Control method and device for fuse used in combiner box protection

Abstract

The present application discloses a control method and device for a fuse used in a combiner box protection, comprising: collecting n power generation current data sets and n power generation voltage data sets from n power generation devices within a first preset time period; obtaining target device data of the combiner box; collecting a first operating data set of the combiner box; determining an expected output current data set based on the target device data, the n power generation current data sets, the n power generation voltage data sets, and the first operating data set; testing the combiner box based on the n power generation current data sets, the expected output current data set, and the first operating data set to obtain a target detection result and detection process data; when the target detection result indicates an operating abnormality, determining a target abnormality score based on the detection process data; determining a target control strategy based on the target abnormality score; and controlling an intelligent fuse to perform a circuit breaking operation based on the target control strategy. By using the embodiments of the present application, flexible circuit control is achieved.

Description

Technical Field

[0001] The present application relates to the field of power system control technology, and in particular to a control method and device for a fuse used to protect a combiner box. Background Art

[0002] Fuses are widely used in high and low voltage power distribution and control systems, as well as in electrical equipment. As short-circuit and overcurrent protectors, they are one of the most commonly used protective devices. However, with the rapid development of new energy industries such as wind power and photovoltaics, new energy power generation systems are characterized by complex and changing operating environments and frequent and large current fluctuations. This has significantly increased the power system's demand for precise equipment control, fault prediction, and flexible regulation.

[0003] Traditional fuses immediately disconnect the circuit upon detecting a short circuit or overcurrent fault. This one-size-fits-all protection approach prevents flexible circuit control. Therefore, achieving flexible circuit control has become a pressing issue. Summary of the Invention

[0004] The embodiments of the present application provide a method and device for controlling a fuse for combining box protection, thereby achieving flexible control of the circuit.

[0005] In a first aspect, an embodiment of the present application provides a method for controlling a fuse for a combiner box protection, the method being applied to a controller in a target power generation system, wherein the target power generation system further comprises: n power generation devices and a combiner box; the combiner box is provided with an intelligent fuse, where n is a positive integer. The method comprises:

[0006] Collecting the generated current data set and the generated voltage data set of the n power generation devices within a first preset time period to obtain n generated current data sets and n generated voltage data sets;

[0007] Acquiring target device data of the combiner box;

[0008] Collecting a first working data set of the combiner box within the first preset time period;

[0009] determining an expected output current dataset based on the target device data, the n generated current datasets, the n generated voltage datasets, and the first operating dataset;

[0010] Detecting the combiner box according to the n generated current data sets, the expected output current data set, and the first working data set to obtain a target detection result and detection process data;

[0011] When the target detection result is a work abnormality, determining a target abnormality score based on the detection process data;

[0012] determining a target control strategy according to the target anomaly score;

[0013] The smart fuse is controlled to perform a circuit breaking operation according to the target control strategy to protect the safety of the target power generation system.

[0014] In a second aspect, an embodiment of the present application provides a control device for a fuse for combining box protection, which is applied to a controller in a target power generation system, wherein the target power generation system further includes: n power generation equipment and a combiner box; the combiner box is provided with an intelligent fuse, where n is a positive integer. The device includes: a collection unit, an abnormality detection unit, and a control unit, wherein:

[0015] The acquisition unit is configured to acquire a power generation current data set and a power generation voltage data set of the n power generation devices within a first preset time period to obtain n power generation current data sets and n power generation voltage data sets; obtain target device data of the combiner box; and acquire a first working data set of the combiner box within the first preset time period;

[0016] The abnormality detection unit is configured to determine an expected output current dataset based on the target device data, the n generated current datasets, the n generated voltage datasets, and the first working dataset; and detect the combiner box based on the n generated current datasets, the expected output current dataset, and the first working dataset to obtain a target detection result and detection process data;

[0017] The control unit is used to determine a target abnormality score based on the detection process data when the target detection result is an operational abnormality; determine a target control strategy based on the target abnormality score; and control the smart fuse to perform a circuit breaking operation according to the target control strategy to protect the safety of the target power generation system.

[0018] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application.

[0020] In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0021] The implementation of this application has the following beneficial effects:

[0022] As can be seen, the control method for a fuse for combiner box protection described in the present application collects generated current datasets and generated voltage datasets of n power generation devices within a first preset time period to obtain n generated current datasets and n generated voltage datasets; obtains target device data of the combiner box; collects a first operating dataset of the combiner box within the first preset time period; determines an expected output current dataset based on the target device data, the n generated current datasets, the n generated voltage datasets, and the first operating dataset; detects the combiner box based on the n generated current datasets, the expected output current dataset, and the first operating dataset to obtain a target detection result and detection process data; when the target detection result is an operational abnormality, determines a target abnormality score based on the detection process data; determines a target control strategy based on the target abnormality score; and controls the smart fuse to perform a circuit breaking operation based on the target control strategy to protect the safety of the target power generation system. In this way, the target control strategy is determined based on the target abnormality score, with different abnormality scores corresponding to different control strategies; and then controls the smart fuse to perform a circuit breaking operation based on the target control strategy, that is, adopts different levels of control measures based on the actual abnormality of the combiner box, thereby achieving flexible control of the circuit. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0024] Figure 1 is a structural diagram of a target power generation system provided in an embodiment of the present application;

[0025] Figure 2 This is a structural diagram of a combiner box provided in an embodiment of the present application;

[0026] Figure 3 This is a scenario application diagram of a method for controlling a fuse for combiner box protection provided by an embodiment of the present application;

[0027] Figure 4 This is a flow chart of a method for controlling a fuse for combiner box protection provided by an embodiment of the present application;

[0028] Figure 5is a schematic diagram of a first straight line provided in an embodiment of the present application;

[0029] Figure 6 This is a flowchart of a method for determining a target anomaly score provided in an embodiment of the present application;

[0030] Figure 7 This is a block diagram of the functional units of a control device for a fuse used to protect a combiner box provided in an embodiment of the present application;

[0031] Figure 8 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0033] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0034] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document indicates that the associated objects are in an "or" relationship. The "plurality" appearing in the embodiments of this application refers to two or more.

[0035] In the embodiments of the present application, "at least one item" or similar expressions refers to any combination of these items, including any combination of single items or plural items, and refers to one or more, and multiple refers to two or more. For example, at least one item (item) of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0036] The "connection" appearing in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not impose any limitations on this.

[0037] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0038] The electronic device described in the embodiments of the present application may include a power generation system.

[0039] The following describes the relevant contents, concepts, meanings, technical issues, technical solutions, beneficial effects, etc. involved in the embodiments of this application.

[0040] First, some professional terms involved in this application are explained:

[0041] A combiner box is a crucial electrical device. Its primary function is to aggregate the current generated by multiple power generation units (such as photovoltaic strings and wind turbine output lines) and transmit it uniformly to inverters or other downstream equipment. For example, in a photovoltaic power generation system, photovoltaic panels are typically connected in series or parallel to form strings. Each string generates relatively low current. Using a combiner box to aggregate the current from multiple strings improves current transmission efficiency and facilitates subsequent energy conversion and processing.

[0042] See also Figure 1 , Figure 1 This is a schematic diagram of the structure of a target power generation system provided by an embodiment of the present application. It can be seen that the target power generation system (hereinafter referred to as the system) may include: a controller, a combiner box, n power generation equipment and other components, which are not limited here. Among them:

[0043] The controller is the "brain" of the system, controlling and coordinating the operations of its various components. It monitors and analyzes the system's operating status based on pre-set rules and algorithms (for example, the control method for fuses used to protect combiner boxes provided in the embodiments of this application). For example, it collects and analyzes various data from combiner boxes and power generation equipment, including parameters such as voltage, current, and power. Based on this data, the controller can determine whether the system is operating properly and make appropriate decisions, such as adjusting the operating parameters of power generation equipment and controlling the opening and closing of intelligent fuses in the combiner box, to ensure efficient, stable, and safe system operation. Furthermore, the controller can communicate with external systems, upload system operating data, and receive external control commands, enabling remote monitoring and management.

[0044] Combiner boxes: Their primary function is to aggregate current. In a system, the current generated by multiple power generation devices often requires centralized processing. Combiner boxes aggregate the output currents of these devices and then transmit them to downstream equipment, such as inverters or other power processing equipment. Combiner boxes also provide protection features, such as lightning protection and overcurrent protection, to prevent damage to the system caused by lightning strikes, current overloads, and other conditions. Furthermore, combiner boxes monitor parameters such as current and voltage on each input branch and transmit this data to the controller, providing a basis for system operational status monitoring and fault diagnosis.

[0045] Generators are the core components of a system, converting other forms of energy into electricity. For example, in a photovoltaic system, the generators are photovoltaic panels, which convert solar energy into direct current (DC) through the photoelectric effect. In a wind power system, the generators are wind turbines, which use wind power to drive the impellers, which in turn cut through magnetic flux lines, generating alternating current (AC). Different types of generators have different operating principles and characteristics, but they all share the goal of converting renewable energy sources from nature or other sources into electricity, providing power for the entire system. The electricity generated by these generators is collected and processed by a combiner box before being transmitted to the grid or other power-consuming devices.

[0046] See also Figure 2 , Figure 2 This is a structural diagram of a combiner box provided in an embodiment of the present application. It can be seen that the combiner box may include: intelligent fuses, busbars, n input ports and other components, which are not limited here. Among them:

[0047] Smart fuses are primarily used for circuit protection and control. They monitor current, voltage, and other parameters in the circuit in real time. When an abnormal condition such as overload, short circuit, or undervoltage occurs, the smart fuse automatically and quickly disconnects the circuit, preventing further escalation of the fault and protecting the combiner box and other equipment in the entire power generation system from damage. Furthermore, the smart fuse can exchange data with an external controller (for example, the system controller) via a communication interface, receiving remote control commands and implementing protective actions, facilitating remote monitoring and management of the combiner box by operation and maintenance personnel.

[0048] The busbar is a key component in the combiner box for collecting and distributing current. Typically made of a highly conductive metal like copper or aluminum, it is a strip conductor that aggregates current from n input ports, enabling centralized current transmission. With its low resistance and high current-carrying capacity, the busbar reduces energy loss and heat generation during the current-collecting process, ensuring efficient current transmission. It also provides a connection point for subsequently distributing the aggregated current to various output lines or devices.

[0049] n input ports: These are the interfaces between the combiner box and external power generation equipment. Each input port can be connected to a corresponding power generation equipment and receives the current generated by the power generation equipment. These input ports ensure a reliable electrical connection between the power generation equipment and the combiner box, allowing the power output of the power generation equipment to be smoothly collected and processed by the combiner box. Furthermore, the input ports may also have various protection measures, such as overcurrent protection and reverse connection protection, to prevent damage to the combiner box due to power generation equipment failure or incorrect connection.

[0050] In one possible embodiment, see Figure 3 , Figure 3 This is a scenario application diagram of a control method for a fuse for combiner box protection provided by an embodiment of the present application. It can be seen that Figure 3 In the embodiment, n is equal to 4, and there are 4 power generation equipment, namely: thermal power generation equipment, wind power generation equipment, hydropower generation equipment, and photovoltaic power generation equipment; these 4 power generation equipment are all connected to the junction box, and the junction box is also connected to the system controller and electrical equipment respectively, wherein the junction box is provided with an intelligent fuse; when these 4 power generation equipment start working, the control method of the junction box protection fuse provided in the embodiment of the present application can be executed by the controller to protect the safety of the system.

[0051] See also Figure 4 , Figure 4This is a flow chart of a method for controlling a fuse for combiner box protection provided in an embodiment of the present application; the method is applied to a controller in a target power generation system, wherein the target power generation system further includes: n power generation devices and a combiner box; the combiner box is provided with an intelligent fuse, where n is a positive integer. The method may include but is not limited to the following steps:

[0052] S401 : Collecting a power generation current data set and a power generation voltage data set of the n power generation devices within a first preset time period to obtain n power generation current data sets and n power generation voltage data sets.

[0053] In the embodiment of the present application, the first preset time period can be preset in advance or defaulted; the power generation equipment may include at least one of the following: thermal power generation equipment, wind power generation equipment, hydropower generation equipment, photovoltaic power generation equipment, etc., which are not limited here; each power generation equipment corresponds to a power generation current data set and a power generation voltage data set.

[0054] In a specific embodiment, for each power generation device, a suitable current sensor (for example, a Hall effect current sensor, etc.) and a voltage sensor (for example, a resistor divider) can be set on the output circuit of the power generation device. In this way, when the starting moment of the first preset time period is reached, the sensor data of the current sensor and the voltage sensor corresponding to each of the n power generation devices can be collected at a first preset time interval (for example, 1 second), thereby obtaining n power generation current data sets and n power generation voltage data sets. It should be explained that the number of data contained in each data set is equal, because the collection period (i.e., the first preset time period) and the collection interval (i.e., the preset time interval) are the same, so the number of data ultimately collected is also the same.

[0055] S402: Acquire target device data of the combiner box.

[0056] In the embodiment of the present application, the target device data may include at least one of the following: device model, operating voltage range, operating current range, etc., which are not limited here.

[0057] In a specific embodiment, the manufacturer of the combiner box can be identified and the technical documentation for the device can be obtained from the manufacturer. For example, the manufacturer's website can be visited and the technical documentation for the combiner box can be downloaded from the website. The target device data can then be obtained from the technical documentation. For example, assuming the target device data is the operating voltage range, the device's technical documentation can be used to find sections related to the device's electrical parameters, technical specifications, or performance indicators based on the directory or index. Key parameters such as the operating voltage range are detailed in these sections. For example, some technical documentation will have a dedicated "Electrical Characteristics" or "Technical Specifications" section that clearly lists the device's operating voltage range.

[0058] S403: Collect a first working data set of the combiner box within the first preset time period.

[0059] In an embodiment of the present application, multiple sensors (for example, current sensors, voltage sensors, temperature sensors, etc.) can be set on the output circuit of the combiner box. When the starting moment of the first preset time period is reached, sensor data of the multiple sensors can be collected at a second preset time interval, that is, a first working data set.

[0060] It should be explained that the second preset time interval may be the same as or different from the first preset time interval. In the embodiment of the present application, in order to facilitate calculation, the second preset time interval and the first preset time interval may be set to the same time interval.

[0061] S404 : Determine an expected output current data set according to the target device data, the n power generation current data sets, the n power generation voltage data sets, and the first operating data set.

[0062] In an embodiment of the present application, the working performance of the combiner box can be determined based on the target device data, and then the processing results of the combiner box on n power generation current data sets and n power generation voltage data sets, that is, the expected output current data set, can be predicted based on the working performance and the first working data set.

[0063] Optionally, in step S404, the first working data set includes a first voltage data set; and determining the expected output current data set based on the target device data, the n generated current data sets, the n generated voltage data sets, and the first working data set may include the following steps:

[0064] S41. Determine a total power generation set corresponding to the n power generation devices according to the n power generation current data sets and the n power generation voltage data sets;

[0065] S42. Determine a theoretical output current data set according to the total generated power set and the first voltage data set;

[0066] S43, determining a target efficiency parameter according to the target device data;

[0067] S44. Determine the expected output current data set according to the target efficiency parameter and the theoretical output current data set.

[0068] In an embodiment of the present application, the total power generation set corresponding to the n power generation devices can be determined based on the n power generation current data sets and the n power generation voltage data sets. Specifically, since power is equal to voltage multiplied by current, the n power generation current data sets can be first multiplied by the corresponding power generation voltage data sets in the n power generation voltage data sets to obtain n power generation sets. Then, the power generation powers corresponding to the n power generation sets at the same time can be added together to obtain a total power generation set.

[0069] Take the first power generation voltage dataset and the first power generation current dataset as an example:

[0070] P i =U i ×I i ;

[0071] Where i is a positive integer, U i represents the i-th power generation voltage data in the first power generation voltage data set; I i represents the i-th generated current data in the first generated current data set; P i represents the power corresponding to the i-th generation voltage data; according to the above formula, the generation power set corresponding to the first generation voltage data set can be obtained; the first generation voltage data set is any generation voltage data set among the n generation voltage data sets, and the first generation current data set is the generation current data set corresponding to the first generation voltage data set among the n generation current data sets.

[0072] Then, the theoretical output current data set can be determined based on the total generated power set and the first voltage data set. Specifically, the total generated power in the total generated power set can be divided by the first voltage data at the same time in the first voltage data set to obtain the theoretical output current data set. Then, the target efficiency parameter can be determined based on the target device data. Specifically, the device model of the combiner box can be determined based on the target device data to obtain the target device model. Then, the reference efficiency parameter can be determined based on the target device model. The mapping relationship between the preset device model and the efficiency parameter can be pre-stored, and the reference efficiency parameter corresponding to the target device model can be determined based on the mapping relationship. Then, the historical working data of the combiner box can be obtained from the system database, and the historical average efficiency parameter of the combiner box can be determined based on the historical working data. Specifically, the historical working data can include the total generated power P on the input side of the combiner box. in And the total output power P on the output side out For each time point or data recording interval, the efficiency parameter is calculated according to the following formula:

[0073] η=P out / P in ×100%;

[0074] By performing the above calculation multiple times, multiple historical efficiency parameters can be obtained. For example, at a certain moment, the input power is 1000 watts and the output power is 950 watts. The instantaneous efficiency at that moment is 950 / 1000×100%=95%. Then, the average of these multiple historical efficiency parameters can be calculated to obtain the historical average efficiency parameter. Next, the deviation between the reference efficiency parameter and the historical average efficiency parameter can be determined to obtain the target deviation. The specific calculation formula for the target deviation is:

[0075] Target deviation = |reference efficiency parameter - historical average efficiency parameter| / historical average efficiency parameter × 100%;

[0076] According to the above formula, the target deviation can be obtained; when the target deviation is less than the preset deviation, the reference efficiency parameter is used as the target efficiency parameter; when the target deviation is not less than the preset deviation, the average value of the reference efficiency parameter and the historical average efficiency parameter can be calculated, and the average value is used as the target efficiency parameter.

[0077] Finally, the expected output current dataset can be determined according to the target efficiency parameter and the theoretical output current dataset. Specifically, the target efficiency parameter can be multiplied by each theoretical output current data in the theoretical output current dataset to obtain the expected output current dataset.

[0078] In this way, by gradually calculating the total power generation set and the theoretical output current data set, and finally combining the target efficiency parameters to obtain the expected output current data set, the actual power generation conditions of the power generation equipment and the energy conversion efficiency of the combiner box are fully considered, avoiding the errors caused by simple estimation, and making the prediction of the combiner box output current more accurate.

[0079] S405 : Detect the combiner box according to the n generated current data sets, the expected output current data set, and the first working data set to obtain a target detection result and detection process data.

[0080] In an embodiment of the present application, it is possible to analyze whether the current magnitudes in the n power generation current data sets are within a normal range. If they are not within the normal range, it can be determined that there is an abnormality in the combiner box. In addition, the expected output current data set can be compared with the first working data set to determine whether there is an abnormality in the combiner box, and various data involved in the detection process can be recorded, such as current fluctuations, the difference between the expected output current and the actual output current, etc., so as to obtain the target detection results and detection process data.

[0081] Optionally, in step S405, the first working data set includes a first output current data set; each power generation current data set includes a plurality of power generation current data and a collection time corresponding to each power generation current data set; and detecting the combiner box based on the n power generation current data sets, the expected output current data set, and the first working data set to obtain a target detection result and detection process data may include the following steps:

[0082] A1. Fitting the generated current data in the n generated current data sets and their corresponding collection times to obtain n curves; each curve corresponds to a generated current data set; the abscissa of each curve represents time, and the ordinate represents generated current data;

[0083] A2. Determine a first current threshold and a second current threshold corresponding to the combiner box; the first current threshold is less than the second current threshold;

[0084] A3. Obtain m difference values ​​based on the difference between each expected output current data in the expected output current data set and the corresponding first output current data in the first output current data set; the detection process data includes the m difference values; m is a positive integer;

[0085] A4. Determine the maximum difference among the m differences;

[0086] A5. If the maximum difference is greater than a preset difference, determining that the target detection result includes the operating abnormality;

[0087] A6. If the maximum difference is not greater than the preset difference, fitting is performed based on the m differences and their corresponding acquisition times to obtain a first straight line, where the abscissa of the first straight line represents time and the ordinate represents the difference. The target occurrence time corresponding to the preset difference is predicted based on the first straight line; and the target duration is determined based on the target occurrence time and the end time of the first preset time period.

[0088] A7. When the target duration is not less than a preset duration, determining that the target detection result includes normal operation;

[0089] A8. When the target duration is less than the preset duration, determine the target detection result and the detection process data according to the n curves, the first current threshold, and the second current threshold.

[0090] In the embodiment of the present application, the preset difference and the preset duration can be preset in advance or defaulted.

[0091] In a specific embodiment, fitting can be performed based on the generation current data in n generation current data sets and their corresponding acquisition times to obtain n curves. Taking the second generation current data set as an example, the acquisition time corresponding to each generation current data in the second generation current data set can be first obtained to obtain multiple acquisition times. These multiple acquisition times and the corresponding generation current data in the second generation current data set are combined into coordinate points to obtain multiple coordinate points. Then, a curve fitting method (for example, a polynomial fitting method, a spline method, etc.) can be used to fit these multiple coordinate points to obtain a first curve, wherein the second generation current data set is any generation current data set in the n generation current data sets. This process is repeated n times to obtain n curves.

[0092] Then, the first current threshold and the second current threshold corresponding to the junction box can be determined; then, m difference values ​​can be obtained based on the difference between each expected output current data in the expected output current data set and the corresponding first output current data in the first output current data set; the detection process data includes m difference values; specifically, the data in the expected output current data set and the first output current data set can be arranged in the same time sequence, and then the two data sets can be traversed in sequence, starting from the first data point in the two data sets, and the data in the expected output current data set and the data at the same position in the first output current data set are taken out in turn and subtracted to obtain m difference values; then, the maximum difference among these m difference values ​​can be found; if the maximum difference is greater than the preset difference value, it can be determined that the target detection result includes an operating abnormality.

[0093] If the maximum difference is not greater than the preset difference, fitting is performed based on the m differences and their corresponding acquisition times to obtain a first straight line. For example, the m differences and their corresponding acquisition times can be combined to obtain m coordinate points. Then, the least squares method can be used to perform straight line fitting on these m coordinate points to obtain the first straight line. Then, the target occurrence time corresponding to the preset difference can be predicted based on the first straight line. Specifically, the linear equation of the first straight line y=kx+r can be obtained first, where y is the difference, x is the time, k is the first slope, and r is the intercept. Then, the preset difference can be substituted into the y in the linear equation, and the corresponding x, which is the target occurrence time, can be solved. Further, the target duration can be determined based on the target occurrence time and the end time of the first preset time period. Specifically, the target duration can be obtained by subtracting the end time of the first preset time period from the target occurrence time. When the target duration is not less than the preset duration, it can be determined that the target detection result includes normal operation.

[0094] When the target duration is less than the preset duration, the target detection result and the detection process data may be determined according to the n curves, the first current threshold, and the second current threshold.

[0095] For an example, see Figure 5 , Figure 5 is a schematic diagram of a first straight line provided in an embodiment of the present application, such as Figure 5 As shown, assuming m=6, straight line L represents the first straight line, point o is the coordinate origin, and there are 6 black dots around the first straight line L, each black dot represents one of the m differences; t1 represents the starting time of the first preset time period, and t2 represents the ending time of the first preset time period; straight line A is a first straight line drawn according to the preset difference and parallel to the x-axis (that is, the equation of straight line A is y=s1, s1 represents the preset difference); the abscissa of the first straight line L is time, which can be in seconds (s), and the ordinate is the difference, which can be in amperes (A); Figure 5 It can be seen that the intersection of the first straight line L and the straight line A is point d, and the coordinates of point d are (t3, s1), that is, t3 is the time when the target occurs.

[0096] In this way, by curve fitting the generated current dataset, the time-varying trend of the generated current can be visually visualized, reflecting the operating status of the power generation equipment. Simultaneously, calculating the difference between the expected and actual output currents allows for a comprehensive assessment of the combiner box's performance from another perspective, integrating multi-dimensional data to achieve a comprehensive evaluation of the combiner box's operating status. Furthermore, by employing a hierarchical approach, a preliminary judgment is made by comparing the maximum difference with a preset difference, followed by further analysis based on a comparison of the target duration with the preset duration, and finally a comprehensive judgment is made by combining the curve and current threshold. This progressively deeper detection mechanism effectively improves the accuracy of detection results and reduces the possibility of misjudgments and missed detections.

[0097] Optionally, step A8, determining the target detection result and the detection process data according to the n curves, the first current threshold, and the second current threshold, may include the following steps:

[0098] B1. Obtaining the maximum values ​​of the n curves within the first preset time period to obtain n maximum values; each curve corresponds to a maximum value; and the detection process data also includes the n maximum values;

[0099] B2. When no value greater than the first current threshold value exists among the n maximum values, determining that the target detection result includes normal operation;

[0100] B3. When there is a value greater than the first current threshold among the n maximum values ​​and no value greater than the second current threshold, determine a first difference between the n maximum values ​​and the first current threshold to obtain n first differences; and determine a first mean corresponding to the n first differences;

[0101] B4. If the first mean is greater than a preset mean, determining that the target detection result includes the operating abnormality;

[0102] B5. If the first mean is not greater than the preset mean, determining that the target detection result includes normal operation;

[0103] B6. When a value greater than the second current threshold exists among the n maximum values, determine that the target detection result includes the operation abnormality.

[0104] In the embodiment of the present application, the preset mean value can be preset in advance or defaulted.

[0105] In a specific embodiment, the maximum values ​​of n curves within the first preset time period can be obtained to obtain n maximum values. Specifically, the curve equation corresponding to each of the n curves can be obtained to obtain n curve equations. Then, the first interval (t1, t2) corresponding to the first preset time period can be determined, where t1 is the starting time of the first preset time period, and t2 is the end time of the first preset time period. Then, the maximum value of each of the n curve equations in the first interval can be determined to obtain n maximum values. The detection process data also includes n maximum values. When there is no value greater than the first current threshold among the n maximum values, that is, when the n maximum values ​​are all less than the first current threshold, it is determined that the target detection result includes normal operation.

[0106] When there is a value greater than the first current threshold among the n maximum values ​​and there is no value greater than the second current threshold, the first difference between each maximum value among the n maximum values ​​and the first current threshold can be calculated to obtain n first differences; the average value of the n first differences, that is, the first mean value, can also be calculated; if the first mean value is greater than the preset mean value, it is determined that the target detection result includes abnormal operation; if the first mean value is not greater than the preset mean value, it is determined that the target detection result includes normal operation.

[0107] When a value greater than the second current threshold exists among the n maximum values, it is determined that the target detection result includes an operation abnormality.

[0108] Thus, by setting the first and second current thresholds, a clear quantitative standard is provided for determining the operating status of the combiner box. By comparing the maximum values ​​of n curves within a first preset time period with these thresholds, different operating conditions can be clearly defined. For example, if all maximum values ​​are no greater than the first current threshold, the combiner box can be directly determined to be operating normally. This clear basis for judgment facilitates rapid conclusions and improves detection efficiency.

[0109] Optionally, step A2, determining the first current threshold and the second current threshold corresponding to the combiner box, may include the following steps:

[0110] C1. Determine the rated current value and the maximum current value corresponding to each of the n power generation devices to obtain n rated current values ​​and n maximum current values;

[0111] C2. Determine a reference first current threshold according to the n rated current values ​​and a first preset coefficient;

[0112] C3. Determine a reference second current threshold according to the n maximum current values ​​and a second preset coefficient;

[0113] C4. Obtaining a target usage scenario corresponding to the target power generation system and target environmental parameters corresponding to the combiner box;

[0114] C5. Determine a first fine-tuning factor corresponding to the target environment parameter; determine a second fine-tuning factor corresponding to the target usage scenario;

[0115] C6. Fine-tune the reference first current threshold according to the first fine-tuning factor and the second fine-tuning factor to obtain a target first current threshold;

[0116] C7. Fine-tune the reference second current threshold according to the first fine-tuning factor and the second fine-tuning factor to obtain a target second current threshold;

[0117] C8. Determine a target maximum current value corresponding to the combiner box based on the target device data;

[0118] C9. When the target second current threshold is not greater than the target maximum current value, determining the first current threshold and the second current threshold according to the target first current threshold and the target second current threshold;

[0119] C10. When the target second current threshold is greater than the target maximum current value, determine the second current threshold according to the target maximum current value; determine a target deviation between the target first current threshold and the target maximum current value; adjust the target first current threshold according to the target deviation to obtain the first current threshold, so that the target maximum current value is greater than the first current threshold.

[0120] In the embodiment of the present application, the first preset coefficient and the second preset coefficient can be preset or defaulted in advance; the target usage scenario may include at least one of the following: industrial electricity usage scenario, commercial electricity usage scenario, residential electricity usage scenario, etc., which are not limited here; the target environmental parameters may include at least one of the following: temperature, humidity, light intensity, wind speed, etc., which are not limited here.

[0121] In a specific embodiment, the rated current value and the maximum current value corresponding to each of the n power generation devices can be determined first to obtain n rated current values ​​and n maximum current values. For example, the equipment manuals of the n power generation devices can be obtained to obtain n equipment manuals. The rated current value and the maximum current value of each power generation device can be queried from these n equipment manuals to obtain n rated current values ​​and n maximum current values. Then, the reference first current threshold can be determined based on the n rated current values ​​and the first preset coefficient. Specifically, the n rated current values ​​can be added together to obtain the total rated current value, and then the first preset coefficient can be multiplied by the total rated current value to obtain the reference first current threshold. Then, the reference second current threshold can be determined based on the n maximum current values ​​and the second preset coefficient. Specifically, the n maximum current values ​​can be added together to obtain the total maximum current value, and then the second preset coefficient can be multiplied by the total maximum current value to obtain the reference second current threshold.

[0122] Next, the target usage scenario corresponding to the target power generation system and the target environmental parameters corresponding to the junction box can be obtained. Specifically, the users served by the target power generation system can be obtained, and the user types of these users can be determined, such as residential users, commercial users, industrial users, etc. Then, the target usage scenario can be determined according to the user type. For example, the target usage scenario corresponding to residential users is the residential electricity usage scenario, the target usage scenario corresponding to industrial users is the industrial electricity usage scenario, and so on. Then, the environmental parameters in the junction box can be detected by the environmental sensor, thereby obtaining the target environmental parameters. For example, assuming that the target environmental parameter is light intensity, the environmental sensor can be a photodiode sensor.

[0123] Next, a first fine-tuning factor corresponding to the target environmental parameter can be determined. Specifically, a mapping relationship between a preset environmental parameter and a fine-tuning factor can be pre-stored, and the first fine-tuning factor corresponding to the target environmental parameter can be determined based on the mapping relationship. Next, a second fine-tuning factor corresponding to the target usage scenario can be determined. Similarly, a mapping relationship between a preset usage scenario and a fine-tuning factor can be pre-stored, and the second fine-tuning factor corresponding to the target usage scenario can be determined based on the mapping relationship. The value range of the first fine-tuning factor and the second fine-tuning factor can both be -0.3 to 0.3. Then, the reference first current threshold can be fine-tuned according to the first fine-tuning factor and the second fine-tuning factor. The specific calculation formula is as follows:

[0124] Target first current threshold = reference first current threshold × (1 + first fine-tuning factor) × (1 + second fine-tuning factor);

[0125] According to the above formula, the target first current threshold can be obtained; then, the reference second current threshold can be fine-tuned according to the first fine-tuning factor and the second fine-tuning factor to obtain the target second current threshold. Specifically, the method for obtaining the target second current threshold can be the same as the method for obtaining the target first current threshold, which will not be repeated here; then, the target maximum current value corresponding to the combiner box can be determined based on the target device data. Specifically, the target device model corresponding to the combiner box can be determined according to the target device data, and then the target maximum current value can be determined according to the target device model. For example, the mapping relationship between the preset device model and the maximum current value can be pre-stored, and the target maximum current value corresponding to the target device model is determined based on the mapping relationship.

[0126] When the target second current threshold is not greater than the target maximum current value, the first current threshold and the second current threshold are determined according to the target first current threshold and the target second current threshold. Specifically, the target first current threshold can be directly used as the first current threshold, and the target second current threshold can be used as the second current threshold.

[0127] When the target second current threshold is greater than the target maximum current value, the second current threshold is determined based on the target maximum current value, that is, the target maximum current value is used as the second current threshold. Then, the target deviation between the target first current threshold and the target maximum current value can be determined. The specific calculation formula is as follows:

[0128] Target deviation = (target first current threshold - target maximum current value) / target maximum current value × 100%;

[0129] According to the above formula, the target deviation can be obtained. Then, the target first current threshold can be adjusted according to the target deviation to obtain the first current threshold, so that the target maximum current value is greater than the first current threshold. For example, when the target deviation is small (for example, less than 30%), a more gentle adjustment method can be adopted, for example, the target first current threshold is reduced by 5%-10% of its current value each time. In this way, the target first current threshold can be gradually made close to the target maximum current value without causing the system to be overly sensitive due to excessive adjustment amplitude. Assuming that the target first current threshold is 110A, the target maximum current value is 100A, and the deviation is 10%, if it is reduced by 5%, it only needs to be reduced twice to obtain the first current threshold; when the target deviation is large (for example, less than 50%), a more aggressive adjustment method can be adopted, for example, the target first current threshold is reduced by 30%-40% of its current value each time.

[0130] In this way, by determining the rated and maximum current values ​​of n power generation devices and calculating the reference current threshold accordingly, the individual differences and performance limitations of each power generation device can be fully accounted for, making the set current threshold more consistent with the actual device operation and avoiding damage to the device due to unreasonable current settings. In addition, by obtaining the target power generation system's usage scenario and the environmental parameters of the combiner box, and determining the corresponding fine-tuning factor to fine-tune the reference current threshold, the current threshold can be adapted to different operating environments and usage requirements. For example, in high-temperature environments, where heat dissipation is difficult for the device, the current threshold may need to be lowered to prevent overheating. In high-load usage scenarios, the current threshold may need to be appropriately increased to meet the power requirements of the power generation system, thereby improving system stability and reliability.

[0131] S406: When the target detection result is an operational abnormality, determine a target abnormality score according to the detection process data.

[0132] In the embodiment of the present application, the anomaly score is a numerical indicator used to quantitatively describe the degree of abnormality in the operation of the power generation system. Specifically, the anomaly score can be a value from 0 to 10, and a larger value indicates a more severe degree of abnormality.

[0133] In a specific embodiment, when the target detection result is an operational abnormality, the detection process data may be analyzed to obtain a target abnormality score.

[0134] Optionally, when the maximum difference is greater than the preset difference, refer to Figure 6 , Figure 6 This is a flow chart of a method for determining a target anomaly score provided by an embodiment of the present application. Step S406, determining the target anomaly score based on the detection process data, may include: Figure 6 Steps shown:

[0135] D1. Determine the ratio of each of the m differences to the preset difference to obtain m ratios;

[0136] D2. Determine a ratio greater than or equal to 1 and b ratios less than 1 among the m ratios; a and b are both natural numbers less than m, and a + b = m;

[0137] D3. Determine a target abnormality ratio based on the a ratio and the b ratio;

[0138] D4. Determine a reference anomaly score corresponding to the target anomaly ratio;

[0139] D5. Determine the difference between the maximum difference and the preset difference to obtain a target difference;

[0140] D6. Determine the target optimization factor corresponding to the target difference;

[0141] D7. Optimize the reference anomaly score according to the target optimization factor to obtain the target anomaly score.

[0142] In the embodiment of the present application, the preset difference value can be preset in advance or defaulted.

[0143] In a specific embodiment, the ratio of each difference value in the m difference values ​​to the preset difference value can be calculated to obtain m ratios. Then, a ratio greater than or equal to 1 and b ratios less than 1 can be found among the m ratios. Then, the target abnormality ratio can be determined based on the a ratio and the b ratio. The specific calculation formula is as follows:

[0144] Target abnormality ratio = a / b × 100%;

[0145] According to the above formula, the target abnormality ratio can be obtained; then, the reference abnormality score corresponding to the target abnormality ratio can be determined. For example, a mapping relationship between a preset abnormality ratio and an abnormality score can be pre-stored, and the reference abnormality score corresponding to the target abnormality ratio can be determined based on the mapping relationship; then, the preset difference can be subtracted from the maximum difference to obtain the target difference; further, the target optimization factor corresponding to the target difference can be determined. For example, a mapping relationship between a preset difference and an optimization factor can be pre-stored, and the target optimization factor corresponding to the target difference can be determined based on the mapping relationship; wherein the value range of the target optimization factor can be -0.12~0.12; finally, the reference abnormality score can be optimized according to the target optimization factor. The specific calculation formula is as follows:

[0146] Target anomaly score = reference anomaly score × (1 + target optimization factor);

[0147] According to the above formula, the target anomaly score can be obtained.

[0148] By calculating the ratio of m differences to a preset value and distinguishing between ratios greater than or equal to 1 and those less than 1, we can comprehensively measure data deviations from the preset value from different perspectives. Ratios greater than or equal to 1 reflect data points with significant anomalies, while ratios less than 1 indicate relatively minor deviations. By comprehensively considering these two ratios to determine the target anomaly ratio, we can more meticulously characterize the anomaly characteristics of the overall data, avoiding focusing on extreme cases while ignoring the overall distribution.

[0149] Optionally, when the first mean is greater than the preset mean, step S406, determining a target anomaly score according to the detection process data, may include the following steps:

[0150] E1. Determine the average difference corresponding to the m differences;

[0151] E2. Determine a first anomaly score corresponding to the average difference;

[0152] E3. Determine the difference between the maximum difference and the preset difference to obtain a second difference;

[0153] E4. Determine a target fine-tuning factor corresponding to the second difference;

[0154] E5. Fine-tune the first anomaly score according to the target fine-tuning factor to obtain a second anomaly score;

[0155] E6. Determine a second mean corresponding to the n maximum values;

[0156] E7. Determine a third anomaly score corresponding to the second mean;

[0157] E8. Determine the target anomaly score according to the second anomaly score and the third anomaly score.

[0158] In an embodiment of the present application, the average value corresponding to the m differences, that is, the average difference, can be calculated; then, the first anomaly score corresponding to the average difference can be determined. Specifically, a mapping relationship between a preset difference and anomaly score can be pre-stored, and the first anomaly score corresponding to the average difference can be determined based on the mapping relationship; then, the preset difference can be subtracted from the maximum difference to obtain a second difference; further, a target fine-tuning factor corresponding to the second difference can be determined. Specifically, a mapping relationship between a preset difference and a fine-tuning factor can be pre-stored, and the target fine-tuning factor corresponding to the second difference can be determined based on the mapping relationship, wherein the value range of the target fine-tuning factor can be -0.15~0.15; then, the first anomaly score can be fine-tuned according to the target fine-tuning factor. The specific calculation formula is as follows:

[0159] Second anomaly score = first anomaly score × (1 + target fine-tuning factor);

[0160] According to the above formula, the second anomaly score can be obtained; then, the average value corresponding to the n maximum values, that is, the second mean value, can be calculated; then, the third anomaly score corresponding to the second mean value can be determined. For example, a mapping relationship between a preset mean value and anomaly score can be pre-stored, and the third anomaly score corresponding to the second mean value can be determined based on the mapping relationship; finally, the target anomaly score can be determined based on the second anomaly score and the third anomaly score. For example, the average anomaly score of the second anomaly score and the third anomaly score can be calculated, and the average anomaly score can be used as the target anomaly score.

[0161] In this way, by calculating the average difference of m differences and determining its corresponding first anomaly score, we can comprehensively reflect the average level of data deviation from the preset value. This helps to understand the degree of anomaly in the system under general circumstances, avoid the excessive influence of individual extreme data points on the overall evaluation, and make the evaluation results more stable and representative. In addition, calculating the difference between the maximum difference and the preset difference (i.e., the second difference) and determining its corresponding target fine-tuning factor can highlight extreme anomalies in the data. Extreme values ​​may indicate serious system problems. By introducing a target fine-tuning factor to fine-tune the first anomaly score to obtain the second anomaly score, we can take into account the impact of extreme cases on the anomaly score, making the score more accurately reflect the actual system status.

[0162] Optionally, when a value greater than the second current threshold exists among the n maximum values, step S406, determining a target abnormality score according to the detection process data, may include the following steps:

[0163] Determine the maximum value among the n maximum values ​​to obtain a target maximum value; determine a target anomaly score based on the target maximum value. Specifically, a mapping relationship between preset maximum values ​​and anomaly scores can be pre-stored, and the target anomaly score corresponding to the target maximum value is determined based on the mapping relationship.

[0164] Optionally, step E8, determining the target anomaly score according to the second anomaly score and the third anomaly score, includes:

[0165] F1. Determine a first acquisition device corresponding to the first working data set;

[0166] F2. Determine a first acquisition accuracy corresponding to the first acquisition device;

[0167] F3. Determine a collection device corresponding to each of the n generation current data sets to obtain n collection devices;

[0168] F4. Obtaining the acquisition accuracy of each of the n acquisition devices to obtain n acquisition accuracies;

[0169] F5. Determine the average acquisition accuracy corresponding to the n acquisition accuracies;

[0170] F6. Determine a first weight and a second weight according to the first acquisition accuracy and the average acquisition accuracy; the sum of the first weight and the second weight is 1;

[0171] F7. Determine the target anomaly score based on the first weight, the second weight, the second anomaly score, and the third anomaly score.

[0172] In the embodiment of the present application, the acquisition device may include at least one of the following: a current sensor, a voltage sensor, a power sensor, a smart meter, a data collector, etc., which are not limited here.

[0173] In a specific embodiment, the first acquisition device corresponding to the first working data set can be determined first. Specifically, the metadata of the first working data set can be queried. Metadata is data about data, and usually contains relevant information of the data set, such as the identification of the acquisition device, the acquisition time, the acquisition location, etc. The first acquisition device can be determined based on the metadata of the first working data set. For example, assuming that there is a "device_id" field in the metadata, which clearly records the device number of the device that collected the data set, or there is a "device_type" field, which records the device type of the acquisition device, the first acquisition device can be determined based on the device number or the device type.

[0174] Next, the first acquisition accuracy corresponding to the first acquisition device can be determined. Specifically, the device manual of the first acquisition device can be obtained. The device manual generally clearly gives the accuracy index of the acquisition device, that is, the first acquisition accuracy. For example, assuming that the first acquisition device is a pressure sensor, its manual will indicate that its measurement accuracy is 0.5%, that is, the first acquisition accuracy is 0.5%.

[0175] Furthermore, the acquisition device corresponding to each of the n generation current data sets is determined to obtain n acquisition devices. Specifically, the method for determining the n acquisition devices can be the same as the method for determining the first acquisition device, which will not be described in detail here. Then, the acquisition accuracy of each of the n acquisition devices can be obtained to obtain n acquisition accuracy. Similarly, the method for obtaining the n acquisition accuracy can be the same as the method for obtaining the first acquisition accuracy, which will not be described in detail here. Then, the average of the n acquisition accuracies, that is, the average acquisition accuracy, can be calculated. Then, the first weight and the second weight can be determined based on the first acquisition accuracy and the average acquisition accuracy. The specific calculation formula is as follows:

[0176] First acquisition accuracy / average acquisition accuracy = first weight / second weight;

[0177] First weight + second weight = 1;

[0178] The above two formulas can be combined to obtain the first weight and the second weight. Finally, a weighted operation can be performed based on the first weight, the second weight, the second anomaly score, and the third anomaly score. The specific calculation formula is as follows:

[0179] Target anomaly score = first weight × second anomaly score + second weight × third anomaly score;

[0180] According to the above formula, the target anomaly score can be obtained.

[0181] Thus, by determining the target anomaly score based on the first weight, second weight, second anomaly score, and third anomaly score, this comprehensive weighted approach fully considers the impact of different factors on the anomaly score. The second and third anomaly scores reflect data anomalies from different perspectives, and the introduction of weights rationally adjusts these two scores based on the accuracy of the data acquisition equipment, avoiding the one-sidedness that can result from a single score. This improves the accuracy and reliability of the target anomaly score, allowing for more accurate identification of anomalies in the power generation system and providing stronger support for subsequent fault diagnosis and maintenance.

[0182] S407: Determine a target control strategy according to the target anomaly score.

[0183] In the embodiment of the present application, the target control strategy may include one of the following: a short-term overload tolerance strategy, a graded current limiting strategy, a fast interruption strategy, etc., which are not limited here.

[0184] In a specific embodiment, the target abnormality level corresponding to the target abnormality score can be determined first. For example, if the target abnormality score is between 0 and 3, the target abnormality level is determined to be a "low abnormality level", if the target abnormality score is between 4 and 7, the target abnormality level is determined to be a "medium abnormality level", and if the target abnormality score is between 8 and 10, the target abnormality level is determined to be a "high abnormality level". Then, the target control strategy is determined according to the target abnormality level. For example, the mapping relationship between the preset abnormality level and the control strategy can be pre-stored, and the control strategy corresponding to the target abnormality level is determined based on the mapping relationship.

[0185] S408: Control the smart fuse to perform a circuit breaking operation according to the target control strategy to protect the safety of the target power generation system.

[0186] In an embodiment of the present application, the smart fuse can be controlled to perform a circuit-breaking operation according to the target control strategy within a second preset time period, thereby protecting the safety of the target power generation system; wherein, the second preset time period can be preset in advance or defaulted; it should be explained that the end time of the second preset time period is equal to or later than the end time of the first preset time period.

[0187] For example, when the target control strategy is a short-term overload tolerance strategy, the smart fuse can monitor the system's current, voltage, and other parameters in real time to calculate the system's load. When it detects that the system load exceeds a certain percentage of the rated load (such as 10% to 20%), the short-term overload tolerance mechanism is activated and a timer begins. During the set tolerance time (such as 3 to 5 minutes), the load situation is continuously monitored. If the load gradually decreases to within the rated load range within the tolerance time, the system continues to operate normally without performing a circuit breaker. If the load is still higher than the rated load after the tolerance time expires, the smart fuse determines that the overload is severe and performs a circuit breaker, severing the connection between the power generation system and the external circuit to protect the power generation system equipment from overload damage.

[0188] When the target control strategy is a graded current limiting strategy, multiple current limiting threshold levels can be set based on system equipment parameters and safety requirements. For example, the first level current limiting threshold is 10%-20% above the rated current, the second level current limiting threshold is 20%-30% above the rated current, and the third level current limiting threshold is more than 30% above the rated current. When the system current reaches the first level current limiting threshold, the smart fuse adjusts its conduction characteristics, such as increasing its on-resistance, to limit the current to a level slightly above the first level current limiting threshold (e.g., approximately 15% above the rated current). If the current continues to rise and reaches the second level current limiting threshold, the smart fuse further increases the current limiting intensity, limiting the current to near the second level current limiting threshold (e.g., approximately 25% above the rated current). Triggering a circuit breaker: If, after implementing graded current limiting measures, the current cannot be effectively controlled and continues to rise above the set maximum current limiting threshold, or other serious abnormalities occur (such as excessive equipment temperature or abnormal voltage fluctuations), the smart fuse will trigger a circuit breaker, disconnecting the circuit to prevent system damage due to overcurrent.

[0189] When the target control strategy is the rapid interruption strategy, the smart fuse directly triggers the circuit-breaking operation. Through the tripping mechanism or electronic switch inside the smart fuse, the circuit is cut off in an extremely short time (usually between a few milliseconds and tens of milliseconds), isolating the power generation system from the external circuit, protecting the equipment safety of the power generation system at the fastest speed, and preventing the fault from further expanding and causing more serious losses.

[0190] It needs to be explained that after the smart fuse is broken, target alarm information can also be generated according to the target abnormality level. Specifically, corresponding target alarm information can be generated according to the high or low target abnormality level. The mapping relationship between the preset abnormality level and the alarm information can be pre-stored, and the target alarm information corresponding to the target abnormality level can be determined based on the mapping relationship. Then, the target alarm information is sent to the system staff as soon as possible. The target alarm information can be sent in the form of text messages, emails, system pop-ups, prompt voices, etc., to transmit the target alarm information to the staff to guide the staff to repair the system or further detect abnormal situations.

[0191] In summary, the control method for a fuse for combiner box protection described in the present application collects generated current datasets and generated voltage datasets of n power generation devices within a first preset time period to obtain n generated current datasets and n generated voltage datasets; obtains target device data of the combiner box; collects a first operating dataset of the combiner box within the first preset time period; determines an expected output current dataset based on the target device data, the n generated current datasets, the n generated voltage datasets, and the first operating dataset; detects the combiner box based on the n generated current datasets, the expected output current dataset, and the first operating dataset to obtain a target detection result and detection process data; when the target detection result indicates an operating abnormality, determines a target abnormality score based on the detection process data; determines a target control strategy based on the target abnormality score; and controls the smart fuse to perform a circuit breaking operation based on the target control strategy to protect the safety of the target power generation system. In this way, the target control strategy is determined based on the target abnormality score, with different abnormality scores corresponding to different control strategies; and then controls the smart fuse to perform a circuit breaking operation based on the target control strategy, that is, adopts different levels of control measures based on the actual abnormality of the combiner box, thereby achieving flexible control of the circuit.

[0192] See also Figure 7 , Figure 7 This is a block diagram of the functional units of a control device 700 for a combiner box protective fuse provided in an embodiment of the present application. The control device 700 for a combiner box protective fuse is applied to a controller in a target power generation system. The target power generation system further includes: n power generation equipment and a combiner box; the combiner box is provided with an intelligent fuse, where n is a positive integer. The control device 700 for a combiner box protective fuse includes: a data acquisition unit 701, an abnormality detection unit 702, and a control unit 703, wherein:

[0193] The acquisition unit 701 is configured to acquire a generated current dataset and a generated voltage dataset of the n generating devices within a first preset time period, thereby obtaining n generated current datasets and n generated voltage datasets; obtain target device data of the combiner box; and acquire a first working dataset of the combiner box within the first preset time period.

[0194] The abnormality detection unit 702 is configured to determine an expected output current dataset based on the target device data, the n generated current datasets, the n generated voltage datasets, and the first working dataset; and detect the combiner box based on the n generated current datasets, the expected output current dataset, and the first working dataset to obtain a target detection result and detection process data.

[0195] The control unit 703 is used to determine a target abnormality score based on the detection process data when the target detection result is an operational abnormality; determine a target control strategy based on the target abnormality score; and control the smart fuse to perform a circuit breaking operation according to the target control strategy to protect the safety of the target power generation system.

[0196] In a specific implementation, the control device 700 for the combiner box protection fuse described in the embodiment of the present invention may also execute other implementations described in the control method for the combiner box protection fuse provided in the above embodiment of the present invention, which will not be described in detail here.

[0197] See also Figure 8 , Figure 8 It is a structural diagram of an electronic device provided in an embodiment of the present application. The electronic device may include a processor, a memory, a communication interface and one or more programs. The processor, memory and communication interface may be interconnected through a bus; the above one or more programs are stored in the above memory and are configured to be executed by the above processor; in the embodiment of the present application, the above program includes a method for executing part or all of the steps in the control method of the junction box protection fuse provided in the above embodiment of the present invention.

[0198] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0199] The present application also provides a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may comprise an electronic device.

[0200] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0201] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0202] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0203] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0204] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, removable hard disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and storage medium can also exist as discrete components in the terminal device or the management device.

[0205] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part through software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part.

[0206] The aforementioned computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible by a computer or a data storage device such as a server or data center that integrates one or more available media.

[0207] The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0208] The modules / units included in the various devices and products described in the above embodiments may be software modules / units, hardware modules / units, or partly software modules / units and partly hardware modules / units. For example, for various devices and products applied to or integrated into a chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least part of the modules / units may be implemented in the form of software programs, which run on a processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated into a chip module, the modules / units included therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as a chip, circuit module, etc.) or different components of the chip module, or at least part of the modules / units may be It is implemented in the form of a software program, which runs on the processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or different components in the terminal equipment, or at least some modules / units can be implemented in the form of a software program, which runs on the processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits.

[0209] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only a specific implementation method of the embodiments of the present application and is not intended to limit the scope of protection of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the scope of protection of the embodiments of the present application.

Claims

1. A method for controlling a fuse for combining box protection, characterized in that: A controller applied to a target power generation system, wherein the target power generation system further comprises: n power generation devices and a combiner box; the combiner box is provided with an intelligent fuse, where n is a positive integer, and the method comprises: Collecting the generated current data set and the generated voltage data set of the n power generation devices within a first preset time period to obtain n generated current data sets and n generated voltage data sets; Acquiring target device data of the combiner box; Collecting a first working data set of the combiner box within the first preset time period; determining an expected output current dataset based on the target device data, the n generated current datasets, the n generated voltage datasets, and the first operating dataset; Detecting the combiner box according to the n generated current data sets, the expected output current data set, and the first working data set to obtain a target detection result and detection process data; When the target detection result is a work abnormality, determining a target abnormality score based on the detection process data; determining a target control strategy according to the target anomaly score; The smart fuse is controlled to perform a circuit breaking operation according to the target control strategy to protect the safety of the target power generation system.

2. The method according to claim 1, wherein The first working data set includes a first voltage data set; and determining the expected output current data set according to the target device data, the n generated current data sets, the n generated voltage data sets, and the first working data set includes: Determining a total power generation set corresponding to the n power generation devices according to the n power generation current data sets and the n power generation voltage data sets; determining a theoretical output current data set according to the total generated power set and the first voltage data set; determining a target efficiency parameter according to the target device data; The expected output current dataset is determined according to the target efficiency parameter and the theoretical output current dataset.

3. The method according to claim 1 or 2, wherein: The first working data set includes a first output current data set; each generated current data set includes a plurality of generated current data and a collection time corresponding to each generated current data set; the combiner box is detected based on the n generated current data sets, the expected output current data set, and the first working data set to obtain a target detection result and detection process data, including: Fitting is performed based on the generated current data in the n generated current data sets and their corresponding collection times to obtain n curves; each curve corresponds to a generated current data set; the abscissa of each curve is time, and the ordinate is generated current data; Determine a first current threshold and a second current threshold corresponding to the combiner box; the first current threshold is less than the second current threshold; m difference values ​​are obtained according to the difference between each expected output current data in the expected output current data set and the corresponding first output current data in the first output current data set; the detection process data includes the m difference values; m is a positive integer; Determining a maximum difference among the m differences; If the maximum difference is greater than a preset difference, determining that the target detection result includes the operating abnormality; If the maximum difference is not greater than the preset difference, a first straight line is obtained by fitting the m differences and their corresponding acquisition times; the abscissa of the first straight line is time, and the ordinate is the difference; the target occurrence time corresponding to the preset difference is predicted based on the first straight line; the target duration is determined based on the target occurrence time and the end time of the first preset time period; When the target duration is not less than a preset duration, determining that the target detection result includes normal operation; When the target duration is less than the preset duration, the target detection result and the detection process data are determined according to the n curves, the first current threshold, and the second current threshold.

4. The method according to claim 3, wherein The determining the target detection result and the detection process data according to the n curves, the first current threshold, and the second current threshold includes: Obtaining the maximum values ​​of the n curves within the first preset time period to obtain n maximum values; each curve corresponds to a maximum value; the detection process data also includes the n maximum values; When no value greater than the first current threshold value exists among the n maximum values, determining that the target detection result includes normal operation; When a value greater than the first current threshold exists among the n maximum values ​​and a value greater than the second current threshold does not exist, determining first differences between the n maximum values ​​and the first current threshold to obtain n first differences; and determining a first mean corresponding to the n first differences; If the first mean is greater than a preset mean, determining that the target detection result includes the operating abnormality; If the first mean is not greater than the preset mean, determining that the target detection result includes normal operation; When a value greater than the second current threshold exists among the n maximum values, it is determined that the target detection result includes the operation abnormality.

5. The method according to claim 3, wherein The determining the first current threshold and the second current threshold corresponding to the combiner box includes: Determining a rated current value and a maximum current value corresponding to each of the n power generation devices to obtain n rated current values ​​and n maximum current values; Determining a reference first current threshold according to the n rated current values ​​and a first preset coefficient; Determining a reference second current threshold according to the n maximum current values ​​and a second preset coefficient; Obtaining a target usage scenario corresponding to the target power generation system and target environmental parameters corresponding to the combiner box; Determining a first fine-tuning factor corresponding to the target environmental parameter; determining a second fine-tuning factor corresponding to the target usage scenario; Fine-tune the reference first current threshold according to the first fine-tuning factor and the second fine-tuning factor to obtain a target first current threshold; Fine-tune the reference second current threshold according to the first fine-tune factor and the second fine-tune factor to obtain a target second current threshold; determining a target maximum current value corresponding to the combiner box based on the target device data; When the target second current threshold is not greater than the target maximum current value, determining the first current threshold and the second current threshold according to the target first current threshold and the target second current threshold; When the target second current threshold is greater than the target maximum current value, the second current threshold is determined according to the target maximum current value; the target deviation between the target first current threshold and the target maximum current value is determined; and the target first current threshold is adjusted according to the target deviation to obtain the first current threshold, so that the target maximum current value is greater than the first current threshold.

6. The method according to claim 3, wherein When the maximum difference is greater than the preset difference, determining a target abnormality score according to the detection process data includes: Determine a ratio between each of the m differences and the preset difference to obtain m ratios; Determine a ratio greater than or equal to 1 and b ratios less than 1 among the m ratios; a and b are both natural numbers less than m, and a+b=m; Determining a target abnormality ratio according to the a ratio and the b ratio; Determining a reference anomaly score corresponding to the target anomaly ratio; Determine the difference between the maximum difference and the preset difference to obtain a target difference; Determining a target optimization factor corresponding to the target difference; The reference anomaly score is optimized according to the target optimization factor to obtain the target anomaly score.

7. The method according to claim 4, wherein When the first mean is greater than the preset mean, determining a target anomaly score according to the detection process data includes: Determine an average difference value corresponding to the m difference values; determining a first anomaly score corresponding to the average difference; Determine the difference between the maximum difference and the preset difference to obtain a second difference; determining a target fine-tuning factor corresponding to the second difference; Fine-tune the first anomaly score according to the target fine-tuning factor to obtain a second anomaly score; Determine a second mean value corresponding to the n maximum values; determining a third anomaly score corresponding to the second mean; The target anomaly score is determined according to the second anomaly score and the third anomaly score.

8. The method according to claim 7, wherein The determining the target anomaly score according to the second anomaly score and the third anomaly score includes: Determining a first acquisition device corresponding to the first working data set; Determining a first acquisition accuracy corresponding to the first acquisition device; Determining a collection device corresponding to each of the n generation current data sets to obtain n collection devices; Obtaining the acquisition accuracy of each of the n acquisition devices to obtain n acquisition accuracies; Determine the average acquisition accuracy corresponding to the n acquisition accuracies; Determining a first weight and a second weight according to the first acquisition accuracy and the average acquisition accuracy; the sum of the first weight and the second weight is 1; The target anomaly score is determined according to the first weight, the second weight, the second anomaly score, and the third anomaly score.

9. A control device for a fuse used to protect a combiner box, characterized in that: A controller applied to a target power generation system, wherein the target power generation system further comprises: n power generation devices and a combiner box; the combiner box is provided with an intelligent fuse, where n is a positive integer; the device comprises: a collection unit, an abnormality detection unit, and a control unit, wherein: The acquisition unit is configured to acquire a power generation current data set and a power generation voltage data set of the n power generation devices within a first preset time period to obtain n power generation current data sets and n power generation voltage data sets; obtain target device data of the combiner box; and acquire a first working data set of the combiner box within the first preset time period; The abnormality detection unit is configured to determine an expected output current dataset based on the target device data, the n generated current datasets, the n generated voltage datasets, and the first working dataset; and detect the combiner box based on the n generated current datasets, the expected output current dataset, and the first working dataset to obtain a target detection result and detection process data; The control unit is used to determine a target abnormality score based on the detection process data when the target detection result is an operational abnormality; determine a target control strategy based on the target abnormality score; and control the smart fuse to perform a circuit breaking operation according to the target control strategy to protect the safety of the target power generation system.

10. A computer-readable storage medium, characterized in that A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 8.

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