A power distribution network intelligent operation and maintenance system and method

Abstract

The application relates to the technical field of power distribution management, in particular to a power distribution network intelligent operation and maintenance system and method, which comprises the following: a temperature rise abnormality detection module collects real-time temperature data of power distribution equipment, extracts temperature gradient values in spatial distribution and time series, calculates the change rate of the temperature gradient values according to the temperature gradient values, compares and judges the gradient change rate of each point, and generates distribution data of the temperature rise abnormality area of the power distribution equipment. In the application, through the collection and processing of real-time temperature data, the spatial distribution and time change characteristics of the temperature gradient values are extracted, the change rate is further calculated and compared and judged, the temperature rise abnormality area of the power distribution equipment is effectively identified, and the fine perception of the equipment operation state is realized. Through the matching analysis of the real-time temperature rise data and the spatial distribution data, the operation state of the contact area is accurately classified, and a health assessment mechanism is constructed in combination with multidimensional data analysis, so that the health state assessment of the circuit breaker is more accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution management, in particular to a power distribution network intelligent operation and maintenance system and method. BACKGROUND

[0002] The technical field of power distribution management mainly involves the distribution and management process of electric energy from the substation to the end user, including the planning, operation, maintenance and optimization of the power supply network. This technical field covers functions such as monitoring, load management, fault location, energy scheduling and power quality management of the power distribution system, aiming to improve the reliability, safety, efficiency and intelligence level of the power distribution system.

[0003] Among them, the power distribution network intelligent operation and maintenance system is an operation and maintenance management system supported by modern information technology, focusing on improving the operation efficiency and reliability of the power distribution network. The system realizes comprehensive perception and efficient management of the operation state of the power distribution network by integrating real-time monitoring, fault diagnosis, maintenance, data analysis and intelligent decision-making functions.

[0004] The prior art lacks fine analysis of temperature gradient changes in the operation state perception aspect, resulting in rough positioning of temperature rise abnormal areas and possible omission of key fault information. The data characteristics of spatial and temporal dimensions are not fully combined in fault diagnosis, and the analysis results are easily affected by single-dimensional data fluctuations, and the health assessment often lacks sufficient precision and diversity. In the micro-grid grid-connected process, fixed parameters are usually used for power regulation, which is difficult to cope with dynamic fluctuations in actual operation, and may lead to uneven energy distribution or intensified power grid fluctuations. The load grouping lacks a dynamic adjustment mechanism based on real-time data, and the management efficiency of power fluctuation abnormal nodes is low, which may exacerbate the negative impact of abnormal loads on the overall network. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art and to provide a power distribution network intelligent operation and maintenance system and method.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: a power distribution network intelligent operation and maintenance system comprises:

[0007] The temperature rise anomaly detection module collects real-time temperature data of power distribution equipment, extracts temperature gradient values in spatial distribution and time series, calculates the change rate of temperature gradient values according to the temperature gradient values, compares and judges the gradient change rate of each point, and generates distribution data of the temperature rise abnormal area of the power distribution equipment;

[0008] The circuit breaker health assessment module collects real-time temperature rise data of a circuit breaker contact area, associates and matches the contact temperature rise value with distribution data of the abnormal temperature rise area of the power distribution equipment, obtains a contact temperature rise state classification result, divides a circuit breaker operation health level according to the contact temperature rise state classification result, and obtains circuit breaker operation state information;

[0009] The micro-grid grid-connected regulation module collects operation data of a micro-grid and a main grid connection point, combines the circuit breaker operation state information, adjusts the power flow distribution of the micro-grid grid-connected point in stages, and obtains a micro-grid grid-connected optimization result;

[0010] The load dynamic grouping module screens a load node with an abnormal power fluctuation amplitude based on the power dynamic allocation of the micro-grid grid-connected optimization result, dynamically clusters and groups the load node with reference to a power allocation value and fluctuation characteristics of the load node, updates a grouping state in real time, and generates a power distribution network operation and maintenance grouping optimization result.

[0011] As a further scheme of the present application, the step of obtaining the change rate of the temperature gradient value is specifically:

[0012] Based on real-time operation data of the power distribution equipment, time series temperature values and spatial temperature difference values of the multi-point temperature sensor are collected, time variation and spatial distribution characteristics associated with the equipment operation state are extracted respectively, and a temperature gradient initial data set is generated;

[0013] Based on the temperature gradient initial data set, the formula:

[0014] ;

[0015] The change rate of the temperature gradient is calculated , to obtain temperature gradient change rate data;

[0016] Wherein, represents the temperature gradient change amount in the spatial direction, represents the temperature gradient change amount in the time direction.

[0017] As a further scheme of the present application, the step of obtaining the distribution data of the abnormal temperature rise area of the power distribution equipment is specifically:

[0018] Based on the temperature gradient change rate data of the power distribution equipment, the temperature gradient change rate values are extracted by traversing the monitoring points and compared with the preset abnormal threshold value point by point, the spatial position and time node of the abnormal points are marked, and an abnormal monitoring point set is generated;

[0019] Based on the abnormal monitoring point set, the spatial coordinates and time series data of each abnormal point are extracted, the continuity in the time dimension and the correlation in the spatial dimension are matched, the time diffusion path of the temperature rise abnormality is formed, and the distribution data of the abnormal temperature rise area of the power distribution equipment is obtained.

[0020] As a further aspect of the present invention, the step of obtaining the contact temperature rise state classification result is specifically as follows:

[0021] Based on the real-time temperature rise data of the circuit breaker contact area, the spatial coordinates of the contact area are filtered and matched with the distribution data of the abnormal temperature rise area of ​​the power distribution equipment to generate an abnormal contact temperature rise data set.

[0022] Based on the aforementioned abnormal contact temperature rise data set, the following formula is used:

[0023] ;

[0024] Calculate the rate of change of the temperature rise gradient of the contact. From the rate of change of the temperature gradient at the contact The maximum value is selected from the results, and the results of the contact temperature rise characteristic analysis are obtained by integrating them.

[0025] in, Indicates time Temperature rise in the contact area at any given moment. Indicates time Temperature rise in the contact area at any given moment. Indicates the time sampling interval;

[0026] Based on the analysis results of the contact temperature rise characteristics, the change rate and maximum value of the contact temperature rise gradient are compared with the preset contact temperature rise threshold, and the temperature rise state is classified point by point to generate the contact temperature rise state classification result.

[0027] As a further aspect of the present invention, the step of obtaining the circuit breaker operating status information specifically includes:

[0028] Based on the classification results of the contact temperature rise status, the following formula is used:

[0029] ;

[0030] Calculate the health score of the circuit breaker Generate circuit breaker rating results;

[0031] in, Indicates the temperature rise of the contact point The weights of each state category, Indicates the first The severity of the condition, This indicates the total number of contact temperature rise status categories;

[0032] The circuit breaker is classified into health levels according to a preset scoring range, and the circuit breaker scoring results are matched with the corresponding health levels to obtain the circuit breaker operating status information.

[0033] As a further scheme of the present application, the obtaining of the micro-grid grid-connection optimization result specifically comprises:

[0034] Based on the circuit breaker operation state information, the circuit breakers with normal state of temperature rise characteristics and power output characteristics are screened by comparing the temperature rise data with the reference temperature rise range point by point, and a normal state circuit breaker operation data set is generated;

[0035] Based on the normal state circuit breaker operation data set, the temperature rise characteristics and power output characteristics thereof are extracted and matched with the voltage value and power flow parameters of the connection point for matching analysis, the power output proportion of the connection point is adjusted by screening the voltage offset value and power fluctuation amplitude, and a micro-grid grid-connection optimization result is obtained.

[0036] As a further scheme of the present application, the obtaining of the power distribution network operation grouping optimization result specifically comprises:

[0037] Based on the micro-grid grid-connection optimization result, the time series data of the power output of the grid-connection point is monitored in real time, the power output value is analyzed by time interval segmentation, the nodes with abnormal power fluctuation amplitude are screened, the abnormal time points and fluctuation trends of the nodes are recorded, and an abnormal load node set is established;

[0038] Based on the abnormal load node set, the power distribution data and fluctuation characteristics of the nodes are obtained, dynamic clustering is performed according to the similarity between the characteristic values, the nodes are grouped according to the power characteristics, and the grouping state is updated in combination with the node characteristics, and a power distribution network operation grouping optimization result is generated.

[0039] A power distribution network intelligent operation method, which is executed based on the above-mentioned power distribution network intelligent operation system, comprises the following steps:

[0040] S1: Based on the real-time temperature data of the power distribution equipment, the temperature gradient value of the equipment in the spatial distribution and time sequence is extracted, the gradient change rate of each point is compared and judged, and power distribution equipment temperature rise abnormal region distribution data is generated;

[0041] S2: Based on the real-time temperature rise data of the circuit breaker contact area, the contact temperature rise value is associated and matched with the power distribution equipment temperature rise abnormal region distribution data, the circuit breaker operation health level is classified according to the contact temperature rise state classification result, and circuit breaker operation state information is obtained;

[0042] S3: Based on the operation data of the micro-grid and the main grid connection point, the power flow distribution of the micro-grid grid-connection point is adjusted in stages in combination with the circuit breaker operation state information, the power distribution of the micro-grid grid-connection is optimized, and a micro-grid grid-connection optimization result is generated;

[0043] S4: Based on the micro-grid grid-connected optimization result, screening the load node with abnormal power fluctuation amplitude, referring to the power allocation value and fluctuation characteristics of the load node, dynamic clustering grouping is carried out, and a load dynamic clustering grouping result is generated;

[0044] S5: Based on the load dynamic clustering grouping result, combined with the dynamically updated grouping state and power allocation demand, the overall operation grouping mode of the power distribution network is iteratively optimized, and a power distribution network operation grouping optimization result is generated.

[0045] Compared with the prior art, the advantages and positive effects of the present application are:

[0046] In the present application, by collecting and processing real-time temperature data, the spatial distribution and time variation characteristics of the temperature gradient value are extracted, and the change rate is further calculated and compared to determine the abnormal temperature rise area of the power distribution equipment, thereby realizing the fine perception of the equipment operation state. Through the matching analysis of real-time temperature rise data and spatial distribution data, the running state of the contact area is accurately classified, and a health assessment mechanism is constructed by combining multi-dimensional data analysis, so that the health state assessment of the circuit breaker is more accurate. In the micro-grid grid-connected process, the running state of the connection point is optimized by adjusting the power flow in stages, which helps to reduce the fluctuation risk and improve the grid-connected efficiency. The dynamic grouping optimization realizes the efficient clustering grouping of the load node by power characteristic similarity analysis, which can respond to the changes of power allocation and fluctuation characteristics in real time, and improve the dynamic adaptability in the operation process. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The system flowchart of the present application is shown in the figure;

[0048] Figure 2 The flowchart for calculating the change rate of the temperature gradient value of the present application is shown in the figure;

[0049] Figure 3 The flowchart for obtaining the distribution data of the abnormal temperature rise area of the power distribution equipment of the present application is shown in the figure;

[0050] Figure 4 The flowchart for obtaining the contact temperature rise state classification result of the present application is shown in the figure;

[0051] Figure 5 The flowchart for obtaining the circuit breaker running state information of the present application is shown in the figure;

[0052] Figure 6 The flowchart for obtaining the micro-grid grid-connected optimization result of the present application is shown in the figure;

[0053] Figure 7 The flowchart for obtaining the power distribution network operation grouping optimization result of the present application is shown in the figure. DETAILED DESCRIPTION

[0054] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0055] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0056] Please refer to Figure 1 The present application provides a technical solution: a power distribution network intelligent operation and maintenance system comprising:

[0057] The temperature rise anomaly detection module collects real-time temperature data of the power distribution equipment, extracts temperature gradient values in spatial distribution and time series, calculates the change rate of the temperature gradient values according to the temperature gradient values using the finite difference method, compares and judges the gradient change rate of each point in combination with a preset anomaly recognition threshold, combines the temperature rise change path in the time dimension with the spatial marker points, and generates distribution data of the temperature rise anomaly area of the power distribution equipment;

[0058] The circuit breaker health assessment module collects real-time temperature rise data of the circuit breaker contact area, associates and matches the contact temperature rise value with the distribution data of the power distribution equipment temperature rise anomaly area, filters the contact temperature rise anomaly data, obtains the gradient change rate and maximum value of the contact temperature rise, and compares them with the preset contact temperature rise threshold to obtain the contact temperature rise state classification result, converts the contact temperature rise state classification result into the health score of the circuit breaker, divides the circuit breaker operation health level in combination with the score range, and obtains the circuit breaker operation state information;

[0059] The microgrid grid-connected regulation module collects operation data of the connection point of the microgrid and the main grid, extracts voltage values, frequency values and power flow parameters of the connection point, distinguishes the operation state of the circuit breaker in the normal state in combination with the circuit breaker operation state information, judges the voltage offset value and power fluctuation amplitude of the connection point according to the temperature rise characteristics and power output characteristics, adjusts the power flow distribution of the microgrid grid-connected point in stages, and obtains the microgrid grid-connected optimization result;

[0060] The load dynamic grouping module extracts real-time distribution data of power output of the grid-connected point based on power dynamic allocation of micro-grid grid-connected optimization results, screens load nodes with abnormal power fluctuation amplitude, dynamically clusters and groups the load nodes with reference to power allocation values and fluctuation characteristics of the load nodes, updates grouping states in real time, and generates distribution network operation and maintenance grouping optimization results.

[0061] The distribution data of the abnormal temperature rise area of the power distribution equipment includes location coordinates of the abnormal area, temperature rise gradient value distribution, and time path information. The circuit breaker operation state information includes classification of contact temperature rise abnormality, health score value, and operation health level. The micro-grid grid-connected optimization results include connection point voltage adjustment value, power fluctuation adjustment range, and phased power injection parameters. The distribution network operation and maintenance grouping optimization results include load grouping node characteristics, power distribution range within the group, and fluctuation characteristic comparison values between groups.

[0062] Please refer to Figure 2 The acquisition step of calculating the temperature gradient change rate is specifically as follows:

[0063] Based on real-time operation data of the power distribution equipment, time series temperature values and spatial temperature difference values of the multi-point temperature sensor are collected, and time variation and spatial distribution characteristics related to the equipment operation state are extracted respectively to generate a temperature gradient initial data set.

[0064] First, real-time temperature values of each monitoring point are collected by a multi-point temperature sensor network deployed on the surface of the power distribution equipment. The sensor network includes multiple sensor nodes distributed on the surface and internal key positions of the equipment. Temperature data is uploaded to the database in time series form in real time. Time and spatial dimension information related to the equipment operation state is extracted from these data, wherein the time dimension refers to continuous temperature change at the equipment operation time, and the extraction method is to record temperature values of each monitoring point at different time points. The spatial dimension refers to the temperature distribution characteristics between different sensor nodes, and the extraction method is to compare temperature differences of adjacent nodes at the same time point. After eliminating possible random noise in the collection process, the time series temperature values and spatial distribution temperature differences of each point are collected and stored to generate a temperature gradient preliminary data set including time series temperature data and spatial temperature difference value data of each monitoring point.

[0065] Based on the temperature gradient initial data set, the formula:

[0066] ;

[0067] The temperature gradient change rate is calculated to obtain temperature gradient change rate data.

[0068] wherein, Temperature gradient change in spatial direction, reflecting the temperature distribution difference between different positions, calculated by the temperature difference between each monitoring point and its adjacent point divided by the distance between the two points, for example , is the distance between the two points, obtained by actual measurement of sensor layout, temperature value and are collected in real time by temperature sensors, Temperature gradient change in time direction, reflecting the temperature change rate of the same point at different times, calculated by the temperature difference between adjacent times of a certain point divided by the time interval, for example , is the time interval, obtained by the data recording interval of the monitoring device, temperature value and are also collected in real time by temperature sensors, is the temperature data of the sensor node with spatial index of the current monitoring point, obtained by reading the node data of the sensor with number represents the sensor node temperature value of the current monitoring point in the spatial direction, obtained by directly reading the real-time data of the sensor with number represents the temperature value of monitoring point , which is the temperature data collected by the temperature sensor installed on the power distribution equipment at the position with spatial coordinates , directly collected and stored by the sensor, represents the temperature value of monitoring point , which is the temperature data collected by the next monitoring point adjacent to the monitoring point in space (i.e. forward interval of one sensor layout distance based on ), for example, if the sensor layout interval is 1 meter, represents the temperature at the adjacent 1 meter, represents the temperature value of the monitoring point at time , which is the temperature data collected by the temperature sensor at time , represents the temperature value of the monitoring point at time , which is the temperature data collected by the temperature sensor at time based on interval of one sampling period

[0069] For example, the interval between adjacent sensors on a certain device is , the sampling period is , and the monitoring point is ​​The temperature values of the monitoring points in the time sequence are and . and .

[0070] Spatial direction temperature gradient change:

[0071] ;

[0072] Temporal direction temperature gradient change:

[0073] ;

[0074] Temperature gradient change rate:

[0075] ;

[0076] The result shows that the temperature gradient change rate of the current monitoring point is , reflecting the comprehensive change intensity of temperature rise in space and time dimensions.

[0077] Please refer to Figure 3 , the distribution data of the power distribution equipment temperature rise abnormal area is obtained by the following steps:

[0078] Based on the temperature gradient change rate data of the power distribution equipment, the temperature gradient change rate value is extracted by traversing the monitoring points and compared with the preset abnormal threshold point by point, the spatial position and time node of the abnormal point are marked, and the abnormal monitoring point set is generated;

[0079] Combined with the calculated temperature gradient change rate and the preset abnormal identification threshold value, the temperature gradient change rate value of each monitoring point is extracted from the gradient change data set, each monitoring point is traversed, and the value of is compared with the abnormal threshold value to determine whether the point has abnormal change, for example, the preset abnormal identification threshold value is , the calculated gradient change rate of a point is , since , the point is marked as an abnormal point; the marking operation is completed by recording the spatial coordinates and time node of the abnormal point, for example, the abnormal point is located at the spatial coordinates , the time , and the record is stored as , the operation is executed for all monitoring points, and the marking information of all abnormal points is stored as an abnormal monitoring point set, which contains the spatial position of the monitoring point and the marking result of the corresponding time, which is used for subsequent abnormal path association analysis.

[0080] Based on the set of abnormal monitoring points, the spatial coordinates and time series data of each abnormal point are extracted, the continuity in time dimension and the correlation in space dimension are matched, the time diffusion path of temperature rise anomaly is formed, and the distribution data of the temperature rise anomaly area of the power distribution equipment is obtained;

[0081] The set of labeled abnormal monitoring points is matched with the time dimension change path in the monitoring data. First, the spatial coordinates and time nodes of each abnormal point are extracted from the set of labeled abnormal monitoring points, for example, the set of abnormal monitoring points contains the following records: 、 、 Subsequently, the time series relationship of the abnormal point is analyzed to determine its continuity in the time dimension, for example, the records in the set of abnormal monitoring points and are time-continuous and the spatial coordinates are increasing, indicating that the abnormal path is expanding in the time dimension. At the same time, it is checked whether the distance change between adjacent points in the spatial dimension conforms to the abnormal diffusion rule, for example, the distance between the two points is . Through the connection of such consecutive points, the abnormal path is formed. The spatial coordinates and time series are integrated to determine the path of the abnormal area, for example, the abnormal area path is The combination of the abnormal point in the spatial and time dimensions finally forms the distribution data of the temperature rise abnormal area of the power distribution equipment, which is used for subsequent analysis of the characteristics of the area.

[0082] Please refer to Figure 4 , the steps for obtaining the contact temperature rise state classification result are as follows:

[0083] Based on the real-time temperature rise data of the circuit breaker contact area, the spatial coordinates of the contact area are screened and matched with the distribution data of the power distribution equipment temperature rise abnormal area to generate an abnormal contact temperature rise data set;

[0084] Collect real-time temperature rise data of the circuit breaker contact area, collect temperature rise value per second through temperature sensors installed in the contact area to form a time series temperature rise data set, and perform noise filtering and abnormal elimination processing on the data. The processed contact temperature rise value is associated and matched with the distribution data of the power distribution equipment temperature rise abnormal area. The matching operation is achieved by checking whether the spatial position of the contact is located within the abnormal distribution area, for example, the spatial coordinates of the circuit breaker contact area are , in the abnormal area distribution data, the abnormal area coverage range includes , it is confirmed that the contact is located within the abnormal distribution area. In addition, it is checked whether the contact temperature rise value is consistent with the time variation trend of the abnormal area, for example, at time and , the temperature rise value of the abnormal area rises from to , and the contact temperature rise value also rises from Rise to After the correlation matching is completed, the contact temperature rise value of the matching success is classified into the abnormal contact temperature rise data set for subsequent analysis.

[0085] Based on the abnormal contact temperature rise data set, the formula:

[0086] ;

[0087] Calculate the contact temperature rise gradient change rate From the contact temperature rise gradient change rate The maximum value is selected to integrate the contact temperature rise characteristic analysis result;

[0088] Wherein, represents the temperature rise value of the contact area at time , which is collected in real time by the contact area temperature sensor, represents the temperature rise value of the contact area at time , and the temperature rise value is also collected in real time by the sensor, represents the time sampling interval, that is, the time difference of the sensor collecting continuous temperature rise values, which is determined by setting the sampling frequency of the sensor.

[0089] For example, if the temperature rise value of the contact area at time s is 50℃, the temperature rise value at time s is 55℃, and the time interval is 2s;

[0090] The temperature rise time sequence is [50℃, 55℃, 60℃, 58℃].

[0091] Calculate the temperature rise gradient change rate :

[0092] ;

[0093] Determine the maximum temperature rise value :

[0094] ;

[0095] The results show that the temperature rise gradient change rate ℃ / s represents the rate of change of the temperature rise of the contact area with time, which is used to determine whether the temperature rise change of the contact exceeds the normal threshold value; the maximum temperature rise value ℃ reflects the highest temperature rise characteristic of the contact area, which is used to further determine whether there is a temperature rise anomaly exceeding the safety limit value;

[0096] Based on the contact temperature rise characteristic analysis result, the contact temperature rise gradient change rate and the maximum value are compared with the preset contact temperature rise threshold value, the temperature rise state is classified point by point, and the contact temperature rise state classification result is generated.

[0097] The contact temperature rise gradient change rate and the maximum temperature rise value are compared with the preset contact temperature rise threshold value. First, the gradient change rate threshold value and the maximum value threshold value are set, for example, the gradient change rate threshold value is , and the maximum value threshold value is Then, the contact temperature rise characteristics are compared point by point with the threshold value. Whether the temperature rise characteristics exceed the threshold value is recorded for each monitoring point, for example, the gradient change rate of a certain contact monitoring point is , and the maximum value is , neither of which exceeds the threshold value, and is classified as “normal state”; the gradient change rate of another contact monitoring point is , and the maximum value is , both of which exceed the threshold value, and is classified as “abnormal state”. The classification results of all monitoring points are stored as a state classification table.

[0098] Please refer to Figure 5 The steps of obtaining the circuit breaker operating state information are as follows:

[0099] Based on the contact temperature rise state classification results, the formula is used:

[0100] ;

[0101] The circuit breaker health score is calculated , and the circuit breaker score result is generated;

[0102] Wherein, represents the weight of the first state classification of the contact temperature rise, reflecting the relative importance of each classification state in the overall score. For example, for the classification result of a serious abnormality, the weight may be higher to emphasize the importance of the state. The weight value can be determined by expert experience, historical statistical data or experimental analysis. For example, collect the operating data of the circuit breaker under different temperature rise states, and count the failure probability corresponding to each state. Then, adjust the weight value according to the failure probability to ensure that the weight distribution is consistent with the actual operating risk. For example, in actual analysis, it is found that the operating risk caused by a slight abnormal state is low, and the weight is 0.3, while the weight of a serious abnormal state is higher, which is set to 0.5, represents the contact temperature rise state classification result, quantitatively describing the severity of the first state. The classification result can be obtained by analyzing the contact temperature rise data. For example, according to the degree to which the gradient change rate or the maximum value of the temperature rise value exceeds the preset threshold value, the temperature rise state is divided into different levels, represents the total number of contact temperature rise state classifications, which is the number of known contact temperature rise classification results.

[0103] Assuming that the weights of the contact temperature rise state classifications are , the state classification results .

[0104] Sum the weighted total:

[0105] ;

[0106] Sum the weight total:

[0107] ;

[0108] Calculate the health score:

[0109] ;

[0110] The results show that the health score of the current circuit breaker is .

[0111] According to the preset score range, the circuit breaker score result is matched to the corresponding health level to obtain the circuit breaker running state information;

[0112] First, define the score range level, for example, the score range is divided into “normal”, the score range is “slight abnormality”, the score range is “serious abnormality”, according to the score , check the interval it falls into, find , the corresponding running health level is “slight abnormality”, record the level information and associate it with the score value to form the running state classification result, and at the same time, mark and store the running state information for subsequent circuit breaker running state evaluation and fault prediction process, and finally generate the circuit breaker running health state information.

[0113] Please refer to Figure 6 , the micro-grid grid-connection optimization result obtaining step is specifically:

[0114] Based on the circuit breaker running state information, the temperature rise data is compared with the reference temperature rise range point by point, the circuit breakers with normal state of temperature rise characteristics and power output characteristics are screened, and a normal state circuit breaker running data set is generated;

[0115] The running state is analyzed by collecting real-time temperature rise data and power output parameters of the circuit breaker. The specific operation includes extracting the temperature rise change trend of the contact area of the circuit breaker, comparing the time series temperature rise data with the reference temperature rise data in the historical normal running state point by point, screening out the circuit breaker with normal temperature rise characteristics, for example, by judging whether the temperature rise change rate and peak value range are within the reference data allowed range, and combining the power output parameters of the circuit breaker, analyzing the stability of the power output, comparing the power fluctuation range in the normal running state, marking the circuit breaker with power output characteristics consistent with the normal state, and finally generating the running data set of the normal state circuit breaker according to the above analysis results.

[0116] Based on the normal state circuit breaker running data set, the temperature rise characteristics and power output characteristics are extracted, and the voltage value and power flow parameters of the connection point are matched and analyzed, the power output proportion of the connection point is adjusted by screening the voltage offset value and power fluctuation amplitude, and the micro-grid grid-connected optimization result is obtained;

[0117] The temperature rise characteristics and power output characteristics are extracted, and the voltage value, frequency value and power flow parameters of the connection point are associated, the real-time voltage value of the connection point is compared with the set stable interval (such as ±5% nominal voltage), the connection point with voltage offset value exceeding the allowed range is screened out, and the time series change of power flow parameters is recorded to analyze the range and trend of power fluctuation, for example, the difference between the peak value and the average value of power flow is taken as the fluctuation amplitude, the connection point with excessive fluctuation amplitude is screened out, and the preset power distribution adjustment rule is combined to gradually adjust the power output proportion of the connection point, the power flow direction and the load distribution of the connection point are optimized step by step, to ensure that the running parameters of the micro-grid grid-connected point meet the voltage and power stability requirements, and finally the optimized connection point running parameters and distribution adjustment scheme are generated.

[0118] Please refer to Figure 7 The acquisition steps of the distribution network operation grouping optimization result are as follows:

[0119] Based on the micro-grid grid-connected optimization result, the time series data of the grid-connected point power output is monitored in real time, the power output value is analyzed by time interval, the nodes with abnormal power fluctuation amplitude are screened out, and the abnormal time points and fluctuation trend of the nodes are recorded to establish an abnormal load node set;

[0120] By monitoring the grid point power output data in real time and extracting the time series of power distribution, the real-time power output values of all load nodes are recorded, the power output values are divided into several segments according to the time interval, the power fluctuation amplitude is screened, the power change range of each load node in the time segment is obtained, the change range is compared with the preset fluctuation amplitude reference value, and the nodes with abnormal power fluctuation amplitude are screened out, for example, the power output value of the node in multiple time periods is significantly larger than that of other nodes, and the sudden abnormal situation is detected by combining the trend chart of the time series, whether the power fluctuation has persistence or periodic change is judged, for example, the power change trend shows sudden rise or fall in a certain period of time, and the time point and node information of the abnormal situation are marked. The load nodes with abnormal power fluctuation amplitude are recorded as an abnormal node set.

[0121] Based on the abnormal load node set, the power distribution data and fluctuation characteristics of the nodes are obtained, dynamic clustering is performed according to the similarity between the characteristic values, the nodes are grouped according to the power characteristics, and the grouping state is updated combined with the node characteristics to generate the distribution network operation grouping optimization result.

[0122] The abnormal load nodes screened out are dynamically clustered and grouped, the power distribution data and fluctuation characteristics of each node are obtained, these data are normalized into standardized characteristic values respectively, the similarity characteristics between nodes are analyzed, nodes with similar power distribution and fluctuation characteristics are classified into the same group, for example, according to the time change trend of the node characteristics, the attribution relationship of the node grouping is adjusted, the grouping state of the node is updated by time period, the node grouping boundary is optimized by using the difference between the characteristic values, for example, the abnormal nodes newly added in a certain time period or the changed characteristics of the original nodes are reexamined and grouped, and the distribution network operation grouping optimization result is generated, including the characteristic information and grouping change record of each group of load nodes, to ensure that the optimized grouping can adapt to the dynamic characteristics of power fluctuation.

[0123] A distribution network intelligent operation method, the distribution network intelligent operation method is executed based on the above-mentioned distribution network intelligent operation system, comprising the following steps:

[0124] S1: Based on the real-time temperature data of the distribution equipment, the temperature gradient value of the equipment in the spatial distribution and time sequence is extracted, the gradient change rate of each point is compared and judged, and the distribution data of the temperature rise abnormal area of the distribution equipment is generated;

[0125] S2: Based on the real-time temperature rise data of the circuit breaker contact area, the contact temperature rise value is associated and matched with the distribution data of the temperature rise abnormal area of the distribution equipment, the circuit breaker operation health level is divided according to the contact temperature rise state classification result, and the circuit breaker operation state information is obtained;

[0126] S3: Based on the operation data of the micro-grid and the main grid connection point, combined with the circuit breaker operation state information, the power flow distribution of the micro-grid grid-connected point is adjusted in stages, the power distribution of the micro-grid grid-connected is optimized, and the micro-grid grid-connected optimization result is generated;

[0127] S4: Based on the micro-grid grid-connected optimization result, the load nodes with abnormal power fluctuation amplitude are screened, the power distribution value and the fluctuation characteristics of the load nodes are referred to, dynamic clustering grouping is performed, and the load dynamic clustering grouping result is generated;

[0128] S5: Based on the load dynamic clustering grouping result, combined with the dynamically updated grouping state and power distribution demand, the overall operation grouping mode of the power distribution network is iteratively optimized, and the power distribution network operation grouping optimization result is generated.

[0129] The above is only a preferred embodiment of the present application, and does not limit the form of the present application, any skilled person in the art can use the disclosed technical content to make changes or modifications as equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made on the basis of the technical essence of the present application to the above embodiments without departing from the technical scheme content of the present application still belongs to the protection scope of the technical scheme of the present application.

Claims

1. A smart operation and maintenance system for power distribution networks, characterized in that, The system includes: The temperature rise anomaly detection module collects real-time temperature data of power distribution equipment, extracts temperature gradient values ​​in spatial distribution and time series, calculates the rate of change of temperature gradient values ​​based on temperature gradient values, compares and judges the rate of change of gradient at each point, and generates distribution data of the temperature rise anomaly area of ​​power distribution equipment. The circuit breaker health assessment module collects real-time temperature rise data of the circuit breaker contact area, correlates and matches the contact temperature rise value with the distribution data of the abnormal temperature rise area of ​​the power distribution equipment, obtains the contact temperature rise status classification result, classifies the circuit breaker operating health level according to the contact temperature rise status classification result, and obtains the circuit breaker operating status information. The microgrid grid connection control module collects the operating data of the connection point between the microgrid and the main grid, and combines it with the circuit breaker operating status information to make phased adjustments to the power flow distribution at the microgrid grid connection point and obtain the microgrid grid connection optimization results. The specific steps for obtaining the microgrid grid connection optimization results are as follows: Based on the circuit breaker operating status information, by comparing the temperature rise data with the reference temperature rise range point by point, circuit breakers whose temperature rise characteristics and power output characteristics meet the normal state are selected, and a set of normal state circuit breaker operating data is generated. Based on the normal state circuit breaker operation data set, its temperature rise characteristics and power output characteristics are extracted and matched with the voltage value and power flow parameters of the connection point. By filtering the voltage deviation value and power fluctuation amplitude, the power output ratio of the connection point is adjusted to obtain the microgrid grid connection optimization result. The load dynamic grouping module, based on the power dynamic allocation of the microgrid grid connection optimization results, filters load nodes with abnormal power fluctuation amplitude, performs dynamic clustering and grouping with reference to the power allocation value and fluctuation characteristics of the load nodes, updates the grouping status in real time, and generates distribution network operation and maintenance grouping optimization results. The specific steps for obtaining the distribution network operation and maintenance group optimization results are as follows: Based on the microgrid grid connection optimization results, by monitoring the time series data of the power output at the grid connection point in real time, the power output value is analyzed in segments according to the time interval, nodes with abnormal power fluctuation amplitude are screened, and a set of abnormal load nodes is established by recording the abnormal time points and fluctuation trends of the nodes. Based on the set of abnormal load nodes, the power distribution data and fluctuation characteristics of the nodes are obtained. Dynamic clustering is performed according to the similarity between characteristic values. The nodes are grouped according to power characteristics, and the grouping status is updated in combination with the node characteristics to generate the distribution network operation and maintenance group optimization results.

2. The intelligent operation and maintenance system for power distribution networks according to claim 1, characterized in that, The specific steps for obtaining the rate of change of the temperature gradient value are as follows: Based on the real-time operating data of power distribution equipment, time-series temperature values ​​and spatial temperature differences from multiple temperature sensors are collected, and the temporal variation and spatial distribution characteristics associated with the equipment operating status are extracted to generate an initial set of temperature gradient data. Based on the initial set of temperature gradient data, the following formula is used: ； Calculate the rate of change of temperature gradient The temperature gradient change rate data are obtained. in, This represents the amount of temperature gradient change in a spatial direction. This represents the change in temperature gradient over time.

3. The intelligent operation and maintenance system for power distribution networks according to claim 2, characterized in that, The specific steps for obtaining the distribution data of the abnormal temperature rise area of ​​the power distribution equipment are as follows: Based on the temperature gradient change rate data of the power distribution equipment, by traversing the monitoring points, the temperature gradient change rate value is extracted and compared with the preset abnormal threshold point by point, the spatial location and time node of the abnormal point are marked, and an abnormal monitoring point set is generated. Based on the set of abnormal monitoring points, the spatial coordinates and time series data of each abnormal point are extracted. The continuity in the time dimension and the correlation in the spatial dimension are matched to form the time diffusion path of the temperature rise anomaly, and the distribution data of the temperature rise anomaly area of ​​the power distribution equipment are obtained.

4. The intelligent operation and maintenance system for power distribution networks according to claim 3, characterized in that, The specific steps for obtaining the contact temperature rise status classification results are as follows: Based on the real-time temperature rise data of the circuit breaker contact area, the spatial coordinates of the contact area are filtered and matched with the distribution data of the abnormal temperature rise area of ​​the power distribution equipment to generate an abnormal contact temperature rise data set. Based on the aforementioned abnormal contact temperature rise data set, the following formula is used: ； Calculate the rate of change of the temperature rise gradient of the contact. From the rate of change of the temperature gradient at the contact The maximum value is selected from the results, and the results of the contact temperature rise characteristic analysis are obtained by integrating them. in, Indicates time Temperature rise in the contact area at any given moment. Indicates time Temperature rise in the contact area at any given moment. Indicates the time sampling interval; Based on the analysis results of the contact temperature rise characteristics, the change rate and maximum value of the contact temperature rise gradient are compared with the preset contact temperature rise threshold, and the temperature rise state is classified point by point to generate the contact temperature rise state classification result.

5. The intelligent operation and maintenance system for power distribution networks according to claim 4, characterized in that, The specific steps for obtaining the circuit breaker operating status information are as follows: Based on the classification results of the contact temperature rise status, the following formula is used: ； Calculate the health score of the circuit breaker Generate circuit breaker rating results; in, Indicates the temperature rise of the contact point The weights of each state category, Indicates the first The severity of the condition, This indicates the total number of contact temperature rise status categories; The circuit breaker is classified into health levels according to a preset scoring range, and the circuit breaker scoring results are matched with the corresponding health levels to obtain the circuit breaker operating status information.

6. A method for intelligent operation and maintenance of a power distribution network, characterized in that, The intelligent operation and maintenance system for power distribution networks according to any one of claims 1-5 includes the following steps: Based on the real-time temperature data of power distribution equipment, the temperature gradient values ​​of the equipment in spatial distribution and time series are extracted, the gradient change rate of each point is compared and judged, and the distribution data of abnormal temperature rise areas of power distribution equipment is generated. Based on the real-time temperature rise data of the circuit breaker contact area, the contact temperature rise value is correlated and matched with the distribution data of abnormal temperature rise areas of the power distribution equipment. The circuit breaker operating health level is classified according to the contact temperature rise status classification result, and the circuit breaker operating status information is obtained. Based on the operational data of the connection point between the microgrid and the main grid, and combined with the circuit breaker operation status information, the power flow distribution at the microgrid connection point is adjusted in stages to optimize the power allocation of the microgrid connection and generate microgrid connection optimization results. Based on the microgrid grid connection optimization results, load nodes with abnormal power fluctuation amplitudes are screened, and dynamic clustering is performed by referring to the power allocation value and fluctuation characteristics of the load nodes to generate load dynamic clustering results. Based on the load dynamic clustering grouping results, combined with the dynamically updated grouping status and power allocation requirements, the overall operation grouping mode of the distribution network is iteratively optimized to generate the distribution network operation and maintenance grouping optimization results.

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