An intelligent monitoring system for the operating status of a distribution transformer

Abstract

The present invention relates to the technical fields of transformers and intelligent monitoring, and provides an intelligent monitoring system for the operating status of distribution transformers; the sensor network module installs sensors on the distribution transformer to collect relevant parameters of the distribution transformer in real time; the data transmission module transmits the sensor data of the sensor network module to the data analysis module in real time, and the data analysis module receives the data transmitted by the data transmission module for analysis; the operation monitoring center obtains the analysis results of the data analysis module and displays them together with the operation data of the distribution transformer; an alarm function and an input interface are provided; the management module memorizes relevant information according to the analysis results of the data analysis module. By introducing fuzzy logic, the present invention provides more accurate decision-making support when processing data with strong fuzziness, and saves the solutions to the corresponding problem descriptions through the management module, improving the response speed of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer energy efficiency monitoring, and more specifically to an intelligent monitoring system for the operating status of distribution transformers. Background Art

[0002] With the rapid development of social economy, continuous growth of power demand, wide access of distributed energy, and continuous expansion of the power market, distribution transformers, as important power equipment in the power system, have higher requirements for their operating status and performance. Their safe and stable operation is crucial for ensuring the quality and reliability of power supply. However, the traditional operation management methods of distribution transformers have many limitations and are difficult to meet the needs of modern intelligent and efficient power systems.

[0003] The following are still deficiencies in the prior art:

[0004] 1. Traditional means of monitoring the operation of distribution transformers often require reaching a certain specific point variable or other data such as temperature to make a diagnosis. However, in reality, many faults occur quietly before reaching this threshold. This monitoring method relying on fixed thresholds has great limitations and is prone to delaying the diagnosis of faults. But when the fault has not reached this threshold, there are many factors affecting the fault, and objective reasons prevent in-depth research on the distribution transformer at this time to explore whether it may actually have an abnormal operating state. If only monitored through thresholds, it is impossible to effectively detect faults in advance. As time goes by, these initial faults may gradually deteriorate and eventually trigger serious faults, bringing great harm to the stable operation of the power system and even possibly causing serious consequences such as large-scale power outages.

[0005] 2. In the existing monitoring of the operating status of distribution transformers, the matching of response measures after a fault occurs is insufficient. When a fault occurs, the prior art usually has a set of emergency treatment plans. However, due to the large number of reasons for abnormal states or faults, it may not be possible to match the corresponding emergency plan according to the current abnormal state or match the wrong emergency plan, resulting in the inability to quickly and effectively take measures to contain the fault during the actual treatment process, thus prolonging the power outage time and bringing great inconvenience and economic losses to users. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention provides an intelligent monitoring system for the operating status of distribution transformers to solve the problems in the prior art.

[0007] The technical solution of the present invention is as follows:

[0008] An intelligent monitoring system for the operating status of distribution transformers, comprising:

[0009] A sensor network module, which is used to install sensors on a distribution transformer to monitor relevant parameters of the distribution transformer in real time and all-round. The relevant parameters include temperature data and voltage data;

[0010] A data transmission module, which is data-connected to the sensor network module and is used to collect and transmit the sensor data in the sensor network module in real time;

[0011] An analysis module, which is data-connected to the data transmission module and is used to receive the data transmitted by the data transmission module and perform an analysis process including fuzzification, fuzzy inference, and defuzzification to realize the evaluation, fault diagnosis, and prediction of the operating state of the distribution transformer;

[0012] A management module, which is data-connected to the analysis module, identifies and matches based on the pre-stored solution information combined with the analysis results of the analysis module, and stores the matching results;

[0013] An operation monitoring center, which is data-connected to the analysis module and the management module, and according to the analysis results of the analysis module and the stored content of the management module, displays the operating state, monitoring data, and matching degree of the solution of the distribution transformer in real time, and provides an alarm function and a data input function;

[0014] The analysis module includes a data fuzzification unit, a fuzzy inference unit, and a data defuzzification unit;

[0015] The data fuzzification unit is used to define temperature and voltage fuzzy sets according to the operating parameters of the distribution transformer and actively perform fuzzy processing on the original data;

[0016] The fuzzy inference unit is used to perform inference according to the input monitoring data combined with fuzzy rules. After the data is fuzzified, the fuzzy inference unit performs inference according to the input monitoring data and the pre-set fuzzy rules;

[0017] The data defuzzification unit is used to convert the result of fuzzy inference into a clear control instruction or decision, and convert the obtained result of fuzzy inference into data or result;

[0018] The data fuzzification unit obtains relevant parameters during normal operation of the distribution transformer, including temperature and voltage, according to the relevant usage instructions of the distribution transformer at the time of factory, and defines fuzzy sets according to the parameters. The fuzzy sets include but are not limited to the normal range, the high range, and the low range; Represents the relevant parameters of the distribution transformer transmitted by the data transmission module;

[0019] The formula for the membership function of the boundary or threshold for relevant determination is:

[0020] High range:

[0021] Wherein, and are two thresholds defining the high range value, is too high, is extremely high, represents the degree to which T belongs to the fuzzy set of the high range; 0 means not belonging to the high range, and 1 means belonging to the extremely high range, represents the degree to which T belongs to the fuzzy set of the high range;

[0022] Normal range:

[0023] Wherein, represents the lower limit of the temperature range in the normal working state, represents the upper limit of the temperature range in the normal working state, represents the middle value of the temperature range of the distribution transformer in the normal working state, 0 means not belonging to the normal range, and represent the degree to which T belongs to the fuzzy set of the normal range;

[0024] Low range:

[0025] Wherein, and are two thresholds defining the low range value, is too low, is extremely low, 0 means not belonging to the low range, and 1 means belonging to the extremely low range, represents the degree to which T belongs to the fuzzy set of the low range. In fuzzy logic, for the relevant parameter T, the degree to which it belongs to the "low range" fuzzy set is measured based on thresholds and membership functions. This degree is not an absolute value of 0 or 1, but a fuzzy value between 0 and 1.

[0026] Preferably, the fuzzy inference unit includes but is not limited to the following rules;

[0027] Rule 1: The relevant parameter is in the normal range and the relevant parameter is in the normal range, adopt the strategy of maintaining monitoring:

[0028]

[0029] Rule 2: The relevant parameter is in the low range and the relevant parameter is in the high range, adopt the strategy of emergency repair:

[0030]

[0031] Among them, W represents the temperature parameter in the relevant parameters, and U represents the voltage parameter in the relevant parameters. ∧ represents logical AND. In fuzzy logic, when performing an operation using ∧, the minimum value of the two is taken to represent the result of logical AND. and are the membership functions obtained by fuzzy inference of Rule 1 and Rule 2 respectively, representing the degree of adopting the strategy.

[0032] Preferably, the calculation result of the data defuzzification unit adopts the centroid method for defuzzification:

[0033]

[0034] Among them, O is the final clear decision result, and R represents the value of the output variable in the membership function obtained by the fuzzy inference unit, and the value range is between 0 and 1. represents the differential symbol, indicating an infinitesimal change amount, that is is the infinitesimal change amount of the output variable R, and it is a part of the integral operation.

[0035] Preferably, the operation monitoring center includes an interface unit, an alarm unit, and an input unit;

[0036] The interface unit is used to display the relevant parameters and monitoring status of the distribution transformer to relevant personnel;

[0037] The alarm unit is used to give an alarm when the data analysis module detects an abnormal state, and the alarm methods include sound alarm, light alarm, and information transmission alarm;

[0038] The input unit is used to provide an input interface for system-related data for the staff.

[0039] Preferably, the management module includes an identification unit and a storage unit; the identification unit calculates the matching degree between the problem description and the solution effect according to the problem description, the solution, and the achieved solution effect, and measures the ability of the solution to solve the problem; and calculates the selection probability of the solution based on the matching degree between the problem description and the solution effect; the storage unit stores the solution with the highest selection probability identified by the identification unit and the corresponding problem description;

[0040] Preferably, the calculation formula of the matching degree and the calculation of the solution selection probability in the identification unit are as follows:

[0041] Calculation of the matching degree d: Distribute the problem description a and the solution b to the feature data space, and mark the coordinate values of a and b; the matching degree d is:

[0042]

[0043] Among them, d represents the matching degree. The smaller the value of the matching degree d, the more likely it indicates that solution b can solve problem description a. n represents the total number of solutions and corresponding problem descriptions, that is, the total number of i; i represents the i-th solution and corresponding problem description in the dimensional space.

[0044] After calculating the matching degree, for the selection probability of the problem description , it is proportional to the matching degree . The calculation formula is:

[0045]

[0046] Among them, i represents the feature index of the i-th solution and problem description in the dimensional space, and j is an index variable used to traverse all solutions; m represents the total number of solutions. By traversing from j = 1 to j = m, the matching degrees of all solutions are summed to calculate the value of the denominator, which is used to determine the probability of a solution being selected.

[0047] Preferably, the sensor network module deploys a variety of sensors including a voltage sensor and a temperature sensor.

[0048] Preferably, the data transmission module transmission methods include wired communication methods and wireless communication methods.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. By introducing fuzzy logic control, the monitoring and control process of the distribution transformer in the present invention is made more intelligent and efficient. Especially when dealing with data with strong uncertainty and ambiguity, the present invention can provide more accurate decision-making support, thereby reducing losses caused by misjudgment or delayed processing.

[0051] 2. In the present invention, the recognition unit in the management module identifies and selects the optimal solution according to the characteristics and objectives of the problem description, and the storage unit can save these solutions and corresponding problem descriptions for reuse in future operations, thereby improving the response speed and accuracy of the system.

[0052] 3. The present invention realizes real-time monitoring and data analysis of the distribution transformer through the sensor network module, ensuring the accuracy and timeliness of the data. At the same time, the interface unit, alarm unit, and input unit of the operation monitoring center provide a friendly input interface and flexible interaction method, enabling relevant staff to conveniently obtain monitoring data, receive alarm information, and perform operation input. Brief Description of the Drawings

[0053] Figure 1 is the working flowchart of the present invention.

[0054] Figure 2 is the analysis flowchart of the data analysis module of the present invention. Specific embodiments

[0055] The following further describes in detail the embodiments of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0056] As Figure 1 - Figure 2 shown, the present invention provides an intelligent monitoring system for the operating state of a distribution transformer, including:

[0057] A sensor network module, which is used to install sensors on the distribution transformer to monitor the relevant parameters of the distribution transformer in real time and all-round. The relevant parameters include temperature data and voltage data;

[0058] A data transmission module, which is data-connected to the sensor network module and is used to collect and transmit the sensor data in the sensor network module in real time;

[0059] A data analysis module, which is data-connected to the data transmission module and is used to receive the data transmitted by the data transmission module and perform an analysis process including fuzzification, fuzzy reasoning, and defuzzification to realize the evaluation, fault diagnosis, and prediction of the operating state of the distribution transformer;

[0060] A management module, which is data-connected to the data analysis module, identifies and matches based on the pre-stored solution information combined with the analysis results of the data analysis module, and stores the matching results;

[0061] An operation monitoring center, which is data-connected to the data analysis module and the management module, and displays the operating state, monitoring data, and matching degree of the solution of the distribution transformer in real time according to the analysis results of the data analysis module and the storage content of the management module, and provides an alarm function and a data input function.

[0062] Embodiment 1: In this embodiment, this system is installed on the distribution transformers in a certain area. The monitoring system monitors the operating state of the distribution transformer through the sensor network module, and a variety of sensors including voltage sensors and temperature sensors are deployed in the sensor network module.

[0063] The data transmission module transmits the voltage data and temperature data of the distribution transformer to the data analysis module in real time through wireless communication. The data analysis module includes a data fuzzification unit, a fuzzy reasoning unit, and a data defuzzification unit;

[0064] The data fuzzification unit is used to define temperature and voltage fuzzy sets based on the operating parameters of the distribution transformer, which actively performs fuzzy processing on the original precise data. For example, for temperature data, according to experience and actual operating requirements, the temperature range can be divided into fuzzy sets such as "low temperature", "medium temperature", "high temperature", etc.; for voltage data, it can also be divided into "low voltage", "normal voltage", "high voltage", etc. The advantage of this fuzzy processing is that it can transform complex precise data into fuzzy language descriptions that are more in line with human thinking patterns and convenient for rule processing.

[0065] The fuzzy inference unit is used to perform inference based on the input monitoring data in combination with fuzzy rules. After the data is fuzzified, the fuzzy inference unit performs inference based on the input monitoring data, that is, on the fuzzified temperature and voltage data and the pre-set fuzzy rules. This step is the core link of the whole process, and it infers relevant information such as the operating status of the distribution transformer based on the fuzzified data and rules;

[0066] The data defuzzification unit is used to convert the results of fuzzy inference into clear control instructions or decisions. For example, if the fuzzy inference shows that the distribution transformer is in a "relatively dangerous" fuzzy state, a specific risk probability can be obtained through defuzzification.

[0067] The data fuzzification unit obtains the relevant parameters of the distribution transformer during normal operation according to the relevant usage instructions of the distribution transformer when it leaves the factory, and defines fuzzy sets based on the parameters. The fuzzy sets include but are not limited to the normal range, high range, and low range; Represents the relevant parameters input by the data transmission module, such as including the operating environment temperature parameter W and voltage parameter U, expressed as ;

[0068] The formula for the membership function of the boundary or threshold for relevant determination is:

[0069] High range:

[0070] Among them, and are two thresholds for defining high range values, is too high, is extremely high, represents the degree to which T belongs to the high range fuzzy set; 0 means not belonging to the high range, and 1 means belonging to the extremely high range, represents the degree to which T belongs to the high range fuzzy set. This degree indicates that the situation is in a fuzzy transition state between normal and abnormal, meaning there is a tendency to develop towards abnormality but has not reached complete abnormality. From a decision-making perspective, preventive measures may need to be taken, which is different from the situation where no treatment is required when it is completely normal and immediate correction is required when it is completely abnormal;

[0071] Normal range:

[0072] Among them, represents the lower limit of the temperature range in the normal working state, represents the upper limit of the temperature range in the normal working state, represents the intermediate value of the temperature range when the distribution transformer is in the normal working state, 0 means not within the normal range, and represent the degree to which T belongs to the fuzzy set of the normal range;

[0073] Low range:

[0074] Among them, and are two thresholds defining the low range values, is too low, is extremely low, 0 means not within the low range, 1 means belonging to the extremely low range, represents the degree to which T belongs to the fuzzy set of the low range. In fuzzy logic, for temperature, the degree to which it belongs to the "low range" fuzzy set is measured through specific threshold-based and membership functions. This degree is not an absolute value of 0 or 1, but a fuzzy value between 0 and 1;

[0075] Among the voltage and data transmitted by the data transmission module, for example, there is a set of existing data, the current temperature value °C, the current voltage value V;

[0076] The membership function determination of temperature data is as follows:

[0077] = 80, is the lower limit threshold of the high range, = 90°C, is the upper limit threshold of the high range;

[0078] According to the determination, calculate that belongs to the high range fuzzy set with a degree of being 0.8;

[0079] Lower limit of the normal temperature range = 65°C, upper limit = 75°C, the intermediate value is = 67.5°C;

[0080] = 60°C, is the lower limit threshold of the low range, = 40°C, is the extremely low temperature threshold;

[0081] The membership function determination of voltage data is as follows:

[0082] = 240 V, which is the lower threshold of the high voltage range, = 255 V, which is the ultra-high voltage threshold;

[0083] Lower limit of the normal voltage range = 220 V, upper limit = 230 V, median value is 225 V.

[0084] = 220 V, which is the lower threshold of the too low range, = 180 V, which is the ultra-low voltage threshold. The current voltage value is 185 V. According to the corresponding membership function, the degree to which U belongs to the low range fuzzy set is calculated is 0.5;

[0085] After the above determination, the temperature in this set of data is in the too high range and the voltage is in the too low range. If the membership function obtained through the determination is directly 1, it means that the distribution transformer has failed, and the information is immediately transmitted to the management module to arrange maintenance;

[0086] Immediately afterwards, the fuzzy inference unit confirms the fuzzy rules. In the data transmitted this time, there are two sets of data, voltage and temperature, including but not limited to the following rules;

[0087] Rule 1: Related parameters is in the normal range and related parameters is in the normal range, adopt the strategy of maintaining monitoring:

[0088]

[0089] Rule 2: Related parameters is in the low range and related parameters is in the high range, adopt the strategy of emergency maintenance:

[0090]

[0091] Among them, W represents the temperature parameter in the related parameters, and U represents the voltage parameter in the related parameters, represents logical AND. In fuzzy logic, when using ∧ for operation, the minimum value of the two is taken to represent the result of logical AND, and are the membership functions obtained by fuzzy inference of Rule 1 and Rule 2 respectively, representing the degree of adopting the strategy.

[0092] After determination, this set of fuzzified data belongs to Rule 2, that is, adopt the strategy of emergency maintenance, and the degree of adopting the strategy of emergency maintenance is ;

[0093] The data defuzzification unit uses the centroid method for defuzzification and calculates according to the following formulas respectively ;

[0094]

[0095] where O is the final clear decision result, R represents the value of the output variable in the membership function obtained by the fuzzy inference unit, and the value range is between 0 and 1. represents the differential symbol, indicating an infinitesimal change amount, that is is the infinitesimal change amount of the output variable R, which is part of the integral operation. The purpose of defuzzification is to convert the fuzzy result after fuzzy inference into a clear result that can be directly used for actual operation, and convert the originally fuzzy concept that the distribution transformer may have some problems into a specific and measurable index;

[0096] By introducing fuzzy logic control, the monitoring process of the distribution transformer becomes more intelligent and efficient. Especially when dealing with data with strong uncertainty and fuzziness, the present invention can provide more accurate decision-making support, thereby reducing losses caused by misjudgment or delayed processing.

[0097] Embodiment 2: As Figure 1 - Figure 2 shown, in this embodiment, the distribution transformer already has abnormal operation, and corresponding solutions need to be matched to solve the problem of abnormal operation of the distribution transformer;

[0098] The operation monitoring center includes an interface unit, an alarm unit, and an input unit;

[0099] The interface unit is used to display the relevant parameters and monitoring status of the distribution transformer to relevant personnel;

[0100] The alarm unit is used to give an alarm when the data analysis module detects an abnormal state, and the alarm methods include sound alarm, light alarm, and information transmission alarm;

[0101] The input unit is used to provide an input interface for system-related data for the staff.

[0102] Through the interface unit, alarm unit, and input unit of the operation monitoring center, an interaction method is provided, enabling relevant staff to conveniently obtain monitoring data, receive alarm information, and perform operation inputs.

[0103] The management module includes an identification unit and a storage unit;

[0104] The identification unit clarifies the problem description, solution, and achieved solution effect, calculates the matching degree between the goal and the solution effect, and measures the ability of the solution to solve the problem;

[0105] The storage unit memorizes the solutions with high matching degrees recognized by the recognition unit and the corresponding problem descriptions, and can store some solution plans in advance according to the determination in Embodiment 1. When the fault determination result appears, it can make a quick response.

[0106] The calculation formula of the matching degree in the recognition unit and the probability calculation of solution selection are as follows:

[0107] Calculation of the matching degree d: Distribute the problem description a and the solution b into the feature data space and mark the coordinate values of a and b; the matching degree d is:

[0108]

[0109] where d represents the matching degree. The smaller the value of the matching degree d, the more likely it is that the solution b can solve the problem description a. n represents the total number of solutions and the corresponding problem descriptions, that is, the total number of i; i represents the i-th solution and the corresponding problem description in the dimensional space.

[0110] After calculating the matching degree, for the problem description the selection probability , is proportional to the matching degree , and the calculation formula is:

[0111]

[0112] where i represents the feature index of the i-th solution and problem description in the dimensional space, and j is an index variable used to traverse all solutions; m represents the total number of solutions. By traversing from j = 1 to j = m, the matching degrees of all solutions are summed to calculate the value of the denominator, which is used to determine the probability of a solution being selected. At the same time, selection can also be made according to the level of the matching degree. Solutions with high matching degrees are recommended first, and those with low matching degrees are used as alternatives. When the matching degrees are equal, selection is made according to the above probability.

[0113] The recognition unit in the management module of this system recognizes and selects the optimal solution according to the characteristics and goals of the problem description, and the storage unit can save these solutions and the corresponding problem descriptions for repeated use in future operations, thereby improving the response speed and accuracy of the system.

[0114] The recognition unit in the management module of this system calculates the matching degree by distributing the problem description and the solution into the feature data space. This matching degree calculation method based on the feature space can more accurately measure the degree of fit between the solution and the problem.

[0115] For example, when faced with a complex fault scenario (problem description) where both the temperature is too high and the voltage is too low simultaneously, the system can accurately evaluate the matching degree of different solutions, avoiding the situation of mis-matching the emergency response plan due to numerous fault causes, thereby improving the pertinence and effectiveness of fault handling.

[0116] The embodiments of the present invention are given for the purposes of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. An intelligent monitoring system for the operating status of a distribution transformer, characterized in that: include: A sensor network module, wherein the sensor network module is used to install sensors on the distribution transformer to monitor the relevant parameters of the distribution transformer in real time and in all directions, including temperature data and voltage data; A data transmission module, which is data-connected to the sensor network module and is used to collect and transmit sensor data in the sensor network module in real time; A data analysis module, which is data-connected to the data transmission module and is used to receive data transmitted by the data transmission module and perform an analysis process including fuzzification, fuzzy reasoning and defuzzification to achieve evaluation, fault diagnosis and prediction of the operating status of the distribution transformer; A management module, which is data-connected to the data analysis module, performs identification and matching based on pre-stored solution information combined with analysis results of the data analysis module, and stores the matching results; An operation monitoring center, which is connected to the data analysis module and the management module, displays the operation status of the distribution transformer, the matching degree of the monitoring data and the solution in real time according to the analysis results of the data analysis module and the storage content of the management module, and provides an alarm function and a data input function; The data analysis module includes a data fuzzification unit, a fuzzy reasoning unit and a data defuzzification unit; The data fuzzification unit is used to define temperature and voltage fuzzy sets according to the operating parameters of the distribution transformer, and actively perform fuzzy processing on the original data; The fuzzy reasoning unit is used to make inferences based on the input monitoring data combined with fuzzy rules. After the data is fuzzified, the fuzzy reasoning unit makes inferences based on the input monitoring data and the pre-set fuzzy rules; The data defuzzification unit is used to convert the result of fuzzy reasoning into clear control instructions or decisions, and convert the obtained fuzzy reasoning result into data or results; The data fuzzification unit obtains relevant parameters of the distribution transformer during normal operation according to the factory instructions of the distribution transformer used, including temperature and voltage, and defines a fuzzy set according to the parameters, the fuzzy set including but not limited to a normal range, a high range, and a low range; Indicates the distribution transformer related parameters transmitted by the data transmission module; The boundary or threshold membership function formula is: High Range: in, and are two thresholds that define the high range value, Too high, For super high, Indicates the degree to which T belongs to the fuzzy set of high range; 0 means it does not belong to the high range, 1 means it belongs to the ultra-high range, Indicates the degree to which T belongs to the fuzzy set of high range; Normal range: in, Indicates the lower limit of the temperature range in normal working state. Indicates the upper limit of the temperature range in normal working state. It indicates the middle value of the temperature range when the distribution transformer is in normal working condition. 0 indicates that it is not in the normal range. and Indicates the degree to which T belongs to the fuzzy set of normal range; Low Range: in, and are two thresholds that define the low range value, Too low, For ultra-low, 0 means it is not in the low range, 1 means it is in the ultra-low range, Indicates the degree to which T belongs to the fuzzy set of low range. In fuzzy logic, for the relevant parameter T, the degree to which it belongs to the "low range" fuzzy set is measured based on the threshold and membership function. This degree is not an absolute value of 0 or 1, but a fuzzy value between 0 and 1.

2. The intelligent monitoring system for the operating status of a distribution transformer according to claim 1, characterized in that: The fuzzy reasoning unit includes but is not limited to the following rules: Rule 1: Relevant parameters is within the normal range and the relevant parameters It is within the normal range, and the strategy of maintaining monitoring is adopted: Rule 2: Related parameters is a low range and related parameter If the range is high, take emergency maintenance strategy: Among them, W represents the temperature parameter among the related parameters, and U represents the voltage parameter among the related parameters. Indicates logical AND. In fuzzy logic, when ∧ is used for operation, the minimum value of the two is taken to represent the result of logical AND. and They are the membership functions obtained by fuzzy reasoning of rule one and rule two, indicating the degree of strategy adopted.

3. The intelligent monitoring system for the operating status of a distribution transformer according to claim 2, characterized in that: The calculation result of the data defuzzification unit Defuzzification using the centroid method: Among them, O is the final clear decision result, R represents the value of the output variable in the membership function obtained by the fuzzy reasoning unit, and the value range is between 0 and 1. Represents the differential symbol, indicating an infinitesimal change, that is, It is an infinitesimal change in the output variable R, which is part of the integral operation.

4. The intelligent monitoring system for the operating status of a distribution transformer according to claim 1, characterized in that: The operation monitoring center includes an interface unit, an alarm unit and an input unit; The interface unit is used to display the relevant parameters and monitoring status of the distribution transformer to personnel; The alarm unit is used to give an alarm when the data analysis module detects an abnormal state, and the alarm modes include sound alarm, light alarm and information transmission alarm; The input unit is used to provide an input interface for system data for the staff.

5. The intelligent monitoring system for the operating status of a distribution transformer according to claim 1, characterized in that: The management module includes an identification unit and a storage unit; the identification unit calculates the matching degree between the problem description and the solution effect according to the problem description and the solution and the achieved solution effect, and measures the ability of the solution to solve the problem; And the probability of selecting a solution is calculated based on the match between the problem description and the solution effect; The storage unit stores the solution with the highest selection probability identified by the identification unit and the corresponding problem description.

6. The intelligent monitoring system for the operating status of a distribution transformer according to claim 5, characterized in that: The calculation formula of the matching degree in the recognition unit and the probability calculation of the scheme selection are as follows: Calculation of matching degree d: Distribute the problem description a and solution b to the feature data space, mark the coordinate values ​​of a and b; the matching degree d is: Where d represents the matching degree. The smaller the matching degree d is, the more likely solution b is to solve problem description a. n represents the total number of solutions and corresponding problem descriptions, that is, the total number of i. i represents the i-th solution and corresponding problem description in the dimensional space. After calculating the matching degree, for the problem description The probability of selection , and the matching degree Proportional, the calculation formula is: Among them, i represents the feature index of the i-th solution and problem description in the dimensional space, j is an index variable used to traverse all solutions; m represents the total number of solutions. By traversing from j=1 to j=m, the matching degrees of all solutions are summed up to calculate the value of the denominator, which is used to determine the probability of a solution being selected.

7. The intelligent monitoring system for the operating status of a distribution transformer according to claim 1, characterized in that: The sensor network module deploys a variety of sensors including voltage sensors and temperature sensors.

8. The intelligent monitoring system for the operating status of a distribution transformer according to claim 1, characterized in that: The transmission mode of the data transmission module includes wired communication mode and wireless communication mode.

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