Photovoltaic power station modular detection system based on Internet of Things and cloud platform

Abstract

The invention discloses a photovoltaic power station modular detection system based on the Internet of Things and a cloud platform, and relates to the technical field of photovoltaic power station detection, the photovoltaic power station modular detection system comprises a sensor, a data collector, a communication module, a cloud platform and a webpage client, the data collector collects sensor data in real time and transmits the collected data to the cloud platform through the communication module, and the webpage client is connected with the sensor through the communication module; the cloud platform stores and processes the data and transmits the processed information to the webpage client to be presented in a WEB user interface, and the cloud platform comprises a data analysis module and a calibration management module. A standard value and a measured value are recorded to calculate a correction factor, detection data are effectively corrected, data accuracy is improved, a powerful basis is provided for troubleshooting and maintenance of a power station by accurately judging specific reasons of performance reduction of the inverter, safe and stable operation of the power station is effectively guaranteed, fault loss is reduced, and the reliability of the power station is improved. And the power generation efficiency and the economic benefit of the photovoltaic power station can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power station detection. Specifically, it relates to a modular detection system for photovoltaic power stations based on the Internet of Things and cloud platform. Background Art

[0002] In the technical field of photovoltaic power station detection, with the booming development of the photovoltaic industry, the scale and quantity of photovoltaic power stations are increasing continuously, and its detection work faces many challenges. The existing technologies have obvious deficiencies, which are specifically as follows:

[0003] Currently, in traditional detection systems, on the one hand, data acquisition devices are scattered and lack integration, making it difficult to comprehensively and real-time collect various parameters of photovoltaic power stations. For example, some early photovoltaic power stations can only collect basic voltage and current data and cannot obtain environmental parameters, resulting in poor data integrity. At the same time, the data transmission method is backward, mostly using wired connections, with complex wiring, high costs, and vulnerable to environmental interference. The stability and timeliness of data transmission cannot be guaranteed, making it difficult to timely feedback the collected data to operation and maintenance personnel for analysis and processing. At the same time, the data analysis ability is limited. The previous data analysis means are simple and cannot deeply explore the data value. For the performance index analysis of photovoltaic power stations, only simple statistics of single data can be carried out, such as recording the daily power generation, and it is difficult to comprehensively evaluate key indicators such as system efficiency, inverter performance, and power quality. When a fault occurs in the power station, it is impossible to quickly and accurately locate the root cause of the problem. For example, when the performance of the inverter decreases and the power generation efficiency decreases, it is impossible to accurately determine whether it is a conversion efficiency problem or a maximum power point tracking (MPPT) efficiency problem.

[0004] In addition, on the other hand, in terms of sensors, there is a lack of effective calibration and management mechanisms. After long-term use, sensors are affected by environmental factors (such as temperature and humidity changes) and their own aging, resulting in a decrease in measurement accuracy. However, there is no unified and standardized calibration process and method, leading to large errors in the collected data and affecting the reliability of the detection results. Moreover, the sensor information management is chaotic. There is no perfect sensor library established, making it difficult to effectively manage and trace information such as the device number, specification, range, and accuracy of sensors. And traditional detection systems are usually designed for specific power stations, with poor flexibility, and it is difficult to quickly adjust and expand according to the scale, type (centralized or distributed), and test requirements of different photovoltaic power stations. When new detection indicators need to be added or the measuring point layout needs to be changed, it often requires re-designing and building the entire detection system, which is costly and time-consuming.

[0005] For the problems in the related technologies, no effective solutions have been proposed yet. Summary of the Invention

[0006] In view of the problems in the related art, the present invention proposes a modular detection system for photovoltaic power stations based on the Internet of Things and cloud platform, which effectively solves the deficiencies of traditional detection systems, has significant advantages in data collection, analysis, management and system flexibility, and provides strong support for the efficient operation and stable development of photovoltaic power stations.

[0007] The technical solution of the present invention is realized as follows:

[0008] A modular detection system for photovoltaic power stations based on the Internet of Things and cloud platform, comprising: sensors, data collectors, communication modules, cloud platforms and web clients. The data collectors collect sensor data in real time and transmit the collected data to the cloud platform through the communication modules. The cloud platform stores and processes the data, and transmits the processed information to the web clients to be presented in the WEB user interface.

[0009] The cloud platform includes a data analysis module and a calibration management module, wherein;

[0010] The data analysis module is used to construct a calculation model according to a preset detection model and detection standard, analyze performance indicators, and automatically count the maximum value, minimum value, average value and cumulative value data of performance indicators at different time periods. When data anomalies are detected, an alarm is triggered. Among them, the performance indicators at least include system efficiency, inverter performance, power quality, component efficiency, imbalance rate between photovoltaic strings and system operating status;

[0011] The calibration management module is used to input standard values and measured values on the calibration system of the sensors during calibration, and obtain the correction factor of each sensor through data analysis and comparison processing of the detection data and store it in the system.

[0012] Furthermore, it further includes: a project planning and construction module, a data query and display module, a data management module and a sensor management module, wherein;

[0013] The project planning and construction module is used to establish a corresponding detection model according to specific test requirements, arrange measurement points at preset positions, and preset calibrated sensors, and automatically generate a power generation calculation model of the power station when analyzing the test data of the photovoltaic power station;

[0014] The data query and display module is used to receive the data transmitted by the data collector in real time, automatically update and display the latest information. Users can view the data in various ways, and the data is automatically saved and efficiently retrieved according to various forms of conditions;

[0015] The data management module is used to generate a detection result report for each photovoltaic power station and statistically analyze the data of multiple photovoltaic power stations;

[0016] The sensor management module is used to establish a sensor library, input the sensor information used in the detection activities, and upload calibration certificates, traceability confirmation records, and correction factors. The correction factors are called in the detection model to correct the detection data. Among them, the sensor information includes at least device number, specification, range, and accuracy information.

[0017] Furthermore, the data analysis module further includes: an energy efficiency ratio application test unit, which is used to collect DC power parameters and AC power parameters processed by the inverter in real time, monitor the environmental parameters of the photovoltaic power station at a preset sampling frequency, and obtain the imbalance and open circuit problems between photovoltaic strings through the analysis of the sampled data of each parameter, and compare the impacts of equipment and installation processes of different manufacturers on power generation and efficiency under the same conditions to form an optimized power output improvement strategy. Among them, the environmental parameters include at least solar radiation intensity, temperature, photovoltaic panel temperature, air humidity, and wind speed, according to the measurement objectives and the requirements of parameter types and quantities.

[0018] Furthermore, the calibration management module includes the following steps:

[0019] Pre-input the standard value and the measured value for each sensor respectively. Among them, the standard value and the measured value are obtained by using a standard device and measuring the sensor in the actual calibration environment;

[0020] Perform data analysis and comparison processing on the input standard value and measured value to determine the deviation between the actual performance and the standard performance of the sensor;

[0021] According to the results of the data analysis and comparison processing, calculate the correction factor for each sensor. The correction factor is used to represent the relationship adjustment parameter between the actual output and the ideal output of the sensor;

[0022] Store the calculated correction factor for each sensor in the system, which is used to call the correction factor in the sensor management module to correct the detection data. During the detection process, when using this sensor to collect data, the calibration management module corrects the collected data according to the pre-stored correction factor to improve the accuracy of the detection data.

[0023] Furthermore, determining the deviation between the actual performance and the standard performance of the sensor includes the following steps:

[0024] Calibrate the same measurement point, the standard value x B and the measured value x C , and obtain the deviation value Δx between the measured value and the standard value, expressed as: Δx = x C -x B .

[0025] Further, the calibration management module corrects the collected data according to the pre-stored correction factors, including the following steps:

[0026] Obtain the deviation ratio P, expressed as: P = Δx / x B ;

[0027] Calibrate the correction factor k according to the deviation ratio P, where it includes:

[0028] If the deviation ratio P is positive, indicating that the measured value is larger, the correction factor should be k = 1 - P;

[0029] If the deviation ratio P is negative, indicating that the measured value is smaller, the correction factor should be k = 1 + P;

[0030] Store the correction factor k in the calibration management module. When using this sensor for measurement, correct the measured value x obtained from the measurement C The corrected measured value x Z , expressed as: x Z = k × x C .

[0031] Further, it also includes setting up the cloud platform, including setting up data channels and configuring software function modules, for realizing that the cloud platform receives, stores and processes data, developing the WEB end and mobile application platforms using JAVA and AndroidStudio, developing the detection platform interface display and various function modules by using the SDK and API provided by the sensor manufacturer and cloud platform service provider, establishing the corresponding detection model in the project planning and component module according to specific test requirements, arranging measurement points, and presetting calibrated sensors.

[0032] Advantages of the present invention:

[0033] 1. The data analysis module of the present invention constructs a calculation model based on the preset model and standards, deeply analyzes multiple key performance indicators such as system efficiency and inverter performance, automatically counts data at different times, can also monitor data anomalies in real time and trigger alarms in a timely manner, and can accurately judge the specific reasons for the decline in inverter performance, providing a strong basis for power station fault troubleshooting and maintenance, effectively ensuring the safe and stable operation of the power station, reducing fault losses. At the same time, the energy efficiency ratio application test unit analyzes the problems of photovoltaic strings by collecting AC and DC power supply parameters and environmental parameters, compares the impacts of equipment from different manufacturers and installation processes on power generation and efficiency, and thus forms an optimal power output improvement strategy, which helps to improve the power generation efficiency and economic benefits of the photovoltaic power station.

[0034] 2. The present invention realizes precise sensor calibration and management, standardizes the sensor calibration process, calculates the correction factor by inputting the standard value and the measured value, effectively corrects the detection data, and improves the data accuracy. The sensor management module establishes a complete sensor library to uniformly manage sensor information, facilitating equipment traceability and detection data correction, and ensuring the reliability of detection results.

[0035] 3. The flexible system configuration and expansion of the present invention can quickly establish a detection model, arrange measuring points and preset sensors according to the scale, type and test requirements of different photovoltaic power stations, with strong system flexibility. At the same time, the detection platform is developed using the SDK and API provided by sensor manufacturers and cloud platform service providers, which is convenient for function expansion and upgrade, reduces the system construction and adjustment costs. Meanwhile, with a rich variety of sensors, it can comprehensively collect various data such as electrical parameters and environmental parameters of the photovoltaic power station. Cooperating with the modular data collector with independent A / D conversion and 4G data transmission functions, it realizes the real-time monitoring of the power station operation status. The 4G-LTE communication module ensures the stable and timely transmission of data to the cloud platform, avoiding the wiring problems and environmental interference problems of traditional wired transmission, and ensuring that operation and maintenance personnel can obtain accurate data in time for analysis and processing. In addition, it realizes convenient data query and management. The data query and display module supports multiple ways to view data, automatically saves and efficiently retrieves, facilitating users to obtain the required information. The data management module generates detection result reports and statistically analyzes the data of multiple power stations, providing data support for power station management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0037] Figure 1 is the principle block diagram of a modular detection system for a photovoltaic power station based on the Internet of Things and cloud platform according to an embodiment of the present invention;

[0038] Figure 2 is the scene schematic diagram of a modular detection system for a photovoltaic power station based on the Internet of Things and cloud platform according to an embodiment of the present invention.

[0039] In the figure:

[0040] 1. Sensor; 2. Data collector; 3. Communication module; 4. Cloud platform; 5. Web client;

[0041] 41. Data analysis module; 42. Calibration management module; 43. Project planning and construction module; 44. Data query and display module; 45. Data management module; 46. Sensor management module. Detailed implementation manners

[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention belong to the protection scope of the present invention.

[0043] According to an embodiment of the present invention, a modular detection system for a photovoltaic power station based on the Internet of Things and a cloud platform is provided.

[0044] As Figure 1 - Figure 2 shown, the modular detection system for a photovoltaic power station based on the Internet of Things and a cloud platform according to an embodiment of the present invention includes: a sensor 1, a data collector 2, a communication module 3, a cloud platform 4, and a web client 5. The data collector 2 collects the data of the sensor 1 in real time and transmits the collected data to the cloud platform 4 through the communication module 3. The cloud platform 4 stores and processes the data, and transmits the processed information to the web client 5 for presentation in the WEB user interface, where;

[0045] The sensor 1 is used to collect the array voltage, the current of each string, the irradiance, and the ambient temperature of the photovoltaic power station, and is also used to detect the temperature of the photovoltaic module, the ambient temperature, humidity, wind speed, irradiance amount, and the output current and voltage of each photovoltaic string on the busbar box or string inverter;

[0046] In this technical solution, the sensor 1 includes current and voltage transmitters, an irradiance meter, and a thermocouple. The sensor 1 for collecting the array voltage, the current of each string, the irradiance, and the ambient temperature of the photovoltaic power station also includes a sensor 1 for detecting the temperature of the photovoltaic module, the ambient temperature, humidity, and selecting a range and accuracy that meet the requirements as needed, and a transmitter for detecting the output current and voltage of each photovoltaic string on the busbar box or string inverter. The sensor 1 needs to be reasonably configured and installed according to specific detection requirements to ensure accurate collection of corresponding data.

[0047] The data collector 2 is used to adopt a modular design, has an independent A / D conversion and 4G data transmission function, collects the operating status and environmental parameters of each device in the photovoltaic power station by arranging the sensor 1, and transmits the data to the cloud platform 4 in real time;

[0048] In this technical solution, the data collector 2 includes voltage, current, power on the AC side and DC side, ambient temperature, humidity, irradiance, etc. Parameters such as the data sampling frequency and data transmission rate of the data collector 2 can be set on the device as needed, and the modular design of the data collector 2 should ensure that it can be flexibly arranged and used according to different power station layouts and detection requirements.

[0049] In addition, the data collector 2 also includes preprocessing the collected data, including eliminating irrelevant, duplicate, and incorrect data, handling missing values in the data set, using statistical methods to correct or delete outliers, and determining whether to delete or merge duplicate records based on the occurrence frequency in a specific time period. A low-pass Gaussian filter is used to filter the photovoltaic power generation data to eliminate random interference in the original power generation signal.

[0050] The communication module 3 is used to use a 4G-LTE communication module 3 to transmit the data collected by the data collector 2 to the data receiving port of the cloud platform 4;

[0051] The cloud platform 4 includes a data analysis module 41, a calibration management module 42, a project planning and construction module 43, a data query and display module 44, a data management module 45, and a sensor management module 46, among which;

[0052] The data analysis module 41 is used to construct a calculation model according to preset detection models and detection standards, analyze performance indicators, and automatically count the maximum, minimum, average, and cumulative values of performance indicators at different times. When data anomalies are detected, an alarm is triggered. Among them, the performance indicators at least include system efficiency, inverter performance, power quality, component efficiency, imbalance rate between photovoltaic strings, and system operating status;

[0053] In this technical solution, for the above-mentioned construction of the calculation model, according to the specific situations of the type (centralized or distributed), scale, and equipment parameters of the photovoltaic power station, combined with industry-wide detection standards such as GB / T 18210-2015 "Field Measurement of I-V Characteristics of Crystalline Silicon Photovoltaic (PV) Arrays", a calculation model is constructed.

[0054] Specifically, basic data such as the array voltage, current of each string, irradiance, and ambient temperature of the photovoltaic power station, as well as electrical parameters such as voltage, current, and power on the AC side and DC side are obtained from the data collector 2 in real time. These data are cleaned and preprocessed to remove outliers and noise interference to ensure the accuracy and integrity of the data.

[0055] In addition, for the analysis of performance indicators, specifically as follows:

[0056] Among them, the system efficiency reflects the comprehensive ability of a photovoltaic power station to convert solar energy into electrical energy. The calculation method is that within a certain period of time, such as 1 hour or 1 day, the total electrical energy output by the photovoltaic power station is obtained by integrating the active power output by the inverter and then divided by the solar radiation energy received by the photovoltaic modules within this period, which is calculated based on irradiance and time integration. The calculation formula is: System efficiency = Total electrical energy output by the inverter ÷ Solar radiation energy received by the photovoltaic modules × 100%. By calculating and analyzing the system efficiency at different times, the stability and effectiveness of the overall performance of the power station can be evaluated.

[0057] Among them, the performance of the inverter. The inverter is a key device in a photovoltaic power station, and its performance directly affects the power quality and power generation efficiency. When analyzing the performance of the inverter, the main indicators to focus on are the conversion efficiency, maximum power point tracking (MPPT) efficiency, harmonic content, etc. The conversion efficiency is calculated by the ratio of the output power to the input power of the inverter; the MPPT efficiency reflects the ability of the inverter to track the maximum power point of the photovoltaic modules; the harmonic content is evaluated by performing a Fourier transform on the output current or voltage of the inverter and analyzing the magnitudes of each harmonic component. Excessive harmonic content will affect the normal operation of the power grid.

[0058] Among them, for the power quality, its power quality indicators include voltage deviation, frequency deviation, three-phase unbalance degree, harmonics, etc. The voltage deviation is calculated by dividing the difference between the actually measured voltage value and the rated voltage value by the rated voltage value; the frequency deviation is the difference between the actual frequency and the rated frequency; the three-phase unbalance degree is obtained by calculating the ratio of the negative sequence component to the positive sequence component of the three-phase voltage or current. The data analysis module 41 monitors these indicators in real time. Once they exceed the allowable range, it is determined that there is a problem with the power quality.

[0059] Among them, for the component efficiency, the component efficiency reflects the ability of a single photovoltaic component to convert solar energy into electrical energy. When calculating, first measure the output power of the component, and then divide it by the power of the component under standard test conditions at its rated power to obtain the component efficiency. By comparing and analyzing the efficiencies of different components, the performance differences of the components can be found, and components with declining performance can be detected in a timely manner.

[0060] Among them, for the imbalance rate between photovoltaic strings, the imbalance between photovoltaic strings will affect the overall power generation efficiency of the power station. The imbalance rate is measured by calculating the ratio of the standard deviation to the average value of the current or voltage of each photovoltaic string. When the imbalance rate exceeds a certain threshold, it indicates that there are significant differences between the strings, which may be caused by component failures, shading, connection problems, etc., and timely investigation and handling are required.

[0061] Among them, for the system operation status, the system operation status is judged by comprehensively analyzing multiple performance indicators. Under normal operation, each performance indicator should fluctuate within a reasonable range; if an abnormality occurs, such as a sudden drop in system efficiency, an inverter fault alarm, or an excessive power quality indicator, the data analysis module 41 will determine that the system is in an abnormal operation state and trigger an alarm in a timely manner.

[0062] In addition, it also includes: performing data statistics and storage, specifically as follows: Different time period statistics: According to preset time intervals such as 15 minutes, 1 hour, 1 day, etc., perform statistics on each performance indicator. Calculate the maximum value, minimum value, average value, and cumulative value of the performance indicator for each time period respectively. For example, statistically calculate the maximum value, minimum value, average value of the system efficiency per hour within a day, and the cumulative value of the system efficiency for that day. These statistical data can intuitively reflect the changes in performance indicators in different time periods. At the same time, store the statistically calculated data in the database of the cloud platform 4, establish detailed data indexes and classifications to facilitate subsequent query and analysis. At the same time, perform regular backups on historical data to prevent data loss. For the convenience of visual display and analysis of data, format the stored data to meet the requirements of chart drawing and data analysis software.

[0063] In addition, establish an abnormal alarm mechanism strategy, specifically as follows:

[0064] According to the design parameters of the photovoltaic power station, industry standards, and actual operation experience, set reasonable threshold ranges for each performance indicator. For example, the system efficiency is lower than 80%, the inverter conversion efficiency is lower than 95%, the voltage deviation exceeds ±5%, etc. are used as abnormal thresholds.

[0065] At the same time, the data analysis module 41 monitors each performance indicator in real time. Once it finds that a certain indicator exceeds the threshold range, it immediately triggers an alarm. The alarm information is pushed to the web client 5 through the cloud platform 4 and notified to relevant personnel, such as power station operation and maintenance personnel, management personnel, etc., in the form of pop-up windows, text messages, emails, etc., to promptly conduct fault troubleshooting and handling to ensure the safe and stable operation of the photovoltaic power station.

[0066] In addition, the data analysis module 41 also includes: an energy efficiency ratio application test unit, which is used to collect DC power parameters and AC power parameters processed by the inverter in real time, monitor the environmental parameters of the photovoltaic power station at a preset sampling frequency, and obtain the imbalance and open circuit problems between photovoltaic strings through the analysis of the sampled data of each parameter, and compare the impacts of equipment and installation processes of different manufacturers on power generation and power generation efficiency under the same conditions to form an optimized power output improvement strategy. Among them, the environmental parameters at least include solar radiation intensity, temperature, photovoltaic panel temperature, air humidity, and wind speed, according to the measurement target and the requirements of parameter type and quantity.

[0067] It also includes setting up the cloud platform 4, including setting up data channels and configuring software function modules, for the cloud platform 4 to receive, store, and process data. The WEB and mobile application platforms are developed using JAVA and Android Studio. The SDK and API provided by the sensor 1 manufacturer and the cloud platform 4 service provider are used for the development of the detection platform interface display and various function modules. According to specific test requirements, corresponding detection models are established in the project planning and construction module 43, measurement points are arranged, and the calibrated sensor 1 is preset.

[0068] The calibration management module 42 is used to input the standard value and the measured value of the sensor 1 into the calibration system during calibration. Through data analysis, comparison, and processing of the detection data, the correction factor of each sensor 1 is obtained and stored in the system.

[0069] The project planning and construction module 43 is used to establish corresponding detection models according to specific test requirements, arrange measurement points at preset positions, and preset the calibrated sensor 1 to automatically generate a power generation calculation model for the power station when analyzing the test data of the photovoltaic power station;

[0070] The data query and display module 44 is used to receive the data transmitted by the data collector 2 in real time, automatically update and display the latest information. Users can view the data in various ways, the data is automatically saved, and efficient data retrieval is performed according to various forms of conditions;

[0071] The data management module 45 is used to generate the detection result report of each photovoltaic power station and perform statistical analysis on the data of multiple photovoltaic power stations;

[0072] The sensor management module 46 is used to establish a sensor library, input the information of the sensor 1 used in the detection activity, and upload the calibration certificate, traceability confirmation record, and correction factor. The correction factor is called in the detection model to correct the detection data. Among them, the sensor 1 information includes at least the device number, specification, range, and accuracy information.

[0073] The calibration management module 42 includes the following steps:

[0074] The standard value and the measured value are input for each sensor 1 separately in advance, where the standard value and the measured value are obtained by using a standard device and measuring the sensor 1 in the actual calibration environment;

[0075] Data analysis and comparison processing are performed on the input standard value and measured value to determine the deviation between the actual performance and the standard performance of the sensor 1;

[0076] Among them, determining the deviation between the actual performance and the standard performance of the sensor 1 includes the following steps:

[0077] Calibrate the same measurement point, standard value xB and the measured value x C , and obtain the deviation value Δx between the measured value and the standard value, expressed as: Δx = x C -x B .

[0078] Based on the results of data analysis and comparison processing, a correction factor for each sensor 1 is calculated, and the correction factor is used to represent the relationship adjustment parameter between the actual output and the ideal output of the sensor 1;

[0079] The calculated correction factor of each sensor 1 is stored in the system and is used to call the correction factor in the sensor management module 46 to correct the detection data. During the detection process, when the sensor 1 is used to collect data, the calibration management module 42 makes corresponding corrections to the collected data based on the pre-stored correction factor to improve the accuracy of the detection data.

[0080] The calibration management module 42 corrects the collected data accordingly according to the pre-stored correction factors, including the following steps:

[0081] Get the deviation ratio P, expressed as: P = Δx / x B ;

[0082] The correction factor k is calibrated according to the deviation ratio P, including:

[0083] If the deviation ratio P is positive, it means that the measured value is too large, and the correction factor should be k = 1-P;

[0084] If the deviation ratio P is negative, it means that the measured value is too small, and the correction factor should be k = 1 + P;

[0085] The correction factor k is stored in the calibration management module 42. When the sensor 1 is used for measurement, the measured value x is C Correction is made, and the corrected measured value x Z , expressed as: x Z =k×x C .

[0086] In summary, with the help of the above technical solution of the present invention, the following effects can be achieved:

[0087] 1. The data analysis module of the present invention constructs a calculation model based on a preset model and standards, deeply analyzes multiple key performance indicators such as system efficiency and inverter performance, automatically counts data at different times, can also monitor data anomalies in real time and trigger alarms in a timely manner. It can accurately determine the specific reasons for the decline in inverter performance, provide a strong basis for power station fault troubleshooting and maintenance, effectively ensure the safe and stable operation of the power station, reduce fault losses. At the same time, the energy efficiency ratio application test unit collects AC and DC power supply parameters and environmental parameters, analyzes photovoltaic string problems, and compares the impacts of equipment and installation processes of different manufacturers on power generation and efficiency, so as to form an optimized power output improvement strategy, which helps to improve the power generation efficiency and economic benefits of the photovoltaic power station.

[0088] 2. The present invention realizes precise sensor calibration and management, standardizes the sensor calibration process, calculates the correction factor by inputting the standard value and the measured value, effectively corrects the detection data, and improves the data accuracy. The sensor management module establishes a perfect sensor library, uniformly manages sensor information, facilitates equipment traceability and detection data correction, and ensures the reliability of detection results.

[0089] 3. The flexible system configuration and expansion of the present invention can quickly establish a detection model, arrange measuring points and preset sensors according to the scale, type and test requirements of different photovoltaic power stations, and the system has strong flexibility. At the same time, the SDK and API provided by sensor manufacturers and cloud platform service providers are used to develop the detection platform, which is convenient for function expansion and upgrade, reduces the system construction and adjustment costs. At the same time, there is a rich variety of sensors, which can comprehensively collect various data such as electrical parameters and environmental parameters of the photovoltaic power station. Cooperating with the modular data collector with independent A / D conversion and 4G data transmission functions, it realizes the real-time monitoring of the power station operation status. The 4G-LTE communication module ensures the stable and timely transmission of data to the cloud platform, avoids the wiring problems and environmental interference problems of traditional wired transmission, and ensures that operation and maintenance personnel can obtain accurate data in a timely manner for analysis and processing. In addition, it realizes convenient data query and management. The data query and display module supports multiple ways to view data, automatically saves and efficiently retrieves, facilitating users to obtain the required information. The data management module generates detection result reports and statistically analyzes the data of multiple power stations, providing data support for power station management and decision-making.

[0090] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will easily think of other implementation schemes of the present disclosure after considering the disclosure in the specification and the embodiments. This application aims to cover any variations, uses or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include the common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0091] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A modular detection system for a photovoltaic power station based on the Internet of Things and cloud platform, characterized in that, Including: A sensor (1), a data collector (2), a communication module (3), a cloud platform (4), and a web client (5). The data collector (2) collects the data of the sensor (1) in real time and transmits the collected data to the cloud platform (4) through the communication module (3). The cloud platform (4) stores and processes the data, and transmits the processed information to the web client (5) to be presented in the WEB user interface. The cloud platform (4) includes a data analysis module (41) and a calibration management module (42), where; The data analysis module (41) is used to construct a calculation model according to a preset detection model and detection standard, analyze performance indicators, and automatically count the maximum value, minimum value, average value, and cumulative value data of the performance indicators at different time periods. When data anomalies are detected, an alarm is triggered. Among them, the performance indicators at least include system efficiency, inverter performance, power quality, component efficiency, imbalance rate between photovoltaic strings, and system operation status; The calibration management module (42) is used to input standard values and measured values on the calibration system of the sensor (1) during calibration. Through data analysis and comparison processing of the detected data, the correction factor of each sensor (1) is obtained and stored in the system.

2. The modular detection system for a photovoltaic power station based on the Internet of Things and cloud platform according to claim 1, characterized in that, It also includes: A project planning and formation module (43), a data query and display module (44), a data management module (45), and a sensor management module (46), where; The project planning and formation module (43) is used to establish a corresponding detection model according to specific test requirements, arrange measurement points at preset positions, and preset calibrated sensors (1), and automatically generate a power generation calculation model of the power station when analyzing the test data of the photovoltaic power station; The data query and display module (44) is used to receive the data transmitted by the data collector (2) in real time, automatically update and display the latest information. Users can view the data in multiple ways, the data is automatically saved, and efficient data retrieval is performed according to various forms of conditions; The data management module (45) is used to generate a detection result report for each photovoltaic power station and perform statistical analysis on the data of multiple photovoltaic power stations; The sensor management module (46) is used to establish a sensor library, input the information of the sensors (1) used in the detection activities, and upload calibration certificates, traceability confirmation records, and correction factors. The correction factors are called in the detection model to correct the detection data. Among them, the information of the sensors (1) at least includes device number, specification, range, and accuracy information.

3. The modular detection system for a photovoltaic power station based on the Internet of Things and a cloud platform according to claim 1, wherein The data analysis module (41) further comprises: an energy efficiency ratio application test unit, which is used to collect direct current power supply parameters and alternating current parameters processed by the inverter in real time, to monitor environmental parameters of the photovoltaic power station at a preset collection frequency, and to obtain imbalance and short circuit problems between photovoltaic strings by analyzing the sampling data of each parameter, and to compare the effects of equipment and installation processes of different manufacturers on power generation and power generation efficiency under the same conditions, so as to form an optimized strategy for improving power generation, wherein the environmental parameters at least include solar radiation intensity, air temperature, photovoltaic panel temperature, air humidity and wind speed, according to the measurement target and the requirements of parameter type and quantity.

4. The modular detection system for a photovoltaic power station based on the Internet of Things and cloud platform according to claim 1, wherein The calibration management module (42) comprises the following steps: A standard value and a measured value are respectively inputted for each sensor (1) in advance, wherein the standard value and the measured value are obtained by using a standard device and measuring the sensor (1) in an actual calibration environment; Performing data analysis and comparison processing on the input standard value and the measured value to determine the deviation between the actual performance of the sensor (1) and the standard performance; Based on the results of data analysis and comparison processing, a correction factor of each sensor (1) is calculated, and the correction factor is used to represent the relationship adjustment parameter between the actual output and the ideal output of the sensor (1); The calculated correction factor of each sensor (1) is stored in the system and used to call the correction factor in the sensor management module (46) to correct the detection data. During the detection process, when the sensor (1) is used to collect data, the calibration management module (42) performs corresponding corrections on the collected data according to the pre-stored correction factor to improve the accuracy of the detection data.

5. The modular detection system for a photovoltaic power station based on the Internet of Things and a cloud platform according to claim 4, wherein Determining the deviation between the actual performance of the sensor (1) and the standard performance comprises the following steps: Calibrate the same measurement point, with the standard value x B and the measured value x C , and obtain the deviation value Δx between the measured value and the standard value, expressed as: Δx = x C - x B .

6. The modular detection system for a photovoltaic power station based on the Internet of Things and a cloud platform according to claim 5, characterized in that, The calibration management module (42) corrects the collected data accordingly according to the pre-stored correction factor, including the following steps: Obtain the deviation ratio P, expressed as: P = Δx / x B ; The correction factor k is calibrated according to the deviation ratio P, including: If the deviation ratio P is positive, it means that the measured value is too large, and the correction factor should be k = 1-P; If the deviation ratio P is negative, it means that the measured value is too small, and the correction factor should be k = 1 + P; The correction factor k is stored in the calibration management module (42). When using the sensor (1) for measurement, the measured actual value x obtained from the measurement is C corrected. The corrected measured value x Z is expressed as: x Z = k × x C .

7. The modular detection system for a photovoltaic power station based on the Internet of Things and cloud platform according to claim 1, characterized in that, The method also includes setting up the cloud platform (4), including setting up data channels and configuring software function modules, so as to enable the cloud platform (4) to receive, store and process data, using JAVA and Android Studio to develop WEB and mobile application platforms, using SDK and API provided by sensor (1) manufacturers and cloud platform (4) service providers to display the detection platform interface and develop various function modules, and establishing corresponding detection models in the project planning and assembly module (43) according to specific test requirements, arranging measurement points, and presetting calibrated sensors (1).

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