Photovoltaic power generation management system based on cloud platform

Abstract

The invention relates to the technical field of energy management, in particular to a photovoltaic power generation management system based on a cloud platform, which comprises a photovoltaic module performance monitoring module, an inverter efficiency analysis module, an energy storage equipment management module and a power distribution equipment state management module. According to the invention, operation and maintenance efficiency and safety of photovoltaic power generation are enhanced through integration of multi-source data and execution of detailed analysis, potential faults are prevented in advance, shutdown and maintenance costs are reduced, configuration of the photovoltaic module is optimized, and power generation efficiency is improved through collection of temperature and voltage data of the photovoltaic module and analysis and identification of abnormal states. The input and output data of an inverter are deeply analyzed; the energy conversion efficiency is improved; the energy consumption is reduced; the charge and discharge states of energy storage equipment are detected and analyzed; and the operation risk is reduced through intelligent early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a photovoltaic power generation management system based on a cloud platform. Background Art

[0002] The field of energy management technology refers to the planning, operation, control and maintenance of energy demand and supply through a systematic approach to improve energy efficiency, reduce energy costs, and ensure sustainable energy supply and environmental protection. In photovoltaic power generation systems, energy management technology is particularly critical, involving real-time monitoring, data analysis, fault prediction and diagnosis of photovoltaic power generation equipment, as well as optimization of power generation efficiency and operation and maintenance management. The technology field uses cloud computing, big data analysis, artificial intelligence and other technologies to make energy systems more intelligent and automated, so as to more effectively respond to power market demand and environmental changes.

[0003] Among them, the photovoltaic power generation management system is a system that uses cloud technology to centrally process and analyze the data of photovoltaic power stations. The system is mainly used to monitor the operating status of photovoltaic power stations in real time, including power generation efficiency, equipment status, environmental factors, etc. By integrating data analysis, fault warning and operation and maintenance support functions, the system can achieve remote monitoring and intelligent operation management, improve the efficiency and safety of power station operation, and reduce maintenance costs. This system is particularly suitable for photovoltaic power stations that are vast or geographically dispersed. Centralized management through a cloud platform can greatly improve management efficiency and response speed.

[0004] Existing photovoltaic power generation management systems are capable of basic monitoring and data analysis, but their efficiency and response speed are still limited when dealing with large-scale and geographically dispersed photovoltaic power plants. This is mainly because existing technologies fail to fully utilize advanced data analysis and real-time feedback mechanisms, resulting in insufficient data processing and fault response speeds, and the inability to adjust and optimize power plant operations in real time. Existing technologies rely on historical data and empirical rules in predicting and diagnosing faults, and lack the ability to deeply analyze real-time data, which limits their effectiveness in preventive maintenance and fault warning. This technical limitation is particularly evident in the face of complex or changing environmental factors. For example, the inability to effectively predict the impact of environmental changes on power plant operations leads to unstable performance of power plants under extreme weather conditions, increasing operation and maintenance risks and costs. Summary of the invention

[0005] This application provides a photovoltaic power generation management system based on a cloud platform. The existing photovoltaic power generation management system is capable of basic monitoring and data analysis, but its efficiency and response speed are still limited when dealing with large-scale and geographically dispersed photovoltaic power stations. This is mainly because the existing technology fails to fully utilize advanced data analysis and real-time feedback mechanisms, resulting in insufficient data processing and fault response speeds, and the inability to adjust and optimize power station operations in real time. The existing technology relies on historical data and empirical rules in predicting and diagnosing faults, and lacks the ability to deeply analyze real-time data, which limits its effectiveness in preventive maintenance and fault warning. This technical limitation is particularly evident in the face of complex or changing environmental factors, such as the inability to effectively predict the impact of environmental changes on power station operations, resulting in unstable performance of power stations under extreme weather conditions, and increased operation and maintenance risks and costs.

[0006] In view of the above problems, the present application provides a photovoltaic power generation management system based on a cloud platform.

[0007] The present application provides a photovoltaic power generation management system based on a cloud platform, wherein the system comprises:

[0008] The photovoltaic module performance monitoring module collects the operating data of photovoltaic modules, analyzes the temperature and voltage parameters of photovoltaic modules, detects the abnormal status of photovoltaic modules, optimizes the configuration of photovoltaic modules through the cloud platform, performs module performance analysis and abnormality detection, and generates photovoltaic module evaluation results;

[0009] The inverter efficiency analysis module counts the input and output data of the inverter based on the evaluation results of the photovoltaic components, calculates the inverter conversion efficiency through the cloud platform, analyzes the inverter working status, detects inverter abnormalities, performs input and output statistics and efficiency calculations, and generates inverter operation performance analysis results;

[0010] The energy storage device management module detects the charge and discharge status of the energy storage device according to the inverter operation performance analysis results, records the capacity and voltage of the energy storage device, analyzes the efficiency of the energy storage device through the cloud platform, performs charge and discharge status detection and efficiency analysis, and generates energy storage device performance management analysis results;

[0011] The power distribution equipment status management module utilizes the energy storage equipment performance management analysis results, combined with the historical data of the power distribution equipment, to perform equipment performance trend analysis through the cloud platform, detect abnormal operating conditions, set alarm thresholds, perform performance trend analysis and anomaly detection, and generate performance operation records.

[0012] Preferably, the method for detecting the abnormal state of the photovoltaic module is:

[0013] Collecting the operating data of the photovoltaic modules, including temperature and voltage parameters, and integrating the operating data to generate an initial data set;

[0014] The initial data set is subjected to preliminary data processing to obtain a processed data set, using the following formula:

[0015]

[0016] Among them, D p represents the processed data set, T i and V i are the temperature and voltage of the ith data point, S i is the solar radiation intensity, k t and k v is the adjustment parameter, n is the total number of data points;

[0017] The processed data set is used to perform anomaly detection to generate an abnormal state detection result, using the following formula:

[0018]

[0019] Among them, A represents the abnormal state detection result, D p,j is the jth data point in the processed data set, μ is D p is the average value, σ is the standard deviation, θ is the set abnormality judgment threshold, and m is the number of data points.

[0020] Preferably, the steps of obtaining the photovoltaic module evaluation results are specifically as follows:

[0021] Using the abnormal state detection result, the data points marked as abnormal are determined to obtain a set of abnormal data points:

[0022] Based on the set of abnormal data points, a risk score calculation is performed to obtain a risk score of the photovoltaic module, using the following formula:

[0023]

[0024] Among them, R is the risk score of the photovoltaic module, D p,k is a data point in the set of abnormal data points, μ and σ are the mean and standard deviation of the data points, and w k is the importance weight of the data point, k temp and k rad is the adjustment factor for temperature and radiation intensity, T k and S k is the corresponding temperature and solar radiation intensity, and p is the number of abnormal data points;

[0025] According to the risk score of the photovoltaic module, the status of each photovoltaic module is determined, and the photovoltaic module evaluation result is established and classified as low risk, medium risk, or high risk, using the following formula:

[0026]

[0027] Among them, E is the evaluation result of the PV module, and λ1 and λ2 are the preset risk score thresholds.

[0028] Preferably, the statistical method of the input and output data of the inverter is:

[0029] Based on the photovoltaic module evaluation results, voltage and current input data are collected from inverter monitoring, and the data within the target time period are counted to generate an input data set, where each data point includes a voltage V i and current I i ;

[0030] Based on the input data set, corresponding output voltage and current data are collected to generate an output data set, wherein each data point includes a voltage V o and current I o ;

[0031] Based on the input data set and the output data set, the following formula is used:

[0032]

[0033] By introducing the exponential form of the frequency adjustment coefficient and the cosine value of the power factor angle, the total input power and total output power are calculated, where P in is the total input power, P out is the total output power, n is the number of data points, V i ,I i 、V o ,I o are the voltage and current at the input and output data points, k v and k o is the adjustment coefficient, φ i and φ o is the corresponding power factor angle;

[0034] By comparing the total input power and the total output power, the conversion efficiency of the inverter is calculated.

[0035] The formula is as follows:

[0036]

[0037] Where η is the conversion efficiency of the inverter, k eff is the efficiency adjustment factor.

[0038] Preferably, the steps of obtaining the inverter operation performance analysis result are specifically as follows:

[0039] Based on the conversion efficiency of the inverter, additional data about the working status of the inverter is collected from the inverter monitoring to generate an integrated data set, wherein the integrated data set includes temperature, frequency and power factor;

[0040] Analyzing the integrated data set, analyzing temperature fluctuations and frequency changes using statistical methods, and obtaining a stability index and a response characteristic index;

[0041] Based on the stability index and the response characteristic index, the conversion efficiency, the stability index and the response characteristic index are summarized, and the inverter performance score is calculated using the following formula:

[0042]

[0043] Among them, P is the inverter performance score, α, β and γ are adjustment coefficients, S is the stability index, η is the inverter conversion efficiency, and R is the response characteristic index;

[0044] Based on the value of the inverter performance score, the operating status of the inverter is determined, an inverter operating performance analysis result is generated, and the inverter operating performance is analyzed.

[0045]

[0046] Among them, E is the analysis result of the inverter operation performance.

[0047] Preferably, the calculation method of the energy storage device capacity and voltage is:

[0048] Based on the inverter operation performance analysis result, the voltage and current data are collected in real time through the monitoring of the energy storage device, and the information of the charging and discharging events is recorded to obtain an input data set;

[0049] Based on the input data set, the peak and valley values ​​of the voltage and current in each charging cycle are extracted, and the average voltage and current are calculated respectively by nonlinear weighted averaging, using the following formula:

[0050]

[0051] Among them, V avg is the average voltage, I avg is the average current, V t and I t are the voltage and current readings at time t, λ V and λ I Adjust the nonlinear weights of voltage and current data, τ V and τ I is the time decay factor;

[0052] Based on the average voltage and current, the capacity of the energy storage device is calculated using the following formula:

[0053]

[0054] Where C is the capacity of the energy storage device, T op is the total time during the operation period, V nom is the standard voltage of the device, δ and ρ are adjustment coefficients.

[0055] Preferably, the steps of obtaining the energy storage device performance management analysis results are specifically as follows:

[0056] Based on the data of the capacity and average voltage of the energy storage device, additional performance parameters are collected from the monitoring, including temperature fluctuation and charge and discharge cycle frequency, to obtain an energy storage device operation data set;

[0057] Based on the energy storage device operation data set, a multivariate analysis method is applied to the collected data to determine the health and performance status of the energy storage device and obtain preliminary health and performance evaluation results.

[0058] Based on the preliminary health and performance assessment results, the energy storage equipment performance index is calculated using the following formula:

[0059]

[0060] Among them, PI is the performance index of energy storage equipment, T fluc is the temperature fluctuation, F cycle is the charge and discharge cycle frequency, κ and ξ are adjustment coefficients, T op is the total operation time, V avg is the average voltage;

[0061] The operating conditions of the energy storage devices are classified according to the values ​​of the energy storage device performance index to obtain the energy storage device performance management analysis results. The judgment formula is as follows:

[0062]

[0063] Where W is the performance management analysis result of the energy storage equipment.

[0064] Preferably, the steps of obtaining the performance operation record are specifically as follows:

[0065] Based on the energy storage device performance management analysis results, combined with the historical performance data of the power distribution equipment, including charge and discharge cycles, battery capacity and temperature data, a set of historical performance data of the power distribution equipment is generated;

[0066] The historical performance data set of the power distribution equipment is analyzed, and the performance degradation trend of the power distribution equipment is calculated through the cloud platform to obtain the performance degradation prediction result of the power distribution equipment. The formula used is as follows:

[0067]

[0068] Among them, P t represents the performance degradation prediction result of power distribution equipment, C i is the initial capacity of the ith charge-discharge cycle, t i is the usage time, α is the basic attenuation coefficient, β and k age is the parameter to adjust the attenuation effect, γ is the environmental influence coefficient, and n is the total number of data points;

[0069] According to the performance degradation prediction results of the power distribution equipment, based on the real-time monitoring data of the power distribution equipment, the voltage, current and temperature are continuously monitored, the monitoring data is compared with the historical data of the power distribution equipment and anomalies are identified. If the monitoring value exceeds the preset threshold, an alarm is automatically triggered and related information is recorded to establish a performance operation record.

[0070] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0071] Enhance the efficiency and safety of photovoltaic power generation operation and maintenance by integrating multi-source data and performing detailed analysis. In practice, by collecting temperature and voltage data of photovoltaic modules and analyzing the data to identify abnormal conditions, potential equipment failures can be prevented in advance, reducing downtime and maintenance costs. Further, by optimizing the configuration of photovoltaic modules, improving power generation efficiency, ensuring maximum energy utilization and stability of power supply, and conducting in-depth analysis of the input and output data of the inverter, calculating its conversion efficiency, and monitoring its working status in real time, the energy conversion efficiency of the system is increased while reducing energy consumption. The charge and discharge status detection and efficiency analysis of energy storage equipment help maintain battery health and extend the service life of the equipment. The performance trend analysis and abnormal monitoring functions of distribution equipment improve the responsiveness and reliability of the power grid, and reduce potential operational risks through intelligent early warning.

[0072] 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

[0073] Figure 1 The module diagram of the photovoltaic power generation management system based on the cloud platform is proposed for the present invention;

[0074] Figure 2 A flow chart of a method for detecting abnormal states of photovoltaic modules in a photovoltaic power generation management system based on a cloud platform is provided for the present invention;

[0075] Figure 3A flow chart of photovoltaic component evaluation results of a photovoltaic power generation management system based on a cloud platform is proposed for the present invention;

[0076] Figure 4 A flow chart of a statistical method for calculating input and output data of an inverter of a photovoltaic power generation management system based on a cloud platform is provided for the present invention;

[0077] Figure 5 A flow chart of the inverter operation performance analysis results of the photovoltaic power generation management system based on the cloud platform is proposed for the present invention;

[0078] Figure 6 A flow chart of a method for calculating the capacity and voltage of energy storage equipment in a photovoltaic power generation management system based on a cloud platform is provided for the present invention;

[0079] Figure 7 A flow chart of the energy storage equipment performance management analysis results of the photovoltaic power generation management system based on the cloud platform is proposed for the present invention;

[0080] Figure 8 A flow chart of performance operation record of a photovoltaic power generation management system based on a cloud platform is proposed for the present invention. DETAILED DESCRIPTION

[0081] This application provides a photovoltaic power generation management system based on a cloud platform.

[0082] Application Overview

[0083] The photovoltaic power generation management system in the existing technology is capable of basic monitoring and data analysis, but its efficiency and response speed are still limited when dealing with large-scale and geographically dispersed photovoltaic power plants. This is mainly because the existing technology fails to fully utilize advanced data analysis and real-time feedback mechanisms, resulting in insufficient data processing and fault response speed, and the inability to adjust and optimize power plant operations in real time. The existing technology relies on historical data and empirical rules in predicting and diagnosing faults, and lacks the ability to deeply analyze real-time data, which limits its effectiveness in preventive maintenance and fault warning. This technical limitation is particularly evident when facing complex or changing environmental factors. For example, it is impossible to effectively predict the impact of environmental changes on power plant operations, resulting in unstable performance of power plants under extreme weather conditions, increasing operation and maintenance risks and costs.

[0084] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows:

[0085] like Figure 1 As shown, the present application provides a photovoltaic power generation management system based on a cloud platform, wherein the system includes:

[0086] The photovoltaic module performance monitoring module collects the operating data of photovoltaic modules, analyzes the temperature and voltage parameters of photovoltaic modules, detects the abnormal status of photovoltaic modules, optimizes the configuration of photovoltaic modules through the cloud platform, performs module performance analysis and abnormality detection, and generates photovoltaic module evaluation results;

[0087] The inverter efficiency analysis module counts the input and output data of the inverter based on the evaluation results of the photovoltaic modules, calculates the inverter conversion efficiency through the cloud platform, analyzes the inverter working status, detects inverter abnormalities, performs input and output statistics and efficiency calculations, and generates inverter operation performance analysis results;

[0088] The energy storage equipment management module detects the charge and discharge status of the energy storage equipment according to the inverter operation performance analysis results, records the capacity and voltage of the energy storage equipment, analyzes the efficiency of the energy storage equipment through the cloud platform, performs charge and discharge status detection and efficiency analysis, and generates energy storage equipment performance management analysis results;

[0089] The distribution equipment status management module uses the performance management analysis results of the energy storage equipment, combined with the historical data of the distribution equipment, to perform equipment performance trend analysis through the cloud platform, detect abnormal operating conditions, set alarm thresholds, perform performance trend analysis and anomaly detection, and generate performance operation records.

[0090] The evaluation results of PV modules include performance indicators and status health. The inverter operation performance analysis results include conversion efficiency indicators, input-output ratio, and status abnormality indications. The energy storage equipment performance management analysis results include capacity efficiency, voltage stability, and charge and discharge cycle health. Performance operation records include trend change analysis, abnormal status records, and alarm threshold settings.

[0091] Specifically, if Figure 2 As shown, the method for detecting the abnormal state of the photovoltaic module is:

[0092] Collecting the operating data of the photovoltaic modules, including temperature and voltage parameters, and integrating the operating data to generate an initial data set;

[0093] Perform preliminary data processing on the initial data set to obtain the processed data set using the following formula:

[0094]

[0095] Among them, D p represents the processed data set, T i and V i are the temperature and voltage of the ith data point, S i is the solar radiation intensity, k t and k v is the adjustment parameter, n is the total number of data points;

[0096] The processed data set is used for anomaly detection to generate abnormal state detection results. The formula used is as follows:

[0097]

[0098] Among them, A represents the abnormal state detection result, D p,j is the jth data point in the processed data set, μ is D p is the average value, σ is the standard deviation, θ is the set abnormality judgment threshold, and m is the number of data points.

[0099] The processed data set formula is:

[0100]

[0101] T i 、V i , S i : represent the temperature, voltage and solar radiation intensity of the ith data point respectively, and the values ​​are directly obtained from the sensor data;

[0102] k t , k v :This is the adjustment coefficient of temperature and voltage, which is used to adjust the contribution of related parameters to the final result. The coefficient can be obtained through historical data analysis to optimize the prediction accuracy of the model;

[0103] n: The total number of data points.

[0104] For each data point, calculate the temperature exponent This reflects the nonlinear effect of temperature on performance. For each data point, calculate This calculation is to synthesize the effects of voltage and solar radiation intensity. The square root of the sum is a common method to synthesize two related influencing factors. The temperature effect and the voltage and solar radiation effects are combined according to their respective weights k. t and k v Add up, sum the processed results of all data points, and then divide by the total number of data points n to get the average result D p .

[0105] Actual example:

[0106] Suppose there are 3 data points, where T = [25, 30, 20], V = [12, 15, 11], S = [300, 350, 280], k t =0.1, k v = 0.2, calculate D p The process is as follows:

[0107] For T1=25, e 25Approximately equal to 7.2×10 10 (Actually calculated using a calculator);

[0108] For V1=12, S1=300, calculate

[0109] Calculate the contribution of the first data point: 0.1×7.2×10 10 +0.2×300.24≈7.2×10 9 +60.048≈7.2×10 9 ;

[0110] Repeat the above steps for all data points, then sum and divide by 3 to get D p .

[0111] The abnormal state detection result formula is:

[0112]

[0113] D p,j : is the jth data point after processing;

[0114] μ: D p The average value of

[0115] σ: is D p The standard deviation of

[0116] θ: is the preset abnormality threshold, which determines what degree of deviation is considered abnormal;

[0117] Calculate all D p The average value μ, calculate D p The standard deviation σ of each D p,j , calculate the deviation from the mean divided by the absolute value of the standard deviation, find the maximum value of all deviation values, and determine whether it is greater than the threshold θ.

[0118] Actual example:

[0119] Assumption D p =[100, 105, 95], μ=100, σ=5, θ=1:

[0120] Calculate |100-100| / 5=0

[0121] Calculate |105-100| / 5=1

[0122] Calculate |95-100| / 5=1

[0123] The maximum deviation is 1, and compared with θ, the result is that it does not exceed the threshold.

[0124] Specifically, if Figure 3 As shown in the figure, the steps for obtaining the evaluation results of photovoltaic modules are as follows:

[0125] Using the abnormal state detection results, determine the data points marked as abnormal and obtain the abnormal data point set:

[0126] Based on the set of abnormal data points, the risk score is calculated to obtain the risk score of the photovoltaic module. The formula is as follows:

[0127]

[0128] Among them, R is the risk score of the photovoltaic module, D p,k is a data point in the set of abnormal data points, μ and σ are the mean and standard deviation of the data points, and w k is the importance weight of the data point, k temp and k rad is the adjustment factor for temperature and radiation intensity, T k and S k is the corresponding temperature and solar radiation intensity, and p is the number of abnormal data points;

[0129] According to the risk score of the PV modules, the status of each PV module is determined, and the PV module assessment results are established and classified as low risk, medium risk, or high risk. The formula used is as follows:

[0130]

[0131] Among them, E is the evaluation result of the PV module, and λ1 and λ2 are the preset risk score thresholds.

[0132] Risk scoring formula for PV panels:

[0133]

[0134] Abnormal data point D p,k : Abnormal data points are data points collected from PV panels that are marked as abnormal according to the anomaly detection algorithm;

[0135] Mean μ and standard deviation σ: The mean and standard deviation statistics are calculated from the entire data set. The mean μ is the average of all data points, and the standard deviation σ is the standard deviation of the data points, indicating the fluctuation of the data;

[0136] The importance weight w of the data point k : This is the weight assigned based on the influence or sensitivity of the data point. For example, the location of the data point in the system makes it more influential on the system performance, so it is assigned a higher weight;

[0137] Adjustment factor k temp and k rad: The coefficients are determined based on historical data and performance analysis to adjust for the effects of temperature and solar radiation intensity on the score;

[0138] Temperature T k and solar radiation intensity S k : directly from the sensor readings associated with the data point;

[0139] Number of anomalous data points p: The total number of data points marked as anomalous by the anomaly detection process.

[0140] Calculation process and actual examples:

[0141] Suppose there are three abnormal data points, and their specific values ​​are as follows:

[0142] D p,1 =120, T1=30, S1=200, w1=0.5

[0143] D p,2 =130, T2=35, S2=220, w2=0.7

[0144] D p,3 =125, T3=32, S3=210, w3=0.6

[0145] Assume that the mean μ = 100, the standard deviation σ = 10, and the adjustment coefficient k temp =0.1, k rad =0.01.

[0146] Steps to calculate the risk score R:

[0147] For each data point D p,k , calculate the square of its deviation from the mean and normalize it:

[0148] (120-100) 2 / 10 2 =4

[0149] (130-100) 2 / 10 2 =9

[0150] (125-100) 2 / 10 2 =6.25

[0151] For each data point, calculate the inverse of the logarithm of the weighted sum of temperature and radiation multiplied by the weight:

[0152] 0.5 / log(0.1·30+0.01·200+1)≈0.147

[0153] 0.7 / log(0.1·35+0.01·220+1)≈0.185

[0154] 0.6 / log(0.1·32+0.01·210+1)≈0.168

[0155] Add and average the above calculations:

[0156]

[0157] Through derivation and actual examples, the specific role and calculation process of each parameter can be seen, ensuring the transparency and accuracy of the PV module evaluation process.

[0158] Specifically, if Figure 4 As shown, the statistical method of the inverter input and output data is:

[0159] Based on the evaluation results of the photovoltaic modules, the voltage and current input data are collected from the inverter monitoring, and the data within the target time period are counted to generate an input data set, where each data point includes the voltage V i and current I i ;

[0160] Based on the input data set, the corresponding output voltage and current data are collected to generate an output data set, where each data point includes a voltage V o and current I o ;

[0161] Based on the input data set and the output data set, the following formula is used:

[0162]

[0163] By introducing the exponential form of the frequency adjustment coefficient and the cosine value of the power factor angle, the total input power and total output power are calculated, where P in is the total input power, P out is the total output power, n is the number of data points, V i ,I i 、V o ,I o are the voltage and current at the input and output data points, k v and k o is the adjustment coefficient, φ i and φ o is the corresponding power factor angle;

[0164] By comparing the total input power and the total output power, the conversion efficiency of the inverter is calculated using the following formula:

[0165]

[0166] Where η is the conversion efficiency of the inverter, k eff is the efficiency adjustment factor.

[0167] Collect voltage and current input and output data from the inverter monitoring. Data points include: Input voltage V i and input current I i , output voltage V o and output current I o ;

[0168] Calculate the total input power P in And the total output power P out , including exponential adjustment of the cosine value of the voltage, current, and power factor angle, as well as the frequency regulation coefficient.

[0169] Total input and output power formula:

[0170]

[0171] The conversion efficiency, η, is calculated by comparing the ratio of total output power to total input power, and is further adjusted to account for nonlinear response, using the formula: Among them, k eff Practical examples of efficiency tuning parameters used to correct the sensitivity of efficiency calculations.

[0172] Assume the following data:

[0173] Input data point: V i =120V, I i =10A,φ i =30°(power factor angle)

[0174] Output data point: V o =110V, I o =9.5A,φ o =25°

[0175] Adjustment factor: k v =0.1, k o =0.1, f i =f o =2

[0176] Efficiency adjustment parameter: k eff =1.5

[0177] Calculate input and output power:

[0178]

[0179] Calculate the conversion efficiency:

[0180]

[0181] Specifically, if Figure 5 As shown, the steps for obtaining the inverter operation performance analysis results are as follows:

[0182] Based on the conversion efficiency of the inverter, additional data about the working status of the inverter is collected from the inverter monitoring to generate an integrated data set, which includes temperature, frequency and power factor;

[0183] Analyze the integrated data set, use statistical methods to analyze temperature fluctuations and frequency changes, and obtain stability index and response characteristic index;

[0184] Based on the stability index and response characteristic index, the conversion efficiency, stability index and response characteristic index are summarized and the inverter performance score is calculated using the following formula:

[0185]

[0186] Among them, P is the inverter performance score, α, β and γ are adjustment coefficients, S is the stability index, η is the inverter conversion efficiency, and R is the response characteristic index;

[0187] Based on the value of the inverter performance score, the operating status of the inverter is determined, the inverter operating performance analysis results are generated, and the inverter operating performance analysis results are classified into excellent, good, general and maintenance. The judgment formula is as follows:

[0188]

[0189] Among them, E is the analysis result of the inverter operation performance.

[0190] Data about the working status of the inverter, including temperature, voltage, current, frequency and power factor, are collected from the monitoring of the inverter. The data are organized into an integrated data set, and the integrated data set is processed using data analysis techniques, including statistical analysis of temperature fluctuations and frequency changes, and calculation of the stability index S and response characteristic index R. These two indices are obtained by analyzing the standard deviation or coefficient of variation of temperature and frequency.

[0191] Inverter performance scoring formula:

[0192]

[0193] η: inverter conversion efficiency obtained from energy efficiency test;

[0194] S: stability index, obtained by calculating the coefficient of variation of temperature data;

[0195] R: Response characteristic index, obtained by analyzing the fast Fourier transform (FFT) result of the frequency response;

[0196] α, β, and γ: Pre-set weight coefficients used to adjust the degree of influence of stability and response characteristics.

[0197] Actual example

[0198] Assume the following parameters:

[0199] Conversion efficiency η = 85% or 0.85

[0200] Stability index S = 0.3 (indicates that the temperature variation coefficient is 0.3)

[0201] Response characteristic index R = 0.2 (based on FFT analysis of frequency variation)

[0202] Weight coefficients α = 0.5, β = 1.0, γ = 2.0

[0203] Calculate P:

[0204] Calculation stability part:

[0205] Calculation response characteristics: β·log(1+0.2) 2 =1.0·log(1.2) 2 =1.0·(log(1.2)×2)=1.0·(0.07918×2)=0.15836

[0206] Final inverter performance rating:

[0207] P=85%·(1+0.2397+0.15836)=0.85·1.39806=118.85%

[0208] The judgment formula for the inverter operation performance analysis result is as follows:

[0209]

[0210] According to the value of P and the above classification standards, the operating status of the inverter is determined.

[0211] Specifically, if Figure 6 As shown, the calculation method of energy storage device capacity and voltage is:

[0212] Based on the analysis results of the inverter operation performance, the voltage and current data are collected in real time through the monitoring of the energy storage device, and the information of the charging and discharging events is recorded to obtain the input data set;

[0213] Based on the input data set, the peak and valley values ​​of voltage and current in each charging cycle are extracted, and the average voltage and current are calculated respectively using nonlinear weighted average, using the following formula:

[0214]

[0215] Among them, V avg is the average voltage, I avg is the average current, V t and I t are the voltage and current readings at time t, λ V and λ I Adjust the nonlinear weights of voltage and current data, τ V and τ I is the time decay factor;

[0216] Based on the average voltage and current, the capacity of the energy storage device is calculated using the following formula:

[0217]

[0218] Where C is the capacity of the energy storage device, T op is the total time during the operation period, V nom is the standard voltage of the device, δ and ρ are adjustment coefficients.

[0219] Average voltage V avg and current I avg The calculation uses an exponential decay model to increase the weight of recent data points, reflecting the performance of the energy storage device in the most recent time period.

[0220]

[0221] V t and I t are the voltage and current readings at time t;

[0222] λ V and λ I It is a nonlinear adjustment parameter of voltage and current, allowing the model to respond more sensitively to changes in voltage and current;

[0223] τ V and τ I is the time decay factor used to reduce the effect of past data on the current average.

[0224] Actual example:

[0225] Assume there are three voltage readings V t = [120, 122, 118] volts and three current readings I t = [5, 5.5, 4.5] amperes, assuming λ V =2,λ I =2,τ V =2,τ I =2, and T=3.

[0226] Calculation of average voltage:

[0227]

[0228] The calculation for average current is similar and uses the same formula.

[0229] Derivation of the energy storage equipment capacity C formula:

[0230]

[0231] I avg is the calculated average current;

[0232] T op is the total time during the operation period;

[0233] V nom is the standard voltage of the device;

[0234] δ and ρ are the adjustment coefficient and power exponent, which are used to adjust the impact of voltage deviation on capacity estimation.

[0235] Actual example:

[0236] Hypothesis I avg =5 amperes, T op =24 hours, V nom =120 volts, δ=0.05, ρ=2.

[0237]

[0238] C=120÷1=120Ah

[0239] Specifically, if Figure 7 As shown, the specific steps for obtaining the performance management analysis results of energy storage equipment are as follows:

[0240] Based on the data of energy storage device capacity and average voltage, additional performance parameters including temperature fluctuation and charge and discharge cycle frequency are collected from monitoring to obtain the energy storage device operation data set;

[0241] Based on the energy storage equipment operation data set, multivariate analysis methods are applied to the collected data to determine the health and performance status of the energy storage equipment and obtain preliminary health and performance assessment results.

[0242] Based on the preliminary health and performance assessment results, the energy storage equipment performance index is calculated using the following formula:

[0243]

[0244] Among them, PI is the performance index of energy storage equipment, T fluc is the temperature fluctuation, F cycleis the charge and discharge cycle frequency, κ and ξ are adjustment coefficients, T op is the total operation time, V avg is the average voltage;

[0245] According to the value of the energy storage equipment performance index, the operating status of the energy storage equipment is classified to obtain the energy storage equipment performance management analysis results. The judgment formula is as follows:

[0246]

[0247] Where W is the performance management analysis result of the energy storage equipment.

[0248] The formula for the energy storage device performance index is:

[0249]

[0250] C: Energy storage device capacity, calculated from current and voltage data. Usually measured by monitoring the charging and discharging behavior of the device over a certain period of time;

[0251] V avg : Average voltage, obtained by the arithmetic mean of the voltage readings. The average value is the simple arithmetic average of the voltage values ​​in all observation periods;

[0252] T op : Total operation time, the total operation time from the time the device is enabled to the present, obtained from the device operation log;

[0253] T fluc : Temperature fluctuation, calculated from the device’s temperature monitoring system, evaluated by measuring the difference between the maximum and minimum temperatures;

[0254] F cycle : Charge and discharge cycle frequency, determined by counting the number of charge and discharge cycles within a specific time;

[0255] κ and ξ: adjustment coefficients, set according to the device type and historical performance data, used to adjust the impact of temperature fluctuations and charge and discharge frequency on performance evaluation.

[0256] Actual example

[0257] Assume the following parameters:

[0258] C = 100 kWh (capacity), V avg =400V (average voltage), T op =8760h (number of hours in a year), T fluc =10℃(temperature fluctuation), F cycle =300 cycles / year (one year's charge and discharge cycle), κ=0.05, ξ=0.1.

[0259] To calculate the performance index PI, first calculate the basic performance ratio:

[0260]

[0261] Calculate the adjustment factor:

[0262]

[0263] Final performance index:

[0264] PI = 4.566 × 5.977 ≈ 27.298

[0265] According to the PI result, the judgment formula is: The performance status of the equipment can be further analyzed, such as whether it is excellent, good, average, and requires maintenance. This method ensures that the calculation of each parameter is based on actual data and reasonable assumptions, ensuring the transparency and reliability of the entire analysis process.

[0266] Specifically, if Figure 8 As shown in the figure, the specific steps for obtaining the performance operation record are as follows:

[0267] Based on the performance management analysis results of energy storage equipment, combined with the historical performance data of distribution equipment, including charge and discharge cycles, battery capacity and temperature data, a collection of historical performance data of distribution equipment is generated;

[0268] The historical performance data set of the power distribution equipment is analyzed, and the performance degradation trend of the power distribution equipment is calculated through the cloud platform to obtain the performance degradation prediction result of the power distribution equipment. The formula used is as follows:

[0269]

[0270] Among them, P t represents the performance degradation prediction result of power distribution equipment, C i is the initial capacity of the ith charge-discharge cycle, t i is the usage time, α is the basic attenuation coefficient, β and k age is the parameter to adjust the attenuation effect, γ is the environmental influence coefficient, and n is the total number of data points;

[0271] According to the prediction results of the performance degradation of the distribution equipment, based on the real-time monitoring data of the distribution equipment, the voltage, current and temperature are continuously monitored, the monitoring data is compared with the historical data of the distribution equipment and anomalies are identified. If the monitoring value exceeds the preset threshold, an alarm is automatically triggered and related information is recorded to establish a performance operation record.

[0272] Distribution equipment performance degradation prediction formula:

[0273]

[0274] C i : The initial capacity of the i-th charge and discharge cycle, obtained from the operation record of the device

[0275] t i : The time since the device was first used, calculated from the device activation date and the data collection date;

[0276] α: basic performance attenuation coefficient, obtained through historical data analysis, reflecting the natural decline trend of equipment performance over time;

[0277] β and k age : The influence of parameter adjustment time and making the attenuation model closer to the actual equipment aging process are determined by fitting historical data;

[0278] γ: Environmental impact coefficient, which takes into account the impact of external conditions (such as temperature and humidity) on equipment performance and is determined based on the correlation analysis of environmental monitoring data and equipment performance;

[0279] n: Total number of data points, i.e., the total number of charge and discharge cycles considered in the analysis.

[0280] Set a variable to accumulate the contribution of each cycle, with an initial value of 0. For each cycle, calculate the adjustment factor of the logarithmic time Calculate the attenuation factor These two factors and the initial capacity C i Combine, calculate the performance contribution of the cycle, add the contribution of each cycle to the sum, divide the sum by the total number of data points n, and get the average performance prediction value P t .

[0281] Actual example:

[0282] Assume there are three data points, the specific parameters are as follows:

[0283] C1=100,t1=2 years,C2=95,t2=3 years,C3=90,t3=4 years

[0284] Parameter settings: α = 0.05, β = 0.03, k age =1.2,γ=1.1,n=3

[0285] Calculation process:

[0286] For the first data point:

[0287] Attenuation Factor:

[0288] Logarithmic time adjustment:

[0289] Performance contribution: 100 0.979 0.867 ≈ 84.8

[0290] Similarly, for the second and third data points we calculate:

[0291] Second point: about 81.4

[0292] The third point: about 75.3

[0293] Average performance prediction:

[0294]

[0295] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations 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 claims of the present application and their equivalents, the present application is intended to include these modifications and variations.

Claims

1. Photovoltaic power generation management system based on cloud platform, characterized by: The system comprises: The photovoltaic module performance monitoring module collects the operating data of photovoltaic modules, analyzes the temperature and voltage parameters of photovoltaic modules, detects the abnormal status of photovoltaic modules, optimizes the configuration of photovoltaic modules through the cloud platform, performs module performance analysis and abnormality detection, and generates photovoltaic module evaluation results; The inverter efficiency analysis module counts the input and output data of the inverter based on the evaluation results of the photovoltaic components, calculates the inverter conversion efficiency through the cloud platform, analyzes the inverter working status, detects inverter abnormalities, performs input and output statistics and efficiency calculations, and generates inverter operation performance analysis results; The energy storage device management module detects the charge and discharge status of the energy storage device according to the inverter operation performance analysis results, records the capacity and voltage of the energy storage device, analyzes the efficiency of the energy storage device through the cloud platform, performs charge and discharge status detection and efficiency analysis, and generates energy storage device performance management analysis results; The power distribution equipment status management module utilizes the energy storage equipment performance management analysis results, combined with the historical data of the power distribution equipment, to perform equipment performance trend analysis through the cloud platform, detect abnormal operating conditions, set alarm thresholds, perform performance trend analysis and anomaly detection, and generate performance operation records.

2. The photovoltaic power generation management system based on the cloud platform according to claim 1 is characterized in that: The method for detecting the abnormal state of the photovoltaic module is: Collecting the operating data of the photovoltaic modules, including temperature and voltage parameters, and integrating the operating data to generate an initial data set; The initial data set is subjected to preliminary data processing to obtain a processed data set, using the following formula: Among them, D p represents the processed data set, T i and V i are the temperature and voltage of the ith data point, S i is the solar radiation intensity, k t and k v is the adjustment parameter, n is the total number of data points; The processed data set is used to perform anomaly detection to generate an abnormal state detection result, using the following formula: Among them, A represents the abnormal state detection result, D p,j is the jth data point in the processed data set, μ is D p is the average value, σ is the standard deviation, θ is the set abnormality judgment threshold, and m is the number of data points.

3. The photovoltaic power generation management system based on the cloud platform according to claim 1 is characterized in that: The steps for obtaining the photovoltaic module evaluation results are specifically as follows: Using the abnormal state detection result, the data points marked as abnormal are determined to obtain a set of abnormal data points: Based on the set of abnormal data points, a risk score calculation is performed to obtain a risk score of the photovoltaic module, using the following formula: Among them, R is the risk score of the photovoltaic module, D p,k is a data point in the set of abnormal data points, μ and σ are the mean and standard deviation of the data points, and w k is the importance weight of the data point, k temp and k rad is the adjustment factor for temperature and radiation intensity, T k and S k is the corresponding temperature and solar radiation intensity, and p is the number of abnormal data points; According to the risk score of the photovoltaic module, the status of each photovoltaic module is determined, and the photovoltaic module evaluation result is established and classified as low risk, medium risk, or high risk, using the following formula: Among them, E is the evaluation result of the PV module, and λ1 and λ2 are the preset risk score thresholds.

4. The photovoltaic power generation management system based on the cloud platform according to claim 1, characterized in that: The statistical method of the input and output data of the inverter is: Based on the photovoltaic module evaluation results, voltage and current input data are collected from inverter monitoring, and the data within the target time period are counted to generate an input data set, where each data point includes a voltage V i and current I i ; Based on the input data set, corresponding output voltage and current data are collected to generate an output data set, wherein each data point includes a voltage V o and current I o ; Based on the input data set and the output data set, the following formula is used: By introducing the exponential form of the frequency adjustment coefficient and the cosine value of the power factor angle, the total input power and total output power are calculated, where P in is the total input power, P out is the total output power, n is the number of data points, V i ,I i 、V o ,I o are the voltage and current at the input and output data points, k v and k o is the adjustment coefficient, φ i and φ o is the corresponding power factor angle; By comparing the total input power and the total output power, the conversion efficiency of the inverter is calculated using the following formula: Where η is the conversion efficiency of the inverter, k eff is the efficiency adjustment factor.

5. The photovoltaic power generation management system based on the cloud platform according to claim 1, characterized in that: The steps for obtaining the inverter operation performance analysis result are specifically as follows: Based on the conversion efficiency of the inverter, additional data about the working status of the inverter is collected from the inverter monitoring to generate an integrated data set, wherein the integrated data set includes temperature, frequency and power factor; Analyzing the integrated data set, analyzing temperature fluctuations and frequency changes using statistical methods, and obtaining a stability index and a response characteristic index; Based on the stability index and the response characteristic index, the conversion efficiency, the stability index and the response characteristic index are summarized, and the inverter performance score is calculated using the following formula: Among them, P is the inverter performance score, α, β and γ are adjustment coefficients, S is the stability index, η is the inverter conversion efficiency, and R is the response characteristic index; Based on the value of the inverter performance score, the operating status of the inverter is determined, the inverter operating performance analysis result is generated, and the inverter operating performance analysis result is classified into excellent, good, general and maintenance. The judgment formula is as follows: Among them, E is the analysis result of the inverter operation performance.

6. The photovoltaic power generation management system based on cloud platform according to claim 1, characterized in that: The calculation method of the energy storage device capacity and voltage is: Based on the inverter operation performance analysis result, the voltage and current data are collected in real time through the monitoring of the energy storage device, and the information of the charging and discharging events is recorded to obtain an input data set; Based on the input data set, the peak and valley values ​​of the voltage and current in each charging cycle are extracted, and the average voltage and current are calculated respectively by nonlinear weighted averaging, using the following formula: Among them, V avg is the average voltage, I avg is the average current, V t and I t are the voltage and current readings at time t, λ V and λ I Adjust the nonlinear weights of voltage and current data, τ V and τ I is the time decay factor; Based on the average voltage and current, the capacity of the energy storage device is calculated using the following formula: Where C is the capacity of the energy storage device, T op is the total time during the operation period, V nom is the standard voltage of the device, δ and ρ are adjustment coefficients.

7. The photovoltaic power generation management system based on the cloud platform according to claim 1, characterized in that: The steps for obtaining the energy storage device performance management analysis results are specifically as follows: Based on the data of the capacity and average voltage of the energy storage device, additional performance parameters are collected from the monitoring, including temperature fluctuation and charge and discharge cycle frequency, to obtain an energy storage device operation data set; Based on the energy storage device operation data set, a multivariate analysis method is applied to the collected data to determine the health and performance status of the energy storage device and obtain preliminary health and performance evaluation results. Based on the preliminary health and performance assessment results, the energy storage equipment performance index is calculated using the following formula: Among them, PI is the performance index of energy storage equipment, T fluc is the temperature fluctuation, F cycle is the charge and discharge cycle frequency, κ and ξ are adjustment coefficients, T op is the total operation time, V avg is the average voltage; The operating conditions of the energy storage devices are classified according to the values ​​of the energy storage device performance index to obtain the energy storage device performance management analysis results. The judgment formula is as follows: Where W is the performance management analysis result of the energy storage equipment.

8. The photovoltaic power generation management system based on a cloud platform according to claim 1, characterized in that: The steps for obtaining the performance operation record are specifically as follows: Based on the energy storage device performance management analysis results, combined with the historical performance data of the power distribution equipment, including charge and discharge cycles, battery capacity and temperature data, a set of historical performance data of the power distribution equipment is generated; The historical performance data set of the power distribution equipment is analyzed, and the performance degradation trend of the power distribution equipment is calculated through the cloud platform to obtain the performance degradation prediction result of the power distribution equipment. The formula used is as follows: Among them, P t represents the performance degradation prediction result of power distribution equipment, C i is the initial capacity of the ith charge-discharge cycle, t i is the usage time, α is the basic attenuation coefficient, β and k age is the parameter to adjust the attenuation effect, γ is the environmental influence coefficient, and n is the total number of data points; According to the performance degradation prediction results of the power distribution equipment, based on the real-time monitoring data of the power distribution equipment, the voltage, current and temperature are continuously monitored, the monitoring data is compared with the historical data of the power distribution equipment and anomalies are identified. If the monitoring value exceeds the preset threshold, an alarm is automatically triggered and related information is recorded to establish a performance operation record.

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