Power equipment risk identification method and system

Abstract

The invention relates to the related field of power risk management, and discloses a risk identification method and system for power equipment, and the method comprises the steps: carrying out the thermal data collection of a photovoltaic power equipment group at a preset period frequency through employing a group remote sensing monitoring mode, and further monitoring the temperature change of each piece of photovoltaic power equipment, compared with a traditional monitoring scheme of arranging a plurality of groups of sensors, the method is more suitable for a large-scale equipment group, and the group remote sensing monitoring mode effectively reduces data sources, reduces the data communication pressure of the equipment of the Internet of Things, and improves the reliability of the equipment of the Internet of Things. And the occurrence of risk misjudgment and the like caused by the failure of the Internet of Things sensing network equipment can be avoided.

Description

Technical Field

[0001] The present invention relates to the field related to power risk management, and in particular to a risk identification method and system for power equipment. Background Art

[0002] As the importance attached to environmental protection increases, the scale of clean energy application is also gradually increasing. Different from traditional electricity production methods, clean energy power collection has the characteristics of decentralization and multiple nodes, especially wind power and photovoltaic power, whose power equipment settings are often large-scale and have more equipment.

[0003] Among them, for the equipment risk monitoring of photovoltaic power equipment, a sensor group is usually used to monitor the corresponding reference data, and then the operating status of the photovoltaic power equipment is judged according to the changes in the reference data. The independent sensor monitoring method has higher monitoring accuracy and is suitable for small-area and multi-region distributed solutions in the process of photovoltaic promotion (such as distribution and installation on rooftops in rural and urban areas). However, with the increase in the scale of clean energy, large-scale clustered photovoltaic power stations are rapidly expanding in wide-area places such as the northwest and the sea. When faced with a large area of ​​photovoltaic power equipment, such a monitoring method will cause the sensors of a large number of photovoltaic power equipment in the Internet of Things network to generate massive sensor data, causing pressure on data transmission, and more independent corresponding photovoltaic power equipment sensor data need to be processed and evaluated one-to-one, resulting in increased computing power requirements. Summary of the invention

[0004] The purpose of the present invention is to provide a risk identification method and system for power equipment to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A risk identification method for power equipment, comprising: The sensor data of the monitoring area is updated and acquired at preset data acquisition cycle intervals, and the sensor data of each acquisition cycle node includes multiple groups of multi-type sensor data that are aligned and stacked; Positioning and matching the data coordinates and distribution coordinates of several photovoltaic objects based on the image data of the sensor data, and obtaining the inclination state data corresponding to the distribution coordinates one by one, wherein the inclination state data is used to characterize the spatial orientation of the photovoltaic panel; Processing the thermal sensor data of several photovoltaic objects to obtain the rising coefficient and the steady-state extreme value of the temperature change state of each photovoltaic object, wherein the rising coefficient and the steady-state extreme value are both associated with the current lighting conditions and the tilt state data; Arranging the rising coefficients and steady-state extreme values ​​of the array based on the continuous sequence of the tilt state data to establish an evaluation sequence, and performing smooth curve fitting evaluation on the rising coefficients and steady-state extreme values ​​in the evaluation sequence, and marking the photovoltaic object for risk if the error fluctuation value of the smooth curve is exceeded; The risk bias evaluation is performed on the photovoltaic object with risk mark. If the rising coefficient or the steady-state extreme value is higher than the smooth curve fitting value, the risk level is marked as high risk. Otherwise, the risk level is marked as to be evaluated. The risk object is visualized based on the risk level.

[0006] As a further solution of the present invention: the step of updating and acquiring the sensor data of the monitoring area at a preset data acquisition period interval specifically includes: Execute the dynamic monitoring step, initialize the tilt states of several photovoltaic objects, so that the photovoltaic objects face away from the illumination direction, and evenly select multiple evaluation nodes between the back illumination direction and the maximum illumination direction after initialization, and control the photovoltaic objects to stay at multiple evaluation nodes in sequence and record corresponding sensor data; The static monitoring step is performed. When the photovoltaic object enters a normal operating posture, the photovoltaic object is monitored at a preset data collection cycle interval to obtain sensor data. The data collection cycle interval is not shared with the dynamic monitoring interval.

[0007] As a further solution of the present invention: it also includes a dirt assessment step, specifically including: Acquire power feeding data per unit time of a plurality of photovoltaic objects, and spatially arrange the plurality of power feeding data based on distribution coordinates of the photovoltaic objects; Calculating the unit illumination intensity of several photovoltaic objects according to the inclination attitude data of the photovoltaic objects and the current ambient illumination intensity, and evaluating the ideal power feeding intensity of the photovoltaic objects through a preset photovoltaic power feeding illumination model; Calculating the difference between the ideal feeding intensity and the feeding data, obtaining a feeding offset value of the corresponding photovoltaic object, and performing spatial surface fitting on several feeding offset values ​​based on the spatial arrangement of the feeding data to obtain a feeding offset distribution image; The feed offset distribution image is divided into deviation level areas by using preset spatial height steps, and the deviation level area with a larger feed offset value has a higher corresponding dirt level.

[0008] As a further solution of the present invention: it also includes a low temperature auxiliary verification step, specifically including: The smoothness of the feed offset distribution image is evaluated, the protruding nodes are marked and the photovoltaic objects corresponding to the distribution coordinates of the protruding nodes are obtained. If the corresponding marked risk level is to be evaluated, the alignment is re-marked and the corresponding risk level is marked as high risk.

[0009] As a further solution of the present invention: it also includes a collaborative optimization management step, specifically including: Acquire power feeding data per unit time of several photovoltaic objects, and perform power feeding curve fitting on the several power feeding data according to the continuous sequence of the tilt state data, wherein the power feeding curve is used to characterize the correlation between the unit light intensity, the steady-state extreme temperature and the power feeding capacity of the photovoltaic object; A peak value is determined for the feeding curve. If the feeding curve has a unique feeding peak value, it indicates that both the unit light intensity and the steady-state extreme value are not greater than the optimal efficiency state. The inclination state data of the photovoltaic object corresponding to the feeding peak value is set as the current optimal posture solution. If the feed curve has a non-unique feed peak, and one side of the feed peak is in an ascending or descending state, and the other side is in a balanced state, then the unit illumination of the photovoltaic unit corresponding to the balanced state exceeds the optimal efficiency state, and the corresponding temperature data is not greater than the optimal efficiency state, and the inclination state data of multiple photovoltaic objects corresponding to the balanced state can be set as the current optimal posture solution; If the feeding curve has a non-unique feeding peak, and the two sides of the feeding peak are in opposite rising or falling states, then the unit light intensity and steady-state extreme value of the photovoltaic unit corresponding to the equilibrium state exceed the optimal efficiency state under the current environment, and the inclination state data of the photovoltaic object corresponding to the equilibrium state is set as the current optimal posture solution.

[0010] An embodiment of the present invention aims to provide a risk identification system for electric power equipment, comprising: A data updating module is used to update and acquire sensor data of the monitoring area at preset data collection period intervals, and the sensor data of each collection period node includes multiple groups of aligned and stacked sensor data of multiple types; An object matching module is used to locate and match the data coordinates and distribution coordinates of several photovoltaic objects based on the image data of the sensor data, and to obtain the inclination state data corresponding to the distribution coordinates one by one, wherein the inclination state data is used to characterize the spatial orientation of the photovoltaic panel; A feature evaluation module is used to process the thermal sensor data of several photovoltaic objects to obtain the rising coefficient and the steady-state extreme value of the temperature change state of each photovoltaic object, wherein the rising coefficient and the steady-state extreme value are both associated with the current lighting conditions and the tilt state data; A risk marking module, used for arranging the rising coefficients and steady-state extreme values ​​of the array based on the continuous sequence of the tilt state data to establish an evaluation sequence, and performing smooth curve fitting evaluation on the rising coefficients and steady-state extreme values ​​in the evaluation sequence respectively, and if the error fluctuation value of the smooth curve is exceeded, the photovoltaic object is marked as risk; The risk assessment module is used to evaluate the risk bias of the photovoltaic object marked with risk. If the rising coefficient or the steady-state extreme value is higher than the smooth curve fitting value, the risk level is marked as high risk, otherwise the risk level is marked as to be evaluated, and the risk object is visualized based on the risk level.

[0011] As a further solution of the present invention: the data updating module comprises: A dynamic update unit is used to execute the dynamic monitoring step, initialize the tilt states of several photovoltaic objects so that the photovoltaic objects face away from the illumination direction, and evenly select multiple evaluation nodes between the back illumination direction and the maximum illumination direction after initialization, and control the photovoltaic objects to stay at multiple evaluation nodes in sequence and record corresponding sensor data; The static updating unit is used to execute the static monitoring step. When the photovoltaic object enters the normal operation posture, the photovoltaic object is monitored at a preset data collection cycle interval to obtain sensor data. The data collection cycle interval is not shared with the dynamic monitoring interval.

[0012] As a further solution of the present invention: it also includes a dirt assessment module, specifically including: A power feeding feedback unit, used to obtain power feeding data of a plurality of photovoltaic objects per unit time, and spatially arrange the plurality of power feeding data based on the distribution coordinates of the photovoltaic objects; An ideal evaluation unit, used to calculate the unit illumination intensity of several photovoltaic objects according to the inclination attitude data of the photovoltaic objects and the current ambient illumination intensity, and evaluate the ideal power feeding intensity of the photovoltaic objects through a preset photovoltaic power feeding illumination model; a deviation evaluation unit, configured to calculate the difference between the ideal power feeding intensity and the power feeding data, obtain a power feeding offset value of the corresponding photovoltaic object, and perform spatial surface fitting on several power feeding offset values ​​based on the spatial arrangement of the power feeding data to obtain a power feeding offset distribution image; The dirt rating unit is used to divide the feed offset distribution image into deviation level areas according to preset spatial height steps, and the deviation level area with a larger feed offset value has a higher corresponding dirt level.

[0013] As a further solution of the present invention: it also includes a low temperature auxiliary verification module; The low-temperature auxiliary verification module is used to evaluate the smoothness of the feed offset distribution image, mark the protruding nodes and obtain the photovoltaic objects corresponding to the distribution coordinates of the protruding nodes. If the corresponding marked risk level is to be evaluated, the alignment is re-marked and the corresponding risk level is marked as high risk.

[0014] As a further solution of the present invention: it also includes a collaborative optimization module, specifically including: A power feeding fitting unit is used to obtain power feeding data per unit time of several photovoltaic objects, and to perform power feeding curve fitting on the several power feeding data according to a continuous sequence of tilt state data, wherein the power feeding curve is used to characterize the correlation between the unit light intensity, the steady-state extreme temperature and the power feeding capacity of the photovoltaic object; A peak determination unit is used to perform peak determination on the feeding curve. If the feeding curve has a unique feeding peak, it indicates that both the unit light intensity and the steady-state extreme value are not greater than the optimal efficiency state, and the inclination state data of the photovoltaic object corresponding to the feeding peak is set as the current optimal posture solution; A balance state determination unit, for if there is a non-unique feed peak value in the feed curve, and one side of the feed peak value is in an ascending or descending state, and the other side is in a balance state, then the unit illumination of the photovoltaic unit corresponding to the balance state exceeds the optimal efficiency state, and the corresponding temperature data is not greater than the optimal efficiency state, and the inclination state data of multiple photovoltaic objects corresponding to the balance state can all be set as the current optimal posture solution; A multi-peak state determination unit is used for, if there is a non-unique feeding peak value in the feeding curve, and the two sides of the feeding peak value are in opposite rising or falling states, then the unit light intensity and the steady-state extreme value of the photovoltaic unit corresponding to the equilibrium state are both beyond the optimal efficiency state under the current environment, and the inclination state data of the photovoltaic object corresponding to the equilibrium state is set as the current optimal posture solution.

[0015] Compared with the prior art, the beneficial effects of the present invention are: by adopting the method of group remote sensing monitoring, thermal data of the photovoltaic power equipment group is collected at a preset periodic frequency, so that the temperature changes of each photovoltaic power equipment can be monitored, and the equipment risk can be judged through the abnormal temperature changes of the photovoltaic power equipment. Compared with the traditional monitoring scheme of setting multiple groups of sensors, it is more suitable for large-scale equipment groups. The group remote sensing monitoring method effectively reduces the data source, reduces the data communication pressure of the Internet of Things equipment, and can avoid the occurrence of risk misjudgment due to the failure of the Internet of Things sensor network equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a flowchart of a risk identification method for power equipment.

[0017] Figure 2 The present invention is a flowchart of the pollution rating assessment step in a risk identification method for power equipment.

[0018] Figure 3 The figure is a block diagram of the composition of a risk identification system for power equipment.

[0019] Figure 4 The present invention is a block diagram of the composition of a contamination assessment module in a risk identification system of an electric power equipment. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0022] like Figure 1 The present invention provides a method for identifying risks of electric power equipment according to an embodiment of the present invention, comprising the following steps: S10, updating and acquiring sensor data of the monitoring area at preset data collection period intervals, wherein the sensor data of each collection period node includes multiple groups of aligned and stacked sensor data of multiple types; S20, positioning and matching the data coordinates and distribution coordinates of several photovoltaic objects based on the image data of the sensor data, and acquiring the inclination state data corresponding to the distribution coordinates one by one, wherein the inclination state data is used to characterize the spatial orientation of the photovoltaic panel; S30, processing the thermal sensing data of several photovoltaic objects to obtain the rising coefficient and the steady-state extreme value of the temperature change state of each photovoltaic object, wherein the rising coefficient and the steady-state extreme value are both associated with the current illumination condition and the tilt state data; S40, arranging the rising coefficients and steady-state extreme values ​​of the array based on the continuous sequence of the tilt state data to establish an evaluation sequence, performing smooth curve fitting evaluation on the rising coefficients and steady-state extreme values ​​in the evaluation sequence, and marking the photovoltaic object for risk if the error fluctuation value of the smooth curve is exceeded; S50, performing risk bias evaluation on the risk-marked photovoltaic object. If the rising coefficient or the steady-state extreme value is higher than the smooth curve fitting value, the risk level is marked as high risk. Otherwise, the risk level is marked as to be evaluated. Visual output of the risk object is performed based on the risk level.

[0023] In this embodiment, a method for identifying the risk of power equipment is provided. By adopting a group remote sensing monitoring method, thermal data of a photovoltaic power equipment group is collected at a preset periodic frequency, so that the temperature change of each photovoltaic power equipment can be monitored, and the risk of the equipment can be judged through the abnormal temperature change of the photovoltaic power equipment. Compared with the traditional monitoring scheme of setting multiple groups of sensors, it is more suitable for large-scale equipment groups. The group remote sensing monitoring method effectively reduces the data source, reduces the data communication pressure of the Internet of Things equipment, and can avoid the risk misjudgment caused by the failure of the Internet of Things sensor network equipment. In the prior art, for the equipment risk monitoring of photovoltaic power equipment, a sensor group is usually used to monitor the corresponding reference data, and then the operating status of the photovoltaic power equipment is judged according to the change of the reference data. Such a management method has high monitoring accuracy. However, when facing a large area of ​​photovoltaic power equipment, such a monitoring method will cause the sensors of a large number of photovoltaic power equipment in the Internet of Things network to generate massive sensor data, causing pressure on data transmission, and more independent corresponding photovoltaic power equipment sensor data need to be processed and evaluated one-to-one, resulting in an increase in computing power requirements. Therefore, when facing a large number of photovoltaic power equipment, such a monitoring method There are many limitations; the solution adopted in this embodiment is to monitor data by means of group remote sensing, for example, by using drone equipment to fly at a rated time interval and perform infrared remote sensing monitoring to obtain temperature remote sensing data of several photovoltaic power equipment in an area, and in the remote sensing data, each photovoltaic power equipment corresponds to a point, so on this basis, when the orientation posture (angle with direct light) of the photovoltaic power equipment at different points is consistent, the temperature rise rate and the limit temperature should be consistent within a small allowable error during the movement toward the sun and after a certain period of time after the orientation is stable. Therefore, if there is a photovoltaic power equipment in the remote sensing data of multiple photovoltaic power equipment with continuously changing postures, the thermal data of the corresponding coordinates is more prominent and different from other objects, it indicates that the photovoltaic power equipment has certain risks (damage to the photovoltaic panel may cause the photovoltaic panel to generate more heat during operation, the temperature rise rate to accelerate, abnormally high temperature or low temperature failure), so it is distinguished and marked, and the subsequent output is convenient for maintenance personnel to handle in time. Through such a management method, the high demand for communication of a large number of data sources and the high computing power demand for data processing are avoided, which is more suitable for the management of large-scale photovoltaic power equipment.

[0024] As another preferred embodiment of the present invention, the step of updating and acquiring the sensor data of the monitoring area at a preset data collection period interval specifically includes: Execute the dynamic monitoring step, initialize the tilt states of several photovoltaic objects, so that the photovoltaic objects face away from the illumination direction, and evenly select multiple evaluation nodes between the back illumination direction and the maximum illumination direction after initialization, and control the photovoltaic objects to stay at multiple evaluation nodes in sequence and record corresponding sensor data; The static monitoring step is performed. When the photovoltaic object enters a normal operating posture, the photovoltaic object is monitored at a preset data collection cycle interval to obtain sensor data. The data collection cycle interval is not shared with the dynamic monitoring interval.

[0025] In this embodiment, the steps for collecting sensor data are specifically described. Since the data used for risk judgment are mainly two parts, one is the dynamic temperature rise rate, and the other is the accumulated heat temperature under stable conditions, two sets of monitoring step schemes are adopted here. For the dynamic monitoring step, it is necessary to cooperate with the adjustment process of the electric photovoltaic equipment to achieve a continuous record. For example, when the sun comes out and reaches a predetermined angle, the photovoltaic panel starts to work. The photovoltaic panel is controlled to rotate step by step from an initialization state facing away from the light, and each step is maintained for a certain time to ensure that the heating of the equipment reaches equilibrium. Then, the temperature rise rate of the equipment can be monitored and obtained, which can be used to screen risky equipment. For the static monitoring step, data can be efficiently acquired by simply setting a data collection cycle for interval collection.

[0026] like Figure 2 As shown, as another preferred embodiment of the present invention, it also includes a dirt assessment step, specifically including: S51, acquiring power feeding data of a plurality of photovoltaic objects per unit time, and spatially arranging the plurality of power feeding data based on distribution coordinates of the photovoltaic objects; S52, calculating the unit illumination intensity of a plurality of photovoltaic objects according to the inclination attitude data of the photovoltaic object and the current ambient illumination intensity, and evaluating the ideal power feeding intensity of the photovoltaic object through a preset photovoltaic power feeding illumination model; S53, calculating the difference between the ideal feeding intensity and the feeding data, obtaining a feeding offset value of the corresponding photovoltaic object, and performing spatial surface fitting on several feeding offset values ​​based on the spatial arrangement of the feeding data to obtain a feeding offset distribution image; S54, dividing the feed offset distribution image into deviation level areas according to a preset spatial height ladder, wherein the deviation level area with a larger feed offset value has a higher corresponding dirt level.

[0027] In this embodiment, a step for evaluating the dirt coverage on the surface of the photovoltaic panel is supplemented. Specifically, the implementation principle is as follows: when the surface of the photovoltaic power device is not dirty, the light intensity received by the surface will be equally induced to generate electricity, but when the surface is dirty, if the light intensity is consistent, the amount of electricity generated will definitely be reduced to a certain extent. Therefore, it can be judged based on the offset between the actual power generation efficiency and the ideal power generation efficiency of the photovoltaic power device. Because in the same area, the distribution of dirt should be continuous and should be in a transitional relationship with adjacent devices, the dirt level can be distinguished by dividing the area according to the height of the offset image. In addition, the evaluation of dirt can be indirectly achieved by evaluating the light reflection efficiency of the photovoltaic panel surface.

[0028] As another preferred embodiment of the present invention, a low temperature auxiliary verification step is also included, specifically including: The smoothness of the feed offset distribution image is evaluated, the protruding nodes are marked and the photovoltaic objects corresponding to the distribution coordinates of the protruding nodes are obtained. If the corresponding marked risk level is to be evaluated, the alignment is re-marked and the corresponding risk level is marked as high risk.

[0029] In this embodiment, based on the contamination judgment step in the previous embodiment, further, for adjacent photovoltaic power equipment in the same area, their surface contamination states caused by environmental influences should be similar. Therefore, when an unusual peak appears in the offset distribution image, it indicates that the photovoltaic power equipment at the corresponding point may be affected by factors other than contamination. Therefore, an overlapping judgment can be made based on the risk mark to be assessed in the content of the aforementioned embodiment. If the two overlap, it means that there is a problem with the equipment and there is a risk that timely maintenance management is required. Therefore, the risk level is remarked as high risk.

[0030] As another preferred embodiment of the present invention, it also includes a collaborative optimization management step, specifically including: Acquire power feeding data per unit time of several photovoltaic objects, and perform power feeding curve fitting on the several power feeding data according to the continuous sequence of the tilt state data, wherein the power feeding curve is used to characterize the correlation between the unit light intensity, the steady-state extreme temperature and the power feeding capacity of the photovoltaic object; A peak value is determined for the feeding curve. If the feeding curve has a unique feeding peak value, it indicates that both the unit light intensity and the steady-state extreme value are not greater than the optimal efficiency state. The inclination state data of the photovoltaic object corresponding to the feeding peak value is set as the current optimal posture solution. If the feed curve has a non-unique feed peak, and one side of the feed peak is in an ascending or descending state, and the other side is in a balanced state, then the unit illumination of the photovoltaic unit corresponding to the balanced state exceeds the optimal efficiency state, and the corresponding temperature data is not greater than the optimal efficiency state, and the inclination state data of multiple photovoltaic objects corresponding to the balanced state can be set as the current optimal posture solution; If the feeding curve has a non-unique feeding peak, and the two sides of the feeding peak are in opposite rising or falling states, then the unit light intensity and steady-state extreme value of the photovoltaic unit corresponding to the equilibrium state exceed the optimal efficiency state under the current environment, and the inclination state data of the photovoltaic object corresponding to the equilibrium state is set as the current optimal posture solution.

[0031] In this embodiment, during the initial execution, it is necessary to set a photovoltaic power device to face the sun to obtain maximum unit area illumination, thereby realizing the critical point of the maximum feeding light threshold (increased light will increase power generation efficiency, but increased temperature will inhibit power generation efficiency, so the higher the light intensity is, the better), and then realizing the control of the unit light intensity (i.e., the angle with the direct direction of the sun) and temperature of the photovoltaic power device, so that the photovoltaic power device is in an optimal power generation state.

[0032] like Figure 3 As shown, the present invention also provides a risk identification system for power equipment, which comprises: The data updating module 100 is used to update and acquire the sensor data of the monitoring area at a preset data collection period interval, and the sensor data of each collection period node includes multiple groups of multi-type sensor data that are aligned and stacked; The object matching module 200 is used to locate and match the data coordinates and distribution coordinates of several photovoltaic objects based on the image data of the sensor data, and obtain the inclination state data corresponding to the distribution coordinates one by one, and the inclination state data is used to characterize the spatial orientation of the photovoltaic panel; The characteristic evaluation module 300 is used to process the thermal sensor data of several photovoltaic objects, and obtain the rising coefficient and the steady-state extreme value of the temperature change state of each photovoltaic object, wherein the rising coefficient and the steady-state extreme value are both associated with the current illumination conditions and the tilt state data; The risk marking module 400 is used to arrange the rising coefficients and the steady-state extreme values ​​of the array based on the continuous sequence of the tilt state data to establish an evaluation sequence, and to perform smooth curve fitting evaluation on the rising coefficients and the steady-state extreme values ​​in the evaluation sequence respectively, and if the error fluctuation value of the smooth curve is exceeded, the photovoltaic object is marked as risk; The risk assessment module 500 is used to evaluate the risk bias of the photovoltaic object marked with risk. If the rising coefficient or the steady-state extreme value is higher than the smooth curve fitting value, the risk level is marked as high risk, otherwise the risk level is marked as to be evaluated, and the risk object is visualized based on the risk level.

[0033] As another preferred embodiment of the present invention, the data updating module includes: A dynamic update unit is used to execute the dynamic monitoring step, initialize the tilt states of several photovoltaic objects so that the photovoltaic objects face away from the illumination direction, and evenly select multiple evaluation nodes between the back illumination direction and the maximum illumination direction after initialization, and control the photovoltaic objects to stay at multiple evaluation nodes in sequence and record corresponding sensor data; The static updating unit is used to execute the static monitoring step. When the photovoltaic object enters the normal operation posture, the photovoltaic object is monitored at a preset data collection cycle interval to obtain sensor data. The data collection cycle interval is not shared with the dynamic monitoring interval.

[0034] like Figure 4 As shown, as another preferred embodiment of the present invention, it also includes a dirt assessment module 600, which specifically includes: A power feeding feedback unit 610 is used to obtain power feeding data of a plurality of photovoltaic objects per unit time, and spatially arrange the plurality of power feeding data based on the distribution coordinates of the photovoltaic objects; An ideal evaluation unit 620 is used to calculate the unit illumination intensity of several photovoltaic objects according to the inclination attitude data of the photovoltaic objects and the current ambient illumination intensity, and evaluate the ideal power feeding intensity of the photovoltaic objects through a preset photovoltaic power feeding illumination model; The deviation evaluation unit 630 is used to calculate the difference between the ideal feeding intensity and the feeding data, obtain the feeding offset value of the corresponding photovoltaic object, and perform spatial surface fitting on several feeding offset values ​​based on the spatial arrangement of the feeding data to obtain a feeding offset distribution image; The dirt rating unit 640 is used to divide the feed offset distribution image into deviation level areas according to a preset spatial height ladder, and the deviation level area with a larger feed offset value has a higher corresponding dirt level.

[0035] As another preferred embodiment of the present invention, it also includes a low temperature auxiliary verification module; The low-temperature auxiliary verification module is used to evaluate the smoothness of the feed offset distribution image, mark the protruding nodes and obtain the photovoltaic objects corresponding to the distribution coordinates of the protruding nodes. If the corresponding marked risk level is to be evaluated, the alignment is re-marked and the corresponding risk level is marked as high risk.

[0036] As another preferred embodiment of the present invention, a collaborative optimization module is also included, specifically including: A power feeding fitting unit is used to obtain power feeding data per unit time of several photovoltaic objects, and to perform power feeding curve fitting on the several power feeding data according to a continuous sequence of tilt state data, wherein the power feeding curve is used to characterize the correlation between the unit light intensity, the steady-state extreme temperature and the power feeding capacity of the photovoltaic object; A peak determination unit is used to perform peak determination on the feeding curve. If the feeding curve has a unique feeding peak, it indicates that both the unit light intensity and the steady-state extreme value are not greater than the optimal efficiency state, and the inclination state data of the photovoltaic object corresponding to the feeding peak is set as the current optimal posture solution; A balance state determination unit, for if there is a non-unique feed peak value in the feed curve, and one side of the feed peak value is in an ascending or descending state, and the other side is in a balance state, then the unit illumination of the photovoltaic unit corresponding to the balance state exceeds the optimal efficiency state, and the corresponding temperature data is not greater than the optimal efficiency state, and the inclination state data of multiple photovoltaic objects corresponding to the balance state can all be set as the current optimal posture solution; A multi-peak state determination unit is used for, if there is a non-unique feeding peak value in the feeding curve, and the two sides of the feeding peak value are in opposite rising or falling states, then the unit light intensity and the steady-state extreme value of the photovoltaic unit corresponding to the equilibrium state are both beyond the optimal efficiency state under the current environment, and the inclination state data of the photovoltaic object corresponding to the equilibrium state is set as the current optimal posture solution.

[0037] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0038] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0039] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for identifying risks of power equipment, characterized in that: Include: The sensor data of the monitoring area is updated and acquired at preset data acquisition cycle intervals, and the sensor data of each acquisition cycle node includes multiple groups of multi-type sensor data that are aligned and stacked; Positioning and matching the data coordinates and distribution coordinates of several photovoltaic objects based on the image data of the sensor data, and obtaining the inclination state data corresponding to the distribution coordinates one by one, wherein the inclination state data is used to characterize the spatial orientation of the photovoltaic panel; Processing the thermal sensor data of several photovoltaic objects to obtain the rising coefficient and steady-state extreme value of the temperature change state of each photovoltaic object, wherein the rising coefficient and steady-state extreme value are both associated with the current lighting conditions and the tilt state data; Arranging the rising coefficients and steady-state extreme values ​​of the array based on the continuous sequence of the tilt state data to establish an evaluation sequence, and performing smooth curve fitting evaluation on the rising coefficients and steady-state extreme values ​​in the evaluation sequence, and marking the photovoltaic object for risk if the error fluctuation value of the smooth curve is exceeded; The risk bias evaluation is performed on the photovoltaic object with risk mark. If the rising coefficient or the steady-state extreme value is higher than the smooth curve fitting value, the risk level is marked as high risk. Otherwise, the risk level is marked as to be evaluated. The risk object is visualized based on the risk level.

2. A method for identifying risks of electric power equipment according to claim 1, characterized in that: The step of updating and acquiring the sensor data of the monitoring area at a preset data collection period interval specifically includes: Execute the dynamic monitoring step, initialize the tilt states of several photovoltaic objects, so that the photovoltaic objects face away from the illumination direction, and evenly select multiple evaluation nodes between the back illumination direction and the maximum illumination direction after initialization, and control the photovoltaic objects to stay at multiple evaluation nodes in sequence and record corresponding sensor data; The static monitoring step is performed. When the photovoltaic object enters a normal operating posture, the photovoltaic object is monitored at a preset data collection cycle interval to obtain sensor data. The data collection cycle interval is not shared with the dynamic monitoring interval.

3. A method for identifying risks of electric power equipment according to claim 2, characterized in that: It also includes a soiling assessment step, which includes: Acquire power feeding data per unit time of a plurality of photovoltaic objects, and spatially arrange the plurality of power feeding data based on distribution coordinates of the photovoltaic objects; Calculating the unit illumination intensity of several photovoltaic objects according to the inclination attitude data of the photovoltaic objects and the current ambient illumination intensity, and evaluating the ideal power feeding intensity of the photovoltaic objects through a preset photovoltaic power feeding illumination model; Calculating the difference between the ideal feeding intensity and the feeding data, obtaining a feeding offset value of the corresponding photovoltaic object, and performing spatial surface fitting on several feeding offset values ​​based on the spatial arrangement of the feeding data to obtain a feeding offset distribution image; The feed offset distribution image is divided into deviation level areas by using preset spatial height steps, and the deviation level area with a larger feed offset value has a higher corresponding dirt level.

4. A method for identifying risks of electric power equipment according to claim 3, characterized in that: It also includes low temperature auxiliary verification steps, including: The smoothness of the feed offset distribution image is evaluated, the protruding nodes are marked and the photovoltaic objects corresponding to the distribution coordinates of the protruding nodes are obtained. If the corresponding marked risk level is to be evaluated, the alignment is re-marked and the corresponding risk level is marked as high risk.

5. A method for identifying risks of electric power equipment according to claim 1, characterized in that: It also includes collaborative optimization management steps, including: Acquire power feeding data per unit time of several photovoltaic objects, and perform power feeding curve fitting on the several power feeding data according to the continuous sequence of the tilt state data, wherein the power feeding curve is used to characterize the correlation between the unit light intensity, the steady-state extreme temperature and the power feeding capacity of the photovoltaic object; A peak value is determined for the feeding curve. If the feeding curve has a unique feeding peak value, it indicates that both the unit light intensity and the steady-state extreme value are not greater than the optimal efficiency state. The inclination state data of the photovoltaic object corresponding to the feeding peak value is set as the current optimal posture solution. If the feed curve has a non-unique feed peak, and one side of the feed peak is in an ascending or descending state, and the other side is in a balanced state, then the unit illumination of the photovoltaic unit corresponding to the balanced state exceeds the optimal efficiency state, and the corresponding temperature data is not greater than the optimal efficiency state, and the inclination state data of multiple photovoltaic objects corresponding to the balanced state can be set as the current optimal posture solution; If the feeding curve has a non-unique feeding peak, and the two sides of the feeding peak are in opposite rising or falling states, then the unit light intensity and steady-state extreme value of the photovoltaic unit corresponding to the equilibrium state exceed the optimal efficiency state under the current environment, and the inclination state data of the photovoltaic object corresponding to the equilibrium state is set as the current optimal posture solution.

6. A risk identification system for power equipment, characterized in that: Include: A data updating module is used to update and acquire sensor data of the monitoring area at preset data collection period intervals, and the sensor data of each collection period node includes multiple groups of aligned and stacked sensor data of multiple types; An object matching module is used to locate and match the data coordinates and distribution coordinates of several photovoltaic objects based on the image data of the sensor data, and to obtain the inclination state data corresponding to the distribution coordinates one by one, wherein the inclination state data is used to characterize the spatial orientation of the photovoltaic panel; A feature evaluation module is used to process the thermal sensor data of several photovoltaic objects to obtain the rising coefficient and the steady-state extreme value of the temperature change state of each photovoltaic object, wherein the rising coefficient and the steady-state extreme value are both associated with the current lighting conditions and the tilt state data; A risk marking module, used for arranging the rising coefficients and steady-state extreme values ​​of the array based on the continuous sequence of the tilt state data to establish an evaluation sequence, and performing smooth curve fitting evaluation on the rising coefficients and steady-state extreme values ​​in the evaluation sequence respectively, and if the error fluctuation value of the smooth curve is exceeded, the photovoltaic object is marked as risk; The risk assessment module is used to evaluate the risk bias of the photovoltaic object marked with risk. If the rising coefficient or the steady-state extreme value is higher than the smooth curve fitting value, the risk level is marked as high risk, otherwise the risk level is marked as to be evaluated, and the risk object is visualized based on the risk level.

7. A risk identification system for electric power equipment according to claim 6, characterized in that: The data updating module comprises: A dynamic update unit is used to execute the dynamic monitoring step, initialize the tilt states of several photovoltaic objects so that the photovoltaic objects face away from the illumination direction, and evenly select multiple evaluation nodes between the back illumination direction and the maximum illumination direction after initialization, and control the photovoltaic objects to stay at multiple evaluation nodes in sequence and record corresponding sensor data; The static updating unit is used to execute the static monitoring step. When the photovoltaic object enters the normal operation posture, the photovoltaic object is monitored at a preset data collection cycle interval to obtain sensor data. The data collection cycle interval is not shared with the dynamic monitoring interval.

8. A risk identification system for electric power equipment according to claim 7, characterized in that: Also included is the Soil Assessment Module, which includes: A power feeding feedback unit, used to obtain power feeding data of a plurality of photovoltaic objects per unit time, and spatially arrange the plurality of power feeding data based on the distribution coordinates of the photovoltaic objects; An ideal evaluation unit, used to calculate the unit illumination intensity of several photovoltaic objects according to the inclination attitude data of the photovoltaic objects and the current ambient illumination intensity, and evaluate the ideal power feeding intensity of the photovoltaic objects through a preset photovoltaic power feeding illumination model; a deviation evaluation unit, configured to calculate the difference between the ideal power feeding intensity and the power feeding data, obtain a power feeding offset value of the corresponding photovoltaic object, and perform spatial surface fitting on several power feeding offset values ​​based on the spatial arrangement of the power feeding data to obtain a power feeding offset distribution image; The dirt rating unit is used to divide the feed offset distribution image into deviation level areas according to preset spatial height steps, and the deviation level area with a larger feed offset value has a higher corresponding dirt level.

9. A risk identification system for electric power equipment according to claim 8, characterized in that: It also includes a cryogenic auxiliary verification module; The low-temperature auxiliary verification module is used to evaluate the smoothness of the feed offset distribution image, mark the protruding nodes and obtain the photovoltaic objects corresponding to the distribution coordinates of the protruding nodes. If the corresponding marked risk level is to be evaluated, the alignment is re-marked and the corresponding risk level is marked as high risk.

10. A risk identification system for electric power equipment according to claim 6, characterized in that: It also includes collaborative optimization modules, including: A power feeding fitting unit is used to obtain power feeding data per unit time of several photovoltaic objects, and to perform power feeding curve fitting on the several power feeding data according to a continuous sequence of tilt state data, wherein the power feeding curve is used to characterize the correlation between the unit light intensity, the steady-state extreme temperature and the power feeding capacity of the photovoltaic object; A peak determination unit is used to perform peak determination on the feeding curve. If the feeding curve has a unique feeding peak, it indicates that both the unit light intensity and the steady-state extreme value are not greater than the optimal efficiency state, and the inclination state data of the photovoltaic object corresponding to the feeding peak is set as the current optimal posture solution; A balance state determination unit, for if there is a non-unique feed peak value in the feed curve, and one side of the feed peak value is in an ascending or descending state, and the other side is in a balance state, then the unit illumination of the photovoltaic unit corresponding to the balance state exceeds the optimal efficiency state, and the corresponding temperature data is not greater than the optimal efficiency state, and the inclination state data of multiple photovoltaic objects corresponding to the balance state can all be set as the current optimal posture solution; A multi-peak state determination unit is used for, if there is a non-unique feeding peak value in the feeding curve, and the two sides of the feeding peak value are in opposite rising or falling states, then the unit light intensity and the steady-state extreme value of the photovoltaic unit corresponding to the equilibrium state are both beyond the optimal efficiency state under the current environment, and the inclination state data of the photovoltaic object corresponding to the equilibrium state is set as the current optimal posture solution.

Images