On-line monitoring management system and method for running state of photovoltaic controller

Abstract

The invention discloses an on-line monitoring management system and method for the running state of a photovoltaic controller, and relates to the technical field of photovoltaic control. According to the on-line monitoring and management system for the running state of the photovoltaic controller, by constructing the technical scheme of coupling feature extraction, working condition matching degree calculation, working condition recognition and low-efficiency running judgment, firstly, multi-dimensional coupling features are constructed through a power change trend cooperation coefficient, a DTW similarity value and a power fitting residual comprehensive feature value; a quantitative matching degree is obtained by combining steady-state and transition working condition feature sets, then a steady-state working condition with stable load power and a transition working condition with transient fluctuation of the load power are distinguished by relying on steady-state establishment minimum time and candidate working condition verification rules, the operation efficiency is calculated only for the steady-state working condition, and low efficiency is judged through the efficiency trend change rate and the deviation coefficient. The defect that in the prior art, a steady-state quantitative judgment standard is lacked is made up on the whole, and the problem that normal efficiency fluctuation under the transition working condition is misjudged to be low-efficiency operation of the controller is avoided.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic control technology, specifically to an online monitoring and management system and method for the operating status of a photovoltaic controller. Background Technology

[0002] In photovoltaic (PV) controller operation status monitoring, accurate identification of steady-state operating conditions is a crucial step in determining inefficient operation. However, in actual PV controller operation scenarios, factors such as load fluctuations and equipment transient adjustments frequently occur, revealing the following shortcomings in existing monitoring solutions: First, most existing technologies rely on threshold judgments based on single power or efficiency indicators, lacking quantitative characterization of multi-dimensional coupled characteristics such as power change trends, fluctuation consistency, and data smoothness, thus failing to fully reflect the essential attributes of steady-state operating conditions. Second, existing solutions ignore the evolution process from transient to steady-state operating conditions, making it difficult to define the true steady-state range. Finally, existing inefficient operation judgments often directly use efficiency data from all operating stages for analysis, failing to clearly distinguish between steady-state and transient operating conditions, equating transient efficiency fluctuations under transient conditions with inefficient operation characteristics.

[0003] Therefore, there is an urgent need for an online monitoring and management solution that can quantify multi-dimensional coupling characteristics, combine equipment operating principles, and accurately distinguish operating conditions to solve the above-mentioned technical bottlenecks and improve the accuracy and reliability of photovoltaic controller inefficiency judgment. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an online monitoring and management system and method for the operating status of photovoltaic controllers. This solves the problem that existing technologies lack quantitative criteria for determining steady-state operating conditions, cannot distinguish between steady-state operating conditions with stable load power and transitional operating conditions with transient load power fluctuations, and are prone to misjudging efficiency fluctuations under transitional operating conditions as inefficient operation of the controller.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an online monitoring and management system for the operating status of a photovoltaic controller, comprising: The coupling feature extraction module is used to extract the coupling feature matrix for each load power time-series segment of the photovoltaic controller.

[0006] The operating condition matching degree calculation module is used to match the coupling feature matrix with the steady-state operating condition feature set and the transition operating condition feature set to obtain the steady-state matching degree and transition matching degree of each load power time sequence segment.

[0007] The operating condition identification module is used to determine the operating condition judgment result of each load power timing segment based on the steady-state matching degree and the transition matching degree, combined with the preset steady-state establishment minimum time.

[0008] The low-efficiency operation determination module is configured to, when the working condition determination result is the steady-state working condition, calculate the steady-state operation efficiency of the corresponding load power time sequence sub-section, and determine whether the photovoltaic controller is in the low-efficiency operation state.

[0009] A photovoltaic controller operation state online monitoring management method for the photovoltaic controller operation state online monitoring management system, comprising the following steps: The coupling feature matrix extraction is performed on each load power time sequence sub-section of the photovoltaic controller.

[0010] The coupling feature matrix is matched with the steady-state working condition feature set and the transition working condition feature set to obtain the steady-state matching degree and the transition matching degree of each load power time sequence sub-section.

[0011] Based on the steady-state matching degree and the transition matching degree, the working condition determination result of each load power time sequence sub-section is determined in combination with the preset steady-state establishment minimum time.

[0012] When the working condition determination result is the steady-state working condition, the steady-state operation efficiency of the corresponding load power time sequence sub-section is calculated, and it is determined whether the photovoltaic controller is in the low-efficiency operation state.

[0013] Compared with the prior art, the present application has the following beneficial effects: The present application has the following beneficial effects:

[0014] Of course, any product implementing the present application does not necessarily need to achieve all the advantages mentioned above. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 It is a module connection schematic diagram of the photovoltaic controller operation state online monitoring management system of the present application.

[0016] Figure 2 It is a flow chart of the coupling feature matrix extraction in the photovoltaic controller operation state online monitoring management system of the present application.

[0017] Figure 3The flow chart for determining whether the photovoltaic controller is in an inefficient running state in the photovoltaic controller running state online monitoring management system method of the present application.

[0018] Figure 4 The flow chart for the photovoltaic controller running state online monitoring management method of the present application. DETAILED DESCRIPTION

[0019] Please refer to Figure 1 The embodiment of the present application provides a technical solution: a photovoltaic controller running state online monitoring management system, comprising: A coupling feature extraction module is configured to extract a coupling feature matrix for each load power time sequence subsegment of the photovoltaic controller.

[0020] It should be noted that the coupling feature matrix is a combination of the power change trend coordination coefficient, the DTW similarity value and the power fitting residual comprehensive feature value. The three parameters are quantified by constructing quantitative indexes from three dimensions of power change trend stability, adjacent subsegment fluctuation consistency and power data smoothness, which provides an explicit and quantifiable basis for determining the steady state. Among them: The power change trend coordination coefficient quantifies the stability of the power change, the DTW similarity value quantifies the continuity of the adjacent time period working condition, and the power fitting residual comprehensive feature value quantifies the fluctuation degree of the power data deviating from the macro trend. The three parameters can be used to distinguish the steady state of the load power and the transition state of the load power.

[0021] Specifically, as Figure 2 shown, the process of coupling feature matrix extraction is as follows: Obtain the target load power time sequence subsegment, calculate the difference value of the power at adjacent time points point by point in time sequence, obtain the first-order difference sequence, which directly reflects the real-time change rate of the power at each time point, calculate the difference value of adjacent time points point by point for the first-order difference sequence, obtain the second-order difference sequence, which reflects the fluctuation trend of the power change rate.

[0022] Compare the change trend of the first-order difference sequence and the second-order difference sequence point by point. If they are positive at the same time or negative at the same time, it means that the trends are in the same direction. The proportion of the number of times that the trends of the first-order difference sequence and the second-order difference sequence are in the same direction to the total number of data points is taken as the power change trend coordination coefficient, that is, the proportion of the number of times that the trends of the first-order difference sequence and the second-order difference sequence are in the same direction to the total number of data points of the second-order difference sequence.

[0023] The closer the power change trend coordination coefficient is to 1, the more stable the power change trend is, which is consistent with the characteristics of the steady state. The farther the coefficient deviates from 1, the more violent the power change rate fluctuation is, which is consistent with the characteristics of the transition state.

[0024] Based on the power fluctuation curve of the load power time sequence sub-section and the previous adjacent sub-section, the DTW similarity value is obtained by combining the DTW algorithm. The smaller the DTW similarity value is, the more consistent the adjacent sub-section fluctuation form is, the continuous working condition is, and the tendency to steady state is.

[0025] It should be noted that the calculation process of the DTW algorithm is prior art. If the load power time sequence sub-section is the first sub-section, the pre-stored rated steady reference sub-section in the reference library is selected, the factory calibration is consistent with the real-time sub-section sampling parameters.

[0026] Considering that the residual reflects the local fluctuation of the load power deviating from the macro trend, the power changes gently in the steady state, the residual should be concentrated in a reasonable range determined by the normal operation noise of the equipment, and a large number of abnormal residuals exceeding the range will be generated in the transition condition due to power mutation; considering that the residual in the steady state is mainly derived from the random noise of the equipment operation, the distribution should present the characteristics of uniform dispersion; and the residual in the transition condition will appear local concentration due to power mutation, resulting in that the distribution of the residual in the numerical interval presents a significant imbalance, so the process of obtaining the power fitting residual comprehensive characteristic value is: The residual sequence is obtained by locally weighted regression fitting the load power time sequence sub-section, the dispersion degree and the distribution uniformity of the residual sequence are statistically fused to obtain the power fitting residual comprehensive characteristic value, which has the physical meaning of the degree of power data deviating from the smooth trend. The smaller the value is, the more gentle the power fluctuation is; the larger the value is, the more obvious the random power mutation is.

[0027] The process of statistically obtaining the dispersion degree and the distribution uniformity of the residual sequence is: The ratio of the number of data points exceeding the set residual range to the total number of data points in the residual sequence is taken as the dispersion degree. The set residual range is determined based on the noise level of the normal operation of the photovoltaic controller, and specifically: the residual sequence in the state of stable power operation without transient fluctuation is collected, and 3 times the standard deviation of the sequence is taken as the upper limit of the residual, and -3 times the standard deviation is taken as the lower limit of the residual.

[0028] The numerical range of the residual sequence is evenly divided into several equal-width intervals, the number of residual data points in each equal-width interval is counted, and the ratio of the maximum value to the minimum value of the number of residual data points is taken as the distribution uniformity.

[0029] The coupling feature matrix is obtained by combining the power change trend correlation coefficient, the DTW similarity value and the power fitting residual comprehensive characteristic value.

[0030] It should be noted that the process of obtaining the residual sequence by locally weighted regression fitting the load power time sequence sub-section is: Take the time of the target time sequence sub-section as the independent variable, and the power value as the dependent variable. Assign a weight to each power data point that is positively correlated with the distance to adjacent points. The weight of adjacent points is higher, and the weight of distant points is lower.

[0031] Based on the weighted data set, a smooth trend curve is fitted, that is, for each target time point, the weighted sum of all power values is calculated with the weight as the coefficient, and then divided by the total weight to obtain the fitted power value at that time point. The essence is the weighted average of local data. The fitted power values of all time points are continuously connected in time sequence. Since each fitted value is derived from the smooth transition of adjacent data, a smooth curve without breakpoints and mutations is finally formed. This curve reflects the macro trend of power and filters local random fluctuations.

[0032] Point-by-point calculation of the difference between the actual power value in the target sub-section and the power value of the corresponding time fitting curve, to obtain the residual sequence.

[0033] The working condition matching degree calculation module is used to match the coupling feature matrix with the steady state working condition feature set and the transition working condition feature set to obtain the steady state matching degree and the transition matching degree of each load power time sequence sub-section.

[0034] It should be noted that the specific process is as follows: Calculate the absolute difference between each feature in the coupling feature matrix and the corresponding reference feature in the steady state working condition feature set and the transition working condition feature set, and then divide the absolute difference by the corresponding feature standard deviation to obtain the standardized relative deviation. The reference feature represents the corresponding reference of the steady state working condition feature set and the transition working condition feature set for the power change trend coordination coefficient, DTW similarity value, and power fitting residual comprehensive feature value.

[0035] Wherein, the steady state working condition feature set is used to illustrate how to obtain the corresponding reference feature and feature standard deviation: First, collect sufficient steady state running data of the photovoltaic controller under normal load conditions, which is confirmed to have no transient fluctuation and stable power transmission. Divide the data into multiple load power time sequence sub-sections according to the pre-set time length.

[0036] Then, execute the coupling feature extraction process described in the patent for each load power time sequence sub-section, that is, calculate the power change trend coordination coefficient, DTW similarity value, and power fitting residual comprehensive feature value to form the steady state working condition corresponding coupling feature vector.

[0037] Finally, statistically analyze the same type of features in all coupling feature vectors to obtain the statistical mean of each type of feature as the corresponding reference feature, and the feature standard deviation represents the natural dispersion degree of this type of feature. Finally, integrate the reference of the three types of features and the dispersion degree parameters to form a steady state working condition feature set that can objectively reflect the typical feature distribution of the steady state working condition.

[0038] The reference features and feature standard deviations corresponding to the transition working condition features are the same as the steady state working condition features.

[0039] The linear proportion conversion is used to obtain the sub-item fitting degrees corresponding to each normalized relative deviation.

[0040] The linear proportion rule for converting the sub-item fitting degrees is that the highest fitting degree corresponds to the normalized relative deviation of 0, that is, the real-time features are completely consistent with the reference features, the lowest fitting degree corresponds to the normalized relative deviation of twice the feature standard deviation, and the sub-item fitting degrees of each feature are converted in proportion according to the actual deviation of the real-time features in the interval, and the result is a value between 0 and 100. The smaller the deviation is, the higher the fitting degree is.

[0041] The linear weighted sum of the sub-item fitting degrees corresponding to each feature in the coupling feature matrix is used to obtain the steady state matching degree and the transition matching degree. The higher the value is, the stronger the fitting degree of the real-time sub-section to the corresponding working condition features is. For example, the weights of the sub-item fitting degrees corresponding to each feature are 0.4, 0.3 and 0.3 respectively.

[0042] The working condition matching degree calculation module calculates the normalized relative deviation of the three features representing the working condition characteristics in the coupling feature matrix with the corresponding reference features of the steady state and transition working condition feature sets respectively, combines the linear proportion conversion of the sub-item fitting degrees and the matching degree quantization results of the weighted sum, and provides a clear and quantifiable fitting degree judgment standard for the working condition, which facilitates the effective differentiation between the steady state working condition of the load power stability and the transition working condition of the load power transient fluctuation.

[0043] The working condition recognition module is used to determine the working condition judgment result of each load power time sequence sub-section based on the steady state matching degree and the transition matching degree, and in combination with the preset steady state establishment minimum time.

[0044] In this embodiment, the specific process is as follows: The load power time sequence sub-section with a steady state matching degree greater than a set steady state matching degree threshold is marked as a steady state candidate working condition.

[0045] The load power time sequence sub-section with a transition matching degree greater than a set transition matching degree threshold is marked as a transition candidate working condition.

[0046] It should be noted that the steady-state matching degree threshold can be determined by the following method: collecting the steady-state matching degree data corresponding to all confirmed steady-state conditions in the steady-state condition characteristic set, statistically analyzing the data to calculate the statistical mean and standard deviation of the data, and determining the 95% confidence interval covering the matching degree range of most normal steady-state conditions based on the normal distribution statistical law, and setting the lower limit of the confidence interval as the steady-state matching degree threshold. The transition matching degree threshold is obtained in the same way as the steady-state matching degree threshold. The steady-state matching degree threshold and the transition matching degree threshold are determined based on the statistical mean, standard deviation and normal distribution law of the condition characteristic set, so as to cover the characteristic range of most normal conditions and ensure the objectivity of the candidate condition label.

[0047] The duration of the steady-state candidate condition and the transition candidate condition is extracted. If the duration of the steady-state candidate condition is not less than the steady-state establishment minimum time, it means that the photovoltaic controller has completed all the physical processes of steady-state establishment, the power transmission and the stable running state, and the condition determination result is a steady-state condition, otherwise it is a non-steady-state condition.

[0048] It should be noted that the steady-state establishment minimum time can be extracted from the photovoltaic controller factory hardware manual and test report, and can be specifically set as the sum of the switching device action delay time, the inductance current stable response time and the MPPT algorithm maximum power point tracking stable time.

[0049] It should be noted that the photovoltaic controller usually needs to complete three types of physical and algorithmic processes in sequence to reach a true steady state: the switching device as a power conversion device, its action delay is the basic time consumption of instruction execution, which determines the initial response efficiency of power transmission; the inductance as a key energy storage element, the current needs to go through transient fluctuations to reach stability, which is the prerequisite for stable power transmission; the MPPT algorithm needs to accurately track the maximum power point of the photovoltaic array through iterative calculation to ensure the stability of the controller running efficiency. These three processes are the necessary links for the controller to transition from transient adjustment to steady state operation. Only when all three processes are completed and reach a stable state, the power output, current characteristics and running efficiency of the controller will be stable simultaneously, so the time consumption of the three processes needs to be added up as the minimum time standard for determining whether the condition has entered the steady state.

[0050] If the preceding adjacent sub-segment of the transition candidate condition is a steady-state condition and the duration of the transition candidate condition is not greater than the set multiple of the steady-state establishment minimum time, it meets the physical limit of transient adjustment of the transition condition, then the condition determination result is a transition condition, otherwise it is a non-transition condition. For example, the set multiple is 2.

[0051] The setting reason for the preceding adjacent sub-segment being a steady-state condition is to meet the physical logic that the transition condition is triggered by the steady state.

[0052] The step-by-step process of candidate operating condition marking and duration verification realizes layer-by-layer screening of operating condition determination, highlights the basic role of feature matching, and supplements the stability verification of the time dimension to ensure the scientificity and rigor of the design.

[0053] It should be noted that when the load power time sequence sub-section simultaneously satisfies the determination conditions of the steady-state candidate operating condition and the transition candidate operating condition, the transition operating condition is determined first, because the physical nature of the transition operating condition is a transient adjustment process of the steady-state operating condition, and the previous adjacent sub-section is usually a steady-state operating condition, which conforms to the evolution logic from steady state to transition. If the load power time sequence sub-section is neither a steady-state candidate operating condition nor a transition candidate operating condition, it is determined to be an abnormal operating condition, and an operating condition abnormality prompt signal is output. This signal can trigger the fault diagnosis process of the photovoltaic controller.

[0054] The low-efficiency operation determination module is used to calculate the steady-state operation efficiency of the corresponding load power time sequence sub-section when the operating condition determination result is a steady-state operating condition, and determine whether the photovoltaic controller is in a low-efficiency operation state.

[0055] It should be noted that since the efficiency data under the steady-state operating condition can truly reflect the actual operation performance of the controller, the efficiency fluctuation of the transition operating condition is a transient adjustment and does not have reference value for determining low efficiency. Therefore, as shown in Figure 3 the process of determining whether the photovoltaic controller is in a low-efficiency operation state is: All load power time sequence sub-sections finally determined by the operating condition recognition module as steady-state operating conditions are screened out, and only the steady-state operating conditions are subjected to efficiency calculation and low-efficiency determination, excluding the interference of transition operating conditions and non-steady-state or non-transition operating conditions. The ratio of the input power to the output power corresponding to the load power time sequence sub-section of the steady-state operating condition is recorded as the steady-state operation efficiency. The input power is the power input from the photovoltaic array to the controller, and the output power is the power output from the controller to the load or energy storage device, both of which are obtained in real time from the power detection unit of the photovoltaic controller.

[0056] The running efficiency data of a plurality of continuous steady-state operating conditions are extracted in chronological order, and these running efficiencies are arranged in time sequence to construct an efficiency time sequence evolution sequence. The efficiency time sequence evolution sequence is constructed in chronological order to capture the continuous change law of the efficiency, avoid the accidental influence of single steady-state operating condition efficiency data, and ensure that the sequence can reflect the efficiency change law of the controller in the continuous stable operation stage. The efficiency trend change rate and the deviation coefficient of the efficiency time sequence evolution sequence are calculated.

[0057] If the efficiency trend change rate is continuously negative and the deviation coefficient exceeds the normal efficiency evolution deviation range, it indicates that the efficiency fluctuation exceeds the allowed interval of normal operation, and the photovoltaic controller is in a low-efficiency operation state; if any of the conditions is not met, it is determined to be in a normal operation state, and the final determination result is output, providing a clear basis for operation and maintenance adjustment of the photovoltaic controller. The normal efficiency evolution deviation range is determined by the following method: collecting the steady-state operation efficiency data of the same type of photovoltaic controller under the rated load condition, constructing the efficiency time evolution sequence and calculating the deviation coefficient, taking the 95% confidence interval of the deviation coefficient as the normal efficiency evolution deviation range, and exceeding the interval is determined to be abnormal.

[0058] The efficiency trend change rate is continuously negative, which means that the efficiency trend change rate of 3 or more consecutive steady-state operating conditions is negative. The number of consecutive steady-state operating conditions can be adjusted to 2-5 according to the operation and maintenance requirements of the equipment.

[0059] The double determination criteria of the trend change rate being continuously negative and the deviation coefficient exceeding the range are adopted because the core features of low-efficiency operation include not only the continuous downward trend of efficiency but also the fluctuation beyond the normal range. The double criteria can comprehensively cover the low-efficiency determination requirements and ensure the pertinence and scientificity of the design.

[0060] The process of calculating the efficiency trend change rate and the deviation coefficient of the efficiency time evolution sequence is as follows: For the constructed efficiency time evolution sequence, the difference between the adjacent two efficiency values in the steady-state efficiency time sequence is calculated in time sequence, that is, the efficiency value of the next steady-state operating condition is subtracted from the efficiency value of the previous steady-state operating condition. All such calculation results are arranged in order to obtain the efficiency difference value sequence.

[0061] Two statistical analyses are performed on the efficiency difference value sequence: one is to count the number of negative difference values in the sequence and calculate the proportion of the number of negative difference values in the total number of difference values, which directly reflects the frequency of efficiency decline, and the arithmetic mean of all efficiency difference values in the efficiency difference value sequence is calculated.

[0062] The proportion of the number of negative difference values and the absolute value of the arithmetic mean are weighted and fused according to the preset weight to obtain the trend change rate, and the sign of the trend change rate is consistent with the sign of the previously calculated arithmetic mean.

[0063] By calculating the adjacent efficiency difference values and counting the proportion of negative numbers and the average amplitude, and then weighting and fusing to obtain the trend change rate, the frequency and amplitude of efficiency decline can be considered, and the key signal of continuous decline can be captured.

[0064] The relative deviation sequence is calculated by calculating the absolute value of the difference between two adjacent efficiency values in the steady-state efficiency time sequence, and then dividing the absolute value by the arithmetic mean of the two efficiency values.

[0065] The arithmetic mean of all relative deviation data in the relative deviation sequence is taken as the deviation coefficient, which objectively reflects the overall level of efficiency fluctuation and conforms to the low-efficiency determination logic when the fluctuation exceeds the normal range.

[0066] The calculation process focuses on the efficiency time sequence evolution sequence of continuous steady-state working conditions, and by quantifying the trend change rate and deviation coefficient of efficiency, it ensures that the low-efficiency determination is based only on the efficiency data characteristics of the true steady-state running stage, effectively avoiding the problem of existing technology that cannot distinguish between steady-state and transient working conditions, and mistakenly including normal efficiency fluctuations in the transient working conditions into the low-efficiency determination basis, making the low-efficiency running determination standard more targeted and reliable.

[0067] In summary, the low-efficiency running determination module first selects the steady-state working condition sub-section confirmed by the working condition identification module, excludes the interference of transient working conditions and non-steady-state or non-transient working conditions from the data source, and only based on the efficiency data of the true steady-state running stage to construct the efficiency time sequence evolution sequence, quantifies the low-efficiency characteristics through the double determination standards of efficiency trend change rate and deviation coefficient, solves the problem of existing technology that mistakenly judges normal efficiency fluctuations in transient working conditions as controller low-efficiency running due to lack of steady-state quantitative distinction ability, and provides a reliable basis for operation and maintenance adjustment.

[0068] A photovoltaic controller running state online monitoring management method, as shown in Figure 4 The method comprises the following steps: S1, extracting the coupling feature matrix of each load power time sequence sub-section of the photovoltaic controller.

[0069] S2, matching the coupling feature matrix with the steady-state working condition feature set and the transient working condition feature set to obtain the steady-state matching degree and the transient matching degree of each load power time sequence sub-section.

[0070] S3, based on the steady-state matching degree and the transient matching degree, combining the preset steady-state establishment minimum time to determine the working condition determination result of each load power time sequence sub-section.

[0071] S4, when the working condition determination result is a steady-state working condition, calculating the steady-state running efficiency of the corresponding load power time sequence sub-section to determine whether the photovoltaic controller is in a low-efficiency running state.

[0072] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product.

[0073] Those skilled in the art can realize that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0074] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0075] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any modification or replacement within the technical scope disclosed by the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0076] Finally, the above is merely preferred embodiments of the present application, and is not intended to limit the present application, and any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A photovoltaic controller operation status online monitoring and management system, comprising: The coupling feature extraction module is used to extract the coupling feature matrix for each load power time segment of the photovoltaic controller. The operating condition matching degree calculation module is used to match the coupling feature matrix with the steady-state operating condition feature set and the transition operating condition feature set to obtain the steady-state matching degree and transition matching degree of each load power time series segment. The operating condition identification module is used to determine the operating condition judgment result of each load power timing segment based on the steady-state matching degree and the transition matching degree, combined with the preset minimum steady-state establishment time. The inefficient operation determination module is used to calculate the steady-state operating efficiency of the corresponding load power time sequence segment when the operating condition determination result is a steady-state operating condition, and to determine whether the photovoltaic controller is in an inefficient operating state.

2. The photovoltaic controller operation status online monitoring and management system according to claim 1, characterized in that, The process of extracting the coupling feature matrix for each load power time-series segment of the photovoltaic controller is as follows: The first-order difference sequence and the second-order difference sequence of the load power time series segment are obtained based on the power difference between adjacent time points. The proportion of the number of times the first-order difference sequence and the second-order difference sequence are in the same direction to the total number of data points is used as the power change trend coordination coefficient. Based on the power fluctuation curves of the load power time sequence segment and the preceding adjacent segment, the DTW similarity value is obtained by combining the DTW algorithm. The residual sequence is obtained by fitting the load power time series segment through local weighted regression. The dispersion and distribution uniformity of the residual sequence are statistically analyzed and weighted and fused to obtain the comprehensive characteristic value of the power fitting residual. The coupling feature matrix is ​​obtained by combining the power change trend coordination coefficient, DTW similarity value, and power fitting residual comprehensive feature value.

3. The photovoltaic controller online monitoring and management system according to claim 2, characterized in that, The process of determining the dispersion and uniformity of the statistical residual sequence is as follows: The degree of dispersion is defined as the ratio of the number of data points in the residual sequence that exceed the set residual range to the total number of data points. The numerical range of the residual sequence is divided into several equally wide intervals. The number of residual data points in each equally wide interval is counted, and the ratio of the maximum to the minimum number of residual data points is used as the uniformity of distribution.

4. The photovoltaic controller operation status online monitoring and management system according to claim 1, characterized in that, The process of matching the coupling feature matrix with the steady-state feature set and the transient feature set to obtain the steady-state matching degree and transient matching degree for each load power time series segment is as follows: The absolute difference between each feature in the coupling feature matrix and the corresponding benchmark feature in the steady-state and transitional operating condition feature sets is calculated. Then, the absolute difference is divided by the corresponding feature standard deviation to obtain the standardized relative deviation. The degree of fit of each standardized relative deviation is obtained by linear proportional conversion; The steady-state matching degree and the transitional matching degree are obtained by linearly weighting and summing the degree of matching of each feature in the coupling feature matrix.

5. The photovoltaic controller online monitoring and management system according to claim 1, characterized in that, The process of determining the operating condition judgment result of each load power time series segment based on steady-state matching degree and transient matching degree, combined with the preset boundary distinction rules, is as follows: Based on the steady-state matching degree and the transient matching degree, determine whether the corresponding load power timing segment is a steady-state candidate operating condition or a transient candidate operating condition; Extract the duration of steady-state candidate operating conditions and transitional candidate operating conditions, and determine the operating condition judgment result based on the duration and the minimum time established by the steady state.

6. The photovoltaic controller operation status online monitoring and management system according to claim 5, characterized in that, The process of determining whether the corresponding load power time segment is a steady-state candidate operating condition or a transitional candidate operating condition based on steady-state matching degree and transition matching degree is as follows: Load power time segments with a steady-state matching degree greater than a set steady-state matching degree threshold are marked as steady-state candidate operating conditions; Load power timing segments with a transition matching degree greater than the set transition matching degree threshold are marked as transition candidate operating conditions.

7. The photovoltaic controller operation status online monitoring and management system according to claim 5, characterized in that, The process of determining the working condition judgment result based on the minimum time established by duration and steady state is as follows: If the duration of a steady-state candidate operating condition is not less than the minimum time for establishing a steady state, the operating condition is determined to be a steady-state operating condition; otherwise, it is a non-steady-state operating condition. If the preceding adjacent segment of the transitional candidate condition is a steady-state condition and the duration of the transitional candidate condition is not greater than a multiple of the minimum steady-state establishment time, then the condition determination result is a transitional condition; otherwise, it is a non-transitional condition.

8. The photovoltaic controller operation status online monitoring and management system according to claim 1, characterized in that, The process of calculating the steady-state operating efficiency of the corresponding load power time segment and determining whether the photovoltaic controller is in an inefficient operating state is as follows: The ratio of input power to output power corresponding to the load power timing segment is denoted as the steady-state operating efficiency. Extract operating efficiency data from multiple consecutive steady-state operating conditions, construct an efficiency time-series evolution sequence, and calculate the efficiency trend change rate and deviation coefficient of the efficiency time-series evolution sequence; If the efficiency trend change rate is consistently negative and the deviation coefficient exceeds the normal efficiency evolution deviation range, the photovoltaic controller is determined to be in an inefficient operating state.

9. The photovoltaic controller operation status online monitoring and management system according to claim 8, characterized in that, The process of calculating the rate of change of efficiency trend and the coefficient of deviation of the efficiency time series evolution sequence is as follows: The difference between two adjacent efficiency values ​​in the steady-state efficiency time series is calculated in chronological order to obtain the efficiency difference sequence. The percentage of negative differences in the efficiency difference sequence is counted, and the arithmetic mean of all efficiency differences in the efficiency difference sequence is calculated. The proportion of negative differences is weighted and fused with the absolute value of the arithmetic mean according to a preset weight to obtain the trend change rate, and the sign of the trend change rate is the same as the sign of the arithmetic mean. The relative deviation sequence is obtained by calculating the relative deviation of two adjacent efficiency values ​​in the steady-state efficiency time series in chronological order. The arithmetic mean of all relative deviation data in the relative deviation sequence is used as the deviation coefficient.

10. A method for online monitoring and management of the operating status of a photovoltaic controller, characterized in that, Includes the following steps: The coupling feature matrix is ​​extracted for each load power time segment of the photovoltaic controller; The coupling feature matrix is ​​matched with the steady-state feature set and the transitional feature set to obtain the steady-state matching degree and the transitional matching degree of each load power time sequence segment; Based on steady-state matching degree and transient matching degree, combined with the preset minimum steady-state establishment time, the operating condition judgment result of each load power timing segment is determined; When the operating condition is determined to be a steady-state condition, the steady-state operating efficiency of the corresponding load power time sequence segment is calculated to determine whether the photovoltaic controller is in an inefficient operating state.

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