Distributed power monitoring system based on internet of things

Abstract

The application discloses a kind of distributed power monitoring systems based on Internet of Things, belong to intelligent photovoltaic technical field, this system is by the distribution of fault cell piece on the surface of photovoltaic board to analyze to evaluate the influence of hot spot in photovoltaic board on photovoltaic board working condition, to carry out timely maintenance replacement to the photovoltaic board with more damaged cell piece quantity and position concentration, avoid the combustion accident caused by the serious hot spot effect of photovoltaic board to cause greater economic loss, and the maintenance purpose of photovoltaic cell board is stronger, can effectively reduce meaningless maintenance alarm.In addition, compared with the specific investigation mode for photovoltaic board in the prior art, the present application can explore the abnormalities existing in the photovoltaic panels in a sub-region of a photovoltaic power station, and promptly discover regional problems, thereby avoiding further expansion of losses.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent photovoltaic technology, and particularly relates to a distributed power monitoring system based on the Internet of Things. BACKGROUND

[0002] The distributed power system is relative to the traditional centralized power supply mode, and the power generation system is arranged in the vicinity of the user in a small scale, small capacity and modular manner, and can independently output electric energy. The system can not only improve the energy utilization efficiency and reduce the power transmission and distribution loss, but also improve the safety and reliability of power supply.

[0003] The distributed power system has various types, and the distributed power generation technology based on renewable energy such as solar photovoltaic is a common distributed power generation system. In order to ensure the output voltage and current, there is a series and parallel relationship between many photovoltaic panels in the photovoltaic power station, which also causes the whole output to be affected when a photovoltaic panel has a problem. Therefore, in the prior art, the photovoltaic panels in the photovoltaic power station need to be monitored and managed to timely find the photovoltaic panels with problems. However, in the prior art, the photovoltaic panels with problems can only be found when there is obvious output abnormality. The hidden dangers of the photovoltaic panels and the system in which the photovoltaic panels are located are difficult to be found in time, that is, the prevention effect is poor. In order to solve the above problems, the application provides the following technical scheme. SUMMARY

[0004] The application aims to provide a distributed power monitoring system based on the Internet of Things, which solves the problem that the safety hidden danger of the photovoltaic panels in the photovoltaic power station caused by geographical factors and external influences cannot be found and solved in time in the prior art.

[0005] The application can be achieved by the following technical scheme.

[0006] A distributed power monitoring system based on the Internet of Things, comprising: a real scene image acquisition unit, which acquires image information of photovoltaic panels in a photovoltaic power station;

[0007] an infrared image acquisition unit, which acquires infrared image information of the photovoltaic panels in the photovoltaic power station;

[0008] an image analysis unit, which is used for analyzing the image information to obtain the position and area of the shielding object on each photovoltaic panel, and is also used for analyzing the infrared image information to obtain the position and area of the hot spot on the photovoltaic panel;

[0009] a monitoring center, which is used for receiving the analysis result data of the image analysis unit and identifying the abnormal area in the photovoltaic power station according to the analysis result.

[0010] an alarm unit for issuing an alarm information;

[0011] The method for the monitoring center to identify the abnormal area part in the photovoltaic power station is as follows:

[0012] Step one, mark the cell belonging to the hot spot coverage area but not belonging to the shelter coverage area as an abnormal cell;

[0013] Step two, when a cell is marked as an abnormal cell for k times continuously, mark it as a fault cell; wherein k is a preset value;

[0014] Step three, divide the photovoltaic power station into several sub-areas, obtain the fault parameter G of each photovoltaic panel in a sub-area in each inspection cycle in the past m inspection cycles, obtain the average value Gj of the fault parameter G of each photovoltaic panel obtained after an inspection cycle, wherein j takes the value of 1 to m, and when the Gj corresponding to a sub-area appears to increase by a certain degree from the average level, mark the sub-area as an abnormal sub-area;

[0015] Step four, when a sub-area is marked as an abnormal sub-area, the alarm unit issues an alarm information to prompt the staff to troubleshoot the problem.

[0016] As a further scheme of the present application, in step two, when the fault parameter G corresponding to a photovoltaic panel is greater than or equal to a preset value Gy, mark the corresponding photovoltaic panel as a high-risk photovoltaic panel, wherein Gy is a preset empirical coefficient;

[0017] When a photovoltaic panel is marked as a high-risk photovoltaic panel, the alarm unit issues an alarm information to remind the staff to timely maintain the corresponding high-risk photovoltaic panel.

[0018] As a further scheme of the present application, the system further comprises a cleaning unit for cleaning the photovoltaic panel with a shelter, the image analysis unit sends the analyzed shelter position information to the cleaning unit, and the cleaning unit focuses on cleaning the corresponding position on the photovoltaic panel when cleaning the corresponding photovoltaic panel.

[0019] As a further scheme of the present application, the calculation method of the fault parameter G of the photovoltaic panel is as follows:

[0020] For a photovoltaic panel, a plane rectangular coordinate system is established with the plane in which the photovoltaic panel is located, and the coordinates (xi, yi) of the geometric center of each fault cell in the coordinate system are obtained, wherein i takes the value of 1 to n, and n is the number of fault cells on the corresponding photovoltaic panel;

[0021] Calculate the standard deviation Z1 of n corresponding xi values and the standard deviation Z2 of n corresponding yi values; then according to the formula G=n*e -Z Calculate the fault parameter G corresponding to the photovoltaic panel, and e is the natural constant; wherein Z=0.5*Z1+0.5*Z2.

[0022] As a further scheme of the present application, the division method of the sub-area is that the photovoltaic panels in the same sub-area are distributed continuously and have the same altitude, or the photovoltaic panels in the same sub-area are distributed continuously and are in the same plane, or the photovoltaic panels in the same sub-area are connected to the same inverter.

[0023] As a further scheme of the present application, the method for obtaining the abnormal sub-area is:

[0024] A coordinate system is established with time as the horizontal axis and numerical value as the vertical axis, and a change curve of the average value Gj of the fault parameter corresponding to the sub-area is represented in the coordinate system, and the area Ma of the closed figure surrounded by the change curve, x=1, x=m and the x-axis is calculated;

[0025] The change curve corresponding to each sub-area and the area Ma of the closed figure surrounded are calculated in turn according to the above method, wherein a takes the value of 1 to f, and f is the number of the divided sub-areas;

[0026] The sub-area corresponding to the Ma with a larger deviation from Mp is marked as an abnormal sub-area, and Mp is the average value of the f corresponding Ma.

[0027] As a further scheme of the present application, the specific method for obtaining the abnormal sub-area further comprises:

[0028] According to the formula The dispersion coefficient F corresponding to the f Ma values is calculated, and when F is greater than or equal to the preset parameter Fy, the corresponding Ma is deleted in the order from large to small according to |Ma-Mp|; and after deleting one Ma, the dispersion coefficient F of the remaining Ma is calculated and updated according to the above formula until the corresponding F satisfies the condition of being less than the preset parameter Fy;

[0029] The sub-area corresponding to the Ma greater than Mp deleted according to the above process is marked as an abnormal sub-area.

[0030] As a further scheme of the present application, the identification of the abnormal sub-area is performed once every m periods of related data every preset number of periods.

[0031] The beneficial effects of the present application are:

[0032] 1. The application evaluates the influence of hot spots on the working state of photovoltaic panels by analyzing the distribution of faulty cell pieces on the surface of the photovoltaic panels, thereby timely repairing and replacing photovoltaic panels with a large number of damaged cell pieces and concentrated locations, avoiding greater economic losses caused by combustion accidents caused by serious hot spot effects of photovoltaic panels, and the maintenance purpose of photovoltaic panels is stronger, which can effectively reduce meaningless maintenance alarms.

[0033] 2. Compared with the specific checking mode of the prior art for photovoltaic panels, the application can explore the abnormalities existing in the photovoltaic panels in a sub-region of a photovoltaic power station, such as damage to photovoltaic panel cell pieces caused by inverter failure, cell piece damage caused by photovoltaic panel deformation due to installation problems of photovoltaic panel supports on slopes, etc. These problems are difficult to directly observe. The application can timely discover regional problems by long-term monitoring of photovoltaic panels in each region, calculating the changes of the corresponding fault parameters of the photovoltaic panels in each sub-region, and avoiding further expansion of losses. BRIEF DESCRIPTION OF DRAWINGS

[0034] The application will be further described below with reference to the accompanying drawings.

[0035] Figure 1 is a schematic diagram of the framework structure of a distributed power monitoring system based on the Internet of Things according to the application;

[0036] Figure 2 is a schematic diagram of the working process of the monitoring center according to the application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments. 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.

[0038] Embodiment 1

[0039] A distributed power monitoring system based on the Internet of Things, as shown in Figure 1 , comprises:

[0040] a real scene image acquisition unit for acquiring image information of each photovoltaic panel in the distributed photovoltaic power station and transmitting the acquired image information to an image analysis unit for analysis;

[0041] an infrared image acquisition unit for acquiring infrared image information of each photovoltaic panel in the distributed photovoltaic power station and transmitting the acquired infrared image information to the image analysis unit for analysis;

[0042] In actual work, the unmanned aerial vehicle can carry the real scene image acquisition unit and the infrared image acquisition unit to fly above the photovoltaic panel and collect and transmit the image information and the infrared image information corresponding to the photovoltaic panel;

[0043] The image analysis unit is configured to receive the image information collected by the real scene image acquisition unit, analyze the image information to obtain the position and area of the shielding object on each photovoltaic panel, and receive the infrared image information collected by the infrared image acquisition unit and analyze the infrared image information to obtain the position and area of the hot spot on the photovoltaic panel;

[0044] The monitoring center is configured to receive the analysis result data of the image analysis unit, and identify the abnormal area in the photovoltaic power station according to the analysis result;

[0045] The monitoring center is further configured to send a cleaning instruction and the position information of the shielding object on each photovoltaic panel to the cleaning unit;

[0046] The cleaning unit is configured to clean the photovoltaic panel with the shielding object;

[0047] The alarm unit is configured to send an alarm information to remind the staff to maintain the high-risk photovoltaic panel and the abnormal sub-area.

[0048] As shown in Figure 2 The monitoring center further analyzes the analysis result data uploaded by the image analysis unit to identify the abnormal area in the photovoltaic power station, and the method comprises the following steps:

[0049] Step one, for a photovoltaic panel, the real scene image acquisition unit and the infrared image acquisition unit collect the image information and the infrared image information of the photovoltaic panel respectively, the image analysis unit analyzes the collected information to obtain the position and area of the shielding object and the position and area of the hot spot on the photovoltaic panel, and marks the cell belonging to the hot spot coverage area but not belonging to the shielding object coverage area as an abnormal cell;

[0050] The cell refers to the basic unit of the photovoltaic panel;

[0051] The image analysis unit sends the analyzed shielding object position information to the cleaning unit, and the cleaning unit focuses on cleaning the corresponding position on the photovoltaic panel when cleaning the photovoltaic panel;

[0052] The focused cleaning can be to increase the cleaning time of the corresponding position, increase the pressing pressure of the cleaning head, etc.

[0053] Step two, repeat the method of obtaining the abnormal cell in step one, and mark the cell as a fault cell when the cell is marked as an abnormal cell for k consecutive times.

[0054] wherein k is a preset value;

[0055] calculating the fault parameter G corresponding to each photovoltaic panel;

[0056] For a photovoltaic panel, a plane rectangular coordinate system is established in the plane where the panel surface is located, and the coordinates (xi, yi) of the geometric center of each fault cell in the coordinate system are obtained, wherein i takes a value from 1 to n, and n is the number of fault cells on the corresponding photovoltaic panel;

[0057] The standard deviation Z1 corresponding to xi and the standard deviation Z2 corresponding to yi are calculated, and then the formula G = n * e -Z The fault parameter G corresponding to the photovoltaic panel is calculated, and e is a natural constant;

[0058] wherein Z = 0.5 * Z1 + 0.5Z2;

[0059] Step three, divide the photovoltaic power station into several sub-regions, and the specific division rule can be determined according to the environment or the connection relationship between photovoltaic panels, such as the photovoltaic panels in the same sub-region are continuous and have the same altitude, or the photovoltaic panels in the same sub-region are continuous and in the same plane, or the photovoltaic panels in the same sub-region are connected to the same inverter;

[0060] Obtain the fault parameter G corresponding to each photovoltaic panel in each inspection cycle in a sub-region in the past m inspection cycles;

[0061] One of the inspection cycles is the process completed by the cleaning unit after the image analysis unit analyzes and obtains the obstacle position after the real image acquisition unit and the infrared image acquisition unit complete the image acquisition of the photovoltaic power station (when the system is facing a region of the photovoltaic power station, the target is the corresponding region). The process of cleaning the photovoltaic panels in the corresponding region is completed.

[0062] For a sub-region, obtain the average value Gj of the fault parameter G corresponding to each photovoltaic panel obtained after one inspection cycle, wherein j takes a value from 1 to m; then establish a coordinate system with time as the horizontal axis and value as the vertical axis, and express the change curve of the average value Gj of the fault parameter of the corresponding sub-region in the coordinate system, and calculate the area Ma of the closed figure surrounded by the change curve, x = 1, x = m and the x-axis;

[0063] According to the above method, the change curve corresponding to each sub-region and the area Ma of the closed figure surrounded are calculated in turn, wherein a takes a value from 1 to f, and f is the number of sub-regions divided;

[0064] Mark the sub-region corresponding to Ma with a larger deviation from Mp as an abnormal sub-region, and Mp is the average value of the f corresponding Ma.

[0065] Compared with the specific checking mode for photovoltaic panels in the prior art, the photovoltaic power station can find the abnormalities of the photovoltaic panels in a sub-region, such as the damage of the photovoltaic panel caused by the inverter failure, the damage of the photovoltaic panel caused by the deformation of the photovoltaic panel caused by the installation problem of the photovoltaic panel support on the slope, and the like. These problems are difficult to be directly observed. The photovoltaic power station can find the regional problems in time and avoid further expansion of the loss through long-term monitoring of the photovoltaic panels in each region.

[0066] In step four, when a sub-region is marked as an abnormal sub-region, the alarm unit sends an alarm information to prompt the staff to check the problem;

[0067] Embodiment two

[0068] On the basis of the embodiment one, in step two, when the fault parameter G of a photovoltaic panel is greater than or equal to the preset value Gy, the corresponding photovoltaic panel is marked as a high-risk photovoltaic panel, wherein Gy is a preset empirical coefficient;

[0069] When a photovoltaic panel is marked as a high-risk photovoltaic panel, the alarm unit sends an alarm information to remind the staff to timely repair the corresponding high-risk photovoltaic panel to avoid the burning of the photovoltaic panel caused by the hot spot;

[0070] Since the larger the area of the hot spot is, the more concentrated the hot spot is, and the greater the influence on the working safety and stability of the photovoltaic panel is, and the small-area hot spot phenomenon is relatively common in the photovoltaic panel and has limited harm to the photovoltaic panel, the photovoltaic panel is evaluated by analyzing the distribution of the fault cell on the surface of the photovoltaic panel to evaluate the influence of the hot spot on the working state of the photovoltaic panel, so that the photovoltaic panel with a large number of damaged cells and concentrated positions is timely repaired and replaced to avoid the burning accident caused by the serious hot spot effect of the photovoltaic panel to cause greater economic loss, and the maintenance purpose of the photovoltaic panel is stronger, and the meaningless maintenance alarm can be effectively reduced.

[0071] Embodiment three

[0072] On the basis of the embodiment one, the embodiment further provides a specific method for obtaining an abnormal sub-region, and specifically, the method is:

[0073] According to the formula The dispersion coefficient F corresponding to the f Ma values is calculated, when F is greater than or equal to the preset parameter Fy, the corresponding Ma is sequentially deleted in the order of |Ma-Mp| from large to small, and after deleting one Ma, the dispersion coefficient F of the remaining Ma is calculated and updated according to the above formula until the corresponding F meets the condition of being less than the preset parameter Fy;

[0074] The sub-region corresponding to the Ma greater than Mp in the Ma deleted according to the above flow is marked as an abnormal sub-region;

[0075] In practical application, the above statistics can be performed once every several preset periods with m periods of relevant data, thereby identifying the abnormal sub-region. For example, the above statistics are performed every 3 periods with 8 periods of relevant data, and the abnormal sub-region existing therein is obtained. In this way, when a sub-region is obviously aged by external factors (referring to the above-mentioned ground change, inverter failure, and other non-battery natural aging factors) in the latest 1-4 periods, it can be discovered in time, thereby avoiding further expansion of loss.

[0076] The above content is merely an example and description of the present application, and those skilled in the art can make various modifications or supplements to the described specific embodiments or replace them with similar ways, as long as they do not deviate from the invention or exceed the scope defined by the present claims, which shall belong to the protection scope of the present application.

Claims

1. An Internet of Things based distributed power monitoring system characterized in that, include: The real-scene image acquisition unit collects image information of photovoltaic panels in a photovoltaic power station; Infrared image acquisition unit, which acquires infrared image information of photovoltaic panels in photovoltaic power station; The image analysis unit is used to analyze image information to obtain the location and area of ​​obstructions on each photovoltaic panel; It is also used to analyze infrared image information to obtain the location and area of ​​hot spots on photovoltaic panels; The monitoring center is used to receive the analysis results data from the image analysis unit and identify abnormal areas in the photovoltaic power station based on the analysis results. An alarm unit is used to send alarm information. The method used by the monitoring center to identify abnormal areas in a photovoltaic power station is as follows: Step 1: Mark the solar cells that belong to the hot spot coverage area but not to the area covered by the obstruction as abnormal solar cells; Step 2: When a solar cell is marked as an abnormal solar cell k times consecutively, it is marked as a faulty solar cell; where k is a preset value. Step 3: Divide the photovoltaic power station into several sub-regions and obtain the fault parameters G of each photovoltaic panel in a sub-region in each inspection cycle over the past m inspection cycles. The average value Gj of the fault parameter G corresponding to each photovoltaic panel is obtained after one inspection cycle, where j takes the value from 1 to m; when the Gj corresponding to a sub-region increases beyond the average level to a certain extent, it is marked as an abnormal sub-region. Step 4: When a sub-area is marked as an abnormal sub-area, the alarm unit issues an alarm message to prompt staff to investigate the problem. The method for calculating the fault parameter G of the photovoltaic panel is as follows: For a photovoltaic panel, a Cartesian coordinate system is established with the plane on which the photovoltaic panel is located, and the coordinates (xi, yi) of the geometric center of each faulty cell in the coordinate system are obtained, where i takes the value from 1 to n, and n is the number of faulty cells on the corresponding photovoltaic panel. The standard deviation Z1 of n corresponding xi values and the standard deviation Z2 of n corresponding yi values are calculated, and then G = n * e -Z The fault parameter G corresponding to the photovoltaic panel is calculated, and e is a natural constant; wherein Z = 0.5 * Z1 + 0.5 * Z2.

2. The distributed power monitoring system based on the Internet of Things according to claim 1, characterized in that, In step two, when the fault parameter G corresponding to a photovoltaic panel is greater than or equal to the preset value Gy, the corresponding photovoltaic panel is marked as a high-risk photovoltaic panel, where Gy is a preset empirical coefficient; When a photovoltaic panel is marked as a high-risk photovoltaic panel, the alarm unit issues an alarm message to remind staff to promptly inspect and repair the corresponding high-risk photovoltaic panel.

3. The distributed power monitoring system based on the Internet of Things according to claim 1, characterized in that, The system also includes a cleaning unit, which is used to clean photovoltaic panels with obstructions. The image analysis unit sends the obtained location information of the obstructions to the cleaning unit. When cleaning the corresponding photovoltaic panel, the cleaning unit focuses on cleaning the corresponding location on the photovoltaic panel.

4. The distributed power monitoring system based on the Internet of Things according to claim 1, characterized in that, The method for dividing the sub-regions is as follows: photovoltaic panels in the same sub-region are continuously distributed and have the same altitude, or photovoltaic panels in the same sub-region are continuously distributed and are located on the same plane, or photovoltaic panels in the same sub-region are connected to the same inverter.

5. A distributed power monitoring system based on the Internet of Things according to claim 4, characterized in that, The method for obtaining abnormal sub-regions is as follows: Establish a coordinate system with time as the horizontal axis and numerical value as the vertical axis, and represent the variation curve of the average fault parameter Gj of the corresponding sub-region in the coordinate system, and calculate the area Ma of the closed figure enclosed by the variation curve, x=1, x=m and the x-axis. The variation curves corresponding to each sub-region and the area Ma of the enclosed figure are calculated sequentially according to the above method, where a takes values ​​from 1 to f, and f is the number of sub-regions defined. The sub-regions corresponding to Ma that deviate significantly from Mp are marked as abnormal sub-regions, where Mp is the average of the f corresponding Ma.

6. A distributed power monitoring system based on the Internet of Things according to claim 5, characterized in that, Specific methods for obtaining abnormal sub-regions also include: According to the formula The discrete coefficients F corresponding to these f Ma values ​​are calculated. When F is greater than or equal to the preset parameter Fy, the corresponding Ma is deleted in descending order of |Ma-Mp|. After each Ma is deleted, the discrete coefficients F of the remaining Ma are calculated and updated according to the above formula until the corresponding F satisfies the condition that it is less than the preset parameter Fy. The sub-regions corresponding to Ma values ​​greater than Mp among the Ma values ​​deleted according to the above procedure are marked as abnormal sub-regions.

7. A distributed power monitoring system based on the Internet of Things according to claim 5, characterized in that, Every preset number of cycles, an abnormal sub-region is identified using relevant data from m cycles.

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