Photovoltaic information processing method and system

Abstract

The application discloses a photovoltaic information processing method and system, the method comprises the following steps: acquiring distribution information of equipment, constructing an information storage matrix according to the distribution information of the equipment; updating power information of each unit in the information storage matrix according to a statistical period; polling and detecting whether the power data of each unit in the information storage matrix compared with the average power data of adjacent units satisfies a deviation condition according to a statistical period as a granularity, the deviation condition is that there is a deviation greater than a first proportion; determining the equipment corresponding to the unit satisfying the deviation condition as a suspected faulty target equipment, marking and tracking the target equipment; continuously tracking the data of the target equipment for multiple statistical periods, when the power data of the target equipment compared with the power data of the equipment in the adjacent units presents a trend that the deviation expands or shrinks with time, determining that the fault type of the equipment is a rotating mechanism fault.

Description

Technical Field

[0001] This application relates to photovoltaic management technology, and in particular to a photovoltaic information processing method and system. Background Technology

[0002] With the development of clean energy technologies, photovoltaics has become an important part of clean energy supply. Typically, photovoltaic facilities need to be widely distributed to generate benefits. However, since they are usually located in underdeveloped areas, maintaining these areas requires considerable human resources. Currently, most equipment can rotate to follow sunlight, generating greater power output compared to fixed photovoltaic panels. If the rotation mechanism fails, the efficiency of the equipment will be greatly reduced. Some solutions use sensors to detect most rotation mechanism failures. However, installing sensors on each photovoltaic panel increases costs. Furthermore, some failure modes, such as mechanism detachment, cannot be detected by monitoring motor sensor status. Moreover, such faults are not particularly urgent, and even maintenance requires a certain timeframe. Therefore, the inventors believe that a lower-cost solution can be proposed that can distinguish between failures caused by the rotation mechanism. Summary of the Invention

[0003] The present invention aims to at least solve one of the technical problems existing in the prior art. To this end, the present invention provides a photovoltaic information processing method and system that can distinguish the type of fault in a rotating mechanism through power information without adding sensors.

[0004] On one hand, embodiments of this application provide a photovoltaic information processing method, including:

[0005] Obtain the distribution information of the devices, construct an information storage matrix based on the distribution information of the devices, and store the power information of the devices through the cells in the information storage matrix;

[0006] The power information of each unit in the information storage matrix is ​​updated according to the statistical period;

[0007] According to the statistical period, the power data of each unit in the information storage matrix is ​​polled to check whether the deviation condition is met compared with the average power data of the adjacent units. The deviation condition is that there is a deviation greater than a first proportion.

[0008] The equipment corresponding to the unit that meets the deviation condition is identified as the target equipment suspected of being faulty, and the target equipment is marked and tracked for monitoring.

[0009] The target equipment is continuously tracked for multiple statistical cycles. When the power data of the target equipment compared with the equipment in the adjacent unit shows a trend of increasing or decreasing deviation over time, the fault type of the equipment is determined to be a rotating mechanism fault.

[0010] In some embodiments, the deviation condition further includes a deviation that is less than a second ratio, wherein the second ratio is greater than a first ratio.

[0011] In some embodiments, when the power data of the target device relative to that of the devices in adjacent units shows a trend of increasing or decreasing deviation over time, the fault type of the device is determined to be a rotating mechanism fault. Specifically:

[0012] When the power data of the target device relative to that of the devices in the adjacent unit shows a trend of increasing or decreasing deviation over time within at least three statistical periods, the fault type of the device is determined to be a rotating mechanism fault.

[0013] In some embodiments, the method further includes the following steps: estimating the angle deviation corresponding to each statistical period by looking up an experience table based on the deviation ratio of power data collected in multiple different statistical periods, and estimating the position where the target device will remain after the fault based on the theoretical angle of normal equipment around the target device in each statistical period; the experience table records the power deviation ratio of different angle deviations relative to the optimal position.

[0014] In some embodiments, the process of estimating the post-fault dwell time of a target device includes:

[0015] Given the theoretical movement trajectory of the target equipment, calculate the theoretical position of the normal equipment in the statistical period, and based on the deviation ratio of the statistical period, look up the angle deviation from the empirical table to determine the estimated position of the target equipment on the theoretical movement trajectory in the statistical period. Calculate the average position of the estimated positions obtained in two or more statistical periods, and use this average position as the position where the target equipment will remain after the failure.

[0016] In some embodiments, the power generation loss for a future period is estimated based on the location of the target equipment after the failure, the revenue loss is estimated based on the power generation loss of all target equipment and the power generation loss caused by other failure types, and the need for advance maintenance is determined based on the revenue loss and the cost of one maintenance.

[0017] In some embodiments, determining whether advance maintenance is needed based on the cost of a single maintenance operation specifically includes:

[0018] Determine the cost of a single maintenance session, and then divide the cost of a single maintenance session by the maintenance interval to obtain the cost incurred in advance each day.

[0019] Calculate the revenue loss to be generated in the next few days. When the revenue loss is greater than a preset multiple of the cost of advance maintenance, a maintenance work order is generated, where the preset multiple is greater than 1.

[0020] In some embodiments, the method further includes the following step: when the deviation ratio of the target device relative to the average power data of adjacent devices remains basically unchanged in multiple statistical periods, the device is determined to be a local fault or local pollution, wherein the deviation ratio remaining basically unchanged means that the absolute value of the difference between the deviation ratios of two adjacent statistical periods is less than a third ratio.

[0021] On the other hand, this application provides a photovoltaic information processing system, including: a memory for storing a program; and a processor for loading the program to execute the photovoltaic information processing method.

[0022] On the other hand, embodiments of this application provide a photovoltaic information processing system, including:

[0023] The acquisition module is used to acquire the distribution information of the devices, construct an information storage matrix based on the distribution information of the devices, and store the power information of the devices through the cells in the information storage matrix;

[0024] The update module is used to update the power information of each unit in the information storage matrix according to the statistical period;

[0025] The polling module is used to poll and detect whether the power data of each unit in the information storage matrix meets the deviation condition compared with the average power data of the adjacent units, according to a statistical period as the granularity. The deviation condition is that there is a deviation greater than a first proportion.

[0026] The monitoring module is used to identify the equipment corresponding to the unit that meets the deviation conditions as the target equipment with suspected faults, and to mark and track the target equipment.

[0027] The determination module is used to continuously track the data of the target device for multiple statistical cycles. When the power data of the target device relative to the devices in the adjacent units shows a trend of increasing or decreasing deviation over time, the fault type of the device is determined to be a rotating mechanism fault.

[0028] Beneficial effects: This application embodiment obtains the distribution information of devices, constructs an information storage matrix based on the distribution information, and stores the power information of devices through the units in the information storage matrix; by constructing an information storage matrix and mapping the distribution information of devices through the power information of devices stored in the units in the information storage matrix, the ordered calculation can be simplified while ensuring a certain level of accuracy; then, the power information of each unit in the information storage matrix is ​​updated according to a statistical period; using a statistical period as the granularity, the power data of each unit in the information storage matrix is ​​polled to detect whether it meets the deviation condition compared with the average power data of the adjacent units, wherein the deviation condition is that there is a deviation greater than a first proportion; By identifying significant deviations, abnormal equipment is determined. Equipment in units meeting the deviation criteria is then identified as potential faulty targets, marked, and monitored. Through continuous data tracking over multiple statistical periods, when the power data of a target device shows a trend of increasing or decreasing deviation compared to adjacent units, the fault type is determined to be a rotating mechanism fault. This solution allows for the identification of equipment with rotating mechanism faults from power data even without dedicated sensors, providing more data support for the system and facilitating targeted maintenance, thus increasing maintenance efficiency. Furthermore, analysis can be performed on existing data without requiring additional hardware, making it easy to deploy and implement. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0030] Figure 1 This is a flowchart of a method provided in an embodiment of this application;

[0031] Figure 2 This is a system module block diagram provided in an embodiment of this application;

[0032] Figure 3 This is another system module block diagram provided in the embodiments of this application. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0034] Reference Figure 1 This application discloses a photovoltaic information processing method. It is understood that photovoltaic systems are typically deployed in large numbers in sunny locations. Due to land costs, these deployment locations are often relatively remote, resulting in certain inspection and maintenance costs. Common faults include, but are not limited to, line faults, partial / entire unit faults in photovoltaic panels, rotating mechanism faults, and panel contamination. This method primarily uses existing collected power data to analyze the fault types and identify rotating mechanism faults, providing maintenance personnel with more information.

[0035] The methods include:

[0036] S1. Obtain the distribution information of the devices, construct an information storage matrix based on the distribution information of the devices, and store the power information of the devices through the units in the information storage matrix.

[0037] Typically, related devices are placed in a planned manner according to areas, usually forming an array. Although the actual distribution is not strictly an N*M matrix, this solution only needs to consider the proximity between devices. Therefore, mapping the device distribution using a matrix facilitates computation without having to look up the distance relationships between devices each time. In other words, in this step, the devices are mapped to an A*B size information storage matrix based on their adjacency.

[0038] Typically, most areas have devices arranged in an array, making mapping relatively easy. The process involves polling the position of each device, identifying the adjacent devices of each mapped device, and then mapping those adjacent devices to their surrounding matrix cells. In some embodiments, pre-constructed blueprints showing the distribution can be used to graphically map these blueprints into the matrix. Alternatively, even with only device coordinates, mapping can be based on distances between devices. The first device placed in the matrix is ​​selected as the origin, and a coordinate system is established using this as the origin. Typically, the first device is a corner device. Mapping then begins with the closest device, filling the matrix sequentially. In practice, the matrix only needs to reflect the relative positions of the devices; the actual distance between them is not critical. The underlying principle is that within a certain distance, different devices theoretically receive the same amount of energy under normal conditions. Therefore, anomalies can be detected by comparing the differences between a device and its surrounding devices.

[0039] S2. Update the power information of each unit in the information storage matrix according to the statistical period.

[0040] Understandably, within each statistical period, the power generated by each device will be output, and this power can be an average value over a period of time. Understandably, power information can be affected by various factors, including: weather conditions (such as cloud cover), seasonal factors (current season's sunlight intensity), the device's receiving angle, as well as device contamination and functional integrity. The statistical period can be one hour, half an hour, or 15 minutes, etc. Understandably, the average output power within the statistical period is typically used as the data to fill the units in the information storage matrix.

[0041] S3. Using a statistical period as the granularity, poll and detect whether the power data of each unit in the information storage matrix meets the deviation condition compared with the average power data of the adjacent units. The deviation condition is that there is a deviation greater than a first proportion.

[0042] It's understandable that the location of a device in the matrix reflects its positional relationship with surrounding devices. Therefore, adjacent cells, being roughly close in location, will encounter similar influencing factors. Thus, anomalies can be detected by comparing the power differences between a device and its neighbors. Furthermore, pre-mapping devices into the matrix simplifies the algorithm. During polling, only data from the eight (or more) cells surrounding the current cell needs to be queried. Even if the matrix only roughly simulates the distribution, as long as the distance between adjacent cells in the matrix is ​​not too large, it will not affect the implementation of the solution.

[0043] S4. Identify the equipment corresponding to the unit that meets the deviation condition as the target equipment with suspected fault, and mark and track the target equipment.

[0044] Typically, when a device experiences a steering mechanism malfunction, contamination, or a partial failure at a certain location, it will reflect a power difference compared to surrounding healthy equipment within a specific statistical period. However, the power difference characteristics reflected in contamination or partial failures differ from those of steering mechanism malfunctions. Contamination or malfunctions produce relatively obvious abrupt changes. Furthermore, since malfunctions and contamination generally do not change over time, their reduction ratio relative to surrounding equipment remains relatively constant. In contrast, for steering mechanism malfunctions, because the equipment cannot rotate, a relatively significant power change will only occur after a period of time. Therefore, the type of malfunction can be distinguished from the contamination.

[0045] S5. Continuously track the data of the target equipment for multiple statistical cycles. When the power data of the target equipment compared with the equipment in the adjacent unit shows a trend of increasing or decreasing deviation over time, the fault type of the equipment is determined to be a rotating mechanism fault.

[0046] Understandably, to identify rotating mechanism faults through output power analysis, it's necessary to observe the changes in power data deviation compared to surrounding equipment over multiple statistical periods. Since the equipment's output is affected by sunlight, it only operates during the day. When the fault occurs early, such as at 9 AM, a power deviation typically develops gradually after the fault occurs, and this deviation tends to increase over time. This is because, as time passes and the faulty equipment stops rotating, the angle deviation from sunlight increases, resulting in a wider difference in output power compared to surrounding normal equipment. If the fault occurs later, near nighttime, its location is around sunset, and the power change may not be observable until the following day. In this case, the angle between sunlight and the equipment is approaching its optimal position, leading to a decreasing power difference. However, the initial difference will be more pronounced. Therefore, using this method, equipment with rotating mechanism faults can be distinguished from other types of faults.

[0047] In some embodiments, the deviation condition further includes a deviation less than a second proportion, where the second proportion is greater than a first proportion. Typically, with and without tracking, the power output deviation can reach 25% to 30%, meaning there will be a significant deviation in power output between the optimal and less optimal positions. However, this deviation still has an upper limit, unlike extremely large deviations. Therefore, if a certain deviation proportion is exceeded, it indicates a possible other type of fault. Therefore, in this solution, a deviation proportion less than the second proportion is set to determine if it is a possible fault in the rotating mechanism. Generally, the deviation between two values ​​can be calculated as (larger value - smaller value) / larger value.

[0048] In some embodiments, the method further includes the following step: when the deviation ratio of the target device relative to the average power data of adjacent devices remains basically unchanged in multiple statistical periods, the device is determined to have a local fault or local pollution. The deviation ratio remaining basically unchanged means that the absolute value of the difference between the deviation ratios of two adjacent statistical periods is less than a third ratio.

[0049] Understandably, since many factors affect output power, even normal equipment output power will fluctuate to some extent. Therefore, these deviations are usually within a certain threshold range. Thus, when the deviation ratio between two statistical periods is less than a certain threshold, it is considered that the deviation ratio remains constant. For example, setting the absolute value of the difference between the deviation ratios to be less than 2% is considered as maintaining constant values.

[0050] In some embodiments, when the power data of the target device relative to that of devices in adjacent units shows a trend of increasing or decreasing deviation over time, the fault type of the device is determined to be a rotating mechanism fault. Specifically:

[0051] When the power data of the target device relative to that of adjacent devices shows a trend of increasing or decreasing deviation over time within at least three statistical periods, the device is determined to be a rotating mechanism failure. Understandably, three statistical periods are generally needed to determine whether a decreasing or increasing trend exists. However, due to the uncertainty of environmental changes, the deviation over time is not necessarily completely monotonic. Individual points of change may appear. For example, within five periods, one period might show a decreasing or stable deviation, while the remaining four periods show a monotonically increasing trend. In such cases, the judgment condition can be relaxed; as long as the deviation shows an increasing or decreasing trend over time for Ns periods out of N periods, the condition is met. N and s can be set empirically, typically with s being a smaller number, such as one or two periods, while Ns must be at least greater than three. Users can determine the relevant judgment settings based on the size of the statistical periods.

[0052] In some embodiments, the following steps are also included: estimating the angle deviation corresponding to each statistical period by looking up an empirical table based on the proportion of the deviation of the power data collected in multiple different statistical periods, and estimating the position where the target device will remain after the fault based on the theoretical angle of the normal equipment around the target device in each statistical period.

[0053] The process of estimating the post-failure latency of the target equipment includes:

[0054] Given the theoretical movement trajectory of the target equipment, calculate the theoretical position of the normal equipment in the statistical period, and based on the deviation ratio of the statistical period, look up the angle deviation from the empirical table to determine the estimated position of the target equipment on the theoretical movement trajectory in the statistical period. Calculate the average position of the estimated positions obtained in two or more statistical periods, and use this average position as the position where the target equipment will remain after the failure.

[0055] It is understandable that the motion trajectory of both single-axis and multi-axis steering mechanisms is related to the direction of sunlight movement, and therefore, the trajectory is generally fixed. Thus, with a fixed trajectory, the center point of the equipment panel is taken as the equipment position. Since the equipment rotates around a point, that point will move along a certain trajectory on a motion surface. At different times of the day, there are optimal positions for the equipment. If the faulty equipment has an angular difference from this position, it will manifest as a loss in power output. Through experience, the losses caused by different angular differences can be determined. Therefore, the angular deviation can be determined by looking up a table based on the proportion of power deviation. With a fixed trajectory, the approximate position of the faulty equipment can be determined. By averaging the approximate positions estimated over multiple periods, the location where the faulty equipment is stuck can be obtained.

[0056] In some embodiments, based on the location of the target equipment after a failure, the power generation loss over a future period is estimated. Revenue loss is estimated based on the power generation loss of all target equipment and power generation losses caused by other failure types. Whether advance maintenance is necessary is determined based on the revenue loss and the cost of a single maintenance operation. Generally, due to the large maintenance area, maintenance is typically performed on an interval. Usually, maintenance is carried out on a fixed number of days, for example, once a week. Since the manpower and traffic light costs for each maintenance operation increase as the maintenance interval shortens, it is necessary to consider whether there are many faulty devices to determine whether advance maintenance is worthwhile.

[0057] In some embodiments, determining whether advance maintenance is needed based on the cost of a single maintenance operation specifically includes:

[0058] Determine the cost of a single maintenance session, and then divide the cost of a single maintenance session by the maintenance interval to obtain the cost incurred in advance each day.

[0059] Calculate the revenue loss to be generated in the next few days. When the revenue loss is greater than a preset multiple of the cost of advance maintenance, a maintenance work order is generated, where the preset multiple is greater than 1.

[0060] Understandably, by determining the cost of a single maintenance session and the maintenance interval, the cost of each maintenance session can be amortized to each day. Then, the cost incurred each day for advance maintenance can be calculated. If the additional expenses incurred by advance maintenance are less than the revenue loss caused by equipment failure, then advance maintenance is feasible. Typically, a certain coefficient can be set, for example, stipulating that advance maintenance should only be performed when the revenue loss exceeds 1.2 times the cost of advance maintenance. This allows for the setting of a certain redundancy value to avoid excessive maintenance.

[0061] Reference Figure 2A photovoltaic information processing system, comprising:

[0062] Memory, used to store programs;

[0063] A processor is used to load the program to execute the photovoltaic information processing method.

[0064] The system can communicate with peripherals through a communication interface and perform further processing.

[0065] Reference Figure 3 A photovoltaic information processing system, comprising:

[0066] The acquisition module is used to acquire the distribution information of the devices, construct an information storage matrix based on the distribution information of the devices, and store the power information of the devices through the cells in the information storage matrix;

[0067] The update module is used to update the power information of each unit in the information storage matrix according to the statistical period;

[0068] The polling module is used to poll and detect whether the power data of each unit in the information storage matrix meets the deviation condition compared with the average power data of the adjacent units, according to a statistical period as the granularity. The deviation condition is that there is a deviation greater than a first proportion.

[0069] The monitoring module is used to identify the equipment corresponding to the unit that meets the deviation conditions as the target equipment with suspected faults, and to mark and track the target equipment.

[0070] The determination module is used to continuously track the data of the target device for multiple statistical cycles. When the power data of the target device relative to the devices in the adjacent units shows a trend of increasing or decreasing deviation over time, the fault type of the device is determined to be a rotating mechanism fault.

[0071] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0072] Note that the above are merely preferred embodiments and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the appended claims.

Claims

1. A photovoltaic information processing method, characterized in that, Photovoltaic equipment is distributed in a matrix, including: The distribution information of the devices is obtained, and an information storage matrix is ​​constructed based on the distribution information of the devices. The power information of the devices is stored in the cells of the information storage matrix. The information storage matrix reflects the positional relationship of the devices, and adjacent cells in the information storage matrix are within a certain distance. The power information of each unit in the information storage matrix is ​​updated according to the statistical period; According to the statistical period, the power data of each unit in the information storage matrix is ​​polled to check whether the deviation condition is met compared with the average power data of the adjacent units in the information storage matrix. The deviation condition is that there is a deviation ratio greater than a first ratio; where adjacent units refer to the eight surrounding units. The equipment corresponding to the unit that meets the deviation condition is identified as the target equipment suspected of being faulty, and the target equipment is marked and tracked for monitoring. The target equipment is continuously tracked for multiple statistical cycles. When the power data of the target equipment compared with the equipment in the adjacent unit shows a trend of increasing or decreasing deviation over time, the fault type of the equipment is determined to be a rotating mechanism fault. The statistical period is one hour; deviations with an absolute value less than 2% are considered to remain unchanged. When the power data of the target device compared to that of the devices in adjacent units shows a trend of increasing or decreasing deviation over time, the fault type of the device is determined to be a rotating mechanism fault. Specifically: When the power data of the target device shows a trend of monotonically increasing or monotonically decreasing deviation ratio over time compared with the power data of the devices in the adjacent unit for at least three statistical periods, the fault type of the device is determined to be a rotating mechanism fault.

2. The method according to claim 1, characterized in that, The deviation condition also includes a deviation that is less than a second ratio, where the second ratio is greater than a first ratio.

3. The method according to claim 1, characterized in that, It also includes the following steps: Based on the deviation ratio of power data collected in multiple different statistical periods, the angle deviation corresponding to each statistical period is estimated by referring to an experience table, and the position where the target device will remain after the fault is estimated based on the theoretical angle of the normal equipment around the target device in each statistical period; the experience table records the power deviation ratio of different angle deviations relative to the optimal position.

4. The method according to claim 3, characterized in that, The process of estimating the post-failure latency of the target equipment includes: Given the theoretical movement trajectory of the target equipment, calculate the theoretical position of the normal equipment in the statistical period, and based on the deviation ratio of the statistical period, look up the angle deviation from the empirical table to determine the estimated position of the target equipment on the theoretical movement trajectory in the statistical period. Calculate the average position of the estimated positions obtained from two or more statistical periods, and use this average position as the position where the target equipment will remain after the failure.

5. The method according to claim 1, characterized in that, Based on the location of the target equipment after the failure, estimate the power generation loss for a future period of time. Based on the power generation loss of all target equipment and the power generation loss caused by other failure types, estimate the revenue loss. Based on the revenue loss and the cost of one maintenance, determine whether early maintenance is necessary.

6. The method according to claim 1, characterized in that, It also includes the following steps: when the deviation ratio of the target device relative to the average power data of adjacent devices remains basically unchanged in multiple statistical periods, the device is determined to be a local fault or local pollution. Here, the deviation ratio remaining basically unchanged means that the absolute value of the difference between the deviation ratios of two adjacent statistical periods is less than a third ratio.

7. A photovoltaic information processing system, characterized in that, include: Memory, used to store programs; A processor for loading the program to execute the photovoltaic information processing method as described in any one of claims 1-6.

Images