Stability evaluation method for water photovoltaic installation equipment

Abstract

The invention discloses a stability evaluation method for water photovoltaic installation equipment, which belongs to the field of photovoltaic installation, and comprises the following steps: collecting regional hydrological information and environmental climate information, and obtaining regional extremum features; constructing a simulation space, wherein the simulation space comprises a front simulation scene module and a rear stability evaluation module; inputting the regional hydrological information, the environmental climate information and the regional extremum features into a parameter search configuration module to obtain configuration parameters; using the parameter search configuration module as a feedforward module of the front simulation scene module, and fitting configuration parameters to a simulation space; and performing stability evaluation by using a rear stability evaluation module to obtain a stability evaluation result. According to the method, the simulation space is constructed, and the parameter optimization is carried out by taking the maximum volatility as the guide, so that the comprehensive and accurate evaluation of the stability of the overwater photovoltaic installation equipment in the extreme environment is realized, and the long-term reliable operation of the equipment is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic installation, and in particular to a stability assessment method for water photovoltaic installation equipment. Background Art

[0002] With the vigorous development of the new energy industry, water-based photovoltaic power generation systems have received widespread attention. However, water-based photovoltaic installation equipment is exposed to complex and changeable natural environments for a long time, and its stability is directly related to the safe operation and economic benefits of the power generation system. In the prior art, the evaluation of the stability of water-based photovoltaic installation equipment is mostly limited to static calculations or simple dynamic simulations. Although these methods take into account the influence of wind loads and wave loads, they do not fully simulate the combination of environmental parameters under extreme weather conditions, and it is difficult to fully reflect the stress conditions and structural responses of the equipment under extreme hydrological and climatic conditions. Therefore, it is difficult to evaluate the stability of the equipment under the most unfavorable conditions, resulting in insufficient safety redundancy of water-based photovoltaic installation equipment, thereby affecting the long-term reliable operation of the equipment. Summary of the invention

[0003] This application provides a stability assessment method for water-based photovoltaic installation equipment, aiming to solve the technical problem in the prior art that the stability of water-based photovoltaic installation equipment in the most unfavorable environment cannot be accurately assessed due to the lack of systematic consideration of extreme hydrological and climatic conditions, thereby affecting the long-term reliable operation of the equipment.

[0004] The present application discloses a stability assessment method for water-based photovoltaic installation equipment, comprising: collecting regional hydrological information and environmental climate information according to a preset period to obtain regional extreme value characteristics; collecting equipment structure and equipment installation data of water-based photovoltaics to construct a simulation space, wherein the simulation space includes a front-end simulation scenario module and a rear-end stability evaluation module; inputting regional hydrological information, environmental climate information, and regional extreme value characteristics into a parameter search configuration module, performing parameter search with maximum volatility as the goal, and obtaining configuration parameters; using the parameter search configuration module as a feedforward module of the front-end simulation scenario module to fit the configuration parameters to the simulation space; performing simulation testing through the simulation space, performing stability evaluation using the rear-end stability evaluation module, and obtaining a stability evaluation result.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] By collecting regional hydrological information and environmental climate information according to preset cycles and obtaining regional extreme value characteristics, the basic data required for the evaluation are obtained, providing real and reliable environmental parameters for subsequent analysis; the equipment structure and equipment installation data of water photovoltaics are collected to build a simulation space. The simulation space includes a pre-simulation scenario module and a post-stability evaluation module, which introduce the actual situation of the evaluation object into the analysis process and build a core framework for simulation evaluation. The pre-simulation scenario module is used to simulate various working conditions, and the post-stability evaluation module is responsible for analyzing the simulation results. The combination of the two ensures the integrity of the evaluation process; the regional hydrological information, environmental climate information, and regional extreme value characteristics are input into the parameter search configuration module, and parameter search is performed with the maximum volatility as the goal to obtain configuration parameters. The most unfavorable hydrological and climate combination characteristics are found through optimization algorithms, rather than using fixed parameters. With the maximum volatility as the goal, the working conditions that may pose the greatest threat to equipment stability are found, thereby ensuring the conservatism and reliability of the evaluation; the parameter search As a feedforward module of the front-end simulation scenario module, the configuration module fits the configuration parameters to the simulation space, realizes the organic combination of the optimized parameters and the simulation model, makes the simulation process closer to the actual extreme situation, and lays the foundation for the subsequent stability evaluation; through the simulation space, the simulation test is carried out, the stability evaluation is carried out by the post-stability evaluation module, the stability evaluation result is obtained, the simulation is run under the most unfavorable parameter setting, and the result is comprehensively analyzed for stability, so as to obtain the performance of the water photovoltaic installation equipment under extreme conditions, and enhance the reliability of the stability evaluation. The technical solution solves the technical problem that the stability of the water photovoltaic installation equipment under the most unfavorable environment cannot be accurately evaluated due to the lack of systematic consideration of extreme hydrological and climatic conditions in the existing technology, thereby affecting the long-term reliable operation of the equipment. By constructing the simulation space and optimizing the parameters with the maximum volatility as the guide, the stability of the water photovoltaic installation equipment under extreme environment is comprehensively and accurately evaluated, which effectively guarantees the long-term reliable operation of the equipment.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic flow chart of a stability assessment method for a water-based photovoltaic installation device is provided for an embodiment of the present application;

[0009] Figure 2 A flow chart of setting development rules in a stability assessment method for a water-based photovoltaic installation device is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0010] The overall idea of ​​the technical solution provided by this application is as follows:

[0011] The embodiment of the present application provides a stability assessment method for water-based photovoltaic installation equipment, which systematically considers extreme hydrological and climatic conditions and optimizes the most unfavorable operating parameters, thereby achieving a comprehensive and accurate assessment of the stability of the equipment in the most severe environment.

[0012] First, regional hydrological information and environmental climate information are collected according to the preset cycle to obtain regional extreme value characteristics and establish the environmental parameters required for evaluation. Secondly, combined with the equipment structure and installation data of water-based photovoltaics, a simulation space including a pre-simulation scenario module and a post-stability evaluation module is constructed to provide a platform for subsequent analysis. Then, a parameter search configuration module is introduced to optimize the search for environmental parameters with the maximum volatility as the goal, and to find the most unfavorable hydrological and climate combination characteristics. After that, the optimized configuration parameters are fitted to the simulation space to make the simulation process closer to the extreme reality. Finally, the test is carried out in the simulation space, and the results are analyzed using the post-stability evaluation module to obtain the final stability evaluation results.

[0013] After introducing the basic principles of the present application, the non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the drawings in the specification.

[0014] like Figure 1 As shown, an embodiment of the present application provides a method for evaluating the stability of a water-based photovoltaic installation device, the method comprising:

[0015] S1: Collect regional hydrological information and environmental climate information according to the preset period to obtain regional extreme value characteristics.

[0016] Specifically, the preset period is a time unit such as day, week, month or quarter, and its selection depends on the frequency of change of the required data and the evaluation accuracy requirements. By collecting data regularly, it can be ensured that the information obtained can fully reflect the dynamic changes of local hydrological and climatic conditions. Collect regional hydrological information and environmental climate information according to the preset period. Among them, regional hydrological information includes but is not limited to parameters such as water level, flow velocity, flow direction, etc. These parameters directly affect the stress condition and stability of water photovoltaic equipment; environmental climate information can cover wind speed, wind direction, precipitation, temperature and other factors, which not only act on hydrological conditions, but also have a direct impact on the photovoltaic equipment itself.

[0017] On the basis of collecting the above information, we further extract regional extreme value features. Regional extreme value features refer to the maximum or minimum values ​​of hydrological and meteorological elements observed within a specific time scale, such as the highest water level, maximum wind speed, maximum 24-hour precipitation, etc., which represent the most unfavorable environmental conditions and are used to evaluate the stability of water-based photovoltaic installation equipment.

[0018] By collecting regional hydrological information and environmental climate information according to preset cycles and obtaining regional extreme value characteristics, the necessary data is provided for subsequent simulation analysis and stability assessment, which helps to fully understand the environmental characteristics of the target area, especially extreme conditions that may endanger the safety of equipment, thereby laying the foundation for the stability assessment of water-based photovoltaic installation equipment.

[0019] S2: Collect the equipment structure and equipment installation data of the water photovoltaic system and construct a simulation space, which includes a front-end simulation scene module and a rear-end stability evaluation module.

[0020] Specifically, the equipment structure data includes the size, weight, material properties of the photovoltaic modules, as well as the geometric shape and mechanical properties of key components such as the support structure and anchoring system; the equipment installation data includes parameters such as the installation position, orientation, inclination, and relative height to the water surface. The equipment structure and equipment installation data of the floating photovoltaic equipment jointly determine the stress characteristics and deformation behavior of the floating photovoltaic equipment under the action of external forces.

[0021] Then, according to the equipment structure and equipment installation data of the water photovoltaic equipment, a simulation space is constructed, which includes a pre-simulation scenario module and a post-stability evaluation module. Among them, the pre-simulation scenario module is used to simulate the external environment of the water photovoltaic equipment, and generate various possible working conditions based on the obtained regional hydrological information and environmental climate information, providing input conditions for subsequent stability analysis; the post-stability evaluation module focuses on evaluating the mechanical response and stability of the equipment under different working conditions, which may involve structural stress analysis, fatigue life prediction, capsizing risk assessment and other aspects.

[0022] Through modular simulation space design, not only can the accuracy of environmental simulation and stability analysis be optimized respectively, but also through data interaction between modules, dynamic evaluation of the stability of water photovoltaic installation equipment throughout its life cycle can be achieved. At the same time, it provides reliable technical support for the stability evaluation of water photovoltaic installation equipment, effectively avoiding the high cost and high risk of conducting large-scale, long-term tests in actual environments.

[0023] S3: Input the regional hydrological information, environmental climate information, and regional extreme value characteristics into a parameter search configuration module, perform parameter search with the maximum volatility as the goal, and obtain configuration parameters.

[0024] Specifically, the most unfavorable environmental parameter combination is obtained through the parameter search configuration module, thereby providing strict test conditions for the stability evaluation of offshore photovoltaic installation equipment.

[0025] The regional hydrological information, environmental climate information and regional extreme value characteristics are input into the parameter search configuration module, and the parameter search is carried out with the maximum volatility as the goal, and the configuration parameters are finally obtained. Among them, the parameter search configuration module uses an optimization algorithm to find the hydrological and climate combination characteristics that make the stability of the water photovoltaic equipment the worst in the given parameter space. In the search process, the regional hydrological information and environmental climate information constitute the basic parameter space, providing the search range and initial conditions. The regional extreme value characteristics serve as constraints to guide the search towards parameter combinations that are more unfavorable to the water photovoltaic installation equipment. With the maximum volatility as the goal, the most unfavorable working conditions are found, so as to simulate the worst situation that the water photovoltaic equipment may face, and provide sufficient safety margin for subsequent stability evaluation. Through the search of the parameter search configuration module, the configuration parameters are output, which collect various factors that may cause equipment instability, such as the superposition of high water level and strong wind speed, the coupling of rapids and heavy rains, etc. These parameters will be used to set up simulation scenarios to verify the stability performance of water photovoltaic installation equipment under extreme conditions.

[0026] By setting up the parameter search configuration module and conducting parameter searches, the comprehensiveness and reliability of the stability assessment of floating photovoltaic installation equipment can be improved, providing a strong guarantee for the safe operation of the equipment.

[0027] S4: Using the parameter search configuration module as a feedforward module of the front simulation scene module to fit the configuration parameters to the simulation space.

[0028] Specifically, the obtained configuration parameters are integrated into the simulation space to prepare for the stability simulation test of the floating photovoltaic installation equipment.

[0029] First, the hierarchical relationship between the parameter search configuration module and the front simulation scenario module is established, that is, the former is positioned as the feedforward module of the latter, so as to realize the unidirectionality of data flow and the decoupling between modules, which is conducive to improving the maintainability and scalability of the simulation process. As a feedforward module, the parameter search configuration module provides input for the front simulation scenario module, and optimizes and screens the input in advance, that is, focusing on the parameter combination that may cause the maximum volatility of the water photovoltaic equipment. Subsequently, the configuration parameters are fitted to the simulation space, and the discrete configuration parameters are converted into a continuous and dynamic simulation environment. In this process, through a variety of technical means such as data smoothing, interpolation, and scale conversion, while maintaining the extremes represented by the configuration parameters, a simulation scene that conforms to physical laws and is close to the real situation is constructed.

[0030] S5: Perform simulation test in the simulation space, perform stability evaluation using a post-stability evaluation module, and obtain a stability evaluation result.

[0031] Specifically, the constructed simulation space is used and the post-stability evaluation module is used to conduct a comprehensive analysis of the performance of offshore photovoltaic equipment under extreme working conditions.

[0032] First, simulation tests are carried out in the simulation space, which integrates multi-dimensional information such as regional hydrological information, environmental climate information, and equipment structure data, which contains the operating parameters that lead to the maximum volatility of the equipment selected by the parameter search configuration module. Testing in an optimized simulation scenario can effectively simulate the stress state and dynamic response of water-based photovoltaic equipment under the most unfavorable conditions. Subsequently, the post-stability evaluation module is called to analyze the results of the simulation test. Through multiple evaluation dimensions, such as structural integrity, anchoring system reliability, and floating body stability, the stability of the water-based photovoltaic installation equipment is comprehensively analyzed to ensure the reliability of the evaluation results. After that, the stability evaluation results are output in the form of safety factor, failure probability, expected service life, etc., which intuitively reflects the adaptability and safety margin of water-based photovoltaic equipment in extreme environments. Based on these indicators, the stability level of the equipment can be objectively judged, and targeted optimization measures can be taken when necessary, such as strengthening structural design, improving anchoring schemes, and adjusting array forms.

[0033] By conducting tests in a simulated environment, the high cost and high risk of conducting large-scale tests in actual waters were avoided, and the stability limits of the water-based photovoltaic installation equipment were explored to the greatest extent possible, providing strong support for the safe and efficient operation of the equipment throughout its life cycle.

[0034] Furthermore, the embodiment of the present application also includes:

[0035] Collect regional hydrological information and environmental climate information according to the preset period, wherein the regional hydrological information includes water level information and water flow velocity, and the environmental climate information includes wind speed, precipitation and duration;

[0036] Obtain hydrological constraint information and environmental climate constraint information for water-based photovoltaic installations;

[0037] An extreme value screening window is set based on the hydrological constraint information and the environmental climate constraint information, and the regional hydrological information and the environmental climate information are traversed to obtain extreme value screening information;

[0038] Feature extraction is performed on the extreme value screening information to obtain the regional extreme value feature.

[0039] In a feasible implementation, first, the environmental parameters to be collected are clearly defined. Regional hydrological information focuses on water level information and water flow velocity. These two parameters determine the buoyancy balance and horizontal force of water photovoltaic equipment; environmental climate information includes wind speed, precipitation and its duration, which not only affects hydrological conditions, but also imposes additional loads on the equipment or changes its working environment. Both regional hydrological information and environmental climate information are collected at preset cycles to fully capture the dynamic changes in the environment. At the same time, the constraint information of water photovoltaic installation and environmental climate constraint information are obtained. These constraint information define the threshold or range of each environmental factor that has a significant impact on the stability of water photovoltaic equipment. It is obtained based on historical cases and expert experience, providing a scientific basis for subsequent extreme value screening, avoiding blindly treating all abnormal values ​​as key factors.

[0040] Then, the extreme value screening window is set using the hydrological constraint information and the environmental climate constraint information. The size of the window corresponds to the value range of each factor in the constraint condition, forming a multi-dimensional information screening area. By traversing the regional hydrological information and environmental climate information and comparing them with the extreme value screening window, extreme value events with potential risks can be efficiently identified to obtain extreme value screening information. Then, through statistical analysis, pattern recognition and other technical means, in-depth feature extraction of extreme value screening information is carried out, aiming to summarize more universal features from the discrete data points obtained by screening, and obtain regional extreme value characteristics, such as the frequency of occurrence, duration, peak intensity, etc. of extreme values, to provide information for subsequent stability assessment.

[0041] Furthermore, the embodiment of the present application also includes:

[0042] Obtained stability exception cases for waterborne photovoltaic installations;

[0043] Classify the stability anomaly cases, construct a multi-level case set, and extract multi-level top events;

[0044] For each of the multi-level case sets, correlation analysis is performed with the multi-level top events as the starting point, and the influencing factors are segmented to obtain a minimum cut set, wherein the minimum cut set includes a minimum factor combination and factor ratio that causes the multi-level top events to occur;

[0045] The hydrological information and the environmental climate information are extracted according to the minimum cut set, and the maximum matching values ​​of the hydrological information and the environmental climate information in the minimum cut set are respectively selected as the hydrological constraint information and the environmental climate constraint information.

[0046] In a preferred embodiment, by processing and mining stability anomaly cases, the environmental factors and their critical values ​​that have a decisive influence on the stability of water photovoltaic equipment are extracted from actual operating experience, thereby providing more targeted and practical hydrological constraint information and environmental climate constraint information for the screening of extreme value features.

[0047] First, the stability anomaly cases of water-based photovoltaic installations are obtained from actual operation records, accident reports or related literature, covering various unstable conditions that occur during the use of the equipment, such as excessive deformation, anchor failure, and floating body capsizing. Through the stability anomaly cases of water-based photovoltaic installations, rich original materials are provided for subsequent analysis. Then, the stability anomaly cases are graded and a multi-level case set is constructed. Among them, the classification basis includes the severity of the accident consequences, the scope of equipment damage, the high or low repair cost, etc. Through the graded classification process, a corresponding relationship is established for stability problems of different degrees. On this basis, multi-level top events are further extracted, that is, the final manifestation of each level of anomaly.

[0048] Subsequently, starting from the multi-level top events, the influencing factors are decomposed layer by layer through top-down correlation analysis to obtain the minimum cut set. Among them, the minimum cut set contains the minimum factor combination and its ratio that cause the top events, reflecting the contribution of various environmental factors to the stability of the water photovoltaic equipment. Specifically, for each level of case set, the corresponding top event is set as the focus of the analysis. Based on this, a top-down correlation analysis is carried out, and the intermediate events and basic events of various levels related to the top events are systematically sorted out through tools such as fault trees and cause-effect diagrams. In this process, different logical relationships (such as "AND" gates and "OR" gates) are used to describe the dependencies between events, thereby constructing a multi-level and multi-path event network. With the deepening of the analysis, various impact paths that may lead to top events gradually become clear. Afterwards, special attention is paid to the basic events that directly act on the water photovoltaic equipment, which are closely related to specific environmental factors, such as strong winds, rapids, and heavy rains. By identifying and extracting these basic events, the transformation from macro stability anomalies to micro influencing factors is achieved.

[0049] On this basis, the minimum cut set is obtained, that is, the minimum factor combination required to cause the top event to occur, and the environmental factors that play a decisive role are screened out from the aforementioned event network. Each minimum cut set not only contains a specific factor combination, but also contains the factor ratio of each factor, that is, the intensity of action, which provides support for the subsequent quantitative assessment of environmental impact. Since a multi-level case set is processed, the output will be a set of minimum cut sets corresponding to stability anomalies of different severity. The hierarchical analysis model helps to fully grasp the vulnerability of water-based photovoltaic equipment under various extreme conditions, laying the foundation for the subsequent formulation of hydrological constraint information and environmental climate constraint information. Afterwards, the ratio values ​​of hydrological information and environmental climate information in each minimum cut set are calculated respectively. If a minimum cut set contains only a single parameter, the parameter ratio is regarded as 1. By comparing the ratios between different cut sets, the maximum ratio values ​​of hydrological information and environmental climate information are selected, and they are set as hydrological constraint information and environmental climate constraint information respectively.

[0050] Furthermore, the embodiment of the present application also includes:

[0051] Randomly create multiple search subsets, each search subset has a guide parameter, and the guide parameter is the regional hydrological and environmental climate parameter with the largest volatility evaluation in the search subset;

[0052] Determine the search scope according to the regional hydrological information and environmental climate information, use the regional extreme value characteristics as constraints, and set development rules;

[0053] Extracting the influencing parameters from the regional hydrological information and environmental climate information, including water level information, water flow velocity, wind speed, precipitation and duration, fitting the linear relationship between each influencing parameter and the volatility of water photovoltaics, and constructing a volatility evaluation function;

[0054] Taking the guiding parameter as the search starting point, performing search subset search and updating according to the development rule and the volatility evaluation function, and updating the guiding parameter through the volatility evaluation function with the maximum volatility as the goal;

[0055] When the search target requirement is reached, the search is stopped and the current update guidance parameters of each search subset are obtained;

[0056] The current update guide parameters of each search subset are sorted according to the evaluation values, and are used as the configuration parameters in sequence according to the sorting.

[0057] In a feasible implementation, guided by maximizing the volatility of water-based photovoltaic equipment, through strategies such as multi-subset parallel search and guided parameter dynamic update, the most unfavorable environmental combination is efficiently identified and extracted in the high-dimensional parameter space composed of regional hydrological information, environmental climate information and regional extreme characteristics, providing configuration parameters for subsequent simulation analysis.

[0058] First, multiple search subsets are randomly created. Each search subset can be regarded as a local exploration unit in the parameter space, and the optimization efficiency is improved by parallel search. A guiding parameter is assigned to each search subset, which is selected from the regional hydrological or environmental climate parameter with the largest volatility evaluation within the subset, so that each subset starts searching from a relatively preferred position, which is conducive to quickly approaching the global optimal solution. Then, based on the regional hydrological information and environmental climate information, the upper and lower limits of the parameter values, that is, the search range, are determined. At the same time, the regional extreme value characteristics are converted into constraints, which not only narrows the effective search space, but also ensures the practical significance of the search results. On this basis, development rules are set to provide guidance for subsequent parameter updates, such as step size selection and direction judgment.

[0059] Subsequently, key influencing parameters, including water level information, water flow velocity, wind speed, precipitation and its duration, are extracted from regional hydrological information and environmental climate information. Through data analysis, the linear relationship between each influencing parameter and the volatility of water photovoltaics is fitted to construct a volatility evaluation function. After that, each subset starts with its own guiding parameter and follows the set development rules to conduct a search. Each set of parameters is evaluated by the volatility evaluation function, driving the search in the direction of maximum volatility, while guiding the update of the guiding parameters, that is, continuously selecting new parameters with greater volatility as the starting point for the next round of search, thereby ensuring the continuous optimization of the search based on the iterative mechanism of evaluation. When the search target requirements are met, such as reaching the maximum number of iterations, the improvement of the evaluation value for several consecutive rounds is lower than the threshold, the search process stops. At this point, each search subset has converged to its own local optimal solution, that is, the currently updated guiding parameter. After that, the currently updated guiding parameters of each search subset are sorted in descending order according to the evaluation value calculated by the volatility evaluation function. The parameter combinations at the top represent more extreme and challenging environmental conditions. These sorted parameters are used as configuration parameters in turn, providing the main test conditions for subsequent simulations.

[0060] Further, such as Figure 2 As shown, the embodiment of the present application also includes:

[0061] Performing data dispersion analysis based on the regional hydrological information and environmental climate information to determine data dispersion;

[0062] Based on the data discreteness, determining a discrete mean configuration search step size;

[0063] Setting a search step size adaptive rule, wherein the search step size adaptive rule includes adjusting the search step size to a discrete minimum value when the guidance parameter update speed reaches a preset deceleration threshold, and restoring the search step size to a discrete mean value when the guidance parameter update speed reaches a preset acceleration threshold;

[0064] Setting an adaptive adjustment limit, and when the adaptive adjustment limit is reached, acquiring a search field, and searching again in the search field;

[0065] The search step size, the search step size adaptive rule and the adaptive adjustment limit are added as incremental rules into the development rule.

[0066] In a preferred embodiment, first, the data dispersion analysis is performed on the regional hydrological information and environmental climate information. Wherein, the dispersion refers to the degree of dispersion of the data distribution, which is measured by statistics such as the coefficient of variation (the ratio of the standard deviation to the mean value) and the interquartile range (the difference between the upper and lower quartiles). For different parameters such as water level, flow velocity, and wind speed, there are significant differences in their dispersion. For example, in some areas, the water level may be relatively stable, while the wind speed fluctuates violently. By determining the data dispersion, a basis is provided for the subsequent step size setting. Then, based on the obtained data dispersion, the discrete mean is calculated, and the discrete mean is the weighted average of the dispersion of each environmental parameter (such as water level, flow velocity, wind speed, etc.), where the weight is determined according to the degree of influence of the parameter on the stability of the water photovoltaic device. For example, if the fluctuation of wind speed has the greatest impact on the equipment, the wind speed dispersion is given a higher weight when calculating the discrete mean. After obtaining the discrete mean, the search step size is configured accordingly. The more drastic the data fluctuations, the larger the search step size is to quickly explore the vast parameter space; conversely, if the data is relatively stable, a smaller search step size is used for a detailed search.

[0067] At the same time, the search step adaptive rule is introduced to dynamically adjust the search step by monitoring the update speed of the guiding parameters (such as the improvement of the evaluation function value within a unit number of iterations). Specifically, when the update speed is lower than the preset deceleration threshold, it means that the search falls into a local flat area. At this time, the search step is reduced to the discrete minimum value (such as the minimum value of the discreteness of all parameters) to conduct more refined local exploration. When the update speed exceeds the preset growth rate threshold, it indicates that the current area contains significant upward potential, so the search step is restored to the previous discrete mean to accelerate convergence. Despite the setting of the search step adaptive rule, the search may still stagnate. To address this problem, an adaptive adjustment limit is set, that is, the maximum number of consecutive inefficient searches allowed. When this limit is reached, the reset procedure of the search field is started, that is, a new search range (such as the current parameter value ± 20%) is delineated around the current optimal point, and random initialization and search are re-performed within this range, thereby getting rid of the local optimum and enhancing the potential for finding the global optimal solution. Afterwards, the search step, search step adaptive rules and adaptive adjustment limits are integrated into incremental rules and incorporated into the development rules to enrich the development rules and provide support for the parameter search of the parameter search configuration module.

[0068] In a preferred embodiment, a volatility evaluation function is constructed, and its expression is:

[0069] Among them, Y represents the volatility evaluation value, which reflects the degree of fluctuation of the water-based photovoltaic installation equipment; X is the influencing parameter, such as water level information, water flow velocity, wind speed, precipitation and duration, etc.; ε is the error term, which represents the random error in the fitting process; is the volatility fitting coefficient corresponding to each influencing parameter, is the sample mean of volatility evaluation, is the parameter mean, It represents the expected value of Y when X is zero, n is the number of influencing parameters, and n is a positive integer. This function provides a quantitative optimization target for the parameter search configuration module by associating the fluctuation characteristics of the floating photovoltaic equipment with the multi-dimensional environmental parameters.

[0070] By constructing a volatility evaluation function, the impact of different influencing parameters on the stability of floating photovoltaic installation equipment is quantitatively evaluated, providing an important basis for subsequent stability evaluation and optimization.

[0071] Furthermore, the embodiment of the present application also includes:

[0072] Based on the equipment structure and equipment installation data of the water photovoltaic system, the stability evaluation of the structure data and installation data is performed;

[0073] According to the stability data, set the grid partition, where the smaller the stability data, the smaller the grid partition;

[0074] Perform stress collection on the grid partitions based on the configuration parameters to obtain stress data of each partition;

[0075] The stability evaluation of the water photovoltaic state is performed according to the stability data and the partition stress data to obtain the stability evaluation result.

[0076] In a preferred embodiment, first, a preliminary evaluation of the stability of the water photovoltaic installation equipment is performed based on the equipment structure and equipment installation data. Among them, the equipment structure data includes the geometric dimensions, material properties, structural form, etc. of the equipment, reflecting the force characteristics of the equipment itself; and the equipment installation data involves the installation position, support method, anchoring form, etc., reflecting the coupling mechanism between the equipment and the external environment. By analyzing these two types of data, the inherent stability level of the water photovoltaic installation equipment is comprehensively judged to lay the foundation for subsequent evaluation. Then, according to the obtained stability data, the water photovoltaic installation equipment is gridded. The smaller the area of ​​stability data, the finer the divided grid, so as to achieve a partitioned evaluation of the equipment and finely characterize the local stability characteristics.

[0077] Afterwards, on each grid unit, the configuration parameters generated by the pre-simulation scenario module are used to perform a force analysis on the water photovoltaic installation equipment to obtain the stress data of each grid unit. The simulated environmental parameters (such as water level, flow velocity, wind speed, etc.) are combined with the geometric properties and material properties of the actual equipment, and the stress distribution of each part of the equipment under specific working conditions is calculated through numerical simulation. Subsequently, the overall stability state of the water photovoltaic installation equipment is evaluated by comprehensively considering the stability data and partition stress data, and the stability evaluation results are obtained.

[0078] Furthermore, the embodiment of the present application also includes:

[0079] According to the stability data of each partition, a force threshold is evaluated, and a force histogram of each partition is generated according to the force threshold;

[0080] Calculating the stability impact value according to the partition stress data, determining the stability loss value, and using the stability loss value to reduce the force histogram of each partition, wherein the stability loss value is calculated based on the functional relationship obtained by fitting the historical sample data;

[0081] The simulation tests of the configuration parameters are completed cumulatively, and the stability evaluation results are obtained according to the reduction histogram.

[0082] In a feasible implementation, first, a force threshold assessment is carried out based on the stability data of each grid partition. Among them, the stability data includes physical quantities such as displacement and strain that reflect the degree of structural deformation. By analyzing the distribution characteristics of the stability data of each region, the upper limit of the bearing capacity, that is, the force threshold, is identified. The size of the force threshold represents the ultimate bearing level of each region and is an important basis for judging the stability of the structure. After obtaining the force threshold, a force histogram of each partition is generated accordingly. The force histogram intuitively represents the force distribution of each region, with the horizontal axis representing the force magnitude and the vertical axis representing the corresponding frequency or frequency. By comparing the force histograms of each region, the force characteristics of each part of the equipment are clarified, and potential stress concentration areas and weak parts are identified.

[0083] Then, the stability impact value is calculated for the partition stress data, and the stability loss value of each area is determined accordingly. The stability impact value reflects the degree of influence of each partition stress level on the overall stability, and is obtained through methods such as eigenvalue decomposition and principal component analysis. On the basis of determining the impact value, the stability loss value is further calculated. The stability loss value characterizes the decrease in structural stability caused by stress, and its size determines the degree of damage to the overall stability of each area. Among them, the stability loss value is not directly calculated from the partition stress data, but is obtained by statistical analysis and function fitting of historical sample data. The historical sample data records a large amount of measured information of existing projects, including stress distribution and corresponding stability performance under various environmental conditions. Through data mining technology, the intrinsic relationship between stress level and stability loss is extracted, and a quantitative functional relationship is established. Through this functional relationship, based on the stress data of each partition, the stability loss value of each area can be evaluated. After obtaining the stability loss value, the generated force histogram is corrected by using it, that is, the original histogram is reduced by the stability loss value. The force histogram after the reduction has changed in form, and the frequency distribution curve will have local depressions, which correspond to areas with severe stability loss. By comparing the force histograms before and after the correction, the stability level of each partition can be intuitively judged and the weak points of the equipment can be identified.

[0084] After the simulation test of all configuration parameters is completed, the stability evaluation result is obtained according to the final reduction histogram. The comprehensive simulation test covers various possible extreme working conditions, which is equivalent to evaluating the stability of the equipment from different sides and angles. The evaluation data under each working condition can be accumulated to fully understand the stability performance of the equipment and determine whether it meets the design requirements. During the test, each set of configuration parameters will generate a corrected force histogram. When the configuration parameters are traversed, a series of reduction histograms are obtained. These reduction histograms are compared and comprehensively analyzed to obtain the qualitative description and quantitative indicators of the overall stability of the equipment. Among them, the qualitative description uses language to give the evaluation results, such as "good stability", "general stability", "poor stability", etc.; quantitative indicators include values ​​such as strain capacity and structural reliability to intuitively quantify the ability of the equipment to resist external disturbances.

[0085] In summary, steps S541 to S543 refine the implementation process of stability evaluation, make full use of the stability data and stress data of each grid partition, and use mathematical methods such as data fitting and histogram analysis to form an effective stability evaluation strategy. This method can not only qualitatively judge the stability level of the water photovoltaic installation equipment, but also provide quantitative evaluation indicators, and at the same time identify weak points and find the most unfavorable working conditions. The evaluation results are comprehensive and reliable, and can provide a scientific basis for the safe operation and optimization of the equipment.

[0086] In summary, the stability assessment method for a water-based photovoltaic installation device provided in the embodiment of the present application has the following technical effects:

[0087] According to the preset cycle, regional hydrological information and environmental climate information are collected to obtain regional extreme value characteristics, obtain the basic data required for the evaluation, and provide real and reliable environmental parameters, laying a data foundation for subsequent parameter optimization and simulation analysis, ensuring the authenticity and comprehensiveness of environmental factors in the evaluation process. Collect the equipment structure and equipment installation data of water photovoltaics, build a simulation space, which includes a pre-simulation scenario module and a post-stability evaluation module, introduce the actual situation of the evaluation object into the analysis process, and build the core framework of the simulation evaluation. The pre-simulation scenario module is used to simulate various working conditions, and the post-stability evaluation module is responsible for result analysis, providing a necessary platform for subsequent simulation and evaluation. Input regional hydrological information, environmental climate information, and regional extreme value characteristics into the parameter search configuration module, perform parameter search with the maximum volatility as the goal, obtain configuration parameters, and find the most unfavorable hydrological and climate combination characteristics through optimization algorithms instead of using fixed parameters, which improves the conservatism and reliability of the evaluation and ensures that the stability analysis will not miss the most potentially dangerous working conditions. The parameter search configuration module is used as the feedforward module of the front-end simulation scenario module, and the configuration parameters are fitted to the simulation space to achieve an organic combination of optimized parameters and simulation, so that the simulation process is closer to the actual extreme situation and ensures that the most unfavorable working conditions can be fully reflected in the simulation. Through the simulation test in the simulation space, the stability evaluation is performed using the post-stability evaluation module to obtain the stability evaluation results, the simulation is run under the most unfavorable parameter settings, and a comprehensive stability analysis is performed on the results to obtain the performance of the water photovoltaic installation equipment under extreme conditions, achieving a comprehensive and accurate evaluation of the stability of the water photovoltaic installation equipment under extreme environments, effectively ensuring the long-term reliable operation of the equipment.

[0088] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0089] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for evaluating the stability of a waterborne photovoltaic installation, characterized in that: The stability assessment method of the above-water photovoltaic installation equipment comprises: Collect regional hydrological information and environmental climate information according to preset cycles to obtain regional extreme value characteristics; Collecting equipment structure and equipment installation data of water photovoltaics to build a simulation space, which includes a front-end simulation scene module and a post-end stability evaluation module; Input the regional hydrological information, environmental climate information, and regional extreme value characteristics into a parameter search configuration module, perform parameter search with the maximum volatility as the goal, and obtain configuration parameters; Using the parameter search configuration module as a feedforward module of the front simulation scene module to fit the configuration parameters to the simulation space; The simulation test is performed through the simulation space, and the stability evaluation is performed using the post-stability evaluation module to obtain the stability evaluation result.

2. The stability assessment method for waterborne photovoltaic installation equipment according to claim 1, characterized in that: The collecting of regional hydrological information and environmental climate information according to a preset period to obtain regional extreme value characteristics includes: Collect regional hydrological information and environmental climate information according to the preset period, wherein the regional hydrological information includes water level information and water flow velocity, and the environmental climate information includes wind speed, precipitation and duration; Obtain hydrological constraint information and environmental climate constraint information for water-based photovoltaic installations; An extreme value screening window is set based on the hydrological constraint information and the environmental climate constraint information, and the regional hydrological information and the environmental climate information are traversed to obtain extreme value screening information; Feature extraction is performed on the extreme value screening information to obtain the regional extreme value feature.

3. The stability assessment method for waterborne photovoltaic installation equipment according to claim 2, characterized in that: The obtaining of hydrological constraint information and environmental climate constraint information of the waterborne photovoltaic installation includes: Obtained stability exception cases for waterborne photovoltaic installations; Classify the stability anomaly cases, construct a multi-level case set, and extract multi-level top events; For each of the multi-level case sets, correlation analysis is performed with the multi-level top events as the starting point, and the influencing factors are segmented to obtain a minimum cut set, wherein the minimum cut set includes a minimum factor combination and factor ratio that causes the multi-level top events to occur; The hydrological information and the environmental climate information are extracted according to the minimum cut set, and the maximum matching values ​​of the hydrological information and the environmental climate information in the minimum cut set are respectively selected as the hydrological constraint information and the environmental climate constraint information.

4. The stability assessment method for waterborne photovoltaic installation equipment according to claim 1, characterized in that: The regional hydrological information, environmental climate information, and regional extreme value characteristics are input into the parameter search configuration module, and parameter search is performed with the maximum volatility as the goal to obtain configuration parameters, including: A plurality of search subsets are randomly created, each search subset having a guiding parameter, wherein the guiding parameter is a regional hydrological and environmental climate parameter with the largest volatility evaluation in the search subset; Determine the search scope according to the regional hydrological information and environmental climate information, use the regional extreme value characteristics as constraints, and set development rules; Extracting the influencing parameters from the regional hydrological information and environmental climate information, including water level information, water flow velocity, wind speed, precipitation and duration, fitting the linear relationship between each influencing parameter and the volatility of water photovoltaics, and constructing a volatility evaluation function; Taking the guiding parameter as the search starting point, performing search subset search and updating according to the development rule and the volatility evaluation function, and updating the guiding parameter through the volatility evaluation function with the maximum volatility as the goal; When the search target requirement is reached, the search is stopped and the current update guidance parameters of each search subset are obtained; The current update guide parameters of each search subset are sorted according to the evaluation values, and are used as the configuration parameters in sequence according to the sorting.

5. The stability assessment method for waterborne photovoltaic installation equipment according to claim 4, characterized in that: The setting of development rules also includes: Performing data dispersion analysis based on the regional hydrological information and environmental climate information to determine data dispersion; Based on the data discreteness, determining a discrete mean configuration search step size; Setting a search step size adaptive rule, wherein the search step size adaptive rule includes adjusting the search step size to a discrete minimum value when the guidance parameter update speed reaches a preset deceleration threshold, and restoring the search step size to a discrete mean value when the guidance parameter update speed reaches a preset acceleration threshold; Setting an adaptive adjustment limit, and when the adaptive adjustment limit is reached, acquiring a search field, and searching again in the search field; The search step size, the search step size adaptive rule and the adaptive adjustment limit are added as incremental rules into the development rule.

6. The stability assessment method for waterborne photovoltaic installation equipment according to claim 4, characterized in that: The volatility evaluation function is constructed, and its expression is: Among them, X is the influencing parameter, ε is the error term, is the volatility fitting coefficient, is the sample mean for volatility evaluation, is the parameter mean, It represents the expected value of Y when X is zero, n is the number of influencing parameters, and n is a positive integer.

7. The stability assessment method for waterborne photovoltaic installation equipment according to claim 1, characterized in that: The post-stability evaluation module is used to perform stability evaluation to obtain a stability evaluation result, including: Based on the equipment structure and equipment installation data of the water photovoltaic system, the stability evaluation of the structure data and installation data is performed; According to the stability data, set the grid partition, where the smaller the stability data, the smaller the grid partition; Perform stress collection on the grid partitions based on the configuration parameters to obtain stress data of each partition; The stability evaluation of the water photovoltaic state is performed according to the stability data and the partition stress data to obtain the stability evaluation result.

8. The stability assessment method for waterborne photovoltaic installation equipment according to claim 7, characterized in that: The stability evaluation of the water photovoltaic state is performed according to the stability data and the partition stress data to obtain the stability evaluation result, including: According to the stability data of each partition, a force threshold is evaluated, and a force histogram of each partition is generated according to the force threshold; Calculating the stability impact value according to the partition stress data, determining the stability loss value, and using the stability loss value to reduce the force histogram of each partition, wherein the stability loss value is calculated based on the functional relationship obtained by fitting the historical sample data; The simulation tests of the configuration parameters are completed cumulatively, and the stability evaluation results are obtained according to the reduction histogram.

Images