A distributed photovoltaic point inspection priority dynamic sorting method based on meteorological factors

Abstract

This invention discloses a dynamic prioritization method for distributed photovoltaic (PV) site inspections based on meteorological factors. The method includes: numbering each PV site and extracting basic information for each site; calculating the lightning strike density at each site and at different distances from the site, and constructing lightning strike density evaluation indicators for each site; statistically analyzing the frequency of each meteorological factor within its corresponding range, and constructing meteorological disaster evaluation indicators for wind speed, temperature, and precipitation at each site; calculating the weights of the evaluation indicators, establishing a meteorological disaster risk assessment calculation model, and providing a dynamic prioritization of distributed PV site inspections. This invention can dynamically assess the meteorological disaster risk at each distributed PV site, providing a priority ranking decision for daily inspections. It overcomes the current situation where existing power plants conduct daily inspections of each site individually, lacking specificity and timeliness in identifying potential hazards at different sites. Simultaneously, it provides a basis for differentiated prevention measures against meteorological disasters at different sites.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power station inspection; in particular, it relates to a dynamic sorting method for the inspection priority of distributed photovoltaic points based on meteorological factors. Background Technology

[0002] Distributed photovoltaic (PV) power stations are generally widely distributed and numerous, with each station operating relatively independently. Due to this dispersed nature, daily inspections are significantly more challenging and demanding than centralized systems. Currently, a step-by-step daily inspection approach is used. However, if a high-risk station is prioritized for inspection, it may not be able to address the problem promptly, posing a significant threat to the station's daily operations. Furthermore, local microclimate conditions at each station are highly relevant to the station's stable operation. Currently, there is a lack of effective methods to prioritize inspections based on meteorological risk assessments of different stations, hindering targeted and timely hazard identification. Summary of the Invention

[0003] The purpose of this invention is to provide a dynamic prioritization method for distributed photovoltaic site inspection based on meteorological factors, in order to solve the technical problem that the existing photovoltaic power station adopts a daily inspection mode of each site one by one, which lacks specificity and timeliness in the investigation of hidden dangers at different sites.

[0004] Therefore, the present invention adopts the following technical solution:

[0005] A dynamic prioritization method for distributed photovoltaic (PV) site inspections based on meteorological factors, characterized by the following steps:

[0006] Step S1: Number each distributed photovoltaic site and extract basic information for each site, including: site envelope, area, and coordinates.

[0007] Step S2: Extract lightning location data from meteorological departments in the areas where each station is located over the past few years, and remove positive ground lightning arrays based on polarity;

[0008] Step S3: Based on the lightning location data processing results in Step S2 and the envelope of each point station in Step 1, calculate the lightning strike density at different distances within and outside each point station.

[0009] Step S4: Based on the lightning strike density results calculated in Step S3, and according to the principle that the farther away the lightning strike point is, the less damage it causes to the photovoltaic power station, construct the lightning strike density evaluation index for each location of the power station.

[0010] Step S5: Extract meteorological sensor data from various locations and stations over a recent period, including wind speed, temperature, and precipitation, and perform daily statistics on the data of each meteorological factor.

[0011] Step S6: Set the range of wind speed, temperature and precipitation, and based on the statistical results of step S5, calculate the daily data of maximum wind speed, maximum temperature and 24-hour cumulative precipitation at each location station, and the frequency of occurrence of these values ​​within the range of wind speed, temperature and precipitation.

[0012] Step S7: Based on the calculation results of step S6, construct meteorological disaster evaluation indicators for wind speed, temperature, and precipitation at each location station;

[0013] Step S8: Calculate the weights of the evaluation indicators based on the evaluation indicator results constructed in steps S4 and S7.

[0014] Step S9: Based on the results of step S8, establish a meteorological disaster risk assessment calculation model;

[0015] Step S10: Based on the results of step S9, dynamically sort the inspection priorities of distributed photovoltaic power station sites.

[0016] The steps S2-S4 and S5-S7 are not ranked in any particular order.

[0017] In step S1, the numbering of each distributed photovoltaic site is done according to... Numbering is performed. Step S1 involves extracting the envelope of the point station, i.e., the ground projection boundary range of the station;

[0018] Step S2 involves extracting lightning location data from the meteorological department, including: lightning strike point coordinates and lightning current polarity;

[0019] Step S3 involves calculating the lightning strike density at different distances within and outside the lightning station at each location. The calculation expression is as follows:

[0020]

[0021] In the formula: For the first Lightning strike density at each location station; For the first The frequency of lightning strikes occurring at each location within the site in recent years (e.g., the last 5 years); For the first The area occupied by each station location; Each of the following is the first The coordinates of each station location are represented by a circle with radii of [missing information]. Lightning strike density within the area; The radii are respectively Frequency of lightning strikes within the area; This refers to the number of distributed photovoltaic (PV) sites. (Unit: km)

[0022] Step S4 describes the construction of lightning strike density evaluation indicators for each location, calculated using the following expression:

[0023]

[0024] The wind speed range set in step S6 is as follows:

[0025]

[0026] In the formula: This is a set of wind speed ranges (unit: m / s).

[0027] The temperature range set in step S6 is as follows:

[0028]

[0029] In the formula: This is a set of temperature ranges (unit: °C).

[0030] Step S6 describes setting the precipitation range. Based on the China Meteorological Administration's standard for classifying heavy rain and rainstorm levels according to 24-hour rainfall, this invention divides the precipitation range as follows:

[0031]

[0032] In the formula: This is a set of precipitation intervals (unit: mm).

[0033] Step S7 describes the construction of wind speed hazard evaluation indicators for each photovoltaic power station location. Based on the principle that higher wind speeds cause greater damage to photovoltaic power stations, the wind speed hazard evaluation calculation expression is as follows:

[0034]

[0035] In the formula: For the first Results of wind speed disaster assessment indicators for individual monitoring stations; For respectively the first The daily maximum wind speeds at each of the monitoring stations over the past year fall within the respective wind speed ranges. Cumulative frequency.

[0036] Step S7 describes the construction of temperature disaster assessment indicators for each site. The maximum output temperature of photovoltaic modules is between 20-30℃. Excessively high or low temperatures significantly impact module output. Therefore, the temperature disaster assessment calculation expression is as follows:

[0037]

[0038] In the formula: For the first Results of temperature-related disaster assessment indicators for individual monitoring stations; For respectively the first The daily maximum temperatures at each monitoring station within one year fall within the temperature range. Cumulative frequency.

[0039] Step S7 describes the construction of rainfall disaster assessment indicators for each photovoltaic power station. Based on the principle that greater rainfall causes greater damage to the photovoltaic power station, the rainfall disaster assessment calculation expression is as follows:

[0040]

[0041] In the formula: For the first Results of disaster assessment indicators for rainfall at individual monitoring stations; For respectively the first The daily maximum rainfall at each monitoring station within one year falls within the rainfall range. Cumulative frequency.

[0042] Step S8 involves calculating the weights of the evaluation indicators. This invention uses the entropy weight method to calculate the weight values ​​of lightning strike density, wind speed, temperature, and precipitation. First, the evaluation matrix is ​​constructed as follows:

[0043]

[0044] The evaluation matrix constructed above is then normalized to the range [0,1], and the calculation expression is as follows:

[0045]

[0046] Based on the normalized results above, calculate the... The first indicator The weight of each point value in this evaluation indicator is calculated using the following expression:

[0047]

[0048] Next, calculate the information entropy value of the indicator. The calculation expression is as follows:

[0049]

[0050] Finally, the weights of the evaluation indicators are calculated as follows:

[0051]

[0052] The meteorological disaster risk assessment calculation model established in step S9 is as follows:

[0053]

[0054] in, The normalized value of the evaluation index The weights are used to evaluate the indicators.

[0055] Step S10 describes the dynamic prioritization of distributed photovoltaic (PV) site inspections, and subsequent actions are based on the newly added priority levels. Tian Distributed Photovoltaics Evaluation indicators for each location sampled every 15 minutes over 24 hours. By inputting the observed values ​​into the meteorological disaster risk assessment calculation model, the results can be obtained. Each location station The values ​​are sorted from highest to lowest, which is the number of distributed photovoltaic [projects]. The priority of on-site inspections is dynamically sorted.

[0056] This invention assigns a number to each distributed photovoltaic (PV) site and extracts basic information for each site. It also extracts lightning location data from meteorological departments in the areas where each site is located over the past five years, calculates the lightning strike density occurring within and at different distances from the site, and constructs lightning strike density evaluation indicators for each site. Furthermore, it extracts meteorological sensor data from each site over the past year, and statistically analyzes the data for each meteorological factor daily, setting ranges for wind speed, temperature, and precipitation, and statistically analyzing the frequency of each meteorological factor occurring within its corresponding range. This allows for the construction of meteorological disaster evaluation indicators for wind speed, temperature, and precipitation at each site. Finally, it uses the entropy weight method to calculate the weights of the evaluation indicators, establishes a meteorological disaster risk assessment calculation model, and provides a dynamic ranking of the inspection priorities for distributed PV sites.

[0057] This invention provides a method for dynamically guiding the prioritization of distributed photovoltaic (PV) power plant inspections based on meteorological disaster risk assessment. It overcomes the shortcomings of existing daily inspection methods that rely on individual points at each power plant location, which lack specificity and timeliness in identifying potential hazards at different sites. Furthermore, it provides a basis for differentiated preventative measures against meteorological disasters at different locations. The invention also features a simple operation process, making it effective for decision-making during the daily inspections of PV power plants. Attached Figure Description

[0058] Figure 1 This invention provides a method for dynamically prioritizing distributed photovoltaic (PV) site inspections based on meteorological factors. Detailed Implementation

[0059] Referring to the accompanying drawings, the present invention provides a dynamic prioritization method for distributed photovoltaic power station inspections based on meteorological factors, comprising:

[0060] Step S1: Number each distributed photovoltaic site and extract basic information for each site, including: site envelope, area, and coordinates.

[0061] Step S2: Extract lightning location data from meteorological departments in the areas where each station is located over the past 5 years, and remove positive ground lightning arrays based on polarity;

[0062] Step S3: Based on the lightning location data processing results in Step S2 and the envelope of each point station in Step 1, calculate the lightning strike density at different distances within and outside each point station.

[0063] Step S4: Based on the lightning strike density results calculated in Step S3, and according to the principle that the farther away the lightning strike point is, the less damage it causes to the photovoltaic power station, construct the lightning strike density evaluation index for each location of the power station.

[0064] Step S5: Extract meteorological sensor data from various locations and stations over the past year, including wind speed, temperature, and precipitation, and perform daily statistics on the data of each meteorological factor.

[0065] Step S6: Set the range of wind speed, temperature and precipitation, and based on the statistical results of Step 5, calculate the daily data of maximum wind speed, maximum temperature and 24-hour cumulative precipitation at each location station, and the frequency of occurrence of these values ​​within the range of wind speed, temperature and precipitation.

[0066] Step S7: Based on the calculation results of step S6, construct meteorological disaster evaluation indicators for wind speed, temperature, and precipitation at each location;

[0067] Step S8: Calculate the weights of the evaluation indicators based on the evaluation indicator results constructed in steps S4 and S7.

[0068] Step S9: Based on the results of step S8, establish a meteorological disaster risk assessment calculation model;

[0069] Step S10: Based on the results of step S9, dynamically sort the inspection priorities of distributed photovoltaic power station sites.

[0070] In step S1, the numbering of each point of the distributed photovoltaic system is carried out according to... Number the points; Step 1 involves extracting the envelope of the station location, i.e., the ground projection boundary of the station.

[0071] Among them, step S2, which involves extracting lightning location data from the meteorological department, includes: lightning strike point coordinates and lightning current polarity;

[0072] Step S3 involves calculating the lightning strike density at different distances within and outside the lightning station at each location. The calculation expression is as follows:

[0073]

[0074] In the formula: For the first Lightning strike density at each location station; For the first Frequency of lightning strikes at each site over the past 5 years; For the first The area occupied by each station location; Each of the following is the first The coordinates of each station location are represented by a circle with radii of [missing information]. Lightning strike density within the area; The radii are respectively Frequency of lightning strikes within the area; This refers to the number of distributed photovoltaic (PV) sites. (Unit: km)

[0075] Step S4 describes the construction of lightning strike density evaluation indicators for each location, calculated using the following expression:

[0076]

[0077] The wind speed range set in step S6 is as follows:

[0078]

[0079] In the formula: This is a set of wind speed ranges (unit: m / s).

[0080] The temperature range set in step S6 is as follows:

[0081]

[0082] In the formula: This is a set of temperature ranges (unit: °C).

[0083] Step S6 describes setting the precipitation range. Based on the China Meteorological Administration's standard for classifying heavy rain and rainstorm levels according to 24-hour rainfall, this invention divides the precipitation range as follows:

[0084]

[0085] In the formula: This is a set of precipitation intervals (unit: mm).

[0086] Step S7 describes the construction of wind speed hazard evaluation indicators for each photovoltaic power station location. Based on the principle that higher wind speeds cause greater damage to photovoltaic power stations, the wind speed hazard evaluation calculation expression is as follows:

[0087]

[0088] In the formula: For the first Results of wind speed disaster assessment indicators for individual monitoring stations; For respectively the first The daily maximum wind speeds at each of the monitoring stations over the past year fall within the respective wind speed ranges. Cumulative frequency.

[0089] Step S7 describes the construction of temperature disaster assessment indicators for each site. The maximum output temperature of photovoltaic modules is between 20-30℃. Excessively high or low temperatures significantly impact module output. Therefore, the temperature disaster assessment calculation expression is as follows:

[0090]

[0091] In the formula: For the first Results of temperature-related disaster assessment indicators for individual monitoring stations; For respectively the first The daily maximum temperatures at each monitoring station within one year fall within the temperature range. Cumulative frequency.

[0092] Step 7 describes the construction of rainfall disaster assessment indicators for each photovoltaic power station. Based on the principle that greater rainfall causes greater damage to the photovoltaic power station, the calculation expression for rainfall disaster assessment is as follows:

[0093]

[0094] In the formula: For the first Results of disaster assessment indicators for rainfall at individual monitoring stations; For respectively the first The daily maximum rainfall at each monitoring station within one year falls within the rainfall range. Cumulative frequency.

[0095] Step S8 involves calculating the weights of the evaluation indicators. This invention uses the entropy weight method to calculate the weight values ​​of lightning strike density, wind speed, temperature, and precipitation. First, the evaluation matrix is ​​constructed as follows:

[0096]

[0097] The evaluation matrix constructed above is then normalized to the range [0,1], and the calculation expression is as follows:

[0098]

[0099] Based on the normalized results above, calculate the... The first indicator The weight of each point value in this evaluation indicator is calculated using the following expression:

[0100]

[0101] Next, calculate the information entropy value of the indicator. The calculation expression is as follows:

[0102]

[0103] Finally, the weights of the evaluation indicators are calculated as follows:

[0104]

[0105] The meteorological disaster risk assessment calculation model established in step S9 is as follows:

[0106]

[0107] Step S10 describes the dynamic prioritization of distributed photovoltaic (PV) site inspections, and subsequent actions are based on the newly added priority levels. Tian Distributed Photovoltaics Evaluation indicators for each location sampled every 15 minutes over 24 hours. By inputting the observed values ​​into the meteorological disaster risk assessment calculation model, the results can be obtained. Each location station The values ​​are sorted from highest to lowest, which is the number of distributed photovoltaic [projects]. The priority of on-site inspections is dynamically sorted.

Claims

1. A dynamic prioritization method for distributed photovoltaic (PV) site inspections based on meteorological factors, characterized in that, Includes the following steps: Step S1: Number each distributed photovoltaic site and extract basic information for each site, including: site envelope, area, and coordinates. Step S2: Extract lightning location data from meteorological departments in the areas where each station is located over the past few years, and remove positive ground lightning arrays based on polarity; Step S3: Based on the lightning location data processing results in Step S2 and the envelope of each point station in Step 1, calculate the lightning strike density at different distances within and outside each point station. Step S4: Based on the lightning strike density results calculated in Step S3, and according to the principle that the farther away the lightning strike point is, the less damage it causes to the photovoltaic power station, construct the lightning strike density evaluation index for each location of the power station. Step S5: Extract meteorological sensor data from various locations and stations over a recent period, including wind speed, temperature, and precipitation, and perform daily statistics on the data of each meteorological factor. Step S6: Set the range of wind speed, temperature and precipitation, and based on the statistical results of step S5, calculate the daily data of maximum wind speed, maximum temperature and 24-hour cumulative precipitation at each location station, and the frequency of occurrence of these values ​​within the range of wind speed, temperature and precipitation. Step S7: Based on the calculation results of step S6, construct meteorological disaster evaluation indicators for wind speed, temperature, and precipitation at each location station; Step S8: Calculate the weights of the evaluation indicators based on the evaluation indicator results constructed in steps S4 and S7. Step S9: Based on the results of step S8, establish a meteorological disaster risk assessment calculation model; Step S10: Based on the results of step S9, dynamically sort the inspection priorities of distributed photovoltaic power station sites. The steps S2-S4 and S5-S7 are not ranked in any particular order.

2. The method for dynamic prioritization of distributed photovoltaic power station inspections based on meteorological factors according to claim 1, characterized in that: Step S2 involves extracting lightning location data from the meteorological department, including: lightning strike point coordinates and lightning current polarity.

3. The method for dynamic prioritization of distributed photovoltaic power station inspections based on meteorological factors according to claim 1, characterized in that: Step S3 involves calculating the lightning strike density at different distances within and outside the lightning station at each location. The calculation expression is as follows: In the formula: For the first Lightning strike density at each location station; For the first The frequency of lightning strikes occurring in recent years at each site; For the first The area occupied by each station location; Each of the following is the first The coordinates of each station location are represented by a circle with radii of [missing information]. Lightning strike density within the area; The radii are respectively Frequency of lightning strikes within the area; This refers to the number of distributed photovoltaic (PV) sites. Unit: km.

4. The method for dynamic prioritization of distributed photovoltaic power station inspections based on meteorological factors according to claim 3, characterized in that: Step S4 describes the construction of lightning strike density evaluation indicators for each location, calculated using the following expression: 。 5. The method for dynamic prioritization of distributed photovoltaic power station inspections based on meteorological factors according to claim 1, characterized in that: Step S6 sets the wind speed range as follows: In the formula: This is a set of wind speed ranges, in m / s. Step S6 sets the temperature range as follows: In the formula: This is a set of temperature ranges, in °C. Step 6: Set the precipitation range. Based on the China Meteorological Administration's standard for classifying heavy rain and rainstorm levels according to 24-hour rainfall, the precipitation range is divided as follows: In the formula: This represents a set of precipitation intervals, in mm.

6. The method for dynamic prioritization of distributed photovoltaic power station inspections based on meteorological factors according to claim 1, characterized in that: Step S7 describes the construction of wind speed hazard evaluation indicators for each photovoltaic power station location. Based on the principle that higher wind speeds cause greater damage to photovoltaic power stations, the wind speed hazard evaluation calculation expression is as follows: In the formula: For the first Results of wind speed disaster assessment indicators for individual monitoring stations; For respectively the first The daily maximum wind speeds at each of the monitoring stations over the past year fall within the respective wind speed ranges. Cumulative frequency. Step S7 describes the construction of temperature hazard assessment indicators for each location station. The temperature hazard assessment calculation expression is as follows: In the formula: For the first Results of temperature-related disaster assessment indicators for individual monitoring stations; For respectively the first The daily maximum temperatures at each monitoring station within one year fall within the temperature range. Cumulative frequency. Step S7 describes the construction of rainfall disaster assessment indicators for each photovoltaic power station. Based on the principle that greater rainfall causes greater damage to the photovoltaic power station, the rainfall disaster assessment calculation expression is as follows: In the formula: For the first Results of disaster assessment indicators for rainfall at individual monitoring stations; For respectively the first The daily maximum rainfall at each monitoring station within one year falls within the rainfall range. Cumulative frequency.

7. The method for dynamic prioritization of distributed photovoltaic site inspections based on meteorological factors according to claim 1, characterized in that: Step S8 involves calculating the weights of the evaluation indicators. The entropy weight method is used to calculate the weight values ​​for lightning strike density, wind speed, temperature, and precipitation. First, the evaluation matrix is ​​constructed as follows: The evaluation matrix constructed above is then normalized to the range [0,1], and the calculation expression is as follows: Based on the normalized results above, calculate the... The first indicator The weight of each point value in this evaluation indicator is calculated using the following expression: Next, calculate the information entropy value of the indicator. The calculation expression is as follows: Finally, the weights of the evaluation indicators are calculated as follows: 。 8. The method for dynamic prioritization of distributed photovoltaic power station inspections based on meteorological factors according to claim 1, characterized in that: The meteorological disaster risk assessment calculation model established in step S9 is as follows: in, The normalized value of the evaluation index The weights are used to evaluate the indicators.

9. The method for dynamic prioritization of distributed photovoltaic power station inspections based on meteorological factors according to claim 8, characterized in that: Step S10 describes the dynamic prioritization of distributed photovoltaic (PV) site inspections, and subsequent actions are based on the newly added priority levels. Tian Distributed Photovoltaics Evaluation indicators for each location sampled every 15 minutes over 24 hours. By inputting the observed values ​​into the meteorological disaster risk assessment calculation model, the results can be obtained. Each location station The values ​​are sorted from highest to lowest, which is the number of distributed photovoltaic [projects]. The priority of on-site inspections is dynamically sorted.

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