Power distribution intelligent operation and maintenance system based on electric power big data

Abstract

The invention discloses a power distribution intelligent operation and maintenance system based on electric power big data, relates to the technical field of intelligent operation and maintenance, and realizes accurate monitoring and dynamic evaluation of a power distribution system by collecting and analyzing various data in a power distribution network in real time based on the electric power big data in combination with the intelligent operation and maintenance technology. The system can carry out real-time detection and early warning on key indexes such as the operation state, meteorological factors and line wear of the power distribution cabinet, and effectively reduces the occurrence of equipment faults and power failure events. Meanwhile, the system has a strong stability analysis function, potential risks can be recognized in advance, resource configuration can be optimized, and therefore the reliability and stability of power supply are improved; besides, the system provides targeted maintenance suggestions and power-off protection schemes through an intelligent evaluation module in combination with factors such as meteorological risks and line wear, and ensures safe operation of the power distribution network under extreme conditions.

Description

Technical Field

[0001] The present invention relates to the field of intelligent operation and maintenance technology, and specifically to a power distribution intelligent operation and maintenance system based on power big data. Background Art

[0002] With the increasing demand for electricity in modern society, the reliability and stability of the distribution system, as an important link in power transmission, directly affect the safety and economy of power supply. Traditional distribution networks mostly rely on manual inspections and routine maintenance. Although these methods have ensured the normal operation of the distribution system to a certain extent, there are still problems such as untimely equipment fault detection, low operation and maintenance efficiency, and unreasonable resource allocation. Especially in the face of complex meteorological environments and aging equipment in long-term operation, traditional methods often fail to detect potential risks in time, leading to power outages.

[0003] In recent years, with the rapid development of the Internet of Things, cloud computing and big data technologies, the concept of smart grids has gradually emerged, and the intelligent operation and maintenance of distribution systems has become a development trend in the power industry. The intelligent operation and maintenance system for distribution based on big data can realize multiple intelligent functions such as status monitoring, fault diagnosis, risk prediction, and optimized scheduling of distribution equipment through real-time collection and in-depth analysis of various data of the distribution system, greatly improving the operation and maintenance efficiency and system reliability. However, most of the existing technologies focus on equipment fault monitoring and anomaly detection. There is still a lack of perfect solutions for how to comprehensively consider various influencing factors such as external meteorological factors, line wear, and power load changes to dynamically evaluate the system operation status and risks.

[0004] Therefore, there is an urgent need for an intelligent power distribution operation and maintenance system based on power big data. Through multi-dimensional data collection and analysis, the various operating parameters of the distribution network can be monitored in real time, and comprehensive evaluation can be conducted based on multiple factors such as meteorology and line wear to identify potential system risks in advance, thereby improving the reliability, safety and operation and maintenance efficiency of the distribution system, reducing the probability of failures, and achieving reasonable allocation and optimal management of power resources. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a power distribution intelligent operation and maintenance system based on power big data, which solves the problems in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a power distribution intelligent operation and maintenance system based on power big data, comprising:

[0007] The distribution cabinet operation status module is used to evaluate the distribution cabinet operation status according to the monitoring data of the distribution cabinet, determine the operation status coefficient, and judge whether the distribution cabinet has a failure risk according to the operation status coefficient. If it is judged that there is a failure risk, the distribution cabinet warning information is generated;

[0008] The stability index determination module is used to obtain the operation status coefficient for stability analysis when the operation status module of the distribution cabinet does not generate warning information, determine the stability index of the operation status coefficient, and then determine the abnormal distribution cabinet according to the stability index of the operation status coefficient, and generate abnormal information of the distribution cabinet;

[0009] Meteorological data collection module, to obtain the meteorological data of the target area at the preset time;

[0010] A meteorological risk factor determination module determines the meteorological risk factor of the target area based on meteorological data;

[0011] A line wear coefficient determination module is used to obtain the historical line failure times of all distribution cabinets in the target area, and determine the line wear coefficient of each distribution cabinet according to the historical line failure times of each distribution cabinet;

[0012] The power outage protection assessment module obtains the operating status coefficient, stability index of the operating status coefficient, meteorological risk coefficient and line wear coefficient of each distribution cabinet in the target area, performs power outage protection assessment to determine the power outage protection coefficient of each distribution cabinet in the target area, and determines whether to generate a power outage protection instruction based on the power outage protection coefficient. When a power outage protection instruction is generated, the power outage protection information of the distribution cabinet is generated at the same time.

[0013] As a further solution of the present invention: in the distribution cabinet operation status module, the monitoring data includes monitoring the voltage, current, temperature, vibration, power factor and insulation resistance in the distribution cabinet.

[0014] As a further solution of the present invention: in the distribution cabinet operation status module, the specific method of evaluating the distribution cabinet operation status according to the monitoring data of the distribution cabinet, determining the operation status coefficient, and judging whether the distribution cabinet has risks according to the operation status coefficient is:

[0015] BS1: Determine the health index of each data in the monitoring data according to the specific value of each data in the monitoring data;

[0016] The health index of each data in the monitoring data is determined according to the specific value of each data in the monitoring data, including the voltage health index RSI V , Current Health Index RSI I , Temperature Health Index RSI T , Vibration Health Index RSI A , Power Factor Health Index RSI pf, Insulation resistance health index

[0017] BS2: Obtain the health index of each monitoring data and determine the operating status coefficient of the power distribution cabinet through the following formula:

[0018]

[0019] Among them, RSI represents the operating status coefficient of the distribution cabinet, and β, γ and α represent weight factors;

[0020] BS3: Get the operating status coefficient RSI of the power distribution cabinet and compare the operating status coefficient RSI with the preset threshold GR1:

[0021] If RSI≤GR1, it means that there is a risk of failure in the operation status of the power distribution cabinet, and the power distribution cabinet warning information is generated;

[0022] If RSI>GR1, it means the distribution cabinet is in good operating condition and no action is required.

[0023] As a further solution of the present invention: in the stability index determination module, the specific method of obtaining the operating state coefficient for stability analysis, determining the stability index of the operating state coefficient, and then determining the abnormal distribution cabinet according to the stability index of the operating state coefficient, and generating the abnormal information of the distribution cabinet is:

[0024] CS1: Get all the operating status coefficients from the current time 2 hours ago;

[0025] CS2: Calculate the average of all operating status coefficients and mark it as RSI enp ;

[0026] CS3: Then use the formula Determine the discrete values ​​EU of all operating state coefficients, where 1≤j≤m, and m represents the total number of all current operating state coefficients;

[0027] CS4: Obtain the discrete value EU and the preset threshold GR2, and determine the stability index of the operating status coefficient by the following formula:

[0028]

[0029] Among them, WD represents the stability index of the operating state coefficient;

[0030] CS5: Get the stability index WD of the operating state coefficient and compare the stability index WD with the abnormal stability index GR3:

[0031] If WD>GR3, the distribution cabinet is marked as an abnormal distribution cabinet and the distribution cabinet abnormal information is generated. No processing is performed in other cases; the abnormal stability index GR3 is a preset value.

[0032] As a further solution of the present invention: in the meteorological data acquisition module, the meteorological data specifically includes temperature parameters, humidity parameters, wind speed parameters, precipitation parameters, lightning activity parameters and frost parameters.

[0033] As a further solution of the present invention: in the meteorological risk coefficient determination module, the specific method of determining the meteorological risk coefficient of the target area according to the meteorological data is:

[0034] DS1: Preprocess the collected meteorological data, including the following steps:

[0035] DS11: Handle missing values ​​and outliers to ensure data integrity and accuracy;

[0036] DS12: Standardize meteorological parameters of different dimensions in meteorological data. For each meteorological parameter X i The normalized value can be expressed as X′ i :

[0037]

[0038] Among them, 1≤i≤6, X 1 Expressed as temperature parameter, X 2 Expressed as humidity parameter, X 3 Expressed as wind speed parameter, X 4 Expressed as precipitation parameter, X 5 Expressed as lightning activity parameter, X 6 Expressed as frost parameter; X max-i Indicates the preset maximum value of the corresponding meteorological parameter, X min-i Indicates the preset minimum value of the corresponding meteorological parameter;

[0039] DS2: For each meteorological parameter X′ i The deviation value of each meteorological parameter is determined by the following formula

[0040]

[0041] Among them, X mid-i is represented by the ideal value of the corresponding meteorological parameter, and X range-i It is expressed as the allowable deviation range of the corresponding meteorological parameters, and X range-i =X max-i -X min-i ;

[0042] DS3: Determine the meteorological risk coefficient CBR through the following formula:

[0043]

[0044] where w i represents the weight coefficient of the deviation value of the corresponding meteorological parameter, and w 1 + w 2 + w 3 + w 4 + w 5 + w 6 = 1.

[0045] As a further solution of the present invention: in the line wear coefficient determination module, the specific method for obtaining the historical line fault times of all power distribution cabinets in the target area and determining the line wear coefficient of each power distribution cabinet according to the historical line fault times of each power distribution cabinet is as follows:

[0046] Obtain all the power distribution cabinets in the abnormal area, and determine the line wear coefficient HX of each power distribution cabinet through the following formula HX = δ·YL according to the historical line fault times of each power distribution cabinet; where YL represents the historical line fault times, and δ represents the influence factor.

[0047] As a further solution of the present invention: in the power-off protection evaluation module, the specific method for obtaining the operating state coefficient, the stability index of the operating state coefficient, the meteorological risk coefficient and the line wear coefficient of each power distribution cabinet in the target area, performing power-off protection evaluation to determine the power-off protection coefficient of each power distribution cabinet in the target area, and judging whether to generate a power-off protection instruction according to the power-off protection coefficient is as follows:

[0048] Perform power-off protection evaluation on it through the following formula to determine the power-off protection coefficient:

[0049]

[0050] where PRG represents the power-off protection coefficient, and k1 and k2 represent the weight factors;

[0051] Then judge whether to generate a power-off protection instruction according to the power-off protection coefficient:

[0052] If the power-off protection coefficient PRG < GR5, generate a power-off protection instruction.

[0053] As a further solution of the present invention: it further includes:

[0054] Maintenance end display module, which obtains and displays the warning information, abnormal information and power-off protection information of the power distribution cabinet, reminds the maintenance personnel to understand the corresponding power distribution cabinet situation at the first moment, and takes corresponding treatment measures.

[0055] The present invention provides a power distribution intelligent operation and maintenance system based on power big data. Compared with the prior art, it has the following beneficial effects:

[0056] Based on power big data and combined with intelligent operation and maintenance technology, the present invention realizes accurate monitoring and dynamic evaluation of the power distribution system by real-time collection and analysis of various data in the power distribution network. The system can perform real-time detection and early warning of key indicators such as the operating status of the distribution cabinet, meteorological factors, and line wear, effectively reducing the occurrence of equipment failures and power outages. At the same time, the system also has a powerful stability analysis function, which can identify potential risks in advance and optimize resource allocation, thereby improving the reliability and stability of power supply.

[0057] In addition, the system uses an intelligent assessment module, combined with factors such as meteorological risks and line wear, to provide targeted maintenance recommendations and power outage protection solutions to ensure the safe operation of the distribution network under extreme conditions. Operation and maintenance personnel can view the system status and risk prediction in real time through the maintenance terminal display module, so as to respond in time and optimize operation and maintenance decisions. Overall, the intelligent operation and maintenance system has significantly improved the operation and maintenance efficiency of the distribution network, reduced maintenance costs, and enhanced the ability to respond to emergencies. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention will be further described below in conjunction with the accompanying drawings.

[0059] Figure 1 This is a structural framework diagram of a power distribution intelligent operation and maintenance system based on power big data in the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Embodiment 1

[0062] See also Figure 1 ,The present invention provides a power distribution intelligent operation and maintenance system based on power big data, including;

[0063] The distribution cabinet operation status module is used to evaluate the distribution cabinet operation status according to the monitoring data of the distribution cabinet, determine the operation status coefficient, and judge whether the distribution cabinet has a failure risk according to the operation status coefficient. If it is judged that there is a failure risk, the distribution cabinet warning information is generated;

[0064] The monitoring data includes monitoring the voltage, current, temperature, vibration, power factor and insulation resistance in the power distribution cabinet;

[0065] Specifically, the data for monitoring the operating status of the power distribution cabinet are obtained and determined through relevant sensors;

[0066] The specific method of evaluating the operation status of the distribution cabinet according to the monitoring data of the distribution cabinet, determining the operation status coefficient, and judging whether there is a risk in the distribution cabinet according to the operation status coefficient is as follows:

[0067] BS1: Determine the health index of each data in the monitoring data according to the specific value of each data in the monitoring data;

[0068] Determining the health index of each data in the monitoring data according to the specific value of each data in the monitoring data includes:

[0069] By formula Determine the voltage health index RSI V ; Among them, V measured Indicates the actual measured voltage value, V rated Expressed as standard voltage value;

[0070] By formula Determine the Current Health Index RSI I ; Among them, I measured Indicates the actual measured current value, I rated Expressed as standard current value;

[0071] By formula Determine the temperature health index RSI T ; Among them, T measured Indicates the actual measured temperature, T max It is expressed as the maximum operating temperature allowed in the power distribution cabinet, T max It is expressed as the minimum operating temperature allowed in the power distribution cabinet;

[0072] By formula Determine the vibration health index RSI A Among them, A measured Indicates the actual measured vibration level, A max Expressed as the maximum vibration level allowed in the switchboard;

[0073] By formula Determine the power factor health index RSI pf ; Among them, pf measured Indicates the actual measured power factor, pf rated Expressed as standard power factor;

[0074] By formula Determining the Insulation Resistance Health Index in, Indicates the actual measured insulation resistance value. It is expressed as the minimum insulation resistance value required for normal operation in the distribution cabinet;

[0075] BS2: Obtain the health index of each monitoring data and determine the operating status coefficient of the power distribution cabinet through the following formula:

[0076]

[0077] Among them, RSI represents the operating status coefficient of the distribution cabinet, and β, γ and α represent weight factors;

[0078] BS3: Get the operating status coefficient RSI of the power distribution cabinet and compare the operating status coefficient RSI with the preset threshold GR1:

[0079] If RSI≤GR1, it means that there is a risk of failure in the operation status of the power distribution cabinet, and the power distribution cabinet warning information is generated;

[0080] If RSI>GR1, it means the power distribution cabinet is in good operating condition and no action is required;

[0081] By real-time monitoring of the operating data of the power distribution cabinet, such as current, voltage, temperature and other parameters, it is possible to accurately determine whether it is in normal working condition; once an abnormality occurs, the module can quickly identify and alarm, avoiding the equipment from being in a fault state for a long time and reducing the risk of fault expansion; through this module, operation and maintenance personnel can obtain the real-time data and health status of the power distribution cabinet, thereby improving the reliability of the power distribution system, reducing power outage time and extending the service life of the equipment;

[0082] The stability index determination module is used to obtain the operation status coefficient for stability analysis when the operation status module of the distribution cabinet does not generate warning information, determine the stability index of the operation status coefficient, and then determine the abnormal distribution cabinet according to the stability index of the operation status coefficient, and generate abnormal information of the distribution cabinet;

[0083] The specific method of obtaining the operating state coefficient for stability analysis, determining the stability index of the operating state coefficient, then determining the abnormal distribution cabinet according to the stability index of the operating state coefficient, and generating the abnormal information of the distribution cabinet is:

[0084] CS1: Get all the operating status coefficients from the current time 2 hours ago;

[0085] It should be noted that all the operating status coefficients for the two hours before the current moment are obtained, which means that within the two hours, the professional staff sets the monitoring data to be collected once every t2 time period and obtains the operating status coefficient, and in general, the monitoring data is collected once every t2 time period and the operating status coefficient is calculated and determined;

[0086] CS2: Calculate the average of all operating status coefficients and mark it as RSI enp ;

[0087] CS3: Then use the formula Determine the discrete values ​​EU of all operating state coefficients, where 1≤j≤m, and m represents the total number of all current operating state coefficients;

[0088] CS4: Obtain the discrete value EU and the preset threshold GR2, and determine the stability index of the operating status coefficient by the following formula:

[0089]

[0090] Among them, WD represents the stability index of the operating state coefficient, and the specific parameters of the preset threshold GR2 are set by professional staff;

[0091] CS5: Get the stability index WD of the operating state coefficient and compare the stability index WD with the abnormal stability index GR3:

[0092] If WD>GR3, the distribution cabinet is marked as an abnormal distribution cabinet and abnormal information of the distribution cabinet is generated. No processing is performed in other cases. The abnormal stability index GR3 is a preset value, which is determined by professional staff.

[0093] This module can help operation and maintenance personnel identify potential risk factors in advance, reduce the occurrence of sudden failures, and improve the operating efficiency of the system by optimizing the operating mode; this module also provides data support for subsequent preventive maintenance measures to ensure the reliability of the distribution system in long-term operation;

[0094] Meteorological data collection module, to obtain the meteorological data of the target area at the preset time;

[0095] The meteorological data at the preset time is represented as the meteorological data at a time period T1 after the current time, starting from the current time; wherein the T1 time period is set by professional staff, and in this embodiment, the T1 time period is 12 hours;

[0096] The meteorological data specifically include temperature parameters, humidity parameters, wind speed parameters, precipitation parameters, lightning activity parameters and frost parameters;

[0097] It should be noted that obtaining the meteorological data of the target area at the preset time can be achieved through a variety of ways, including weather forecast models, weather forecast service APIs, satellite remote sensing data, and ground meteorological stations. The meteorological data of the target area at the preset time is an existing mature technology and will not be described in detail here. The target area can be represented as a city or a preset area, which is determined by professional staff.

[0098] A meteorological risk factor determination module determines the meteorological risk factor of the target area based on meteorological data;

[0099] The specific method of determining the meteorological risk coefficient of the target area based on meteorological data is:

[0100] DS1: Preprocess the collected meteorological data, including the following steps:

[0101] DS11: Handle missing values ​​and outliers to ensure data integrity and accuracy;

[0102] DS12: Standardize meteorological parameters of different dimensions in meteorological data. For each meteorological parameter X i The normalized value can be expressed as X′ i :

[0103]

[0104] Among them, 1≤i≤6, X 1 Expressed as temperature parameter, X 2 Expressed as humidity parameter, X 3 Expressed as wind speed parameter, X 4 Expressed as precipitation parameter, X 5 Expressed as lightning activity parameter, X 6 Expressed as frost parameter; X max-i Indicates the preset maximum value of the corresponding meteorological parameter, X min-i Indicates the preset minimum value of the corresponding meteorological parameter, X max-i and X min-i The specific parameters are set by professional staff;

[0105] DS2: For each meteorological parameter X′ i The deviation value of each meteorological parameter is determined by the following formula

[0106]

[0107] Among them, X mid-i is represented by the ideal value of the corresponding meteorological parameter, and X range-i It is expressed as the allowable deviation range of the corresponding meteorological parameters, and Xrange-i =X max-i -X min-i ;

[0108] DS3: The meteorological risk factor CBR is determined by the following formula:

[0109]

[0110] Among them, w i is expressed as the weight coefficient corresponding to the deviation value of the meteorological parameter, and w 1 +w 2 +w 3 +w 4 +w 5 +w 6 =1;

[0111] Based on the collected meteorological data, the meteorological risk coefficient determination module further quantifies the impact of different meteorological conditions on the distribution system through big data analysis and machine learning algorithms, and calculates the meteorological risk coefficient. This coefficient reflects the risk level that power equipment may face under specific weather conditions. For example, under high wind speed or heavy rain weather conditions, the system can start equipment protection or adjust the operation mode in advance according to the meteorological risk coefficient to avoid equipment failures caused by excessive load or extreme climate. The beneficial effect of this module is that by quantifying the risk coefficient, it helps operation and maintenance personnel understand the potential threats brought by the weather in real time, thereby improving the efficiency and accuracy of emergency response and ensuring the safe operation of the power grid.

[0112] A line wear coefficient determination module is used to obtain the historical line failure times of all distribution cabinets in the target area, and determine the line wear coefficient of each distribution cabinet according to the historical line failure times of each distribution cabinet;

[0113] The specific method of obtaining the historical line failure times of all the distribution cabinets in the target area and determining the line wear coefficient of each distribution cabinet according to the historical line failure times of each distribution cabinet is as follows:

[0114] Obtain all the distribution cabinets in the abnormal area, and determine the line wear coefficient HX of each distribution cabinet according to the historical line failure times of each distribution cabinet through the following formula HX=δ·YL; where YL represents the historical line failure times, and δ represents the impact factor;

[0115] It should be noted that a power distribution cabinet usually corresponds to the power supply of a specific area or a group of devices; the function of the power distribution cabinet is to distribute the power of the main power supply to each load or area, and it distributes and protects the power according to the required power level and equipment configuration; in large buildings, factories or industrial parks, a power distribution cabinet may be responsible for providing power to a specific area (such as a floor, a workshop, a section, etc.); the power consumption requirements of different areas are different, so different power distribution cabinets will be configured, and the power distribution cabinet transports the power through the wire lines; historical line faults refer to the line short - circuit faults, open - circuit faults, overload faults, etc. that have occurred in all the lines connected to a power distribution cabinet in the past. Each time a fault occurs, it is recorded as the number of historical line faults.

[0116] The power - off protection evaluation module obtains the operation status coefficient, the stability index of the operation status coefficient, the meteorological risk coefficient, and the line wear coefficient of each power distribution cabinet in the target area, conducts a power - off protection evaluation to determine the power - off protection coefficient of each power distribution cabinet in the target area, and judges whether to generate a power - off protection instruction according to the power - off protection coefficient. When generating a power - off protection instruction, the power - off protection information of the power distribution cabinet is generated at the same time.

[0117] The specific method of obtaining the operation status coefficient, the stability index of the operation status coefficient, the meteorological risk coefficient, and the line wear coefficient of each power distribution cabinet in the target area, conducting a power - off protection evaluation to determine the power - off protection coefficient of each power distribution cabinet in the target area, and judging whether to generate a power - off protection instruction according to the power - off protection coefficient is as follows:

[0118] Specifically, the following formula is used to conduct a power - off protection evaluation to determine the power - off protection coefficient:

[0119]

[0120] Among them, PRG represents the power - off protection coefficient, and k1 and k2 represent weight factors;

[0121] Then, it is judged whether to generate a power - off protection instruction according to the power - off protection coefficient:

[0122] If the power - off protection coefficient PRG < GR5, a power - off protection instruction is generated, and no other processing is done in other cases; among them, GR5 is a preset value, which is specifically set by professional staff.

[0123] Generating a power - off protection instruction to cut off the power of the power distribution cabinet can prevent the power distribution cabinet and the involved power distribution lines from being damaged and malfunctioning in subsequent bad weather.

[0124] Embodiment 2

[0125] In the specific implementation process of this embodiment, on the basis of Embodiment 1, and the difference from Embodiment 1 is that this embodiment further includes a maintenance - end display module;

[0126] The maintenance end display module obtains and displays the power distribution cabinet warning information, power distribution cabinet abnormal information and power distribution cabinet power failure protection information, reminding the maintenance personnel to understand the corresponding power distribution cabinet situation at the first moment and take corresponding treatment measures;

[0127] The maintenance-end display module is the decision-making support center of the entire system. It is responsible for presenting the data, evaluation results and risk predictions collected by all modules to the operation and maintenance personnel through a visual interface. Through charts, alarms, trend analysis and other forms, the operation and maintenance personnel can quickly understand the operating status, potential problems and risk points that need to be focused on in the distribution system. The beneficial effect of this module is that it improves the work efficiency of the operation and maintenance personnel through clear visual displays, enabling them to quickly identify fault points and take corresponding measures.

[0128] Embodiment 3

[0129] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0130] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0131] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A power distribution intelligent operation and maintenance system based on power big data, characterized in that: include: The distribution cabinet operation status module is used to evaluate the distribution cabinet operation status according to the monitoring data of the distribution cabinet, determine the operation status coefficient, and judge whether the distribution cabinet has a failure risk according to the operation status coefficient. If it is judged that there is a failure risk, the distribution cabinet warning information is generated; The stability index determination module is used to obtain the operation status coefficient for stability analysis when the operation status module of the distribution cabinet does not generate warning information, determine the stability index of the operation status coefficient, and then determine the abnormal distribution cabinet according to the stability index of the operation status coefficient, and generate abnormal information of the distribution cabinet; Meteorological data collection module, to obtain the meteorological data of the target area at the preset time; A meteorological risk factor determination module determines the meteorological risk factor of the target area based on meteorological data; A line wear coefficient determination module is used to obtain the historical line failure times of all distribution cabinets in the target area, and determine the line wear coefficient of each distribution cabinet according to the historical line failure times of each distribution cabinet; The power outage protection assessment module obtains the operating status coefficient, stability index of the operating status coefficient, meteorological risk coefficient and line wear coefficient of each distribution cabinet in the target area, performs power outage protection assessment to determine the power outage protection coefficient of each distribution cabinet in the target area, and determines whether to generate a power outage protection instruction based on the power outage protection coefficient. When a power outage protection instruction is generated, the power outage protection information of the distribution cabinet is generated at the same time.

2. According to the power distribution intelligent operation and maintenance system based on power big data according to claim 1, it is characterized in that: In the distribution cabinet operation status module, the monitoring data includes monitoring the voltage, current, temperature, vibration, power factor and insulation resistance in the distribution cabinet.

3. According to the power distribution intelligent operation and maintenance system based on power big data according to claim 2, it is characterized in that: In the distribution cabinet operation status module, the specific method of evaluating the distribution cabinet operation status according to the monitoring data of the distribution cabinet, determining the operation status coefficient, and judging whether there is a risk in the distribution cabinet according to the operation status coefficient is: BS1: Determine the health index of each data in the monitoring data according to the specific value of each data in the monitoring data; The health index of each data in the monitoring data is determined according to the specific value of each data in the monitoring data, including the voltage health index RSI V , Current Health Index RSI I , Temperature Health Index RSI T , Vibration Health Index RSI A , Power Factor Health Index RSI pf , Insulation resistance health index BS2: Obtain the health index of each monitoring data and determine the operating status coefficient of the power distribution cabinet through the following formula: Among them, RSI represents the operating status coefficient of the distribution cabinet, and β, γ and α represent weight factors; BS3: Get the operating status coefficient RSI of the power distribution cabinet and compare the operating status coefficient RSI with the preset threshold GR1: If RSI≤GR1, it means that there is a risk of failure in the operation status of the power distribution cabinet, and the power distribution cabinet warning information is generated; If RSI>GR1, it means the distribution cabinet is in good operating condition and no action is required.

4. According to the power distribution intelligent operation and maintenance system based on power big data according to claim 3, it is characterized in that: In the stability index determination module, the specific method of obtaining the operating state coefficient for stability analysis, determining the stability index of the operating state coefficient, and then determining the abnormal distribution cabinet according to the stability index of the operating state coefficient and generating the abnormal information of the distribution cabinet is as follows: CS1: Get all the operating status coefficients from the current time 2 hours ago; CS2: Calculate the average of all operating status coefficients and mark it as RSI enp ; CS3: Then use the formula Determine the discrete values ​​EU of all operating state coefficients, where 1≤j≤m, and m represents the total number of all current operating state coefficients; CS4: Obtain the discrete value EU and the preset threshold GR2, and determine the stability index of the operating status coefficient by the following formula: Among them, WD represents the stability index of the operating state coefficient; CS5: Get the stability index WD of the operating state coefficient and compare the stability index WD with the abnormal stability index GR3: If WD > GR3, mark this power distribution cabinet as an abnormal power distribution cabinet and generate power distribution cabinet abnormal information. No other processing is performed in other cases. The abnormal stability index GR3 is a preset value.

5. According to claim 4, a power distribution intelligent operation and maintenance system based on power big data is characterized in that: In the meteorological data collection module, the meteorological data specifically includes temperature parameters, humidity parameters, wind speed parameters, precipitation parameters, lightning activity parameters, and frost parameters.

6. The power distribution intelligent operation and maintenance system based on power big data according to claim 5 is characterized in that: In the meteorological risk coefficient determination module, the specific method for determining the meteorological risk coefficient of the target area according to the meteorological data is as follows: DS1: Preprocess the collected meteorological data, including the following steps: DS11: Process missing values and outliers to ensure the integrity and accuracy of the data; DS12: Standardize meteorological parameters of different dimensions in meteorological data. For each meteorological parameter X i The normalized value can be expressed as X′ i : Where, 1≤i≤6, X1 represents the temperature parameter, X2 represents the humidity parameter, X3 represents the wind speed parameter, X4 represents the precipitation parameter, X5 represents the lightning activity parameter, and X6 represents the frost parameter; X max-i Indicates the preset maximum value of the corresponding meteorological parameter, X min-i Indicates the preset minimum value of the corresponding meteorological parameter; DS2: For each meteorological parameter X′ i The deviation value of each meteorological parameter is determined by the following formula Among them, X mid-i is represented by the ideal value of the corresponding meteorological parameter, and X range-i It is expressed as the allowable deviation range of the corresponding meteorological parameters, and X range-i =X max-i -X min-i ; DS3: Determine the meteorological risk coefficient CBR through the following formula: Among them, w i It is expressed as the weight coefficient corresponding to the deviation value of the meteorological parameter, and w1+w2+w3+w4+w5+w6=1.

7. The power distribution intelligent operation and maintenance system based on power big data according to claim 6 is characterized in that: In the line wear coefficient determination module, the specific method for obtaining the historical line fault times of all power distribution cabinets in the target area and determining the line wear coefficient of each power distribution cabinet according to the historical line fault times of each power distribution cabinet is as follows: Obtain all the power distribution cabinets in the abnormal area, and determine the line wear coefficient HX of each power distribution cabinet through the following formula HX = δ·YL according to the historical line fault times of each power distribution cabinet. Wherein, YL represents the historical line fault times, and δ represents the influence factor.

8. The power distribution intelligent operation and maintenance system based on power big data according to claim 7 is characterized in that: In the power-off protection evaluation module, the specific method for obtaining the operation status coefficient, the stability index of the operation status coefficient, the meteorological risk coefficient, and the line wear coefficient of each power distribution cabinet in the target area, performing power-off protection evaluation to determine the power-off protection coefficient of each power distribution cabinet in the target area, and judging whether to generate a power-off protection instruction according to the power-off protection coefficient is as follows: Perform power-off protection evaluation on it through the following formula to determine the power-off protection coefficient: Among them, PRG represents the power-off protection coefficient, and k1 and k2 represent weight factors; Then judge whether to generate a power-off protection instruction according to the power-off protection coefficient: If the power-off protection coefficient PRG < GR5, generate a power-off protection instruction.

9. The power distribution intelligent operation and maintenance system based on power big data according to claim 1 is characterized in that: It also includes: A maintenance end display module, which obtains and displays power distribution cabinet warning information, power distribution cabinet abnormal information, and power distribution cabinet power-off protection information, reminding maintenance personnel to understand the corresponding power distribution cabinet situation at the first moment and take corresponding treatment measures.

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