A power distribution cabinet system convenient for maintenance

Abstract

The present invention relates to the field of power distribution cabinet detection technology, and in particular to a power distribution cabinet system that is easy to maintain. The cabinet status acquisition module monitors the ambient temperature and humidity, the point status acquisition module captures the temperature and visual information of key components, the current and voltage monitoring module tracks electrical parameters in real time, and the central control module integrates and analyzes data, uses algorithms to determine the fault type of the power distribution cabinet, and generates alarm information. Accurate fault type judgment is achieved through multi-dimensional data from the inside to the outside and then to the whole, avoiding dependence on manual judgment. The prompt positioning module provides fault information through a visual interface. The efficiency of power distribution cabinet maintenance is improved and the dependence on personnel expertise is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of power distribution cabinet detection, in particular to a power distribution cabinet system which is convenient for maintenance. Background Art

[0002] In traditional distribution cabinet systems, maintenance work often faces difficulties in locating fault points and fault types, mainly due to the complexity of the system and the lack of effective monitoring and diagnostic tools. Usually, maintenance personnel need to rely on experience and manual testing methods to identify and solve problems, which is not only time-consuming and inefficient, but also very dependent on the professionalism of technicians. In addition, manual maintenance has difficulties in identifying and locating subtle fault points. Manual maintenance relies on experience and manual testing, which is not only inefficient, but also difficult to find and diagnose some hidden or non-obvious faults, such as minor temperature anomalies or current fluctuations. Since these detailed problems are usually precursors to large faults, ignoring them may cause system failure or serious damage. Therefore, developing a distribution cabinet system that can accurately identify and prompt these subtle problems is the key to improving maintenance efficiency and avoiding the recurrence of faults. Summary of the invention

[0003] In order to solve the above problems, the present invention provides a power distribution cabinet system which is easy to maintain.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] A power distribution cabinet system that is easy to maintain, comprising a cabinet status acquisition module, a point status acquisition module, a current and voltage monitoring module, a central control module and a prompt positioning module, wherein the cabinet status acquisition module, the point status acquisition module, the current and voltage monitoring module and the prompt positioning module are respectively connected to the central control module;

[0006] The cabinet status acquisition module is used to collect environmental data in the power distribution cabinet and send it to the central control module;

[0007] The point status acquisition module is used to collect the temperature and characterization images of several points in the power distribution cabinet;

[0008] The current and voltage monitoring module is used to obtain the voltage and current data of each power supply in the power distribution cabinet and send it to the central control module;

[0009] The central control module is used to determine the fault type of the power distribution cabinet according to the environmental data in the power distribution cabinet, the temperature of the point, the characterization image and the voltage and current data, and send an alarm message to the prompt positioning module;

[0010] The prompt positioning module is used to receive the alarm information sent by the central control module and provide a fault positioning prompt according to the alarm information.

[0011] Furthermore, the environmental data inside the power distribution cabinet includes temperature and humidity.

[0012] Furthermore, the determining of the fault type of the power distribution cabinet comprises the following steps:

[0013] Conduct preliminary fault location based on voltage and current data;

[0014] Perform point matching based on the preliminary positioning results, continuously obtain the temperature of the fault point and the characterization image of the fault point, and generate preliminary judgment results based on the temperature change of the fault point and the dynamic change of the fault point;

[0015] Based on the initial judgment result and the environmental data inside the distribution cabinet, a fusion analysis is performed to generate the fault type of the distribution cabinet.

[0016] Further, the preliminary fault location according to the voltage and current data includes the following steps:

[0017] According to the change rate of current value and voltage value, determine whether there is a short circuit fault, open circuit fault and overload fault;

[0018] The fault is initially located based on the current and voltage values ​​of each node.

[0019] Furthermore, performing point matching according to the preliminary positioning result includes:

[0020] Extract features from the representation image, identify physical components and associate them with components in a preset virtual circuit diagram;

[0021] Perform point matching based on the preliminary positioning results.

[0022] Further, generating the initial judgment result according to the temperature change of the fault point and the dynamic change of the fault point includes:

[0023] Perform pattern recognition on the temperature data of the fault point based on machine learning to identify the abnormal temperature pattern related to the fault;

[0024] Based on the fault-related temperature anomaly pattern, the edge detection algorithm is used to analyze and match the representation image of the fault point to generate an initial judgment result.

[0025] Furthermore, the fusion analysis based on the initial judgment result and the environmental data in the power distribution cabinet includes the following steps:

[0026] Combine the initial judgment results with the environmental data inside the power distribution cabinet to determine the impact relationship;

[0027] The initial judgment result is corrected according to the influence relationship to generate the fault type of the distribution cabinet.

[0028] Furthermore, the influence relationship includes the influence of the environment inside the cabinet on the fault and the influence of the fault on the environment inside the cabinet.

[0029] Furthermore, providing a fault location prompt according to the alarm information includes prompting the fault type, fault location and potential cause of the fault through a visual interface.

[0030] The beneficial effects of the present invention are as follows: the present invention automatically monitors the environmental data in the power distribution cabinet through the cabinet status acquisition module, and sends these data to the central control module. The point status acquisition module collects the temperature and characterization image of specific points in the power distribution cabinet. The current and voltage monitoring module monitors the voltage and current data of each power supply in the power distribution cabinet in real time. Then, the central control module comprehensively analyzes the data provided by all modules, uses algorithms to determine the fault type of the power distribution cabinet, and generates alarm information. Accurate fault type judgment is achieved through multi-dimensional data from the inside to the outside and then to the whole, avoiding dependence on manual judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a structural schematic diagram of a power distribution cabinet system that is easy to maintain in the present invention.

[0032] Figure 2 It is a flowchart of the steps for determining the fault type of the power distribution cabinet in the present invention. DETAILED DESCRIPTION

[0033] See also Figure 1-2 As shown, the present invention relates to a power distribution cabinet system that is easy to maintain, including a cabinet status acquisition module, a point status acquisition module, a current and voltage monitoring module, a central control module and a prompt positioning module, wherein the cabinet status acquisition module, the point status acquisition module, the current and voltage monitoring module and the prompt positioning module are respectively connected to the central control module;

[0034] The cabinet status acquisition module is used to collect environmental data in the power distribution cabinet and send it to the central control module;

[0035] The point status acquisition module is used to collect the temperature and characterization images of several points in the power distribution cabinet;

[0036] The current and voltage monitoring module is used to obtain the voltage and current data of each power supply in the power distribution cabinet and send it to the central control module;

[0037] The central control module is used to determine the fault type of the power distribution cabinet according to the environmental data in the power distribution cabinet, the temperature of the point, the characterization image and the voltage and current data, and send an alarm message to the prompt positioning module;

[0038] The prompt positioning module is used to receive the alarm information sent by the central control module and provide a fault positioning prompt according to the alarm information.

[0039] It should be noted that the cabinet status acquisition module contains multiple sensors, which are set at key locations in the power distribution cabinet. For example, the temperature sensor is used to monitor the temperature changes in the cabinet, while the humidity sensor is used to detect the humidity level in the air. These sensors can capture subtle environmental changes and provide accurate data support for fault prevention and timely maintenance. The data collected by the sensor is sent to the central control module periodically or continuously. The transmission of data can be achieved through wired connections (such as Ethernet) or wireless technologies (such as Wi-Fi or Bluetooth). This flexible data transmission method ensures that data can be transmitted reliably even in complex industrial environments. Considering the harsh working environment that the power distribution cabinet may face, such as high temperature, humidity or vibration, these sensors and modules are designed to be very durable and reliable. They usually have a high level of protection and can operate stably in various industrial environments.

[0040] The point status acquisition module contains several infrared temperature sensors that are deployed in key locations within the distribution cabinet, such as near circuit breakers, cable joints, and other important electrical components. By precisely positioning these sensors, the system ensures that all potential hot spots are effectively monitored. One of the main advantages of infrared sensors is that they provide a contactless measurement method. This means that the sensor can accurately measure the surface temperature of the object being measured without physical contact with it. This is especially important for electrical equipment, as contactless measurement reduces the risk of introducing faults or interference. Infrared sensors are able to monitor the surface temperature of key components in real time. Any abnormal temperature changes, such as overheating caused by overload, short circuit or other electrical problems, will be quickly detected and reported to the central control module. The point status acquisition module also includes high-resolution cameras to capture real-time images of key points within the distribution cabinet. These cameras can capture images of wiring errors, physical damage or other visual faults, providing important visual information for fault analysis and diagnosis.

[0041] The current and voltage monitoring module is used to monitor current and voltage data at key nodes of the distribution cabinet, such as input and output terminals, branch circuits and main circuits.

[0042] The signal collected by the prompt positioning module includes detailed information about various potential faults occurring in the distribution cabinet, such as overload, short circuit, abnormal temperature or other electrical faults. It may include the specific location of the faulty component, the recommended repair path and the necessary safety measures. In some systems, it may also include an interactive distribution cabinet layout diagram indicating the exact location of the fault point.

[0043] Furthermore, the environmental data inside the power distribution cabinet includes temperature and humidity;

[0044] Specifically, unlike temperature monitoring at specific points, the ambient temperature monitoring in the cabinet focuses on the average temperature of the entire distribution cabinet. To this end, temperature sensors are evenly distributed in different areas of the distribution cabinet to comprehensively monitor the temperature distribution in the cabinet. This comprehensive monitoring method can effectively capture any overheating that may occur in the entire distribution cabinet and prevent equipment damage or failure due to excessive heating. Humidity monitoring is equally important, especially in controlling corrosion and condensation of electrical equipment. Humidity sensors are used to continuously track the air humidity level in the distribution cabinet to ensure that the environmental conditions meet the operating standards of the electrical equipment. Excessive humidity may cause corrosion of electrical components, reduced insulation performance, and even short circuits. For example, a distribution cabinet operating in a high humidity environment may require additional drying measures to prevent damage caused by moisture.

[0045] Furthermore, the determining of the fault type of the power distribution cabinet comprises the following steps:

[0046] Conduct preliminary fault location based on voltage and current data;

[0047] Perform point matching based on the preliminary positioning results, continuously obtain the temperature of the fault point and the characterization image of the fault point, and generate preliminary judgment results based on the temperature change of the fault point and the dynamic change of the fault point;

[0048] Based on the initial judgment result and the environmental data inside the distribution cabinet, a fusion analysis is performed to generate the fault type of the distribution cabinet.

[0049] Further, the preliminary fault location according to the voltage and current data includes the following steps:

[0050] According to the change rate of current value and voltage value, determine whether there is a short circuit fault, open circuit fault and overload fault;

[0051] The fault is initially located based on the current and voltage values ​​of each node.

[0052] In some embodiments, the system continuously collects current and voltage data from each node in the power distribution cabinet. These data are captured in real time by sensors of the current and voltage monitoring module, and can reflect the instantaneous electrical status of each power supply in the power distribution cabinet. The core is to analyze the rate of change of current and voltage values. The system performs a timing analysis on the collected data to identify rapid changes in current and voltage, which are usually obvious indications of faults. For example, a short circuit fault usually causes a sudden increase in current and a drop in voltage, while an open circuit fault may manifest as a sudden drop in current value to zero. If a sharp increase in current is detected while a sharp drop in voltage is detected, the system may determine it as a short circuit fault. If the current of a node suddenly drops to zero and the voltage value remains unchanged, this may be a sign of an open circuit fault. When the current value of a node is continuously higher than the normal operating range and is accompanied by a drop in voltage, the system may identify it as an overload fault.

[0053] Furthermore, performing point matching according to the preliminary positioning result includes:

[0054] Extract features from the representation image, identify physical components and associate them with components in a preset virtual circuit diagram;

[0055] Perform point matching based on the preliminary positioning results.

[0056] In some embodiments, first, the high-definition camera in the point status acquisition module captures real-time images of key parts in the distribution cabinet. These images provide a visual representation of the components in the distribution cabinet, providing a basis for further fault analysis. The system analyzes the captured images by applying image processing and image recognition technology. This includes extracting key visual features such as shape, edges, color, texture, etc. from the image. These features help to identify individual electrical components and devices in the image. Subsequently, the system uses machine learning or deep learning algorithms to analyze the extracted features to identify physical components in the image. This may include identifying specific electrical components such as switches, terminal blocks, circuit breakers, etc. The identified physical components are then matched and associated with the corresponding components in the preset virtual circuit diagram. The virtual circuit diagram is a digital representation of the distribution cabinet, which contains the location and connection relationship of all electrical components.

[0057] The collected current and voltage data for each node is analyzed. This includes comparing the data differences between different nodes and analyzing the time series changes of the data on a single node. Through this comparison, the system is able to identify nodes with abnormal data, which may be caused by faults. If the current of a node suddenly increases or decreases, and the current and voltage of adjacent nodes show corresponding changes, this may indicate that a fault has occurred near the node. In the case of a short circuit fault, the current behind the faulty node may drop to zero, while the current of the previous node remains unchanged. For overload faults, the faulty node may show a continuous high current reading. By comprehensively analyzing the current and voltage data of each node, the system is able to preliminarily locate the fault. For example, if the current of a node is found to be continuously abnormal, and a significant difference is found when compared with the data of other nodes, the system can preliminarily determine that the fault occurred near the node.

[0058] Further, generating the initial judgment result according to the temperature change of the fault point and the dynamic change of the fault point includes:

[0059] Perform pattern recognition on the temperature data of the fault point based on machine learning to identify the abnormal temperature pattern related to the fault;

[0060] Based on the fault-related temperature anomaly pattern, the edge detection algorithm is used to analyze and match the representation image of the fault point to generate an initial judgment result.

[0061] In some embodiments, first, machine learning uses a convolutional neural network (CNN) model, and the training of the CNN model begins with a large amount of historical temperature data, which is derived from the normal operating status of the distribution cabinet system and known fault events. During the training phase, the model learns to identify different types of temperature change patterns, which may include rapid temperature rise, sustained high temperature, or irregular fluctuations. For example, a rapid temperature rise may be associated with a short circuit, while a sustained high temperature may indicate an overload condition. In terms of feature extraction, CNN automatically extracts key features from temperature data through its multi-layer structure. These features include not only basic statistical information about temperature, but also more complex patterns, such as trends and periodicity of temperature changes. This feature of CNN makes it particularly suitable for processing temperature data, which usually has time series characteristics and complex patterns. After training is completed, the CNN model is used to monitor the temperature data of the distribution cabinet in real time. When the system detects abnormal temperature patterns, such as a sudden and continuous increase in temperature around a circuit breaker, the CNN model analyzes these patterns and determines whether they match known fault types. If the model identifies a temperature pattern associated with a certain fault, it will alert the system. In addition, the advantage of the CNN model is that it can continue to learn and adapt. As time goes by and more fault data is accumulated, the model will continuously adjust its parameters to improve the accuracy of fault identification. This means that the fault detection ability of the system will improve over time.

[0062] Next, edge detection algorithms are applied to these characterization images. Edge detection is a common image processing technique used to identify the boundaries and contours of objects in an image. In a power distribution cabinet system, edge detection can reveal the physical condition of electrical components, such as whether the wiring is loose, whether the component has signs of ablation, or whether there is other visible damage. By analyzing the edge information of these images, the system can identify visible changes associated with temperature anomaly patterns. For example, if temperature monitoring shows that a circuit breaker is overheating, edge detection may reveal color changes or structural deformations on the surface of the circuit breaker, which are typical signs of overheating. Finally, the system combines the results of temperature anomaly patterns and image edge detection to generate a preliminary fault judgment. If both temperature data and image analysis point to the same fault, the system will identify the nature and location of the fault. For example, if the temperature anomaly of a component corresponds to visible damage in its image, the system may preliminarily judge that the fault is physical damage caused by overheating. In this way, the power distribution cabinet system can not only identify potential faults based on temperature data, but also further verify and accurately locate the fault through image analysis. This method of combining multiple sensor data and advanced analysis techniques greatly improves the accuracy and efficiency of fault diagnosis, and provides strong support for the maintenance and management of power distribution cabinet systems.

[0063] Furthermore, the fusion analysis based on the initial judgment result and the environmental data in the power distribution cabinet includes the following steps:

[0064] Combine the initial judgment results with the environmental data inside the power distribution cabinet to determine the impact relationship;

[0065] The initial judgment result is corrected according to the influence relationship to generate the fault type of the distribution cabinet.

[0066] The influence relationship includes the influence of the environment in the cabinet on the fault and the influence of the fault on the environment in the cabinet.

[0067] It should be noted that this step integrates data from different sensors, including temperature changes at the fault point, characterization images, and environmental data within the distribution cabinet (such as overall temperature and humidity). For example, if the temperature of a fault point (such as a circuit breaker) rises abnormally, this may be caused by internal circuit problems (such as overload). However, if the overall temperature of the distribution cabinet also rises abnormally, this may indicate that environmental factors (such as insufficient heat dissipation or external high temperature environment) are also affecting the condition of this fault point. Conversely, a local fault may also affect the environment within the entire cabinet. For example, an overheated electrical component may cause the temperature of the entire distribution cabinet to rise, thereby affecting the normal operation of other components. In this case, a local fault may cause a wider system problem.

[0068] In some embodiments, the temperature data of the fault point and the overall environmental data of the distribution cabinet (such as temperature and humidity) are first compared in time series. This involves looking at the changes in environmental data before and after the fault occurs, and the time correspondence with the temperature anomaly at the fault point. If the overall temperature in the distribution cabinet is also rising when the fault occurs, it may indicate that environmental factors contribute to the fault. By analyzing the change pattern of the temperature at the fault point (such as the rate and duration of temperature increase) and the pattern of overall environmental changes in the distribution cabinet, it is determined whether there is a direct correlation between the two. For example, a slow but continuous temperature rise may indicate a long-term overheating problem, while a sudden temperature surge may indicate a sudden fault event. After confirming the correlation of time and pattern, the system further performs causal reasoning. This involves analyzing the causes and effects of the fault, and how environmental conditions may affect the development of the fault. For example, if faults occur frequently under high temperature and high humidity environmental conditions, it can be inferred that these environmental conditions may have exacerbated the development of the fault. The system applies logical reasoning and preset analysis rules to determine the specific relationship between the fault and environmental factors. This can include expert systems or decision trees that explain the interaction between data based on specific logic and known fault models.

[0069] After analyzing and determining the mutual influence between the temperature anomaly at the fault point and the environment in the distribution cabinet, the fault type initially judged is adjusted based on the correlation between the environmental data and the fault point data. For example, if the original judgment points to a single fault of a specific component, but the environmental analysis shows that the fault may be related to the high temperature environment of the distribution cabinet as a whole, the fault type may be revised to "electrical component failure caused by environmental conditions." Combining the revised judgment and the comprehensive analysis results, the system generates the final fault type. This process can involve many considerations, including the cause of the fault, the scope of impact, the possible consequences, and the most appropriate countermeasures. The generated fault type not only identifies the nature of the fault, but also provides detailed information about the cause of the fault and possible solutions. This is very valuable for the maintenance team, who can develop more effective maintenance plans and preventive measures based on this information. The final generated fault type helps guide maintenance decisions and operations, thereby ensuring the stable operation and safety of the distribution cabinet system. For example, if the fault type points to a heat dissipation problem in the system, the maintenance team may need to take measures to improve the ventilation of the distribution cabinet or upgrade its cooling system.

[0070] Further, providing a fault location prompt according to the alarm information includes prompting the fault type, fault location and potential cause of the fault through a visual interface;

[0071] In some embodiments, after a fault is detected and the fault type is diagnosed, an alarm mechanism is triggered. This alarm is not just a simple notification, but contains rich information to help operators quickly understand and respond to the fault. These alarm messages are transmitted to the visual interface of the system. This interface is usually designed to be intuitive and easy to understand so that even non-professionals can quickly understand the fault situation. The specific fault type, such as "circuit breaker overheating", "cable short circuit", etc., will be displayed on the interface so that operators can quickly identify the problem. Further, the specific location information of the fault can be provided on the visual interface. This is usually achieved by highlighting the fault point on the digital layout of the distribution cabinet. For example, if the fault occurs on a specific circuit breaker, the location of the circuit breaker is accurately marked on the distribution cabinet layout on the interface. Information about the potential cause of the fault can also be provided on the interface. This is based on the previous fault analysis process, which not only identifies the type and location of the fault, but also speculates on the potential cause of the fault. For example, if the fault type is diagnosed as overheating of the circuit breaker due to overload, the system will prompt "may be caused by overload" on the interface. This method of providing fault location prompts through a visual interface greatly improves the efficiency and accuracy of fault response. Operators can quickly obtain key information and formulate countermeasures accordingly, thereby reducing system downtime and ensuring the stable operation of the distribution system.

[0072] The above implementation modes are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary engineering and technical personnel in the field shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A power distribution cabinet system that is easy to maintain, characterized in that: It includes a cabinet status acquisition module, a point status acquisition module, a current and voltage monitoring module, a central control module and a prompt positioning module, and the cabinet status acquisition module, the point status acquisition module, the current and voltage monitoring module and the prompt positioning module are respectively connected to the central control module; The cabinet status acquisition module is used to collect environmental data in the power distribution cabinet and send it to the central control module; The point status acquisition module is used to collect the temperature and characterization images of several points in the power distribution cabinet; The current and voltage monitoring module is used to obtain the voltage and current data of each power supply in the power distribution cabinet and send it to the central control module; The central control module is used to determine the fault type of the power distribution cabinet according to the environmental data in the power distribution cabinet, the temperature of the point, the characterization image and the voltage and current data, and send an alarm message to the prompt positioning module; The prompt positioning module is used to receive the alarm information sent by the central control module and provide a fault positioning prompt according to the alarm information; The environmental data inside the power distribution cabinet includes temperature and humidity; Determining the fault type of the power distribution cabinet comprises the following steps: Conduct preliminary fault location based on voltage and current data; Perform point matching based on the preliminary positioning results, continuously obtain the temperature of the fault point and the characterization image of the fault point, and generate preliminary judgment results based on the temperature change of the fault point and the dynamic change of the fault point; The generating of the initial judgment result according to the temperature change of the fault point and the dynamic change of the fault point includes: Perform pattern recognition on the temperature data of the fault point based on machine learning to identify the abnormal temperature pattern related to the fault; Based on the fault-related temperature anomaly pattern, the edge detection algorithm is used to analyze and match the representation image of the fault point to generate an initial judgment result; Based on the initial judgment result and the environmental data in the power distribution cabinet, a fusion analysis is performed to generate a fault type of the power distribution cabinet; The fusion analysis based on the initial judgment result and the environmental data in the power distribution cabinet includes the following steps: The influence relationship is determined by combining the initial judgment result with the environmental data in the power distribution cabinet, including: performing a time series comparison between the temperature data of the fault point and the environmental data of the power distribution cabinet; determining the correlation between the two by analyzing the change pattern of the temperature of the fault point and the change pattern of the overall environment in the power distribution cabinet, wherein the change pattern includes the rate and duration of temperature rise; the influence relationship includes the influence of the environment in the cabinet on the fault and the influence of the fault on the environment in the cabinet; The initial judgment result is corrected according to the influence relationship to generate the fault type of the distribution cabinet.

2. A power distribution cabinet system that is easy to maintain according to claim 1, characterized in that: The preliminary fault location according to the voltage and current data comprises the following steps: According to the change rate of current value and voltage value, determine whether there is a short circuit fault, open circuit fault and overload fault; The fault is initially located based on the current and voltage values ​​of each node.

3. The power distribution cabinet system that is easy to maintain according to claim 1 is characterized in that: The point matching according to the preliminary positioning result includes: Extract features from the representation image, identify physical components and associate them with components in a preset virtual circuit diagram; Perform point matching based on the preliminary positioning results.

4. The power distribution cabinet system that is easy to maintain according to claim 1 is characterized in that: Providing a fault location prompt according to the alarm information includes prompting the fault type, fault location and potential cause of the fault through a visual interface.

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