Intelligent operation and maintenance system and method for power distribution network

Abstract

The invention relates to the technical field of power distribution management, in particular to an intelligent operation and maintenance system and method for a power distribution network, and the system comprises a temperature rise anomaly detection module which collects the real-time temperature data of power distribution equipment, extracts the temperature gradient values in spatial distribution and time series, and calculates the change rate of the temperature gradient values according to the temperature gradient values; and comparing and judging the gradient change rate of each point, and generating distribution data of the temperature rise abnormal area of the power distribution equipment. According to the invention, through acquisition and processing of real-time temperature data, extraction of spatial distribution and time change characteristics of temperature gradient values, further calculation of the change rate, comparison and judgment, the temperature rise abnormal area of the power distribution equipment is effectively identified, and refined sensing of the operation state of the equipment 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 multi-dimensional data analysis, so that the health state assessment of the circuit breaker is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution management, and in particular to a smart operation and maintenance system and method for a power distribution network. Background Art

[0002] The field of power distribution management technology mainly involves the distribution and management process of electric energy from substations to end users, 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 dispatching and power quality management of the distribution system, with the goal of improving the reliability, safety, efficiency and intelligence level of the distribution system.

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

[0004] Existing technologies lack refined analysis of temperature gradient changes in terms of operating status perception, resulting in rough positioning of abnormal temperature rise 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 fluctuations in single-dimensional data. Health assessments often lack sufficient accuracy and diversity. During the microgrid connection process, power regulation usually relies on fixed parameters, which makes it difficult to cope with dynamic fluctuations in actual operation, and may lead to uneven energy distribution or increased grid fluctuations. Load grouping lacks a dynamic adjustment mechanism based on real-time data, and the management efficiency of abnormal power fluctuation nodes is low, which may aggravate the negative impact of abnormal loads on the overall network. Summary of the invention

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

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: A distribution network intelligent operation and maintenance system comprises:

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

[0008] 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 abnormal temperature rise area of ​​the power distribution equipment, obtains the contact temperature rise state classification result, divides the circuit breaker operation health level according to the contact temperature rise state classification result, and obtains the circuit breaker operation state information;

[0009] The microgrid connection control module collects the operation data of the connection point between the microgrid and the main grid, and combines the circuit breaker operation status information to make a phased adjustment to the power flow distribution of the microgrid connection point to obtain the microgrid connection optimization result;

[0010] The load dynamic grouping module screens load nodes with abnormal power fluctuation amplitudes based on the power dynamic distribution of the microgrid grid-connected optimization results, dynamically clusters and groups the load nodes with reference to the power distribution values ​​and fluctuation characteristics, updates the grouping status in real time, and generates distribution network operation and maintenance grouping optimization results.

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

[0012] Based on the real-time operation data of the power distribution equipment, the time series temperature values ​​and spatial temperature differences of multi-point temperature sensors are collected, and the time variation and spatial distribution characteristics associated with the equipment operation status are extracted respectively to generate the initial temperature gradient data set;

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

[0014] ;

[0015] Calculate the rate of change of temperature gradient , get the temperature gradient change rate data;

[0016] in, Represents the temperature gradient change in the spatial direction, Indicates the change in temperature gradient in the time direction.

[0017] As a further solution of the present invention, the step of acquiring the distribution data of the abnormal temperature rise area of ​​the power distribution equipment is specifically as follows:

[0018] Based on the temperature gradient change rate data of the power distribution equipment, by traversing the monitoring points, extracting the temperature gradient change rate values ​​and comparing them point by point with the preset abnormal threshold, marking the spatial position and time node of the abnormal point, and generating an abnormal monitoring point set;

[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 anomaly is formed, and the distribution data of the temperature rise abnormal area of ​​the distribution equipment is obtained.

[0020] As a further solution 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 screened and associated 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 abnormal contact temperature rise data set, the formula is used:

[0023] ;

[0024] Calculate the contact temperature rise gradient change rate , from the contact temperature rise gradient change rate Select the maximum value and integrate it to get the contact temperature rise characteristic analysis result;

[0025] in, Indicates at time The temperature rise value of the contact area at the moment, Indicates at time The temperature rise value of the contact area at the moment, represents the time sampling interval;

[0026] 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, the temperature rise state is classified point by point, and the contact temperature rise state classification result is generated.

[0027] As a further solution of the present invention, the step of obtaining the circuit breaker operation status information is specifically as follows:

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

[0029] ;

[0030] Calculating Circuit Breaker Health Score , generate circuit breaker scoring results;

[0031] in, Indicates the contact temperature rise The weight of each state category, Indicates The severity of the condition, Indicates the total number of contact temperature rise status classifications;

[0032] The health levels are divided according to a preset scoring range, the circuit breaker scoring results are matched to corresponding health levels, and the circuit breaker operation status information is obtained.

[0033] As a further solution of the present invention, the steps for obtaining the microgrid connection optimization result are specifically as follows:

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

[0035] Based on the normal state circuit breaker operation data set, its temperature rise characteristics and power output characteristics are extracted, and matched and analyzed with the voltage value and power flow parameters of the connection point. By screening the voltage offset value and the power fluctuation amplitude, the power output ratio of the connection point is adjusted to obtain the microgrid grid-connected optimization result.

[0036] As a further solution of the present invention, the steps for obtaining the distribution network operation and maintenance group optimization results are specifically as follows:

[0037] Based on the microgrid grid connection optimization result, by real-time monitoring of the time series data of the power output of the grid connection point, the power output value is segmented and analyzed by time interval, the nodes with abnormal power fluctuation amplitude are screened, and by recording the abnormal time point and fluctuation trend of the node, 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 status is updated in combination with the node characteristics to generate the distribution network operation and maintenance grouping optimization results.

[0039] A distribution network intelligent operation and maintenance method, the distribution network intelligent operation and maintenance method is executed based on the above distribution network intelligent operation and maintenance system, comprising 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 series is extracted, the gradient change rate of each point is compared and judged, and the distribution data of the abnormal temperature rise area of ​​the power distribution equipment is generated;

[0041] S2: 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 the abnormal temperature rise area of ​​the power distribution equipment, and the circuit breaker operation health level is divided according to the contact temperature rise state classification result to obtain the circuit breaker operation state information;

[0042] S3: Based on the operation data of the connection point between the microgrid and the main grid, combined with the circuit breaker operation status information, the power flow distribution of the microgrid connection point is adjusted in stages, the power distribution of the microgrid connection is optimized, and the microgrid connection optimization result is generated;

[0043] S4: Based on the microgrid grid-connected optimization result, select load nodes with abnormal power fluctuation amplitude, refer to the power distribution value and fluctuation characteristics of the load nodes, perform dynamic clustering grouping, and generate load dynamic clustering grouping results;

[0044] S5: Based on the load dynamic clustering grouping result, combined with the dynamically updated grouping status and power allocation requirements, iteratively optimize the overall operation grouping mode of the distribution network to generate distribution network operation and maintenance grouping optimization results.

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

[0046] In the present invention, the spatial distribution and time variation characteristics of the temperature gradient value are extracted through the collection and processing of real-time temperature data, and the rate of change is further calculated and compared to effectively identify the abnormal temperature rise area of ​​the distribution equipment, so as to achieve refined perception of the equipment operation status. Through the matching analysis of real-time temperature rise data and spatial distribution data, the operation status of the contact area is accurately classified, and a health assessment mechanism is constructed in combination with multi-dimensional data analysis to make the health status assessment of the circuit breaker more accurate. During the microgrid connection process, the operation status of the connection point is optimized through the phased adjustment of power flow, which helps to reduce the risk of fluctuations and improve the efficiency of grid connection. Dynamic grouping optimization uses power characteristic similarity analysis to achieve efficient clustering and grouping of load nodes, which can respond to changes in power distribution and fluctuation characteristics in real time, and improve the dynamic adaptability during operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a system flow chart of the present invention;

[0048] Figure 2 A flow chart for calculating the rate of change of the temperature gradient value of the present invention;

[0049] Figure 3 A flow chart for obtaining distribution data of abnormal temperature rise areas of power distribution equipment according to the present invention;

[0050] Figure 4 A flow chart of obtaining the contact temperature rise state classification result of the present invention;

[0051] Figure 5 A flow chart of obtaining circuit breaker operation status information according to the present invention;

[0052] Figure 6 A flow chart of obtaining microgrid grid connection optimization results according to the present invention;

[0053] Figure 7 The present invention is a flow chart for obtaining the distribution network operation and maintenance group optimization results. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0056] See also Figure 1 The present invention provides a technical solution: a 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 the temperature gradient values ​​in the spatial distribution and time series, and uses the finite difference method to calculate the change rate of the temperature gradient value based on the temperature gradient value. Combined with the preset anomaly recognition threshold, the gradient change rate of each point is compared and judged, and the temperature rise change path in the time dimension is combined with the spatial marking point to generate the distribution data of the temperature rise abnormal 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 abnormal temperature rise area of ​​the power distribution equipment, screens the abnormal contact temperature rise data, obtains the gradient change rate and maximum value of the contact temperature rise, and compares it 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 according to the score range, and obtains the circuit breaker operation status information;

[0059] The microgrid grid-connected control module collects the operating data of the connection point between the microgrid and the main grid, extracts the voltage value, frequency value and power flow parameters of the connection point, and distinguishes the operating state of the circuit breaker under normal conditions in combination with the circuit breaker operating status information. According to its temperature rise characteristics and power output characteristics, the voltage offset value and power fluctuation amplitude of the connection point are determined, and the power flow distribution of the microgrid grid-connected point is adjusted in stages to obtain the microgrid grid-connected optimization results;

[0060] The load dynamic grouping module extracts the real-time distribution data of the power output of the grid connection point based on the dynamic power distribution of the microgrid grid connection optimization results, screens the load nodes with abnormal power fluctuation amplitude, and dynamically clusters and groups the load nodes according to the power distribution value and fluctuation characteristics. It updates the grouping status in real time and generates the distribution network operation and maintenance grouping optimization results.

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

[0062] See also Figure 2 , the steps for calculating the rate of change of the temperature gradient value are as follows:

[0063] Based on the real-time operation data of the power distribution equipment, the time series temperature values ​​and spatial temperature differences of multi-point temperature sensors are collected, and the time variation and spatial distribution characteristics related to the equipment operation status are extracted respectively to generate the initial temperature gradient data set;

[0064] Firstly, the real-time temperature value of each monitoring point is collected through a multi-point temperature sensor network deployed on the surface of the power distribution equipment. The sensor network contains multiple sensor nodes distributed on the surface of the equipment and at key internal positions. The temperature data is uploaded to the database in real time in the form of time series, and the time and space dimension information related to the equipment operation status is extracted from these data. The time dimension refers to the continuous temperature change when the equipment is running. The extraction method is to record the temperature value of each monitoring point at different time points; the spatial dimension refers to the temperature distribution characteristics between different sensor nodes. The extraction method is to compare the temperature difference of adjacent nodes at the same time point. After eliminating the random noise that may exist in the collection process, the time series temperature value and spatial distribution temperature difference of each point are collected and stored to generate a preliminary temperature gradient data set containing the time series temperature data and spatial temperature difference data of each monitoring point.

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

[0066] ;

[0067] Calculate the rate of change of temperature gradient , get the temperature gradient change rate data;

[0068] in, It indicates the change of temperature gradient in the spatial direction, reflecting the difference in temperature distribution between different locations. This value is calculated by dividing the temperature difference between each monitoring point and its adjacent point by the distance between the two points. For example , is the distance between two points, obtained through actual measurement of the sensor layout, and the temperature value and It is collected in real time through temperature sensors. It indicates the change of temperature gradient in the time direction, reflecting the temperature change rate of the same point at different times. This value is calculated by dividing the temperature difference of adjacent time points by the time interval. For example , is the time interval, obtained by the data recording interval of the monitoring device, the temperature value and It is also collected in real time by the temperature sensor. is the spatial index of the current monitoring point The temperature data of the sensor node is obtained by reading the sensor number Node data obtained Indicates the temperature value of the sensor node at the current monitoring point in the spatial direction. By directly reading the current number The sensors collect data in real time. Indicates monitoring point The temperature value is obtained by the temperature sensor installed on the power distribution equipment at the spatial coordinate The temperature data collected at the location is directly collected and stored by the sensor. Indicates monitoring point The temperature value is determined by the monitoring point The next spatially adjacent monitoring point (i.e. Based on the forward spacing of one sensor layout distance ) collected temperature data, for example, if the sensor arrangement interval is 1 meter, then Indicates the temperature at the adjacent 1 meter. Indicates the monitoring point at time The temperature value is determined by the temperature sensor at the time The collected temperature data, Indicates the monitoring point at time The temperature value is determined by the temperature sensor at Based on the time interval backward one sampling period Temperature data collected at all times.

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

[0070] Temperature gradient changes in spatial direction:

[0071] ;

[0072] Temperature gradient change in time direction:

[0073] ;

[0074] Temperature gradient change rate:

[0075] ;

[0076] The results show that the temperature gradient change rate of the current monitoring point is , reflecting the comprehensive change intensity of temperature rise in both spatial and temporal dimensions.

[0077] See also Figure 3 ,The specific steps for obtaining the distribution data of the abnormal temperature rise area of ​​the distribution equipment are as follows:

[0078] Based on the temperature gradient change rate data of the power distribution equipment, by traversing the monitoring points, extracting the temperature gradient change rate value and comparing it point by point with the preset abnormal threshold, marking the spatial position and time node of the abnormal point, and generating an abnormal monitoring point set;

[0079] Combined with the calculated temperature gradient change rate The temperature gradient change rate value of each monitoring point is extracted from the gradient change data set and the preset abnormal identification threshold is used to traverse each monitoring point. The value of and the abnormal threshold are used to determine whether there is an abnormal change at the point. For example, if the preset abnormal recognition threshold is , the calculated gradient change rate at a certain point is ,because , marking the point as an outlier; the marking operation is completed by recording the spatial coordinates and time nodes of the outlier point. For example, the outlier point is located at the spatial coordinates ,time , then store as a record ,This operation is executed cyclically 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 the 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 the time dimension and the correlation in the space dimension are matched, the time diffusion path of the temperature rise anomaly is formed, and the distribution data of the temperature rise anomaly area of ​​the distribution equipment is obtained;

[0081] Match the marked abnormal monitoring point set with the time dimension change path in the monitoring data. First, extract the spatial coordinates and time nodes of each abnormal point from the marked abnormal monitoring point set. For example, the abnormal monitoring point set contains the following records: , , Then, the time series relationship of the abnormal points is analyzed to determine their continuity in the time dimension. For example, and The time is continuous and the spatial coordinates are increasing, indicating that the abnormal path is expanding in the time dimension. At the same time, check whether the distance change between adjacent points in the spatial dimension conforms to the law of abnormal diffusion. For example, the distance between two points is By connecting these continuous points, an abnormal path is formed. The spatial coordinates and time series are combined to determine the path of the abnormal area. For example, the path of the abnormal area is ,The combination of abnormal points in space and time dimensions eventually forms the ,distribution data of the temperature rise abnormal area of ​​the ,distribution equipment, which is used for the subsequent regional ,characteristics analysis.

[0082] See also Figure 4 , the specific steps for obtaining the contact temperature rise status classification results are:

[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 correlated with the distribution data of the abnormal temperature rise area of ​​the power distribution equipment to generate an abnormal contact temperature rise data set;

[0084] Collect the real-time temperature rise data of the circuit breaker contact area. The temperature rise value per second is collected by the temperature sensor installed in the contact area to form a time series temperature rise data set. The data is filtered for noise and abnormality is eliminated. The processed contact temperature rise value is associated and matched with the distribution data of the abnormal temperature rise area of ​​the distribution equipment. The matching operation is achieved by checking whether the spatial position of the contact is located in 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 includes , it is confirmed that the contact is located in the abnormal distribution area; in addition, check whether the contact temperature rise value is consistent with the time change trend of the abnormal area, for example, at time and , the temperature rise value in the abnormal area is from Rise to The contact temperature rise value also changes from Rise to After the associated matching is completed, the successfully matched contact temperature rise value 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 is used:

[0086] ;

[0087] Calculate the contact temperature rise gradient change rate , from the contact temperature rise gradient change rate Select the maximum value and integrate it to get the contact temperature rise characteristic analysis result;

[0088] in, Indicates at time The temperature rise value of the contact area at the moment is collected in real time by the temperature sensor in the contact area. Indicates at time The temperature rise value of the contact area at each moment is also collected in real time by the sensor. It indicates the time sampling interval, that is, the time difference between the sensor collecting consecutive temperature rise values, which is determined by setting the sampling frequency of the sensor.

[0089] For example, if the contact area is known at time The temperature rise at s is 50℃. The temperature rise at s is 55°C, and the time interval is 2s;

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

[0091] Calculate the temperature rise gradient rate :

[0092] ;

[0093] Determine the maximum temperature rise :

[0094] ;

[0095] The results show that the temperature rise gradient change rate ℃ / s indicates the rate at which the temperature rise of the contact area changes over time. This value is used to determine whether the temperature rise of the contact exceeds the normal threshold; the maximum temperature rise value ℃ reflects the maximum temperature rise characteristics of the contact area, which is used to further determine whether there is a temperature rise abnormality that exceeds the safety limit;

[0096] Based on the analysis results of the contact temperature rise characteristics, the contact temperature rise gradient change rate and the maximum value are compared with the preset contact temperature rise threshold, 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. First, the gradient change rate threshold and the maximum value threshold are set. For example, the gradient change rate threshold is , the maximum value threshold is , and then compare the contact temperature rise characteristics with the threshold point by point, and record whether the temperature rise characteristics exceed the threshold for each monitoring point. For example, the gradient change rate of a contact monitoring point is , the maximum value is , all of which did not exceed the threshold and were classified as “normal state”; the gradient change rate of the other contact monitoring point was , the maximum value is , both of which exceed the threshold and are classified as “abnormal state”. The classification results of all monitoring points are stored as a state classification table.

[0098] See also Figure 5 , the specific steps for obtaining the circuit breaker operation status information are:

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

[0100] ;

[0101] Calculating Circuit Breaker Health Score , generate circuit breaker scoring results;

[0102] in, Indicates the contact temperature rise The weight of each state classification reflects the relative importance of each classification state in the overall score. For example, for the classification result of severe abnormality, the weight may be higher to emphasize the importance of this state. The weight value can be determined by expert experience, historical statistical data or experimental analysis. For example, the operation data of the circuit breaker under different temperature rise states are collected, and the failure probability corresponding to each state is counted. Then, the weight value is adjusted according to the failure probability to ensure that the weight distribution is consistent with the actual operation risk. For example, in the actual analysis, it is found that the operation risk caused by the slight abnormal state is lower, and the weight is 0.3, while the weight of the severe abnormal state is higher, set to 0.5. Indicates the classification result of contact temperature rise state, quantitatively describing the The severity of each state can be determined by analyzing the contact temperature rise data. For example, the temperature rise state can be divided into different levels according to the gradient change rate of the temperature rise value or the degree to which the maximum value exceeds the preset threshold. Indicates the total number of contact temperature rise status classifications, which is the number of known contact temperature rise classification results.

[0103] Assuming the weight of the contact temperature rise state classification , status classification results .

[0104] Find the weighted sum:

[0105] ;

[0106] Find the sum of the weights:

[0107] ;

[0108] Calculate the health score:

[0109] ;

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

[0111] The health level is divided according to the preset scoring range, the circuit breaker scoring results are matched to the corresponding health level, and the circuit breaker operation status information is obtained;

[0112] First, define the rating range level. For example, the rating range is divided into is "normal", For "slight abnormality", For "severe abnormality", according to the score , check the interval it falls into, and find , the corresponding operation health level is "slightly abnormal", and the level information is recorded and associated with the score value to form an operation status classification result. At the same time, the operation status information is marked and stored for subsequent circuit breaker operation status evaluation and fault prediction processes, and finally the circuit breaker operation health status information is generated.

[0113] See also Figure 6 , the specific steps for obtaining the microgrid grid-connected optimization results are:

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

[0115] By collecting the real-time temperature rise data and power output parameters of the circuit breaker, the operating status is analyzed. The specific operations include extracting the temperature rise change trend of the circuit breaker contact area, comparing the time series temperature rise data with the benchmark temperature rise data under the historical normal operating state point by point, and screening out the circuit breakers whose temperature rise characteristics meet the normal state. For example, by judging whether the temperature rise change rate and peak range are within the allowable range of the benchmark data, and combining the power output parameters of the circuit breaker, the stability of its power output is analyzed, and the power fluctuation range during normal operation is compared. The circuit breakers whose power output characteristics are consistent with the normal state are marked. Finally, based on the above analysis results, a set of operating data of the circuit breaker in the normal state is generated.

[0116] Based on the normal state circuit breaker operation data set, the temperature rise characteristics and power output characteristics are extracted, and matched with the voltage value and power flow parameters of the connection point for analysis. By screening the voltage offset value and power fluctuation amplitude, the power output ratio of the connection point is adjusted to obtain the microgrid grid-connected optimization result;

[0117] The temperature rise characteristics and power output characteristics are extracted, and associated with the voltage value, frequency value and power flow parameters of the connection point. By comparing the real-time voltage value of the connection point with the set stability range (such as ±5% of the nominal voltage), the connection points with voltage offset values ​​exceeding the allowable range are screened out. At the same time, by recording the time series changes of the power flow parameters, the range and trend of power fluctuations are analyzed. For example, the difference between the peak value and the average value of the power flow is used as the fluctuation amplitude, and the connection points with excessive fluctuation amplitude are screened out. Combined with the preset power distribution adjustment rules, the power output ratio of the connection point is gradually adjusted. By optimizing the power flow direction and the load distribution of the connection point step by step, it is ensured that the operating parameters of the microgrid connection point meet the voltage and power stability requirements, and finally the optimized connection point operating parameters and distribution adjustment plan are generated.

[0118] See also Figure 7 ,The specific steps for obtaining the distribution network operation and maintenance group optimization results are as follows:

[0119] Based on the optimization results of microgrid connection, the power output time series data of the grid connection point is monitored in real time, and the power output value is segmented and analyzed by time interval to screen out the nodes with abnormal power fluctuation amplitude. By recording the abnormal time point and fluctuation trend of the node, a set of abnormal load nodes is established;

[0120] By real-time monitoring of the power output data of the grid-connected point, extracting the time series of power distribution, recording the real-time power output values ​​of all load nodes, dividing the power output values ​​into several segments according to the time interval, and screening the power fluctuation amplitude, the power variation range of each load node in the time segment is obtained, and the variation range is compared with the preset fluctuation amplitude reference value to screen out nodes with abnormal power fluctuation amplitude. For example, the power output value fluctuation of the node in multiple time periods is significantly greater than that of other nodes. At the same time, the trend chart of the time series is combined to detect sudden abnormal situations and determine whether there is continuous or periodic change in power fluctuation. For example, the power variation trend in a certain period of time shows a sudden rise or fall. By marking the time point and node information of the abnormal situation, 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, and dynamic clustering is performed according to the similarity between the characteristic values. The nodes are grouped according to the power characteristics, and the group status is updated based on the node characteristics to generate the distribution network operation and maintenance group optimization results;

[0122] Dynamically cluster and group the screened abnormal load nodes, obtain the power distribution data and fluctuation characteristics of each node, normalize these data into standardized characteristic values, and classify the nodes with similar power distribution and fluctuation characteristics into the same group by analyzing the similarity characteristics between nodes. For example, adjust the affiliation of node groups according to the time change trend of node characteristics, update the grouping status of nodes in each time period, and optimize the node grouping boundaries by using the differences between characteristic values. For example, the abnormal nodes added in a specific time period or the changing characteristics of the original nodes are reviewed and adjusted to generate the distribution network operation and maintenance grouping optimization results, including the characteristic information of each group of load nodes and the grouping change records, to ensure that the optimized grouping can adapt to the dynamic characteristics of power fluctuations.

[0123] A distribution network intelligent operation and maintenance method, which is executed based on the above distribution network intelligent operation and maintenance system, includes the following steps:

[0124] 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 series is extracted, the gradient change rate of each point is compared and judged, and the distribution data of the abnormal temperature rise area of ​​the power 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 correlated and matched with the distribution data of the abnormal temperature rise area of ​​the power distribution equipment, and the circuit breaker operation health level is divided according to the contact temperature rise status classification result to obtain the circuit breaker operation status information;

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

[0127] S4: Based on the microgrid grid-connected optimization results, select the load nodes with abnormal power fluctuation amplitude, refer to the power distribution value and fluctuation characteristics of the load nodes, perform dynamic clustering grouping, and generate load dynamic clustering grouping results;

[0128] S5: Based on the dynamic clustering grouping results of the loads, 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.

[0129] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A distribution network intelligent operation and maintenance system, characterized in that: The system comprises: The temperature rise anomaly detection module collects real-time temperature data of the power distribution equipment, extracts the temperature gradient values ​​in the spatial distribution and time series, calculates the change rate of the temperature gradient value based on the temperature gradient value, compares and judges the gradient change rate of each point, and generates the distribution data of the temperature rise anomaly area of ​​the power distribution equipment; 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 abnormal temperature rise area of ​​the power distribution equipment, obtains the contact temperature rise state classification result, divides the circuit breaker operation health level according to the contact temperature rise state classification result, and obtains the circuit breaker operation state information; The microgrid connection control module collects the operation data of the connection point between the microgrid and the main grid, and combines the circuit breaker operation status information to make a phased adjustment to the power flow distribution of the microgrid connection point to obtain the microgrid connection optimization result; The load dynamic grouping module screens load nodes with abnormal power fluctuation amplitudes based on the power dynamic distribution of the microgrid grid-connected optimization results, dynamically clusters and groups the load nodes with reference to the power distribution values ​​and fluctuation characteristics, updates the grouping status in real time, and generates distribution network operation and maintenance grouping optimization results.

2. The intelligent operation and maintenance system for distribution network according to claim 1, characterized in that: The step of obtaining the rate of change of the calculated temperature gradient value is specifically as follows: Based on the real-time operation data of the power distribution equipment, the time series temperature values ​​and spatial temperature differences of multi-point temperature sensors are collected, and the time variation and spatial distribution characteristics associated with the equipment operation status are extracted respectively to generate the initial temperature gradient data set; Based on the temperature gradient initial data set, the formula is used: ； Calculate the rate of change of temperature gradient , get the temperature gradient change rate data; in, Represents the temperature gradient change in the spatial direction, Indicates the change in temperature gradient in the time direction.

3. The intelligent operation and maintenance system for distribution network according to claim 2, characterized in that: The steps for acquiring the distribution data of the abnormal temperature rise area of ​​the power distribution equipment are specifically as follows: Based on the temperature gradient change rate data of the power distribution equipment, by traversing the monitoring points, extracting the temperature gradient change rate values ​​and comparing them point by point with the preset abnormal threshold, marking the spatial position and time node of the abnormal point, and generating an abnormal monitoring point set; 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 anomaly is formed, and the distribution data of the temperature rise abnormal area of ​​the distribution equipment is obtained.

4. The intelligent operation and maintenance system for distribution network according to claim 3, characterized in that: The steps for obtaining the contact temperature rise status classification result are specifically as follows: Based on the real-time temperature rise data of the circuit breaker contact area, the spatial coordinates of the contact area are screened and associated 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 abnormal contact temperature rise data set, the formula is used: ； Calculate the contact temperature rise gradient change rate , from the contact temperature rise gradient change rate Select the maximum value and integrate it to get the contact temperature rise characteristic analysis result; in, Indicates at time The temperature rise value of the contact area at the moment, Indicates at time The temperature rise value of the contact area at the moment, represents the time sampling interval; 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, the temperature rise state is classified point by point, and the contact temperature rise state classification result is generated.

5. The intelligent operation and maintenance system for distribution network according to claim 4, characterized in that: The steps for obtaining the circuit breaker operation status information are specifically as follows: Based on the contact temperature rise state classification results, the formula is used: ； Calculating Circuit Breaker Health Score , generate circuit breaker scoring results; in, Indicates the contact temperature rise The weight of each state category, Indicates The severity of the condition, Indicates the total number of contact temperature rise status classifications; The health levels are divided according to a preset scoring range, the circuit breaker scoring results are matched to corresponding health levels, and the circuit breaker operation status information is obtained.

6. The intelligent operation and maintenance system for distribution network according to claim 5, characterized in that: The steps for obtaining the microgrid grid-connected optimization result are specifically as follows: Based on the circuit breaker operation status information, by comparing the temperature rise data with the reference temperature rise range point by point, the circuit breakers whose temperature rise characteristics and power output characteristics meet the normal state are screened, and a normal state circuit breaker operation data set is generated; Based on the normal state circuit breaker operation data set, its temperature rise characteristics and power output characteristics are extracted, and matched and analyzed with the voltage value and power flow parameters of the connection point. By screening the voltage offset value and the power fluctuation amplitude, the power output ratio of the connection point is adjusted to obtain the microgrid grid-connected optimization result.

7. The intelligent operation and maintenance system for distribution network according to claim 6, characterized in that: The steps for obtaining the distribution network operation and maintenance group optimization result are specifically as follows: Based on the microgrid grid connection optimization result, by real-time monitoring of the time series data of the power output of the grid connection point, the power output value is segmented and analyzed by time interval, the nodes with abnormal power fluctuation amplitude are screened, and by recording the abnormal time point and fluctuation trend of the node, an abnormal load node set is established; 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 status is updated in combination with the node characteristics to generate the distribution network operation and maintenance grouping optimization results.

8. A distribution network intelligent operation and maintenance method, characterized in that: The intelligent operation and maintenance system for distribution network according to any one of claims 1 to 7 is implemented, comprising the following steps: Based on the real-time temperature data of the power distribution equipment, the temperature gradient values ​​of the equipment in the spatial distribution and time series are extracted, the gradient change rate of each point is compared and judged, and the distribution data of the abnormal temperature rise area of ​​the 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 the abnormal temperature rise area of ​​the power distribution equipment, and the circuit breaker operation health level is divided according to the contact temperature rise state classification result to obtain the circuit breaker operation status information; Based on the operation data of the connection point between the microgrid and the main grid, combined with the circuit breaker operation status information, the power flow distribution of the microgrid connection point is adjusted in stages, the power distribution of the microgrid connection is optimized, and the microgrid connection optimization result is generated; Based on the microgrid grid-connected optimization result, load nodes with abnormal power fluctuation amplitude are screened, and dynamic clustering and grouping are performed with reference to the power distribution value and fluctuation characteristics of the load nodes to generate load dynamic clustering and grouping 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 distribution network operation and maintenance grouping optimization results.

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