CN112886923A - Photovoltaic power station operation and maintenance method and device in thunder and lightning weather - Google Patents

Photovoltaic power station operation and maintenance method and device in thunder and lightning weather Download PDF

Info

Publication number
CN112886923A
CN112886923A CN202110045887.6A CN202110045887A CN112886923A CN 112886923 A CN112886923 A CN 112886923A CN 202110045887 A CN202110045887 A CN 202110045887A CN 112886923 A CN112886923 A CN 112886923A
Authority
CN
China
Prior art keywords
lightning
preset area
photovoltaic power
preset
thunder
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110045887.6A
Other languages
Chinese (zh)
Other versions
CN112886923B (en
Inventor
不公告发明人
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Jianyi Energy Research Institute Co ltd
Original Assignee
Individual
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Individual filed Critical Individual
Priority to CN202110045887.6A priority Critical patent/CN112886923B/en
Publication of CN112886923A publication Critical patent/CN112886923A/en
Application granted granted Critical
Publication of CN112886923B publication Critical patent/CN112886923B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
    • H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Educational Administration (AREA)
  • Development Economics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Game Theory and Decision Science (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Photovoltaic Devices (AREA)

Abstract

The application discloses a photovoltaic power station operation and maintenance method and equipment in thunder weather, wherein thunder characteristic data are input into a thunder prediction model so as to determine thunder occurrence time and thunder falling point position information in a preset area, and the preset area comprises a photovoltaic power station; determining lightning risk factors of a preset area and lightning risk levels according to the information of the lightning landing point, the geographical position information of the photovoltaic power station in the preset area and lightning data; and determining the operation and maintenance priority level of the photovoltaic power station in the preset area and generating maintenance information of the photovoltaic power station according to the lightning occurrence time, the photovoltaic power station information in the preset area and the lightning risk level. By the method, the photovoltaic power station in the range influenced by thunder and lightning is subjected to targeted operation, maintenance and protection aiming at the thunder and lightning occurrence time, the thunder and lightning risk level and the lightning falling point position.

Description

Photovoltaic power station operation and maintenance method and device in thunder and lightning weather
Technical Field
The application relates to the technical field of solar power generation, in particular to a photovoltaic power station operation and maintenance method and device in thunder and lightning weather.
Background
In recent years, the nation vigorously carries out a green, environment-friendly and sustainable development concept, and photovoltaic power generation is continuously developed and becomes a green energy project encouraged by the nation. In order to ensure the stable operation of the photovoltaic power station, the operation and maintenance work of the photovoltaic power station is particularly important. In the prior art, an intelligent operation and maintenance system of a photovoltaic power station is mainly used for maintaining normal operation of equipment, detecting equipment faults, predicting generated energy and the like. However, under special climatic conditions, different devices are susceptible to different damage degrees, and all the devices are monitored in the same way, which easily causes resource waste.
In addition, in special climatic conditions, the influence of lightning weather on the photovoltaic power station is large, and in the prior art, aiming at the lightning weather, the intelligent operation and maintenance method of the photovoltaic power station in the lightning weather is lacked by designing a lightning protection system, arranging a lightning rod and the like; when equipment has a fault premonition under special climatic conditions, maintenance personnel are required to go to the site for maintenance, and the danger coefficient of the maintenance personnel is increased.
Disclosure of Invention
The embodiment of the application provides a photovoltaic power station operation and maintenance method and equipment in thunder and lightning weather, which are used for solving the following technical problems in the prior art: the photovoltaic power station intelligent operation and maintenance system lacks a targeted operation and maintenance method under the thunder and lightning weather condition, the power generation efficiency is influenced by the damage of equipment components under the thunder and lightning weather condition, and potential safety hazards exist in field maintenance of maintenance personnel.
On one hand, the embodiment of the application provides a photovoltaic power station operation and maintenance method in thunder and lightning weather, and the method comprises the following steps: inputting lightning characteristic data into a lightning prediction model to determine lightning occurrence time and lightning landing point position information in a preset area, wherein the preset area comprises a photovoltaic power station; determining thunder and lightning data in a preset area according to the thunder and lightning occurrence time and the information of the lightning landing point; determining lightning risk factors of a preset area according to the information of the lightning landing point, the geographical position information of one or more photovoltaic power stations in the preset area and lightning data in the preset area; the lightning data in the preset area comprise any one or more of the following items: thunderstorm days in a preset time, lightning density of a preset area and lightning current intensity of the preset area; determining the lightning risk level of a preset area according to the lightning risk factors; and determining the operation and maintenance priority level of the photovoltaic power stations in the preset area and generating maintenance information of the photovoltaic power stations according to the thunder occurrence time, the information of one or more photovoltaic power stations in the preset area and the thunder risk level.
The photovoltaic power station operation and maintenance method of thunder and lightning weather provided by the embodiment of the application determines the thunder and lightning occurrence time and the position of the lightning falling point through the thunder and lightning prediction model, determines the operation and maintenance priority level of the photovoltaic power station according to the thunder and lightning risk level and generates maintenance information, realizes that the photovoltaic power station in the range influenced by thunder and lightning carries out targeted operation and maintenance and protection aiming at the thunder and lightning occurrence time, the thunder and lightning risk level and the position of the lightning falling point, effectively avoids the waste of operation and maintenance resources, improves the operation and maintenance efficiency, and realizes key operation and maintenance.
In an implementation of the present application, according to the landmine point position information, the geographical position information of one or more photovoltaic power stations in the preset area, and the thunder and lightning data in the preset area, the thunder and lightning risk factor is determined, which specifically includes: determining the number of thunderstorm days in a preset time period in a preset area by taking the information of the position of the thunderstorm point as the center and the preset value as the radius; determining the annual average lightning current intensity of the previous year of the lightning strike time by taking the information of the lightning strike point position as a center; determining the size relation between the height of each photovoltaic power station in a preset area and the height of the highest building in the preset area, and determining the relative height between the photovoltaic power stations in the preset area and the highest building in the preset area; determining the lightning strike density of the preset area, wherein the lightning strike density is the number of lightning strikes per unit area in the preset area within one year; determining lightning risk factors according to the number of thunderstorm days in preset time, the lightning current intensity of a preset area, the relative height between a photovoltaic power station in the preset area and a highest building in the preset area and the lightning stroke density of the preset area; the lightning risk factor is positively correlated with thunderstorm days, lightning current intensity, relative height of the photovoltaic power station and the highest building and lightning stroke density of a preset area.
In the embodiment of the application, the lightning-falling point position is used as the center, the preset value is used as the radius, the preset area is determined, the lightning data of the preset area is further obtained, the lightning influence range is enlarged, the range of the photovoltaic power station which is possibly damaged by lightning is enlarged, and the economic loss caused by the undersize monitoring range is avoided.
In an implementation manner of the present application, according to the landmine point position information, the geographical position information of one or more photovoltaic power stations in the preset area and the lightning data in the preset area, the lightning risk factor of the preset area is determined, specifically: according to g ═ Dm×Im×Cg×Sg×ρgGenerating a lightning risk factor; wherein g is a lightning risk factor, and g is positively correlated with the lightning risk level; dm is a thunderstorm day factor, and is positively correlated with the thunderstorm days in a preset time period; im is a lightning current intensity factor, and Im is positively correlated with the lightning current intensity in a preset area; cg is a position factor of the photovoltaic power station, and the Cg is positively correlated with the relative height of the photovoltaic power station and the highest building in the preset area; sg is a floor area factor of the photovoltaic power station in the preset area, and the Sg is positively correlated with the total floor area of all the photovoltaic power stations in the preset area; rhogIs a lightning strike density factor, rho, of a photovoltaic power stationgAnd positively correlated with the lightning strike density in the preset area.
In one implementation of the present application, the lightning risk factor g in a preset area satisfies 10-5＜g＜3.125×10-2In the case of (1), the lightning risk level is a secondary risk level; the lightning risk factor g in the preset area meets 3.125 multiplied by 10-2If g is less than 1, the lightning risk grade is a first-grade risk grade; wherein the primary risk level is higher than the secondary risk level.
In one implementation manner of the application, rho satisfies the condition that the lightning strike density rho in the preset area is greater than or equal to 0 and less than or equal to rho < 2gThe value is 0.1; for the condition that the lightning strike density rho in the preset area meets 2-3 rho, rhogThe value is 0.5; for the condition that the lightning strike density rho in the preset area meets 3-rho, rhogThe value is 1; for the condition that d is more than or equal to 0 and less than 25 in the thunderstorm days d of one year in the preset area, the value Dm is 0.1; for the condition that d is more than or equal to 25 and less than 60, the number of thunderstorm days d in one year in the preset area is 0.5; for in a preset areaThe value Dm of the thunderstorm days d in one year is 1 when d is more than or equal to 60 and less than or equal to 365; for the condition that the height of a building in a preset area is higher than the height of the photovoltaic power station, the Cg value is 0.1; for the condition that the difference value between the height of the photovoltaic power station and the height of a building in a preset area is lower than a preset threshold value, the Cg value is 0.5; for the condition that no other objects exist around the photovoltaic power station in a preset area, the Cg value is 1; the Im value is 0.1 when the lightning current intensity I in the preset area meets the condition that I is more than or equal to 0 and less than 20; the Im value is 0.5 when the lightning current intensity I in the preset area meets the condition that I is more than or equal to 20 and less than 40; the Im value is 1 when the lightning current intensity I in the preset area meets the condition that I is not less than 40; the occupied area S of all photovoltaic power stations in the preset area meets the condition that S is more than or equal to 0 and less than 5000, and the value of Sg is 0.1; for the condition that the occupied area S of all photovoltaic power stations in the preset area is equal to or more than 5000 and less than 7500, the value of Sg is 0.5; and for the condition that the occupied area S of all photovoltaic power stations in the preset area meets 7500-S, the value of Sg is 1.
In one implementation manner of the present application, after determining the operation and maintenance priority level of the corresponding photovoltaic power station and generating the maintenance information of the photovoltaic power station, the method includes: under the condition that the lightning occurrence time is a first preset time period and the lightning risk level is a first-level risk level, remotely lifting a lightning receiving device of a corresponding photovoltaic power station and cutting off the circuit connection of a photovoltaic assembly; the first preset time period is a time period corresponding to the angle between the photovoltaic module and the light source being less than 10 degrees; under the condition that the lightning occurrence time is a second preset time period and the lightning risk level is a first-level risk level, remotely lifting a lightning receiving device of the corresponding photovoltaic power station or remotely lifting the lightning receiving device of the corresponding photovoltaic power station and simultaneously cooling a diode in the photovoltaic module based on historical lightning data of the photovoltaic power station in a preset area; the second preset time period is a time period corresponding to the angle between the photovoltaic module and the light source being greater than or equal to 10 degrees; and under the condition that the lightning occurrence time is a second preset time period and the lightning risk grade is a secondary risk grade, remotely lifting the lightning receiving device of the corresponding photovoltaic power station.
According to the embodiment of the application, different lightning protection measures are implemented according to the lightning occurrence time, the lightning risk level and historical lightning data of the photovoltaic power station in the preset area, so that lightning damage of the photovoltaic module is effectively avoided and the power generation amount is ensured according to different lightning occurrence times and lightning risk levels; in addition, the maintenance measures can be operated remotely, and the personal safety of maintenance personnel in thunder and lightning weather is guaranteed.
In an implementation of the present application, the thunder and lightning characteristic data is input into the thunder and lightning prediction model to determine the thunder and lightning occurrence time and the thunder and lightning location information in the preset area, which specifically includes: determining thunder characteristic data in a preset area, wherein the thunder characteristic data comprises atmospheric electric field information and radar echo intensity information, and the atmospheric electric field information is atmospheric electric field characteristic data continuously acquired when thunder occurs; the radar echo intensity is radar echo data in the lightning occurrence region; and inputting the lightning characteristic data into the trained neural network model to obtain the lightning occurrence time and the lightning strike point position.
In an implementation manner of the present application, determining lightning characteristic data in a preset area further includes: the method comprises the steps that a connection line collects the maximum value of electric field intensity, the change rate of the electric field intensity, the average value of the electric field intensity and the variance of the electric field intensity in the electric field time sequence process in a preset area, when the maximum value of the electric field intensity, the change rate of the electric field intensity, the average value of the electric field intensity and the variance of the electric field intensity are higher than an electric field information preset threshold value, the corresponding time is determined to be lightning occurrence time, the electric field time sequence process is a process of collecting continuous electric field intensity in a fixed time window width, and the electric field information preset threshold value is corresponding electric; and acquiring the maximum echo intensity, the block echo center intensity and the echo block area of the preset height in the air, and when the maximum echo intensity, the block echo center intensity and the echo block area of the preset height in the air in a preset area are higher than a radar echo preset threshold value, determining that the corresponding area is the position of a lightning-down point, wherein the radar echo preset threshold value is corresponding radar echo data when lightning occurs.
In an implementation of the present application, before inputting the lightning characteristic data into the trained neural network model and obtaining the lightning occurrence time and the lightning strike point position, the method further includes: acquiring lightning occurrence time corresponding to historical lightning and the lightning characteristic data of the lightning landing point position as training sample data; classifying training sample data into an atmospheric electric field information data set and a radar echo intensity data set, wherein the atmospheric electric field information data set is related to lightning occurrence time, and the radar echo intensity data set is related to a lightning landing point position; screening the atmospheric electric field information data set and the radar echo intensity data set; and respectively inputting the processed atmospheric electric field information data set and radar echo intensity data set into a neural network model to train the neural network model.
On the other hand, this application embodiment still provides a photovoltaic power plant fortune dimension equipment of thunder and lightning weather, and equipment includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform a method of lightning weather photovoltaic plant operation and maintenance according to any one of claims 1 to 9.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of a photovoltaic power plant operation and maintenance method in lightning weather according to an embodiment of the present application;
fig. 2 is a schematic diagram of a lightning strike point monitoring range and a lightning influence range according to an embodiment of the present application;
fig. 3 is an internal structure schematic diagram of photovoltaic power plant operation and maintenance equipment in thunder and lightning weather provided by the embodiment of the application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more apparent, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person skilled in the art without making any inventive step based on the embodiments in the description belong to the protection scope of the present application.
In recent years, the nation vigorously carries out a green, environment-friendly and sustainable development concept, and photovoltaic power generation is continuously developed and becomes a green energy project encouraged by the nation. In order to ensure the stable operation of the photovoltaic power station, the operation and maintenance work of the photovoltaic power station is particularly important. In the prior art, an intelligent operation and maintenance system of a photovoltaic power station is mainly used for maintaining normal operation of equipment, detecting equipment faults, predicting generated energy and the like. However, under special climatic conditions, different devices are susceptible to different damage degrees, and all the devices are monitored in the same way, which easily causes resource waste.
In addition, in special climatic conditions, lightning weather has a large influence on the photovoltaic power station, and in the prior art, aiming at the lightning weather, the lightning protection system is mainly designed, the lightning rod is arranged, and the like, so that an intelligent operation and maintenance method in the lightning weather is lacked, and when the lightning weather with different risk levels occurs, which appropriate lightning protection measure is adopted to minimize the economic loss is unknown; when equipment has a fault premonition under special climatic conditions, maintenance personnel are required to go to the site for maintenance, and the danger coefficient of the maintenance personnel is increased.
The embodiment of the application provides a photovoltaic power station operation and maintenance method and equipment in thunder and lightning weather, through the analysis to thunder and lightning occurrence time, thunder and lightning point position and thunder and lightning risk factor, photovoltaic power station in to the thunder and lightning region of different thunder and lightning risk grades, thunder and lightning occurrence time sets up the operation and maintenance priority and carries out remote operation, the problem of photovoltaic power station intelligence operation and maintenance system lack the pertinence operation and maintenance method under thunder and lightning weather condition is solved, the equipment assembly is impaired under thunder and is influenced generating efficiency, the technical problem of potential safety hazard exists in the field maintenance of maintainer. The method has the advantages that targeted key operation and maintenance are realized, operation and maintenance efficiency is improved, in addition, targeted remote lightning protection measures are taken according to lightning risk levels, historical lightning data of the photovoltaic power station and other conditions in thunderstorm weather, circuit connection is timely cut off under necessary conditions, loss minimization is realized, and personal safety of maintenance personnel is guaranteed.
It is clear to those skilled in the art that except for lightning weather, other extreme weather such as strong wind, sand and dust, extreme temperature and the like all need to perform targeted operation and maintenance on a photovoltaic power station, and in an actual situation, risk grade division can be performed according to different extreme weather types, so that a targeted operation and maintenance method is determined. The main discussion of this application is under thunder and lightning weather, carries out thunder and lightning risk grade to the region that photovoltaic power plant is located and divides, sets up the operation and maintenance priority to the photovoltaic power plant in the thunderbolt region according to different thunder and lightning risk grade, emergence time, realizes the pertinence operation and maintenance method. The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a photovoltaic power station operation and maintenance method in lightning weather provided in an embodiment of the present application. It is clear to those skilled in the art that the execution subject in the embodiment of the present application may be a server or a remote operation platform and any kind of device with processing capability. As shown in fig. 1, the photovoltaic power plant operation and maintenance method in lightning weather mainly includes the following steps:
step 101, predicting lightning occurrence time and lightning falling point positions.
The photovoltaic power station operation and maintenance method in thunder and lightning weather provided by the embodiment of the application firstly predicts the thunder and lightning occurrence time and the lightning drop point position. And after lightning data in a preset area are determined, inputting the lightning characteristic data into a neural network model to obtain the lightning occurrence time and the lightning drop point position.
In one embodiment of the present application, the neural network model first needs to be trained before inputting the lightning characteristic data into the neural network model.
Specifically, training sample data is first selected. The thunder characteristic data of a thunder point area and a thunder time corresponding to historical thunder can be acquired from the Internet, wherein the thunder characteristic data comprise atmospheric electric field information and radar echo intensity, and a large number of historical thunder moments and areas are selected in order to improve the accuracy of a training model.
As will be clear to those skilled in the art, with the support of the state on photovoltaic power stations, photovoltaic power stations are spread all over the country, and besides large centralized photovoltaic power stations, there are many household photovoltaic power stations, and therefore, when training sample data is selected, a lightning area should be selected in a large range. For example, areas where lightning occurs in coastal areas, inland areas, plateau areas, plain areas, hilly areas, south and northwest of China and the like can be simultaneously selected, and the lightning occurrence time includes any time of 24 hours. And secondly, classifying the training sample data to obtain an atmospheric electric field information data set related to the thunder and lightning occurrence time and a radar echo intensity data set related to the lightning landing point position. Then, the training sample data is screened, inaccurate data contained in the current training sample data is removed, for example, the corresponding lightning historical time is corresponding to the condition that other extreme climates occur besides lightning, and a training data set is obtained. And finally, respectively inputting the obtained training data sets into a neural network model for training so as to output the lightning occurrence time and the lightning strike point position until the output converges, thereby obtaining the neural network training model.
In an embodiment of the application, the atmospheric electric field information is atmospheric electric field characteristic data continuously collected in a lightning region when the current lightning occurs, and includes one or more of the following items: the maximum value of the electric field intensity when the electric field fluctuates, the rate of change of the electric field intensity, the average value of the electric field intensity, and the variance of the electric field intensity.
When the maximum value of the electric field strength, the change rate of the electric field strength, the average value of the electric field strength, and the variance of the electric field strength at a certain time are greater than a preset threshold value, it is determined that the lightning occurrence time is short.
It should be clear to those skilled in the art that different areas have different atmospheric electric fields when lightning occurs due to differences in meteorological elements such as atmospheric temperature and humidity. Therefore, in the embodiment of the present application, the preset value of the atmospheric electric field information sets different threshold values according to different regions. Taking the atmospheric electric field data of a meteorological observation station in a certain area in the south as an example, when the maximum value of the electric field intensity is more than 1.5kV/m, the change rate of the electric field intensity is more than 0, the average value of the electric field intensity is more than 3kV/m when the electric field fluctuates and the variance of the electric field intensity is more than 0, the lightning occurs in the area; and when the maximum value of the electric field intensity is more than 3kV/m, lightning occurs according to statistical data of a certain area in the north. Because the corresponding atmospheric electric field information under the thunder and lightning weather is influenced by the region, the embodiment of the application does not specifically limit the atmospheric electric field information threshold value.
In one embodiment of the present application, the radar return intensity is radar return data in a lightning occurrence area, and includes one or more of the following items: the maximum echo intensity, the block echo center intensity and the echo block area of the air with preset height are obtained. When the maximum echo intensity, the block echo center intensity and the echo block area at the preset height of the preset area are higher than a radar echo preset threshold value, determining that the corresponding area is the position of a lightning-down point, wherein the radar echo preset threshold value is radar echo data corresponding to lightning.
The position of the lightning strike point is related to the radar echo intensity, and is determined according to the echo intensity characteristics in historical lightning weather. The preset threshold value is set to be 1/3 that the maximum echo intensity at the height of-10 ℃ in the air is greater than or equal to 40dBZ, the central intensity of the block echo is greater than or equal to 50dBZ, and the area of the echo block is compact and greater than the area of a radar echo map of a preset area.
As can be clearly understood by those skilled in the art, the occurrence of echo intensity of 40dBZ at the height of-10 ℃ can be taken as the basis of lightning strike, the central intensity of the block echo is greater than or equal to 50dBZ, and 1/3, which is the area of the echo block is compact and greater than the radar echo map area of the preset area, is the radar echo characteristic of the lightning occurrence area.
Furthermore, the thunder characteristic data is input into the neural network model to obtain the predicted thunder occurrence time and the lightning drop point position. Specifically, the thunder characteristic data are input into a neural network training model through an input layer of a neural network, then the thunder characteristic data are processed and classified through a hidden layer, and thunder occurrence time and thunder falling point positions are respectively output on an output layer.
And 102, determining the lightning risk level.
And after the predicted lightning occurrence time and the lightning landing point position are obtained, determining the lightning risk level according to the lightning risk factors. The lightning risk factors are related to lightning point position information, photovoltaic power station geographic information and lightning data in a preset area.
In an embodiment of the application, before the lightning risk factor is determined, lightning data in a preset area is obtained through lightning occurrence time and a lightning drop point position. The lightning data in the preset area comprises any one or more of the following items: thunderstorm days in a preset time, lightning strike density of a preset area and lightning current intensity of the preset area.
In an embodiment of the present application, a preset area of a lightning strike point determined according to the information of the location of the lightning strike point is shown in fig. 2. Wherein the solid black dots 200 represent the predicted lightning strike point location information and the circle 210 range represents the lightning impact range including one or more photovoltaic power plants. And determining a preset area of the lightning drop point by taking the lightning drop point 200 as a circle center and taking the preset value as a radius, wherein the preset area of the lightning drop point comprises one or more photovoltaic power stations, as shown by a circle 220.
Specifically, for example, when a lightning strike point is 200 points, the center of the circle is 200 points, one or more photovoltaic power stations in an area with a lightning influence range of 2km radius, namely the area of the circle 210, are influenced by lightning, and a preset area, namely the area of the circle 220, can be generated by setting a preset value to 2.5 km. The method is characterized in that the range is expanded on the basis of the lightning influence range to obtain a preset area of a lightning falling point, the range can be set according to the situation, the influence range is expanded, and economic loss caused by a small monitoring range is prevented.
In one embodiment of the present application, the lightning risk factor is generated by the following formula:
g＝Dm×Im×Cg×Sg×ρg
wherein g is a lightning risk factor, Dm is a thunderstorm day factor, Im is a predicted lightning current intensity factor, Cg is a position factor of the photovoltaic power station, Sg is a floor area factor of the photovoltaic power station in a target monitoring range, and rho isgThe lightning density factor of the photovoltaic power station.
In an embodiment of the present application, Dm is a thunderstorm day factor, and a value of the thunderstorm day factor is positively correlated with the number of thunderstorm days d in a preset time period in a preset area, so as to represent that a lightning risk is from low to high. Specifically, the thunderstorm days of a preset area in a preset time period are counted.
It should be noted that the preset time period is one year before the lightning occurrence time.
Further, the value rule of the thunderstorm day factor Dm is as follows: when d is more than or equal to 0 and less than 25, the value Dm is 0.1; when d is more than or equal to 25 and less than 60, the value Dm is 0.5; when d is more than or equal to 60 and less than or equal to 365, the value Dm is 1; wherein d is the number of thunderstorm days in the preset time period in the preset area, and when the number of thunderstorm days in the preset area is less than 25 days, the thunderstorm day factor value is 0.1, which indicates that the lightning risk in the preset area is low; when the number of thunderstorm days in the preset area is between 25 days and 60 days, the thunderstorm day factor value is 0.5, which indicates that the lightning risk in the preset area is moderate; when the number of thunderstorm days in the preset area is more than 60 days and less than 365 days, the thunderstorm day factor value is 1, and the lightning risk in the preset area is high.
For example, the lightning occurrence time in the preset area is 12 months and 10 days in 2020, the number of lightning occurrence days between 12 months and 9 days in 2019 and 12 months and 9 days in 2020 in the preset area is counted as 2 days, in the first value range, the lightning risk is low, and correspondingly, the thunderstorm day factor takes a value of 0.1.
In an embodiment of the application, Im is a lightning current intensity factor, and a value of the lightning current intensity factor is positively correlated with an average lightning current intensity of a year in the lightning occurrence time in a preset area, and is used for indicating that the lightning risk is from low to high. Specifically, the average lightning current intensity is obtained by calculating a ratio of the total lightning current intensity of the preset area in one year to the lightning frequency of the preset area in the lightning strike time.
Further, the Im value rule is as follows: when I is more than or equal to 0 and less than 20, Im is 0.1; when I is more than or equal to 20 and less than 40, Im is 0.5; when I is more than or equal to 40, Im is 1; wherein I is the average lightning current intensity of a preset area in one year in the lightning strike time, and the unit is kA; when the average lightning current intensity is less than 20kA, the lightning current intensity factor is 0.1, which indicates that the lightning current intensity is lower and the lightning risk is lower when lightning occurs in the preset area in the last year; when the average lightning current intensity is between 20kA and 40kA, the lightning current intensity factor value is 0.5, which indicates that the lightning current intensity is medium and the lightning risk is medium when lightning occurs in the last year in the preset area; when the average lightning current intensity is larger than 40kA, the lightning current intensity factor is 1, which shows that the lightning current intensity is larger and the lightning risk is higher when lightning occurs in the preset area in the last year. For example, the lightning occurrence time in the preset area is predicted to be 10 days at 12 months and 10 days in 2020, the number of lightning occurrences in the preset area between 1 day at 2019 and 31 days at 12 months and 31 days in 2019 is counted to be 10 times, the total lightning current intensity when 10 lightning occurrences is 160kA, the average lightning current intensity in 2019 in one year is calculated to be 16kA, and in the first value range, the lightning risk in the preset area is low, and correspondingly, the lightning current intensity factor is 0.1.
In an embodiment of the application, Cg is a position factor, and a value of the position factor is positively correlated with a relative height between a photovoltaic power station in a preset area and a highest building in the preset area, and is used for indicating that a lightning risk is from low to high. Specifically, the relative height between the photovoltaic power station in the preset area and the highest building in the preset area is obtained by calculating the size relationship between the height of the photovoltaic power station and the height of the highest building in the preset area.
Further, the value rule of the position factor Cg is as follows: when the relative height is negative, namely the height of a building in the preset area is higher than that of the photovoltaic power station, and Cg is 0.1, the high building can attract lightning more easily, and the lightning risk of the photovoltaic power station in the preset area is small; when the relative height between the height of the photovoltaic power station and the height of the building in the preset area is lower than a preset threshold value, namely the height of the photovoltaic power station in the preset area is close to the height of the building, Cg is 0.5, and the risk of lightning stroke of the building and the photovoltaic power station is low; when relative height is positive, do not have other objects around the photovoltaic power plant in the predetermined area promptly, Cg value is 1, and under this condition, photovoltaic power plant is in spacious department, and its probability of being struck by lightning is bigger, and the thunder and lightning risk is higher. For example, when a building higher than the photovoltaic power station exists around the photovoltaic power station in the preset area of the lightning point according to the position of the lightning point, the building can attract lightning, correspondingly, the lightning risk of the photovoltaic power station is relatively low, and the value of the position factor Cg is 0.1.
In an embodiment of the present application, the floor area factor Sg is related to the floor area of all the photovoltaic power stations in the preset area, and the corresponding value rule is as follows: when S is more than or equal to 0 and less than 5000, the value of Sg is 0.1; when S is more than or equal to 5000 and less than 7500, the value of Sg is 0.5; when 7500 is less than or equal to S, Sg is 1; and S is the floor area of all photovoltaic power stations in a preset area, and the unit is square meter. The larger the area of the photovoltaic power stations in the preset area is, the more the number of the covered photovoltaic power stations is, and at the moment, the larger the damage is when lightning occurs, so that the occupied area factors are sequentially 0.1, 0.5 and 1 along with the increase of the occupied area so as to represent the trend of lightning risk increase.
In one embodiment of the present application, the lightning strike density factor ρgAnd positively correlating with the lightning stroke density, wherein the lightning stroke density is the number of lightning occurrences per unit area of one year in the preset area. Lightning strike density factor ρgThe corresponding value rule is as follows: when rho is more than or equal to 0 and less than 2, the rhogThe value is 1; when rho is more than or equal to 2 and less than 3, the rhogThe value is 0.5; when 3 is less than or equal to rho, the rhogThe value is 0.1; wherein rho is the lightning strike density and the unit is sub/meter2. The higher the lightning strike density is, the more the number of lightning strikes per unit area in the preset area is, the easier the lightning strike is, and therefore the corresponding lightning strike density factor is larger. For example, lightning is predicted to occur in 12 months and 10 days in 2020 and in 10 months in the preset area, the number of times of lightning occurring in the preset area in 2019 is counted as 100 times, the floor area of the preset area is 200 square meters, the lightning density is calculated as 0.5, namely the ratio of 100 times to 200 square meters, and in the first value range, the value of the lightning density factor is 0.1 at the moment.
Further, substituting the above parameters into the formula of lightning risk factor when 10-5＜g＜3.125×10-2The lightning risk level is a second level riskGrade; when the ratio is 3.125X 10-2When g is less than 1, the lightning risk grade is a first-grade risk grade; wherein the lightning risk of the first level risk level is higher than the lightning risk of the second level risk level.
It should be noted that when the values of the thunderstorm day factor, the lightning current intensity factor, the photovoltaic power station position factor, the floor area factor and the lightning strike density factor are all 0.1, the lightning risk corresponding to each parameter is extremely low, and at this time, the lightning risk factor in the preset area is 10-5. When the thunderstorm day factor, the lightning current intensity factor, the photovoltaic power station position factor, the floor area factor and the lightning density factor all take values of 0.5, the lightning risks represented by all parameters are moderate, and the lightning risk factor in the preset area is 3.125 multiplied by 10-2And represents a critical value of the lightning risk in a preset area. When thunderstorm day factor, thunder current intensity factor, photovoltaic power plant position factor, area factor and lightning density factor all take the value to be 1, the thunder and lightning risk that each parameter corresponds all is in high state, and the thunder and lightning risk factor that obtains the predetermined area at this moment is 1, and it is higher to show the thunder and lightning risk.
It will be clear to a person skilled in the art that the number of thunderstorm days, the cumulative number of thunderstorm times, the lightning current strength, etc. of the past time can be obtained from the lightning location system statistics.
And step 103, determining the operation and maintenance priority.
Specifically, according to the determined preset area, one or more photovoltaic power station information in the preset area is sent to an expert data platform. And the expert data platform sets the operation and maintenance priority level of the corresponding photovoltaic power station in the preset area according to the lightning occurrence time, the lightning risk level and the photovoltaic power station information in the preset area.
In an embodiment of the application, after a preset area of a good lightning strike point is determined, photovoltaic power station information of one or more photovoltaic power stations in the range is sent to an expert data platform, wherein the photovoltaic power station information includes one or more of the following: operation information, power generation equipment information, and historical lightning data information. The operation information comprises the operation state of the photovoltaic power station and the number of the photovoltaic power stations in the operation state, the power generation equipment information comprises the operation condition and the operation data of the power generation equipment, and the historical lightning data information comprises the number of photovoltaic equipment faults, fault types, power generation amount and corresponding maintenance measures in lightning weather.
It should be noted that information of one or more photovoltaic power stations in a preset area of a lightning drop point is sent to the expert data platform, the expert data platform determines the overall operation condition of the photovoltaic power stations in the range according to the operation state of the photovoltaic power stations in the preset area and the number of the photovoltaic power stations, and the failed photovoltaic power stations are found in time. And monitoring the operation condition of the power generation equipment of each photovoltaic power station according to the operation condition and the operation data of the power generation equipment, and further finding out the power generation equipment with faults in the photovoltaic power stations with faults. In addition, the photovoltaic power station which is easy to damage is located according to the number of faults in the historical lightning data information, maintenance is conducted according to the fault type, the purpose of monitoring the power generation amount is to monitor the power generation situation under the historical lightning weather so as to be convenient for comparing with the power generation situation under the actual lightning weather, and whether the power generation amount is abnormal under the actual lightning weather is judged. And sending maintenance measures in the thunder weather to form reference opinions and judging whether the photovoltaic power station is suitable for the maintenance measures in the thunder weather.
And 104, generating maintenance information.
Specifically, determining the operation and maintenance priority level of the photovoltaic power station in the preset area of the lightning drop point and generating maintenance information. In one embodiment of the application, in the case that the lightning occurrence time is a first preset time period and the lightning risk level is a primary risk level, the maintenance information is to remotely raise the lightning receptor of the corresponding photovoltaic power station and to disconnect the circuit connection of the photovoltaic module.
It should be noted that, when the lightning occurrence time is the first preset time period, the angle between the photovoltaic module and the light source is less than 10 degrees, at this time, the light source received by the photovoltaic module is less, the power generation amount of the photovoltaic power station is low, and at this time, if lightning of the first-level risk level occurs, the influence on the power generation equipment is large, and the protection effect of the lightning receptor for the power generation equipment is small. Therefore, when the generated energy is less and the lightning risk is greater, the circuit connection is cut off in time, the loss is reduced, the benefit maximization is ensured, and the damage minimization is realized.
In another implementation manner of the embodiment of the application, the lightning occurrence time is a second preset time period, the angle between the photovoltaic module and the light source in the second preset time period is greater than or equal to 10 degrees, and the lightning risk level is a first-level risk level, and according to historical lightning data of the photovoltaic power station in a preset area, the lightning receptor in the north of the corresponding photovoltaic power station is remotely lifted or the lightning receptor in the corresponding photovoltaic power station is remotely lifted, and meanwhile, the diode in the photovoltaic module is cooled.
It should be noted that the lightning occurrence time is a second preset time period, the angle between the photovoltaic module and the light source in the second preset time period is greater than or equal to 10 degrees, the power generation amount of the photovoltaic power station is greater than the first preset time period, a first-level lightning risk occurs at this time, and finally adopted maintenance operation depends on historical data of the photovoltaic power station in lightning weather. If historical data under the thunder weather shows, take under the thunder weather of one-level thunder and lightning risk to rise to connect the sudden strain of a muscle device to carry out lightning protection after, equipment is not harmed and the generated energy change is little, then explains to this photovoltaic power plant, rises to connect the sudden strain of a muscle device alright in order effectually carrying out lightning protection, consequently can continue to adopt this lightning protection operation. If photovoltaic power plant historical data shows, under the thunder and lightning weather of one-level thunder and lightning risk level, the inside diode temperature of this photovoltaic power plant's photovoltaic module rises, for avoiding the temperature to lead to the fact the damage to photovoltaic module after continuously rising, the in-process that rises the lightning arrester is in step to the inside diode of photovoltaic module operation of cooling down. It should be noted that, because the lightning arrester has a certain height, when the angle between the photovoltaic module and the light source is greater than or equal to 10 °, the light source shielding is formed on the photovoltaic module, and the hot spot effect is easily generated due to long-time shielding, so that the temperature of the diode inside the photovoltaic module rises and the photovoltaic module is damaged.
In another implementation manner of the embodiment of the application, when the lightning occurrence time is a second preset time period and the lightning risk level is a second level risk level, the power generation amount of the photovoltaic power station is greater than the first preset time period, the lightning risk level is lower, and the lightning protection is performed by remotely lifting the lightning receptor in the north of the corresponding photovoltaic power station.
It should be noted that the photovoltaic power station in the embodiment of the present application may be a centralized photovoltaic power station, or may be a distributed photovoltaic power station. It should also be noted that, the photovoltaic power station in the embodiment of the present application is all installed with a sensor, such as a temperature sensor, for collecting real-time data, and is used for monitoring the temperature of the photovoltaic module. The photovoltaic power plant in this application embodiment has all set up arrester and remote operation device to be used for remote operation to rise the arrester and/or long-rangely to photovoltaic module cooling operation and/or long-rangely cut off photovoltaic module circuit connection.
In one embodiment of the application, after the steps are carried out, the operation information and the power generation equipment information of one or more photovoltaic power stations in a preset area are monitored in real time in the thunder and lightning weather, and the operation state and the power generation amount of the equipment are analyzed and evaluated. The circuit connection of the photovoltaic modules is timely cut off aiming at the photovoltaic power station with poor equipment running state and power generation capacity, and the photovoltaic power station is prevented from being damaged due to failure of lightning protection measures.
Specifically, the real-time running state and the real-time power generation amount of the equipment are compared with the photovoltaic power station information sent to the expert data platform in the step 103 by analyzing the running information data and the power generation equipment data of the photovoltaic power station in the preset area of the lightning point in the thunder and lightning weather, and the running state and the power generation amount of the equipment are judged. If the equipment runs normally and the power generation amount is normal, the adopted lightning protection measures are appropriate. On the contrary, if the conditions of abnormal operation of equipment and sharp reduction of generated energy occur, the circuit connection of the photovoltaic power station is cut off in time, and the phenomenon that the power generation equipment is damaged by lightning and larger property loss is caused is avoided.
The photovoltaic power station operation and maintenance method for the thunder weather can predict the thunder occurrence time, the preset area of the thunder drop point and the thunder risk level, and achieves the targeted operation and maintenance method. The method mainly embodies that the maintenance range is targeted, the maintenance object is targeted, and the maintenance means is targeted, thereby effectively avoiding the waste of operation and maintenance resources, being beneficial to improving the operation and maintenance efficiency and realizing the key operation and maintenance. In addition, in thunderstorm weather, a targeted remote lightning protection measure is taken according to the conditions of lightning risk level, equipment power generation amount and the like, and circuit connection is cut off in time under necessary conditions, so that the loss minimization is realized, and the personal safety of maintenance personnel is guaranteed.
The photovoltaic power station operation and maintenance equipment based on the lightning weather is further provided based on the same invention concept.
Fig. 3 is an internal structure schematic diagram of photovoltaic power plant operation and maintenance equipment in thunder and lightning weather provided by the embodiment of the application. As shown in fig. 3, the apparatus includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of lightning weather photovoltaic plant operation and maintenance as described above.
In one embodiment of the application, the processor is used for inputting the thunder and lightning characteristic data into the thunder and lightning prediction model so as to determine the thunder and lightning occurrence time and the thunder and lightning landing point position information in the preset area; the lightning data in a preset area is determined according to the lightning occurrence time and the information of the lightning landing point, and the preset area comprises a photovoltaic power station; (ii) a The lightning risk factor determining module is further used for determining lightning risk factors of the preset area according to the information of the lightning landing point, the geographical position information of one or more photovoltaic power stations in the preset area and lightning data in the preset area; the lightning data in the preset area comprise any one or more of the following items: thunderstorm days in a preset time, lightning density of a preset area and lightning current intensity of the preset area; the processor is further used for determining the lightning risk level of a preset area according to the lightning risk factors, determining the operation and maintenance priority level of the photovoltaic power stations in the preset area according to the lightning occurrence time, the information of one or more photovoltaic power stations in the preset area and the lightning risk level, and generating maintenance information of the photovoltaic power stations.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (10)

1. A photovoltaic power station operation and maintenance method for thunder weather is characterized by comprising the following steps:
inputting the lightning characteristic data into a lightning prediction model to determine the lightning occurrence time and the lightning falling point position information in a preset area; the preset area comprises a photovoltaic power station;
determining thunder and lightning data in the preset area according to the thunder and lightning occurrence time and the information of the lightning landing point; determining lightning risk factors of the preset area according to the lightning landing point position information, the geographic position information of one or more photovoltaic power stations in the preset area and lightning data in the preset area; the lightning data in the preset area comprise any one or more of the following items: thunderstorm days in a preset time, lightning strike density of the preset area and lightning current intensity of the preset area;
determining the lightning risk level of a preset area according to the lightning risk factors;
and determining the operation and maintenance priority level of the photovoltaic power stations in the preset area and generating maintenance information of the photovoltaic power stations according to the lightning occurrence time, the information of one or more photovoltaic power stations in the preset area and the lightning risk level.
2. The operation and maintenance method for photovoltaic power stations in thunder and lightning weather according to claim 1, wherein the determining a thunder and lightning risk factor according to the information of the lightning landing point, the information of the geographical locations of one or more photovoltaic power stations in the preset area, and the thunder and lightning data in the preset area specifically comprises:
determining the number of thunderstorm days of the preset area in a preset time period by taking the information of the lightning strike point as a center and a preset value as a radius; determining annual average lightning current intensity of the previous year of the lightning strike time by taking the information of the lightning strike point as a center and a preset value as a radius; determining the size relation between the height of each photovoltaic power station in the preset area and the height of the highest building in the preset area, and determining the relative height between the photovoltaic power stations in the preset area and the highest building in the preset area; determining the lightning strike density of the preset area, wherein the lightning strike density is the number of lightning strikes per unit area in the preset area within one year;
determining the lightning risk factor according to the number of thunderstorm days in the preset time, the lightning current intensity of the preset area, the relative height between the photovoltaic power station in the preset area and the highest building in the preset area and the lightning stroke density of the preset area; the lightning risk factor is positively correlated with the number of thunderstorm days, lightning current intensity, the relative height of the photovoltaic power station and the highest building and lightning stroke density of a preset area.
3. The operation and maintenance method for photovoltaic power stations in thunder and lightning weather according to claim 2, wherein lightning risk factors of the preset area are determined according to the information of the lightning landing point, the information of the geographical locations of one or more photovoltaic power stations in the preset area and the lightning data in the preset area, and specifically are as follows:
according to g ═ Dm×Im×Cg×Sg×ρgGenerating a lightning risk factor; wherein g is a lightning risk factor, and g is positively correlated with the lightning risk level; dm is a thunderstorm day factor, and is positively correlated with the thunderstorm days in a preset time period; im is a lightning current intensity factor, and the Im is positively correlated with the lightning current intensity in a preset area; cg is a position factor of the photovoltaic power station, and the Cg is positively correlated with the relative height of the photovoltaic power station and the highest building in the preset area; sg is a floor area factor of the photovoltaic power stations in the preset area, and the Sg is positively correlated with the total floor area of all the photovoltaic power stations in the preset area; rhogIs a lightning strike density factor of a photovoltaic power station, the rhogAnd positively correlated with the lightning strike density in the preset area.
4. The lightning weather photovoltaic power plant operation and maintenance method of claim 3,
satisfies 10 for the lightning risk factor g in the preset area-5＜g＜3.125×10-2The lightning risk level is a secondary risk level;
the lightning risk factor g in the preset area meets 3.125 multiplied by 10-2If g is less than 1, the lightning risk grade is a first-grade risk grade;
wherein the primary risk level is higher than the secondary risk level.
5. The lightning weather photovoltaic power plant operation and maintenance method of claim 4,
for the condition that the lightning strike density rho in the preset area meets the condition that rho is more than or equal to 0 and less than 2, wherein rhogThe value is 0.1;
for the condition that the lightning strike density rho in the preset area meets 2-3,the rhogThe value is 0.5;
for the condition that the lightning strike density rho in the preset area meets 3-rho, wherein rho is not more than rhogThe value is 1;
for the condition that d is more than or equal to 0 and less than 25 in the thunderstorm days d of one year in the preset area, the value Dm is 0.1;
for the condition that d is more than or equal to 25 and less than 60, the number of thunderstorm days d in one year in a preset area is 0.5;
for the condition that d is more than or equal to 60 and less than or equal to 365 in the thunderstorm days d of one year in the preset area, the value Dm is 1;
for the condition that the height of a building in a preset area is higher than the height of the photovoltaic power station, the Cg value is 0.1;
for the condition that the difference value between the height of the photovoltaic power station and the height of a building in a preset area is lower than a preset threshold value, the Cg value is 0.5;
for the condition that no other objects exist around the photovoltaic power station in a preset area, the Cg value is 1;
the Im is 0.1 when the lightning current intensity I in the preset area meets the condition that I is more than or equal to 0 and less than 20;
the Im is 0.5 when the lightning current intensity I in the preset area meets the condition that I is more than or equal to 20 and less than 40;
the Im value is 1 when the lightning current intensity I in the preset area meets the condition that I is not less than 40;
the occupied area S of all photovoltaic power stations in the preset area meets the condition that S is more than or equal to 0 and less than 5000, and the value of Sg is 0.1;
the occupied area S of all photovoltaic power stations in the preset area meets the condition that S is more than or equal to 5000 and less than 7500, and the value of Sg is 0.5;
and for the condition that the occupied area S of all photovoltaic power stations in the preset area meets 7500-S, the value of Sg is 1.
6. The method of claim 1, wherein after the determining the operation priority level of the corresponding photovoltaic power station and the generating the maintenance information of the photovoltaic power station, the method comprises:
under the condition that the lightning occurrence time is a first preset time period and the lightning risk level is a first-level risk level, remotely lifting a lightning receiving device of a corresponding photovoltaic power station and cutting off the circuit connection of a photovoltaic assembly; the first preset time period is a time period corresponding to the angle between the photovoltaic module and the light source being less than 10 degrees;
under the condition that the lightning occurrence time is a second preset time period and the lightning risk level is a first-level risk level, remotely lifting a lightning receiving device of the corresponding photovoltaic power station or remotely lifting the lightning receiving device of the corresponding photovoltaic power station and simultaneously cooling a diode inside the photovoltaic module based on historical lightning data of the photovoltaic power station in the preset area; the second preset time period is a time period corresponding to the angle between the photovoltaic module and the light source being greater than or equal to 10 degrees;
and under the condition that the lightning occurrence time is a second preset time period and the lightning risk grade is a secondary risk grade, remotely lifting the lightning receiving device of the corresponding photovoltaic power station.
7. The operation and maintenance method for the photovoltaic power station in the thunder weather is characterized in that thunder characteristic data are input into a thunder prediction model to determine thunder occurrence time and thunder drop point position information in a preset area, and specifically comprises the following steps:
determining thunder characteristic data in the preset area, wherein the thunder characteristic data comprises atmospheric electric field information and radar echo intensity information, and the atmospheric electric field information is atmospheric electric field characteristic data continuously acquired when thunder occurs; the radar echo intensity is radar echo data in the lightning occurrence region;
and inputting the lightning characteristic data into a trained neural network model to obtain the lightning occurrence time and the lightning strike point position.
8. The lightning weather photovoltaic power station operation and maintenance method according to claim 1, wherein the determining lightning characteristic data in the preset area further comprises:
the method comprises the steps that a connection line collects the maximum value of electric field intensity, the change rate of the electric field intensity, the average value of the electric field intensity and the variance of the electric field intensity in the electric field time sequence process in a preset area, when the maximum value of the electric field intensity, the change rate of the electric field intensity, the average value of the electric field intensity and the variance of the electric field intensity are higher than a preset threshold value of electric field information, the corresponding time is determined as lightning occurrence time, the electric field time sequence process is a process of collecting continuous electric field intensity in a fixed time window width, and the preset threshold value of the electric field information is corresponding electric field data when lightning; and
the method comprises the steps of collecting the maximum echo intensity, the block echo center intensity and the echo block area of the preset height in the air, determining that the corresponding area is the position of a lightning-down point when the maximum echo intensity, the block echo center intensity and the echo block area of the preset height in the air are higher than a radar echo preset threshold value in the preset area, and determining radar echo data corresponding to the preset threshold value of the radar echo when lightning occurs.
9. The method of claim 7, wherein the step of inputting the lightning characteristic data into a trained neural network model to obtain the lightning occurrence time and the lightning strike point position further comprises:
acquiring the lightning occurrence time corresponding to historical lightning and the lightning characteristic data of the lightning landing point position as training sample data;
classifying the training sample data into an atmospheric electric field information dataset and a radar echo intensity dataset, wherein the atmospheric electric field information dataset is related to the lightning occurrence time, and the radar echo intensity dataset is related to the lightning strike point position;
screening the atmospheric electric field information data set and the radar echo intensity data set;
and respectively inputting the processed atmospheric electric field information data set and the radar echo intensity data set into the neural network model to train the neural network model.
10. A photovoltaic power plant operation and maintenance equipment for thunder weather, which is characterized by comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of lightning weather photovoltaic plant operation as claimed in any one of claims 1 to 9.
CN202110045887.6A 2021-01-14 2021-01-14 Photovoltaic power station operation and maintenance method and device in thunder and lightning weather Active CN112886923B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110045887.6A CN112886923B (en) 2021-01-14 2021-01-14 Photovoltaic power station operation and maintenance method and device in thunder and lightning weather

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110045887.6A CN112886923B (en) 2021-01-14 2021-01-14 Photovoltaic power station operation and maintenance method and device in thunder and lightning weather

Publications (2)

Publication Number Publication Date
CN112886923A true CN112886923A (en) 2021-06-01
CN112886923B CN112886923B (en) 2022-08-12

Family

ID=76045872

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110045887.6A Active CN112886923B (en) 2021-01-14 2021-01-14 Photovoltaic power station operation and maintenance method and device in thunder and lightning weather

Country Status (1)

Country Link
CN (1) CN112886923B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114019255A (en) * 2022-01-10 2022-02-08 中科边缘智慧信息科技（苏州）有限公司 Method and system for positioning and predicting thunderstorm center
CN114089091A (en) * 2021-09-27 2022-02-25 杭州柯林电气股份有限公司 Power transmission line on-line monitoring method and system based on non-contact sensor
KR20230070795A (en) * 2021-11-15 2023-05-23 현대로오텍(주) Lightning stroke damage prevention system of solar photovoltaic power station using artificial intelligence and lightning stroke damage prevention method of solar photovoltaic power station thereof

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103018604A (en) * 2012-12-07 2013-04-03 浙江省电力公司电力科学研究院 Assessment method of electricity grid lightning strike risk and device thereof
CN104682305A (en) * 2015-02-03 2015-06-03 国网上海市电力公司 Method for differentiated lightning protection of urban 10 kV distribution line
CN105067911A (en) * 2015-07-23 2015-11-18 国网电力科学研究院武汉南瑞有限责任公司 Intelligent transformer station light current system lightning protection performance assessing method
CN105279565A (en) * 2014-05-27 2016-01-27 北京中科九章软件有限公司 Lightning early warning method and lightning early warning system
CN105868872A (en) * 2016-05-30 2016-08-17 东北大学 Power distribution network lightning disaster failure prediction method
CN106410792A (en) * 2016-10-17 2017-02-15 国网江苏省电力公司苏州供电公司 Power grid lightning protection method and system
CN107045669A (en) * 2017-03-16 2017-08-15 武汉水院电气有限责任公司 Distribution line damage to crops caused by thunder methods of risk assessment based on compartmentalization
CN108921370A (en) * 2018-05-15 2018-11-30 陕西省地方电力（集团）有限公司 A kind of appraisal procedure and system of transmission line of electricity section damage to crops caused by thunder risk
CN110739655A (en) * 2019-11-05 2020-01-31 成都中航信虹科技股份有限公司 Regional active lightning protection system and installation information determination method
CN111709610A (en) * 2020-05-25 2020-09-25 北京中科九章软件有限公司 Lightning risk assessment method and device, storage medium and electronic equipment
CN111817248A (en) * 2020-06-05 2020-10-23 云南电网有限责任公司大理供电局 Comprehensive lightning protection method for 35kV power transmission line

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103018604A (en) * 2012-12-07 2013-04-03 浙江省电力公司电力科学研究院 Assessment method of electricity grid lightning strike risk and device thereof
CN105279565A (en) * 2014-05-27 2016-01-27 北京中科九章软件有限公司 Lightning early warning method and lightning early warning system
CN104682305A (en) * 2015-02-03 2015-06-03 国网上海市电力公司 Method for differentiated lightning protection of urban 10 kV distribution line
CN105067911A (en) * 2015-07-23 2015-11-18 国网电力科学研究院武汉南瑞有限责任公司 Intelligent transformer station light current system lightning protection performance assessing method
CN105868872A (en) * 2016-05-30 2016-08-17 东北大学 Power distribution network lightning disaster failure prediction method
CN106410792A (en) * 2016-10-17 2017-02-15 国网江苏省电力公司苏州供电公司 Power grid lightning protection method and system
CN107045669A (en) * 2017-03-16 2017-08-15 武汉水院电气有限责任公司 Distribution line damage to crops caused by thunder methods of risk assessment based on compartmentalization
CN108921370A (en) * 2018-05-15 2018-11-30 陕西省地方电力（集团）有限公司 A kind of appraisal procedure and system of transmission line of electricity section damage to crops caused by thunder risk
CN110739655A (en) * 2019-11-05 2020-01-31 成都中航信虹科技股份有限公司 Regional active lightning protection system and installation information determination method
CN111709610A (en) * 2020-05-25 2020-09-25 北京中科九章软件有限公司 Lightning risk assessment method and device, storage medium and electronic equipment
CN111817248A (en) * 2020-06-05 2020-10-23 云南电网有限责任公司大理供电局 Comprehensive lightning protection method for 35kV power transmission line

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
冯鹤: "并网光伏发电系统雷电灾害风险评估研究", 《科学技术与工程 》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114089091A (en) * 2021-09-27 2022-02-25 杭州柯林电气股份有限公司 Power transmission line on-line monitoring method and system based on non-contact sensor
KR20230070795A (en) * 2021-11-15 2023-05-23 현대로오텍(주) Lightning stroke damage prevention system of solar photovoltaic power station using artificial intelligence and lightning stroke damage prevention method of solar photovoltaic power station thereof
KR102641838B1 (en) * 2021-11-15 2024-02-29 현대로오텍(주) Damage prevention system of solar photovoltaic power station using artificial intelligence from lightning stroke
CN114019255A (en) * 2022-01-10 2022-02-08 中科边缘智慧信息科技（苏州）有限公司 Method and system for positioning and predicting thunderstorm center
CN114019255B (en) * 2022-01-10 2022-03-11 中科边缘智慧信息科技（苏州）有限公司 Method and system for positioning and predicting thunderstorm center

Also Published As

Publication number Publication date
CN112886923B (en) 2022-08-12

Similar Documents

Publication Publication Date Title
CN112886923B (en) Photovoltaic power station operation and maintenance method and device in thunder and lightning weather
US8606416B2 (en) Energy generating system and control thereof
CN107169645B (en) Power transmission line fault probability online evaluation method considering influence of rainstorm disaster
CN111523699A (en) Overhead line fault probability prediction method based on comprehensive state health degree
CN102508046B (en) Real-time lightning stroke prewarning method and device for outdoor electric equipment
CN109828182A (en) A kind of network system accident analysis method for early warning based on failure modes processing
CN109902948A (en) A kind of power transmission line monitoring system and method based on big data
CN111612315A (en) Novel power grid disastrous gale early warning method
CN104122488A (en) Fault check and diagnosis method for power transmission line
CN105279612A (en) Poisson distribution-based power transmission line tripping risk assessment method
CN116882638B (en) Management method and system of perfluoro compound pollution monitoring equipment
CN109460923A (en) A kind of ice covering on transmission lines probability forecasting method
CN113176420A (en) Wind speed forecasting and correcting system for power grid tower point
CN113177678A (en) Meteorological risk early warning method and device for different types of foreign body intrusion
CN110570628B (en) Power transmission line pole tower geological disaster monitoring, early warning and analyzing system and using method
CN116050599A (en) Line icing fault prediction method, system, storage medium and equipment
CN111257686A (en) Intelligent tripping analysis system based on data sharing analysis
CN112257329A (en) Method for judging influence of typhoon on line
CN117009731A (en) Power failure early warning method based on meteorological factors
CN115796577A (en) Lightning risk early warning method for distribution network
CN112578232A (en) Lightning early warning method and lightning early warning device of wind generating set
CN115713236A (en) Power distribution network lightning damage risk assessment method based on lightning stroke data space autocorrelation analysis
US11243238B2 (en) Method for warning about lightning activity in wind farms
CN114358165A (en) Detection method for preventing ground fault of photovoltaic module based on multi-source data fusion
CN111882173A (en) Power grid static security risk assessment system and assessment method based on meteorological information

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right
TA01 Transfer of patent application right

Effective date of registration: 20220721

Address after: 519000 room 525-123, fifth floor, No. 8, Pingdong Sixth Road, Nanping Science and Technology Industrial Park, Zhuhai, Guangdong Province (centralized office area)

Applicant after: Guangdong Jianyi Energy Research Institute Co.,Ltd.

Address before: 318000 Room 502, building 44, Baiyun New Village, Jiaojiang District, Taizhou City, Zhejiang Province

Applicant before: Chen Ling

GR01 Patent grant
GR01 Patent grant