A fault recording system for a photovoltaic system and a configuration adaptive method

Abstract

This invention discloses a fault recording system and adaptive configuration method for photovoltaic systems. The adaptive method includes: designing corresponding recording configuration tables for different fault types; packaging data sampled by the fault recording acquisition device into an operation log and uploading it to the cloud; using the uploaded data as a sample set, parsing the operation data of the same type of fault during the occurrence period in the sample set to obtain feature information corresponding to the corresponding fault; establishing a fault prediction model based on the triggering causes of different types of faults; predicting the type of the next fault based on the real-time data collected by the fault recording acquisition device; and issuing the corresponding recording configuration table based on the predicted fault type to configure the fault recording acquisition device. The fault recording system and adaptive configuration method provided by this invention improve the effectiveness and relevance of the data obtained from fault recording, facilitating the location and analysis of fault problems.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, and in particular to a fault recording system and configuration adaptive method for photovoltaic systems. Background Technology

[0002] Existing fault recording methods are constrained by hardware storage capacity, leading to limitations in the number of sampled variables, recording time, and sampling accuracy. These methods require pre-setting parameters such as sampling variables and frequency. When a pre-defined fault is detected, recording and storage are performed according to the preset configuration and parameters. However, different fault types require different recording variables, times, and accuracy, making it impossible to analyze all fault types using a single set of preset waveforms. Therefore, an adaptive fault recording method is urgently needed. Summary of the Invention

[0003] To overcome the shortcomings of existing technologies, this invention provides a fault recording system and configuration adaptive method for photovoltaic systems, the specific technical solution of which is as follows:

[0004] On the one hand, an adaptive method for fault recording configuration in photovoltaic systems is provided, comprising the following steps:

[0005] S1. Design corresponding waveform recording configuration tables based on the characteristics of different fault types;

[0006] S2. Package the data sampled by the fault recording and acquisition equipment into an operation log and upload it to the cloud;

[0007] S3. Use the uploaded data as a sample set, and analyze the operational data of the same type of fault during the same period in the sample set to obtain the feature information corresponding to the fault.

[0008] S4. Based on the triggering causes of different types of faults, establish a fault prediction model and quantify the correlation between different fault types and their corresponding feature information.

[0009] S5. The fault prediction model predicts the type of fault that will occur next based on the data collected in real time by the fault recording and acquisition device.

[0010] S6. Based on the predicted fault type, issue the corresponding waveform recording configuration table to configure the fault waveform recording acquisition device.

[0011] Furthermore, in step S2, the cloud periodically summarizes and sorts the uploaded data, extracts various fault information, including fault parameters, fault time, and fault location. Different fault types are classified and summarized according to the fault parameters, and the operation logs of the fault time and the time period before and after it are summarized to form a corresponding database.

[0012] Furthermore, in step S3, during the analysis process, if the data corresponding to the fault parameter exceeds a preset threshold, it is used as feature information.

[0013] Furthermore, in step S4, a fault prediction model is established using expert experience and / or the analytic hierarchy process.

[0014] Furthermore, the fault prediction model establishes a weight relationship between each fault type and each feature information, and assigns a corresponding weight to each fault type's associated feature information;

[0015] When the feature information obtained after processing the real-time collected data is a single feature, if the feature information corresponds to multiple fault types at the same time, the weight of the feature information in the corresponding fault type is compared, and the fault type corresponding to the maximum weight is taken as the predicted fault type.

[0016] When the real-time collected data is processed to obtain multiple feature information, one or more fault types are associated with the feature information, the sum of the weights of the corresponding feature information in the corresponding fault type is calculated, and the fault type corresponding to the maximum value of the sum of weights is taken as the predicted fault type.

[0017] Furthermore, in step S5, if the range of data collected in real time does not contain feature information after parsing, it is necessary to further expand the range of data collected this time and upload it until the uploaded data contains feature information after parsing, so that the fault prediction model can find the associated fault type and make a judgment and prediction based on the feature information.

[0018] Furthermore, after step S6, the following is also included:

[0019] S7. If the predicted fault type is different from the actual fault type, the relevant data of the actual fault type is processed and added to the sample set to correct the fault prediction model.

[0020] Furthermore, the waveform recording configuration table includes waveform recording parameters, waveform recording time, and waveform recording interval.

[0021] On the other hand, a fault recording system for a photovoltaic system is provided, including a fault recording acquisition device, a local information uploading device, and a cloud. The fault recording acquisition device is communicatively connected to the local information uploading device, and the cloud is communicatively connected to both the fault recording acquisition device and the local information uploading device. The fault recording acquisition device samples the inverter side of the photovoltaic system according to a recording configuration table issued by the cloud. The local information uploading device processes the sampled data from the fault recording acquisition device and uploads it to the cloud. The cloud establishes a fault prediction model based on the uploaded data to predict the next fault type and issues a recording configuration table corresponding to that fault type.

[0022] Furthermore, the local information uploading device includes a DSP unit and an ARM processor. The DSP unit is used to package the sampling data of the fault recording acquisition device into an operation log. The ARM processor is communicatively connected to the DSP unit and the cloud, and is used to transmit the operation log to the cloud.

[0023] Furthermore, the fault recording system also includes a host computer, which is connected to the local information uploading device and the cloud. The host computer is used to remotely monitor the fault recording data of the photovoltaic system and upload it to the cloud.

[0024] Furthermore, the fault recording system also includes a Flash unit, which is communicatively connected to the DSP unit and is used to locally store the sampling data of the fault recording acquisition device.

[0025] The present invention has the following advantages: it improves the effectiveness and relevance of fault recording data, and facilitates the location and analysis of fault problems. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the adaptive fault recording configuration method for photovoltaic systems provided in an embodiment of the present invention.

[0027] Figure 2 This is a schematic diagram of the framework of a fault recording system for photovoltaic systems provided in an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0030] In one embodiment of the present invention, an adaptive method for fault recording configuration of a photovoltaic system is provided, see [link to relevant documentation]. Figure 1 This includes the following steps:

[0031] S1. During the design phase, based on actual working conditions and the characteristics of different fault types, a corresponding waveform recording configuration table is designed; the waveform recording configuration table includes waveform recording parameters, waveform recording time, waveform recording interval, etc.

[0032] For example, for transient faults like inverter-side overcurrent, engineering projects focus not only on physical quantities directly related to the fault, such as grid-side voltage sampling values, grid-connected current sampling values, and BUS voltage sampling values, but also require the highest possible data accuracy at the time of the fault. Therefore, the parameters in the waveform recording configuration table are set as fault-related parameters, the sampling frequency is set to a maximum of 16kHz, the sampling frequency is set by the waveform recording interval, and the sampling and analysis period during the fault is set by the waveform recording time. However, for faults like GFCI, which require a relatively long time to determine, more attention is paid to GFCI sampling than to the various grid-side parameters, and a longer waveform recording time is desired to clearly observe the data fluctuation trend. Therefore, the parameters in the waveform recording configuration table are set as GFCI fault-related parameters, and the sampling frequency is set to 1kHz.

[0033] S2. Package the data sampled by the fault recording and acquisition equipment into an operation log and upload it to the cloud;

[0034] The cloud platform periodically summarizes and organizes the uploaded data, extracts various fault information, including fault parameters, fault time, and fault location. Different fault types are classified and summarized according to the fault parameters, and the operation logs of the fault time and the time period before and after it are summarized to form a corresponding database.

[0035] Specifically, during operation, the DSP unit packages the sampled data (including fault information and fault data) into an operation log and transmits it via ARM through a pass-through mechanism. The log is then uploaded to the cloud via WiFi or 4G. The backend periodically summarizes and organizes the uploaded data, primarily extracting fault information, including fault parameters, fault time, and fault location. Different fault types are categorized and summarized based on the fault parameters. The operation logs for the fault time and the period before and after it are also summarized, including fault parameters and fault location information. If a certain fault type occurs too frequently in the summary, indicating a higher likelihood of that type of fault, the data for the parameters involved in that fault type can be increased to accurately predict the next occurrence of that type of fault, thus further building a database.

[0036] S3. Use the uploaded data as a sample set, and analyze the operational data of the same type of fault during the same period in the sample set to obtain the feature information corresponding to the fault.

[0037] Specifically, the database formed in the above process is used as a sample set. Within this sample set, operational data at the time of occurrence of similar faults is analyzed. Significantly changing physical quantities or information are extracted and listed as fault-related feature information, such as time period, weather conditions, equipment temperature, historical faults, etc. There are two methods for identifying significantly changing physical quantities: one is to directly judge based on the data, identifying obviously abnormal data, such as parameters exceeding normal values ​​or preset thresholds within a certain range; this method is suitable for situations with a small amount of data. The other method is to plot the data at each time point and identify obviously abnormal data based on the graph; this method is suitable for situations with a large amount of data.

[0038] S4. Based on the triggering causes of different types of faults, establish a fault prediction model and quantify the correlation between different fault types and their corresponding feature information.

[0039] Specifically, based on theoretical experience regarding the causes of different types of faults and the data content of the sample set, an algorithm is constructed using expert experience and analytic hierarchy process to obtain a fault prediction model. The fault prediction model establishes and quantifies the correlation between each fault type and each feature information, that is, it establishes the weight relationship between each fault type and each feature information. Because different fault types may involve the same feature information, it is necessary not only to judge the weight of this feature information, but also to combine the weight of other feature information to comprehensively judge the next fault type.

[0040] Specifically, the fault prediction model establishes a weighted relationship between each fault type and each feature information, assigning a corresponding weight to each fault type's associated feature information. For example, in fault A, feature information a and feature information b have weights of 0.6 and 0.4, respectively; in fault B, feature information a and feature information c have weights of 0.7 and 0.3, respectively.

[0041] S5. The fault prediction model predicts the type of fault that will occur next based on the data collected in real time by the fault recording and acquisition device.

[0042] When the feature information obtained after processing the real-time collected data is a single feature, if the feature information corresponds to only one fault type, then that fault type is taken as the predicted fault type; if the feature information corresponds to multiple fault types, then the weight of the feature information in the corresponding fault types is compared, and the fault type corresponding to the maximum weight is taken as the predicted fault type; for example, if the only feature information obtained is feature b, then the predicted fault type is A; if the only feature information obtained is feature a, since 0.7 is greater than 0.6 in the corresponding weight, the predicted fault type is B.

[0043] When multiple feature information results are obtained from the real-time collected data, these feature information are associated with one or more fault types. The sum of the weights of the corresponding feature information within each fault type is calculated, and the fault type corresponding to the maximum sum of weights is taken as the predicted fault type. For example, if only feature information a and feature information b are obtained, and the sum of the weights of the corresponding feature information in fault A is 1, while the sum of the weights of the corresponding feature information in fault B is 0.7, then the predicted fault type is A. It is important to note that the association is considered complete if the fault type contains even one of the obtained feature information.

[0044] If the range of data collected in real time does not contain feature information after parsing, the range of data collected needs to be further expanded, and steps S2-S5 need to be repeated until the uploaded data contains feature information after parsing. This allows the fault prediction model to find the associated fault type and make a judgment and prediction based on the feature information. It should be noted that expanding the range of data collected in this instance involves extending the data range both forward and backward from the original data interval, and then uploading and processing the expanded data again.

[0045] Before using the fault prediction model for prediction, the total amount of data in the sample set needs to be verified. If the total amount of data in the sample set is lower than the set value, and there are sample waveform data for each type of fault, then steps S2-S5 are repeated to supplement the sample data. If the total amount of data in the sample set is higher than or equal to the set value, then the type of fault that will occur next is predicted.

[0046] Specifically, when making predictions, it is important to note that if the total amount of data in the sample set is small, the prediction model may not be able to correctly predict the type of failure that will occur next. In this case, it is necessary to repeat the process from S2 to S5 above to expand the sample set, improve the feature information, and re-establish and quantify the correlation between each failure type and each feature information to improve the reliability and accuracy of the prediction results. If the total amount of data in the sample set is sufficient, the weight relationship between the failure type and each feature information is used to determine the next failure type, and the weights of each feature information are sorted to predict the next failure type.

[0047] S6. Based on the predicted fault type, issue the corresponding waveform recording configuration table to configure the fault waveform recording acquisition device.

[0048] To further improve the accuracy of subsequent predictions, the following steps are included after step S6:

[0049] S7. If the predicted fault type is different from the actual fault type, the relevant data of the actual fault type is processed and added to the sample set to correct the fault prediction model.

[0050] In one embodiment of the present invention, a fault recording system for a photovoltaic system is provided, including a fault recording acquisition device, a local information uploading device, and a cloud platform. The fault recording acquisition device is communicatively connected to the local information uploading device, and the cloud platform is communicatively connected to both the fault recording acquisition device and the local information uploading device. The fault recording acquisition device samples the inverter side of the photovoltaic system according to a recording configuration table issued by the cloud platform. The local information uploading device processes the sampled data from the fault recording acquisition device and uploads it to the cloud platform. The cloud platform establishes a fault prediction model based on the uploaded data to predict the next fault type and issues a recording configuration table corresponding to that fault type. Fault recording acquisition devices at different sites can upload data to the same cloud platform through their respective local information uploading devices. The cloud platform can utilize data from multiple sites to enrich the database and improve the prediction model. Furthermore, the cloud platform can simultaneously issue configuration commands to multiple fault recording acquisition devices to achieve recording configuration across the entire area, making it particularly suitable for fault recording of photovoltaic inverters.

[0051] Among them, see Figure 2 The local information uploading device includes a DSP unit and an ARM processor. The DSP unit and the ARM processor are connected via SCI / CAN. The DSP unit is used to package the sampling data of the fault recording and acquisition device into an operation log. The ARM processor is connected to the cloud via GPRS / WiFi / LAN. The ARM processor is used to transmit the operation log to the cloud.

[0052] The fault recording system also includes a host computer and a Flash unit. The host computer is connected to the cloud and transmits information with the ARM processor via the 485 communication protocol or USB. The host computer is used to remotely monitor the fault recording data of the photovoltaic system and upload it to the cloud to supplement the database. The Flash unit is connected to the DSP unit via the SPI protocol and is used to locally store the sampling data of the fault recording acquisition device.

[0053] The fault recording system and configuration adaptive method for photovoltaic systems provided by this invention improve the effectiveness and relevance of the fault recording data, and facilitate the location and analysis of fault problems.

[0054] The above description is merely a preferred embodiment of the present invention and does not limit its patent scope. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, whether directly or indirectly applied to other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An adaptive method for fault recording configuration in photovoltaic systems, characterized in that, Includes the following steps: S1. During the design phase, corresponding waveform recording configuration tables are designed according to the characteristics of different fault types. The waveform recording configuration tables include waveform recording parameters, waveform recording time, and waveform recording interval. S2. Package the data sampled by the fault recording and acquisition device into an operation log and upload it to the cloud. The cloud periodically summarizes and sorts the uploaded data, extracts various fault information, including fault parameters, fault time and fault location. Different fault types are classified and summarized according to the fault parameters, and the operation logs within the fault time and the time period before and after it are summarized to form a corresponding database. For fault types that occur multiple times in the summary, data of the parameters involved in the fault type are uploaded to further form a database. S3. Using the database as a sample set, analyze the operating data of the same type of fault during the time period in the sample set to obtain the feature information corresponding to the corresponding fault. If the data corresponding to the fault parameter exceeds the preset threshold, it is used as feature information. Alternatively, the data at each time moment is plotted into a graph, and the abnormal data is determined as feature information based on the graph. S4. Based on the triggering causes of different types of faults, establish a fault prediction model and quantify the correlation between different fault types and their corresponding feature information, including establishing the weight relationship between each fault type and each feature information, and assigning corresponding weights to the associated feature information for each fault type. S5. The fault prediction model predicts the type of the next fault based on the real-time data collected by the fault recording acquisition device, including: when the feature information obtained after processing the real-time data is a single feature, if the feature information corresponds to multiple fault types, the weight of the feature information in the corresponding fault types is compared, and the fault type corresponding to the maximum weight is taken as the predicted fault type; when the feature information obtained after processing the real-time data is multiple feature information, the feature information is associated with one or more fault types, the weights of the corresponding feature information in the corresponding fault type are calculated, and the fault type corresponding to the maximum weight is taken as the predicted fault type. S6. Based on the predicted fault type, issue the corresponding waveform recording configuration table to configure the fault waveform recording acquisition device.

2. The adaptive fault recording configuration method for photovoltaic systems according to claim 1, characterized in that, In step S4, a fault prediction model is established using expert experience and / or the analytic hierarchy process.

3. The adaptive fault recording configuration method for photovoltaic systems according to claim 1, characterized in that, When the feature information obtained from the real-time collected data is a single feature, if the feature information corresponds to only one fault type, then that fault type is taken as the predicted fault type.

4. The adaptive fault recording configuration method for photovoltaic systems according to claim 1, characterized in that, In step S5, if the range of data collected in real time does not have feature information after parsing, it is necessary to further expand the range of data collected this time and upload it until the uploaded data obtains feature information after parsing, so that the fault prediction model can find the associated fault type and make a judgment and prediction based on the feature information.

5. The adaptive fault recording configuration method for photovoltaic systems according to claim 1, characterized in that, The process after step S6 also includes: S7. If the predicted fault type is different from the actual fault type, the relevant data of the actual fault type is processed and added to the sample set to correct the fault prediction model.

6. A fault recording system for photovoltaic systems, characterized in that, The adaptive fault recording configuration method for photovoltaic systems as described in claim 1 includes a fault recording acquisition device, a local information uploading device, and a cloud platform. The fault recording acquisition device is communicatively connected to the local information uploading device, and the cloud platform is communicatively connected to both the fault recording acquisition device and the local information uploading device. The fault recording acquisition device samples the inverter side of the photovoltaic system according to a recording configuration table issued by the cloud platform. The local information uploading device processes the sampled data from the fault recording acquisition device and uploads it to the cloud platform. The cloud platform establishes a fault prediction model based on the uploaded data to predict the next fault type and issues a recording configuration table corresponding to that fault type.

7. The fault recording system for photovoltaic systems according to claim 6, characterized in that, The local information uploading device includes a DSP unit and an ARM processor. The DSP unit is used to package the sampling data of the fault recording acquisition device into an operation log. The ARM processor is communicatively connected to the DSP unit and the cloud, and is used to transmit the operation log to the cloud.

8. The fault recording system for a photovoltaic system according to claim 6, characterized in that, It also includes a host computer, which is connected to the local information uploading device and the cloud. The host computer is used to remotely monitor the fault recording data of the photovoltaic system and upload it to the cloud.

9. The fault recording system for a photovoltaic system according to claim 7, characterized in that, It also includes a Flash unit, which is communicatively connected to the DSP unit and is used to locally store the sampling data of the fault recording acquisition device.

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