Fault Detection Method and System for Photovoltaic Water Pump Inverter

Abstract

The present application relates to the field of photovoltaic water pump inverter control, and discloses a fault detection method and system for a photovoltaic water pump inverter. The method includes: obtaining the timing broadcast voltage value and the timing broadcast current value of the master inverter in the master-slave inverter control group, and obtaining the timing output voltage value and the timing output current value of each slave inverter in the multiple slave inverters of the inverter control group; determining the timing difference corresponding to the timing broadcast voltage value and the timing output voltage value of each slave inverter as the timing voltage deviation value; and determining the timing difference between the timing broadcast current value and the timing output current value of each slave inverter as the timing current deviation value; through the inverter state recognition model, according to the timing voltage deviation value and the timing current deviation value of the multiple slave inverters in the master-slave inverter control group, determining the fault probability values corresponding to the multiple slave inverters respectively. The present application can quickly locate the photovoltaic inverter with poor working state in the master-slave control scheme.

Description

Technical Field

[0001] This application relates to the technical field of photovoltaic water pump inverter control, and more specifically, to a fault detection method and system for a photovoltaic water pump inverter. Background Art

[0002] A photovoltaic water pump inverter is a device specifically used in a photovoltaic power generation system. Its main function is to convert the direct current generated by photovoltaic panels into alternating current to drive a water pump to work. A photovoltaic water pump inverter usually includes components such as an inverter bridge, control logic, and a filter circuit. The photovoltaic water pump inverter can adjust the output frequency in real time according to the change of sunlight intensity to achieve maximum power point tracking (MPPT), thereby maximizing the utilization of solar energy. In addition, the photovoltaic water pump inverter also has a variety of operating modes and MPPT control methods to choose from, can freely set the water pump speed regulation range according to the actual system situation, and has protection functions such as lightning protection, overvoltage protection, undervoltage protection, overcurrent protection, and overload protection.

[0003] As the capacity of the electrical energy demand of AC loads is getting larger and larger, the power of photovoltaic inverters is also becoming larger and larger. Increasing the power of photovoltaic inverters will, on the one hand, cause the appearance volume to become larger and larger, making it inconvenient for the movement and transportation of the inverter; on the other hand, it will increase the difficulty in selecting internal electronic components. To meet this demand, the function of multiple photovoltaic inverters in parallel at the output end to jointly bear the AC load exceeding their own power has become an essential function of photovoltaic inverters. There are mainly 4 existing inverter parallel control methods: the master-slave control scheme, the centralized control scheme, the wireless parallel droop control scheme, and the instantaneous average current control. Currently, the master-slave control scheme can quickly identify the photovoltaic inverter as the host and achieve the rapid positioning of the master and slave machines. However, in the current master-slave control scheme, it is not possible to quickly locate the photovoltaic inverter with a poor working state, resulting in an impact on the reliability of the master-slave control method of the photovoltaic inverter. Summary of the Invention

[0004] The purpose of this application is to provide a fault detection method and system for a photovoltaic water pump inverter, which solves the technical problem that in the current master-slave control scheme, it is not possible to quickly locate the photovoltaic inverter with a poor working state, and achieves the technical effect of quickly locating the photovoltaic inverter with a poor working state in the master-slave control scheme.

[0005] A fault detection method for a photovoltaic water pump inverter provided by an embodiment of the present application, the method includes: obtaining the timing broadcast voltage value and the timing broadcast current value of the master inverter in the master-slave inverter control group, and obtaining the timing output voltage value and the timing output current value of each slave inverter in the multiple slave inverters of the inverter control group; determining the timing difference corresponding to the timing broadcast voltage value and the timing output voltage value of each slave inverter as the timing voltage deviation value; and determining the timing difference between the timing broadcast current value and the timing output current value of each slave inverter as the timing current deviation value; through the inverter state recognition model, according to the timing voltage deviation values and the timing current deviation values of the multiple slave inverters in the master-slave inverter control group, determining the corresponding fault probability values of the multiple slave inverters in the master-slave inverter control group respectively; when the fault probability value of the first slave inverter is greater than the preset fault probability value, marking the first slave inverter as a faulty slave inverter.

[0006] In a possible implementation manner, the method further includes: determining the timing voltage fluctuation value corresponding to the timing voltage deviation value of each slave inverter, and determining the timing current fluctuation value corresponding to the timing current deviation value of each slave inverter; wherein, the timing voltage fluctuation value includes the fluctuation moment and the fluctuation amplitude of the timing voltage deviation value, and the timing current fluctuation value includes the fluctuation moment and the fluctuation amplitude of the timing current deviation value; clustering the multiple slave inverters in the master-slave inverter control group according to the similar characteristics of the timing voltage fluctuation values and the timing current fluctuation values of each slave inverter to obtain multiple slave inverter subsets, and determining the mean value of the timing voltage fluctuation values and the mean value of the timing current fluctuation values of the multiple slave inverters in each slave inverter subset; through the inverter state recognition model, according to the mean value of the timing voltage fluctuation values and the mean value of the timing current fluctuation values of the multiple slave inverter subsets, determining the corresponding fault probability values of the multiple slave inverter subsets respectively, when the fault probability value of the first slave inverter subset is greater than the preset fault probability value, marking the first slave inverter subset as a faulty slave inverter subset, and determining the faulty slave inverters in the faulty slave inverter subset.

[0007] In another possible implementation, clustering multiple slave inverters in the master-slave inverter control group according to the similar characteristics of the timing voltage fluctuation values and timing current fluctuation values of each slave inverter includes: obtaining the timing irradiance change value and the timing temperature change value of the photovoltaic solar panel corresponding to each slave inverter, and determining the first coincidence time period of the timing voltage fluctuation value, the timing current fluctuation value and the timing irradiance change value of each slave inverter, and determining the second coincidence time period of the timing voltage fluctuation value, the timing current fluctuation value and the timing temperature change value; in the timing voltage fluctuation values and the timing current fluctuation values of each slave inverter, deleting the timing voltage fluctuation values and the timing current fluctuation values corresponding to the first coincidence time period and the second coincidence time period; clustering multiple slave inverters in the master-slave inverter control group according to the similar characteristics of the timing voltage fluctuation values and the timing current fluctuation values of each slave inverter.

[0008] In another possible implementation, determining the faulty slave inverters in the subset of faulty slave inverters includes: determining the voltage fluctuation variance value of the timing voltage fluctuation value and the current fluctuation variance value of the timing current fluctuation value of each slave inverter in the subset of faulty slave inverters; and determining the mean value of the voltage fluctuation variance values and the mean value of the current fluctuation variance values of all slave inverters in the subset of faulty slave inverters; when the first voltage fluctuation variance value of the first slave inverter is greater than or equal to the mean value of the voltage fluctuation variance values, or the first current fluctuation variance value of the first slave inverter is greater than or equal to the mean value of the current fluctuation variance values, determining the first slave inverter as a faulty inverter.

[0009] In another possible implementation, the method further includes: determining the target subset of slave inverters with the smallest failure probability value among multiple subsets of slave inverters, and determining the voltage fluctuation variance value of the timing voltage fluctuation value and the current fluctuation variance value of the timing current fluctuation value of each slave inverter in the target subset of slave inverters, and determining the sum of the voltage fluctuation variance value and the current fluctuation variance value of each slave inverter in the target subset of slave inverters as the comprehensive evaluation value of the slave inverter; determining the target slave inverter with the smallest comprehensive evaluation value of the slave inverter in the target subset of slave inverters, and using the target slave inverter as a standby master inverter; obtaining the master inverter failure probability value of the master inverter, and when the master inverter failure probability value is greater than or equal to the preset master inverter failure probability value, switching the standby master inverter to be the master inverter.

[0010] In another possible implementation, the method further includes: determining a preset number of target slave inverter subsets arranged in ascending order of failure probability values among a plurality of slave inverter subsets, determining the communication failure probability between each slave inverter and the master inverter in each target slave inverter subset, and determining the average communication failure probability of all slave inverters in each target slave inverter subset; determining the target slave inverter subset with the minimum average communication failure probability among the preset number of target slave inverter subsets, determining the target slave inverter with the minimum comprehensive evaluation value of the slave inverters in the target slave inverter subset, and using the target slave inverter as the standby master inverter.

[0011] In another possible implementation, obtaining the master inverter failure probability value of the master inverter includes: obtaining the failure probability value of the master inverter and obtaining the communication failure probability between the master inverter and a plurality of slave inverters; determining the sum of the product of the first weight factor and the failure probability value and the product of the second weight factor and the communication failure probability as the master inverter failure probability value; wherein, the first weight factor is the difference between 1 and the second weight factor, and the second weight factor is determined according to the output load fluctuation condition of the master-slave inverter control group.

[0012] In another possible implementation, determining the second weight factor according to the output load fluctuation condition of the master-slave inverter control group includes: obtaining the fluctuation variance value of the output load of the master-slave inverter control group within a preset time period, and obtaining the maximum preset fluctuation variance of the output load of the master-slave inverter control group; determining the ratio of the fluctuation variance value to the maximum preset fluctuation variance as the second weight factor.

[0013] In another possible implementation, the method further includes: obtaining the number of inverters in the master-slave inverter control group and obtaining the inverter number threshold of the master-slave inverter control group; determining the ratio of the number of inverters to the inverter number threshold as the inverter number adjustment factor; multiplying the second weight factor by the inverter number adjustment factor to adjust the second weight factor.

[0014] An embodiment of the present application also provides a fault detection system for a photovoltaic water pump inverter, including a unit for executing the method described in any one of the above.

[0015] The beneficial effects of the embodiment of the present application compared with the prior art are:

[0016] An embodiment of the present application provides a fault detection method for a photovoltaic water pump inverter. The method includes: obtaining the timing broadcast voltage value and the timing broadcast current value of the master inverter in the master-slave inverter control group, and obtaining the timing output voltage value and the timing output current value of each slave inverter in the multiple slave inverters of the inverter control group; determining the timing difference corresponding to the timing broadcast voltage value and the timing output voltage value of each slave inverter as the timing voltage deviation value; and determining the timing difference between the timing broadcast current value and the timing output current value of each slave inverter as the timing current deviation value; through the inverter state recognition model, according to the timing voltage deviation value and the timing current deviation value of the multiple slave inverters in the master-slave inverter control group, determining the corresponding fault probability values of the multiple slave inverters in the master-slave inverter control group respectively; when the fault probability value of the first slave inverter is greater than the preset fault probability value, marking the first slave inverter as a faulty slave inverter. The method in the embodiment of the present application can determine the corresponding fault probability values of the multiple slave inverters in the master-slave inverter control group according to the timing voltage deviation value and the timing current deviation value of the multiple slave inverters in the master-slave inverter control group, realize the identification of the faulty inverters among the multiple slave inverters, improve the identification accuracy of the faulty slave inverters in the master-slave control scheme, and can improve the stability of the inverter system in the master-slave control scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is a schematic flowchart of the first fault detection method for a photovoltaic water pump inverter provided by an embodiment of the present application;

[0019] Figure 2 It is a schematic flowchart of the second fault detection method for a photovoltaic water pump inverter provided by an embodiment of the present application;

[0020] Figure 3 It is a schematic flowchart of the third fault detection method for a photovoltaic water pump inverter provided by an embodiment of the present application;

[0021] Figure 4 It is a schematic flowchart of the fourth fault detection method for a photovoltaic water pump inverter provided by an embodiment of the present application;

[0022] Figure 5 It is a schematic flowchart of the fifth fault detection method for a photovoltaic water pump inverter provided by an embodiment of the present application;

[0023] Figure 6 This is a schematic diagram of the logical structure of a fault detection system for a photovoltaic water pump inverter provided by an embodiment of the present application. Detailed implementation manners

[0024] It should be understood that when used in the specification and appended claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0025] It should also be understood that the term "and / or" used in the specification and appended claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0026] As used in the specification and appended claims of the present application, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" according to the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" according to the context.

[0027] In addition, in the description of the specification and appended claims of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0028] The description of referring to "one embodiment" or "some embodiments" in the specification of the present application means that specific features, structures, or characteristics described in connection with the embodiment are included in one or more embodiments of the present application. Thus, the statements "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0029] The master-slave control scheme can quickly identify the photovoltaic inverter acting as the master, achieving the quick positioning of the master and slave machines. However, in the current master-slave control scheme, the photovoltaic inverter with a poor working state cannot be quickly positioned, resulting in the reliability of the master-slave control method of the photovoltaic inverter being affected.

[0030] For the above reasons, the embodiments of the present application provide a fault detection method for a photovoltaic water pump inverter. The method includes: obtaining the timing broadcast voltage value and the timing broadcast current value of the master inverter in the master-slave inverter control group, and obtaining the timing output voltage value and the timing output current value of each slave inverter in the multiple slave inverters of the inverter control group; determining the timing difference corresponding to the timing broadcast voltage value and the timing output voltage value of each slave inverter as the timing voltage deviation value; and determining the timing difference between the timing broadcast current value and the timing output current value of each slave inverter as the timing current deviation value; through the inverter state recognition model, according to the timing voltage deviation value and the timing current deviation value of the multiple slave inverters in the master-slave inverter control group, determining the fault probability values corresponding to the multiple slave inverters in the master-slave inverter control group respectively; when the fault probability value of the first slave inverter is greater than the preset fault probability value, marking the first slave inverter as a faulty slave inverter. The method in the embodiments of the present application can determine the fault probability values corresponding to the multiple slave inverters in the master-slave inverter control group according to the timing voltage deviation value and the timing current deviation value of the multiple slave inverters in the master-slave inverter control group, realize the identification of the faulty inverter among the multiple slave inverters, improve the identification accuracy of the faulty slave inverter in the master-slave control scheme, and can improve the stability of the inverter system in the master-slave control scheme.

[0031] In some scenarios, a fault detection method for a photovoltaic water pump inverter according to an embodiment of the present application can be applied to the inverter control in the master-slave control scheme, which can improve the working stability of the inverter in the master-slave control scheme and improve the use effect of the master-slave control scheme.

[0032] The following specifically describes a fault detection method for a photovoltaic water pump inverter provided by the embodiments of the present application with specific examples.

[0033] Figure 1 FIG. is a schematic flow chart of the first fault detection method for a photovoltaic water pump inverter provided by the embodiments of the present application. As Figure 1 shown, the method includes S110 to S120. The following specifically describes S110 to S120.

[0034] S110. Obtain the timing broadcast voltage value and the timing broadcast current value of the master inverter in the master-slave inverter control group, and obtain the timing output voltage value and the timing output current value of each slave inverter in the multiple slave inverters of the inverter control group. Determine the timing difference corresponding to the timing broadcast voltage value and the timing output voltage value of each slave inverter as the timing voltage deviation value. And determine the timing difference between the timing broadcast current value and the timing output current value of each slave inverter as the timing current deviation value.

[0035] In the master-slave parallel control scheme of the inverter, the master-slave control scheme realizes the stable operation of the parallel system by setting a master inverter and multiple slave inverters. The master inverter is responsible for voltage control, while the slave inverters perform current control. The master inverter ensures a constant output voltage of the entire system by adjusting the amplitude and phase of the output voltage, while the slave inverters adjust their own output currents according to the instructions of the master module, thereby achieving an equal distribution of the load current. In the master-slave parallel control scheme, the master inverter usually adopts a voltage control mode (such as SPWM control), is responsible for regulating the reference voltage of the system, and exchanges data with the slave inverters through a communication method (such as pulse synchronization or high-speed communication). At the same time, the slave inverters adopt a current control mode and adjust their own output currents according to the instructions provided by the master inverter to achieve an equal distribution of the load current.

[0036] In the master-slave parallel control scheme, when a fault occurs in the master inverter, the system can upgrade a certain slave inverter to the master inverter through a switching mechanism, thereby ensuring the continuous operation of the system.

[0037] In the master-slave parallel control scheme, in order to accurately identify the faults of the inverters, the sequential broadcast voltage value and the sequential broadcast current value of the master inverter in the master-slave inverter control group can be obtained first. The sequential broadcast voltage value and the sequential broadcast current value are the control voltage values and control current values distributed by the master inverter to multiple slave inverters at multiple moments in chronological order. Furthermore, the sequential broadcast voltage value and the sequential broadcast current value can be used as indicators to judge the working state of the slave inverters.

[0038] In the master-slave parallel control scheme, the sequential output voltage value and the sequential output current value of each slave inverter in the multiple slave inverters of the inverter control group can also be obtained. The sequential output voltage value and the sequential output current value are the actual output values of each slave inverter during control. Furthermore, the output state of the slave inverters can be evaluated based on the sequential output voltage value and the sequential output current value to determine the faulty slave inverter.

[0039] After obtaining the sequential output voltage value, the sequential difference corresponding to the sequential broadcast voltage value and the sequential output voltage value of each slave inverter can be determined as the sequential voltage deviation value, and the sequential voltage deviation value characterizes the sequential voltage output characteristics of the slave inverter.

[0040] After obtaining the sequential output current value, the sequential difference between the sequential broadcast current value and the sequential output current value of each slave inverter can be determined as the sequential current deviation value, and the sequential current deviation value characterizes the sequential current output characteristics of the slave inverter.

[0041] S120. Based on the inverter state recognition model, determine the fault probability values corresponding to multiple slave inverters in the master-slave inverter control group according to the timing voltage deviation values and timing current deviation values of the multiple slave inverters in the master-slave inverter control group. When the fault probability value of the first slave inverter is greater than the preset fault probability value, mark the first slave inverter as a faulty slave inverter.

[0042] After obtaining the timing voltage deviation values and timing current deviation values, it is possible to determine the fault probability values corresponding to multiple slave inverters in the master-slave inverter control group according to the timing voltage deviation values and timing current deviation values of the multiple slave inverters in the master-slave inverter control group through the inverter state recognition model, realizing the simultaneous evaluation of multiple slave inverters in the master-slave inverter control group.

[0043] It should be noted that since multiple slave inverters in the master-slave inverter control group work under the control of the master inverter and the multiple slave inverters in the master-slave inverter control group work together, in order to efficiently identify multiple slave inverters in the master-slave inverter control group, in the embodiments of the present application, overall fault identification is performed through multiple slave inverters in the master-slave inverter control group. The deviation data of multiple slave inverters in the master-slave inverter control group can form spatio-temporal correlation features (such as phase deviation synchronization, current deviation amplitude consistency), and then accidental interference can be excluded through cross-validation. For example, if the voltage deviation of a certain slave inverter is abnormal but the synchronization of the other slave inverters is normal, it is more likely to be local noise; if multiple slave inverter devices show the same-direction deviation, it is more likely to point to a master inverter fault or system parameter drift. Then, the master inverter fault or system parameter drift problem can be excluded to achieve accurate identification of multiple faulty slave inverters among multiple slave inverters.

[0044] In addition, based on the health status assessment of population data, a more accurate maintenance plan can be formulated. For example, if the fault probability of a certain slave inverter continuously exceeds the population mean, priority can be given to arranging maintenance instead of adopting unified cycle maintenance, improving the maintenance efficiency of slave inverters.

[0045] After obtaining the fault probability value of the slave inverter, when the fault probability value of the first slave inverter is greater than the preset fault probability value, mark the first slave inverter as a faulty slave inverter, and then accurately identify the faulty slave inverter.

[0046] Exemplarily, the preset fault probability value can be 0.6, 0.7, or 0.8.

[0047] Exemplarily, the inverter state recognition model can be trained by the timing voltage deviation values and the timing current deviation values of multiple slave inverters in the marked master-slave inverter control group, and the failure probability values respectively corresponding to the multiple slave inverters in the master-slave inverter control group. The failure probability values respectively corresponding to the multiple slave inverters in the master-slave inverter control group can be predicted through the inverter state recognition model.

[0048] The beneficial effect of the above implementation manner is that, according to the timing voltage deviation values and the timing current deviation values of multiple slave inverters in the master-slave inverter control group, the failure probability values respectively corresponding to the multiple slave inverters in the master-slave inverter control group are determined, so as to realize the identification of the faulty inverters among the multiple slave inverters, improve the identification accuracy of the faulty slave inverters in the master-slave control scheme, and can improve the stability of the inverter system in the master-slave control scheme.

[0049] The beneficial effect of the above implementation manner is also that, through the overall failure identification of multiple slave inverters in the master-slave inverter control group, the deviation data of the multiple slave inverters in the master-slave inverter control group can form spatio-temporal correlation features, and accidental interference can be excluded through cross-validation, thereby improving the failure identification accuracy of the multiple slave inverters in the master-slave inverter control group.

[0050] Figure 2 The flow diagram of the second fault detection method for the photovoltaic water pump inverter provided by the embodiment of the present application is shown as Figure 2 shown. The above method further includes S210 to S230, and the following is a specific description of S210 to S230.

[0051] S210. Determine the timing voltage fluctuation value corresponding to the timing voltage deviation value of each slave inverter, and determine the timing current fluctuation value corresponding to the timing current deviation value of each slave inverter. Wherein, the timing voltage fluctuation value includes the fluctuation moment and the fluctuation amplitude of the timing voltage deviation value, and the timing current fluctuation value includes the fluctuation moment and the fluctuation amplitude of the timing current deviation value.

[0052] When identifying the faults of the slave inverters, when the number of slave inverters is large, for example, when the number of slave inverters reaches 1000, in order to identify the overall faults of multiple slave inverters in the slave inverter control group at this time, it may cause the feature dimensions of the multiple slave inverters input to the inverter state recognition model to be too large. Therefore, the input feature dimensions of the multiple slave inverters can be reduced, and thus the fault identification of the multiple slave inverters can be realized.

[0053] When determining a faulty inverter among multiple master-slave inverters with a large number of inverters, the timing voltage fluctuation value corresponding to the timing voltage deviation value of each slave inverter and the timing current fluctuation value corresponding to the timing current deviation value of each slave inverter can be determined. The timing voltage fluctuation value and the timing current fluctuation value characterize the voltage fluctuation and current fluctuation of each slave inverter. Furthermore, the slave inverters with similar characteristics can be clustered based on the voltage fluctuation and current fluctuation, so as to identify the faulty slave inverter through the set of clustered slave inverters, improving the accuracy of faulty identification for the set of slave inverters.

[0054] It should be noted that the timing voltage fluctuation value includes the fluctuation moment and amplitude of the timing voltage deviation value. The timing voltage fluctuation value can be the voltage fluctuation value in time sequence obtained by subtracting the reference timing voltage deviation value from the timing voltage deviation value, which realizes the filtering of smaller voltage fluctuation values. Through the timing voltage fluctuation value, the voltage fluctuation characteristics of the slave inverter in chronological order can be characterized.

[0055] It should be noted that the timing current fluctuation value includes the fluctuation moment and amplitude of the timing current deviation value. The timing current fluctuation value can be the current fluctuation value in time sequence obtained by subtracting the reference timing current deviation value from the timing current deviation value, which realizes the filtering of smaller current fluctuation values. Through the timing current fluctuation value, the current fluctuation characteristics of the slave inverter in chronological order can be characterized.

[0056] S220. Cluster multiple slave inverters in the master-slave inverter control group according to the similarity characteristics of the timing voltage fluctuation value and the timing current fluctuation value of each slave inverter, obtain multiple subsets of slave inverters, and determine the mean value of the timing voltage fluctuation values and the mean value of the timing current fluctuation values of the multiple slave inverters in each subset of slave inverters.

[0057] After obtaining the timing voltage fluctuation value and the timing current fluctuation value, multiple slave inverters in the master-slave inverter control group can be clustered according to the similarity characteristics of the timing voltage fluctuation value and the timing current fluctuation value of each slave inverter, obtaining multiple subsets of slave inverters. The timing voltage fluctuation values and the timing current fluctuation values of the slave inverters in each subset of slave inverters are similar.

[0058] Exemplarily, when clustering the slave inverters according to the similarity characteristics of the timing voltage fluctuation value and the timing current fluctuation value, the slave inverters can be clustered according to the timing voltage fluctuation value and the timing current fluctuation value based on the K-means++ clustering algorithm.

[0059] In the embodiments of the present application, after obtaining multiple slave inverter subsets, the mean values of the timing voltage fluctuation values and the mean values of the timing current fluctuation values of the multiple slave inverters in each slave inverter subset can be determined. Furthermore, fault identification can be performed on each slave inverter subset according to the mean values of the timing voltage fluctuation values and the mean values of the timing current fluctuation values of the multiple slave inverters, so as to locate the faulty slave inverter subsets among the multiple slave inverter subsets.

[0060] It should be noted that in the embodiments of the present application, through similarity clustering of fluctuation characteristics, devices can be divided into slave inverter subsets with similar behavior patterns. For example, in a photovoltaic power station, when the light changes in the early morning, the voltage fluctuation amplitudes of the slave inverters in a slave inverter subset increase synchronously, while there is no such phenomenon in other slave inverter subsets. This pattern difference can help quickly locate regional faults related to the MPPT algorithm.

[0061] In addition, by determining the mean values of the timing voltage fluctuation values and the mean values of the timing current fluctuation values of the multiple slave inverters, the observability of gradual faults can be further enhanced through mean value analysis. For example, if the voltage fluctuation amplitude of a slave inverter increases by 0.5% per month due to insulation aging, this may be ignored in a single slave inverter, but the mean value curve in the slave inverter subset will clearly show this linear growth trend, facilitating fault observation.

[0062] S230. Through the inverter state recognition model, according to the mean values of the timing voltage fluctuation values and the mean values of the timing current fluctuation values of the multiple slave inverter subsets, determine the fault probability values corresponding to the multiple slave inverter subsets respectively. When the fault probability value of the first slave inverter subset is greater than the preset fault probability value, mark the first slave inverter subset as a faulty slave inverter subset, and determine the faulty slave inverter in the faulty slave inverter subset.

[0063] When identifying the faulty slave inverter subsets among the multiple slave inverter subsets, the inverter state recognition model can be used to determine the fault probability values corresponding to the multiple slave inverter subsets respectively according to the mean values of the timing voltage fluctuation values and the mean values of the timing current fluctuation values of the multiple slave inverter subsets. The fault probability value of each slave inverter subset represents the probability value of each slave inverter subset having a fault.

[0064] After obtaining the fault probability values corresponding to the multiple slave inverter subsets respectively, when the fault probability value of the first slave inverter subset is greater than the preset fault probability value, it indicates that the probability of the inverters in the first slave inverter subset having a fault is relatively high. Furthermore, the first slave inverter subset can be marked as a faulty slave inverter subset.

[0065] After obtaining multiple subsets of slave inverters through clustering in the embodiments of the present application, the synchronism at the fluctuation moment can be analyzed, the phase correlation between devices can be revealed, and then the fault analysis of the subsets of slave inverters can be carried out according to the phase correlation. For example, when multiple inverters simultaneously exhibit current fluctuation peaks near the zero crossing of the grid voltage, it may indicate a systematic fault in the phase-locked loop parameter drift, and such phase-related characteristics are difficult to be found through the independent analysis of a single slave inverter.

[0066] After obtaining the subset of faulty slave inverters, further identification of the inverters in the subset of faulty slave inverters can determine the faulty slave inverters in the subset of faulty slave inverters, realizing the accurate identification of the faulty slave inverters.

[0067] Exemplarily, the inverter state recognition model can be trained by the mean of the time-series voltage fluctuation values, the mean of the time-series current fluctuation values of multiple labeled subsets of slave inverters, and the fault probability values respectively corresponding to the multiple subsets of slave inverters. The fault probability value of the subset of slave inverters can be recognized through the inverter state recognition model.

[0068] The beneficial effect of the above implementation manner is that when overall fault identification is performed on multiple slave inverters in the slave inverter control group, the feature scales of the inputs corresponding to the multiple slave inverters to the inverter state recognition model may be too large. Through clustering, the input feature scales of the multiple slave inverters can be reduced, and then efficient fault identification of multiple master-slave inverters can be realized.

[0069] The beneficial effect of the above implementation manner is also that by determining the mean of the time-series voltage fluctuation values and the mean of the time-series current fluctuation values of multiple slave inverters, the observability of the gradual fault can be enhanced through further mean analysis, improving the identification accuracy of the subset of faulty slave inverters.

[0070] The beneficial effect of the above implementation manner is also that after obtaining multiple subsets of slave inverters through clustering, the synchronism at the fluctuation moment can be analyzed, the phase correlation between devices can be revealed, and then the fault analysis of the subsets of slave inverters can be carried out according to the phase correlation, improving the accurate identification of the faults of the subsets of slave inverters.

[0071] In some implementation manners, in S220 above, clustering the multiple slave inverters in the master-slave inverter control group according to the similarity characteristics of the time-series voltage fluctuation values and the time-series current fluctuation values of each slave inverter includes S221 to S222, and the following specifically describes S221 to S222.

[0072] S221. Obtain the time - series irradiance change value and the time - series temperature change value of the photovoltaic solar panel corresponding to each slave inverter, determine the first coincidence period of the time - series voltage fluctuation value, the time - series current fluctuation value, and the time - series irradiance change value of each slave inverter, and determine the second coincidence period of the time - series voltage fluctuation value, the time - series current fluctuation value, and the time - series temperature change value.

[0073] When detecting the working state of the slave inverter, in order to accurately detect the state of the slave inverter, the influence of the light intensity of the solar panel and the temperature of the solar panel can be excluded, further improving the accuracy of detecting the state of the inverter.

[0074] When excluding the influence of the light intensity of the solar panel and the temperature of the solar panel, the time - series irradiance change value and the time - series temperature change value of the photovoltaic solar panel corresponding to each slave inverter can be obtained. The time - series irradiance change value and the time - series temperature change value of the photovoltaic solar panel can be obtained through the light sensor data and the temperature sensor data on the solar panel.

[0075] After obtaining the time - series irradiance change value and the time - series temperature change value, the first coincidence period of the time - series voltage fluctuation value, the time - series current fluctuation value, and the time - series irradiance change value of each slave inverter can be determined, and the second coincidence period of the time - series voltage fluctuation value, the time - series current fluctuation value, and the time - series temperature change value can be determined. Furthermore, the influence of the light intensity of the solar panel and the temperature of the solar panel can be excluded according to the first coincidence period and the second coincidence period.

[0076] S222. In the time - series voltage fluctuation value and the time - series current fluctuation value of each slave inverter, delete the time - series voltage fluctuation value and the time - series current fluctuation value corresponding to the first coincidence period and the second coincidence period. Cluster the multiple slave inverters in the master - slave inverter control group according to the similar characteristics of the time - series voltage fluctuation value and the time - series current fluctuation value of each slave inverter.

[0077] When excluding the influence of the light intensity of the solar panel and the temperature of the solar panel, in the time - series voltage fluctuation value and the time - series current fluctuation value of each slave inverter, the time - series voltage fluctuation value and the time - series current fluctuation value corresponding to the first coincidence period and the second coincidence period can be deleted, so that only the time - series voltage fluctuation value and the time - series current fluctuation value after excluding the influence of the light intensity of the solar panel and the temperature of the solar panel are retained in the time - series voltage fluctuation value and the time - series current fluctuation value of each slave inverter.

[0078] After obtaining the adjusted time - series voltage fluctuation value and the time - series current fluctuation value, the multiple slave inverters in the master - slave inverter control group can be clustered according to the similar characteristics of the time - series voltage fluctuation value and the time - series current fluctuation value of each slave inverter, improving the accuracy of subsequent fault identification of the multiple slave inverters based on the clustering results.

[0079] The beneficial effects of the above implementation method are that it can accurately detect the state of the slave inverter, eliminate the influence of the light intensity and temperature of the solar panel, and further improve the accuracy of detecting the state of the slave inverter.

[0080] In some implementation methods, in the above S230, determining the faulty slave inverter in the subset of slave inverters includes S231 to S232, and the following is a specific description of S231 to S232.

[0081] S231. Determine the voltage fluctuation variance value of the time-sequence voltage fluctuation value and the current fluctuation variance value of the time-sequence current fluctuation value of each slave inverter in the subset of faulty slave inverters; and determine the average value of the voltage fluctuation variance values and the average value of the current fluctuation variance values of all slave inverters in the subset of faulty slave inverters.

[0082] When determining the faulty slave inverter in the subset of faulty slave inverters, the voltage fluctuation variance value of the time-sequence voltage fluctuation value and the current fluctuation variance value of the time-sequence current fluctuation value of each slave inverter in the subset of faulty slave inverters can be determined. The voltage fluctuation variance value and the current fluctuation variance value characterize the voltage and current fluctuation amplitudes of each slave inverter in the subset of faulty slave inverters.

[0083] At the same time, the average value of the voltage fluctuation variance values and the average value of the current fluctuation variance values of all slave inverters in the subset of faulty slave inverters can be determined, and then the faulty slave inverter in the subset of faulty slave inverters can be identified based on the average value of the voltage fluctuation variance values and the average value of the current fluctuation variance values of all slave inverters in the subset of faulty slave inverters.

[0084] S232. When the first voltage fluctuation variance value of the first slave inverter is greater than or equal to the average value of the voltage fluctuation variance values, or the first current fluctuation variance value of the first slave inverter is greater than or equal to the average value of the current fluctuation variance values, determine the first slave inverter as a faulty inverter.

[0085] Within the range of the subset of faulty slave inverters, when the voltage or current fluctuation amplitude of the first slave inverter in the subset of faulty slave inverters is too large, that is, when the first voltage fluctuation variance value of the first slave inverter is greater than or equal to the average value of the voltage fluctuation variance values, or the first current fluctuation variance value of the first slave inverter is greater than or equal to the average value of the current fluctuation variance values, determine the first slave inverter as a faulty inverter, and then the first slave inverter within the range of the subset of faulty slave inverters can be accurately located.

[0086] The beneficial effects of the above implementation method are as follows. When the first voltage fluctuation variance value of the first slave inverter is greater than or equal to the average value of the voltage fluctuation variance values, or when the first current fluctuation variance value of the first slave inverter is greater than or equal to the average value of the current fluctuation variance values, it is determined that the first slave inverter is a faulty inverter, achieving the purpose of further accurately determining the faulty inverter within the range of the faulty slave inverter subset and improving the detection accuracy of the faulty inverter.

[0087] Figure 3 The flowchart of the third method for detecting faults in a photovoltaic water pump inverter provided by an embodiment of the present application is shown in Figure 3 As shown, the above method further includes S310 to S320, and the following is a specific description of S310 to S320.

[0088] S310: Determine the target slave inverter subset with the smallest fault probability value among multiple slave inverter subsets, and determine the voltage fluctuation variance value of the time-series voltage fluctuation value and the current fluctuation variance value of the time-series current fluctuation value of each slave inverter in the target slave inverter subset, and determine the sum of the voltage fluctuation variance value and the current fluctuation variance value of each slave inverter in the target slave inverter subset as the comprehensive evaluation value of the slave inverter.

[0089] When performing master-slave control on the inverter, the target slave inverter subset with the smallest fault probability value among multiple slave inverter subsets can be determined. The slave inverters in the target slave inverter subset have the smallest fault probability, and thus the slave inverter can be determined from the target slave inverter subset.

[0090] After obtaining the target slave inverter subset, the voltage fluctuation variance value of the time-series voltage fluctuation value and the current fluctuation variance value of the time-series current fluctuation value of each slave inverter in the target slave inverter subset can be determined, and the sum of the voltage fluctuation variance value and the current fluctuation variance value of each slave inverter in the target slave inverter subset is determined as the comprehensive evaluation value of the slave inverter. The comprehensive evaluation value of the slave inverter comprehensively represents the total sum of the voltage fluctuation amplitude and the current fluctuation amplitude of the slave inverter.

[0091] S320: Determine the target slave inverter with the smallest comprehensive evaluation value of the slave inverter in the target slave inverter subset, and use the target slave inverter as the standby main inverter. Obtain the main inverter fault probability value of the main inverter. When the main inverter fault probability value is greater than or equal to the preset main inverter fault probability value, switch the standby main inverter to the main inverter.

[0092] After obtaining the comprehensive evaluation value of the slave inverter of the inverter, the target slave inverter with the smallest comprehensive evaluation value in the target slave inverter subset can be determined. The target slave inverter is the slave inverter with the smallest failure probability among the target slave inverter subsets with the smallest failure probability. Furthermore, the target slave inverter can be used as the standby main inverter. The standby main inverter can be used as a replacement inverter for the current main inverter, improving the switching speed of the main inverter.

[0093] When controlling the main inverter, the main inverter failure probability value of the main inverter can be obtained. When the main inverter failure probability value is greater than or equal to the preset main inverter failure probability value, the standby main inverter is switched to the main inverter to achieve an effective switch of the main inverter.

[0094] Exemplarily, the preset main inverter failure probability value can be 0.6, 0.65, or 0.7.

[0095] The beneficial effect of the above implementation method is to determine the target slave inverter subset with the smallest failure probability value among multiple slave inverter subsets, and determine the target slave inverter with the smallest comprehensive evaluation value in the target slave inverter subset as the standby main inverter, which can improve the switching speed of the standby main inverter and improve the control effect on the main inverter in the main - slave inverter control group.

[0096] Figure 4 The flowchart of the fourth fault detection method for a photovoltaic water pump inverter provided by the embodiment of the present application is shown as Figure 4 shown. The above - mentioned method further includes S410 to S420. The following will specifically describe S410 to S420.

[0097] S410: Determine a preset number of target slave inverter subsets arranged in ascending order of failure probability values among multiple slave inverter subsets, determine the communication failure probability between each slave inverter and the main inverter in each target slave inverter subset, and determine the average communication failure probability of all slave inverters in each target slave inverter subset.

[0098] The communication failure of the slave inverter will also affect the working effect after the slave inverter is switched to the main inverter. Therefore, the communication status of the slave inverter can be evaluated to further improve the accuracy of determining the standby main inverter among multiple slave inverters.

[0099] When determining the standby main inverter, a preset number of target slave inverter subsets arranged in ascending order of failure probability values among multiple slave inverter subsets can be determined. The preset number of target slave inverter subsets are multiple slave inverter subsets with relatively small failure probabilities. Furthermore, slave inverters with excellent communication status can be determined as the standby main inverter in the preset number of target slave inverter subsets.

[0100] When determining a standby main inverter, the communication failure probability between each slave inverter and the main inverter in each target subset of slave inverters can be determined, and the average value of the communication failure probabilities of all the slave inverters in each target subset of slave inverters can be determined, and the communication status of the target subset of slave inverters can be evaluated based on the average value of the communication failure probabilities.

[0101] S420. Determine the target subset of slave inverters with the minimum average communication failure probability among a preset number of target subsets of slave inverters, determine the target slave inverter with the minimum comprehensive evaluation value among the slave inverters in the target subset of slave inverters, and use the target slave inverter as the standby main inverter.

[0102] After obtaining the average value of the communication failure probabilities, the target subset of slave inverters with the minimum average communication failure probability among a preset number of target subsets of slave inverters can be determined. The failure probability of the slave inverters in the target subset of slave inverters is relatively small and the communication failure probability is the minimum. Furthermore, a standby main inverter can be determined within the target subset of slave inverters.

[0103] After obtaining the target subset of slave inverters, when determining the target slave inverter with the minimum comprehensive evaluation value among the slave inverters in the target subset of slave inverters, the target slave inverter can be used as the standby main inverter. Furthermore, the slave inverters can be replaced according to the standby main inverter.

[0104] Exemplarily, when determining the target slave inverter with the minimum comprehensive evaluation value among the slave inverters in the target subset of slave inverters, the standby main inverter can be determined according to the method in S310 to S320.

[0105] The beneficial effect of the above implementation manner is that by determining the target subset of slave inverters with the minimum average communication failure probability among a preset number of target subsets of slave inverters, and determining the target slave inverter with the minimum comprehensive evaluation value among the slave inverters in the target subset of slave inverters, the accuracy of determining the standby main inverter is improved, and the control effect on the master-slave inverter control group is improved.

[0106] In some implementation manners, in S320 above, obtaining the main inverter failure probability value of the main inverter includes S321 to S322. S321 to S322 are specifically described below.

[0107] S321. Obtain the failure probability value of the main inverter, and obtain the communication failure probabilities between the main inverter and multiple slave inverters.

[0108] When evaluating the state of the main inverter, the fault probability value of the main inverter can be obtained, and the fault probability value is the working fault probability of the main inverter. At the same time, the communication fault probability between the main inverter and multiple slave inverters can be obtained, and the communication fault probability is the communication fault probability of the main inverter during operation. After obtaining the fault probability value and the communication fault probability, the working state of the main inverter can be evaluated based on the fault probability value and the communication fault probability.

[0109] S322. Determine the sum of the product of the first weight factor and the fault probability value and the product of the second weight factor and the communication fault probability as the fault probability value of the main inverter. Among them, the first weight factor is the difference between 1 and the second weight factor, and the second weight factor is determined according to the output load fluctuation of the master-slave inverter control group.

[0110] After obtaining the fault probability value and the communication fault probability of the main inverter, the sum of the product of the first weight factor and the fault probability value and the product of the second weight factor and the communication fault probability can be determined as the fault probability value of the main inverter, and then it can be evaluated whether the main inverter needs to be switched to a standby main inverter according to the fault probability value of the main inverter.

[0111] Exemplarily, the second weight factor is determined according to the output load fluctuation of the master-slave inverter control group. When the output load fluctuation states of the master-slave inverter control group are different, the communication requirements for the main inverter in the master-slave inverter control group are different, and thus the second weight factor can be adjusted according to the output load fluctuation of the master-slave inverter control group.

[0112] Exemplarily, the first weight factor is the difference between 1 and the second weight factor, so that the first weight factor can be dynamically adjusted according to the value of the second weight factor.

[0113] The beneficial effect of the above implementation method is that the working state of the main inverter is evaluated according to the fault probability value and the communication fault probability. When the output load fluctuation states of the master-slave inverter control group are different, the communication requirements for the main inverter in the master-slave inverter control group are different, and the second weight factor is adjusted according to the output load fluctuation of the master-slave inverter control group, realizing the accuracy of the working state evaluation of the main inverter.

[0114] In some implementation methods, in the above S322, determining the second weight factor according to the output load fluctuation of the master-slave inverter control group includes S322a to S322b, and the following is a specific description of S322a to S322b.

[0115] S322a. Obtain the fluctuation variance value of the output load of the master-slave inverter control group within a preset time period, and obtain the maximum preset fluctuation variance of the output load of the master-slave inverter control group.

[0116] When determining the second weight factor, the fluctuation variance value of the output load of the master-slave inverter control group within a preset time period can be obtained. The output load of the master-slave inverter control group is the power grid that the master-slave inverter control group needs to output to the power grid. The master-slave inverter control group needs to adjust the output of the master-slave inverter control group according to the requirements of the power grid. When adjusting the output of the master-slave inverter control group, communication needs to be carried out from the master inverter to multiple slave inverters within the master-slave inverter control group. The level of communication from the master inverter to multiple slave inverters affects the level of communication quality requirements within the master-slave inverter control group. Furthermore, the level of communication quality requirements can be evaluated through the fluctuation variance value of the output load of the master-slave inverter control group within a preset time period.

[0117] After obtaining the fluctuation variance value of the output load of the master-slave inverter control group within a preset time period, the preset maximum fluctuation variance value of the output load of the master-slave inverter control group can be obtained, and the communication quality requirements of the master-slave inverter control group can be evaluated based on the preset maximum fluctuation variance value.

[0118] S322b. Determine the ratio of the fluctuation variance value to the preset maximum fluctuation variance value as the second weight factor.

[0119] After obtaining the fluctuation variance value and the preset maximum fluctuation variance value, the ratio of the fluctuation variance value to the preset maximum fluctuation variance value can be determined as the second weight factor. The second weight factor characterizes the evaluation of the level of communication quality requirements by the master-slave inverter control group.

[0120] The beneficial effect of the above implementation method is that by evaluating the level of communication quality requirements through the fluctuation variance value of the output load of the master-slave inverter control group within a preset time period, and determining the ratio of the fluctuation variance value to the preset maximum fluctuation variance value as the second weight factor, the accuracy of dynamically evaluating the working state of the master inverter can be improved.

[0121] Figure 5 It is a schematic flowchart of the fifth fault detection method for a photovoltaic water pump inverter provided by an embodiment of the present application. As Figure 5 shown, the above method further includes S510 to S520, and the following provides a specific description of S510 to S520.

[0122] S510. Obtain the number of inverters in the master-slave inverter control group, and obtain the inverter number threshold of the master-slave inverter control group. Determine the ratio of the number of inverters to the inverter number threshold as the inverter number adjustment factor.

[0123] When evaluating the communication quality requirements of the master-slave inverter control group, the number of inverters in the master-slave inverter control group can also be obtained. The size of the number of inverters characterizes the size of the communication burden of the master-slave inverter control group. Furthermore, the communication burden of the master-slave inverter control group can be evaluated based on the number of inverters.

[0124] Meanwhile, the threshold value of the number of inverters in the master-slave inverter control group can be obtained. The threshold value of the number of inverters is the maximum number of inverters corresponding to the master-slave inverter control group. Furthermore, the ratio of the number of inverters to the threshold value of the number of inverters can be determined as the inverter number adjustment factor, and the inverter number adjustment factor characterizes the size of the communication burden of the master-slave inverter control group.

[0125] S520. Multiply the second weight factor by the inverter number adjustment factor to adjust the second weight factor.

[0126] After obtaining the inverter number adjustment factor, the second weight factor can be multiplied by the inverter number adjustment factor to adjust the second weight factor, further achieving the purpose of adjusting the second weight factor according to the number of inverters.

[0127] The beneficial effect of the above implementation method is that the second weight factor is multiplied by the inverter number adjustment factor to adjust the second weight factor, and further the communication requirements of the master-slave inverter control group are evaluated according to the number of inverters, improving the accuracy of determining the standby master inverter of the master-slave inverter control group.

[0128] The embodiment of the present application also provides a fault detection system for a photovoltaic water pump inverter, including a unit for executing the method described in any one of the above.

[0129] Figure 6 As shown in the schematic logical structure diagram of a fault detection system for a photovoltaic water pump inverter provided by an embodiment of the present application, Figure 6 as shown, the system 1 of this embodiment includes a processing unit 11, a storage unit 12, and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12, and the transceiver unit 13 cooperate with each other to implement the above method. The beneficial effects of the embodiment of the present application have been described in the above method and will not be repeated here.

[0130] It should be noted that the information interaction, execution process, etc. between the above devices / units, due to being based on the same concept as the method embodiment of the present application, for their specific functions and the technical effects brought, please refer to the method embodiment part specifically, and will not be repeated here.

[0131] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment and will not be elaborated here.

[0132] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above method embodiments of this application, a computer program can be used to instruct the relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the photographing device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0133] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0134] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0135] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0136] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] The above-described embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included in the protection scope of this application.

Claims

1. A fault detection method for a photovoltaic water pump inverter, characterized in that: The method comprises: Obtain the time-series broadcast voltage value and the time-series broadcast current value of the master inverter in the master-slave inverter control group, and obtain the time-series output voltage value and the time-series output current value of each slave inverter in the multiple slave inverters of the inverter control group; determine the time-series difference value corresponding to the time-series broadcast voltage value and the time-series output voltage value of each slave inverter as the time-series voltage deviation value; and determine the time-series difference value between the time-series broadcast current value and the time-series output current value of each slave inverter as the time-series current deviation value; By using the inverter state identification model, according to the sequential voltage deviation values ​​and the sequential current deviation values ​​of the multiple slave inverters in the master-slave inverter control group, the fault probability values ​​corresponding to the multiple slave inverters in the master-slave inverter control group are determined; when the fault probability value of the first slave inverter is greater than the preset fault probability value, the first slave inverter is marked as a faulty slave inverter; The method further comprises: Determine the sequential voltage fluctuation value corresponding to the sequential voltage deviation value of each slave inverter, and determine the sequential current fluctuation value corresponding to the sequential current deviation value of each slave inverter; wherein the sequential voltage fluctuation value includes the fluctuation time and fluctuation amplitude of the sequential voltage deviation value, and the sequential current fluctuation value includes the fluctuation time and fluctuation amplitude of the sequential current deviation value; Clustering multiple slave inverters in the master-slave inverter control group according to similar characteristics of the sequential voltage fluctuation value and the sequential current fluctuation value of each slave inverter to obtain multiple slave inverter subsets, and determining the mean value of the sequential voltage fluctuation value and the mean value of the sequential current fluctuation value of the multiple slave inverters in each slave inverter subset; Through the inverter state identification model, the fault probability values ​​corresponding to the multiple slave inverter subsets are determined according to the mean values ​​of the sequential voltage fluctuation values ​​and the mean values ​​of the sequential current fluctuation values ​​of the multiple slave inverter subsets. When the fault probability value of the first slave inverter subset is greater than the preset fault probability value, the first slave inverter subset is marked as a faulty slave inverter subset, and the faulty slave inverter in the faulty slave inverter subset is determined.

2. The method according to claim 1, characterized in that Clustering multiple slave inverters in the master-slave inverter control group is performed according to similar characteristics of the sequential voltage fluctuation value and the sequential current fluctuation value of each slave inverter, including: Obtain the sequential irradiance change value and the sequential temperature change value of the photovoltaic solar panel corresponding to each slave inverter, and determine the first coincidence time period of the sequential voltage fluctuation value, the sequential current fluctuation value and the sequential irradiance change value of each slave inverter, and determine the second coincidence time period of the sequential voltage fluctuation value, the sequential current fluctuation value and the sequential temperature change value; Among the sequential voltage fluctuation values ​​and sequential current fluctuation values ​​of each slave inverter, the sequential voltage fluctuation values ​​and sequential current fluctuation values ​​corresponding to the first coincidence time period and the second coincidence time period are deleted; and multiple slave inverters in the master-slave inverter control group are clustered according to similar characteristics of the sequential voltage fluctuation values ​​and sequential current fluctuation values ​​of each slave inverter.

3. The method according to claim 2, characterized in that A faulty slave inverter in a subset of faulty slave inverters is determined, including: Determine the voltage fluctuation variance value of the sequential voltage fluctuation value and the current fluctuation variance value of the sequential current fluctuation value of each slave inverter in the faulty slave inverter subset; and determine the mean value of the voltage fluctuation variance value and the mean value of the current fluctuation variance value of all slave inverters in the faulty slave inverter subset; When the first voltage fluctuation variance value of the first slave inverter is greater than or equal to the voltage fluctuation variance value mean, or when the first current fluctuation variance value of the first slave inverter is greater than or equal to the current fluctuation variance value mean, the first slave inverter is determined to be a faulty inverter.

4. The method according to claim 3, characterized in that The method further comprises: Determine a target slave inverter subset with the smallest fault probability value among multiple slave inverter subsets, determine a voltage fluctuation variance value of a sequential voltage fluctuation value and a current fluctuation variance value of a sequential current fluctuation value of each slave inverter in the target slave inverter subset, and determine the sum of the voltage fluctuation variance value and the current fluctuation variance value of each slave inverter in the target slave inverter subset as a comprehensive evaluation value of the slave inverter; Determine the target slave inverter with the smallest slave inverter comprehensive evaluation value in the target slave inverter subset, and use the target slave inverter as the backup master inverter; obtain the main inverter failure probability value of the main inverter, and when the main inverter failure probability value is greater than or equal to the preset main inverter failure probability value, switch the backup master inverter to the main inverter.

5. The method according to claim 4, characterized in that The method further comprises: Determine a preset number of target slave inverter subsets whose failure probability values ​​in the multiple slave inverter subsets are arranged from small to large, determine a communication failure probability between each slave inverter in each target slave inverter subset and the master inverter, and determine a mean value of the communication failure probability of all slave inverters in each target slave inverter subset; A target slave inverter subset with the smallest mean value of communication failure probability among a preset number of target slave inverter subsets is determined, and a target slave inverter with the smallest slave inverter comprehensive evaluation value among the target slave inverter subset is determined, and the target slave inverter is used as a backup master inverter.

6. The method according to claim 5, characterized in that Obtain the main inverter failure probability value of the main inverter, including: Obtaining a fault probability value of a master inverter, and obtaining a communication fault probability between the master inverter and a plurality of slave inverters; Determine the sum of the product of the first weight factor and the fault probability value and the product of the second weight factor and the communication fault probability as the main inverter fault probability value; wherein the first weight factor is 1 minus the difference between the second weight factor, and the second weight factor is determined according to the output load fluctuation of the master-slave inverter control group.

7. The method according to claim 6, characterized in that The second weight factor is determined according to the output load fluctuation of the master-slave inverter control group, including: Obtaining a fluctuation variance value of an output load of a master-slave inverter control group within a preset time period, and obtaining a preset maximum value of the fluctuation variance of an output load of the master-slave inverter control group; A ratio of the volatility variance value to a preset volatility variance maximum value is determined as a second weight factor.

8. The method according to claim 7, characterized in that The method further comprises: Obtaining the number of inverters of the master-slave inverter control group and obtaining a threshold value of the number of inverters of the master-slave inverter control group; determining a ratio of the number of inverters to the threshold value of the number of inverters as an inverter number adjustment factor; The second weight factor is multiplied by the inverter quantity adjustment factor to adjust the second weight factor.

9. A fault detection system for a photovoltaic water pump inverter, characterized in that: Comprising means for performing the method according to any one of claims 1 to 8.

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