Method and device for evaluating toughness of photovoltaic system under freezing disaster and medium

Abstract

The invention provides a method and equipment for evaluating the toughness of a photovoltaic system under a freezing disaster. The evaluation method comprises the following steps: step 1) constructing an ice disaster database; 2) constructing a photovoltaic system icing growth model under the ice disaster weather; 3) constructing a time sequence prediction model based on LSTM to predict a load sequence; 4) acquiring real-time meteorological data and photovoltaic system parameters of the measured area, inputting the real-time meteorological data and the photovoltaic system parameters into the time sequence model to predict and obtain a load sequence, and predicting an ice force load of a power transmission line and an ice force load of a photovoltaic panel according to the predicted icing thickness; step 5) according to the grade of the icing thickness, combining the reliability index with different target evaluation indexes to form a combined index; 6, the reliability index and each target evaluation index are calculated respectively and weighted, and a comprehensive toughness evaluation result is obtained.By means of the method, the toughness level of the photovoltaic system in the ice disaster weather can be evaluated accurately and comprehensively, and the reliability and stability of the system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic system monitoring, in particular to a photovoltaic system resilience evaluation method and device under ice disaster and a medium. BACKGROUND

[0002] With the intensification of global climate change, the frequency and intensity of extreme weather events are increasing, which poses a serious challenge to the stable operation of photovoltaic systems. In particular, in winter or cold regions, ice disaster weather causes particularly serious damage to photovoltaic systems. Under ice disaster weather conditions, photovoltaic panels are easily covered with ice layers, which directly leads to a significant reduction in photoelectric conversion efficiency, reduces photoelectric conversion efficiency, and in severe cases, even causes photovoltaic systems to malfunction, thereby affecting the continuity and reliability of power supply, causing power outages and other serious consequences.

[0003] Currently, for the resilience evaluation of photovoltaic systems, the existing technology usually adopts a manual experience judgment method or a simple evaluation using a single indicator. The manual experience judgment method has low evaluation efficiency and relies on manual experience, and in actual evaluation, the dynamic changes in the performance of photovoltaic systems under ice disaster weather conditions and the possible long-term effects are often overlooked, so it is difficult to accurately predict and evaluate the resilience of photovoltaic systems under extreme weather conditions. The evaluation method using a single indicator, such as evaluating the recovery situation based on the power generation of photovoltaic systems after extreme weather, cannot comprehensively reflect the overall resilience of photovoltaic systems under extreme weather conditions, nor can it comprehensively evaluate the cooperative working ability and overall resilience of system components under extreme weather conditions. The influence of different degrees of icing on system resilience is also different, and the single indicator evaluation method cannot accurately evaluate the adaptability and recovery ability of the system under extreme weather conditions. SUMMARY

[0004] The embodiments of the present application provide a photovoltaic system resilience evaluation method, device and medium under ice disaster, which can accurately and comprehensively evaluate the resilience level of photovoltaic systems under ice disaster weather, and improve the reliability and stability of photovoltaic system operation.

[0005] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: On the one hand, the embodiments of the present application provide a photovoltaic system resilience evaluation method under ice disaster, the steps of which include: Step 1) Collect meteorological data when ice disaster weather occurs, environmental parameters of the area affected by ice disaster weather, and performance parameters of the photovoltaic system, and construct an ice disaster database containing time series features, wherein the performance parameters of the photovoltaic system include structural parameters; Step 2) constructing an icing growth model of the photovoltaic system under ice disaster weather according to the ice disaster database, the icing growth model of the photovoltaic system being a relationship model between the icing thickness of the photovoltaic system and the meteorological data at the time of the ice disaster weather and the environmental parameters of the area affected by the ice disaster weather; Step 3) constructing an LSTM-based time series prediction model based on the historical load time series data and the meteorological data in the ice disaster database to predict the load sequence at a future time; Step 4) obtaining real-time meteorological data of the measured area and real-time performance parameters of the measured photovoltaic system, inputting the real-time meteorological data and the real-time performance parameters into the time series prediction model to predict the load sequence at a future time, and predicting the icing thickness of the measured photovoltaic system under the ice disaster weather according to the icing growth model of the photovoltaic system, and calculating the icing force load of the photovoltaic system transmission line and the icing force load of the photovoltaic panel according to the predicted icing thickness; Step 5) judging the grade of the predicted icing thickness, combining the reliability index with different target evaluation indexes according to the grade of the icing thickness to form a combined index, the reliability index being an index for evaluating the ability of the system to maintain normal operation according to the icing force load of the photovoltaic system transmission line and the icing force load of the photovoltaic panel; Step 6) calculating the reliability index and each target evaluation index in the combined index and obtaining a comprehensive resilience evaluation result by weighting, to evaluate the resilience state of the measured photovoltaic system, according to the real-time meteorological data of the measured area, the real-time performance parameters of the measured photovoltaic system, the predicted icing force load of the measured photovoltaic system transmission line and the photovoltaic panel, and the load sequence at a future time.

[0006] Further, the meteorological data includes any one or more of wind speed, water density, ice density, precipitation rate, air moisture content, and ice disaster duration, and the structural parameters of the photovoltaic system include any one or more of cable diameter, photovoltaic panel icing force load threshold, transmission line icing force load threshold, photovoltaic panel installation angle, photovoltaic panel length, photovoltaic panel width, and anti-icing coating reflectivity.

[0007] Further, in step 2), the constructed icing growth model of the photovoltaic system under ice disaster weather is: wherein, R represents the icing thickness, T represents the ice disaster duration, p w represents the water density, p i represents the ice density, r represents the precipitation rate, v represents the wind speed, W represents the air moisture content, q represents the photovoltaic panel installation angle,l Represents the reflectivity of the anti-icing coating.

[0008] Furthermore, the ice load on the photovoltaic system transmission line under ice disaster weather is predicted according to the following formula: L 1 and photovoltaic panel ice load L 2: in, D represents the cable diameter, q Represents the angle at which the photovoltaic panels are installed, L Represents the length of the photovoltaic panel, W Represents the width of the photovoltaic panel, p i represents the density of ice, Represents the predicted ice thickness.

[0009] Furthermore, step 3) includes: S31. Collect historical time series data from the system ice disaster database, including system load time series P ( t ),temperature T ( t ), wind speed v ( t ) and relative humidity H ( t ), and then normalized to form the input matrix X As model input; S32, build a time series prediction model based on LSTM, and input the matrix in step S51 Input into the LSTM network, and the output is the load sequence of the predicted future time { P ( t ), ,…, }, T is the length of the prediction time; After the time series forecasting model predicts the load series at future moments in step 4), key time points are extracted. The steps include: S41, the forecast load sequence output by the model { P ( t ), ,…, Perform first-order difference processing to calculate the load increment between adjacent moments and obtain the approximate instantaneous rate of change , and set a significant drop threshold for identifying load drop phases , , used to identify the significant rising threshold during the load recovery phase 、 , the jitter tolerance for judging whether the system enters the platform period , ; S42, start from the post-disaster recovery window starting time t0, scan backward, and record the first time point satisfying as the load rapid drop starting point ; continue scanning after , record the first time point satisfying and near the minimum value of this stage as ; scan after , record the first time point satisfying as the load recovery starting time ; scan after , record the first time point satisfying and as ; scan after , record the time point after recovery when is satisfied again as ; scan after , record the first time point making as the complete recovery time ; record each time point as the key time point required to be extracted.

[0010] Further, the target evaluation index includes a strain force index, a resistance force index, and a recovery force index, the strain force index is used to evaluate the ability of the photovoltaic system to adjust and reconfigure in long-term changes, the resistance force index is used to evaluate the ability of the photovoltaic system to absorb and slow down the impact when the impact occurs; and the recovery force index is used to evaluate the time required for the photovoltaic system to recover to a normal operating state after being subjected to the impact; the strain force index, the resistance force index, and the recovery force index are calculated according to the predicted load sequence and the extracted key time points respectively. The calculation expression of the strain force index is: In the formula, represents an initial time point of normal operation of the photovoltaic system before the ice disaster weather starts, represents a time point at which the photovoltaic system starts to operate with reduced load after the ice disaster weather starts; represents the normal power of the photovoltaic system when the ice disaster weather does not occur; represents the predicted power at time t, represents the power of the photovoltaic system after being affected by the ice disaster weather; The expression of the resistance force index is: ​In the formula, represents the time point when the photovoltaic system takes emergency recovery measures after the icing weather ends; The calculation expression of the recovery force index is: In the formula, w 1 、 w 2 、 w 3 represents the weight value of the three indexes; AR 41 represents the emergency recovery force, which is used to reflect the ability of the photovoltaic system to take emergency recovery measures immediately after the icing ends; AR 42 represents the recovery progress force, which is used to reflect the ability of the photovoltaic system to further recover to the normal operation state after the emergency recovery measures; AR 43 represents the complete recovery force, which is used to reflect the ability of the photovoltaic system to completely recover to the normal state after experiencing the influence of the icing weather; represents the time point when the photovoltaic system starts to gradually recover to the normal operation after taking the emergency measures; represents the time point when the photovoltaic system further recovers to the normal operation; represents the time point when the photovoltaic system completely recovers to the normal state after experiencing the influence of the icing weather; represents the power at represents the power at represents the power at represents the power at represents the power at represents the power at represents the power at represents the power at

[0011] Further, step 5) comprises: If the predicted ice thickness is less than the first preset threshold, it is judged to belong to the light grade icing, and the combination index is formed by combining the reliability index and the strain force index; If the predicted ice thickness is greater than the first preset threshold and less than the second preset threshold, it is judged to belong to the moderate grade icing, and the combination index is formed by combining the reliability index and the strain force index, the resistance force index; If the predicted ice thickness is greater than the second preset threshold, it is judged to belong to the heavy grade icing, and the combination index is formed by combining the reliability index and the strain force index, the resistance force index, and the recovery force index, the first preset threshold < the second preset threshold < the third preset threshold.

[0012] Further, the expression of the reliability index is: wherein, l 1 (t) and x 1 (t) represent the mean and standard deviation of the ice force load on the power transmission line of the photovoltaic system when the photovoltaic system is subjected to icing weather, l 2 (t) and x 2 (t represent the mean and standard deviation of the ice force load on the photovoltaic panel of the photovoltaic system when the photovoltaic system is subjected to icing weather, Lmax 1 represents the ice force load threshold of the photovoltaic panel, Lmax 2 represents the ice force load threshold of the photovoltaic panel, L 1 and L 2 represent the ice force load on the power transmission line of the photovoltaic system and the ice force load on the photovoltaic panel under icing weather.

[0013] In another aspect, the embodiments of the present application also provide a computing device, comprising a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to perform the above method.

[0014] In another aspect, the embodiments of the present application also provide a storage medium, wherein the storage medium stores a computer program, and the computer program, when executed, implements the steps of the above method.

[0015] Compared with the prior art, the present application has the following beneficial effects: by collecting meteorological data under icing weather, environmental parameters of the area affected by the icing weather, and performance parameters of the photovoltaic system, the present application constructs an ice growth model of the photovoltaic system, predicts the ice thickness of the measured photovoltaic system under icing weather by using the ice growth model of the photovoltaic system, and further predicts the ice force load on the power transmission line of the measured photovoltaic system and the ice force load on the photovoltaic panel, and simultaneously determines the toughness evaluation index according to the grade of the ice thickness, and calculates the comprehensive toughness evaluation result by using the determined toughness evaluation index, which not only can comprehensively consider the meteorological data under icing weather, the environmental parameters of the area affected by the icing weather, and the performance parameters of the photovoltaic system, accurately and comprehensively evaluate the toughness state of the photovoltaic system under icing weather, but also can fully consider the influence of different environments on the system, adaptively determine different toughness evaluation indexes to comprehensively evaluate the system, more effectively adapt to different environmental conditions to accurately evaluate the toughness state of the photovoltaic system under icing weather, and improve the reliability and precision of the evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0016] The present application will be described in more detail below based on embodiments and with reference to the drawings. In which: Figure 1 is the implementation flow diagram of the photovoltaic system resilience assessment method under ice disaster in the embodiments of the present application.

[0017] Figure 2 is the result diagram of the system load obtained in the specific application embodiments of the present application. DETAILED DESCRIPTION

[0018] The present application will be described in more detailed below in conjunction with the drawings and specific embodiments, but the protection scope of the present application is not limited by this.

[0019] Figure 1 The implementation flow of the photovoltaic system resilience assessment method under ice disaster in the embodiments is shown, and the detailed steps include: Step 1) Collect the meteorological data when the ice disaster weather occurs, the environmental parameters of the area affected by the ice disaster weather, and the performance parameters of the photovoltaic system, and build an ice disaster database, the performance parameters of the photovoltaic system including the load function curve and the structure parameters.

[0020] Specifically, the meteorological data when the ice disaster weather occurs is collected, the environmental parameters of the area affected by the ice disaster weather when the ice disaster weather occurs, and the performance parameters of the photovoltaic system under the ice disaster weather are collected. Among them, the meteorological data includes wind speed, water density, ice density, precipitation rate, air moisture content, ice disaster duration, etc., the structure parameters of the photovoltaic system include cable diameter, photovoltaic panel ice force load threshold, power transmission line load ice force load threshold, photovoltaic panel installation angle, photovoltaic panel length, photovoltaic panel width, etc., and the environmental parameters include altitude, temperature decrement rate, temperature influence coefficient, etc. The above data is collected to form an ice disaster database.

[0021] Step 2) Construct a photovoltaic system ice accretion growth model under ice disaster weather according to the ice disaster database, and the photovoltaic system ice accretion growth model is a relationship model between ice thickness and meteorological data when the ice disaster weather occurs and environmental parameters of the area affected by the ice disaster weather.

[0022] Specifically, the photovoltaic system ice accretion growth model under ice disaster weather is constructed based on the ice disaster data and photovoltaic structure parameters collected in the ice disaster database, and the model can be used to predict the ice thickness of the actual photovoltaic system under the ice disaster weather.

[0023] As a preferred embodiment, the constructed photovoltaic system ice accretion growth model under ice disaster weather can be expressed as: (1) Among them, Rrepresenting ice thickness, T representing ice disaster duration, p w representing water density, p i representing ice density, r representing precipitation rate, v representing wind speed, W representing air moisture content, q representing photovoltaic panel installation angle, l representing anti-icing coating reflectivity.

[0024] The above photovoltaic system icing growth model can fully consider the influence of different environmental factors on temperature, accurately characterize the relationship between photovoltaic system icing thickness and altitude, temperature and the like of the area affected by ice disaster weather, and thus accurately realize the photovoltaic system icing thickness under ice disaster weather.

[0025] Ice force load of photovoltaic system transmission line under ice disaster weather L 1 and photovoltaic panel ice force load L 2. L 1 and photovoltaic panel ice force load L 2.

[0026] Step 3) Based on the historical load time series data and meteorological data in the ice disaster database, an LSTM-based time series prediction model is constructed to predict the load sequence at the future moment.

[0027] In a specific application embodiment, the following steps can be used to construct the time series prediction model: S31, collect historical time series data in the system ice disaster database, including system load time series P ( t ), temperature T ( t ), wind speed v ( t ) and relative humidity H ( t ), and normalize to form an input matrix X as model input; S32, construct an LSTM-based time series prediction model, input the input matrix into the LSTM network for training, and the output of the model is the predicted load sequence at the future moment P ( t ), , …, }, Tthe length of time for prediction.

[0028] Step 4) Obtain real-time meteorological data of the measured area and real-time performance parameters of the measured photovoltaic system, input into the time series prediction model to predict the load sequence at the future time, and predict the ice thickness of the measured photovoltaic system under ice storm weather according to the ice accretion model of the photovoltaic system, and calculate the ice force load of the photovoltaic system transmission line and the ice force load of the photovoltaic panel according to the predicted ice thickness.

[0029] After obtaining the real-time meteorological data of the measured area and the real-time performance parameters of the measured photovoltaic system, input into the trained time series prediction model, the load sequence at the future time can be predicted, and input into the ice accretion model of the photovoltaic system as formula (1) can predict the ice thickness of the measured photovoltaic system under ice storm weather, and then calculate the ice force load of the photovoltaic system transmission line and the ice force load of the photovoltaic panel according to the predicted ice thickness.

[0030] As a preferred embodiment, the ice force load of the photovoltaic system transmission line under ice storm weather can be predicted according to the following formula L 1 and the ice force load of the photovoltaic panel L 2: (2) Wherein, D represents the diameter of the cable, q represents the angle of the photovoltaic panel installation, L represents the length of the photovoltaic panel, W represents the width of the photovoltaic panel, p i represents the density of ice, represents the predicted ice thickness.

[0031] As shown in formula (2), the ice force load of the photovoltaic system transmission line under ice storm weather L 1, by calculating the difference of the cross-sectional area of the transmission line before and after icing, the change of the cross-sectional area caused by icing can be more accurately characterized, so as to accurately predict the ice force load of the photovoltaic system transmission line under ice storm weather L 1, especially when the ice thickness R is relatively large relative to the cable diameter D. At the same time, fully considering the influence of the installation angle of the photovoltaic panel on the load, the ice force load of the photovoltaic panel L 2, the calculation accuracy can be further improved.

[0032] Specifically, real-time meteorological data of the measured area is acquired, the meteorological data including wind speed, water density, ice density, total precipitation, rainfall rate, ice disaster duration, sea level temperature, etc., and environmental parameters of the measured area including altitude, temperature reduction rate, temperature influence coefficient, etc.; meanwhile, load function curve and structure parameters of the measured photovoltaic system are acquired, the structure parameters including cable diameter, photovoltaic panel ice force load threshold, power transmission line load ice force load threshold, angle of photovoltaic panel installation, photovoltaic panel length, photovoltaic panel width, etc.

[0033] After the real-time meteorological data of the measured area and the real-time performance parameters of the measured photovoltaic system are acquired, the ice thickness of the measured photovoltaic system under ice disaster weather is predicted according to a photovoltaic system icing growth model (as shown in formula (1)), and meanwhile, the power transmission line ice force load L 1 and photovoltaic panel ice force load L 2 of the measured photovoltaic system under ice disaster weather are predicted according to the predicted ice thickness and formula (2).

[0034] Further, after the time series prediction model predicts the load sequence at the future moment, key time point extraction is further included for subsequent calculation of evaluation indexes, the steps of the key time point extraction including: S41, first-order difference processing is performed on the predicted load sequence output by the model P ( t ), , …, }, the load increment between adjacent moments is calculated to obtain an approximate instantaneous change rate , and a significant drop threshold , for identifying a load drop stage, a significant rise threshold , for identifying a load recovery stage, and a jitter tolerance , for judging whether the system enters a platform period or not are set; S42, starting from the post-disaster recovery window starting moment t0, scanning backward, the first moment satisfying < is recorded as the load rapid drop starting point ; after , continue scanning, the first moment satisfying and reaching the vicinity of the minimum value of the stage is recorded as ; after , scan, the first moment satisfying is recorded as the load recovery starting moment ; after , scan, the first moment satisfying and the time point of ; after scanning, recovery, again meet the time point of ; after scanning, recovery, again meet the time point of ; after scanning, first make the time point of complete recovery , record each time point as the key time point required to extract.

[0035] The results of the system load extracted by the above method and each key time point in the specific application embodiment are as shown in Figure 2 .

[0036] Step 5) judging the grade of the predicted ice thickness, combining the reliability index with different target evaluation indexes according to the grade of the ice thickness to form a combined index, the reliability index being an index for evaluating the ability of the system to maintain normal operation according to the ice force load of the photovoltaic system transmission line and the ice force load of the photovoltaic panel.

[0037] Considering that the influence on the system resilience is different under different ice thicknesses, for example, light ice generally does not cause significant impact on the system, moderate ice causes greater stress on the system, which may cause the photovoltaic system structure to deform to a certain extent and the operation stability of the system to be affected, and heavy ice causes the system to bear a large ice load, which may greatly affect the system. The present embodiment can more accurately evaluate the resilience state of the photovoltaic system by selecting different evaluation index combinations according to the predicted ice thickness, which can adapt to different environmental states.

[0038] Specifically, according to the predicted ice thickness, a resilience evaluation index is selected. The resilience evaluation index includes a reliability index and other various target evaluation indexes, for example, the target evaluation indexes include a strain force index, a resistance index and a recovery index, the strain force index is used to evaluate the ability of the photovoltaic system to adjust and reconfigure in long-term changes, the resistance index is used to evaluate the ability of the photovoltaic system to absorb and slow down the impact when the impact occurs; and the recovery index is used to evaluate the time required for the photovoltaic system to recover to a normal operation state after being subjected to the impact.

[0039] Specifically, combining the reliability index with different target evaluation indexes according to the grade of the ice thickness to form a combined index includes: (1) if the predicted ice thickness is less than a first preset threshold, it is judged to belong to light ice, and the combined index is formed by combining the reliability index with the strain force index; (2) if the predicted ice thickness is greater than the first preset threshold and less than a second preset threshold, it is judged to belong to moderate ice, and the combined index is formed by combining the reliability index with the strain force index and the resistance index; (3) If the predicted ice thickness is greater than the second preset threshold, it is judged to belong to the heavy icing grade, and a combined index is formed by the reliability index and the strain force index, the resistance index, and the recovery force index, and the first preset threshold < the second preset threshold < the third preset threshold.

[0040] For example, according to the ice growth model R The ice thickness of the region is calculated; according to different ranges of ice thickness, light icing: 0-5 mm; moderate icing: 5-15 mm; heavy icing: > 15 mm; if it is calculated that the region belongs to light icing, select reliability and strain force as evaluation indexes, light icing generally does not cause significant impact on the system, therefore mainly focus on the reliability and strain force of the system to ensure that the system can continue to run and adapt to changes; if it is calculated that the region belongs to moderate icing, select reliability, strain force and resistance as evaluation indexes, moderate icing, the system is under greater stress, which may cause a certain degree of deformation of the photovoltaic system structure and the running stability of the system may be affected, evaluating the strain force of the system can help determine the deformation capacity of the system under the load of ice, evaluating the reliability of the system can help determine whether the system can maintain normal operation when facing increasing ice thickness, and evaluating the resistance of the system can help determine whether the system can effectively resist the load of heavy ice and maintain the stability and safety of the system; if it is calculated that the region belongs to heavy icing, select reliability, strain force, resistance and recovery force as evaluation indexes, when heavy icing, the system needs to withstand a large ice load, and the system may be greatly affected, therefore it is necessary to comprehensively consider various indexes to evaluate the specific performance of the system in the face of heavy icing.

[0041] Step 6) According to the real-time meteorological data of the measured region, the real-time performance parameters of the measured photovoltaic system, the predicted ice force load of the measured photovoltaic system transmission line and the photovoltaic panel, and the load sequence at the future time, the reliability index in the combined index and each target evaluation index are calculated and weighted to obtain a comprehensive resilience evaluation result, so as to evaluate the resilience state of the measured photovoltaic system.

[0042] Specifically, the comprehensively determined resilience evaluation index (combination of reliability index and multiple target evaluation indexes) is used to construct a photovoltaic system resilience evaluation model under ice disaster weather, to comprehensively evaluate the resilience of the photovoltaic system, calculate the resilience score, and adapt to different environmental states to comprehensively and comprehensively evaluate the reliability, strain force, resistance and recovery force of the measured photovoltaic system, and improve the reliability and accuracy of system resilience state evaluation under different environmental states.

[0043] As a preferred embodiment, the reliability index is calculated according to real-time meteorological data of the measured area, real-time performance parameters of the measured photovoltaic system, predicted ice force load of the photovoltaic system power transmission line and ice force load of the photovoltaic panel, and the expression of the reliability index can be represented as: (3) wherein, l 1 (t) and x 1 (t) represents the mean and standard deviation of the photovoltaic system power transmission line when the photovoltaic system power transmission line faces ice disaster weather, l 2 (t) and x 2 (t) represents the mean and standard deviation of the photovoltaic system when the photovoltaic system faces ice disaster weather, L max1 represents the ice force load threshold of the photovoltaic panel, L max2 represents the ice force load threshold of the photovoltaic panel, L 1 and L 2 represent the ice force load of the photovoltaic system power transmission line and the ice force load of the photovoltaic panel under ice disaster weather.

[0044] In this embodiment, the strain force index, the resistance force index and the recovery force index are calculated according to real-time meteorological data of the measured area, real-time performance parameters of the measured photovoltaic system, predicted load sequence and extracted key time points, respectively.

[0045] As a preferred embodiment, the calculation expression of the strain force index can be represented as: (4) wherein, represents the initial time point of the normal operation of the photovoltaic system before the ice disaster weather starts, represents the time point when the photovoltaic system starts to reduce load operation after the ice disaster weather starts; represents the normal power of the photovoltaic system when the ice disaster weather does not occur; represents the predicted power at time t, represents the power of the photovoltaic system after being affected by the ice disaster weather.

[0046] As a preferred embodiment, the expression of the resistance force index can be represented as: (5) wherein, represents the time point when the photovoltaic system takes emergency recovery measures after the ice disaster weather ends; As a preferred embodiment, the calculation expression of the resilience index can be expressed as: (6) (7) In the formula, w 1 、 w 2 、 w 3 is expressed as a weight value of three indexes; AR 41 represents the emergency resilience, which is used to reflect the ability of the photovoltaic system to take emergency recovery measures immediately after the end of the ice disaster; AR 42 represents the recovery progress force, which is used to reflect the ability of the photovoltaic system to further recover to the normal operation state after the emergency recovery measures; AR 43 represents the complete recovery force, which is used to reflect the ability of the photovoltaic system to completely recover to the normal state after experiencing the impact of ice disaster weather; represents the time point at which the photovoltaic system starts to gradually recover to the normal operation after taking emergency measures; represents the time point at which the photovoltaic system further recovers to the normal operation; represents the time point at which the photovoltaic system completely recovers to the normal state after experiencing the impact of ice disaster weather; represents the power at ; represents the power at ; represents the power at ; represents the power at .

[0047] Further, according to the selected resilience evaluation index, a comprehensive resilience evaluation model of the photovoltaic system is constructed, for example, the expression is: (8) In the formula, when the ice thickness is less than 5 mm , n=2; when the ice thickness is greater than 5 mm, and less than 15 mm , n=3; when the ice thickness is greater than 15 mm , n=4; s 1 is a weight value of the strain force index of the photovoltaic system; s 2 is a weight value of the resistance force index of the photovoltaic system; s 3 is a weight value of the resilience index of the photovoltaic system; s 4The weight value of the photovoltaic system reliability index; here, equal weight is set; AR The comprehensive resilience score of the photovoltaic system under ice disaster weather, with a value range of [0, 100].

[0048] Further, according to the comprehensive resilience score calculated by the photovoltaic system resilience evaluation model under ice disaster weather, the photovoltaic system can be divided into three resilience levels, such as the resilience level of the score in the interval [0, 40) is low, the resilience level of the score in the interval [40, 80) is medium, and the resilience level of the score in the interval [80, 100) is high. It can be understood that the specific classification data and classification method can be configured according to actual needs.

[0049] In summary, the present application collects meteorological data under ice disaster weather, environmental parameters of the area affected by ice disaster weather, and performance parameters of the photovoltaic system, constructs an ice accretion model of the photovoltaic system, predicts the ice thickness of the measured photovoltaic system under ice disaster weather by using the ice accretion model of the photovoltaic system, and further predicts the ice force load of the power transmission line and the ice force load of the photovoltaic panel. At the same time, the resilience evaluation index is determined according to the level of ice thickness, and the comprehensive resilience evaluation result is calculated by using the determined resilience evaluation index. Not only can the meteorological data under ice disaster weather, the environmental parameters of the area affected by ice disaster weather, and the performance parameters of the photovoltaic system be comprehensively considered, but also the resilience state of the photovoltaic system under ice disaster weather can be accurately and comprehensively evaluated. In addition, the influence of different environments on the system can be fully considered, different resilience evaluation indexes can be adaptively determined for comprehensive evaluation of the system, the resilience state of the photovoltaic system under ice disaster weather can be more effectively and accurately evaluated under different environmental conditions, and the reliability and precision of the evaluation can be improved.

[0050] Through the above-mentioned scheme of the present application, the resilience level of the photovoltaic system under ice disaster weather can be comprehensively understood, and the problems and weak links of the photovoltaic system can be found, which is helpful to take targeted measures to improve the resilience of the system, thereby reducing the failure rate and improving the reliability and stability of the system.

[0051] The present application also provides a photovoltaic system resilience evaluation device under ice disaster, comprising a processor and a memory, the memory is used for storing a computer program, and the processor is used for executing the computer program to execute the above-mentioned method.

[0052] The present application also provides a computer readable storage medium storing a computer program, which is executed by a processor to implement the above-mentioned method.

[0053] In the embodiments of the present disclosure, it should be understood that the disclosed apparatus and method can also be implemented in other manners. The described apparatus embodiments are merely exemplary. For example, the flowcharts and block diagrams in the accompanying drawings show the possible implementation architectures, functions and operation of the apparatus, method and computer program product according to the embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logic function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks can actually be executed in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and flowcharts, and the combination of blocks in the block diagrams and flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0054] Although the present application has been described with reference to the preferred embodiments, various modifications and changes can be made thereto without departing from the scope of the present application. In particular, the technical features mentioned in each of the embodiments can be combined in any manner as long as there is no structural conflict. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for evaluating the resilience of a photovoltaic system under freezing disasters, characterized in that the steps include: Step 1) collecting meteorological data when ice disaster weather occurs, environmental parameters of the area affected by the ice disaster weather, and performance parameters of the photovoltaic system to construct an ice disaster database containing time series characteristics, wherein the performance parameters of the photovoltaic system include structural parameters; Step 2) constructing a photovoltaic system ice growth model under ice disaster weather based on the ice disaster database, wherein the photovoltaic system ice growth model is a relationship model between the thickness of photovoltaic system ice and meteorological data during ice disaster weather and environmental parameters of the area affected by the ice disaster weather; Step 3) Based on the historical load time series data and meteorological data in the ice disaster database, a time series prediction model based on LSTM is constructed to predict the load series at future moments; Step 4) obtaining real-time meteorological data of the measured area and real-time performance parameters of the measured photovoltaic system, inputting the data into the time series prediction model to predict the load sequence at a future time, and predicting the ice thickness of the measured photovoltaic system under ice disaster weather based on the photovoltaic system ice growth model, and calculating the ice load on the photovoltaic system transmission line and the photovoltaic panel based on the predicted ice thickness; Step 5) Determining the predicted ice thickness level, and combining a reliability index with different target evaluation indicators based on the ice thickness level to form a combined index, wherein the reliability index is an indicator of the system's ability to maintain normal operation based on the ice load on the photovoltaic system's transmission lines and the photovoltaic panels; Step 6) Based on the real-time meteorological data of the measured area, the real-time performance parameters of the measured PV system, the predicted ice loads on the transmission lines and PV panels of the measured PV system, and the load sequence at future times, the reliability index and each target evaluation index in the combined index are calculated and weighted to obtain a comprehensive resilience evaluation result to evaluate the resilience status of the measured PV system.

2. The photovoltaic system resilience assessment method under freezing disasters according to claim 1 is characterized in that: The meteorological data includes any one or more of wind speed, water density, ice density, precipitation rate, water content in the air, and duration of ice disasters; the structural parameters of the photovoltaic system include any one or more of cable diameter, photovoltaic panel ice load threshold, transmission line ice load threshold, photovoltaic panel installation angle, photovoltaic panel length, photovoltaic panel width, and anti-icing coating reflectivity.

3. The photovoltaic system resilience assessment method under freezing disasters according to claim 1, characterized in that: In step 2), the ice growth model of the photovoltaic system under ice disaster weather is constructed as follows: in, R represents the ice thickness, T Represents the duration of the ice disaster, ρ w represents the density of water, ρ i represents the density of ice, r represents the precipitation rate, v represents wind speed, W Represents the water content in the air, θ Represents the installation angle of the photovoltaic panel, λ Represents the reflectivity of the anti-icing coating.

4. The photovoltaic system resilience assessment method under freezing disasters according to claim 3 is characterized in that: Predict the ice load on the photovoltaic system transmission line under ice disaster weather according to the following formula L 1 and photovoltaic panel ice load L 2: in, D represents the cable diameter, θ Represents the angle at which the photovoltaic panels are installed, L Represents the length of the photovoltaic panel, W Represents the width of the photovoltaic panel, ρ i represents the density of ice, Represents the predicted ice thickness.

5. The photovoltaic system resilience assessment method under freezing disasters according to claim 1 is characterized in that: Step 3) includes: S31. Collect historical time series data from the system ice disaster database, including system load time series P ( t ),temperature T ( t ), wind speed v ( t ) and relative humidity H ( t ), and then normalized to form the input matrix X As model input; S32, build a time series prediction model based on LSTM, and input the matrix Input into the LSTM network for training, the output of the model is the load sequence predicted at the future moment { P ( t ), ,…, }, T is the length of the prediction time; After the time series forecasting model predicts the load series at future moments in step 4), key time points are extracted. The steps include: S41, the forecast load sequence output by the model { P ( t ), ,…, Perform first-order difference processing to calculate the load increment between adjacent moments and obtain the approximate instantaneous rate of change , and set a significant drop threshold for identifying load drop phases , , used to identify the significant rising threshold during the load recovery phase 、 , used to determine whether the system has entered the plateau period jitter tolerance , ; S42, scan backward from the start time t0 of the post-disaster recovery window, and find the first < The moment of rapid load reduction is recorded as the starting point ;exist Then continue scanning and find the first one that satisfies both and The moment when the minimum value of this stage is reached is recorded as ;exist After scanning, the first one that meets The time of load recovery is recorded as the load recovery start time ;exist After scanning, the first one that meets and The moment is recorded as ;exist After scanning and recovery, satisfy again The moment is recorded as ;exist After scanning, the first one makes The time of complete recovery is recorded as , and take each recorded moment as the key time point to be extracted.

6. The photovoltaic system resilience assessment method under freezing disasters according to claim 5 is characterized in that: The target evaluation indicators include a strain index, a resistance index, and a resilience index. The strain index is used to evaluate the ability of the photovoltaic system to adjust and reconfigure in the face of long-term changes. The resilience index is used to evaluate the ability of the photovoltaic system to absorb and mitigate the impact when a shock occurs. The resilience index is used to evaluate the time required for the photovoltaic system to return to normal operation after suffering a shock. The strain index, resistance index, and resilience index are calculated based on the real-time meteorological data of the measured area, the real-time performance parameters of the measured photovoltaic system, the predicted load sequence, and the extracted key time points. The calculation expression of the strain index is: Where, Indicates the initial time point of normal operation of the photovoltaic system before the ice disaster begins. Indicates the time when the PV system starts to reduce load after the ice disaster begins; Indicates the normal power of the photovoltaic system when there is no ice disaster; represents the predicted power at time t, Indicates the power of the photovoltaic system after being affected by the ice disaster weather; The resilience index expression is: Where, Indicates the time point when emergency restoration measures are taken for the photovoltaic system after the ice disaster ends; The calculation expression of the resilience index is: Where, ω 1 、 ω 2 、 ω 3 Expressed as the weight values ​​of the three indicators; AR 41 It represents emergency recovery capacity, which reflects the ability of the PV system to take emergency recovery measures immediately after the ice disaster ends; AR 42 Indicates the restoration progress, which is used to reflect the ability of the PV system to further recover to normal operating conditions after emergency restoration measures; AR 43 It represents full recovery capacity, which is used to reflect the ability of the photovoltaic system to fully recover to normal state after experiencing ice disaster weather; Indicates the time point when the PV system begins to gradually return to normal operation after emergency measures are taken; Indicates the time point when the photovoltaic system further returns to normal operation; Indicates the time point when the photovoltaic system fully recovers to normal state after experiencing the impact of ice disaster weather; Indicates Power of the moment; Indicates Power of the moment; Indicates Power of the moment; Indicates The power of the moment.

7. The method for assessing the resilience of a photovoltaic system under freezing disasters according to any one of claims 1 to 6, characterized in that: Step 5) includes: If the predicted ice thickness is less than a first preset threshold, it is determined to be a light ice condition, and a combined index is formed by combining the reliability index and the strain index; If the predicted ice thickness is greater than a first preset threshold and less than a second preset threshold, it is determined to be moderate ice thickness, and a combined index is formed by combining the reliability index with the strain index and the resistance index; If the predicted ice thickness is greater than the second preset threshold, it is judged to be a severe level of icing, and a combination index is formed by combining the reliability index with the strain index, the resistance index and the recovery index, and the first preset threshold < the second preset threshold < the third preset threshold.

8. The method for assessing the resilience of a photovoltaic system under freezing disasters according to any one of claims 1 to 6, wherein: The reliability index is calculated based on the real-time meteorological data of the tested area, the real-time performance parameters of the tested photovoltaic system, the predicted ice load on the transmission line of the tested photovoltaic system, and the ice load on the photovoltaic panels. The calculation expression is: in, λ 1 (t) and ξ 1 (t) Represents the mean and standard deviation of the photovoltaic system transmission line when facing ice disaster weather, λ 2 (t) and ξ 2 (t ) represents the mean and standard deviation of the photovoltaic system photovoltaic panels when facing ice disaster weather, L max1 Represents the ice load threshold of photovoltaic panels, L max2 Represents the ice load threshold of photovoltaic panels, L 1 and L 2 Represents the ice load on the photovoltaic system transmission lines and photovoltaic panels under ice disaster weather.

9. A photovoltaic system resilience assessment device under freezing disasters, comprising a processor and a memory, wherein the memory is used to store a computer program, characterized in that: The processor is configured to execute the computer program to perform the method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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