Method for monitoring internal environment of power distribution cabinet

Abstract

The invention relates to the technical field of power equipment monitoring, and discloses a power distribution cabinet internal environment monitoring method, which comprises the following steps: selecting a low-power-consumption, high-precision and wireless temperature and humidity sensor, a gas sensor, a partial discharge sensor and a laser dust sensor, installing the sensors on a power distribution cabinet, reasonably deploying a plurality of data acquisition nodes in the power distribution cabinet, the data acquisition nodes acquire data of peripheral sensors and transmit the acquired data to the main data receiving device. According to the method for monitoring the internal environment of the power distribution cabinet, the temperature, the humidity, the gas concentration, the dust content, the electrical parameters and the multi-dimensional parameters of partial discharge in the power distribution cabinet are comprehensively monitored, and advanced sensors and data acquisition and transmission technologies are adopted, so that the limitation of traditional monitoring is avoided, and the accuracy of monitoring data is improved; through real-time data analysis and historical data modeling, operation and maintenance personnel can know abnormal changes of the internal environment of the power distribution cabinet in time and take measures in advance to prevent faults.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment monitoring, in particular to a power distribution cabinet internal environment monitoring method. BACKGROUND

[0002] In modern power systems, power distribution cabinets, as the key hub of power distribution and control, bear the important task of safely and stably distributing power from the upper power source to various power consumption devices. The internal environment conditions, such as temperature, humidity, gas concentration, dust content, and electrical parameters, have a crucial impact on the normal operation, service life, and reliability of electrical equipment in the power distribution cabinet.

[0003] Traditional temperature and humidity sensors mostly use wired connection methods. In the complex wiring environment inside the power distribution cabinet, installation is difficult, and a large amount of manpower and time is required for wiring construction. Moreover, when maintaining or replacing the sensors later, it is inconvenient to operate, which may affect the normal operation of the power distribution cabinet. At the same time, wired connection methods also have the problem of signal transmission being easily disturbed, especially in strong electromagnetic environments, the accuracy and stability of sensor data are difficult to guarantee. For example, in the power distribution cabinets of some industrial production sites, due to the presence of a large number of large electric machines, electric welders, and other strong electromagnetic interference sources, the temperature data transmitted by traditional wired sensors fluctuates greatly, and cannot truly reflect the actual temperature conditions inside the power distribution cabinet.

[0004] Therefore, the present application provides a power distribution cabinet internal environment monitoring method to solve the above-mentioned problems. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a power distribution cabinet internal environment monitoring method, which has the advantages of solving potential problems caused by internal environment changes during the operation of the power distribution cabinet, efficiently mastering the internal environment conditions of the power distribution cabinet, and providing a solid guarantee for the stable operation of the power system, etc., solving the above-mentioned problems.

[0006] To achieve the above-mentioned purposes, the present application provides the following technical solution: a power distribution cabinet internal environment monitoring method, comprising the following steps: Step one: first select low-power, high-precision, and wireless temperature and humidity sensors, gas sensors, partial discharge sensors, and laser dust sensors, and install them on the power distribution cabinet; Step two: reasonably deploy multiple data acquisition nodes inside the power distribution cabinet, the data acquisition nodes use STM32 series microcontrollers as core control units, collect data from multiple surrounding sensors, and transmit the collected data to the main data receiving equipment; Step three: the data receiving device sends the received raw data to the data analysis server, which uses the real-time data analysis engine Apache Flink to process the data in real time; By setting data thresholds and data analysis models, the changes in the internal environmental parameters of the power distribution cabinet are analyzed in real time to determine whether there are abnormalities. Step four: use big data technology to deeply mine and analyze historical monitoring data, establish a change model of internal environmental parameters of the power distribution cabinet, and through learning from historical data, analyze the correlation between different environmental parameters and their change rules over time. Step five: combine real-time data analysis results and historical data modeling results to realize intelligent diagnosis and prediction of internal environmental abnormalities and electrical equipment failures of the power distribution cabinet.

[0007] Preferably, in step one, wireless temperature sensors are installed at the busbar of the power distribution cabinet, switch contact parts, and wireless humidity sensors are installed at the bottom of the power distribution cabinet where condensation is likely to occur and the corners inside the power distribution cabinet.

[0008] Preferably, the gas sensor is an electrochemical gas sensor with high sensitivity and selectivity for carbon monoxide and sulfur dioxide gas, and an infrared gas sensor suitable for monitoring sulfur hexafluoride and its decomposition product gas.

[0009] Preferably, the partial discharge sensor is a transient ground wave sensor and an ultrasonic sensor; the transient ground wave sensor and the ultrasonic sensor monitor the partial discharge signals in the switch cabinet, and the running state of the equipment is comprehensively judged in combination with the changes of electrical parameters.

[0010] Preferably, in step two, the data collection nodes form a Mesh network for data transmission, and the collected data is gradually transmitted to the main data receiving end located outside the power distribution cabinet. Even if one of the nodes fails, other nodes can still ensure normal data transmission.

[0011] Preferably, the data collection node uses AES encryption algorithm to encrypt the transmitted data to ensure the security of the data during transmission.

[0012] Preferably, in step three, when the temperature data exceeds the preset normal working temperature range, the system automatically sends a warning signal; through continuous monitoring and analysis of humidity data, it is determined whether there is a rapid rise in humidity or a long period of high humidity. If so, it may indicate a risk of condensation and timely warning.

[0013] Preferably, in the fourth step, the relationship model between temperature and load is established by analyzing historical temperature data and load current data, and the possible temperature change trend is predicted according to the current load condition; the machine learning algorithm is used to train the historical partial discharge data, and the partial discharge fault prediction model is constructed to predict the potential fault of the electrical equipment in advance, and the machine learning algorithm includes support vector machine and decision tree.

[0014] Preferably, in the fifth step, when the system detects that the environmental parameters are abnormal or the electrical equipment has abnormal characteristics, the fault type and cause are quickly and accurately judged by comparing and analyzing the historical data and the fault case library, and the development trend of the fault is predicted.

[0015] Compared with the prior art, the power distribution cabinet internal environment monitoring method has the following beneficial effects: 1. The power distribution cabinet internal environment monitoring method comprehensively monitors the temperature, humidity, gas concentration, dust content, electrical parameters and multi-dimensional parameters of partial discharge in the power distribution cabinet, and adopts advanced sensors and data acquisition and transmission technology, effectively avoiding the limitations of traditional monitoring methods, and greatly improving the accuracy and completeness of the monitoring data.

[0016] 2. The power distribution cabinet internal environment monitoring method can make the operation and maintenance personnel understand the abnormal changes of the internal environment of the power distribution cabinet in time through real-time data analysis and historical data modeling, and take measures to prevent faults in advance. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. EMBODIMENT

[0018] A power distribution cabinet internal environment monitoring method comprises the following steps: Step one: first, low-power, high-precision and wireless temperature and humidity sensors, gas sensors, partial discharge sensors and laser dust sensors are selected and installed on the power distribution cabinet; Wireless temperature sensors are installed at busbar and switch contact parts of the switchgear, and wireless humidity sensors are installed at the bottom of the switchgear where condensation is likely to occur and in the corners inside the switchgear. The gas sensor is an electrochemical gas sensor with high sensitivity and selectivity to carbon monoxide and sulfur dioxide gas, and an infrared gas sensor suitable for monitoring sulfur hexafluoride and its decomposition products. The partial discharge sensor is a transient ground wave sensor and an ultrasonic sensor. The partial discharge signals in the switchgear are monitored by the transient ground wave sensor and the ultrasonic sensor, and the equipment operating state is comprehensively judged in combination with the changes in electrical parameters. Step two: Reasonably deploy multiple data acquisition nodes inside the switchgear. The data acquisition node uses an STM32 series microcontroller as the core control unit, acquires data from multiple sensors around it, and transmits the acquired data to the main data receiving device. The data acquisition nodes form a Mesh network for data transmission, and the acquired data is gradually transmitted to the main data receiving end located outside the switchgear. Even if one of the nodes fails, other nodes can still ensure normal data transmission. To ensure the safety of data during transmission, the data acquisition node uses the AES encryption algorithm to encrypt the transmitted data. Step three: The data receiving device sends the received raw data to the data analysis server, which uses the real-time data analysis engine Apache Flink to process the data in real time. When the temperature data exceeds the preset normal operating temperature range, the system automatically sends a warning signal. By continuously monitoring and analyzing humidity data, it can determine whether there is a rapid increase in humidity or a long period of high humidity. If so, it may indicate a condensation risk and send an early warning. By setting data thresholds and data analysis models, the system can analyze the changes in internal environmental parameters of the switchgear in real time and determine whether there is an anomaly. Step four: Use big data technology to deeply mine and analyze historical monitoring data, establish a model of changes in internal environmental parameters of the switchgear, and through learning from historical data, analyze the correlation between different environmental parameters and their variation over time. By analyzing historical temperature data and load current data, a relationship model between temperature and load is established to predict possible temperature trends based on current load conditions. Machine learning algorithms, including support vector machines and decision trees, are used to train historical partial discharge data and build a partial discharge fault prediction model to predict potential electrical equipment failures in advance. Step five: Combine real-time data analysis results and historical data modeling results to achieve intelligent diagnosis and prediction of internal environmental anomalies and electrical equipment failures in the switchgear. When the system detects that the environmental parameters are abnormal or the electrical equipment has abnormal characteristics, the type and cause of the fault are quickly and accurately judged by comparison and analysis with historical data and a fault case library, and the development trend of the fault is predicted.

[0019] The power distribution cabinet internal environment monitoring method has the advantages that the temperature, humidity, gas concentration, dust content, electrical parameters and multi-dimensional parameters of local discharge inside the power distribution cabinet are comprehensively monitored, advanced sensors and data acquisition and transmission technologies are used, the limitations of traditional monitoring methods are effectively avoided, the accuracy and completeness of monitoring data are greatly improved, real-time data analysis and historical data modeling are used, operation and maintenance personnel can timely understand the abnormal changes of the internal environment of the power distribution cabinet, and measures are taken in advance to prevent faults.

[0020] Although the embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the internal environment of a power distribution cabinet, characterized in that: The following steps are involved: Step 1: First, select low-power, high-precision and wireless temperature and humidity sensors, gas sensors, partial discharge sensors and laser dust sensors, and install them on the power distribution cabinet; Step 2: Rationally deploy multiple data acquisition nodes inside the power distribution cabinet. The data acquisition nodes use STM32 series microcontrollers as core control units to collect data from multiple peripheral sensors and transmit the collected data to the main data receiving device. Step 3: The data receiving device sends the received raw data to the data analysis server, which uses the real-time data analysis engine Apache Flink to process the data in real time. By setting data thresholds and data analysis models, the changes in the internal environmental parameters of the power distribution cabinet can be analyzed in real time to determine whether there are any abnormalities. Step 4: Use big data technology to deeply mine and analyze historical monitoring data, establish a change model for the internal environmental parameters of the distribution cabinet, and analyze the correlation between different environmental parameters and their changes over time by learning from historical data; Step 5: Combine real-time data analysis results with historical data modeling results to achieve intelligent diagnosis and prediction of abnormal internal environment of distribution cabinets and electrical equipment failures.

2. A method for monitoring the internal environment of a power distribution cabinet according to claim 1, characterized in that: In the step 1, wireless temperature sensors are installed at the busbars and switch contacts of the distribution cabinet, and wireless humidity sensors are installed at the bottom of the distribution cabinet and in the corners inside the distribution cabinet where condensation is likely to occur.

3. The method for monitoring the internal environment of a power distribution cabinet according to claim 1, wherein: The gas sensor is an electrochemical gas sensor with high sensitivity and selectivity to carbon monoxide and sulfur dioxide gases, and an infrared gas sensor suitable for monitoring sulfur hexafluoride and its decomposition product gases.

4. The method for monitoring the internal environment of a power distribution cabinet according to claim 1, wherein: The partial discharge sensors are transient ground wave sensors and ultrasonic sensors. The partial discharge signals in the switch cabinet are monitored by the transient ground wave sensors and ultrasonic sensors, and the equipment operating status is comprehensively judged in combination with the changes in electrical parameters.

5. The method for monitoring the internal environment of a power distribution cabinet according to claim 1, wherein: In the step 2, the data collection nodes form a Mesh network for data transmission, and the collected data is gradually transmitted to the main data receiving terminal located outside the distribution cabinet. Even if one of the nodes fails, the other nodes can still ensure normal data transmission.

6. A method for monitoring the internal environment of a power distribution cabinet according to claim 5, characterized in that: In order to ensure the security of data during transmission, the data collection node uses the AES encryption algorithm to encrypt the transmitted data.

7. The method for monitoring the internal environment of a power distribution cabinet according to claim 1, characterized in that: In step three, when the temperature data exceeds the preset normal operating temperature range, the system automatically issues a warning signal; through continuous monitoring and analysis of humidity data, it is determined whether there is a rapid increase in humidity or a long-term high humidity state. If so, it may indicate a condensation risk and a warning is issued in time.

8. The method for monitoring the internal environment of a power distribution cabinet according to claim 1, characterized in that: In step 4, by analyzing historical temperature data and load current data, a relationship model between temperature and load is established, and possible temperature change trends are predicted based on the current load conditions. A machine learning algorithm is used to train the historical partial discharge data to construct a partial discharge fault prediction model to predict potential faults of electrical equipment in advance. The machine learning algorithm includes a support vector machine and a decision tree.

9. The method for monitoring the internal environment of a power distribution cabinet according to claim 1, characterized in that: In step five, when the system detects abnormal environmental parameters or abnormal characteristics of electrical equipment, it quickly and accurately determines the type and cause of the fault and predicts the development trend of the fault by comparing and analyzing with historical data and a fault case library.