Distributed power monitoring system based on Internet of Things

Abstract

The invention discloses a distributed power monitoring system based on Internet of Things, which belongs to the field of intelligent photovoltaic technology, and evaluates the influence of hot spots in a photovoltaic panel on the working state of the photovoltaic panel by analyzing the distribution of fault cells on the surface of the photovoltaic panel. Therefore, the photovoltaic panel with a large number of damaged battery pieces and concentrated positions can be maintained and replaced in time, greater economic loss caused by accidents such as combustion and the like due to serious hot spot effect of the photovoltaic panel is avoided, the maintenance purposiveness of the photovoltaic panel is higher, and meaningless maintenance alarm can be effectively reduced. In addition, compared with a specific troubleshooting mode for the photovoltaic panel in the prior art, the method can explore the abnormity of the photovoltaic panel in a sub-region in a photovoltaic power station, timely discover regional problems, and avoid further expansion of loss.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent photovoltaics, and specifically relates to a distributed power monitoring system based on the Internet of Things. Background Art

[0002] Distributed power system is relative to the traditional centralized power supply mode. It arranges power generation systems near users in a small-scale, small-capacity, modular way and can output electric energy independently. This system can not only improve energy utilization efficiency and reduce transmission and distribution losses, but also improve the safety and reliability of power supply.

[0003] There are many types of distributed power systems, among which 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, many photovoltaic panels in a photovoltaic power station are connected in series and parallel. This also means that when a photovoltaic panel has a problem, it will affect the overall output. Therefore, in the prior art, it is necessary to monitor and manage the photovoltaic panels in the photovoltaic power station to promptly discover the problematic photovoltaic panels. However, in the prior art, the abnormal photovoltaic panel can only be discovered when there is an obvious output abnormality in the photovoltaic panel. It is difficult to discover the hidden dangers of the photovoltaic panel and the system where the photovoltaic panel is located in time, that is, the prevention effect is poor. In order to solve the above problem, a method is provided for promptly discovering the hidden dangers of damage to photovoltaic panels in a photovoltaic power station. The present invention provides the following technical solutions. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed power monitoring system based on the Internet of Things to solve the problem in the prior art that it is difficult to timely discover and solve the safety hazards of photovoltaic panels caused by geographical factors, external influences, etc. in photovoltaic power stations.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A distributed power monitoring system based on the Internet of Things comprises: a real-scene image acquisition unit for collecting image information of photovoltaic panels in a photovoltaic power station;

[0007] Infrared image acquisition unit, which collects infrared image information of photovoltaic panels in photovoltaic power stations;

[0008] An image analysis unit is used to analyze the image information to obtain the location and area of ​​the obstructions on each photovoltaic panel; and is also used to analyze the infrared image information to obtain the location and area of ​​the hot spot on the photovoltaic panel;

[0009] The monitoring center is used 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;

[0010] An alarm unit, used for issuing an alarm message;

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

[0012] Step 1: Mark the cells that belong to the hot spot coverage area but not the obstruction coverage area as abnormal cells;

[0013] Step 2: When a battery cell is marked as an abnormal battery cell k times in a row, it is marked as a faulty battery cell; wherein k is a preset value;

[0014] Step 3: Divide the photovoltaic power station into several sub-areas, obtain the fault parameters G corresponding to 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 parameters G corresponding to each photovoltaic panel obtained after one inspection cycle, where j ranges from 1 to m; when Gj corresponding to a sub-area increases to a certain extent beyond the average level, it is marked as an abnormal sub-area;

[0015] Step 4: When a sub-area is marked as an abnormal sub-area, the alarm unit sends an alarm message to prompt the staff to troubleshoot the problem.

[0016] As a further solution of the present invention, in step 2, when the fault parameter G corresponding to a photovoltaic panel is greater than or equal to a preset value Gy, the corresponding photovoltaic panel is marked as a high-risk photovoltaic panel, where Gy is a preset empirical coefficient;

[0017] When a photovoltaic panel is marked as a high-risk photovoltaic panel, the alarm unit sends an alarm message to remind the staff to carry out timely maintenance on the corresponding high-risk photovoltaic panel.

[0018] As a further solution of the present invention, the system also includes a cleaning unit, which is used to clean the photovoltaic panels where there are obstructions. The image analysis unit sends the obstruction position information obtained through analysis to the cleaning unit. When the cleaning unit cleans the corresponding photovoltaic panel, the cleaning unit focuses on cleaning the corresponding position on the photovoltaic panel.

[0019] As a further solution of the present invention, the calculation method of the fault parameter G of the photovoltaic panel is:

[0020] For a photovoltaic panel, a rectangular coordinate system is established with the plane where the photovoltaic panel surface is located, and the coordinates (xi, yi) of the geometric center of each faulty cell in the coordinate system are obtained, where i ranges from 1 to n, and n is the number of faulty 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 use the formula G = n*e -Z The fault parameter G of the corresponding photovoltaic panel is calculated, and e is a natural constant; wherein Z=0.5*Z1+0.5*Z2.

[0022] As a further solution of the present invention, the sub-area division method is: the photovoltaic panels in the same sub-area are distributed continuously and at the same altitude, or the photovoltaic panels in the same sub-area are distributed continuously and in the same plane, or the photovoltaic panels in the same sub-area are connected to the same inverter.

[0023] As a further solution of the present invention, a method for obtaining an abnormal sub-region is:

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

[0025] According to the above method, the change curve corresponding to each sub-region and the area Ma of the enclosed figure are calculated in sequence, where a ranges from 1 to f, and f is the number of the delineated sub-regions;

[0026] The sub-region corresponding to Ma that deviates greatly from Mp is marked as an abnormal sub-region, and Mp is the average value of f corresponding Ma.

[0027] As a further solution of the present invention, a specific method for obtaining an abnormal sub-region also includes:

[0028] According to the formula The discrete coefficients F corresponding to the f Ma values ​​are calculated. When F is greater than or equal to the preset parameter Fy, the corresponding Ma are deleted in order from large to small according to |Ma-Mp|; and 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;

[0029] The sub-regions corresponding to Ma greater than Mp among the Ma deleted according to the above process are marked as abnormal sub-regions.

[0030] As a further solution of the present invention, the abnormal sub-region is identified once every several preset cycles using the relevant data of m cycles.

[0031] Beneficial effects of the present invention:

[0032] 1. The present invention evaluates the influence of hot spots in photovoltaic panels on the working state of photovoltaic panels by analyzing the distribution of faulty cells on the surface of photovoltaic panels, so as to timely repair and replace photovoltaic panels with a large number of damaged cells and concentrated positions, thereby avoiding accidents such as combustion caused by serious hot spot effects of photovoltaic panels, which would cause greater economic losses. The maintenance of photovoltaic panels is more purposeful and can effectively reduce meaningless maintenance alarms.

[0033] 2. Compared with the prior art method of conducting specific inspections on photovoltaic panels, the present invention can discover the anomalies existing in the photovoltaic panels in a sub-area of ​​a photovoltaic power station, such as damage to the photovoltaic panel cells due to inverter failure, damage to the cells caused by deformation of the photovoltaic panel due to installation problems of the photovoltaic panel bracket on a slope, etc. These problems are difficult to observe directly. The present invention conducts long-term monitoring of the photovoltaic panels in each area, calculates the changes in the fault parameters corresponding to the photovoltaic panels in each sub-area, and promptly discovers such regional problems to avoid further expansion of losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below in conjunction with the accompanying drawings.

[0035] Figure 1 This is a schematic diagram of the framework structure of a distributed power monitoring system based on the Internet of Things of the present invention;

[0036] Figure 2 It is a schematic diagram of the workflow of the monitoring center described in the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Example 1

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

[0040] A real-scene image acquisition unit is used to acquire image information of each photovoltaic panel in a distributed photovoltaic power station, and transmit the acquired image information to an image analysis unit for analysis;

[0041] The infrared image acquisition unit is used to acquire infrared image information of each photovoltaic panel in the distributed photovoltaic power station, and transmit the acquired infrared image information to the image analysis unit for analysis;

[0042] In actual work, the drone can carry the real-scene image acquisition unit and the infrared image acquisition unit at the same time to fly over the photovoltaic panels, and collect and transmit the image information and infrared image information corresponding to the photovoltaic panels;

[0043] The image analysis unit is used to receive the image information collected by the real-scene image acquisition unit, and analyze it to obtain the position and area of ​​the obstructions on each photovoltaic panel; it is also used to receive the infrared image information collected by the infrared image acquisition unit, and analyze it to obtain the position and area of ​​the hot spot on the photovoltaic panel;

[0044] The monitoring center is used 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] It is also used to send cleaning instructions to the cleaning unit and the location information of the obstructions on each photovoltaic panel;

[0046] A cleaning unit, used for cleaning photovoltaic panels with obstructions;

[0047] The alarm unit is used to send out alarm information to remind the staff to perform maintenance on abnormal high-risk photovoltaic panels and abnormal sub-areas.

[0048] like Figure 2 As shown, 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. The method includes the following steps:

[0049] Step 1: for a photovoltaic panel, the real-scene image acquisition unit and the infrared image acquisition unit are used to respectively acquire the image information and the infrared image information of the photovoltaic panel, and the image analysis unit is used to analyze and acquire the position and area of ​​the obstruction existing on the photovoltaic panel and the position and area of ​​the hot spot existing on the photovoltaic panel, and the cell that belongs to the hot spot coverage area but not to the obstruction coverage area is marked as an abnormal cell;

[0050] The cell refers to the basic unit that constitutes a photovoltaic panel;

[0051] The image analysis unit sends the obtained information of the location of the obstruction to the cleaning unit. When the cleaning unit cleans the corresponding photovoltaic panel, it focuses on cleaning the corresponding location on the photovoltaic panel.

[0052] The focused cleaning may be performed by increasing the cleaning time of the corresponding position, increasing the pressing pressure of the cleaning head, etc.

[0053] Step 2, repeat the method of obtaining abnormal battery cells in step 1, and when a battery cell is marked as an abnormal battery cell k times in a row, it is marked as a faulty battery cell;

[0054] Where k is a preset value;

[0055] Calculate and obtain the fault parameter G corresponding to each photovoltaic panel;

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

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

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

[0059] Step 3: Divide the photovoltaic power station into several sub-areas. The specific division rules can be determined according to the environment or the connection relationship between photovoltaic panels, such as photovoltaic panels in the same sub-area are distributed continuously and at the same altitude, photovoltaic panels in the same sub-area are distributed continuously and are in the same plane, or photovoltaic panels in the same sub-area are connected to the same inverter;

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

[0061] One inspection cycle is the process in which the real-scene image acquisition unit and the infrared image acquisition unit complete the image acquisition inside the photovoltaic power station (when the system is facing an area of ​​the photovoltaic power station, the target is the corresponding area), the image analysis unit analyzes and obtains the location of the obstacle, and finally the cleaning unit completes the process of cleaning the photovoltaic panels in the corresponding area.

[0062] For a sub-area, obtain the average value Gj of the fault parameter G corresponding to each photovoltaic panel obtained after an inspection cycle, where j ranges from 1 to m; then establish a coordinate system with time as the horizontal axis and the value as the vertical axis, and represent the change curve of the fault parameter average value Gj of the corresponding sub-area 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 enclosed figure are calculated in sequence, where a ranges from 1 to f, and f is the number of the delineated sub-regions;

[0064] The sub-region corresponding to the Ma with a large deviation from Mp is marked as an abnormal sub-region, and Mp is the average value of f corresponding Ma;

[0065] Compared with the method of conducting specific inspections on photovoltaic panels in the prior art, the present invention can discover anomalies existing in photovoltaic panels in a sub-area of ​​a photovoltaic power station, such as damage to photovoltaic panel cells due to inverter failure, damage to cells caused by deformation of photovoltaic panels due to installation problems of photovoltaic panel brackets on slopes, etc. These problems are difficult to observe directly. The present invention conducts long-term monitoring of photovoltaic panels in various areas to promptly discover such regional problems and avoid further expansion of losses.

[0066] Step 4: When a sub-area is marked as an abnormal sub-area, the alarm unit sends an alarm message to prompt the staff to conduct problem investigation;

[0067] Embodiment 2

[0068] On the basis of the first embodiment, in step 2 of this embodiment, when the fault parameter G corresponding to a photovoltaic panel is greater than or equal to a preset value Gy, the corresponding photovoltaic panel is marked as a high-risk photovoltaic panel, where Gy is a preset empirical coefficient;

[0069] When a photovoltaic panel is marked as a high-risk photovoltaic panel, the alarm unit will send out an alarm message to remind the staff to promptly inspect and repair the corresponding high-risk photovoltaic panel to avoid accidents such as photovoltaic panel burning caused by hot spots;

[0070] Since the larger and more concentrated the hot spot area is, the greater the impact on the working safety and stability of the photovoltaic panel, while the relatively dispersed small-area hot spot phenomenon is more common in photovoltaic panels and has limited harm to photovoltaic panels. Therefore, the present invention analyzes the distribution of faulty cells on the surface of the photovoltaic panel to evaluate the impact of hot spots in the photovoltaic panel on the working state of the photovoltaic panel, so as to promptly repair and replace photovoltaic panels with a large number of damaged cells and concentrated locations, thereby avoiding accidents such as combustion caused by the hot spot effect of the photovoltaic panels, which causes greater economic losses, and the maintenance of the photovoltaic panels is more purposeful, which can effectively reduce meaningless maintenance alarms.

[0071] Embodiment 3

[0072] On the basis of the first embodiment, this embodiment further provides a specific method for obtaining an abnormal sub-region. Specifically, the method is:

[0073] According to the formula The discrete coefficients F corresponding to the f Ma values ​​are calculated. When F is greater than or equal to the preset parameter Fy, the corresponding Ma are deleted in order from large to small according to |Ma-Mp|; and 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;

[0074] Mark the sub-regions corresponding to Ma greater than Mp among the Ma deleted according to the above process as abnormal sub-regions;

[0075] In practical applications, the above statistics can be performed once every several preset cycles with relevant data of m cycles to identify abnormal sub-regions. For example, the above statistics can be performed once every 3 cycles with relevant data within 8 cycles to obtain the abnormal sub-regions therein. In this way, when a sub-region has obvious battery cell aging due to external factors (referring to the aforementioned non-natural battery cell aging factors such as foundation changes and inverter failures) in the most recent 1-4 cycles, it can be discovered in time to avoid further expansion of losses.

[0076] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. A distributed power monitoring system based on the Internet of Things, characterized in that: include: A real-scene image acquisition unit, which collects image information of photovoltaic panels in a photovoltaic power station; Infrared image acquisition unit, which collects infrared image information of photovoltaic panels in photovoltaic power stations; An image analysis unit, used to analyze the image information to obtain the location and area of ​​the 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 result data of the image analysis unit, and identify the abnormal area in the photovoltaic power station according to the analysis result; An alarm unit, used for issuing an alarm message; The method for the monitoring center to identify the abnormal area in the photovoltaic power station is: Step 1: Mark the cells that belong to the hot spot coverage area but not the obstruction coverage area as abnormal cells; Step 2: When a battery cell is marked as an abnormal battery cell k times in a row, it is marked as a faulty battery cell; k is a preset value; Step 3: divide the photovoltaic power station into several sub-areas, and obtain the fault parameters G corresponding to 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 corresponding to each photovoltaic panel obtained after an inspection cycle, where j ranges from 1 to m; when the Gj corresponding to a sub-area increases to a certain extent beyond the average level, it is marked as an abnormal sub-area; Step 4: When a sub-area is marked as an abnormal sub-area, the alarm unit sends an alarm message to prompt the staff to troubleshoot the problem.

2. According to claim 1, a distributed power monitoring system based on the Internet of Things is characterized in that: In step 2, when the fault parameter G corresponding to a photovoltaic panel is greater than or equal to a 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 sends an alarm message to remind the staff to carry out timely maintenance on the corresponding high-risk photovoltaic panel.

3. According to the distributed power monitoring system based on the Internet of Things in claim 1, it is 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 obstruction position information obtained through analysis to the cleaning unit. When cleaning the corresponding photovoltaic panel, the cleaning unit focuses on cleaning the corresponding position on the photovoltaic panel.

4. The distributed power monitoring system based on the Internet of Things according to claim 1 is characterized in that: The calculation method of the fault parameter G of the photovoltaic panel is: For a photovoltaic panel, a rectangular coordinate system is established with the plane where the photovoltaic panel surface is located, and the coordinates (xi, yi) of the geometric center of each faulty cell in the coordinate system are obtained, where i ranges from 1 to n, and n is the number of faulty cells on the corresponding photovoltaic panel; Calculate the standard deviation Z1 of n corresponding xi values ​​and the standard deviation Z2 of n corresponding yi values; then use the formula G = n*e - Z The fault parameter G of the corresponding photovoltaic panel is calculated, and e is a natural constant; wherein Z=0.5*Z1+0.5*Z2.

5. The distributed power monitoring system based on the Internet of Things according to claim 1 is characterized in that: The sub-area division method is as follows: the photovoltaic panels in the same sub-area are distributed continuously and at 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.

6. A distributed power monitoring system based on the Internet of Things according to claim 5, characterized in that: The method to obtain the abnormal sub-area is: A coordinate system is established with time as the horizontal axis and value as the vertical axis, and a variation curve of the fault parameter average value Gj of the corresponding sub-area is represented in the coordinate system, and the area Ma of the closed figure formed by the variation curve, x=1, x=m and the x-axis is calculated; According to the above method, the change curve corresponding to each sub-region and the area Ma of the enclosed figure are calculated in sequence, where a ranges from 1 to f, and f is the number of the delineated sub-regions; The sub-region corresponding to Ma that deviates greatly from Mp is marked as an abnormal sub-region, and Mp is the average value of f corresponding Ma.

7. A distributed power monitoring system based on the Internet of Things according to claim 6, characterized in that: The specific method for obtaining the abnormal sub-region also includes: According to the formula The discrete coefficients F corresponding to the f Ma values ​​are calculated. When F is greater than or equal to the preset parameter Fy, the corresponding Ma are deleted in order from large to small according to |Ma-Mp|; and 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 greater than Mp among the Ma deleted according to the above process are marked as abnormal sub-regions.

8. The distributed power monitoring system based on the Internet of Things according to claim 6 is characterized in that: The abnormal sub-region is identified once every several preset cycles using the relevant data of m cycles.

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