Intelligent monitoring control system of power distribution cabinet

Abstract

The invention discloses a power distribution cabinet intelligent monitoring control system, and relates to the technical field of power distribution cabinet monitoring control. The system is used for monitoring and controlling the operation state of the power distribution cabinet in real time and comprises a plurality of sensor units, a data acquisition module, an intelligent analysis module and a control execution module, the sensor units are used for acquiring various operation parameters of the power distribution cabinet, the data acquisition module is used for collecting data of the sensor units, and the intelligent analysis module is used for analyzing the data of the sensor units. And the intelligent analysis module is used for analyzing data and identifying an abnormal state. Operation parameters of the power distribution cabinet are collected in real time through the sensor unit, the data collection module carries out primary processing and verification and then transmits the operation parameters to the intelligent analysis module, and the module identifies and analyzes the operation state and potential faults of the power distribution cabinet by utilizing historical operation data and current abnormal data and combining a sensor position relation and a dynamic adjustment coefficient. And a corresponding early warning strategy is selected according to the fault type, and related control operation is executed through the control execution module.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution cabinet monitoring and control, and in particular to an intelligent monitoring and control system for a power distribution cabinet. Background Art

[0002] The distribution cabinet is divided into power distribution cabinet, lighting distribution cabinet and metering cabinet, which is the final equipment of the power distribution system. The distribution cabinet is a general term for the motor control center. The distribution cabinet is used in places where the load is relatively dispersed and there are fewer circuits; the motor control center is used in places where the load is concentrated and there are more circuits. They distribute the power of a circuit of the upper-level distribution equipment to the nearest load. This set of equipment should provide protection, monitoring and control for the load. At present, the distribution cabinet equipment is generally equipped with indicator lights on the cabinet body to indicate the working status of the distribution cabinet.

[0003] With the continuous expansion of the scale of power systems and the improvement of their intelligence, the reliable operation of distribution cabinets is of vital importance. Traditional distribution cabinets mainly rely on manual inspection and monitoring, which have problems such as delayed detection, slow response, and high maintenance costs. Therefore, an intelligent distribution cabinet monitoring and control system is needed to realize real-time monitoring and control of the operating status of the distribution cabinet and improve the reliability and safety of the distribution cabinet. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent monitoring and control system for a power distribution cabinet to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent monitoring and control system for a power distribution cabinet, which is used to monitor and control the operating status of the power distribution cabinet in real time. The system includes multiple sensor units, a data acquisition module, an intelligent analysis module and a control execution module. The sensor unit is used to collect various operating parameters of the power distribution cabinet, the data acquisition module is used to collect data from the sensor unit, the intelligent analysis module is used to analyze the data and identify abnormal conditions, and the control execution module is used to perform corresponding control operations according to the analysis results. The intelligent monitoring and control method of the power distribution cabinet includes:

[0006] Acquire historical operation data of a first sensor unit, where the first sensor is any unit among a plurality of sensor units for monitoring;

[0007] Acquire current abnormal operation data of a second sensor unit, where the second sensor unit is any unit that detects an abnormal state of the power distribution cabinet;

[0008] Based on the data acquisition module, the data of all sensor units are collected in real time, and preliminary processing and verification are performed;

[0009] The intelligent analysis module analyzes historical operation data and current abnormal operation data to identify the operating status and potential faults of the distribution cabinet.

[0010] As a specific solution of the technical solution of the present application, the monitoring strategy based on the spatial position relationship between the sensor units includes:

[0011] Acquiring relative position information between the first sensor unit and the second sensor unit;

[0012] Based on the relative position information, calculating the mutual influence coefficient of the sensor units as a first adjustment coefficient;

[0013] The intelligent analysis module adjusts the monitoring priority and sampling frequency of the first sensor unit in combination with the historical operation data and the first adjustment coefficient;

[0014] Based on the current abnormal operation data and the first adjustment coefficient, the monitoring accuracy and response speed of the second sensor unit are dynamically adjusted.

[0015] As a specific solution of the technical solution of this application, the abnormality identification and early warning method based on data analysis includes:

[0016] Conduct statistical analysis based on historical operation data and establish a normal operation mode library;

[0017] Identify the abnormal type based on the comparison between the current abnormal operation data and the normal operation mode library;

[0018] Based on the abnormality type, the corresponding early warning measures are selected from the preset early warning strategies and the control execution module is triggered to execute.

[0019] As a specific solution of the technical solution of this application, the confidence assessment and decision optimization method based on abnormal data includes:

[0020] Collect historical event sequence data corresponding to the current abnormal data;

[0021] Use time series analysis methods to assess the confidence of current abnormal data;

[0022] If the confidence level is lower than the preset threshold, the algorithm parameters of the intelligent analysis module are adjusted or additional sensor data is added for review.

[0023] As a specific solution of the technical solution of this application, the intelligent control strategy based on real-time data and prediction model includes:

[0024] Collect data from sensor units in real time and pre-process them through intelligent analysis modules;

[0025] Establish a distribution cabinet operation status prediction model based on machine learning algorithm;

[0026] Predict the operating status of the power distribution cabinet in the future based on the prediction model;

[0027] Based on the prediction results, the control execution module takes preventive measures in advance.

[0028] As a specific solution of the technical solution of this application, the system also includes a remote monitoring and fault diagnosis module.

[0029] The remote monitoring module receives the real-time data uploaded by the data acquisition module through network communication;

[0030] The fault diagnosis module automatically diagnoses the fault type of the power distribution cabinet based on the output of the intelligent analysis module;

[0031] Based on the diagnostic structure, the remote monitoring module sends fault reports and recommended maintenance measures to managers.

[0032] An intelligent monitoring and control system for a power distribution cabinet includes a plurality of sensor units, a data acquisition module, an intelligent analysis module and a control execution module. The system also includes a remote monitoring and fault diagnosis module and a central processing unit. The central processing unit includes:

[0033] A data interface, used to receive data transmitted by the sensor unit and the data acquisition module;

[0034] Intelligent processors, responsible for running data analysis algorithms, prediction models, and fault diagnosis logic;

[0035] A control signal generator generates control instructions based on the analysis results of the intelligent processor and sends them to the control execution module;

[0036] The network communication module realizes data interaction and command transmission between the remote monitoring and fault diagnosis modules.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The intelligent monitoring and control system of the distribution cabinet collects the operating parameters of the distribution cabinet in real time through the sensor unit, and the data acquisition module transmits them to the intelligent analysis module after preliminary processing and verification. The module uses historical operating data and current abnormal data, combined with the sensor position relationship and dynamic adjustment coefficient, to identify and analyze the operating status and potential faults of the distribution cabinet, and selects the corresponding early warning strategy according to the fault type, and executes related control operations through the control execution module. The remote monitoring and fault diagnosis module receives data and diagnoses the fault type, and sends fault reports and maintenance suggestions to the management personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1It is a schematic diagram of the flow of the intelligent monitoring and control method of the power distribution cabinet of the present invention;

[0040] Figure 2 A schematic diagram of the process flow of the abnormality identification and early warning method for data analysis of the present invention;

[0041] Figure 3 It is a flow chart of the intelligent control strategy of the real-time data and prediction model of the present invention. DETAILED DESCRIPTION

[0042] 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.

[0043] It should be noted that, in the description of the present invention, the terms "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc. indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation on the present invention.

[0044] Furthermore, it should be understood that for the sake of ease of description, the sizes of the various components shown in the drawings are not drawn according to actual proportions. For example, the thickness or width of certain layers may be exaggerated relative to other layers.

[0045] It should be noted that like reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined or described in one drawing, it will not require further detailed discussion and description in the description of the subsequent drawings.

[0046] like Figure 1-Figure 3 As shown, the present invention provides a technical solution: an intelligent monitoring and control system for a power distribution cabinet, which is used to monitor and control the operating status of the power distribution cabinet in real time. The system includes multiple sensor units, a data acquisition module, an intelligent analysis module and a control execution module. The sensor unit is used to collect various operating parameters of the power distribution cabinet, the data acquisition module is used to collect data from the sensor unit, the intelligent analysis module is used to analyze the data and identify abnormal conditions, and the control execution module is used to perform corresponding control operations according to the analysis results. The intelligent monitoring and control method of the power distribution cabinet includes:

[0047] Acquire historical operation data of a first sensor unit, wherein the first sensor is any unit among a plurality of sensor units and is used for monitoring; acquire historical operation data of the first sensor unit through a data storage system, wherein the first sensor is any unit among a plurality of sensor units and is used for detecting various operation parameters of a power distribution cabinet, such as current, voltage, and temperature;

[0048] Obtaining the current abnormal operation data of the second sensor unit, where the second sensor unit is any unit that detects the abnormal state of the power distribution cabinet. It should be clear that in the present application, the current abnormal behavior data of the second sensor unit is obtained by the real-time data monitoring system, where the second sensor unit is any unit that monitors the abnormal state of the power distribution cabinet. The real-time data monitoring system continuously monitors the outputs of all sensor units. When the second sensor detects abnormal data, the system immediately captures and stores the abnormal data.

[0049] The data acquisition module collects data from all sensor units in real time, and performs preliminary processing and verification. The data processing subsystem in the data acquisition module collects data from all sensor units in real time, and performs preliminary processing and verification. The data processing subsystem is responsible for receiving the raw data from each sensor unit, performing preliminary processing steps such as data cleaning, data format conversion, and data verification to ensure the accuracy and availability of the data. The processed data is then passed to the intelligent analysis module for further analysis and diagnosis;

[0050] The intelligent analysis module analyzes historical operation data and current abnormal operation data to identify the operating status and potential faults of the distribution cabinet. The intelligent analysis module uses a built-in data analysis engine that integrates multiple algorithms and models, such as machine learning algorithms, rule engines, etc. These algorithms and models are used to process and analyze historical operation data and current abnormal operation data from sensor units.

[0051] Monitoring strategies based on the spatial relationship between sensor units include:

[0052] Acquiring relative position information between the first sensor unit and the second sensor unit;

[0053] Based on the relative position information, calculating the mutual influence coefficient of the sensor units as a first adjustment coefficient;

[0054] The intelligent analysis module adjusts the monitoring priority and sampling frequency of the first sensor unit in combination with the historical operation data and the first adjustment coefficient;

[0055] Based on the current abnormal operation data and the first adjustment coefficient, the monitoring accuracy and response speed of the second sensor unit are dynamically adjusted.

[0056] It should be clear that in the embodiment of the present application, the relative position information between the first sensor unit and the second sensor unit is obtained by the position sensing system. The position sensing system relies on physical layout diagrams, GPS positioning, or signal strength measurement based on wireless communication to determine the spatial relationship between the sensor units. Then, the algorithm module is used to calculate the mutual influence coefficient between the sensor units based on the relative position information. The coefficient reflects the degree of mutual influence that may occur between them due to spatial proximity. The intelligent analysis module combines the historical operation data and the first adjustment coefficient to perform weighted processing on the historical data to reflect the data deviation or correlation that may occur between different sensor units due to mutual influence. Based on the weighted data, the intelligent analysis module dynamically adjusts the monitoring priority and sampling frequency of the first sensor unit to ensure that key areas or susceptible sensors are given higher attention and more frequent monitoring. When the current abnormal operation data is monitored, the intelligent analysis module also uses the first adjustment coefficient to dynamically adjust the monitoring accuracy and response speed of the second adjustment sensor unit according to the mutual influence between the sensor units. If the first sensor unit detects an abnormality, other sensor units close to its spatial position may also be required to monitor with higher accuracy to quickly confirm and locate potential faults.

[0057] Abnormal identification and early warning methods based on data analysis include:

[0058] Conduct statistical analysis based on historical operation data and establish a normal operation mode library;

[0059] Identify the abnormal type based on the comparison between the current abnormal operation data and the normal operation mode library;

[0060] Based on the abnormality type, the corresponding early warning measures are selected from the preset early warning strategies, and the control execution module is triggered to execute. It should be clear that the abnormality identification and early warning method based on data analysis can realize the timely discovery and handling of potential faults in the distribution cabinet or similar system by collecting historical data, establishing a normal operation mode library, real-time monitoring of current data, identifying abnormal types, and taking early warning measures.

[0061] Confidence assessment and decision optimization methods based on abnormal data include:

[0062] Collect historical event sequence data corresponding to the current abnormal data;

[0063] Use time series analysis methods to assess the confidence of current abnormal data;

[0064] If the confidence is lower than the preset threshold, the algorithm parameters of the intelligent analysis module are adjusted or additional sensor data is added for review. It should be clear that the confidence assessment and decision optimization method based on abnormal data can achieve comprehensive analysis and processing of abnormal data by collecting historical data, conducting time series analysis, assessing confidence, and adjusting algorithm parameters or adding data for review.

[0065] Intelligent control strategies based on real-time data and predictive models include:

[0066] Collect data from sensor units in real time and pre-process them through intelligent analysis modules;

[0067] Establish a distribution cabinet operation status prediction model based on machine learning algorithm;

[0068] Predict the operating status of the power distribution cabinet in the future based on the prediction model;

[0069] Based on the prediction results, the control execution module takes preventive measures in advance.

[0070] The system also includes remote monitoring and fault diagnosis modules.

[0071] The remote monitoring module receives the real-time data uploaded by the data acquisition module through network communication;

[0072] The fault diagnosis module automatically diagnoses the fault type of the power distribution cabinet based on the output of the intelligent analysis module;

[0073] Based on the diagnostic structure, the remote monitoring module sends fault reports and recommended maintenance measures to managers.

[0074] An intelligent monitoring and control system for a power distribution cabinet includes a plurality of sensor units, a data acquisition module, an intelligent analysis module and a control execution module. The system also includes a remote monitoring and fault diagnosis module and a central processing unit. The central processing unit includes:

[0075] A data interface, used to receive data transmitted by the sensor unit and the data acquisition module;

[0076] Intelligent processors, responsible for running data analysis algorithms, prediction models, and fault diagnosis logic;

[0077] A control signal generator generates control instructions based on the analysis results of the intelligent processor and sends them to the control execution module;

[0078] The network communication module realizes data interaction and command transmission between the remote monitoring and fault diagnosis modules.

[0079] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. An intelligent monitoring and control system for a power distribution cabinet, which is used to monitor and control the operating status of the power distribution cabinet in real time. The system includes multiple sensor units, a data acquisition module, an intelligent analysis module and a control execution module. The sensor unit is used to collect various operating parameters of the power distribution cabinet, the data acquisition module is used to collect data from the sensor unit, the intelligent analysis module is used to analyze the data and identify abnormal conditions, and the control execution module is used to perform corresponding control operations according to the analysis results. It is characterized in that: The intelligent monitoring and control method of the power distribution cabinet comprises: Acquire historical operation data of a first sensor unit, where the first sensor is any unit among a plurality of sensor units for monitoring; Acquire current abnormal operation data of a second sensor unit, where the second sensor unit is any unit that detects an abnormal state of the power distribution cabinet; Based on the data acquisition module, the data of all sensor units are collected in real time, and preliminary processing and verification are performed; The intelligent analysis module analyzes historical operation data and current abnormal operation data to identify the operating status and potential faults of the distribution cabinet.

2. The intelligent monitoring and control system for a power distribution cabinet according to claim 1 is characterized in that: The monitoring strategy based on the spatial position relationship between the sensor units includes: Acquiring relative position information between the first sensor unit and the second sensor unit; Based on the relative position information, calculating the mutual influence coefficient of the sensor units as a first adjustment coefficient; The intelligent analysis module adjusts the monitoring priority and sampling frequency of the first sensor unit in combination with the historical operation data and the first adjustment coefficient; Based on the current abnormal operation data and the first adjustment coefficient, the monitoring accuracy and response speed of the second sensor unit are dynamically adjusted.

3. The intelligent monitoring and control system for a power distribution cabinet according to claim 1 is characterized in that: The abnormality identification and early warning method based on data analysis includes: Conduct statistical analysis based on historical operation data and establish a normal operation mode library; Identify the abnormal type based on the comparison between the current abnormal operation data and the normal operation mode library; Based on the abnormality type, the corresponding early warning measures are selected from the preset early warning strategies and the control execution module is triggered to execute.

4. The intelligent monitoring and control system for a power distribution cabinet according to claim 1 is characterized in that: The confidence evaluation and decision optimization method based on abnormal data includes: Collect historical event sequence data corresponding to the current abnormal data; Use time series analysis methods to assess the confidence of current abnormal data; If the confidence level is lower than the preset threshold, the algorithm parameters of the intelligent analysis module are adjusted or additional sensor data is added for review.

5. The intelligent monitoring and control system for a power distribution cabinet according to claim 1 is characterized in that: The intelligent control strategy based on real-time data and prediction model includes: Collect data from sensor units in real time and pre-process them through intelligent analysis modules; Establish a distribution cabinet operation status prediction model based on machine learning algorithm; Predict the operating status of the power distribution cabinet in the future based on the prediction model; Based on the prediction results, the control execution module takes preventive measures in advance.

6. According to the intelligent monitoring and control system of a power distribution cabinet in claim 1, the system further comprises a remote monitoring and fault diagnosis module, characterized in that: The remote monitoring module receives the real-time data uploaded by the data acquisition module through network communication; The fault diagnosis module automatically diagnoses the fault type of the power distribution cabinet based on the output of the intelligent analysis module; Based on the diagnostic structure, the remote monitoring module sends fault reports and recommended maintenance measures to managers.

7. An intelligent monitoring and control system for a power distribution cabinet, comprising a plurality of sensor units, a data acquisition module, an intelligent analysis module and a control execution module, the system also comprising a remote monitoring and fault diagnosis module, and a central processing unit, characterized in that: The central processing unit comprises: A data interface, used to receive data transmitted by the sensor unit and the data acquisition module; Intelligent processors, responsible for running data analysis algorithms, prediction models, and fault diagnosis logic; A control signal generator generates control instructions based on the analysis results of the intelligent processor and sends them to the control execution module; The network communication module realizes data interaction and command transmission between the remote monitoring and fault diagnosis modules.

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