Transmission method of photovoltaic intelligent operation and maintenance data

Abstract

The invention discloses a transmission method of photovoltaic intelligent operation and maintenance data, which belongs to the technical field of data transmission, and comprises the following steps of: classifying operation and maintenance data groups according to the types and layout positions of sensors to form a data group set, and verifying by using waveform similarity to ensure the accuracy of data classification. On the basis, high-risk operation and maintenance data sets are identified, and the sampling frequency and the transmission priority are dynamically adjusted for different data set sets, so that under the condition that the bandwidth is limited, transmission of key data is preferentially guaranteed, the data transmission delay and the packet loss rate are reduced, and the real-time performance and the reliability of an operation and maintenance system are improved. According to the method, the correction sampling frequency of the data group set is calculated, and the sampling frequency and the transmission priority of the non-high-risk and high-risk data groups are dynamically adjusted based on the network condition, so that intelligent distribution of data transmission resources is realized. And meanwhile, the transmission guarantee of high-risk data is improved, the bandwidth pressure is effectively relieved, and the system construction cost is reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data transmission, and particularly relates to a transmission method for photovoltaic intelligent operation and maintenance data. BACKGROUND

[0002] With the rapid development of photovoltaic power generation technology, the scale of photovoltaic power stations is continuously expanding, and intelligent operation and maintenance systems play a key role in ensuring the efficient and stable operation of power stations. The photovoltaic intelligent operation and maintenance system collects device operation data, environmental data, operation and maintenance management data, and efficiency analysis data in real time through the deployment of various sensors. These data are important basis for state monitoring, fault diagnosis and efficiency optimization.

[0003] In practical applications, the transmission of photovoltaic operation and maintenance data faces many challenges. First, the number of sensors is large, and the amount of data is huge, resulting in high transmission bandwidth demand. When network resources are limited, data transmission delay or packet loss may occur, affecting the real-time performance of operation and maintenance. Second, the data collected by different sensors have different importance and risk characteristics. For example, some sensors are more likely to produce abnormal data due to installation location or their own characteristics, but existing transmission methods often lack effective identification and priority processing of high-risk data, resulting in critical data not being transmitted in time when bandwidth is insufficient. In addition, existing technologies usually use fixed sampling frequency and transmission strategy, which is difficult to adapt to dynamically changing network conditions and data characteristics, and is likely to cause resource waste or monitoring blind spots. Therefore, how to design a transmission method that can intelligently classify data, identify high-risk data, and dynamically adjust sampling frequency and transmission priority has become a key problem to improve the performance of photovoltaic intelligent operation and maintenance systems. SUMMARY

[0004] The purpose of the present application is to provide a transmission method for photovoltaic intelligent operation and maintenance data, which solves the problem that the existing technology lacks effective identification and priority processing of high-risk data, resulting in critical data not being transmitted in time when bandwidth is insufficient, and the problem that the existing technology usually uses fixed sampling frequency and transmission strategy, which is difficult to adapt to dynamically changing network conditions and data characteristics, and is likely to cause resource waste or monitoring blind spots.

[0005] The purpose of the present application can be achieved by the following technical solutions: A transmission method for photovoltaic intelligent operation and maintenance data, comprising the following steps: Step 1, collecting photovoltaic intelligent operation and maintenance data through the deployed sensors, numbering each sensor, and associating each sensor with its corresponding photovoltaic intelligent operation and maintenance data, each sensor corresponding to an operation and maintenance data group; The photovoltaic intelligent operation and maintenance data includes device operation data, environmental data, operation and maintenance management data, and efficiency analysis data; Step 2, according to the sensor type and the laying position condition, the operation and maintenance data group corresponding to the sensor is classified, a plurality of data group sets are obtained, different operation and maintenance data groups in each data group set have highly similar waveform characteristics; Step 3, identifying the high-risk operation and maintenance data group in each data group set; Step 4, for each data group set after removing the high-risk operation and maintenance data group, the modified sampling frequency corresponding to the data group set is calculated, and the sampling frequency and transmission priority of the operation and maintenance data group in each data group set is dynamically adjusted according to the network condition of the data transmission unit.

[0006] As a further scheme of the application, the method for judging whether two operation and maintenance data groups have highly similar waveform characteristics in Step 2 is: According to the time sequence, the waveforms corresponding to the two operation and maintenance data groups are drawn; After completing the time sequence alignment, the corresponding parameter values at the same time on the two waveforms are collected respectively, the parameter values collected by one waveform are marked as C1i, and the parameter values collected on the other waveform are marked as C2i; Then the specific value of is calculated, when the value is less than the preset threshold value, it is considered that the two have high similarity, otherwise, it is considered that the two do not have high similarity; wherein n is the number of samples.

[0007] As a further scheme of the application, the specific method for identifying the high-risk operation and maintenance data group in Step 3 is: For a data group set, according to the time sequence, the corresponding parameter values Djk are obtained in each operation and maintenance data group at the same time interval, wherein j is the index of the operation and maintenance data group, and k is the sample index; For the m parameter values collected at the same time, the standard deviation S of the m parameter values is calculated, and the corresponding Djk value is deleted in the order of |Djk-Dp| from large to small, until the standard deviation S is less than or equal to the preset value, and the operation and maintenance data group corresponding to the deleted Djk is marked; wherein Dp is the average value of the corresponding m Djk values; The number of times of marking each operation and maintenance data group is counted, when the number of times of marking an operation and maintenance data group is greater than a preset value, or the ratio of the number of times of marking to the total number of samples n1 is greater than a preset value, the operation and maintenance data group is determined as a high-risk operation and maintenance data group.

[0008] As a further scheme of the application, the method for calculating the modified sampling frequency in Step 4 is: The average value Ep of the number of times of marking each operation and maintenance data group in the data group set in Step 3 is obtained; According to the formula H1=H+Q Ep*αThe modified sampling frequency H1 is calculated, wherein when the calculated value of Ep*α is less than 1, the value is 1, H is the initial set sampling frequency of the sensor corresponding to the data set, Q is a set value greater than 1 and less than 1.2, and α is the importance influence parameter set for the data set.

[0009] As a further scheme of the application, the manner of dynamically adjusting the sampling frequency and the transmission priority in Step 4 is as follows: The non-high-risk operation and maintenance data set in each data set is sampled according to the updated modified sampling frequency, or is sampled according to the updated modified sampling frequency when the data transmission unit does not meet the low-delay and low-packet-loss requirements.

[0010] As a further scheme of the application, the manner of dynamically adjusting the sampling frequency and the transmission priority further includes: For the high-risk operation and maintenance data set in the data set, the sampling frequency is increased when the data transmission unit meets the low-delay and low-packet-loss requirements, and the sampling frequency is maintained or increased and the transmission priority is increased when the data transmission unit does not meet the requirements.

[0011] As a further scheme of the application, when the data transmission still does not meet the low-delay and low-packet-loss requirements after sampling according to the modified sampling frequency, the modified sampling frequency corresponding to each data set is proportionally reduced until the data transmission meets the requirements.

[0012] The application has the following beneficial effects: The application classifies operation and maintenance data sets according to the types and layout positions of sensors, forms data set groups, and verifies the data classification using waveform similarity, thereby ensuring the accuracy of data classification. On this basis, high-risk operation and maintenance data sets are identified, and the sampling frequency and transmission priority are dynamically adjusted for different data set groups, thereby preferentially guaranteeing the transmission of critical data under limited bandwidth, reducing data transmission delay and packet loss rate, and improving the real-time performance and reliability of the operation and maintenance system.

[0013] The application dynamically adjusts the sampling frequency and transmission priority of non-high-risk and high-risk data sets based on network conditions by calculating the modified sampling frequency of the data set group, thereby achieving intelligent allocation of data transmission resources. The method can automatically reduce the sampling frequency of non-critical data when the bandwidth is insufficient, reduce the data transmission amount, and improve the transmission guarantee of high-risk data, thereby effectively relieving the bandwidth pressure and reducing the system construction cost.

[0014] The application adopts a high-risk data identification mechanism based on standard deviation and label number, can accurately capture abnormal sensor data, and dynamically adjusts the sampling frequency combined with the importance influence parameter, so that the system can adapt to different sensor data update frequency and importance difference. In addition, by regularly updating the correction sampling frequency and the equal proportion reduction strategy, the adaptability of the system to network fluctuations and data changes is further enhanced, ensuring the continuity and accuracy of operation and maintenance monitoring. Improve the fine level of operation and maintenance management: distinguish between device operation data, environmental data, operation and maintenance management data, and efficiency analysis data, and implement independent processing on high-risk data. BRIEF DESCRIPTION OF DRAWINGS

[0015] The application will be further described below in conjunction with the accompanying drawings.

[0016] Figure 1 is a flowchart of a photovoltaic intelligent operation and maintenance data transmission method of the application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the application will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the application.

[0018] A photovoltaic intelligent operation and maintenance data transmission method, as shown in Figure 1 includes the following steps: Step 1, collecting photovoltaic intelligent operation and maintenance data through the laid sensors, numbering each sensor, and associating each sensor with its corresponding photovoltaic intelligent operation and maintenance data, each sensor corresponding to an operation and maintenance data group; The photovoltaic intelligent operation and maintenance data includes: Device operation data, such as voltage, current, etc. Environmental data, temperature, humidity, illumination, etc. Operation and maintenance management data, cleaning frequency, equipment replacement record, fault response time, etc. Efficiency analysis data, PR value, loss traceability analysis, etc. Step 2, according to the conditions such as sensor type and layout position, classify the operation and maintenance data group corresponding to the sensor, and obtain a plurality of data group sets; For example, temperature sensors laid in the same area should have the same or similar temperature values; For example, voltage sensors laid in the same area should have the same or similar power generation voltage because the light conditions in the same area are the same. When the working state is normal (normal working state here includes the normal operation of the sensor itself and the normal operation of the components monitored by the sensor), the different operation and maintenance data groups in each data set have highly similar waveform characteristics. Here, waveform refers to the graph formed by plotting the datasets in the operation and maintenance data group according to time sequence. The method for determining whether two operation and maintenance data groups have highly similar waveform characteristics is as follows: Draw the waveforms corresponding to the two maintenance data groups according to the time sequence; After timing alignment is completed, the corresponding parameter values ​​are collected at the same time on the two waveforms respectively. The parameter values ​​collected on one waveform are marked as C1i, and the parameter values ​​collected on the other waveform are marked as C2i. Then calculate The specific value is determined by the threshold value. If the value is less than the preset threshold, the two are considered to be highly similar. Otherwise, they are considered not to be highly similar. Where n is the number of samples, that is, the number of parameter values ​​collected on a waveform.

[0019] Step 3: Identify high-risk operation and maintenance data groups in each data group set; Specifically: For a set of data groups, obtain all operation and maintenance data groups within the set of data groups, and according to the time sequence, at the same time interval, obtain the corresponding parameter value Djk in each operation and maintenance data group; Where j ranges from 1 to m, where m is the number of operation and maintenance data groups in the corresponding data group set; k ranges from 1 to n1, where n1 is the number of samples collected, that is, the total number of parameter values ​​collected in an operation and maintenance data group. For m parameter values ​​collected at the same time, calculate the standard deviation S of these m parameter values; and delete the corresponding Djk values ​​in descending order of |Djk-Dp| until the standard deviation S is less than or equal to the preset value, and mark the operation and maintenance data group corresponding to the deleted Djk. Where Dp is the average of the corresponding m Djk values; After processing all samples, the number of times each operation and maintenance data group was labeled was counted; When an operations and maintenance data group is marked more than a preset number of times, the corresponding operations and maintenance data group is considered a high-risk operations and maintenance data group, or When the ratio of the number of times an operation and maintenance data group is marked to n1 is greater than a preset value, the corresponding operation and maintenance data group is considered to be a high-risk operation and maintenance data group. The second discrimination mode is that in actual operation, the data collected by different sensors has different data update frequencies, so when discriminating the high-risk operation data group, different data group sets may have different sample numbers corresponding to the collection, at this time, it is not suitable to set a quantity threshold or a unified quantity threshold, and the ratio threshold is more suitable for different sensor sampling conditions.

[0020] This step identifies the high-risk operation data group, and the identified high-risk operation data group is the data corresponding to the sensor whose collected data is more prone to abnormality. Due to installation position, sensor abnormality and other reasons, the probability of these data being abnormal is greater, so in the subsequent operation data transmission process, the collection frequency and transmission priority of these data are adjusted independently, which can ensure the timeliness of the transmission of these high-risk data when the bandwidth is insufficient; Step 4, first, the processing object in this step is each data group set after removing the high-risk operation data group; For a data group set, the number of times each operation data group is marked when processed according to the method in Step 3 is obtained, and the average value Ep of the number of times each operation data group is marked in a data group set is calculated; Then, according to the formula H1=H+Q Ep*α , the corrected sampling frequency H1 corresponding to the data group set is calculated; Where H is the initial set sampling frequency of the sensor corresponding to the data group set, and Q is a set value greater than 1 and less than 1.2, such as 1.08; Wherein, the greater Ep is, the greater the corrected sampling frequency is; Wherein, when Ep*alpha is less than 1, it is taken as 1; Alpha is the importance influence parameter set for the sensor corresponding to each data group set according to the importance of the parameters collected by the sensor and the importance of the position monitored by the sensor. For a data group set, the greater the corresponding importance influence parameter is, the greater the corresponding corrected sampling frequency is; The present application calculates the corrected sampling frequency corresponding to each data group set, and dynamically adjusts the data sampling frequency of the sensor corresponding to each data group set in the subsequent data transmission process, so as to ensure stable transmission of data resources and reduce the influence on abnormal monitoring under the condition of limited and fluctuating data transmission resources.

[0021] Example 2 The corrected sampling frequency corresponding to each data group set is updated at intervals, and the following method can be used for intelligent operation data transmission adjustment: 1. Non-high-risk operation and maintenance data groups in each data set are always sampled according to the updated corrected sampling frequency, thereby reducing the data transmission pressure on the data transmission unit used for data transmission and reducing the bandwidth construction requirements for data transmission. 2. Non-high-risk operation and maintenance data groups in each data group set are sampled according to the initially set corrected sampling frequency. When the data transmission unit does not meet the requirements of low latency and low packet loss rate, the non-high-risk operation and maintenance data groups in each data group set are sampled according to the updated corrected sampling frequency. For high-risk operation and maintenance data groups in the data set, increase their sampling frequency when the data transmission unit meets the requirements of low latency and low packet loss rate. When the data transmission unit does not meet the requirements of low latency and low packet loss rate, its sampling frequency is maintained or increased, and its transmission priority is increased. If the transmission of data obtained after sampling non-high-risk operation and maintenance data groups in each data set according to the updated corrected sampling frequency still does not meet the requirements of low latency and low packet loss rate, the corrected sampling frequency corresponding to each data set is reduced proportionally until the transmission of data meets the requirements of low latency and low packet loss rate. Specifically: S1. Every preset time interval T1, the corrected sampling frequency corresponding to each data set is reduced proportionally by a preset ratio. S2. If the data transmission still does not meet the requirements of low latency and low packet loss rate, then continue to execute S1 until the data transmission meets the requirements of low latency and low packet loss rate.

[0022] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A method for transmitting photovoltaic intelligent operation and maintenance data, characterized in that, The method comprises the following steps: Step 1: collecting photovoltaic intelligent operation and maintenance data through sensors, numbering each sensor, and associating each sensor with corresponding photovoltaic intelligent operation and maintenance data, each sensor corresponding to an operation and maintenance data group; The photovoltaic intelligent operation and maintenance data comprises device operation data, environmental data, operation and maintenance management data, and efficiency analysis data; Step 2: classifying operation and maintenance data groups corresponding to the sensors according to sensor types and layout positions, obtaining a plurality of data group sets, and each data group set having highly similar waveform characteristics between different operation and maintenance data groups; Step 3: identifying high-risk operation and maintenance data groups in each data group set; Step 4: calculating a corrected sampling frequency corresponding to each data group set after removing the high-risk operation and maintenance data groups, and dynamically adjusting the sampling frequency and transmission priority of the operation and maintenance data groups in each data group set according to network conditions of a data transmission unit. 2.The photovoltaic intelligent operation and maintenance data transmission method of claim 1, wherein, The method for determining whether two operation and maintenance data groups have highly similar waveform characteristics in Step 2 is as follows: Draw waveforms corresponding to the two operation and maintenance data groups in time sequence; After time sequence alignment, collect parameter values at the same time on the two waveforms, mark the parameter values collected on one waveform as C1i, and mark the parameter values collected on the other waveform as C2i; Then the specific value of is calculated, and when the value is less than a preset threshold, it is considered that the two have high similarity, otherwise, it is considered that the two do not have high similarity; wherein n is the number of samples. 3.The photovoltaic intelligent operation and maintenance data transmission method of claim 1, wherein, The specific method for identifying the high-risk operation and maintenance data groups in Step 3 is as follows: For a data group set, obtain corresponding parameter values Djk in each operation and maintenance data group at equal time intervals in time sequence, where j is the index of the operation and maintenance data group, and k is the sample index; For m parameter values collected at the same time, calculate the standard deviation S of the m parameter values, and sequentially delete corresponding Djk values in descending order of |Djk-Dp| until the standard deviation S is less than or equal to a preset value, and mark the operation and maintenance data group corresponding to the deleted Djk; wherein Dp is the average value of the corresponding m Djk values; Count the number of times each operation and maintenance data group is marked, and determine that the operation and maintenance data group is a high-risk operation and maintenance data group when the number of times the operation and maintenance data group is marked is greater than a preset value, or the ratio of the number of times the operation and maintenance data group is marked to the total number of samples n1 is greater than a preset value. 4.The photovoltaic intelligent operation and maintenance data transmission method of claim 1, wherein, The method for calculating the corrected sampling frequency in Step 4 is as follows: Obtain the average value Ep of the number of times each operation and maintenance data group in the data group set is marked in Step 3 processing; According to the formula H1=H+Q Ep*α A corrected sampling frequency H1 is calculated, where Ep*α is less than 1, then the value is 1, H is the initial set sampling frequency of the sensor corresponding to the data set, Q is a set value greater than 1 and less than 1.2, and α is the importance influence parameter set for the data set corresponding sensor. 5.The photovoltaic intelligent operation and maintenance data transmission method of claim 1, wherein, The method for dynamically adjusting the sampling frequency and transmission priority in Step 4 is as follows: Non-high-risk operation and maintenance data groups in each data group set are sampled according to the updated corrected sampling frequency, or are sampled according to the updated corrected sampling frequency when the data transmission unit does not meet the low delay and low packet loss rate requirements. 6.The photovoltaic intelligent operation and maintenance data transmission method of claim 5, wherein, The method for dynamically adjusting the sampling frequency and transmission priority further comprises: For high-risk operation and maintenance data groups in the data group set, increase the sampling frequency when the data transmission unit meets the low delay and low packet loss rate requirements, maintain or increase the sampling frequency when the data transmission unit does not meet the requirements, and increase the transmission priority.

7. The photovoltaic intelligent operation and maintenance data transmission method according to claim 6, characterized in that, When the data transmission still does not meet the low delay and low packet loss rate requirements after being sampled according to the modified sampling frequencies, the modified sampling frequencies corresponding to each data group set are proportionally reduced until the data transmission meets the requirements.

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