Photovoltaic module online power monitoring method, electronic device and storage medium

Abstract

This application provides a method, electronic device, and storage medium for online power monitoring of photovoltaic modules. The method includes: constructing verification conditions; determining a valid test time based on pre-established verification conditions (string operating status verification, irradiance integer value verification, and irradiance stability verification); real-time power correction of the module based on the measured irradiance and cell temperature at the valid test time; determining the online power test value based on the corrected power value under the same integer irradiance reference value; when the integer irradiance reference value reaches a preset high irradiance threshold, correcting the online power test value to standard test conditions to obtain a comparable online power test value; determining the current control reference for the current test cycle based on its corresponding factory power correction value; and updating the dynamic iterative control reference based on the current control reference and historical control references to perform power anomaly monitoring. This application achieves accurate online testing and anomaly identification of photovoltaic module power.

Description

Technical Field

[0001] This application relates to the field of online monitoring and evaluation technology of photovoltaic module performance, and in particular to an online power monitoring method, electronic device and storage medium for photovoltaic modules. Background Technology

[0002] Currently, online monitoring of photovoltaic power plant module power generally faces problems such as low data validity, poor calibration accuracy, weak benchmark adaptability, and delayed anomaly identification. String-level or array-level acquisition cannot reflect the true output of individual modules and does not dynamically respond to irradiance fluctuations and cell temperature changes; existing online data lacks a rigorous string operating condition verification mechanism, irradiance values ​​are arbitrary and stability is not verified, resulting in a large amount of noisy data entering the analysis process; power calibration often relies solely on a single irradiance factor, ignoring temperature coupling effects and measurement timing biases; anomaly judgment methods that use the factory rated power as a fixed benchmark do not take into account the normal degradation gradient over the module's operating years; historical trend analysis is lacking, making it difficult to capture the slowly evolving performance degradation process. Summary of the Invention

[0003] The purpose of this application is to provide a method, electronic device and storage medium for online power monitoring of photovoltaic modules, so as to alleviate the problems of high dispersion of power assessment results, false alarms and false alarms coexisting in the prior art, which makes it difficult to support refined operation and maintenance decision-making.

[0004] In a first aspect, the present invention provides a method for online power monitoring of photovoltaic modules, comprising: Construct verification conditions and determine the valid test time based on the pre-established verification conditions; the verification conditions include string working status verification, irradiance integer value verification, and irradiance stability verification. The module's power is calibrated in real time based on the measured irradiance and battery temperature at the effective test time, and the online power test value is determined based on the calibrated power value under the same integer irradiance reference value. When the integer irradiance reference value reaches the preset high irradiance threshold, the online power test value is corrected to the standard test conditions to obtain a comparable online power test value. Based on the comparable online power test values ​​of multiple photovoltaic modules in the same batch or array and their corresponding factory power correction values, determine the current control benchmark for the current test cycle; The dynamic iterative control benchmark is updated based on the current control benchmark and the historical control benchmark, so as to monitor power anomalies according to the dynamic iterative control benchmark and the deviation value of the photovoltaic module in the previous test cycle.

[0005] In an optional implementation, verification conditions are constructed, and valid test times are determined based on pre-established verification conditions, including: Collect string voltage, string current, measured irradiance, and battery temperature at multiple time intervals within a continuous period; Calculate the theoretical string power at each moment based on the string voltage and string current; Based on the theoretical string power and actual string power at each time point, the average deviation rate is calculated, and when the average deviation rate is within the preset tolerance range, the string working status is confirmed to be normal. The irradiance is divided into multiple integer irradiance reference values ​​with a preset integer interval. For each integer irradiance reference value, it is determined whether the measured irradiance falls within a value range centered on the integer irradiance reference value and with a preset percentage width, so as to verify the integer irradiance value. For the measured irradiance falling within the range of values, multiple irradiance samples are collected at the same time intervals in a continuous period after the collection time corresponding to the measured irradiance, the average value is calculated, and it is determined whether the deviation rate between the average value and the corresponding integer irradiance reference value is within the preset tolerance range, so as to verify the stability of the irradiance. When the array is working normally and the measured irradiance meets the requirements for integer values ​​and stability, the corresponding acquisition time is determined as the valid test time.

[0006] In an optional implementation, the module's real-time power is calibrated based on the measured irradiance and battery temperature at the effective test time, and the online power test value is determined based on the calibrated power value under the same integer irradiance reference value, including: At each valid test moment, the real-time voltage and real-time current of the photovoltaic module are continuously collected according to the preset collection frequency, and multiple real-time power values ​​are calculated. For each real-time power value, the real-time power of the module is corrected for irradiance and temperature based on the measured irradiance and battery temperature at the effective test time to obtain the initial corrected power value. The initial corrected power value under the same integer irradiance reference value is corrected twice to determine the online power test value corresponding to the integer irradiance reference value.

[0007] In an optional implementation, for each real-time power value, based on the measured irradiance and battery temperature at the effective test time, the real-time power of the module is irradiance-corrected and temperature-corrected to obtain an initial corrected power value, including: For each real-time power value, irradiance correction is performed based on the measured irradiance and integer irradiance reference value at the corresponding time to obtain the irradiance-corrected power value. For each irradiation-corrected power value, temperature correction is performed based on the measured battery temperature and module power temperature coefficient at the corresponding time to obtain the temperature-corrected power value.

[0008] In an optional implementation, when the integer-digit irradiance reference value reaches a preset high irradiance threshold, the online power test value is corrected to standard test conditions to obtain a comparable online power test value, including: Identify all integer-bit irradiance reference values ​​that are greater than or equal to the preset high irradiance threshold; For each integer irradiance reference value, standard test conditions are corrected based on the corresponding online power test value, the integer irradiance reference value, and the average battery temperature at the corresponding time to obtain a comparable online power test value under the integer irradiance reference value. When multiple comparable online power test values ​​exist, their arithmetic mean is calculated to obtain the comparable online power test value of the component within the test date.

[0009] In an optional implementation, the current control benchmark for the current testing cycle is determined based on the comparable online power test values ​​of multiple photovoltaic modules in the same batch or array and their corresponding factory power correction values, including: For no less than a preset number of photovoltaic modules in the same batch or array, calculate the factory power correction value of the photovoltaic modules based on the time difference between the production date and the test date and the annual degradation rate of the modules. The relative deviation value is determined based on the comparable online power test value and the factory power correction value; The mean of all relative deviations is calculated to determine the current control baseline for the current test cycle.

[0010] In an optional implementation, a dynamic iterative control benchmark is updated based on the current control benchmark and historical control benchmarks to monitor power anomalies according to the dynamic iterative control benchmark and the deviation value of the photovoltaic module in the previous test cycle, including: The current control benchmark for the current test period is weighted and averaged with the historical control benchmark for at least one historical test period according to a preset weighting coefficient to generate a dynamic iterative control benchmark. For each photovoltaic module in the current testing cycle, the relative deviation value is compared with the dynamic iterative control benchmark. When the deviation exceeds the first preset deviation threshold, the photovoltaic module is marked as the first type of abnormal state to be confirmed. Obtain the relative deviation value of the photovoltaic module in the previous test cycle, and calculate the difference between the current relative deviation value and the relative deviation value in the previous test cycle; When the decrease in the difference of the relative deviation value exceeds the second preset deviation threshold, the component is marked as a second type of abnormal state pending confirmation. When a component is in either the first type of abnormality pending confirmation state or the second type of abnormality pending confirmation state, it is confirmed that the component has experienced a power abnormality.

[0011] In an optional implementation, the method further includes: Once a power anomaly is confirmed, obtain a sequence of comparable online power test values ​​for the abnormal photovoltaic module over the most recent consecutive test cycles. Monotonicity analysis and slope mutation detection are performed on the comparable online power test value sequence to identify the accelerated power decay segment; When there is a power degradation acceleration phase and the duration of the power degradation acceleration phase exceeds the preset trend confirmation time, a component performance degradation warning is generated and associated with the operation and maintenance work order system.

[0012] In a second aspect, the present invention provides an electronic device including a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the photovoltaic module online power monitoring method of any of the foregoing embodiments.

[0013] Thirdly, the present invention provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are invoked and executed by a processor, the computer-executable instructions cause the processor to implement the photovoltaic module online power monitoring method of any of the foregoing embodiments.

[0014] The photovoltaic module online power monitoring method, electronic device, and storage medium provided in this application ensure that the test is conducted in a real steady-state operating environment by constructing multiple verification conditions, including string operating status verification, irradiance integer value verification, and irradiance stability verification. This improves the representativeness and reliability of the data from the source and overcomes the problem of missing effective data caused by string anomalies or irradiance fluctuations in existing technologies. The real-time power of the module is corrected based on the measured irradiance and cell temperature at the effective test time, and the online power test value is determined based on the correction result under the same integer irradiance reference value. This achieves refined isolation of environmental factors and significantly enhances the consistency and reproducibility of power measurement. When the baseline irradiance reaches the preset high irradiance threshold, the online power test values ​​are uniformly corrected to standard test conditions to ensure the horizontal comparability of test results at different times and locations. By statistically analyzing the deviations between comparable online power test values ​​and factory power correction values ​​of multiple modules in the same batch or array, a control baseline for the current test cycle is established. This baseline is then dynamically iterated and updated in conjunction with historical control baselines, allowing it to adaptively evolve with the overall degradation pattern of the modules and avoid systematic misjudgments caused by static baselines. Based on this, power anomaly monitoring is conducted by dynamically iterating the control baseline and the deviation trend of the previous test cycle, achieving coordinated identification of the degree and rate of anomalies. Overall, this achieves precision, robustness, and intelligence in the online power monitoring of photovoltaic modules. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating an online power monitoring method for photovoltaic modules provided in this application embodiment; Figure 2 This application provides a schematic diagram of the composition of an online power testing system for photovoltaic modules. Figure 3 A flowchart illustrating a specific online power testing method for photovoltaic modules provided in this application embodiment; Figure 4 A schematic diagram of a verification rule provided for an embodiment of this application; Figure 5 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0018] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0020] This application provides an online power monitoring method for photovoltaic modules. See [link to relevant documentation] Figure 1 As shown, the method mainly includes the following steps: S110, Construct verification conditions and determine the valid test time based on the pre-established verification conditions; wherein, the verification conditions include string working status verification, irradiance integer bit value verification, and irradiance stability verification.

[0021] The string operation status verification includes randomly selecting multiple moments within a preset time window and collecting the measured irradiance, module cell temperature, inverter DC-side string voltage, and string current at each moment. Based on the maximum power point value, power temperature coefficient, and irradiance response coefficient recorded in the module's factory test report, and combined with standard test conditions, the theoretical string power at each moment is calculated. Simultaneously, the actual string power at each moment is calculated based on the product of the measured string voltage and string current. The average value of the theoretical string power at multiple moments and the average value of the actual string power at multiple moments are taken, and the deviation rate is calculated. When the deviation rate does not exceed 10%, the string operation status is determined to be normal, and the data for that test date proceeds to the next verification stage.

[0022] The verification of the integer value of the irradiance is to divide the irradiance into an integer range with each unit as an example, 100 watts per square meter, including 100 watts per square meter, 200 watts per square meter, ... up to 1 kilowatt per square meter; for any measured irradiance value, if it is within a tolerance range of 5% above or below a certain integer reference value, then it is confirmed that the value requirement of that integer is met at that moment.

[0023] Irradiance stability verification involves continuously collecting irradiance data starting from the moment the integer value requirement is met. The collection frequency can be set to, for example, once per minute, to obtain multiple irradiance sampling points. The average irradiance of these multiple sampling points is calculated and compared with the corresponding integer reference value. When the deviation rate between the average value and the integer reference value does not exceed a preset threshold, the irradiance is determined to be stable, and the component voltage, current, and battery temperature data at the corresponding moment are confirmed as valid test moment data.

[0024] S120 performs real-time power correction of the module based on the measured irradiance and battery temperature at the effective test time, and determines the online power test value based on the corrected power value under the same integer irradiance reference value.

[0025] For each valid test moment confirmed by S110, the module-level monitoring unit collects the module's real-time voltage and current at a preset frequency and calculates the real-time power. Real-time power values ​​are continuously collected under the same integer-digit irradiance reference value. The real-time power obtained for each collection is corrected based on the measured irradiance and battery temperature at that collection moment. For example, the measured irradiance can be corrected first, followed by temperature correction; alternatively, the battery temperature can be corrected first, followed by measurement irradiance correction.

[0026] S130: When the integer irradiance reference value reaches the preset high irradiance threshold, the online power test value is corrected to the standard test conditions to obtain a comparable online power test value.

[0027] When the integer-digit irradiance reference value is greater than or equal to the preset reference, the online power test value under that integer-digit irradiance reference value is corrected to the standard test conditions. In one specific correction process, the online power test value can be extrapolated proportionally to 1 kilowatt per square meter of irradiance, and combined with the temperature difference between the average of the three collected cell temperatures and 25 degrees Celsius, temperature compensation is performed according to the module power temperature coefficient. The result obtained is the comparable online power test value. If there are multiple integer-digit irradiance reference values ​​that are not lower than the preset reference within the same test date, the corresponding comparable online power test values ​​are calculated separately, and their arithmetic mean is taken as the final comparable online power test value of the module within that test date.

[0028] S140, based on the comparable online power test values ​​of multiple photovoltaic modules in the same batch or array and their corresponding factory power correction values, determine the current control benchmark for the current test cycle.

[0029] After completing S130, retrieve basic information of a sufficient number of photovoltaic modules belonging to the same array or batch participating in this test from the database, including the unique identifier of each module, the factory maximum power point value, and the production date; calculate the time difference between the current test date and the production date of each module, and perform attenuation correction on the factory maximum power point value of each module according to the annual degradation rate promised by the manufacturer, to obtain the factory power correction value; for each module, calculate its deviation rate in this test, that is, the percentage of the difference between its reference online power test value and the factory power correction value to the factory power correction value; calculate the arithmetic mean of the deviation rates of all modules to obtain the current control benchmark for the current test cycle.

[0030] S150 updates the dynamic iterative control benchmark based on the current control benchmark and the historical control benchmark, so as to monitor power anomalies according to the dynamic iterative control benchmark and the deviation value of the photovoltaic module in the previous test cycle.

[0031] The system automatically retrieves historical test records of the array or batch to which the component belongs from the database, obtaining the control benchmarks determined by all previous historical tests. Following a preset weighting strategy, it performs a weighted average of the current control benchmark and all historical control benchmarks to obtain a dynamic iterative control benchmark. This strategy embodies the principle of "newer is heavier," meaning the control benchmark corresponding to the most recent test is assigned a higher weight, with the weights of previous tests decreasing sequentially. For any component to be evaluated, after obtaining its deviation rate in the current test, a horizontal judgment is first performed: if its current deviation rate is lower than the dynamic iterative control benchmark by a preset margin, its power is determined to be lower than the group average attenuation level, constituting a serious anomaly. Secondly, a vertical judgment is performed: the deviation rate of the component in the previous test cycle is retrieved; if the current deviation rate is lower than the previous cycle by a preset margin, its attenuation rate is determined to be abnormally accelerated, constituting an acceleration anomaly. The system performs a logical "OR" operation on the two judgment results; if either condition is met, the component is marked as having a power anomaly, and an anomaly list is generated in the visualization unit, recording the judgment basis, supporting manual review and confirmation by maintenance personnel.

[0032] For ease of understanding, the following provides a detailed description of an online power monitoring method for photovoltaic modules provided in the embodiments of this application.

[0033] The above-mentioned construction of verification conditions, and the determination of valid test times based on pre-established verification conditions, may include the following steps 1.1 to 1.6 in specific implementation: Step 1.1: Collect string voltage, string current, measured irradiance, and battery temperature at multiple time intervals within a continuous time period.

[0034] After the test task is started, the data acquisition task is initiated. Within a representative continuous time period, multiple acquisition actions are triggered synchronously at preset time intervals. Each acquisition action simultaneously acquires the string voltage, string current, measured irradiance, and module cell temperature at that moment. The acquired data is cached in real time and accurately timestamped. The length of this continuous time period and the specific values ​​of the time intervals can be adaptively configured according to the geographical and climatic characteristics of the power station and the statistical patterns of historical operating data.

[0035] For example, the continuous time period can typically be set to thirty minutes, and the preset time interval can be set to once every six minutes, so as to obtain data at no less than five sampling times within the time period; however, the above time parameters are only one feasible implementation method, and those skilled in the art can make equivalent adjustments according to the actual monitoring system performance, network bandwidth or accuracy requirements, which does not constitute a limitation on the scope of protection.

[0036] Step 1.2: Calculate the theoretical string power at each moment based on the string voltage and string current.

[0037] For the string voltage and string current acquired at each acquisition moment, the factory test report of the components contained in the string is retrieved to extract the power temperature coefficient, irradiance response coefficient, and maximum power point power value of a single component. Combined with the measured irradiance and battery temperature acquired synchronously at that moment, the maximum power point power of a single component is corrected for irradiance ratio and temperature compensation according to the component photoelectric conversion physical model. Then, the corrected single component power is multiplied by the number of components in the string to obtain the theoretical string power corresponding to that moment. This power value is used as the theoretical input for subsequent deviation analysis.

[0038] Step 1.3: Calculate the average deviation rate based on the theoretical string power and actual string power at each time point, and confirm that the string is working normally when the average deviation rate is within the preset tolerance range.

[0039] The actual string power is obtained by directly multiplying the string voltage and string current collected in step 1.1, and is used to characterize the actual operating state of the system. The average deviation rate is a statistical consistency measure between the theoretical and actual values ​​across multiple sampling points. Its calculation does not rely on single-point instantaneous errors, but rather smooths random disturbances through the mean, improving the robustness of judgment. The preset tolerance range is a manually set engineering acceptable error threshold used to define whether there are systemic problems affecting the overall output, such as significant string mismatch, bypass diode malfunction, local shading, or wiring faults.

[0040] The theoretical string power calculated in step 1.2 at each moment is compared with the actual string power obtained by multiplying the string voltage and string current at the same moment. The arithmetic mean of the theoretical string power at all sampling moments is calculated to obtain the average theoretical string power. The arithmetic mean of the actual string power at all sampling moments is calculated to obtain the average actual string power. The percentage of the difference between the two to the average theoretical string power is calculated as the average deviation rate. When the average deviation rate is within the preset tolerance range, the system determines that the string is in a working state with normal electrical connection and no significant abnormal loss within this test window; otherwise, the subsequent verification process is paused, and the maintenance personnel are prompted to check the health status of the string.

[0041] For example, the preset tolerance range can be set to ±10%; however, this range is set based on the allowable power tolerance of typical photovoltaic modules under standard operating conditions and on-site measurement experience, and can be adjusted for adaptability according to different module technical routes or power plant acceptance standards, and does not constitute a limitation on the technical solution.

[0042] Step 1.4: Divide the irradiance into multiple integer irradiance reference values ​​with a preset integer interval. For each integer irradiance reference value, determine whether the measured irradiance falls within a value range centered on the integer irradiance reference value and with a preset percentage width, in order to verify the integer irradiance value.

[0043] A set of equally spaced integer irradiance reference values ​​is pre-configured, which covers the normal operating irradiance range of photovoltaic modules; for the measured irradiance at each acquisition time in step 1.1, the integer irradiance reference values ​​are compared sequentially; if the difference between a measured irradiance and a certain integer irradiance reference value is within a preset percentage width as a percentage of the reference value, the measured irradiance is identified as belonging to that integer digit, and that time is marked as a candidate time for "verification by integer digit value".

[0044] For example, the preset integer interval can be set to 100 watts per square meter to generate a reference value sequence of 100, 200... up to 1 kilowatt per square meter; the preset percentage width can be set to ±5%; however, both the interval and the width are engineering optimization parameters and can be adjusted according to the accuracy of the irradiator, regional climate characteristics, or the convergence requirements of the correction algorithm.

[0045] Step 1.5: For the measured irradiance falling within the range, collect multiple irradiance samples at the same time interval within a continuous time period after the collection time corresponding to the measured irradiance, calculate the average value, and determine whether the deviation rate between the average value and the corresponding integer irradiance reference value is within the preset tolerance range, so as to verify the stability of the irradiance.

[0046] For each collection moment determined to belong to a certain integer irradiance reference value, the system initiates a special monitoring of irradiance stability from that moment. Within a preset short continuous time period, multiple irradiance samples are continuously collected at the same time intervals. The arithmetic mean of the obtained samples is calculated. This mean is compared with the integer irradiance reference value to which that moment belongs, and the deviation rate between the two is calculated. If the deviation rate is within the preset tolerance range, the integer irradiance is determined to be stable on a short time scale, and that moment passes the irradiance stability verification; otherwise, it is considered unstable and that moment is removed.

[0047] For example, the short continuous time period can be set to five minutes, the number of samples collected can be set to five, and the preset tolerance range can be set to plus or minus 10%. However, the above parameter combination is only a typical configuration to ensure the effectiveness of the short-term stability criterion. Those skilled in the art can make equivalent substitutions according to the irradiation change inertia, component thermal time constant, or algorithm robustness requirements.

[0048] Step 1.6: When the string is working normally and the measured irradiance meets the requirements for integer values ​​and stability, the corresponding acquisition time is determined as the valid test time.

[0049] For each acquisition moment in step 1.1, a triple verification status mark is applied: whether the string working status verification (from step 1.3) is passed, whether the irradiance integer value verification is passed (from step 1.4) and whether the irradiance stability verification is passed (from step 1.5). Only when a certain acquisition moment has all three "pass" marks is it officially confirmed as a "valid test moment", and all its corresponding original parameters (component voltage, current, battery temperature, irradiance) and timestamp are packaged and sent to the S120 power correction module. All moment data that fails any verification is automatically filtered by the system and does not participate in any subsequent calculations.

[0050] Furthermore, the above-mentioned real-time power correction of the module based on the measured irradiance and battery temperature at the effective test time, and the determination of the online power test value based on the corrected power value under the same integer irradiance reference value, may include the following steps 2.1 to 2.3 in specific implementation: Step 2.1: At each valid test moment, continuously collect the real-time voltage and real-time current of the photovoltaic module according to the preset collection frequency, and calculate multiple real-time power values.

[0051] After confirming a specific moment as a valid test moment, the real-time voltage and current of the component are synchronously collected at a preset sampling frequency at that moment and several consecutive adjacent moments thereafter. The voltage and current values ​​obtained each time are directly multiplied to obtain the real-time power value at the corresponding moment. Multiple real-time power values ​​can be identified by marking the same timestamp (i.e., the valid test moment to which they belong).

[0052] Step 2.2: For each real-time power value, based on the measured irradiance and battery temperature at the effective test time, perform irradiance correction and temperature correction on the real-time power of the module to obtain the initial corrected power value.

[0053] Irradiance correction refers to restoring the measured power to the equivalent power under the current integer irradiance reference value. Essentially, it eliminates power scaling errors caused by slight deviations in measured irradiance from the reference value, allowing data from different effective testing times to be compared horizontally under the same irradiance reference. Temperature correction further compensates for power deviations caused by battery temperature deviating from the standard reference temperature. Its compensation direction aligns with the inherent temperature characteristics of the module (i.e., power decreases with increasing temperature). The initial corrected power value is an intermediate result after completing the above two-stage physical mapping, having removed instantaneous disturbances from irradiance and temperature, but not yet achieving statistical integration under the same reference value.

[0054] For each real-time power value obtained in step 2.1, the real-time power is first normalized according to the ratio between the measured irradiance and the integer irradiance reference value to obtain the irradiance-corrected power value; then, according to the power temperature coefficient provided in the component factory test report and the measured battery temperature at that moment, the irradiance-corrected power value is subjected to temperature compensation processing to obtain the initial corrected power value corresponding to the real-time power value.

[0055] Step 2.3: Perform secondary correction on the initial corrected power value under the same integer irradiance reference value to determine the online power test value corresponding to the integer irradiance reference value.

[0056] The secondary correction referred to in this embodiment is not a new physical model, but rather a statistical processing operation performed on multiple initial correction power values ​​based on the completed irradiation and temperature corrections. Its purpose is to suppress random measurement noise and transient fluctuations, and improve the representativeness and reproducibility of single test results. Here, it specifically refers to arithmetic averaging.

[0057] The initial corrected power values ​​generated at all valid test times belonging to the same integer irradiance reference value are aggregated; the arithmetic mean of all aggregated initial corrected power values ​​is calculated, and the result is the online power test value corresponding to that integer irradiance reference value.

[0058] Furthermore, for each real-time power value, based on the measured irradiance and battery temperature at the effective test time, the real-time power of the module is corrected for irradiance and temperature to obtain an initial corrected power value. In specific implementation, this may include the following steps 2.2.1 and 2.2.2: Step 2.2.1: For each real-time power value, perform irradiance correction based on the measured irradiance and integer irradiance reference value at the corresponding time to obtain the irradiance-corrected power value.

[0059] Using an integer-digit irradiance reference value as the target irradiance reference value, the real-time power of the component under the measured irradiance conditions is linearly mapped to the power level corresponding to the integer-digit irradiance reference value through a proportional conversion relationship.

[0060] Specifically, under the premise of satisfying the data extraction rule verification (i.e., the string is in good working condition, the irradiance falls within the specified integer tolerance range, and the irradiance remains stable in the following 5 minutes), the component-level monitoring unit collects the real-time voltage V_i, current I_i, and synchronous measured irradiance G_meas_i for the i-th time, and calculates the real-time power P_real_i=V_i×I_i; then, it calls the integer irradiance reference value G_ref that has been confirmed as valid by rules 2 and 3, and performs irradiance correction on P_real_i according to the correction formula P_G_i=P_real_i×(G_ref / G_meas_i) to obtain the irradiance-corrected power value P_G_i.

[0061] Step 2.2.2: For each irradiation-corrected power value, perform temperature correction based on the measured battery temperature and module power temperature coefficient at the corresponding time to obtain the temperature-corrected power value.

[0062] Based on the completed irradiance correction, the inherent thermosensitive characteristic parameter of the module—the module power temperature coefficient α—is further introduced to dynamically correct P_G_i based on the measured cell temperature T_i, thereby eliminating the power drift caused by temperature changes and restoring the module's power output capability under standard cell temperature conditions. Specifically, the module-level monitoring unit synchronously collects the i-th measured cell temperature T_i and retrieves the module power temperature coefficient α recorded in the corresponding module's factory test report from the module's basic information database; according to the correction formula P_cal_i=P_G_i×[1+α×(25 [T_i)], perform temperature correction on the power value P_G_i after irradiation correction to obtain the power value P_cal_i after temperature correction.

[0063] The above methods significantly improve the physical integrity of online test power values ​​and the comparability across time periods and components, enabling component power data acquired in non-standard field environments to reflect their performance status relative to standard test conditions in a true, stable, and reproducible manner. This provides a core data foundation with high signal-to-noise ratio, high consistency, and high traceability for the subsequent establishment of dynamic control benchmarks and the implementation of multi-dimensional anomaly judgment.

[0064] Furthermore, when the integer irradiance reference value reaches the preset high irradiance threshold, the online power test value is corrected to the standard test conditions to obtain a comparable online power test value. In specific implementation, this may include the following steps 3.1 to 3.3: Step 3.1: Identify all integer irradiance reference values ​​that are greater than or equal to the preset high irradiance threshold.

[0065] In one example, 600W / m² can be used as a preset high irradiance threshold to filter all integer irradiance baseline values ​​retained after verification by data extraction rules within the current test date, retaining only integer irradiance baseline values ​​with values ​​≥600W / m².

[0066] Specifically, after verifying the data extraction rules, the system obtains several sets of valid integer-digit irradiance reference values ​​G_ref (such as 600, 700, 800 W / m², etc.). The system iterates through this set, comparing each value with the preset high irradiance threshold of 600 W / m², and includes integer-digit irradiance reference values ​​that satisfy G_ref ≥ 600 W / m² in the subsequent correction processing range. This step, by setting a reasonable lower limit, eliminates the adverse effects of excessively low irradiance (such as ≤ 500 W / m²), such as enhanced nonlinearity in component output, hysteresis in temperature response, and a significant decrease in signal-to-noise ratio, providing a high-quality input prerequisite for STC correction.

[0067] Step 3.2: For each integer irradiance reference value, perform standard test condition correction based on the corresponding online power test value, the integer irradiance reference value, and the average battery temperature at the corresponding time to obtain the comparable online power test value under the integer irradiance reference value.

[0068] Using a single integer irradiance reference value G_ref as the starting point for correction, and combining the calculated online power test value P_test at that integer digit, and the arithmetic mean T_avg of the battery temperature obtained from three acquisitions at that integer digit, a two-factor joint correction is performed to normalize P_test to the STC condition. Specifically, the system retrieves the P_test value associated with the integer irradiance reference value G_ref, and simultaneously reads the battery temperature T_i (i=1,2,3) from three acquisitions at that integer digit, calculating its average value T_avg=(T_1+T_2+T_3) / 3; subsequently, according to the correction formula P_comp=P_test×(1000 / G_ref)×[1+α×(T_avg)], a two-factor joint correction is performed to normalize P_test to the STC condition. 25)] / [1+α×(25 [T_avg)], perform STC correction on P_test, where α is the component power temperature coefficient (taken from the factory test report), and finally obtain the reference online power test value P_comp under the integer irradiance reference value G_ref.

[0069] Step 3.3: When there are multiple comparable online power test values, calculate their arithmetic mean to obtain the comparable online power test value of the component within the test date.

[0070] When multiple valid integer-bit irradiance reference values ​​(e.g., 600, 700, 800 W / m²) satisfying G_ref≥600W / m² are identified within the same test date, the arithmetic mean of the P_comp values ​​calculated for each integer bit is performed to generate a single, robust, and accidental-resistant final characterization quantity.

[0071] Specifically, the system counts the number of integer irradiance reference values ​​selected in step 3.1. If the number is ≥2, the P_comp values ​​corresponding to each G_ref are summed and divided by the total number to obtain the final comparable online power test value P_comp_final for the component within the test date. If only one G_ref meets the condition, its P_comp is directly used as P_comp_final.

[0072] Furthermore, the above-mentioned determination of the current control benchmark for the current testing cycle based on the comparable online power test values ​​of multiple photovoltaic modules in the same batch or array and their corresponding factory power correction values ​​may, in specific implementation, include the following steps 4.1 to 4.3: Step 4.1: For no less than a preset number of photovoltaic modules in the same batch or array, calculate the factory power correction value of the photovoltaic modules based on the time difference between the production date and the test date and the annual degradation rate of the modules.

[0073] Using the actual service time of the module as the axis, the maximum power point power value P_stc recorded in the factory test report is linearly attenuated according to the annual attenuation rate γ promised by the module manufacturer, so as to obtain the power level that the module should theoretically maintain at the current test time, i.e., the factory power correction value.

[0074] Specifically, the system retrieves the factory test report associated with the unique ID of each component to be analyzed from the component basic information database, and reads the P_stc value and production date recorded therein; at the same time, it obtains the test date of this test and calculates the time difference Δt (unit: year, accurate to two decimal places) between the two; then it retrieves the preset annual component degradation rate γ (default is 0.5%, but different values ​​can also be configured according to component model or supplier); according to the calculation formula P_stc_cal=P_stc×(1 γ×Δt), calculate the factory power correction value P_stc_cal for each component.

[0075] Step 4.2: Determine the relative deviation value based on the comparable online power test value and the factory power correction value.

[0076] Using the factory power correction value P_stc_cal as the denominator and the comparable online power test value P_comp_final obtained by the component within the same test date as the numerator, the percentage deviation relative to the theoretical power after attenuation is calculated to generate a relative deviation value characterizing the individual performance state of a single component. Specifically, for each component for which P_stc_cal has been calculated in step 4.1, the system retrieves its corresponding P_comp_final value and calculates it according to the formula ΔP_i=(P_comp_final_i The relative deviation value ΔP_i of the component in the current test cycle is calculated by P_stc_cal_i) / P_stc_cal_i×100%.

[0077] Step 4.3: Calculate the mean of all relative deviation values ​​to determine the current control baseline for the current test cycle.

[0078] The relative deviation values ​​ΔP_i corresponding to all components that meet the quantity threshold in the same batch or array are summarized and averaged to generate the current control baseline B_1, which represents the overall performance deviation level of the component group in the current test cycle.

[0079] Specifically, the system determines whether the number of components involved in the calculation reaches a preset number (e.g., "not less than 30 pieces"). If it does, all ΔP_i are summed and divided by the total number of components n. According to the formula B_1=(ΔP_1+ΔP_2+…+ΔP_n) / n, the current control benchmark B_1 for the current test cycle is obtained. This value is the benchmark threshold used in the "comparison with dynamic control benchmark" step in the subsequent anomaly determination.

[0080] Furthermore, the aforementioned dynamic iterative control benchmark update based on the current control benchmark and historical control benchmark, in order to monitor power anomalies according to the dynamic iterative control benchmark and the deviation value of the photovoltaic module in the previous test cycle, may include the following steps 5.1 to 5.5 in specific implementation: Step 5.1: The current control benchmark of the current test cycle and the historical control benchmark of at least one historical test cycle are weighted and averaged according to the preset weight coefficient to generate a dynamic iterative control benchmark.

[0081] Retrieve the current control baseline B_k (i.e., B_1 obtained in step 4.3) for the current test cycle from the data storage unit, as well as the historical control baseline (such as B_{k) for at least one historical test cycle. 1}、B_{k 2} etc.); then, according to the principle that the newer the test cycle, the greater the weight, the more weight is set (for example, the weight of the most recent test is 0.3, the previous one is 0.2, and the weight of earlier tests decreases in sequence), a weighted average operation is performed on B_k and each historical control benchmark, and the dynamic iterative control benchmark B_iter is generated according to the formula B_iter=(w_1B_1+w_2B_2+…+w_kB_k) / (w_1+w_2+…+w_k).

[0082] Step 5.2: For each photovoltaic module in the current test cycle, compare the relative deviation value with the dynamic iterative control benchmark. When the deviation exceeds the first preset deviation threshold, mark the photovoltaic module as a first-type abnormal state to be confirmed.

[0083] For each component, retrieve its relative deviation value ΔP_current = (P_comp_final) calculated within the current test cycle. P_stc_cal) / P_stc_cal×100%, and compare the value with B_iter; when ΔP_current≤B_iter is satisfied... 5% (i.e., the deviation exceeds the first preset deviation threshold) When the percentage is 5%, the component is marked as "Type 1 Abnormal Status to be Confirmed".

[0084] Step 5.3: Obtain the relative deviation value of the photovoltaic module in the previous test cycle, and calculate the difference between the current relative deviation value and the relative deviation value in the previous test cycle.

[0085] Based on the component's unique ID and the relative deviation value ΔP_prev corresponding to the component in the previous test cycle, calculate the difference between the current relative deviation value ΔP_current and ΔP_prev, ΔP = ΔP_current. ΔP_prev.

[0086] Step 5.4: When the decrease in the difference of the relative deviation value exceeds the second preset deviation threshold, the component is marked as a second type of abnormal state pending confirmation.

[0087] In one example, determine whether ΔΔP satisfies ΔΔP≤ 2% (i.e., the degree of decline exceeds the second preset deviation threshold) If true, mark the component as "Type II Abnormal Status Pending Confirmation" in the database (2%).

[0088] Step 5.5: When a component is in a first-type abnormality pending confirmation state or a second-type abnormality pending confirmation state, confirm that the component has experienced a power abnormality.

[0089] If any component is marked as "Type I anomaly pending confirmation" or "Type II anomaly pending confirmation," the system automatically upgrades its status to "power anomaly," which can then be highlighted in the visualization unit, an anomaly list generated, and a warning message pushed. Simultaneously, the system records the corresponding judgment criteria (such as "deviation from dynamic baseline"). "5.8%" or "accelerated deterioration compared to the previous cycle" "2.3%", supports manual review.

[0090] Furthermore, once a power anomaly is determined, the method further includes the following steps 6.1 to 6.3: Step 6.1: Obtain the sequence of comparable online power test values ​​for the abnormal photovoltaic module in the most recent consecutive test cycles.

[0091] Once a component is confirmed to have a power anomaly, its historical records are retrieved, prioritizing the acquisition of the three most recent (current cycle, previous cycle, and cycle two before that) P_comp_final values, forming an ordered sequence of length three: {P_comp_final_k, P_comp_final_{k}}. 1},P_comp_final_{k 2}}; If there are fewer than three historical data points, the actual number of available cycles shall prevail, but it shall include at least the current cycle and the previous cycle (i.e., no less than two points) to meet the premise of basic trend judgment.

[0092] Step 6.2: Perform monotonicity analysis and slope abrupt change detection on the reference online power test value sequence to identify the power decay acceleration segment.

[0093] First, calculate the power change ΔP_i between adjacent test cycles = P_comp_final_i P_comp_final_{i 1}(i=k,k 1) Calculate the corresponding rate of change r_i = ΔP_i / Δt_i (where Δt_i is the time difference between the two weeks, in years, converted from the difference in test dates); then compare r_k with r_{k The numerical relationship of r_k: <r_{k 1} (i.e., the rate of descent increases), and this relationship holds true in two consecutive adjacent intervals (e.g., r_k). <r_{k 1} and r_{k 1} <r_{k 2}), then it is determined that there is a period of accelerated power decay, the starting point of which is the earliest period in which the rate of increase occurs (e.g., k). (2 cycles), the termination point is the current cycle (k cycles).

[0094] Step 6.3: When there is a power attenuation acceleration segment and the duration of the power attenuation acceleration segment exceeds the preset trend confirmation time, generate a component performance degradation warning and associate it with the operation and maintenance work order system.

[0095] The system calculates the time span covered by the accelerated power degradation phase, which is the time difference between the test date of the start period and the test date of the end period of the phase. When the time difference is greater than or equal to the preset trend confirmation duration (which is consistent with the test period, i.e., the default is half a year), the system generates a "component performance degradation warning" event. The system then automatically pushes the event, along with key information such as component ID, start and end period of the accelerated phase, power degradation value, and change in degradation rate, to the power plant's existing operation and maintenance work order system through a standard API interface or message queue, triggering the generation of a preventive maintenance work order with priority marking.

[0096] To implement the above method, an online power testing system for photovoltaic modules is also provided, see [link to relevant documentation]. Figure 2 As shown, the system consists of the following components: 1. Component-level monitoring unit: Deployed on each component to collect real-time voltage (V), current (I), and battery temperature (T). The data collection frequency is configurable (default is once per minute). The form of this unit is not limited, as long as it meets the data collection accuracy requirements (voltage accuracy ±0.5%, current accuracy ±1%, temperature accuracy ±0.5℃).

[0097] 2. Irradiance monitoring unit: Irradiance meters are evenly distributed in the photovoltaic array area (at least one unit is configured for every 100 modules) to collect real-time irradiance (G). The collection frequency is consistent with that of the module-level monitoring unit, and the irradiance measurement accuracy is ±2%.

[0098] 3. Inverter Data Interface: Access the DC side monitoring data of the inverter through the communication interface to obtain string-level voltage and current data for verifying the string's operating status.

[0099] 4. Data transmission module Communication network: Wired (Ethernet, RS485) or wireless (LoRa, 4G / 5G) communication methods are used to transmit collected data to the data processing platform; encrypted data transmission and breakpoint resume are supported to ensure data integrity.

[0100] Communication protocols: Compatible with industrial standard communication protocols such as Modbus and IEC61850, it can be seamlessly integrated with existing power plant monitoring systems without large-scale modifications.

[0101] 5. Data Processing and Analysis Platform Data storage unit: stores basic information of the storage components (ID, factory test report, production date), real-time acquired data (voltage, current, temperature, irradiance), and test result data (corrected power, deviation value, control reference).

[0102] Algorithm processing unit: Integrates core algorithms such as data extraction rule verification, multi-factor power correction, dynamic benchmark iteration, and multi-dimensional anomaly detection, and is the core functional module of the system.

[0103] 6. Visualization Unit: Displays information such as real-time component power, historical test results, list of abnormal components, and control baseline change trends, for data export and report generation.

[0104] II. Online Power Testing Methods for Photovoltaic Modules This method achieves accurate testing and anomaly identification of component power through standardized testing procedures, scientific data screening and correction, and dynamic benchmark iteration. (See [link to relevant documentation]). Figure 3 As shown, the specific steps are as follows: 1. Basic Information Preprocessing Component Information Entry: Enter basic information such as the component's unique ID, factory test report (including maximum power point power value P_stc), and production date into the data processing platform to establish a component information database; supports batch import and manual editing to ensure information integrity.

[0105] Test cycle setting: Set the test cycle according to the power plant operation and maintenance needs (once every six months by default). The platform will automatically generate a test plan and specify the test dates for each batch of components.

[0106] 2. Verification of test data extraction rules See [link / reference] within the set test dates. Figure 4 As shown, the following rules are used to filter valid test data to ensure stable and reliable test conditions: Rule 1: Verification of String Working Status Data acquisition: At least 5 random times are selected within 30 minutes, and the real-time irradiance G_i, module cell temperature T_i, and the corresponding string voltage U_str_i and string current I_str_i on the DC side of the inverter are collected at each time.

[0107] Theoretical power calculation: Based on the irradiance G_i, the battery temperature T_i, and the temperature coefficient and irradiance response coefficient in the module's factory test report, the theoretical string power P_theo_i at each moment is calculated as follows: P_theo_i = N × [P_stc × (G_i / 1000) × (1 + α × (T_i - 25))], where N is the number of modules in the string, and α is the module power temperature coefficient (taken from the factory report).

[0108] Actual power calculation: Calculate the actual string power P_act_i = U_str_i × I_str_i at each time moment.

[0109] Status determination: Calculate the theoretical average string power P_theo_avg and the actual average string power P_act_avg at 5 time points. When the deviation rate |(P_act_avg-P_theo_avg) / P_theo_avg|≤10%, the string is considered to be in good working condition and the test data for that test date is valid; otherwise, the test is paused and restarted after the string returns to normal.

[0110] Rule 2: Verification of integer digits of irradiation dose Integer setting: Irradiation dose is set to an integer digit every 100W / m² (e.g., 100, 200, ..., 1000W / m²).

[0111] Value condition: When the measured irradiance G satisfies "integer G_ref - 5% × G_ref ≤ G ≤ integer G_ref + 5% × G_ref", it is determined that the integer value requirement is met, and the current irradiance G and the corresponding component data are recorded.

[0112] Rule 3: Verification of Irradiation Stability Continuous data monitoring: After the integer value of Rule 2 is satisfied, continue to monitor several irradiance values ​​within 5 minutes (the monitoring frequency is consistent with the data acquisition frequency, i.e., once per minute, for a total of 5 data points G_1~G_5).

[0113] Stability determination: Calculate the average value of the 5 irradiance values ​​G_avg=(G_1+G_2+G_3+G_4+G_5) / 5. When the deviation rate|(G_avg-G_ref) / G_ref|≤10%, the irradiance is determined to be stable; otherwise, it is determined to be unstable, the test data corresponding to the integer part is invalid, and the data set is discarded.

[0114] 3. Component power acquisition and multi-factor correction Real-time power acquisition: Under the test conditions that simultaneously meet rules 1 to 3, the component-level monitoring unit acquires the real-time voltage V and current I of the component at a set frequency (default once per minute) and calculates the real-time power P_real=V×I; after acquiring the data 3 times consecutively, 3 real-time power values ​​P_real_1, P_real_2, and P_real_3 are obtained.

[0115] Power multi-factor correction: Combining the measured irradiance G_ref with the battery temperature T, the three real-time power values ​​are corrected to obtain the online module power test value at the integer digits of the irradiance. Step 1: Irradiance correction. Correct the real-time power to the standard power under the current integer irradiance. The correction formula is: P_G_i=P_real_i×(G_ref / G_meas_i), where G_meas_i is the measured irradiance at the i-th acquisition.

[0116] Step 2: Temperature Correction. The power is corrected a second time based on the battery temperature. The correction formula is: P_cal_i=P_G_i×[1+α×(25-T_i)], where T_i is the module battery temperature at the i-th data acquisition, and α is the module power temperature coefficient (taken from the factory report).

[0117] Step 3: Average value calculation. Take the average of the power values ​​after three corrections to obtain the online component power test value P_test=(P_cal_1+P_cal_2+P_cal_3) / 3 at the integer digits of the irradiance.

[0118] 4. Calculation of reference power under standard test conditions High Irradiance Screening: When the integer part of the irradiance G_ref ≥ 600 W / m², the online module power test value P_test at this integer part is corrected to the standard test conditions (STC: irradiance 1000 W / m², cell temperature 25℃) to obtain the reference online power test value P_comp. P_comp=P_test×(1000 / G_ref)×[1+α×(T_avg-25)] / [1+α×(25-T_avg)] Where T_avg is the average battery temperature from the three data collections.

[0119] Multi-integer data fusion: If there are multiple irradiance integers ≥600W / m² (such as 600, 700, 800W / m²) within the same test date, calculate the reference online power test value corresponding to each integer, and take the average value as the final reference online power test value P_comp_final of the component within that test date.

[0120] 5. Establishment of dynamic iterative control benchmark, see [link / reference] Figure 5 As shown: Single test cycle benchmark calculation: Within the same test date, select several components (no less than 30) from the same array or batch, and calculate the deviation ΔP_i between the reference online power test value P_comp_final and the corresponding component's factory maximum power point correction value P_stc_cal. Factory power correction value calculation: Based on the time difference Δt (years) between the module production date and the test date, combined with the annual degradation rate γ promised by the module manufacturer (default ≤0.5%), calculate the correction value after normal degradation of the module: P_stc_cal=P_stc×(1-γ×Δt).

[0121] Deviation calculation: ΔP_i=(P_comp_final_i-P_stc_cal_i) / P_stc_cal_i×100%.

[0122] Single-cycle control baseline determination: Calculate the average value of the component deviation ΔP_i for this batch, and use it as the control baseline B_1=(ΔP_1+ΔP_2+...+ΔP_n) / n for this test cycle, where n is the number of components.

[0123] Multi-period benchmark iteration: As the test period increases (e.g., the 2nd, 3rd test), the control benchmark B_k of the new test period is weighted and averaged with all historical control benchmarks (B_1, B_2, ..., B_{k-1}), and the control benchmark is iteratively updated. B_iter=(w_1×B_1+w_2×B_2+...+w_k×B_k) / (w_1+w_2+...+w_k) Where w_i is the weight coefficient, which is set according to the principle that the newer the test period, the greater the weight (e.g., the weight of the most recent test is 0.3, the previous one is 0.2, and the weight of earlier tests decreases accordingly) to ensure that the control benchmark dynamically adapts to the overall decay law of the component.

[0124] 6. Multi-dimensional component power anomaly detection By combining two judgment methods, abnormal component power can be accurately identified, avoiding false positives and false negatives. Judgment Method 1: Comparison with Dynamic Control Benchmark Calculate the deviation value of a single component in the current test cycle ΔP_current=(P_comp_final-P_stc_cal) / P_stc_cal×100%. When ΔP_current≤B_iter-5%, the component power is determined to be abnormal (i.e. the component power is more than 5% lower than the overall average attenuation level).

[0125] Judgment Method Two: Comparison with Historical Test Results Retrieve the deviation value ΔP_prev from the previous test cycle of the component. When ΔP_current-ΔP_prev≤-2%, the component is determined to have abnormal power (i.e., the component's attenuation rate is more than 2% faster than the previous cycle, exceeding the normal attenuation range).

[0126] 7. Anomaly Warning and Report Generation When a component meets either of the two judgment methods mentioned above, the system marks the component as having abnormal power and records the basis for the abnormal judgment (such as deviation value, historical comparison data); manual review is supported to ensure the accuracy of the judgment.

[0127] In summary, the workflow of the system of this invention includes: First, the test is automatically triggered or manually initiated by the user at a preset cycle; then, the basic information of the tested component is retrieved from the database, including the component ID, factory test report, and production date; next, the voltage, current, temperature, irradiance, and inverter string data of the component are collected synchronously, and the string operating status, irradiance integer digits, and stability are verified according to set rules to filter valid data; then, the valid data is corrected for irradiance and temperature, and the comparable online power test value under standard test conditions is calculated; after that, the current cycle control benchmark is calculated based on the test results of the same batch of components, and iteratively updated with historical benchmarks; abnormal components are identified through a multi-dimensional judgment mechanism, an abnormality list is generated, and an early warning is issued; finally, the system automatically generates a power test report containing the test results of a single component, the control benchmark, and details of abnormal components, and supports export and printing.

[0128] This invention comprehensively improves the accuracy, reliability, and engineering applicability of online power testing for photovoltaic modules. It ensures input quality through scientific data screening rules, enhances power measurement accuracy by combining multi-factor correction for irradiance and temperature, establishes a dynamic control benchmark that evolves over time to accurately reflect the degradation patterns of the entire module population, and employs a two-dimensional anomaly detection mechanism combining lateral deviation and vertical trend analysis to effectively identify two typical problems: severe anomalies and slow, accelerated degradation. The system is algorithm-driven, requires no additional dedicated hardware, and can be seamlessly integrated into existing monitoring platforms, significantly reducing deployment and maintenance costs. Ultimately, it achieves routine, standardized, and highly comparable online performance evaluation of all modules, providing a reliable data foundation and decision-making basis for refined power plant operation and maintenance, health status prediction, and asset lifecycle management.

[0129] This application also provides an electronic device, such as... Figure 5 The diagram shows the structure of the electronic device 100, which includes a processor 51 and a memory 50. The memory 50 stores computer-executable instructions that can be executed by the processor 51. The processor 51 executes the computer-executable instructions to implement any of the above-mentioned photovoltaic module online power monitoring methods.

[0130] exist Figure 5 In the illustrated embodiment, the electronic device further includes a bus 52 and a communication interface 53, wherein the processor 51, the communication interface 53, and the memory 50 are connected via the bus 52.

[0131] The memory 50 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 53 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 52 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 52 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0132] The processor 51 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 51 or by instructions in software form. The processor 51 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory. The processor 51 reads the information in the memory and, in conjunction with its hardware, completes the steps of the photovoltaic module online power monitoring method of the aforementioned embodiment.

[0133] This application also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the above-described online power monitoring method for photovoltaic modules. For specific implementation details, please refer to the foregoing method embodiments, which will not be repeated here.

[0134] The computer program products of the photovoltaic module online power monitoring method, electronic device and storage medium provided in the embodiments of this application include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0135] Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application.

[0136] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0137] In the description of this application, it should be noted that the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for online power monitoring of photovoltaic modules, characterized in that, include: Construct verification conditions and determine the valid test time based on the pre-established verification conditions; wherein, the verification conditions include string working state verification, irradiance integer bit value verification, and irradiance stability verification; The module's power is calibrated in real time based on the measured irradiance and battery temperature at the effective test time, and the online power test value is determined based on the calibrated power value under the same integer irradiance reference value. When the integer irradiance reference value reaches the preset high irradiance threshold, the online power test value is corrected to the standard test conditions to obtain a comparable online power test value. Based on the comparable online power test values ​​and their corresponding factory power correction values ​​of multiple photovoltaic modules in the same batch or array, the current control benchmark for the current test cycle is determined. The dynamic iterative control benchmark is updated based on the current control benchmark and the historical control benchmark, so as to monitor power anomalies according to the dynamic iterative control benchmark and the deviation value of the photovoltaic module in the previous test cycle.

2. The online power monitoring method for photovoltaic modules according to claim 1, characterized in that, Construct verification conditions and determine valid test times based on pre-established verification conditions, including: Collect string voltage, string current, measured irradiance, and battery temperature at multiple time intervals within a continuous period; Calculate the theoretical string power at each moment based on the string voltage and the string current; Based on the theoretical string power and actual string power at each moment, the average deviation rate is calculated, and when the average deviation rate is within the preset tolerance range, the string working status is confirmed to be normal. The irradiance is divided into multiple integer irradiance reference values ​​with a preset integer interval. For each integer irradiance reference value, it is determined whether the measured irradiance falls within a value range centered on the integer irradiance reference value and with a preset percentage width, so as to verify the integer irradiance value. For the measured irradiance falling within the range of values, multiple irradiance samples are collected at the same time interval within a continuous time period after the collection time corresponding to the measured irradiance, the average value is calculated, and it is determined whether the deviation rate between the average value and the corresponding integer irradiance reference value is within the preset tolerance range, so as to verify the stability of the irradiance. When the array is working normally and the measured irradiance meets the requirements for integer values ​​and stability, the corresponding acquisition time is determined as the valid test time.

3. The online power monitoring method for photovoltaic modules according to claim 1, characterized in that, The module's real-time power is calibrated based on the measured irradiance and battery temperature at the effective test time, and the online power test value is determined based on the calibrated power value under the same integer irradiance reference value, including: At each valid test moment, the real-time voltage and real-time current of the photovoltaic module are continuously collected according to the preset collection frequency, and multiple real-time power values ​​are calculated. For each real-time power value, based on the measured irradiance and battery temperature at the effective test time, the real-time power of the module is corrected for irradiance and temperature to obtain an initial corrected power value. The initial corrected power value under the same integer irradiance reference value is corrected a second time to determine the online power test value corresponding to the integer irradiance reference value.

4. The online power monitoring method for photovoltaic modules according to claim 1, characterized in that, For each real-time power value, based on the measured irradiance and battery temperature at the effective test time, the real-time power of the module is corrected for irradiance and temperature to obtain an initial corrected power value, including: For each real-time power value, irradiance correction is performed based on the measured irradiance and integer irradiance reference value at the corresponding time to obtain the irradiance-corrected power value. For each irradiation-corrected power value, temperature correction is performed based on the measured battery temperature and module power temperature coefficient at the corresponding time to obtain the temperature-corrected power value.

5. The online power monitoring method for photovoltaic modules according to claim 1, characterized in that, When the integer-digit irradiance reference value reaches a preset high irradiance threshold, the online power test value is corrected to standard test conditions to obtain a comparable online power test value, including: Identify all integer-bit irradiance reference values ​​that are greater than or equal to the preset high irradiance threshold; For each integer irradiance reference value, standard test conditions are corrected based on the corresponding online power test value, the integer irradiance reference value, and the average battery temperature at the corresponding time to obtain a comparable online power test value under the integer irradiance reference value. When multiple comparable online power test values ​​exist, their arithmetic mean is calculated to obtain the comparable online power test value of the component within the test date.

6. The online power monitoring method for photovoltaic modules according to claim 1, characterized in that, Based on the comparable online power test values ​​and their corresponding factory power correction values ​​of multiple photovoltaic modules in the same batch or array, the current control benchmark for the current testing cycle is determined, including: For no less than a preset number of photovoltaic modules in the same batch or array, the factory power correction value of the photovoltaic modules is calculated based on the time difference between the production date and the test date and the annual degradation rate of the modules. The relative deviation value is determined based on the comparable online power test value and the factory power correction value; The mean of all relative deviations is calculated to determine the current control baseline for the current test cycle.

7. The online power monitoring method for photovoltaic modules according to claim 6, characterized in that, The dynamic iterative control benchmark is updated based on the current control benchmark and the historical control benchmark to monitor power anomalies according to the dynamic iterative control benchmark and the deviation value of the photovoltaic module in the previous test cycle, including: The current control benchmark for the current test period is weighted and averaged with the historical control benchmark for at least one historical test period according to a preset weighting coefficient to generate a dynamic iterative control benchmark. For each photovoltaic module in the current testing cycle, the relative deviation value is compared with the dynamic iterative control benchmark. When the deviation exceeds the first preset deviation threshold, the photovoltaic module is marked as a first type of abnormal pending confirmation state. Obtain the relative deviation value of the photovoltaic module in the previous test cycle, and calculate the difference between the current relative deviation value and the relative deviation value in the previous test cycle; When the decrease in the difference of the relative deviation values ​​exceeds the second preset deviation threshold, the component is marked as a second type of abnormal pending confirmation state. When a component is in either the first type of abnormality pending confirmation state or the second type of abnormality pending confirmation state, it is confirmed that the component has experienced a power abnormality.

8. The online power monitoring method for photovoltaic modules according to claim 1, characterized in that, The method further includes: Once a power anomaly is confirmed, obtain a sequence of comparable online power test values ​​for the abnormal photovoltaic module in the most recent consecutive test cycles. Monotonicity analysis and slope abrupt change detection are performed on the reference online power test value sequence to identify the power decay acceleration segment; When there is a power attenuation acceleration phase and the duration of the power attenuation acceleration phase exceeds the preset trend confirmation time, a component performance degradation warning is generated and associated with the operation and maintenance work order system.

9. An electronic device, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the online power monitoring method for photovoltaic modules according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the online power monitoring method for photovoltaic modules as described in any one of claims 1 to 7.

Images