Simulation operation evaluation method and device for power distribution network equipment

Abstract

The invention provides a simulation operation evaluation method and device for power distribution network equipment. The method comprises the following steps: acquiring physical parameters of key equipment in a power distribution network; performing three-dimensional modeling on the key equipment based on the physical parameters to obtain a key equipment model; based on the key equipment model, establishing a digital twinborn model of the power distribution network; acquiring simulation operation data of the key equipment model of the digital twin model in a preset period; performing feature extraction on the simulation operation data to obtain feature data; establishing a factor set and a fuzzy evaluation set of key equipment model operation state evaluation based on the feature data; acquiring historical operation data of key equipment in the power distribution network, and determining a comprehensive membership value matrix of the key equipment model based on the historical operation data, the factor set and the fuzzy evaluation set; and evaluating the operation result of the key equipment model based on the comprehensive membership value matrix. According to the invention, the operation condition of the actual power distribution network equipment can be accurately reflected through the model, and the accuracy and efficiency of power distribution network management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of operation evaluation of distribution network equipment, and in particular to a simulated operation evaluation method and device for distribution network equipment. Background Art

[0002] The distribution network is the power network in the power system that exits from the step-down distribution substation (high-voltage distribution substation) to the user end. It is responsible for transmitting electric energy from the high-voltage transmission network or regional power plant to the end user. The distribution network equipment refers to the general term for various equipment used to achieve electric energy distribution, transmission, protection and control in the distribution network. The distribution network equipment plays a vital role in the power system. They work together to ensure that electric energy can be effectively transmitted from the power source to the user end, while ensuring the safety and stability of the distribution process.

[0003] With the development of smart grids, the complexity and reliability requirements of distribution networks are increasing. By modeling the distribution network, the operating status of the distribution network can be monitored more accurately and the operating efficiency of the distribution network can be improved. Traditional two-dimensional design mainly represents power equipment through abstract symbols, which cannot intuitively display the actual structure of the equipment and the spatial relationship between them, limiting the efficiency of inspection, operation and maintenance. At the same time, simulation based on two-dimensional design is difficult to accurately reflect the actual operation of the power grid in three-dimensional space, resulting in greater uncertainty in the design and operation and maintenance process. Therefore, traditional two-dimensional design methods can no longer meet the needs of accurate simulation and efficient management of the power grid operation status.

[0004] The traditional three-dimensional modeling method of distribution network equipment cannot reflect the operating status of power grid equipment in real time and dynamically, which limits the improvement of intelligent management and operation and maintenance level of power grid. In addition, the traditional three-dimensional modeling method of distribution network equipment does not have an accurate evaluation method for the simulated operation evaluation of distribution network equipment. When the simulated operation status of each equipment model is uncertain, it is mostly judged as a poor simulated operation status. There is a lack of further judgment methods for the operating status of the equipment model, resulting in the inability to fully and accurately reflect the actual status of the equipment model in the simulated operation. Summary of the invention

[0005] The present invention provides a simulated operation evaluation method and device for distribution network equipment, so as to solve the problem that current technical means cannot fully and accurately reflect the real state of the equipment model in the simulated operation.

[0006] In a first aspect, the present invention provides a method for evaluating simulated operation of distribution network equipment, comprising:

[0007] Obtain the physical parameters of key equipment in the distribution network;

[0008] Carry out 3D modeling of key equipment based on physical parameters to obtain key equipment models; establish a digital twin model of the distribution network based on the key equipment models;

[0009] Acquire the simulated operation data of the key equipment model of the digital twin model within the preset period; extract features from the simulated operation data to obtain the feature data of the key equipment model; establish the factor set and fuzzy evaluation set for the operation status evaluation of the key equipment model based on the feature data;

[0010] Obtain the historical operation data of key equipment in the distribution network, and determine the comprehensive membership value matrix of the key equipment model based on the historical operation data, factor set and fuzzy evaluation set;

[0011] The operating results of the key equipment model are evaluated based on the comprehensive membership value matrix.

[0012] In a possible implementation, the comprehensive membership value matrix of the key equipment model includes: comprehensive membership values ​​of poor operating status, comprehensive membership values ​​of medium operating status, and comprehensive membership values ​​of good operating status.

[0013] In a possible implementation, the operation results of the key equipment model are evaluated based on the comprehensive membership value matrix, including:

[0014] If the comprehensive membership value of the key equipment model with a good operating state is greater than the sum of the comprehensive membership value of the poor operating state and the comprehensive membership value of the medium operating state, the operation result of the key equipment model is determined to be qualified;

[0015] If the comprehensive membership value of the poor operating state of the key equipment model is greater than the comprehensive membership value of the medium operating state and the comprehensive membership value of the poor operating state of the key equipment model is greater than the comprehensive membership value of the good operating state, it is determined that the operating result of the key equipment model is unqualified.

[0016] In a possible implementation, after evaluating the operation results of the key equipment model based on the comprehensive membership value matrix, the following is further included:

[0017] If the operation results of the key equipment model are qualified, the key equipment model will be applied to the subsequent operation of the digital twin model;

[0018] If the operation result of the key equipment model is unqualified, the parameters of the key equipment model are adjusted, and the process returns to the step of performing three-dimensional modeling of the key equipment based on the physical parameters to obtain the key equipment model.

[0019] In a possible implementation, a comprehensive membership value matrix of a key equipment model is determined based on historical operation data, a factor set, and a fuzzy evaluation set, including:

[0020] Establish membership functions based on historical operating data and factor sets;

[0021] Determine the comprehensive membership value matrix of key equipment models based on membership functions and fuzzy evaluation sets;

[0022] The membership functions include:

[0023]

[0024] Among them, X ij is the jth characteristic data in the i-th key equipment model. When the key equipment corresponding to the i-th key equipment model in the actual distribution network fails, the numerical range of each characteristic data is [X 1ij ,X 3ij ], the lower interval is [X 4ij ,X 6ij ], the numerical range of each characteristic data of the key equipment model of the i-th key equipment in the actual distribution network when the key equipment does not fail is [X 2ij ,X 5ij ].

[0025] In a possible implementation, a comprehensive membership value matrix of a key equipment model is determined based on a membership function and a fuzzy evaluation set, including:

[0026] The membership function A1(X ij )、A2(X ij ) and A3(X ij ) are put into the same plane rectangular coordinate system to obtain the overall membership function of the key equipment model. The overall membership function is divided into seven intervals from I1 to I7, and the corresponding intervals are [0, X 1ij ),[X 1ij ,X 2ij ),[X 2ij ,X 3ij ),[X 3ij ,X 4ij ),[X 4ij ,X 5ij ),[X 5ij ,X 6ij ),[X 6ij , 1];

[0027] Based on the overall membership function and fuzzy evaluation set, the comprehensive membership value matrix of the key equipment model is determined.

[0028] In a possible implementation, based on the overall membership function and the fuzzy evaluation set, a comprehensive membership value matrix of the key equipment model is determined, including:

[0029] Based on the overall membership function and the fuzzy evaluation set, the fuzzy evaluation rules are determined;

[0030] Based on the overall membership function and fuzzy evaluation rules, the comprehensive membership value matrix of the key equipment model is determined.

[0031] The fuzzy evaluation rule is expressed as:

[0032] When the feature data X ij When it belongs to I1, the operating status of the i-th key equipment model under the j-th characteristic data is poor;

[0033] When the feature data X ij When it belongs to I2, the operating status of the i-th key equipment model under the j-th characteristic data is poor or medium;

[0034] When the feature data X ij When it belongs to I3, the operation status of the i-th key equipment model under the j-th characteristic data is medium or good;

[0035] When the feature data X ij When it belongs to I4, the operation status of the i-th key equipment model under the j-th characteristic data is good;

[0036] When the feature data X ij When it belongs to I5, the operation status of the i-th key equipment model under the j-th characteristic data is good or medium;

[0037] When the feature data X ij When it belongs to I6, the operation status of the i-th key equipment model under the j-th characteristic data is medium or poor;

[0038] When the feature data X ij When it belongs to I7, the operating status of the i-th key equipment model under the j-th characteristic data is poor.

[0039] In a possible implementation, key equipment includes: a transformer, a switch cabinet, a capacitor cabinet, a control cabinet, and a transmission line.

[0040] In a possible implementation, feature extraction is performed on the simulation operation data to obtain feature data of key equipment models, including:

[0041] Perform time domain feature extraction and / or frequency domain feature extraction on the simulation operation data to obtain feature data of the key equipment model.

[0042] In a second aspect, the present invention provides a simulated operation evaluation device for distribution network equipment, comprising:

[0043] An acquisition module is used to obtain the physical parameters of key equipment in the distribution network;

[0044] The acquisition module is also used to obtain the simulated operation data of the key equipment model of the digital twin model within a preset period;

[0045] The acquisition module is also used to obtain the historical operation data of key equipment in the distribution network.

[0046] A modeling module is used to perform three-dimensional modeling of key equipment based on physical parameters to obtain a key equipment model;

[0047] The modeling module is also used to establish a digital twin model of the distribution network based on key equipment models.

[0048] The extraction module is used to extract features from the simulation operation data to obtain feature data of key equipment models.

[0049] The fuzzy evaluation module is used to establish a factor set and a fuzzy evaluation set for evaluating the operating status of key equipment models based on characteristic data.

[0050] The fuzzy evaluation module is also used to determine the comprehensive membership value matrix of the key equipment model based on historical operation data, factor sets and fuzzy evaluation sets.

[0051] The evaluation module is used to evaluate the operating status of the key equipment model based on the comprehensive membership value matrix.

[0052] The present invention provides a method and device for evaluating the simulated operation of distribution network equipment. The method comprises obtaining physical parameters of key equipment in the distribution network; performing three-dimensional modeling on the key equipment based on the physical parameters to obtain a key equipment model; establishing a digital twin model of the distribution network based on the key equipment model; obtaining simulated operation data of the key equipment model of the digital twin model within a preset period; performing feature extraction on the simulated operation data to obtain feature data of the key equipment model; establishing a factor set and a fuzzy evaluation set for evaluating the operation status of the key equipment model based on the feature data; obtaining historical operation data of the key equipment in the distribution network, and determining a comprehensive membership value matrix of the key equipment model based on the historical operation data, the factor set and the fuzzy evaluation set; and evaluating the operation results of the key equipment model based on the comprehensive membership value matrix. The present invention establishes a digital twin model of the distribution network, obtains characteristic data of the key equipment model within a preset period, obtains historical data of the operation of the key equipment in the distribution network, establishes a membership function and determines a fuzzy evaluation rule based on the historical data, determines the membership value of the characteristic data of the key equipment model within the membership function interval based on the membership function and the fuzzy evaluation rule, and combines them into a comprehensive membership value matrix. Based on the comprehensive membership value matrix, the operating status of the key equipment model is evaluated. The digital twin model can accurately reflect the operating conditions of the actual distribution network, thereby improving the accuracy and efficiency of distribution network management. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0054] Figure 1 It is a flow chart of an implementation of a simulation operation evaluation method for distribution network equipment provided by an embodiment of the present invention;

[0055] Figure 2 is an overall membership function diagram provided by an embodiment of the present invention;

[0056] Figure 3 It is a structural schematic diagram of a simulated operation evaluation device for distribution network equipment provided by an embodiment of the present invention;

[0057] Figure 4 is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0059] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0060] 3D modeling is a process of creating 3D digital models using 3D modeling software. Digital twin technology is a method of creating virtual models of physical objects. These models simulate behaviors and monitor operations through real-time data and can be widely used in many fields. 3D modeling technology is combined with big data, artificial intelligence and other technologies to build equipment status data sets, which can achieve scientific evaluation and prediction and early warning of equipment status, and promote the application of digital twin technology in power systems.

[0061] Figure 1 is a flow chart of a simulation operation evaluation method for distribution network equipment provided by an embodiment of the present invention, referring to Figure 1In this embodiment, firstly, the physical parameters of the key equipment in the distribution network are obtained to perform three-dimensional modeling of the key equipment, and a three-dimensional scene is built based on the digital twin technology to simulate the operation process of the distribution network. Furthermore, a monitoring cycle is set to obtain the operation data of each key equipment model in each cycle when the distribution network is running, and feature extraction is performed on the pre-processed operation data of each key equipment model, as detailed below:

[0062] In step 101, physical parameters of key equipment in the distribution network are obtained.

[0063] In a possible implementation, key equipment may include but is not limited to transformers, switch cabinets, capacitor cabinets, control cabinets, and transmission lines. Among them, the transformer is used to transform the voltage and convert high voltage electricity into low voltage electricity suitable for the distribution system; the switch cabinet is used to open and close switches and distribution protection devices to control and protect the power system to ensure the normal flow and distribution of electric energy; the capacitor cabinet is used to compensate for the power load and improve the quality and power factor of electric energy; the control cabinet is used to control, protect and monitor the working status of the controlled electrical equipment, does not directly participate in the flow of electric energy, and indirectly affects the distribution and use of electric energy by controlling the switch status of the electrical equipment; the transmission line is used to transmit electric energy from one place to another, ensuring that the electric energy can flow to each user end according to the predetermined path.

[0064] In one possible implementation, the physical parameters of key equipment include size, shape, material description, etc., which can be obtained through design drawings.

[0065] In step 102, three-dimensional modeling is performed on key equipment based on physical parameters to obtain a key equipment model.

[0066] In a possible implementation, the design drawing of each key device in the distribution network is imported into SolidWorks software, and a three-dimensional modeling operation is performed on each key device to obtain a three-dimensional model of each key device.

[0067] Exemplarily, the number of each key device is the same as the number of corresponding key devices in the actual distribution network, that is, if the actual distribution network includes S transformers, the three-dimensional model of each transformer is constructed according to the respective design drawings of the S transformers.

[0068] In step 103, a digital twin model of the distribution network is established based on the key equipment model.

[0069] In one possible implementation, a digital twin model of the distribution network is established based on the three-dimensional model of each key device and the connection status of each key device in the actual distribution network.

[0070] The environmental data in the digital twin model is associated with the environmental data of the distribution network, and the same data as the corresponding key equipment in the actual distribution network is imported into the detailed data of the three-dimensional model of each key equipment in the digital twin model. Exemplarily, the data can be operating parameter data. The digital twin model of the distribution network is simulated to simulate the operating status of each key equipment when the distribution network is operating normally.

[0071] In step 104, the simulated operation data of the key equipment model of the digital twin model within a preset period is obtained.

[0072] In one possible implementation, a monitoring cycle is set, and the simulated operation data of each key equipment model in the distribution network digital twin model is monitored in real time during each monitoring cycle.

[0073] Exemplarily, for the transformer equipment model, within the preset monitoring period, the voltage, current and power data in each transformer equipment model are obtained in real time through the sensor simulation module in the digital twin model, and the changes in the oil temperature and winding temperature in each transformer equipment model are simulated using the thermal simulation module. The sensor simulation module is a component in the digital twin model used to simulate the actual sensor behavior. Through algorithms and models, it simulates the working process of the sensor in the distribution network, including data acquisition, conversion and transmission. The thermal simulation module is a component in the digital twin model used to simulate the thermal behavior of the equipment. Based on the basic principles of heat transfer and fluid mechanics, it simulates the heat transfer and distribution of the equipment during operation through numerical calculation methods, and can predict the temperature distribution and change trend of the equipment under different working conditions.

[0074] Exemplarily, for the switch cabinet equipment model, within a preset monitoring period, the current and voltage data in each switch cabinet equipment model are obtained through the sensor simulation module in the digital twin model, the local discharge signal is obtained by simulating the electric field distribution inside each switch cabinet equipment model and the aging process of the insulating material, and the temperature data on the surface of each switch cabinet equipment model is obtained through the thermal simulation module.

[0075] Exemplarily, for the capacitor cabinet device model, within the preset monitoring period, the capacitance value, current and voltage data of each capacitor cabinet device model are obtained through the electrical parameter simulation module in the digital twin model, and the temperature data on the surface of each capacitor cabinet device model is obtained through the thermal simulation module. The harmonic content of each capacitor cabinet device model is obtained by simulating the harmonic source in the power grid, wherein the electrical parameter simulation module is a component in the digital twin model used to simulate the electrical characteristics of the device, and the electrical behavior of the device in the power grid is simulated through algorithms and models, including the calculation and analysis of electrical parameters such as voltage, current, power factor, and harmonic content.

[0076] Exemplarily, for the control cabinet equipment model, within a preset monitoring period, the internal temperature and humidity data of each control cabinet equipment model is obtained through the environmental simulation module, which simulates the changes in environmental conditions inside each control cabinet.

[0077] Exemplarily, for the transmission line simulation equipment, within a preset monitoring period, the voltage and current data at the end of each transmission line are obtained in real time through the sensor simulation module in the digital twin model.

[0078] In step 105, feature extraction is performed on the simulation operation data to obtain feature data of the key equipment model.

[0079] In a possible implementation, the simulation operation data is preprocessed before feature extraction is performed on the simulation operation data.

[0080] Exemplarily, data cleaning and signal denoising operations are performed on the collected simulation operation data to remove the collected abnormal values, and for data that may contain noise, such as partial discharge signals, a filtering method is used to perform denoising. The filtering method can be selected from low-pass filtering or wavelet transform denoising.

[0081] In a possible implementation, time domain feature extraction and / or frequency domain feature extraction are performed on the preprocessed simulation operation data to obtain feature data of the key equipment model.

[0082] Exemplarily, for the pre-processed voltage and current data, time domain features are extracted, including calculating the average value and variance of the current and voltage in each monitoring cycle, and detecting transient changes in the current and voltage waveforms, such as selecting peak values, rise time, fall time, etc. The time domain signal is converted into a frequency domain signal through Fourier transform, and frequency domain features are extracted, including spectrum peak value and spectrum bandwidth.

[0083] The voltage and current data of all key equipment models are subjected to the above feature extraction operation.

[0084] Exemplarily, for the preprocessed power data, time domain features are extracted, including the calculated active power, reactive power and apparent power in each monitoring period.

[0085] Exemplarily, for the preprocessed temperature data, time domain features are extracted, including calculating the average temperature change rate of each monitoring period and analyzing the maximum value of each temperature data of each monitoring period and the duration of the maximum value.

[0086] The oil temperature, winding temperature and key equipment model surface temperature data are all feature extracted according to the above feature extraction method;

[0087] Exemplarily, for the pre-processed partial discharge signal data, time domain features are extracted, including analyzing and obtaining the discharge times, discharge amount, and discharge phase of the partial discharge signal data in each monitoring period.

[0088] Exemplarily, for the preprocessed harmonic content, a fast Fourier transform is performed on the harmonic signal to obtain a spectrum diagram of the signal, and then frequency domain features are extracted, including the harmonic amplitude, harmonic phase and harmonic order of each harmonic signal.

[0089] In step 101 to step 105, by obtaining the design drawings of key equipment in the distribution network and performing accurate three-dimensional modeling, a digital twin model that is highly similar to the actual equipment is constructed, which provides a solid foundation for the operation simulation of the distribution network. By setting a monitoring cycle and obtaining the operation data of the key equipment model in real time, and performing comprehensive feature extraction on these data, rich data support is provided for the status monitoring, fault diagnosis and predictive maintenance of the distribution network, which not only improves the safety and reliability of the distribution network operation, but also provides a powerful technical means for realizing the refined management of the smart grid.

[0090] After completing steps 101 to 105, this embodiment establishes a factor set and a fuzzy evaluation set for evaluating the operating status of each key equipment model based on the characteristic data, obtains the historical operating data of the distribution network, and establishes a membership function. Based on the membership function, the interval to which the operating status of each characteristic data of each key equipment model belongs is determined, and the comprehensive membership value matrix of the characteristic data in each key equipment model is further determined, as described in detail as follows:

[0091] In step 106, a factor set and a fuzzy evaluation set for evaluating the operating status of the key equipment model are established based on the characteristic data.

[0092] The factor set refers to a set of various factors that affect the evaluation object, which are the characteristics of each key equipment model in this embodiment. The fuzzy evaluation set refers to a set of various evaluation results that the evaluator may make on the evaluation object.

[0093] In a possible implementation, a factor set and a fuzzy evaluation set for evaluating the operating status of each key equipment model are established, and U i express, Where n i Represents the total number of features extracted by the i-th key device model in the running order queue.

[0094] In a possible implementation, the fuzzy evaluation set is {bad operating status, medium operating status, good operating status}.

[0095] In step 107, historical operation data of key equipment in the distribution network is obtained, and a comprehensive membership value matrix of the key equipment model is determined based on the historical operation data, the factor set and the fuzzy evaluation set.

[0096] In one possible implementation, obtaining historical operation data of key equipment in the distribution network includes obtaining the operation order of key equipment in the distribution network. Each key equipment is sorted from first to last and stored in the operation order queue. In step 108, the operation results of each key equipment model in the digital twin model are evaluated in sequence according to this order. Multiple key equipment of the same type are independent of each other and are sorted as independent equipment. For example, if the operation order of transformer A is before the operation order of transformer B, and electric energy is transmitted through transmission line C, the operation results are evaluated in the order of transformer A, transmission line C and transformer B.

[0097] In a possible implementation, historical operation data of key equipment in the distribution network including operation data of each key equipment is obtained, the operation data of each key equipment is processed to obtain characteristic data corresponding to each key equipment, and it is determined whether each key equipment fails.

[0098] The overall idea of ​​establishing a membership function based on historical operation data, factor set and fuzzy evaluation set, and determining the comprehensive membership value matrix of the key equipment model based on the membership function and fuzzy evaluation set is as follows: establishing a membership function based on historical operation data and factor set, and then performing membership analysis on each feature data in each key equipment model for fuzzy evaluation, and obtaining the membership value vector A of this feature data for each fuzzy evaluation in the fuzzy evaluation set ij , combine the membership value vectors of each fuzzy evaluation in the fuzzy evaluation set for all feature data in the same key equipment model to obtain the fuzzy relationship matrix B i , combine each feature data with the corresponding weight to obtain the comprehensive membership value matrix C of each key equipment model i The following describes the process in detail:

[0099] Membership function is a core concept in fuzzy logic, which is used to express the degree to which an element belongs to a fuzzy set. The domain of a membership function refers to the set of all possible input values ​​defined by the membership function, that is, the range of all values ​​that an element in the fuzzy set can take.

[0100] Fuzzy labeling is a concept in fuzzy logic, which is used to deal with uncertainty and ambiguity. Fuzzy labeling is usually used together with membership functions.

[0101] The membership value is a single numerical value that indicates the degree to which an element belongs to a fuzzy set. The membership value usually ranges from 0 to 1, where 0 means that the element does not belong to the set at all and 1 means that the element completely belongs to the set. The membership value is calculated by the membership function.

[0102] A membership vector is a set of numerical values ​​that represents the membership value of a group of elements to a fuzzy set. If there is a fuzzy set and there are n elements that need to be evaluated to what extent they belong to the set, then the membership values ​​of these n elements can form a membership vector.

[0103] The membership vector can be viewed as a combination of multiple membership values. For each element, there is a corresponding membership value, which is arranged in the order of the elements to form a membership vector. The membership vector provides a way to consider the relationship between multiple elements and fuzzy sets at the same time.

[0104] In a possible implementation, a membership function is established based on historical operating data and a factor set.

[0105] First, the linear normalization method is used to perform the same normalization processing on the characteristic data of each key equipment in the distribution network and the characteristic data of each key equipment model in the digital twin model. After normalization, the domain of each feature is [0,1], which is the domain of the membership function.

[0106] The numerical intervals of each characteristic data when each key device is not faulty and the numerical intervals of each characteristic data when a fault occurs are counted respectively. For example, the numerical intervals of each characteristic data when a key device corresponding to the i-th key device model in the actual distribution network fails are recorded as [C 1ij ,X 3ij ], the lower interval is [n 4ij ,X 6ij ], the numerical range of each characteristic data of the key equipment model of the i-th key equipment in the actual distribution network when the key equipment does not fail is [X 2ij ,X 5ij ].

[0107] Each feature data of each key equipment model is fuzzily labeled. For example, for quantity feature data, it is labeled as: "small", "medium", "large", and for rate feature data, it is labeled as "low", "medium", "high".

[0108] Then a quadratic parabolic membership function is established, where the membership functions of the features fuzzily labeled as "small" and "low" are:

[0109]

[0110] The membership function of the feature fuzzily labeled as "medium" is:

[0111]

[0112] The membership functions for the features fuzzily labeled "large" and "tall" are:

[0113]

[0114] Among them, X ij is the jth characteristic data in the i-th key equipment model, and in the i-th key equipment model, the value range of j is {1,2,…,n i}.

[0115] Put the above three membership functions into the same plane rectangular coordinate system to obtain the overall membership function of each key equipment model, such as Figure 2 As shown, in Figure 2 The horizontal axis of the overall membership function is divided into 7 intervals, namely I1-I7, and the corresponding intervals are [0,X 1ij ),[X 1ij ,X 2ij ),[X 2ij ,X 3ij ),[X 3ij ,X 4ij ),[X 4ij ,X 5ij ),[X 5ij ,X 6ij ) and [X 6ij ,1].

[0116] According to the membership function, the fuzzy evaluation rules are set. For example, for quantitative feature data, when the fuzzy mark is "small" or "large", the operation state of the corresponding key equipment model is relatively poor, and when the fuzzy mark is "medium", the operation state of the corresponding key equipment model is relatively good. For rate feature data, when the fuzzy mark is "low" or "high", the operation state of the corresponding key equipment model is relatively poor, and when the fuzzy mark is "medium", the operation state of the corresponding key equipment model is relatively good.

[0117] The fuzzy evaluation rules are specifically expressed as:

[0118] When the feature data X ij When it belongs to I1, the operating status of the i-th key equipment model under the j-th characteristic data is poor;

[0119] When the feature data X ij When it belongs to I2, the operating status of the i-th key equipment model under the j-th characteristic data is poor or medium;

[0120] When the feature data X ijWhen it belongs to I3, the operation status of the i-th key equipment model under the j-th characteristic data is medium or good;

[0121] When the feature data X ij When it belongs to I4, the operation status of the i-th key equipment model under the j-th characteristic data is good;

[0122] When the feature data X ij When it belongs to I5, the operation status of the i-th key equipment model under the j-th characteristic data is good or medium;

[0123] When the feature data X ij When it belongs to I6, the operation status of the i-th key equipment model under the j-th characteristic data is medium or poor;

[0124] When the feature data X ij When it belongs to I7, the operating status of the i-th key equipment model under the j-th characteristic data is poor.

[0125] The membership of each characteristic data of the key equipment model obtained during the monitoring period and its corresponding fuzzy mark is calculated respectively, and the corresponding fuzzy evaluation rule is determined according to the interval where each characteristic data is located, and the calculated membership is assigned to the corresponding fuzzy evaluation result as the membership value of the fuzzy evaluation result.

[0126] For example, for quantitative feature data, when X ij ∈I1, its membership to “small” is calculated to be 1, then the membership value of the characteristic data to the critical equipment operation status difference of the i-th critical equipment model is 1; when X ij ∈I2, its membership to “small” is calculated to be A1, and its membership to “medium” is Z2, and A1>A2, then the membership value of this feature data to the poor operation status of the key equipment model of the i-th key equipment is A1, and the membership value to the medium operation status is A2; when X ij ∈I3, its membership to “small” is calculated to be Z3, and its membership to “medium” is Z4, and A3≤A4, then the membership value of this feature data to the medium operation status of the key equipment model of the i-th key equipment is A3, and the membership value to the good operation status is A4; when X ij ∈I4, its membership to “medium” is calculated to be 1, then the membership value of this feature data to the i-th key equipment model key equipment good operation status is 1; when X ij ∈I5, its membership to “medium” is calculated to be A5, and its membership to “large” is A6, and A5>A6, then the membership value of this feature data to the i-th key equipment model with good key equipment operation status is A5, and the membership value to the medium operation status is A6; when X ij∈I6, its membership to “medium” is calculated to be A7, and its membership to “large” is A8, and A7≤A8, then the membership value of this feature data to the medium operation status of the key equipment model of the i-th key equipment is A7, and the membership value to the poor operation status is A8; when X ij ∈I7, its membership to “large” is calculated to be 1, and the membership value of the characteristic data to the critical equipment operating status difference of the i-th critical equipment model is 1.

[0127] For rate-type feature data, the membership value of each fuzzy evaluation result is calculated using the same method as above.

[0128] Based on the membership value data of the feature data, the membership value vector A of each feature data for each fuzzy evaluation ({bad running state, medium running state, good running state}) in the fuzzy evaluation set is obtained. ij , A ij =(A 1ij A 2ij A 3ij ), A 1ij , A 2ij , A 3ij are the membership value vectors for each feature data pair with poor operating status, medium operating status, and good operating status respectively.

[0129] The fuzzy relationship matrix B of each monitoring cycle is obtained by applying the membership value vector set of each fuzzy evaluation to all feature data in the same key equipment model. i ,in

[0130] Calculate the weight matrix K of the feature data of each key equipment model i ,in Exemplarily, the entropy weight method can be selected to calculate the weight matrix of each feature data. The entropy weight method measures the degree of variation or the amount of information of each indicator by calculating the information entropy of each indicator, and then determines the weight of each indicator.

[0131] The fuzzy relationship matrix B of each key equipment model in each monitoring cycle i And the weight matrix K of each feature data i Combined, the comprehensive membership value matrix C of each key equipment model in each monitoring cycle is obtained i , where C i =K i ·B i =(C 1i C 2i C 3i ), where C 1i , C 2i , C 3iIt is the comprehensive membership value of poor operating status, medium operating status and good operating status for each key equipment model.

[0132] By establishing a factor set and a fuzzy evaluation set for the operating status evaluation of each key equipment model, and combining the historical distribution network operation data to establish an overall membership function, the operating status interval of each characteristic data is determined, and a refined evaluation of the operating status of the key equipment model is achieved. Using the overall membership function and fuzzy evaluation rules, the value of each characteristic data can be converted into a membership degree for the fuzzy evaluation, which becomes the data basis for evaluating the operating results of the key equipment model under different characteristics, and provides strong data support for fault prediction and maintenance management of the distribution network.

[0133] By calculating the membership value vector of each characteristic data for each fuzzy evaluation in the fuzzy evaluation set, and combining the membership value vectors of all characteristic data to obtain the fuzzy relationship matrix, the operating status of the key equipment model under each fuzzy evaluation can be fully reflected. The weight of each characteristic data is calculated using the entropy weight method, and combined with the fuzzy relationship matrix to obtain a comprehensive membership value matrix, which makes the evaluation of the operating status of the key equipment model more scientific and accurate. This not only provides a data basis for the subsequent simulation operation evaluation of each key equipment model, but also improves the accuracy and comprehensiveness of the evaluation of the operating status of distribution network equipment.

[0134] In step 108, the operation results of the key equipment model are evaluated based on the comprehensive membership value matrix.

[0135] In a possible implementation, the operation results of the key equipment model are evaluated based on the comprehensive membership value matrix, including:

[0136] If the comprehensive membership value matrix of the key equipment model with good operation status is greater than the sum of the comprehensive membership value matrix of the key equipment model with poor operation status and the comprehensive membership value matrix of the key equipment model with medium operation status, the operation result of the key equipment model is determined to be qualified;

[0137] If the comprehensive membership value matrix of the poor operating state of the key equipment model is greater than the comprehensive membership value matrix of the medium operating state and the comprehensive membership value matrix of the poor operating state of the key equipment model is greater than the comprehensive membership value matrix of the good operating state, it is determined that the operating result of the key equipment model is unqualified.

[0138] In one possible implementation, if the operation result of the key equipment model is qualified, the key equipment model is applied to the subsequent operation of the digital twin model;

[0139] If the operation result of the key equipment model is unqualified, the parameters of the key equipment model are adjusted, and the process returns to the step of performing three-dimensional modeling of the key equipment based on the physical parameters to obtain the key equipment model.

[0140] For example, if C 3i >C 1i +C 2i , it means that the simulation operation result of the i-th key equipment model in the current monitoring period is qualified, and the key equipment model can be directly applied to the subsequent digital twin model.

[0141] If C 1i >C 2i And C 1i >C 3i , it means that the simulation operation result of the i-th key equipment model in the current monitoring period is unqualified, and the key equipment model cannot be directly used in the subsequent digital twin model. The simulation process of the distribution and disconnection digital twin model is stopped, and the unqualified key equipment model is adjusted.

[0142] In addition to the above two situations, it means that the simulation operation results of the i-th key equipment model in the current monitoring period are uncertain, and further judgment is required to determine whether the simulation operation results of the i-th key equipment model in the current monitoring period are qualified.

[0143] Exemplarily, the current monitoring cycle is placed in the buffer queue of the corresponding model. The maximum span of the periodic data in the buffer queue is 4 periods. If the maximum span of the periodic data in the buffer queue exceeds 4 periods, the first periodic data in the buffer queue is deleted, and further judgment is made based on the simulation operation evaluation results of the next monitoring cycle model, specifically:

[0144] If the simulation result of the model in the next monitoring cycle is unqualified, the data in the buffer queue will be deleted;

[0145] If the simulation result of the model in the next monitoring cycle is qualified, the simulation result of the next cycle will be judged. If the simulation results of the marked model in the next two consecutive cycles are qualified, the data in the buffer queue will be deleted;

[0146] If the simulation result of the model in the next cycle is uncertain, the current cycle data will be placed in the buffer queue of the corresponding model, and the number of data in the buffer queue will be counted. If the number of data in the buffer queue exceeds 2, the model operation result will be judged as unqualified, and the data in the buffer queue will be cleared.

[0147] After determining that the simulation operation result of the i-th key equipment model is unqualified, the current key equipment is taken as the initial fault equipment, and the operation status parameters of the model need to be readjusted, and then return to step 102, three-dimensional modeling of the key equipment is performed based on physical parameters to obtain the key equipment model, and then complete the subsequent steps.

[0148] If the simulation operation results of the equipment model are still unqualified after the operating status parameters of the model are readjusted, the equipment needs to be remodeled; after the entire process of the distribution network digital twin model is completed, if the simulation operation results of all key equipment models in all detection cycles are not unqualified, the distribution network model will be applied to the simulation of the actual distribution network operation process.

[0149] Through the comprehensive membership value matrix, the simulation operation results of each key equipment model are evaluated, and corresponding operations are performed according to the evaluation results, realizing the refined management and control of the operation status of the key equipment models. Not only can the key equipment models with unqualified simulation operation results be discovered and processed in a timely manner, but also the uncertain simulation operation results can be further judged and processed through the buffer queue mechanism, which improves the accuracy and robustness of the evaluation, and the operation status parameters of unqualified key equipment models are readjusted or remodeled to ensure that the final distribution network model can accurately reflect the operation process of the actual distribution network.

[0150] Figure 3 1 is a schematic diagram of the structure of a simulation operation evaluation device for distribution network equipment provided by an embodiment of the present invention, which is described in detail as follows:

[0151] Reference Figure 3 A simulated operation evaluation device 3 for distribution network equipment includes: an acquisition module 31, a modeling module 32, an extraction module 33, a fuzzy evaluation module 34 and an evaluation module 35.

[0152] The acquisition module 31 is used to acquire the physical parameters of key equipment in the distribution network.

[0153] The acquisition module 31 is also used to obtain the simulated operation data of the key equipment model of the digital twin model within a preset period;

[0154] The acquisition module 31 is also used to acquire the historical operation data of key equipment in the distribution network.

[0155] A modeling module 32 is used to perform three-dimensional modeling of key equipment based on physical parameters to obtain a key equipment model;

[0156] The modeling module 32 is also used to establish a digital twin model of the distribution network based on the key equipment model.

[0157] The extraction module 33 is used to extract features from the simulation operation data to obtain feature data of the key equipment model.

[0158] The fuzzy evaluation module 34 is used to establish a factor set and a fuzzy evaluation set for evaluating the operating status of the key equipment model based on the characteristic data.

[0159] The fuzzy evaluation module 34 is also used to determine the comprehensive membership value matrix of the key equipment model based on the historical operation data, the factor set and the fuzzy evaluation set.

[0160] The evaluation module 35 is used to evaluate the operating status of the key equipment model based on the comprehensive membership value matrix.

[0161] In a possible implementation, the comprehensive membership value matrix of the key equipment model includes: comprehensive membership values ​​of poor operating status, comprehensive membership values ​​of medium operating status, and comprehensive membership values ​​of good operating status.

[0162] In a possible implementation, the evaluation module 35 is specifically used for:

[0163] If the comprehensive membership value of the key equipment model with a good operating state is greater than the sum of the comprehensive membership value of the poor operating state and the comprehensive membership value of the medium operating state, the operation result of the key equipment model is determined to be qualified;

[0164] If the comprehensive membership value of the poor operating state of the key equipment model is greater than the comprehensive membership value of the medium operating state and the comprehensive membership value of the poor operating state of the key equipment model is greater than the comprehensive membership value of the good operating state, it is determined that the operating result of the key equipment model is unqualified.

[0165] In a possible implementation, the evaluation module 35 is further configured to:

[0166] If the operation results of the key equipment model are qualified, the key equipment model will be applied to the subsequent operation of the digital twin model.

[0167] If the operation result of the key equipment model is unqualified, the parameters of the key equipment model are adjusted, and the process returns to the step of performing three-dimensional modeling of the key equipment based on the physical parameters to obtain the key equipment model.

[0168] In a possible implementation, the fuzzy evaluation module 34 is specifically used for:

[0169] Establish membership functions based on historical operating data and factor sets;

[0170] The comprehensive membership value matrix of the key equipment model is determined based on the membership function and fuzzy evaluation set.

[0171] The membership functions include:

[0172]

[0173] Among them, X ij is the jth characteristic data in the i-th key equipment model. When the key equipment corresponding to the i-th key equipment model in the actual distribution network fails, the numerical range of each characteristic data is [X 1ij ,X3ij ], the lower interval is [X 4ij ,X 6ij ], the numerical range of each characteristic data of the key equipment model of the i-th key equipment in the actual distribution network when the key equipment does not fail is [X 2ij ,X 5ij ].

[0174] In a possible implementation, the fuzzy evaluation module 34 is further configured to:

[0175] The membership function A1(X ij )、A2(X ij ) and A3(X ij ) are put into the same plane rectangular coordinate system to obtain the overall membership function of the key equipment model. The overall membership function is divided into seven intervals from I1 to I7, and the corresponding intervals are [0, X 1ij ),[X 1ij ,X 2ij ),[X 2ij ,X 3ij ),[X 3ij ,X 4ij ),[X 4ij ,X 5ij ),[X 5ij ,X 6ij ),[X 6ij , 1];

[0176] Based on the overall membership function and fuzzy evaluation set, the comprehensive membership value matrix of the key equipment model is determined.

[0177] In a possible implementation, the fuzzy evaluation module 34 is further configured to:

[0178] Based on the overall membership function and the fuzzy evaluation set, the fuzzy evaluation rules are determined;

[0179] Based on the overall membership function and fuzzy evaluation rules, the comprehensive membership value matrix of the key equipment model is determined.

[0180] The fuzzy evaluation rule is expressed as:

[0181] When the feature data X ij When it belongs to I1, the operating status of the i-th key equipment model under the j-th characteristic data is poor;

[0182] When the feature data X ij When it belongs to I2, the operating status of the i-th key equipment model under the j-th characteristic data is poor or medium;

[0183] When the feature data X ijWhen it belongs to I3, the operation status of the i-th key equipment model under the j-th characteristic data is medium or good;

[0184] When the feature data X ij When it belongs to I4, the operation status of the i-th key equipment model under the j-th characteristic data is good;

[0185] When the feature data X ij When it belongs to I5, the operation status of the i-th key equipment model under the j-th characteristic data is good or medium;

[0186] When the feature data X ij When it belongs to I6, the operation status of the i-th key equipment model under the j-th characteristic data is medium or poor;

[0187] When the feature data X ij When it belongs to I7, the operating status of the i-th key equipment model under the j-th characteristic data is poor.

[0188] In a possible implementation, key equipment includes: a transformer, a switch cabinet, a capacitor cabinet, a control cabinet, and a transmission line.

[0189] In a possible implementation, the extraction module 33 is specifically used to:

[0190] Perform time domain feature extraction and / or frequency domain feature extraction on the simulation operation data to obtain feature data of the key equipment model.

[0191] Figure 4 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processor 40. When the processor 40 executes the computer program 42, the steps in each of the above-mentioned embodiments of the simulation operation evaluation method for distribution network equipment are implemented, such as Figure 1 Alternatively, when the processor 40 executes the computer program 42, the functions of each module / unit in the above-mentioned device embodiments are realized, for example, Figure 3 The functions of the modules 31 to 35 are shown.

[0192] Exemplarily, the computer program 42 may be divided into one or more modules, and the one or more modules / units are stored in the memory 41 and executed by the processor 40 to implement the present invention. The one or more modules may be a series of computer program instruction segments that can implement specific functions, and the instruction segments are used to describe the execution process of the computer program 42 in the electronic device 4. For example, the computer program 42 may be divided into Figure 3 Modules / units 31 to 35 are shown.

[0193] The electronic device 4 may be a computing device such as a desktop computer and a notebook. The electronic device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 4 and does not constitute a limitation of the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0194] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0195] The memory 41 may be an internal storage unit of the electronic device 4, such as a hard disk or memory of the electronic device 4. The memory 41 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 41 is used to store the computer program and other programs and data required by the electronic device. The memory 41 may also be used to temporarily store data that has been output or is to be output.

[0196] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0197] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0198] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0199] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0200] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0201] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0202] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned simulation operation evaluation method embodiment of each distribution network equipment can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0203] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A simulation operation evaluation method for distribution network equipment, characterized in that: include: Obtain the physical parameters of key equipment in the distribution network; Performing three-dimensional modeling on the key equipment based on the physical parameters to obtain the key equipment model; Based on the key equipment model, establishing a digital twin model of the distribution network; Acquire the simulated operation data of the key equipment model of the digital twin model within a preset period; perform feature extraction on the simulated operation data to obtain feature data of the key equipment model; and establish a factor set and a fuzzy evaluation set for evaluating the operation status of the key equipment model based on the feature data; Acquire historical operation data of the key equipment in the distribution network, and determine a comprehensive membership value matrix of the key equipment model based on the historical operation data, the factor set and the fuzzy evaluation set; The operation results of the key equipment model are evaluated based on the comprehensive membership value matrix.

2. A simulated operation evaluation method for distribution network equipment according to claim 1, characterized in that: The comprehensive membership value matrix of the key equipment model includes: comprehensive membership values ​​of poor operating status, comprehensive membership values ​​of medium operating status, and comprehensive membership values ​​of good operating status.

3. A simulated operation evaluation method for distribution network equipment according to claim 2, characterized in that: The operation results of the key equipment model are evaluated based on the comprehensive membership value matrix, including: If the comprehensive membership value of the good operation state of the key equipment model is greater than the sum of the comprehensive membership value of the poor operation state and the comprehensive membership value of the medium operation state, it is determined that the operation result of the key equipment model is qualified; If the comprehensive membership value of the poor operating state of the key equipment model is greater than the comprehensive membership value of the medium operating state and the comprehensive membership value of the poor operating state of the key equipment model is greater than the comprehensive membership value of the good operating state, it is determined that the operating result of the key equipment model is unqualified.

4. A simulated operation evaluation method for distribution network equipment according to claim 3, characterized in that: After evaluating the operation result of the key equipment model based on the comprehensive membership value matrix, the method further includes: If the operation result of the key equipment model is qualified, the key equipment model is applied to the subsequent operation of the digital twin model; If the operation result of the key equipment model is unqualified, the parameters of the key equipment model are adjusted, and the process returns to the step of performing three-dimensional modeling on the key equipment based on the physical parameters to obtain the key equipment model.

5. A simulated operation evaluation method for distribution network equipment according to claim 1, characterized in that: Determining a comprehensive membership value matrix of the key equipment model based on the historical operation data, the factor set and the fuzzy evaluation set includes: Establishing a membership function based on the historical operation data and the factor set; Determine a comprehensive membership value matrix of the key equipment model based on the membership function and the fuzzy evaluation set; The membership function includes: Among them, X ij is the jth characteristic data in the i-th key equipment model. When the key equipment corresponding to the i-th key equipment model in the actual distribution network fails, the numerical range of each characteristic data is [X 1ij , X 3ij ], the lower interval is [X 4ij , X 6ij ], the numerical range of each characteristic data of the key equipment model of the i-th key equipment in the actual distribution network when the key equipment does not fail is [X 2ij , X 5ij ].

6. A simulated operation evaluation method for distribution network equipment according to claim 5, characterized in that: Determining a comprehensive membership value matrix of the key equipment model based on the membership function and the fuzzy evaluation set includes: The membership function A1(X ij )、A2(X ij ) and A3(X ij ) are put into the same plane rectangular coordinate system to obtain the overall membership function of the key equipment model, which is divided into seven intervals from I1 to I7, corresponding to the intervals [0, X 1ij ),[X 1ij , X 2ij ),[X 2ij , X 3ij ),[X 3ij , X 4ij ),[X 4ij , X 5ij ),[X 5ij , X 6ij ),[X 6ij , 1]; Based on the overall membership function and the fuzzy evaluation set, a comprehensive membership value matrix of the key equipment model is determined.

7. A simulated operation evaluation method for distribution network equipment according to claim 6, characterized in that: Based on the overall membership function and the fuzzy evaluation set, a comprehensive membership value matrix of the key equipment model is determined, including: Determining fuzzy evaluation rules based on the overall membership function and the fuzzy evaluation set; Determining a comprehensive membership value matrix of the key equipment model based on the overall membership function and the fuzzy evaluation rule; The fuzzy evaluation rule is expressed as: When the feature data X ij When it belongs to I1, the operating status of the i-th key equipment model under the j-th characteristic data is poor; When the feature data X ij When it belongs to I2, the operating status of the i-th key equipment model under the j-th characteristic data is poor or medium; When the feature data X ij When it belongs to I3, the operation status of the i-th key equipment model under the j-th characteristic data is medium or good; When the feature data X ij When it belongs to I4, the operation status of the i-th key equipment model under the j-th characteristic data is good; When the feature data X ij When it belongs to I5, the operation status of the i-th key equipment model under the j-th characteristic data is good or medium; When the feature data X ij When it belongs to I6, the operation status of the i-th key equipment model under the j-th characteristic data is medium or poor; When the feature data X ij When it belongs to I7, the operating status of the i-th key equipment model under the j-th characteristic data is poor.

8. A simulated operation evaluation method for distribution network equipment according to claim 1, characterized in that: The key equipment includes: transformer, switch cabinet, capacitor cabinet, control cabinet and transmission line.

9. A simulated operation evaluation method for distribution network equipment according to claim 1, characterized in that: Feature extraction is performed on the simulation operation data to obtain feature data of the key equipment model, including: Perform time domain feature extraction and / or frequency domain feature extraction on the simulation operation data to obtain feature data of the key equipment model.

10. A simulated operation evaluation device for distribution network equipment, characterized in that: include: An acquisition module is used to obtain the physical parameters of key equipment in the distribution network; The acquisition module is also used to obtain the simulated operation data of the key equipment model of the digital twin model within a preset period; The acquisition module is also used to acquire the historical operation data of the key equipment in the distribution network; A modeling module, used for performing three-dimensional modeling on the key equipment based on the physical parameters to obtain the key equipment model; The modeling module is further used to establish a digital twin model of the distribution network based on the key equipment model; An extraction module, used for performing feature extraction on the simulation operation data to obtain feature data of the key equipment model; A fuzzy evaluation module, used to establish a factor set and a fuzzy evaluation set for evaluating the operating status of the key equipment model based on the characteristic data; A fuzzy evaluation module is further used to determine a comprehensive membership value matrix of the key equipment model based on the historical operation data, the factor set and the fuzzy evaluation set; An evaluation module is used to evaluate the operating status of the key equipment model based on the comprehensive membership value matrix.

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