Fault diagnosis method and system for new energy access power distribution network

Abstract

The invention discloses a fault diagnosis method and system for new energy access to a power distribution network, and the method comprises the steps: obtaining the operation data of the power distribution network, and building an initial fault type set according to the operation data of the power distribution network; based on the initial fault type set, constructing a multi-level fault knowledge graph comprising an equipment layer, a system layer and an environment layer; based on the multi-level fault knowledge graph, designing a mixed reasoning mechanism combining rule reasoning and probabilistic reasoning, and performing fault diagnosis by using the mixed reasoning mechanism to obtain a fault diagnosis result; and according to a fault diagnosis result, calculating a fault diagnosis coverage rate, and optimizing a fault diagnosis process. According to the method, the coverage range and the accuracy of fault diagnosis can be continuously improved, and challenges caused by access of high-proportion new energy to the power distribution network can be effectively handled. Therefore, the fault diagnosis system can be continuously improved by itself along with the evolution of the power distribution network and the occurrence of new fault types.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault diagnosis, and in particular to a fault diagnosis method and system for connecting new energy to a distribution network. Background Art

[0002] With the large-scale access of new energy to the distribution network, not only has the energy composition of the distribution network been greatly enriched, but its topology has also become increasingly complex and changeable. The traditional distribution network structure is relatively simple, and the power flow distribution is relatively stable, but the access of new energy breaks this balance, making the power flow distribution more difficult to predict and control, which in turn affects the accuracy of fault location.

[0003] To make matters more complicated, new energy access equipment itself may carry new failure risks. For example, photovoltaic inverters may fail due to overheating, overloading, etc., and wind turbines may shut down due to mechanical failure or electrical problems. These types of failures are not common in traditional distribution networks, so they bring new challenges to troubleshooting and repair work.

[0004] In addition, the high proportion of new energy access also makes the operation mode of the distribution network more flexible and diverse. Under certain conditions, the distribution network can operate in an isolated island or interconnect multiple microgrids. Although these new operation modes have improved the flexibility and reliability of the distribution network, they have also made the fault characteristics significantly different from those of the traditional distribution network. Traditional fault location methods often seem powerless when faced with these new fault characteristics, and it is difficult to quickly and accurately locate the fault point. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a fault diagnosis method for a new energy access distribution network to solve the uncertainty in the fault diagnosis process of a high proportion of new energy access distribution network, as well as the problem that the accuracy and robustness of the diagnosis method are not high.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a fault diagnosis method for a new energy source access distribution network, comprising:

[0009] Acquire distribution network operation data, and establish an initial fault type set according to the distribution network operation data;

[0010] Based on the initial fault type set, a multi-level fault knowledge graph including a device layer, a system layer, and an environment layer is constructed;

[0011] Based on the multi-level fault knowledge graph, a hybrid reasoning mechanism combining rule reasoning and probability reasoning is designed, and the hybrid reasoning mechanism is used to perform fault diagnosis and obtain fault diagnosis results;

[0012] According to the fault diagnosis results, the fault diagnosis coverage is calculated and the fault diagnosis process is optimized.

[0013] As a preferred solution of the fault diagnosis method for connecting new energy to the distribution network of the present invention, the initial fault type set is established,

[0014] Based on literature research and expert knowledge, an initial set of fault types including traditional distribution network faults and new energy system faults is established;

[0015] Using the Apriori association rule algorithm, new fault patterns are discovered from historical fault data, and the support and confidence calculations of the Apriori association rule algorithm are calculated;

[0016] When the support and confidence meet the preset thresholds at the same time, a new fault type is found and added to the initial fault type set;

[0017] The K-means clustering algorithm is used to perform cluster analysis on the fault data to find potential new fault types. The objective function of the K-means algorithm is:

[0018]

[0019] in, is the i-th data point in the j-th class, c j is the cluster center of the jth class;

[0020] By minimizing the objective function, the optimal clustering result is obtained. For a newly formed cluster, if the newly formed cluster feature is different from the existing fault type, it is added as a new fault type to the initial fault type set.

[0021] As a preferred solution of the fault diagnosis method for connecting new energy to the distribution network of the present invention, the multi-level fault knowledge graph includes:

[0022] The RDF triple <subject, attribute, object> is used to formally represent the multi-level fault knowledge graph.

[0023] As a preferred solution of the fault diagnosis method for connecting new energy to the distribution network of the present invention, the device layer, the system layer and the environment layer include:

[0024] The equipment layer describes the fault characteristics and interrelationships of various equipment in the distribution network;

[0025] The system layer describes the overall topology, operating status and fault propagation rules of the distribution network;

[0026] The environmental layer contains external factors that affect the operation of the distribution network.

[0027] As a preferred solution of the fault diagnosis method for connecting new energy to the distribution network of the present invention, a hybrid reasoning mechanism combining rule reasoning and probability reasoning is designed, including:

[0028] The rule reasoning is based on expert knowledge and fault knowledge graph to establish a rule base in the form of IF-THEN;

[0029] Use Bayesian networks for probabilistic reasoning to handle uncertainty in fault diagnosis;

[0030] Combining the results of rule reasoning and probability reasoning, DS evidence theory is used for decision fusion. Let m1 and m2 be the basic probability distribution functions obtained by rule reasoning and probability reasoning respectively. The fused result can be expressed as:

[0031]

[0032] Among them, A, B, and C are subsets of the fault type set, B∩C=A, and k is a normalization factor.

[0033] As a preferred solution of the fault diagnosis method for connecting new energy to a distribution network according to the present invention, the fault diagnosis coverage rate is calculated according to the fault diagnosis result, including:

[0034] Fault diagnosis coverage is the ratio of the number of fault types that the system can correctly diagnose to the total number of fault types, which can be expressed as:

[0035]

[0036] Among them, N d is the number of fault types that can be correctly diagnosed; N t is the total number of fault types.

[0037] As a preferred solution of the fault diagnosis method for connecting new energy to the distribution network of the present invention, it also includes:

[0038] According to the importance and frequency of different fault types, the weighted fault diagnosis coverage is introduced and expressed as:

[0039]

[0040] Among them, wi is the weight of the i-th fault type; I i is the indicator function. When the i-th fault can be correctly diagnosed, I i =1, otherwise I i =0.

[0041] In a second aspect, the present invention provides a system for fault diagnosis of new energy access to a distribution network, comprising:

[0042] A data acquisition module is used to acquire distribution network operation data;

[0043] A fault diagnosis module is used to establish an initial fault type set according to the distribution network operation data; based on the initial fault type set, a multi-level fault knowledge graph including a device layer, a system layer and an environment layer is constructed; based on the multi-level fault knowledge graph, a hybrid reasoning mechanism combining rule reasoning and probabilistic reasoning is designed, and the hybrid reasoning mechanism is used to perform fault diagnosis and obtain fault diagnosis results;

[0044] The optimization module is used to calculate the fault diagnosis coverage and optimize the fault diagnosis process according to the fault diagnosis results.

[0045] In a third aspect, the present invention provides a computing device, comprising:

[0046] Memory and processor;

[0047] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the fault diagnosis method for connecting new energy to the distribution network are implemented.

[0048] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the fault diagnosis method for connecting new energy to a distribution network.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention can continuously improve the coverage and accuracy of fault diagnosis, and effectively cope with the challenges brought by the high proportion of new energy access to the distribution network. The core advantages of this method lie in its dynamic adaptability and multi-dimensional knowledge fusion, which enables the fault diagnosis system to continuously improve itself with the evolution of the distribution network and the emergence of new fault types. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0051] Figure 1 The present invention is a schematic diagram of the overall process of a fault diagnosis method for connecting a new energy source to a distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0055] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0056] At the same time, in the description of the present invention, it should be noted that the orientations or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0057] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] Example 1

[0059] Reference Figure 1 , as an embodiment of the present invention, provides a fault diagnosis method for a new energy source access distribution network, comprising:

[0060] S100: Acquire distribution network operation data, and establish an initial fault type set according to the distribution network operation data;

[0061] In the embodiment of the present application, the distribution network operation data includes but is not limited to: fault records, real-time measurement values ​​of current and voltage, power quality parameters such as power factor and frequency, equipment operation information such as switch status and protection action records, and external environmental factors such as weather conditions and load demand. After obtaining the distribution network operation data, the data is preprocessed, and the preprocessing may include data cleaning, normalization, and outlier processing.

[0062] Preferably, based on literature research and expert knowledge, an initial set of fault types including traditional distribution network faults and new energy system faults is established;

[0063] In the embodiment of the present application, the initial fault type set is represented as:

[0064] F={F t ,F n}

[0065] Where F is the fault type set; F t is the fault subset of the traditional distribution network; F n It is a subset of new energy system faults.

[0066] Preferably, the Apriori association rule algorithm is used to discover new fault modes from historical fault data, and the support and confidence of the Apriori association rule algorithm are calculated; when the support and confidence meet the preset thresholds at the same time, a new fault type is discovered and added to the initial fault type set;

[0067] In the embodiment of the present application, let X be a fault feature set, Y be a fault type set, and the association rule can be expressed as X→Y. The support and confidence calculation formulas of the association rule are expressed as:

[0068] support(X→Y)=P(X∪Y)

[0069] confidence(X→Y)=P(Y|X)

[0070] Preferably, a K-means clustering algorithm is used to perform cluster analysis on the fault data to find potential new fault types. The objective function of the K-means algorithm is:

[0071]

[0072] in, is the i-th data point in the j-th class, c j is the cluster center of the jth class;

[0073] By minimizing the objective function, the optimal clustering result is obtained. For the newly formed cluster, if the newly formed cluster characteristics are different from the existing fault types, they are added as new fault types to the initial fault type set.

[0074] It should be noted that the present invention makes full use of the diversity and richness of distribution network operation data to construct an initial fault type set that includes both traditional distribution network faults and new energy system faults; through the joint application of the Apriori association rule algorithm and the K-means clustering algorithm, not only new fault modes are mined from historical data, but also potential unknown fault types are discovered, thereby greatly expanding the coverage and accuracy of the fault type set.

[0075] S102: Based on the initial fault type set, a multi-level fault knowledge graph including the device layer, the system layer and the environment layer is constructed;

[0076] Preferably, RDF triples <subject, attribute, object> are used to formally represent the multi-level fault knowledge graph;

[0077] In the embodiment of the present application, the multi-level fault knowledge graph is represented as:

[0078] G=(E,R,A)

[0079] Among them, E is the entity set; R is the relationship set; A is the attribute set.

[0080] Preferably, the device layer describes the fault characteristics and interrelationships of various types of equipment in the distribution network;

[0081] Exemplarily, taking a transformer as an example, its knowledge representation may include:

[0082] <Transformer, type, oil immersed>;

[0083] <Transformer, fault type, winding short circuit>;

[0084] <Winding short circuit, fault characteristics, three-phase current unbalance>.

[0085] Preferably, the system layer describes the overall topology, operating status and fault propagation law of the distribution network;

[0086] For example,

[0087] <distribution lines, connections, substations>;

[0088] <Distribution line, operation mode, dual circuit>;

[0089] <Fault, impact, power supply reliability>.

[0090] Preferably, the environmental layer includes external factors that affect the operation of the distribution network;

[0091] In a possible embodiment, external factors may include weather conditions, geographical environment, etc.;

[0092] For example,

[0093] <Lightning, causing, insulation breakdown>;

[0094] <High temperature, impact, equipment load capacity>;

[0095] <Marine environment, accelerated, equipment corrosion>.

[0096] In the embodiments of the present application, the interaction between different levels is described, for example,

[0097] <Equipment failure, leading to,system failure>;

[0098] <Environmental factors, impacts, equipment performance>.

[0099] It should be noted that the multi-level fault knowledge graph constructed by the present invention based on the initial fault type set effectively integrates the key fault information and relationships of the equipment layer, system layer and environment layer through the formal representation of RDF triples. This structured knowledge representation method not only improves the readability and comprehensibility of fault information, but also facilitates intelligent reasoning and fault analysis; by clearly describing the equipment fault characteristics, system operating status and external environmental impact, the multi-level fault knowledge graph provides strong support for fault prediction, rapid positioning and repair of the distribution network, and improves the efficiency and accuracy of fault handling.

[0100] S104: Based on the multi-level fault knowledge graph, a hybrid reasoning mechanism combining rule reasoning and probability reasoning is designed, and the hybrid reasoning mechanism is used to perform fault diagnosis and obtain fault diagnosis results;

[0101] Preferably, rule reasoning is based on expert knowledge and fault knowledge graph to establish a rule base in the form of IF-THEN;

[0102] In the embodiments of the present application, the general form of the rule is:

[0103] IF(condition 1) AND(condition 2)...THEN(conclusion)

[0104] For example:

[0105] IF (three-phase current unbalance) AND (transformer oil temperature rises) THEN (transformer winding short circuit fault, CF = 0.9)

[0106] Among them, CF is the confidence factor, which is used to express the uncertainty of the rule.

[0107] Preferably, a Bayesian network is used for probabilistic reasoning to handle the uncertainty in fault diagnosis;

[0108] In the embodiment of the present application, the Bayesian network is composed of a directed acyclic graph (DAG) and a conditional probability table (CPT). Let X be the set of observed variables and Y be the fault type. The posterior probability of fault diagnosis can be expressed as:

[0109]

[0110] According to the structure of the Bayesian network, the joint probability can be decomposed into the product of conditional probabilities:

[0111]

[0112] Among them, pa(X i ) indicates X i The parent node set of .

[0113] Preferably, the results of rule reasoning and probability reasoning are combined, and DS evidence theory is used for decision fusion. Let m1 and m2 be the basic probability distribution functions obtained by rule reasoning and probability reasoning respectively, then the fused result can be expressed as:

[0114]

[0115] Among them, A, B, and C are subsets of the fault type set, B∩C=A, and k is a normalization factor.

[0116] It should be noted that the present invention combines a hybrid reasoning mechanism of rule reasoning and probabilistic reasoning, and provides a more comprehensive and accurate solution for distribution network fault diagnosis on the basis of a multi-level fault knowledge graph; rule reasoning utilizes expert knowledge and IF-THEN rules in the fault knowledge graph, which can quickly locate common fault modes and express the uncertainty of the rules through confidence factors; while the Bayesian network effectively handles the complexity and uncertainty in fault diagnosis through probabilistic reasoning, thereby improving the accuracy and robustness of diagnosis; the results of rule reasoning and probabilistic reasoning are fused through decision-making through DS evidence theory, further enhancing the reliability and consistency of the diagnosis results; this hybrid reasoning mechanism improves the efficiency and accuracy of fault diagnosis, and helps to ensure the safe and stable operation of the power grid.

[0117] S106: Calculate the fault diagnosis coverage rate according to the fault diagnosis result, and optimize the fault diagnosis process;

[0118] Preferably, the fault diagnosis coverage is the ratio of the number of fault types that the system can correctly diagnose to the total number of fault types, which is expressed by the formula:

[0119]

[0120] Among them, N d is the number of fault types that can be correctly diagnosed; N t is the total number of fault types.

[0121] Preferably, according to the importance and occurrence frequency of different fault types, a weighted fault diagnosis coverage is introduced, which is expressed as follows:

[0122]

[0123] Among them, w i is the weight of the i-th fault type; I i is the indicator function. When the i-th fault can be correctly diagnosed, I i =1, otherwise I i =0.

[0124] In the embodiment of the present application, based on the diagnostic coverage evaluation results, the following strategies are adopted for optimization:

[0125] a. For fault types with lower FDC, focus on collecting relevant data and expanding the knowledge graph;

[0126] b. Design special diagnostic rules or adjust the Bayesian network structure for fault types that are difficult to diagnose;

[0127] c. Improve the observability of key features by adding sensors or optimizing data collection strategies.

[0128] In the embodiment of the present application, an iterative optimization process is designed to continuously improve the fault diagnosis coverage:

[0129] a. Assess current diagnostic coverage;

[0130] b. Identify fault types with low diagnostic coverage;

[0131] c. Implement targeted optimization strategies;

[0132] d. Reassess diagnostic coverage;

[0133] e. If the preset target is reached or converged, then end; otherwise return to step b.

[0134] It should be noted that through the above steps, the method of the present invention can continuously improve the fault diagnosis coverage of the distribution network with a high proportion of new energy access, and effectively respond to the challenges brought by the access of new energy. The method of the present invention has strong adaptability and scalability, and can be continuously optimized and improved with the development of new energy technology and the emergence of new fault types.

[0135] The above is a schematic scheme of a fault diagnosis method for connecting a new energy source to a distribution network in this embodiment. It should be noted that the technical scheme of the fault diagnosis system for connecting a new energy source to a distribution network and the technical scheme of the fault diagnosis method for connecting a new energy source to a distribution network belong to the same concept. For details not described in detail in the technical scheme of the fault diagnosis system for connecting a new energy source to a distribution network in this embodiment, please refer to the description of the technical scheme of the fault diagnosis method for connecting a new energy source to a distribution network.

[0136] The fault diagnosis system for connecting new energy to the distribution network in this embodiment includes:

[0137] A data acquisition module is used to acquire distribution network operation data;

[0138] The fault diagnosis module is used to establish an initial fault type set based on the distribution network operation data; based on the initial fault type set, a multi-level fault knowledge graph including the equipment layer, system layer and environment layer is constructed; based on the multi-level fault knowledge graph, a hybrid reasoning mechanism combining rule reasoning and probabilistic reasoning is designed, and the hybrid reasoning mechanism is used to perform fault diagnosis and obtain fault diagnosis results;

[0139] The optimization module is used to calculate the fault diagnosis coverage and optimize the fault diagnosis process according to the fault diagnosis results.

[0140] This embodiment further provides a computing device, which is applicable to the case of fault diagnosis of new energy access to a distribution network, and includes:

[0141] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the fault diagnosis method for connecting new energy to the distribution network as proposed in the above embodiment.

[0142] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the fault diagnosis method for connecting new energy to a distribution network as proposed in the above embodiment is implemented.

[0143] The storage medium proposed in this embodiment and the fault diagnosis method for realizing the access of new energy to the distribution network proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0144] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0145] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A fault diagnosis method for connecting new energy to a distribution network, characterized in that: include: Acquire distribution network operation data, and establish an initial fault type set according to the distribution network operation data; Based on the initial fault type set, a multi-level fault knowledge graph including a device layer, a system layer, and an environment layer is constructed; Based on the multi-level fault knowledge graph, a hybrid reasoning mechanism combining rule reasoning and probability reasoning is designed, and the hybrid reasoning mechanism is used to perform fault diagnosis and obtain fault diagnosis results; According to the fault diagnosis results, the fault diagnosis coverage is calculated and the fault diagnosis process is optimized.

2. The fault diagnosis method for connecting new energy to a distribution network according to claim 1, characterized in that: Establishing the initial set of fault types includes, Based on literature research and expert knowledge, an initial set of fault types including traditional distribution network faults and new energy system faults is established; Using the Apriori association rule algorithm, new fault patterns are discovered from historical fault data, and the support and confidence calculations of the Apriori association rule algorithm are calculated; When the support and confidence meet the preset thresholds at the same time, a new fault type is found and added to the initial fault type set; The K-means clustering algorithm is used to perform cluster analysis on the fault data to find potential new fault types. The objective function of the K-means algorithm is: in, is the i-th data point in the j-th class, c j is the cluster center of the jth class; By minimizing the objective function, the optimal clustering result is obtained. For a newly formed cluster, if the newly formed cluster feature is different from the existing fault type, it is added as a new fault type to the initial fault type set.

3. The fault diagnosis method for connecting new energy to a distribution network as claimed in claim 1 or 2, characterized in that: The multi-level fault knowledge graph includes: The RDF triple <subject, attribute, object> is used to formally represent the multi-level fault knowledge graph.

4. The fault diagnosis method for connecting new energy to a distribution network as claimed in claim 3, characterized in that: The device layer, system layer and environment layer include, The equipment layer describes the fault characteristics and interrelationships of various equipment in the distribution network; The system layer describes the overall topology, operating status and fault propagation rules of the distribution network; The environmental layer contains external factors that affect the operation of the distribution network.

5. The fault diagnosis method for connecting new energy to a distribution network as claimed in claim 4, characterized in that: Design a hybrid reasoning mechanism that combines rule-based reasoning and probabilistic reasoning, including: The rule reasoning is based on expert knowledge and fault knowledge graph to establish a rule base in the form of IF-THEN; Use Bayesian networks for probabilistic reasoning to handle uncertainty in fault diagnosis; Combining the results of rule reasoning and probability reasoning, DS evidence theory is used for decision fusion. Let m1 and m2 be the basic probability distribution functions obtained by rule reasoning and probability reasoning respectively. The fused result can be expressed as: Among them, A, B, and C are subsets of the fault type set, B∩C=A, and k is a normalization factor.

6. The fault diagnosis method for connecting new energy to a distribution network as claimed in claim 5, characterized in that: According to the fault diagnosis results, the calculation of fault diagnosis coverage includes: Fault diagnosis coverage is the ratio of the number of fault types that the system can correctly diagnose to the total number of fault types, which can be expressed as: Among them, N d is the number of fault types that can be correctly diagnosed; N t is the total number of fault types.

7. The fault diagnosis method for connecting new energy to a distribution network as claimed in claim 6, characterized in that: Also includes, According to the importance and frequency of different fault types, the weighted fault diagnosis coverage is introduced and expressed as: Among them, w i is the weight of the i-th fault type; I i is the indicator function. When the i-th fault can be correctly diagnosed, I i =1, otherwise I i =0.

8. A system for fault diagnosis of new energy access to distribution network, characterized in that: include, A data acquisition module is used to acquire distribution network operation data; A fault diagnosis module is used to establish an initial fault type set according to the distribution network operation data; based on the initial fault type set, a multi-level fault knowledge graph including a device layer, a system layer and an environment layer is constructed; Based on the multi-level fault knowledge graph, a hybrid reasoning mechanism combining rule reasoning and probability reasoning is designed, and the hybrid reasoning mechanism is used to perform fault diagnosis and obtain fault diagnosis results; The optimization module is used to calculate the fault diagnosis coverage and optimize the fault diagnosis process according to the fault diagnosis results.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the fault diagnosis method for connecting new energy to the distribution network as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the fault diagnosis method for connecting new energy to a distribution network as described in any one of claims 1 to 7.

Images