Photovoltaic off-grid combined annular transformer and detection method thereof

Abstract

The invention relates to the technical field of electrical equipment, in particular to a photovoltaic off-grid combined annular transformer and a detection method thereof. The ring transformer A and the ring transformer B are combined to form the off-grid combined ring transformer to replace a single large ring transformer, so that the outer diameter can be greatly reduced and the length of a case can be increased under the condition of ensuring the same function, and the requirements of a long-strip-shaped inverter case can be met. According to the innovative installation mode of the inductor, the inductor and the transformer A are fixed on the same screw, and through combination of a series of parts, compared with a conventional external suspension type and dispensing fixing mode, the inductor is more stable and reliable.

Description

Technical Field

[0001] The invention relates to the technical field of electrical equipment, in particular to an off-grid combined toroidal transformer for photovoltaic use and a detection method thereof. Background Art

[0002] With the rapid development of photovoltaic power generation technology, off-grid photovoltaic inverters have been increasingly widely used in remote areas, emergency power supplies and other scenarios due to their flexibility and convenience. Traditional off-grid photovoltaic inverters mostly use bulky and heavy single toroidal transformers, which limits the use of inverters in some application scenarios with limited space or high portability requirements. In addition, traditional toroidal transformers also have some shortcomings in the winding process. For example, the secondary leakage inductance is difficult to control, resulting in a large no-load current of the inverter, affecting the overall efficiency and waveform quality. At the same time, the winding process of traditional toroidal transformers has high requirements for the consistency of the transformer, which increases the production cost and difficulty.

[0003] In order to overcome the above problems, in recent years, some researchers have tried to use combined toroidal transformers to replace traditional single toroidal transformers. The combined toroidal transformer can effectively reduce the outer diameter of the transformer by combining multiple small toroidal transformers, while meeting the requirements of the chassis size for some specific application scenarios, such as the long strip inverter chassis commonly seen on the market. However, there is still room for optimization in the inductor installation method and preparation process of the existing combined toroidal transformer. In view of this, the present invention proposes a combined toroidal transformer suitable for off-grid inverters for photovoltaics and a detection method thereof. Summary of the invention

[0004] The present invention provides a photovoltaic off-grid combined toroidal transformer and a detection method thereof to solve the limitation of the space requirement of the toroidal transformer by the long strip chassis in the inverter market.

[0005] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: The present invention discloses an off-grid combined toroidal transformer for photovoltaic use, comprising a toroidal transformer A, a toroidal transformer B and an inductor; The toroidal transformer A is provided with 4 ports, including a first port, a second port, a third port and a fourth port; the toroidal transformer B is also provided with 4 ports, including a first port, a second port, a third port and a fourth port; The fourth port of the toroidal transformer A is connected to the third port of the toroidal transformer B, the third port of the toroidal transformer A is connected to the inductor, the other end of the inductor is connected to the negative pole of the battery 0VDC, the fourth port of the toroidal transformer B is connected to the positive pole of the battery 48VDC; the first port of the toroidal transformer A is connected to the first port of the toroidal transformer B, the second port of the toroidal transformer A is connected to the second port of the toroidal transformer B; Through this connection method, the two windings on the battery side of the off-grid combined toroidal transformer are connected in series and the two windings on the 0-220V side are connected in parallel.

[0006] Furthermore, in a preferred embodiment of the present invention, the toroidal transformer A includes screws, nuts, an iron core A and a plurality of gaskets, and the inductor, the iron core A and the plurality of gaskets are all mounted on the screws and fixed by the nuts; wherein the plurality of gaskets include iron flat gaskets.

[0007] Furthermore, in a preferred embodiment of the present invention, hardware fixings are pre-embedded on the toroidal transformer A to strengthen the fastening force of the iron flat washer.

[0008] The present invention also discloses a detection method for an off-grid combined toroidal transformer for photovoltaic use, which is applied to any one of the off-grid combined toroidal transformers for photovoltaic use, and comprises the following steps: After the toroidal transformer A and the toroidal transformer B are combined and wound into an off-grid combined toroidal transformer, an electrical test is performed on the off-grid combined toroidal transformer; During the electrical test, the test characteristic parameters of the off-grid combined toroidal transformer are obtained at several preset time nodes, and the test characteristic parameters are classified to obtain a set of test characteristic parameters of the off-grid combined toroidal transformer within a preset time period; Acquiring working performance requirement information of the off-grid combined toroidal transformer, and determining a standard characteristic parameter set of the off-grid combined toroidal transformer according to the working performance requirement information; Compare and analyze the test characteristic parameter set with the standard characteristic parameter set to obtain the performance status of the off-grid combined toroidal transformer; If the performance state of the off-grid combined toroidal transformer is abnormal, further analyzing the off-grid combined toroidal transformer to obtain suspicious functional components of the off-grid combined toroidal transformer; After obtaining the suspicious functional components of the off-grid combined toroidal transformer, obtaining actual electrical parameters of the suspicious functional components during the electrical testing process; Determine whether the actual electrical parameters of the suspicious functional component during the electrical test process are within a preset parameter range; if not, determine the suspicious functional component as a faulty component; The off-grid combined toroidal transformer is re-identified as a faulty component and is repaired and replaced, so that the characteristic parameters of the off-grid combined toroidal transformer meet the production requirements.

[0009] Furthermore, in a preferred embodiment of the present invention, the test characteristic parameter set is compared and analyzed with the standard characteristic parameter set to obtain the performance status of the off-grid combined toroidal transformer, specifically: Constructing a first splitting tree, and importing the test feature parameter set into the first splitting tree, constructing a root node in the first splitting tree according to the test feature parameter set, and continuously splitting the test feature parameter set in the first splitting tree according to the root node to form a plurality of leaf nodes, until only one test feature parameter exists in each leaf node, the splitting ends, and a first splitting tree model is obtained; Constructing a second splitting tree, and importing the standard feature parameter set into the second splitting tree, constructing a root node in the second splitting tree according to the standard feature parameter set, and continuously splitting the standard feature parameter set in the second splitting tree according to the root node to form a plurality of leaf nodes, until only one standard feature parameter exists in each leaf node, the splitting ends, and a second splitting tree model is obtained; An iterative closest point matching algorithm is introduced, and the first split tree model and the second split tree model are registered based on the iterative closest point matching algorithm to obtain a registered split tree model, and two leaf nodes closest to each other in the registered split tree model are marked as a leaf node pair; Calculate the cosine similarity of each leaf node pair in the split tree model after registration, and perform weighted average of the cosine similarities of all leaf node pairs to obtain a similarity score; Determine whether the similarity score is greater than a preset score value; if greater than, mark the performance status of the off-grid combination toroidal transformer as a normal state; if not greater than, mark the performance status of the off-grid combination toroidal transformer as an abnormal state.

[0010] Furthermore, in a preferred embodiment of the present invention, if the performance state of the off-grid combined toroidal transformer is abnormal, the off-grid combined toroidal transformer is further analyzed to obtain suspicious functional components of the off-grid combined toroidal transformer, specifically: Obtaining the composition of functional components in the off-grid combined toroidal transformer, and obtaining functional characteristic information of each functional component in the off-grid combined toroidal transformer; and obtaining various characteristic parameters of the off-grid combined toroidal transformer; The cross-attention mechanism is introduced to analyze the correlation between the functional characteristic information of each functional component in the off-grid combined toroidal transformer and various characteristic parameters, and obtain the correlation between each functional component and each characteristic parameter. If the correlation between a certain functional component and a certain characteristic parameter is greater than a preset correlation threshold, the functional component is marked as the correlation functional component of the characteristic parameter; repeat this judgment process until the correlation between each functional component and each characteristic parameter is judged and analyzed, and the correlation functional components of various characteristic parameters are obtained; Constructing a knowledge graph, and importing correlation functional components of various feature parameters into the knowledge graph; If the performance state of the off-grid combined toroidal transformer is abnormal, the cosine similarity of each leaf node pair is compared with a preset similarity threshold, and only the leaf node pairs whose cosine similarity is not greater than the preset similarity threshold are marked; Acquire characteristic parameters attached to leaf node pairs whose cosine similarity is not greater than a preset similarity threshold, and define them as abnormal characteristic parameters of the off-grid combined toroidal transformer; The abnormal characteristic parameters of the off-grid combined toroidal transformer are imported into the knowledge graph for pairing, and the correlation functional components of the abnormal characteristic parameters are obtained by pairing, and the correlation functional components obtained by pairing are defined as suspicious functional components of the off-grid combined toroidal transformer.

[0011] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: the off-grid combined toroidal transformer A and B are combined to replace a single large toroidal transformer, which can greatly reduce the outer diameter and increase the chassis length while ensuring the same functions, and can meet the needs of long inverter chassis. In addition, the innovative installation method of the inductor fixes it and the transformer A on the same screw, and through a series of component combinations, it is more stable and reliable than the conventional external suspension and glue fixing methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0013] Figure 1 It is a structural schematic diagram of an off-grid combined toroidal transformer; Figure 2 Schematic diagram of the connection structure between the toroidal transformer A and the inductor; Figure 3 It is the structural schematic diagram of toroidal transformer B; The description of the accompanying drawings is as follows: 101, screw; 102, mounting base; 103, rubber pad; 104, sponge pad; 105, iron core A; 106, iron flat washer; 107, spring washer; 108, nut; 109, black iron cover; 202, inductor; 208, hardware fasteners; 1011, white silicone tube; 304, iron core B. DETAILED DESCRIPTION

[0014] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. The preferred embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thoroughly understood.

[0015] The present invention discloses a photovoltaic off-grid combined toroidal transformer, comprising a toroidal transformer A, a toroidal transformer B and an inductor 202; The toroidal transformer A is provided with 4 ports, including a first port, a second port, a third port and a fourth port; the toroidal transformer B is also provided with 4 ports, including a first port, a second port, a third port and a fourth port; The fourth port of the toroidal transformer A is connected to the third port of the toroidal transformer B, the third port of the toroidal transformer A is connected to the inductor 202, the other end of the inductor 202 is connected to the negative pole of the battery 0VDC, the fourth port of the toroidal transformer B is connected to the positive pole of the battery 48VDC; the first port of the toroidal transformer A is connected to the first port of the toroidal transformer B, the second port of the toroidal transformer A is connected to the second port of the toroidal transformer B; Through this connection method, the two windings on the battery side of the off-grid combined toroidal transformer are connected in series and the two windings on the 0-220V side are connected in parallel.

[0016] It should be noted that this toroidal transformer uses toroidal transformer A (220VAC:15VAC) and toroidal transformer B (220VAC:15VAC). The two transformers are combined into an off-grid combined toroidal transformer (inverter system), which has the same function as a large toroidal transformer (capacity = A+B) (220V:30VAC). Figure 1As shown, the fourth port of transformer A is connected to the third port of transformer B, the third port of transformer A is connected to the inductor, the other end of the inductor is connected to the negative pole of the battery 0VDC, and the fourth port of transformer B is connected to the positive pole of the battery 48VDC. The first port of transformer A is connected to the first port of transformer B, and the second port of transformer A is connected to the second port of transformer B. In this way, the two windings on the battery side are connected in series, and the two windings on the 0-220V side are connected in parallel. Compared with using a large toroidal transformer (capacity = A+B), the outer diameter of two toroidal transformers can be greatly reduced, and the length of the chassis occupied is lengthened, which can meet the needs of long strip inverter chassis on the market.

[0017] like Figure 2 As shown, the toroidal transformer A includes screws 101, a mounting base plate 102, a rubber pad 103, a sponge pad 104, an iron core A 105, an iron flat pad 106, a spring washer 107, a nut 108, a black iron cover 109, a rubber pad 103, a rubber pad 103, a black iron cover 109, an iron flat pad 106, a spring washer 107 and a nut 108. Hardware fixings 208 are pre-embedded on the toroidal transformer A to strengthen the fastening force of the iron flat pad.

[0018] It should be noted that the inductor and the core A are fixed on the same screw. When the screw passes through the inductor, a white silicone tube 1011 is sleeved to isolate the inductor enameled wire from the screw. This inductor fixing method is more stable than the conventional external hanging installation and glue fixing method.

[0019] like Figure 3 As shown, the toroidal transformer B includes screws 101, mounting base plate 102, rubber pads 103, sponge pads 104, iron core B304, iron flat pads 106, spring washers 107, and nuts 108. Hardware fixings 208 are embedded in the toroidal transformer B to strengthen the fastening force of the iron flat pads.

[0020] In summary, the combination of toroidal transformers A and B to form an off-grid combined toroidal transformer to replace a single large toroidal transformer can greatly reduce the outer diameter and increase the chassis length while ensuring the same functions, which can meet the needs of long inverter chassis. In addition, the innovative installation method of the inductor fixes it with the core A on the same screw and combines a series of components, which is more stable and reliable than the conventional external hanging and glue fixing methods.

[0021] The present invention also discloses a detection method for an off-grid combined toroidal transformer for photovoltaic use, which is applied to any one of the off-grid combined toroidal transformers for photovoltaic use, and comprises the following steps: After the toroidal transformer A and the toroidal transformer B are combined and wound into an off-grid combined toroidal transformer, an electrical test is performed on the off-grid combined toroidal transformer; During the electrical test, the test characteristic parameters of the off-grid combined toroidal transformer are obtained at several preset time nodes, and the test characteristic parameters are classified to obtain a set of test characteristic parameters of the off-grid combined toroidal transformer within a preset time period; Acquiring working performance requirement information of the off-grid combined toroidal transformer, and determining a standard characteristic parameter set of the off-grid combined toroidal transformer according to the working performance requirement information; Compare and analyze the test characteristic parameter set with the standard characteristic parameter set to obtain the performance status of the off-grid combined toroidal transformer; If the performance state of the off-grid combined toroidal transformer is abnormal, further analyzing the off-grid combined toroidal transformer to obtain suspicious functional components of the off-grid combined toroidal transformer; After obtaining the suspicious functional components of the off-grid combined toroidal transformer, obtaining actual electrical parameters of the suspicious functional components during the electrical testing process; Determine whether the actual electrical parameters of the suspicious functional component during the electrical test process are within a preset parameter range; if not, determine the suspicious functional component as a faulty component; The off-grid combined toroidal transformer is re-identified as a faulty component and is repaired and replaced, so that the characteristic parameters of the off-grid combined toroidal transformer meet the production requirements.

[0022] Among them, the test characteristic parameters of the off-grid combined toroidal transformer include but are not limited to input voltage, output voltage, voltage ratio, operating frequency, operating efficiency, leakage inductance, and operating temperature.

[0023] The actual electrical parameters of the suspicious functional component during the electrical test process include but are not limited to operating voltage, operating current, resistance, voltage transmission characteristics and phase characteristics.

[0024] It should be noted that during the electrical test, test characteristic parameters are obtained at several preset time nodes. This means that it is not just one measurement, but multiple measurements at different time points. Different time points may correspond to the performance of the transformer under different working conditions (such as startup moment, stable working state, load change, etc.). The working performance requirement information of the off-grid combined toroidal transformer is obtained, and the standard characteristic parameter set is determined based on this information. The working performance requirement information may come from design specifications, industry standards or user needs. The standard characteristic parameter set is the basis for measuring whether the transformer is qualified. The processed test characteristic parameter set is compared and analyzed with the standard characteristic parameter set. Through this comparison, the performance status of the off-grid combined toroidal transformer can be comprehensively evaluated to determine whether it meets the design and use requirements. If the comparison result shows that the performance status of the off-grid combined toroidal transformer is abnormal, further analysis is required to find out the problem. This step reflects the fault diagnosis capability of the process method, which not only determines whether the product is qualified, but also can further locate the problem when it is unqualified. The off-grid combined toroidal transformer is further analyzed to obtain suspicious functional components. This involves a separate analysis of each component of the transformer (such as windings, cores, inductors, etc.) or a preliminary judgment of the components that may have problems based on experience and principles. After the suspicious functional component is determined, the actual electrical parameters of the suspicious functional component during the electrical test process are obtained. These actual electrical parameters can more directly reflect the working status of the suspicious component. It is determined whether the actual electrical parameters of the suspicious functional component during the electrical test process are within the preset parameter range. If not within the preset range, the suspicious functional component is determined as a faulty component. This step is a key step in accurately determining the faulty component. By comparing with the preset parameter range, it can be clear whether the component is actually faulty. The faulty component is inspected and replaced. This is a necessary measure to ensure that the off-grid combined toroidal transformer can work normally. By replacing the faulty component, the normal function of the transformer can be restored. After inspection and replacement, the characteristic parameters of the off-grid combined toroidal transformer meet the production requirements. This ensures the quality of the final product so that it can meet the design specifications and usage requirements.

[0025] In summary, by obtaining test characteristic parameters at multiple preset time nodes and classifying them, the performance of the off-grid combined toroidal transformer under different working conditions can be fully and accurately reflected. Compared with the test at a single time point, this method can capture more performance change information, such as the transient characteristics of the transformer at startup and the stability after long-term operation.

[0026] When an abnormality occurs, the suspicious functional components are determined by step-by-step analysis, and the faulty components are further determined by comparing the actual electrical parameters with the preset parameter range. This hierarchical fault diagnosis method can accurately locate the problematic components, avoid blind repair or replacement of the entire transformer, improve maintenance efficiency, and reduce maintenance costs. Ultimately, it ensures that the characteristic parameters of the off-grid combination toroidal transformer meet the production requirements, which guarantees the quality of the product. Whether from the perspective of meeting the design specifications to achieve the expected functions or from the perspective of complying with industry standards to ensure product reliability and safety, this detection method helps to improve the overall quality of the product. And it can determine the standard characteristic parameter set based on the work performance requirement information, so that the detection method can adapt to different work requirements. Whether it is for different power requirements, different usage environments or customized requirements for different users, the effective production and quality control of the off-grid combination toroidal transformer can be achieved by adjusting the standard characteristic parameter set.

[0027] Furthermore, in a preferred embodiment of the present invention, the test characteristic parameter set is compared and analyzed with the standard characteristic parameter set to obtain the performance status of the off-grid combined toroidal transformer, specifically: Constructing a first splitting tree, and importing the test feature parameter set into the first splitting tree, constructing a root node in the first splitting tree according to the test feature parameter set, and continuously splitting the test feature parameter set in the first splitting tree according to the root node to form a plurality of leaf nodes, until only one test feature parameter exists in each leaf node, the splitting ends, and a first splitting tree model is obtained; Constructing a second splitting tree, and importing the standard feature parameter set into the second splitting tree, constructing a root node in the second splitting tree according to the standard feature parameter set, and continuously splitting the standard feature parameter set in the second splitting tree according to the root node to form a plurality of leaf nodes, until only one standard feature parameter exists in each leaf node, the splitting ends, and a second splitting tree model is obtained; An iterative closest point matching algorithm is introduced, and the first split tree model and the second split tree model are registered based on the iterative closest point matching algorithm to obtain a registered split tree model, and two leaf nodes closest to each other in the registered split tree model are marked as a leaf node pair; Calculate the cosine similarity of each leaf node pair in the split tree model after registration, and perform weighted average of the cosine similarities of all leaf node pairs to obtain a similarity score; Determine whether the similarity score is greater than a preset score value; if greater than, mark the performance status of the off-grid combination toroidal transformer as a normal state; if not greater than, mark the performance status of the off-grid combination toroidal transformer as an abnormal state.

[0028] It should be noted that, first, a data structure of a first split tree is constructed, and the test feature parameter set is imported into this tree structure. This tree structure is a hierarchical data organization form that can effectively manage and analyze complex data sets. A root node is constructed according to the test feature parameter set, and this root node is the starting point of the entire split tree. Then, starting from the root node, the test feature parameter set is continuously split according to certain rules. This process is like gradually decomposing a large data set into smaller subsets. Each split will form a new node until there is only one test feature parameter in each leaf node (i.e., the bottom node), and the first split tree model is completed. This splitting method helps to deeply analyze the relationship between each individual test feature parameter and the overall performance. Similar to the construction of the first split tree, a second split tree is constructed and the standard feature parameter set of the off-grid combined toroidal transformer is imported into it. The standard feature parameter set is determined according to the working performance requirements of the transformer and is a benchmark for measuring whether the transformer is working properly. Similarly, a root node is constructed according to the standard feature parameter set, and then the standard feature parameter set is continuously split until there is only one standard feature parameter in each leaf node, thereby obtaining a second split tree model.

[0029] The iterative closest point matching algorithm is introduced, which is an algorithm for finding the best matching relationship between two data sets. Here, the first split tree model and the second split tree model are taken as input and the algorithm is used to register them. The purpose of the registration is to align the corresponding nodes in the two tree models as accurately as possible for subsequent comparative analysis. In the split tree model after registration, the two leaf nodes with the closest distance are marked as leaf node pairs. For each leaf node pair in the split tree model after registration, the cosine similarity between them is calculated. Cosine similarity is an indicator that measures the similarity between two vectors (here, the feature parameters in the leaf nodes can be regarded as vectors), and its value range is between -1 and 1. The closer to 1, the more similar the two vectors are. The cosine similarities of all leaf node pairs are weighted averaged to obtain a comprehensive similarity score. This weighted average process takes into account the importance of each leaf node pair in the overall comparison, so that the final similarity score can more comprehensively reflect the similarity between the test feature parameter set and the standard feature parameter set. Finally, this similarity score is compared with the preset score value. If the similarity score is greater than the preset score value, it means that the test feature parameter set is similar enough to the standard feature parameter set, and the performance status of the off-grid combined toroidal transformer is marked as normal; otherwise, if it is not greater than the preset score value, it is marked as abnormal.

[0030] By constructing a split tree model to process the test feature parameter set and the standard feature parameter set respectively, and then using the iterative nearest point matching algorithm for alignment, the cosine similarity of the leaf node pairs after alignment is calculated and the weighted average is used to obtain the similarity score, so as to judge the performance status of the off-grid combined toroidal transformer. It can comprehensively and meticulously compare the test feature parameters and the standard feature parameters, and fully consider the role of each individual feature parameter in the overall performance evaluation. Compared with the simple direct comparison of parameter values, it can more accurately judge the performance status of the transformer, and the judgment of both normal and abnormal status is more scientific and reliable.

[0031] Furthermore, in a preferred embodiment of the present invention, if the performance state of the off-grid combined toroidal transformer is abnormal, the off-grid combined toroidal transformer is further analyzed to obtain suspicious functional components of the off-grid combined toroidal transformer, specifically: Obtaining the composition of functional components in the off-grid combined toroidal transformer, and obtaining functional characteristic information of each functional component in the off-grid combined toroidal transformer; and obtaining various characteristic parameters of the off-grid combined toroidal transformer; The cross-attention mechanism is introduced to analyze the correlation between the functional characteristic information of each functional component in the off-grid combined toroidal transformer and various characteristic parameters, and obtain the correlation between each functional component and each characteristic parameter. If the correlation between a certain functional component and a certain characteristic parameter is greater than a preset correlation threshold, the functional component is marked as the correlation functional component of the characteristic parameter; repeat this judgment process until the correlation between each functional component and each characteristic parameter is judged and analyzed, and the correlation functional components of various characteristic parameters are obtained; It should be noted that the functional components related to the leaf node pairs with large differences are determined based on the correlation between the characteristic parameters and the functional components of the off-grid combined toroidal transformer. For example, if the characteristic parameters related to the winding are greatly different, then there may be a problem with the winding components; if there is a problem with the characteristic parameters related to the inductor installation method, then the inductor installation component may be the source of the abnormality; Constructing a knowledge graph, and importing correlation functional components of various feature parameters into the knowledge graph; If the performance state of the off-grid combined toroidal transformer is abnormal, the cosine similarity of each leaf node pair is compared with a preset similarity threshold, and only the leaf node pairs whose cosine similarity is not greater than the preset similarity threshold are marked; Acquire characteristic parameters attached to leaf node pairs whose cosine similarity is not greater than a preset similarity threshold, and define them as abnormal characteristic parameters of the off-grid combined toroidal transformer; The abnormal characteristic parameters of the off-grid combined toroidal transformer are imported into the knowledge graph for pairing, and the correlation functional components of the abnormal characteristic parameters are obtained by pairing, and the correlation functional components obtained by pairing are defined as suspicious functional components of the off-grid combined toroidal transformer.

[0032] It should be noted that when the performance status of the off-grid combined toroidal transformer is abnormal, the composition of its functional components, the functional characteristic information of each functional component, and the various characteristic parameters of the transformer must be obtained first. This step is the basis for subsequent analysis. The composition of the functional components clarifies the scope of the analysis object, the functional characteristic information helps to deeply understand the role and characteristics of each component, and the characteristic parameters are an important basis for judging whether the component is abnormal. The cross-attention mechanism is introduced to perform correlation analysis on the functional characteristic information of each functional component and various characteristic parameters. The cross-attention mechanism can focus on the correlation between different information, and here the intrinsic connection between the functional components and the characteristic parameters can be effectively mined. Through this mechanism, the degree of correlation between each functional component and each characteristic parameter can be quantified. If the correlation between a functional component and a characteristic parameter is greater than the preset correlation threshold, the functional component is marked as the correlation functional component of the characteristic parameter. This process will judge all functional components and characteristic parameters one by one until all the analysis is completed, so as to obtain the correlation functional components corresponding to various characteristic parameters. This step establishes a direct connection between functional components and characteristic parameters, which provides a basis for the subsequent determination of suspicious functional components.

[0033] Build a knowledge graph and import the previously obtained correlation functional components of various feature parameters into it. The knowledge graph is a structured knowledge representation that can clearly present the relationship between different entities (here, feature parameters and functional components) to facilitate subsequent query and reasoning.

[0034] When the performance status is abnormal, the cosine similarity of each leaf node pair calculated previously is compared with the preset similarity threshold, and only the leaf node pairs whose cosine similarity is not greater than the preset similarity threshold are marked. The characteristic parameters attached to these leaf node pairs are defined as abnormal characteristic parameters of the off-grid combined toroidal transformer. In this step, characteristic parameters with large differences from the normal state are screened out by comparing the similarity. These abnormal characteristic parameters are likely to be factors that cause abnormal transformer performance. The abnormal characteristic parameters are imported into the knowledge graph for pairing. Since the correlation relationship between characteristic parameters and functional components has been stored in the knowledge graph, the correlation functional components of the abnormal characteristic parameters can be obtained through this pairing. These correlation functional components are defined as suspicious functional components of the off-grid combined toroidal transformer. This step is the ultimate goal of the entire analysis process. Through a series of previous analysis and preparation work, the suspicious functional components that may cause abnormal transformer performance are finally determined, providing a clear direction for subsequent maintenance and troubleshooting.

[0035] Through a series of complex analysis processes, including obtaining functional component information, analyzing correlations using the cross-attention mechanism, building a knowledge graph, and determining abnormal feature parameters based on cosine similarity and finally pairing suspicious functional components, it is possible to accurately locate suspicious functional components that may have problems when the off-grid combined toroidal transformer has abnormal performance. Compared with simple troubleshooting methods, it fully considers the complex relationship between functional components and feature parameters, and uses advanced analysis mechanisms (such as the cross-attention mechanism) and knowledge representation forms (such as knowledge graphs) to improve the accuracy and efficiency of troubleshooting, providing strong support for the rapid repair of off-grid combined toroidal transformers, thereby effectively improving production efficiency.

[0036] The test characteristic parameters are classified and processed to obtain a test characteristic parameter set of the off-grid combined toroidal transformer within a preset time period, specifically: Constructing a three-dimensional grid coordinate system, and dividing the three-dimensional grid coordinate system into a plurality of sub-grids; The collected test characteristic parameters are discretized, and the discretized individual test characteristic parameters are respectively mapped to a separate sub-grid; Perform feature extraction processing on the test feature parameters in each sub-grid to obtain data feature information of the test feature parameters in each sub-grid; Calculate the mutual information value between the data feature information of the test feature parameters in each sub-grid; and merge the two sub-grids with the largest mutual information value to obtain several grid groups; Calculate the parameter mean of the test feature parameters in each grid group, and use the calculated parameter mean as the grid center of the corresponding grid group; Calculate the Euclidean distance between each test feature parameter and the corresponding grid center in each grid group, perform weighted average processing on the Euclidean distance between each test feature parameter and the corresponding grid center in each grid group, and obtain the compactness of each grid group; If the compactness of each grid group is greater than the preset compactness, the merging iteration operation is stopped; If the compactness of at least one grid group is not greater than the preset compactness, each grid group is re-considered as a sub-grid and then merged. The merging iteration operation is stopped when the compactness of each grid group is greater than the preset compactness. When the instruction to stop the merging iteration operation is received, each grid group is cut out in the three-dimensional grid coordinate system to obtain a test characteristic parameter set of the off-grid combined toroidal transformer within a preset time period.

[0037] It should be noted that a three-dimensional grid coordinate system is constructed, which provides a spatial framework for the subsequent processing of the test characteristic parameters. Several sub-grids are cut out in this three-dimensional space, and these sub-grids will serve as the basic units for discretizing the test characteristic parameters. The collected test characteristic parameters are discretized, which means that the continuous test characteristic parameters are converted into discrete values ​​or states according to certain rules. Then the discrete individual test characteristic parameters are mapped to a separate sub-grid, so that the preliminary positioning of the test characteristic parameters in the three-dimensional grid space is achieved. Feature extraction processing is performed on the test characteristic parameters in each sub-grid, with the aim of obtaining the data feature information (such as mean, median, mode, standard deviation, etc.) of these test characteristic parameters. These data feature information can more concisely and effectively represent the characteristics of the test characteristic parameters in the sub-grid, such as some statistical features or specific features related to transformer performance.

[0038] Calculate the mutual information value between the data feature information of the test feature parameters in each sub-grid. Mutual information is a metric that measures the degree of mutual dependence between two random variables. Here, it is used to measure the degree of correlation between the data feature information in different sub-grids. Through the mutual information value, the sub-grid with the strongest correlation can be found. The two sub-grids with the largest mutual information value are merged, and several grid groups are obtained by repeating this process. This merging process is a clustering operation based on the degree of correlation of data feature information. The sub-grids where the data feature information with strong correlation is located are clustered together to form a grid group. For each grid group, calculate the parameter mean of the test feature parameters therein, and use this mean as the grid center of the corresponding grid group. The grid center can be regarded as a representative position of this grid group in the test feature parameter space, which reflects a certain average state of the test feature parameters in the grid group.

[0039] The Euclidean distance between each test characteristic parameter and the corresponding grid center in each grid group is calculated. The Euclidean distance is a common method for measuring the spatial distance between two points. Then, these distances are weighted averaged to obtain the compactness of each grid group. The compactness reflects the degree of aggregation of the test characteristic parameters in the grid group relative to the grid center. If the compactness is high, it means that the test characteristic parameters in the grid group are relatively concentrated near the grid center. Determine whether the compactness of each grid group is greater than the preset compactness. If so, stop the merging iteration operation, which means that the current grid group division has met certain compactness requirements; if there is at least one grid group whose compactness is not greater than the preset compactness, each grid group is re-considered as a sub-grid and merged again until the compactness of all grid groups meets the requirements. This iterative process is to continuously optimize the division of the grid group so that the test characteristic parameters in each grid group have a higher compactness. When the merging iteration operation is stopped, each grid group is cut out in the three-dimensional grid coordinate system, and these grid groups constitute the test characteristic parameter set of the off-grid combined toroidal transformer in the preset time period. This test feature parameter set is obtained after a series of complex processing and optimization, and it integrates the original test feature parameters in a more orderly and representative way.

[0040] By constructing a three-dimensional grid coordinate system, performing discrete mapping, feature extraction, subgrid merging based on mutual information, calculating the compactness of the grid group and performing iterative merging operations on the test characteristic parameters, the test characteristic parameter set is finally obtained, which can effectively classify the test characteristic parameters and mine the intrinsic relationship between the test characteristic parameters, so that the final test characteristic parameter set has good structure and representativeness. Compared with the simple method of directly processing the test characteristic parameters, it can better reflect the complex performance characteristics of the off-grid combined toroidal transformer within a preset time period, and provide a more reliable data basis for the subsequent comparative analysis with the standard characteristic parameter set and the accurate judgment of the transformer performance status.

[0041] The above is based on the ideal embodiment of the present invention, and its description is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

Claims

1. A photovoltaic off-grid combined toroidal transformer, characterized in that: It includes a toroidal transformer A, a toroidal transformer B and an inductor; The toroidal transformer A is provided with 4 ports, including a first port, a second port, a third port and a fourth port; the toroidal transformer B is also provided with 4 ports, including a first port, a second port, a third port and a fourth port; The fourth port of the toroidal transformer A is connected to the third port of the toroidal transformer B, the third port of the toroidal transformer A is connected to the inductor, the other end of the inductor is connected to the negative pole of the battery 0VDC, the fourth port of the toroidal transformer B is connected to the positive pole of the battery 48VDC; the first port of the toroidal transformer A is connected to the first port of the toroidal transformer B, the second port of the toroidal transformer A is connected to the second port of the toroidal transformer B; Through this connection method, the two windings on the battery side of the off-grid combined toroidal transformer are connected in series and the two windings on the 0-220V side are connected in parallel.

2. The photovoltaic off-grid combined toroidal transformer according to claim 1, characterized in that: The annular transformer A comprises screws, nuts, an iron core A and a plurality of gaskets. The inductor, the iron core A and the plurality of gaskets are all mounted on the screws and fixed by the nuts. The plurality of gaskets comprise iron flat gaskets.

3. The photovoltaic off-grid combined toroidal transformer according to claim 2, characterized in that: The toroidal transformer A is pre-embedded with hardware fixings to strengthen the fastening force of the iron flat washer.

4. A detection method for an off-grid combined toroidal transformer for photovoltaic use, applied to an off-grid combined toroidal transformer for photovoltaic use as claimed in any one of claims 1 to 3, characterized in that: The following steps are involved: After the toroidal transformer A and the toroidal transformer B are combined and wound into an off-grid combined toroidal transformer, an electrical test is performed on the off-grid combined toroidal transformer; During the electrical test, the test characteristic parameters of the off-grid combined toroidal transformer are obtained at several preset time nodes, and the test characteristic parameters are classified to obtain a set of test characteristic parameters of the off-grid combined toroidal transformer within a preset time period; Acquire working performance requirement information of the off-grid combined toroidal transformer, and determine a standard characteristic parameter set of the off-grid combined toroidal transformer according to the working performance requirement information; Compare and analyze the test characteristic parameter set with the standard characteristic parameter set to obtain the performance status of the off-grid combined toroidal transformer; If the performance state of the off-grid combined toroidal transformer is abnormal, further analyzing the off-grid combined toroidal transformer to obtain suspicious functional components of the off-grid combined toroidal transformer; After obtaining the suspicious functional components of the off-grid combined toroidal transformer, obtaining actual electrical parameters of the suspicious functional components during the electrical testing process; Determining whether the actual electrical parameters of the suspicious functional component during the electrical test process are within a preset parameter range; If not, the suspicious functional component is determined to be a faulty component; The off-grid combined toroidal transformer is re-identified as a faulty component and is repaired and replaced, so that the characteristic parameters of the off-grid combined toroidal transformer meet the production requirements.

5. The detection method of a photovoltaic off-grid combined toroidal transformer according to claim 4, characterized in that: The test characteristic parameter set is compared and analyzed with the standard characteristic parameter set to obtain the performance status of the off-grid combined toroidal transformer, specifically: Constructing a first splitting tree, and importing the test feature parameter set into the first splitting tree, constructing a root node in the first splitting tree according to the test feature parameter set, and continuously splitting the test feature parameter set in the first splitting tree according to the root node to form a plurality of leaf nodes, until only one test feature parameter exists in each leaf node, the splitting ends, and a first splitting tree model is obtained; Constructing a second splitting tree, and importing the standard feature parameter set into the second splitting tree, constructing a root node in the second splitting tree according to the standard feature parameter set, and continuously splitting the standard feature parameter set in the second splitting tree according to the root node to form a plurality of leaf nodes, until only one standard feature parameter exists in each leaf node, the splitting ends, and a second splitting tree model is obtained; An iterative closest point matching algorithm is introduced, and the first split tree model and the second split tree model are registered based on the iterative closest point matching algorithm to obtain a registered split tree model, and two leaf nodes closest to each other in the registered split tree model are marked as a leaf node pair; Calculate the cosine similarity of each leaf node pair in the split tree model after registration, and perform weighted average of the cosine similarities of all leaf node pairs to obtain a similarity score; Determine whether the similarity score is greater than a preset score value; If it is greater than, the performance status of the off-grid combined toroidal transformer is marked as normal; If it is not greater than, the performance status of the off-grid combined toroidal transformer is marked as an abnormal state.

6. A detection method for off-grid combined toroidal transformer for photovoltaic use according to claim 5, characterized in that: If the performance status of the off-grid combined toroidal transformer is abnormal, further analysis is performed on the off-grid combined toroidal transformer to obtain suspicious functional components of the off-grid combined toroidal transformer, specifically: Obtaining the composition of functional components in the off-grid combined toroidal transformer, and obtaining functional characteristic information of each functional component in the off-grid combined toroidal transformer; and obtaining various characteristic parameters of the off-grid combined toroidal transformer; The cross-attention mechanism is introduced to analyze the correlation between the functional characteristic information of each functional component in the off-grid combined toroidal transformer and various characteristic parameters, and obtain the correlation between each functional component and each characteristic parameter. If the correlation between a certain functional component and a certain characteristic parameter is greater than a preset correlation threshold, the functional component is marked as the correlation functional component of the characteristic parameter; repeat this judgment process until the correlation between each functional component and each characteristic parameter is judged and analyzed, and the correlation functional components of various characteristic parameters are obtained; Constructing a knowledge graph, and importing correlation functional components of various feature parameters into the knowledge graph; If the performance state of the off-grid combined toroidal transformer is abnormal, the cosine similarity of each leaf node pair is compared with a preset similarity threshold, and only the leaf node pairs whose cosine similarity is not greater than the preset similarity threshold are marked; Acquire characteristic parameters attached to leaf node pairs whose cosine similarity is not greater than a preset similarity threshold, and define them as abnormal characteristic parameters of the off-grid combined toroidal transformer; The abnormal characteristic parameters of the off-grid combined toroidal transformer are imported into the knowledge graph for pairing, and the correlation functional components of the abnormal characteristic parameters are obtained by pairing, and the correlation functional components obtained by pairing are defined as suspicious functional components of the off-grid combined toroidal transformer.

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