within a certain filtering time, it is considered a valid alarm; otherwise, it is invalid.

[0077] like Figure 4 As shown, taking the fault shutdown data curve of a certain inverter as an example, an abnormal signal appears at t2 and recovers at t3; an abnormal signal appears at t4 and recovers at t10; an abnormal signal appears at t11 ​​and persists until t14. When the filtering time is 3 minutes, the abnormal signal at t2 will be judged as an invalid fault and thus filtered out without generating an alarm; the value of t4-t6 equals 1, a valid alarm signal is generated at t6, and the alarm recovers at t10; similarly, a valid alarm signal is generated at t13. It should be noted that this type of alarm needs to be combined with the "daytime start, nighttime stop" filtering principle, triggering only during "daytime" hours. Combined with the inverter modeling attributes, a common calculation function is configured on the IoT platform based on the model, such as... Figure 5 As shown. This invention reduces the likelihood of inverter fault signals generating sporadic alarm signals during operation due to external factors (such as data acquisition interference, temporary overload self-protection, etc.) by setting the filtering time, thereby improving the accuracy of the alarms provided by this invention.

[0078] The IoT platform collects power generation data from the photovoltaic (PV) power station via inverters and combines this data with the cumulative daily radiation values ​​collected by the weather station to calculate the daily power generation efficiency of the PV power station. Specifically, the IoT platform first calculates the equivalent hours of the PV power station, which is a reference indicator for measuring the power generation efficiency of the PV power station. This calculation is completed using the power generation data collected by the inverter (dtml:XNYGF-Inverter-01-00:1), and the calculation formula is as follows:

[0079] Equivalent hours (h) = Actual power generation (kW·h) / Installed capacity (kW)

[0080] The equivalent hours of a photovoltaic power plant are affected not only by the failures of the photovoltaic power plant itself, but also by environmental factors, such as the equivalent hours being lower on cloudy or rainy days than on sunny days. Therefore, the data of the equivalent hours alone cannot directly reflect the operating status of the photovoltaic power plant equipment. The interference of the cumulative daily radiation value should be excluded. Therefore, the concept of photovoltaic power plant power generation efficiency (Performance Ratio, PR) should be introduced.

[0081] The daily power generation efficiency (PR) of a photovoltaic (PV) power plant is calculated as: Equivalent hours (h) / Daily cumulative radiation value (MJ / m²) * 3.6 * 100%. PV PR is a core indicator for measuring the overall efficiency of a power plant. It is calculated by combining the daily cumulative radiation value (WB_DayCumOfRainTotalRadiat) from a weather station (dtml:XNYGF-WS-01-00:1). After excluding the influence of environmental factors, it directly reflects the ratio of the actual power generation of the PV power plant to its theoretical maximum power generation. Therefore, when the daily PR of a PV power plant is lower than expected, it may indicate various abnormal conditions such as PV panel shading, contamination, or even electrical faults (e.g., module string mismatch), and an alarm should be promptly sent to the business side. In the formula for calculating the daily PR of a PV power plant, the installed capacity is an adjustable parameter, set according to the actual situation of each power plant. In this embodiment of the invention, the installed capacity is 5MW. Because radiation levels vary across regions, the daily power generation efficiency limits for photovoltaic (PV) power plants also differ. Therefore, the threshold for daily power generation efficiency is set as an adjustable variable, customized by each power plant according to its own needs. In this embodiment of the invention, the daily power generation efficiency threshold is defined as 70%. Actual power generation refers to the daily power generation attribute (INV - Remote Measure / Daily Power Generation) collected by the inverter. Figure 6 The image shows a two-day historical data curve of the power generation collected by the inverter at a power station. Power generation begins around 7:00 AM ("daytime") and peaks at 6:20 PM ("nighttime"). The IoT platform performs a power generation efficiency check once at the start of the nighttime period to calculate whether the photovoltaic power station's operating efficiency for the day meets expectations. The IoT platform uses common calculation functions based on model configuration, such as... Figure 7 As shown.

[0082] The IoT platform collects power output data from photovoltaic power plants through grid-connected meters and calculates the power factor of the photovoltaic power plants. Specifically, the IoT platform performs calculations using two attributes of the grid-connected meter (dtml:HLXNYGF-EM-01-00:1): Total Forward Active Energy (ToMerEnergyOfFor) and Total Reverse Active Energy (ToMerEnergyOfOpp). The calculation formula is as follows:

[0083]

[0084] The IoT platform takes the difference in power data from the early morning of two consecutive days. Because different regional power grids have different requirements for the power factor of photovoltaic power plants, the alarm threshold is adjustable. In this embodiment of the invention, the alarm threshold for the power factor of the photovoltaic power plant is 0.95. The IoT platform configures common calculation functions based on the model, such as... Figure 8As shown, the power factor of a photovoltaic (PV) power plant is a key indicator for measuring the quality of its output power, directly affecting grid stability and the plant's economic benefits. When the power factor of a PV power plant is lower than expected, the IoT platform can detect this in a timely manner and prompt on-site adjustments such as implementing SVG (Static Var Generator) or capacitor bank compensation strategies, adjusting transformer turns ratios, and optimizing voltage levels.

[0085] The technical effects of the present invention are described in detail below.

[0086] After data is integrated into the IoT platform, discrete data is uniformly categorized through device modeling, enabling a unified description of attribute parameters in alarm calculation logic. Therefore, alarm configuration for each power station no longer needs to be implemented independently; only a single alarm rule needs to be configured on the IoT platform. If rules change later, only one modification is required, thus adapting to the alarm operation of all power stations. This significantly improves the workload and efficiency of system implementation and subsequent operation and maintenance, while also effectively reducing system resource load.

[0087] This invention integrates the "daytime start, nighttime stop" operation mechanism of photovoltaic power plants into equipment-type switch alarms, achieving nighttime filtering. This means that equipment alarms are avoided during nighttime operation, generating alarm messages only during the day, greatly reducing the occurrence of invalid alarms. Furthermore, this solution uses the instantaneous radiation values ​​from meteorological stations as the basis for determining day and night, rather than simply relying on fixed time intervals. This avoids the discrepancies between day and night caused by various external factors such as different seasons, regions, and altitudes.

[0088] This invention optimizes traditional single-parameter threshold alarms by using calculation formulas such as equivalent hours and power generation efficiency. It combines various data such as the actual power generation of the inverter and radiation value for joint calculation and then compares the thresholds. It filters out environmental interference factors and uses day and night judgment as a prerequisite to accurately realize alarm judgment of power plant operation efficiency analysis and promptly prompt on-site to clean the component boards and perform electrical maintenance.

[0089] This invention utilizes the daily power generation efficiency calculation formula, performs joint calculations on the phased differences between two attributes—total positive active power and total negative active power—from the grid-connected meter, and then compares thresholds to achieve abnormal power factor detection of the power plant, promptly alerting the power plant site to adjust its operating strategy.

[0090] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution; or the direct application of the inventive concept and technical solution to other situations without modification, are all within the protection scope of the present invention.

Claims

1. A photovoltaic power plant alarm filtering system based on Internet of Things (IoT) modeling, characterized in that: It includes a meteorological data acquisition module, a fault detection module, a power generation data acquisition module, an electrical energy data acquisition module, and an Internet of Things (IoT) platform. The meteorological data acquisition module is used to collect solar radiation data received by the photovoltaic power station, the fault detection module is used to collect fault signals of the photovoltaic power station, the power generation data acquisition module is used to collect power generation data of the photovoltaic power station, and the electrical energy data acquisition module is used to collect electrical energy output data of the photovoltaic power station. The output of the meteorological data acquisition module is connected to the input of the IoT platform, the output of the fault detection module is connected to the input of the IoT platform, the output of the power generation data acquisition module is connected to the input of the IoT platform, and the output of the power acquisition module is connected to the input of the IoT platform.

2. The photovoltaic power plant alarm filtering system based on Internet of Things modeling as described in claim 1, characterized in that: The meteorological acquisition module outputs solar radiation data to the Internet of Things (IoT) platform, the fault detection module outputs fault signals of the photovoltaic power station to the IoT platform, the power generation acquisition module outputs power generation data of the photovoltaic power station to the IoT platform, and the power energy acquisition module outputs power output data of the photovoltaic power station to the IoT platform.

3. The photovoltaic power plant alarm filtering system based on Internet of Things modeling as described in claim 1, characterized in that: The meteorological data acquisition module includes a meteorological station and an edge data acquisition device. The meteorological station is connected to the edge data acquisition device, and the edge data acquisition device is connected to the Internet of Things platform via a wireless network.

4. The photovoltaic power plant alarm filtering system based on Internet of Things modeling as described in claim 1, characterized in that: The fault detection module includes an inverter and an edge collector. The inverter is connected to the edge collector, and the edge collector is connected to the Internet of Things platform via a wireless network.

5. The photovoltaic power plant alarm filtering system based on Internet of Things modeling as described in claim 1, characterized in that: The power generation acquisition module includes an inverter and an edge acquisition unit. The inverter is connected to the edge acquisition unit, and the edge acquisition unit is connected to the Internet of Things platform via a wireless network.

6. The photovoltaic power plant alarm filtering system based on Internet of Things modeling as described in claim 1, characterized in that: The power acquisition module includes a grid-connected meter and an edge collector. The grid-connected meter is connected to the edge collector, and the edge collector is connected to an Internet of Things (IoT) platform via a wireless network.

7. A method for a photovoltaic power plant alarm filtering system based on Internet of Things modeling as described in any one of claims 1-6, characterized in that: Step 1: The IoT platform models the weather station in the meteorological acquisition module, the fault detection module, the inverter in the power generation acquisition module, and the grid-connected metering meter in the power acquisition module. Step 2: The IoT platform determines day and night based on solar radiation data collected by weather stations; Step 3: The IoT platform determines that the current time is daytime and uses the fault signals collected by the inverter to determine whether a fault has occurred in the photovoltaic power station; The IoT platform collects power generation data from photovoltaic power plants through inverters and calculates the daily power generation efficiency of photovoltaic power plants by combining the daily cumulative radiation value collected by meteorological stations. The daily power generation efficiency of photovoltaic power plants is compared with the daily power generation efficiency threshold. When the daily power generation efficiency of photovoltaic power plants is less than the daily power generation efficiency threshold, an alarm is triggered. The IoT platform collects power output data from photovoltaic power plants through grid-connected metering meters and calculates the power factor of the photovoltaic power plants. It then compares the power factor of the photovoltaic power plants with the alarm threshold for the power factor of the photovoltaic power plants. When the power factor of the photovoltaic power plants is less than the alarm threshold for the power factor of the photovoltaic power plants, an alarm is triggered.

8. The method for a photovoltaic power station alarm filtering system based on Internet of Things modeling as described in claim 7, characterized in that: In step one, the IoT platform converts and configures data points for the data collected by the weather station, inverter, and grid-connected meter through the edge collectors corresponding to the weather station, inverter, and grid-connected meter.

9. The method for a photovoltaic power station alarm filtering system based on Internet of Things modeling as described in claim 7, characterized in that: In step two, the IoT platform collects instantaneous radiation values ​​through the meteorological data acquisition module. If the instantaneous radiation value remains in a daytime state within a set time, it is judged as daytime; otherwise, it is judged as nighttime.

10. The method for a photovoltaic power plant alarm filtering system based on Internet of Things modeling as described in claim 7, characterized in that: In step three, the IoT platform sets a filtering time. If the fault signal collected by the IoT through the inverter remains unchanged within the filtering time, the fault signal is considered valid.

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