Intelligent power distribution network fault early warning system and method based on Internet of Things

Abstract

The invention relates to the technical field of power distribution network fault early warning systems and methods, in particular to an intelligent power distribution network fault early warning system and method based on the Internet of Things, and the system comprises a sensing unit, a data processing unit, a data analysis unit, a fault early warning unit, a monitoring management unit, and a personnel inspection subsystem unit. The sensing unit is used for installing a current sensor, a voltage sensor, a temperature sensor and a humidity sensor at key node positions of a transformer, a switch cabinet and a line of the power distribution network, the current sensor is used for monitoring the current in the line in real time, the voltage sensor is used for monitoring the voltage value, and the temperature sensor is used for monitoring the operating temperature of equipment; faults are analyzed and judged from multiple angles, misjudgment and missed judgment are reduced, whether fault hidden dangers exist or not is judged more accurately, the operation state of equipment is evaluated in real time through monitoring of the Internet of Things, the frequency of manual inspection is appropriately reduced, the labor cost is saved, and the resource utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network fault early warning systems and methods, and in particular to an intelligent power distribution network fault early warning system and method based on the Internet of Things. Background Art

[0002] With the rapid development and intelligent transformation of the power industry, the stability and security of the distribution network have become particularly important. Traditional distribution network monitoring and fault handling methods can no longer meet the needs of modern power systems. In addition, distribution network fault detection methods often rely solely on manual inspections or post-analysis, resulting in problems such as untimely warnings and low accuracy.

[0003] For example, the patent with the prior art announcement number CN114977519A discloses a power Internet of Things network management system and method, which relates to the technical field of power management systems. In order to solve the problem that the existing network management system cannot effectively utilize network resources, the faults and potential problems encountered in the network greatly reduce the service quality of the network, resulting in the problem that the computer network cannot operate normally and stably for a long time. A power Internet of Things network management system and method, including a power management system and a network management system; the power management system includes an electric energy telemetry subsystem, a distribution monitoring subsystem and a load management subsystem; the network management system includes an equipment fault management subsystem and an optimized operation subsystem; the network resources are planned, configured, monitored, analyzed and controlled through the equipment fault management subsystem, so that network resources can be effectively utilized.

[0004] However, during the use of the system, it was found that the system is not easy to realize the intelligent combination of the system and artificial intelligence, which reduces the analysis and judgment of distribution network faults from multiple angles and reduces the convenience of use. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides an intelligent distribution network fault warning system and method based on the Internet of Things, which analyzes and judges faults from multiple angles, reduces misjudgments and missed judgments, more accurately judges whether there are hidden fault hazards, conducts real-time evaluation of the operating status of equipment through Internet of Things monitoring, appropriately reduces the frequency of manual inspections, saves labor costs, and improves resource utilization efficiency.

[0006] The present invention provides an intelligent distribution network fault warning system based on the Internet of Things, comprising a sensing unit, a data processing unit, a data analysis unit, a fault warning unit, a monitoring management unit and a personnel inspection subsystem unit; The sensing unit is used to install current sensors, voltage sensors, temperature sensors and humidity sensors at key nodes of transformers, switch cabinets and lines of the power distribution network. The current sensor is used to monitor the current size in the line in real time, the voltage sensor is used to monitor the voltage value, the temperature sensor is used to monitor the operating temperature of the equipment, and the humidity sensor is used to monitor the humidity; different sensors collect the operating parameters of the power distribution network at a sampling frequency of once per second; The data processing unit is used to pre-process the data collected by the sensor by filtering, amplifying and converting the collected data information into analog-to-digital data; removing noise interference during the collection process, then amplifying the weak sensor signal to a set range for processing, and then converting the analog signal into a digital signal to facilitate data transmission and analysis; The data analysis unit uses the data analysis algorithm to analyze the pre-processed data, and classifies and predicts the fault type by adopting the support vector machine learning algorithm; by cleaning the data and removing duplicate data, and then extracting features and analyzing the cleaned data, a model of normal operating status is constructed, and then the collected data model is compared with the normal status model to calculate the deviation value. When it is found that the distribution network operating parameters exceed the normal range or show abnormal trends, fault information is issued; The fault warning unit sends alarm information to the maintenance personnel and the supervision platform based on the fault information sent by the data analysis unit, informing them of the location of the fault point, the temperature of the faulty equipment and the possible fault type; different warning methods are set according to the severity and type of the fault. For minor faults, the alarm information is displayed on the monitoring platform. For serious faults, in addition to being displayed on the monitoring platform, an emergency SMS notification is sent to the operation and maintenance personnel, and the sound and light alarm of the on-site equipment is triggered; The monitoring management unit is used to provide maintenance personnel with an intuitive monitoring interface, through which the operating status of the distribution network can be viewed in real time, and the maintenance personnel can manage the sensors and intelligent devices in the distribution network through the monitoring management unit; the monitoring management unit displays the voltage, current, temperature and humidity parameters of each node of the distribution network, and displays the fault information, so as to facilitate real-time monitoring of each node and facilitate maintenance personnel to quickly reach the fault point; The manual inspection subsystem unit is used to formulate a manual inspection plan based on the scale, equipment type, importance and inspection route of the distribution network, and to adjust the manual inspection plan based on the system's evaluation results of the equipment's operating status; for important distribution network segments, a comprehensive inspection is conducted at least once a week, and for ordinary distribution network segments, a comprehensive inspection is formulated once a month. When formulating the inspection route, the distribution of equipment is considered to determine the inspection time, route and key inspection content, as shown below: 1. Manual inspection plan formulation The formulation function of the manual inspection plan Pplan: Pplan=f(Pscale,S,I,R) Where Pscale represents the scale of the distribution network; 2. Adjust the plan according to the equipment operation status Adjusted manual inspection plan Padjusted: Padjusted=g(Pplan,E) 3. Inspection frequency regulations For important distribution network sections (I=I1): Carry out a comprehensive inspection at least once a week. Let the number of inspections be N1, then N1 ≥ 1 / week For a normal distribution network segment (I=I2): A comprehensive inspection is carried out once a month. Let the number of inspections be N2, then N2=1 / month 4. Inspection route Assume that the inspection route determination function is h, the key inspection content is C, and the inspection time is T: R=h(Sdistribution,Pscale) C=k(S,I) T=m(S,I,Pscale) Among them, functions f, g, h, k, and m represent different logical relationships. IoT monitoring can obtain a large amount of equipment operation data in real time, and manual inspection can make intuitive inspections and empirical judgments on the equipment. By combining the two, faults can be analyzed and judged from multiple angles, reducing misjudgments and missed judgments, and more accurately judging whether there are hidden fault hazards. The operating status of the equipment can be evaluated in real time through IoT monitoring. For equipment in good operating status, the frequency of manual inspections can be appropriately reduced, thereby saving labor costs. At the same time, manual inspections can improve resource utilization efficiency by focusing on weak links in IoT monitoring.

[0007] Preferably, the personnel inspection subsystem unit also includes an expert fault diagnosis unit, a training unit and an inspection equipment management unit; The expert fault diagnosis unit uses the professional knowledge of experts in the power field to build an expert system platform to achieve rapid location and preliminary diagnosis of equipment faults. At the same time, it uses the professional knowledge of experts in the power field to formulate distribution network maintenance and operation training content; The training unit is used to train patrol personnel in power equipment knowledge, safe operating procedures, and fault identification skills using the established distribution network maintenance training content; enable patrol personnel to master the internal structure of the transformer, know how to check the oil level and oil temperature parameters of the transformer, and understand the working principles, structural composition, and common fault types of different equipment. It ensures that patrol personnel can comply with safety regulations during the inspection process, wear insulating protective equipment correctly, and maintain a safe distance when working near high-voltage equipment. By showing real-time cases of the appearance, sound, smell and other characteristics of equipment under different fault conditions, the patrol personnel's ability to identify faults is improved, allowing patrol personnel to quickly determine whether there is a fault in the equipment; The inspection equipment management unit is used to equip the inspection personnel with necessary inspection tools and equipment, improve the inspection efficiency and ensure the inspection personnel's work safety; The inspection coordination unit is used to collect the equipment name, location, fault phenomenon, and preliminary judgment of fault factors recorded by the inspectors during the inspection process, and then send the information to the monitoring management unit. The maintenance personnel of the monitoring management unit integrate and compare the information results of manual inspections with the data monitored by the Internet of Things. When both show that a certain equipment has a fault or a risk of fault, or one party finds an abnormality, the data of the other party can provide further explanation or verification, so as to more accurately determine the cause of the fault and take effective maintenance measures. For problems that cannot be solved, they are reported to the expert fault diagnosis unit, and the fault is further eliminated after discussion by experts.

[0008] Preferably, the personnel inspection subsystem unit further includes a feedback optimization unit and an inspection auxiliary unit; The feedback optimization unit is used to collect the equipment operation status during the manual inspection process and feed back the inspection information to the monitoring management unit. Through manual inspection, the actual operation status of the equipment can be intuitively understood. When the inspectors find potential safety hazards in the equipment, the inspectors will feedback this information. After that, the monitoring management unit will re-evaluate the risk level of the equipment in the area based on the information fed back from the site, and adjust and optimize the subsequent inspection plan. The inspection auxiliary unit is used to use system data to assist inspectors in on-site inspections. Before the start of manual inspections, inspectors access the IoT data in the intelligent distribution network fault warning system through mobile terminals to view the recent voltage and current fluctuation curves of the equipment, understand the historical fault records of the equipment, and conduct targeted inspections on equipment or parameters that may have problems, thereby improving inspection efficiency.

[0009] Preferably, the inspection equipment management unit further includes an equipment monitoring unit; The equipment monitoring unit is used to collect and record the data results sent by the inspection equipment. During the manual inspection process, the inspectors use various tools to detect the equipment. The inspection results of the tools are recorded and uploaded to the perception unit. When the sensor of the perception unit cannot accurately obtain the data of a certain part of the equipment due to the installation position or its own accuracy problems, the data of the area is accurately measured through manual inspection. The manual inspection data supplements the deficiencies of IoT monitoring and makes the system's judgment on the operating status of the equipment more comprehensive and accurate.

[0010] Preferably, it also includes a network transmission unit; The network transmission unit packages the data collected by the sensing unit according to a predefined data transmission protocol, and then uses the Internet of Things communication to transmit the packaged data to the data processing unit. During the data transmission process, encryption technology is used to improve data security.

[0011] Preferably, it also includes a data storage unit; A large database is established to store the massive amount of data collected. The database adopts a combination of relational database and non-relational database. The relational database is used to store structured data such as basic information of equipment and historical fault records, while the non-relational database is used to store unstructured data such as real-time operation data collected by sensors.

[0012] Preferably, the personnel inspection subsystem unit further includes a sharing unit and a scheduling unit; The sharing unit is used to create a network sharing platform. The data analysis unit, experts and inspection personnel can publish the latest fault types, fault phenomena and fault solutions on the platform, so that fault information can be communicated and shared on the platform, promoting the knowledge update and skill improvement of personnel; The dispatch unit is used to reasonably allocate inspection positions for personnel. When the data analysis unit detects abnormal conditions in equipment in a certain area, it quickly allocates appropriate inspection personnel to the area based on the current inspection personnel's location, working status and the importance of the area. At the same time, based on the probability of equipment failure in different time periods, the number of inspection personnel allocated near important equipment is increased during peak power consumption periods.

[0013] Preferably, it also includes a maintenance suggestion unit; The maintenance recommendation unit is used to regularly evaluate the health of power equipment in the distribution network. By analyzing and processing the data collected by sensors, it evaluates the operating health status and service life of the equipment and makes maintenance recommendations to improve the safety of equipment operation.

[0014] A fault early warning method for an intelligent power distribution network based on the Internet of Things comprises the following steps: S1. Install current sensors, voltage sensors, temperature sensors and humidity sensors at key nodes of transformers, switch cabinets and lines of the power distribution network. The current sensor is used to monitor the current in the line in real time, the voltage sensor is used to monitor the voltage value, the temperature sensor is used to monitor the operating temperature of the equipment, and the humidity sensor is used to monitor the humidity. Different sensors collect the operating parameters of the power distribution network at a sampling frequency of once per second. S2. Install current sensors, voltage sensors, temperature sensors and humidity sensors at key nodes of transformers, switch cabinets and lines of the power distribution network. The current sensor is used to monitor the current in the line in real time, the voltage sensor is used to monitor the voltage value, the temperature sensor is used to monitor the operating temperature of the equipment, and the humidity sensor is used to monitor the humidity; different sensors collect the operating parameters of the power distribution network at a sampling frequency of once per second; S3. Analyze the preprocessed data using a data analysis algorithm, and classify and predict the fault type by using a support vector machine learning algorithm; clean the data, remove duplicate data, extract features and analyze the cleaned data, build a normal operating state model, and then compare the collected data model with the normal state model to calculate the deviation value. When it is found that the distribution network operating parameters exceed the normal range or show abnormal trends, a fault message is issued; S4. Send alarm information to maintenance personnel and the monitoring platform based on the fault information sent, informing them of the location of the fault point, the temperature of the faulty equipment and the possible fault type. Set different early warning methods according to the severity and type of the fault. For minor faults, display the alarm information on the monitoring platform. For serious faults, in addition to displaying it on the monitoring platform, send emergency SMS notifications to the operation and maintenance personnel and trigger the sound and light alarm of the on-site equipment. S5. By viewing the operating status of the power distribution network in real time on the interface, the maintenance personnel manage the sensors and intelligent devices in the power distribution network through the monitoring management unit. The monitoring management unit displays the voltage, current, temperature and humidity parameters of each node in the power distribution network, and displays the fault information, so as to facilitate real-time monitoring of each node and facilitate maintenance personnel to quickly reach the fault point; S6. Develop a manual inspection plan based on the scale, equipment type, importance and inspection routes of the distribution network. Adjust the manual inspection plan based on the system's evaluation of the equipment's operating status. For important distribution network segments, conduct a comprehensive inspection at least once a week. For ordinary distribution network segments, plan a comprehensive inspection once a month. When developing the inspection route, consider the distribution of equipment to determine the time, route and key inspection content.

[0015] Preferably, in S6, an expert system platform is constructed to achieve rapid location and preliminary diagnosis of equipment failures. At the same time, the professional knowledge of experts in the power field is used to formulate distribution network maintenance and operation training content, and patrol personnel are trained in power equipment knowledge, safe operating procedures, and fault identification skills. The patrol personnel can master the internal structure of the transformer and know how to check the oil level and oil temperature parameters of the transformer. The patrol personnel understand the working principles, structural composition and common fault types of different equipment, ensure that the patrol personnel can comply with safety regulations during the inspection process, correctly wear insulating protective equipment, and maintain a safe distance when working near high-voltage equipment. By displaying real-time cases of the appearance, sound, smell and other characteristics of the equipment under different fault conditions, the patrol personnel's ability to identify faults is improved, allowing the patrol personnel to quickly determine whether there is a fault in the equipment.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: Internet of Things monitoring can obtain a large amount of equipment operation data in real time, and manual inspection can perform intuitive inspection and empirical judgment on the equipment. By combining the two, faults can be analyzed and judged from multiple angles, reducing misjudgments and missed judgments, and more accurately judging whether there are hidden fault hazards. The operating status of the equipment can be evaluated in real time through Internet of Things monitoring. For equipment in good operating status, the frequency of manual inspections can be appropriately reduced, thereby saving labor costs. At the same time, manual inspections can improve resource utilization efficiency by focusing on weak links in Internet of Things monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the system structure of the present invention; Figure 2 It is a schematic diagram of the axonometric local structure of the connection between the perception unit and the data processing unit; Figure 3 It is a schematic diagram of the axonometric partial structure of the connection between the monitoring management unit and the personnel inspection subsystem unit; Figure 4 It is a schematic diagram of the axonometric local structure of the connection between the expert fault diagnosis unit and the training unit; Figure 5 It is a structural diagram of the perception unit. DETAILED DESCRIPTION

[0018] In order to facilitate understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0019] Example 1

[0020] like Figures 1 to 5As shown, the present invention is an intelligent distribution network fault warning system based on the Internet of Things; including a sensing unit, a data processing unit, a data analysis unit, a fault warning unit, a monitoring management unit and a personnel inspection subsystem unit; The sensing unit is used to install current sensors, voltage sensors, temperature sensors and humidity sensors at key nodes of transformers, switch cabinets and lines of the power distribution network. The current sensor is used to monitor the current size in the line in real time, the voltage sensor is used to monitor the voltage value, the temperature sensor is used to monitor the operating temperature of the equipment, and the humidity sensor is used to monitor the humidity; different sensors collect the operating parameters of the power distribution network at a sampling frequency of once per second; The data processing unit is used to pre-process the data collected by the sensor by filtering, amplifying and converting the collected data information into analog-to-digital data; removing noise interference during the collection process, then amplifying the weak sensor signal to a set range for processing, and then converting the analog signal into a digital signal to facilitate data transmission and analysis; The data analysis unit uses the data analysis algorithm to analyze the pre-processed data, and classifies and predicts the fault type by adopting the support vector machine learning algorithm; by cleaning the data and removing duplicate data, and then extracting features and analyzing the cleaned data, a model of normal operating status is constructed, and then the collected data model is compared with the normal status model to calculate the deviation value. When it is found that the distribution network operating parameters exceed the normal range or show abnormal trends, fault information is issued; The fault warning unit sends alarm information to the maintenance personnel and the supervision platform based on the fault information sent by the data analysis unit, informing them of the location of the fault point, the temperature of the faulty equipment and the possible fault type; different warning methods are set according to the severity and type of the fault. For minor faults, the alarm information is displayed on the monitoring platform. For serious faults, in addition to being displayed on the monitoring platform, an emergency SMS notification is sent to the operation and maintenance personnel, and the sound and light alarm of the on-site equipment is triggered; The monitoring management unit is used to provide maintenance personnel with an intuitive monitoring interface, through which the operating status of the distribution network can be viewed in real time, and the maintenance personnel can manage the sensors and intelligent devices in the distribution network through the monitoring management unit; the monitoring management unit displays the voltage, current, temperature and humidity parameters of each node of the distribution network, and displays the fault information, so as to facilitate real-time monitoring of each node and facilitate maintenance personnel to quickly reach the fault point; The manual inspection subsystem unit is used to formulate a manual inspection plan based on the scale, equipment type, importance and inspection route of the distribution network, and to adjust the manual inspection plan based on the system's evaluation results of the equipment's operating status; for important distribution network segments, a comprehensive inspection is conducted at least once a week, and for ordinary distribution network segments, a comprehensive inspection is formulated once a month. When formulating the inspection route, the distribution of equipment is considered to determine the inspection time, route and key inspection content, as shown below: 1. Manual inspection plan formulation The formulation function of the manual inspection plan Pplan: Pplan=f(Pscale,S,I,R) Where Pscale represents the scale of the distribution network; 2. Adjust the plan according to the equipment operation status Adjusted manual inspection plan Padjusted: Padjusted=g(Pplan,E) 3. Inspection frequency regulations For important distribution network sections (I=I1): Carry out a comprehensive inspection at least once a week. Let the number of inspections be N1, then N1 ≥ 1 / week For a normal distribution network segment (I=I2): A comprehensive inspection is carried out once a month. Let the number of inspections be N2, then N2=1 / month 4. Inspection route Assume that the inspection route determination function is h, the key inspection content is C, and the inspection time is T: R=h(Sdistribution,Pscale) C=k(S,I) T=m(S,I,Pscale) Among them, functions f, g, h, k, and m represent different logical relationships; The personnel inspection subsystem unit also includes an expert fault diagnosis unit, a training unit, and an inspection equipment management unit; The expert fault diagnosis unit uses the professional knowledge of experts in the power field to build an expert system platform to achieve rapid location and preliminary diagnosis of equipment faults. At the same time, it uses the professional knowledge of experts in the power field to formulate distribution network maintenance and operation training content; The training unit is used to train patrol personnel in power equipment knowledge, safe operating procedures, and fault identification skills using the established distribution network maintenance training content; enable patrol personnel to master the internal structure of the transformer, know how to check the oil level and oil temperature parameters of the transformer, and understand the working principles, structural composition, and common fault types of different equipment. It ensures that patrol personnel can comply with safety regulations during the inspection process, wear insulating protective equipment correctly, and maintain a safe distance when working near high-voltage equipment. By showing real-time cases of the appearance, sound, smell and other characteristics of equipment under different fault conditions, the patrol personnel's ability to identify faults is improved, allowing patrol personnel to quickly determine whether there is a fault in the equipment; The inspection equipment management unit is used to equip the inspection personnel with necessary inspection tools and equipment, improve the inspection efficiency and ensure the inspection personnel's work safety; The inspection coordination unit is used to collect the equipment name, location, fault phenomenon, and preliminary judgment of fault factors recorded by the inspectors during the inspection process, and then send the information to the monitoring management unit. The maintenance personnel of the monitoring management unit integrate and compare the information results of manual inspections with the data monitored by the Internet of Things. When both show that a certain equipment has a fault or a risk of fault, or one party finds an abnormality, the data of the other party can provide further explanation or verification, so as to more accurately determine the cause of the fault and take effective maintenance measures. For problems that cannot be solved, they are reported to the expert fault diagnosis unit, and the fault is further eliminated after discussion by experts.

[0021] The personnel inspection subsystem unit also includes a feedback optimization unit and an inspection auxiliary unit; The feedback optimization unit is used to collect the equipment operation status during the manual inspection process and feed back the inspection information to the monitoring management unit. Through manual inspection, the actual operation status of the equipment can be intuitively understood. When the inspectors find potential safety hazards in the equipment, the inspectors will feedback this information. After that, the monitoring management unit will re-evaluate the risk level of the equipment in the area based on the information fed back from the site, and adjust and optimize the subsequent inspection plan. The inspection auxiliary unit is used to use system data to assist inspectors in on-site inspections. Before the start of manual inspections, inspectors access the IoT data in the intelligent distribution network fault warning system through mobile terminals to view the recent voltage and current fluctuation curves of the equipment, understand the historical fault records of the equipment, and conduct targeted inspections on equipment or parameters that may have problems, thereby improving inspection efficiency.

[0022] The inspection equipment management unit also includes an equipment monitoring unit; The equipment monitoring unit is used to collect and record the data results sent by the inspection equipment. During the manual inspection process, the inspectors use various tools to detect the equipment. The inspection results of the tools are recorded and uploaded to the perception unit. When the sensor of the perception unit cannot accurately obtain the data of a certain part of the equipment due to the installation position or its own accuracy problems, the data of the area is accurately measured through manual inspection. The manual inspection data supplements the deficiencies of IoT monitoring and makes the system's judgment on the operating status of the equipment more comprehensive and accurate.

[0023] Also includes a network transmission unit; The network transmission unit packages the data collected by the sensing unit according to a predefined data transmission protocol, and then uses the Internet of Things communication to transmit the packaged data to the data processing unit. During the data transmission process, encryption technology is used to improve data security.

[0024] Also included is a data storage unit; A large database is established to store the massive amount of data collected. The database adopts a combination of relational database and non-relational database. The relational database is used to store structured data such as basic information of equipment and historical fault records, while the non-relational database is used to store unstructured data such as real-time operation data collected by sensors.

[0025] The personnel inspection subsystem unit also includes a sharing unit and a scheduling unit; The sharing unit is used to create a network sharing platform. The data analysis unit, experts and inspection personnel can publish the latest fault types, fault phenomena and fault solutions on the platform, so that fault information can be communicated and shared on the platform, promoting the knowledge update and skill improvement of personnel; The dispatch unit is used to reasonably allocate inspection positions for personnel. When the data analysis unit detects abnormal conditions in equipment in a certain area, it quickly allocates appropriate inspection personnel to the area based on the current inspection personnel's location, working status and the importance of the area. At the same time, based on the probability of equipment failure in different time periods, the number of inspection personnel allocated near important equipment is increased during peak power consumption periods.

[0026] Also included is a maintenance advice unit; The maintenance suggestion unit is used to regularly evaluate the health of power equipment in the distribution network. By analyzing and processing the data collected by sensors, it evaluates the operating health status and service life of the equipment and makes maintenance suggestions to improve the safety of equipment operation. In this embodiment, IoT monitoring can obtain a large amount of equipment operation data in real time, and manual inspections can perform intuitive inspections and empirical judgments on the equipment. By combining the two, faults can be analyzed and judged from multiple angles, reducing misjudgments and missed judgments, and more accurately determining whether there are hidden fault hazards. The operating status of the equipment can be evaluated in real time through IoT monitoring. For equipment in good operating condition, the frequency of manual inspections can be appropriately reduced, thereby saving labor costs. At the same time, manual inspections can improve resource utilization efficiency by focusing on weak links in IoT monitoring.

[0027] Example 2

[0028] On the basis of Example 1, a fault warning method for an intelligent power distribution network based on the Internet of Things includes the following steps: S1. Install current sensors, voltage sensors, temperature sensors and humidity sensors at key nodes of transformers, switch cabinets and lines of the power distribution network. The current sensor is used to monitor the current in the line in real time, the voltage sensor is used to monitor the voltage value, the temperature sensor is used to monitor the operating temperature of the equipment, and the humidity sensor is used to monitor the humidity. Different sensors collect the operating parameters of the power distribution network at a sampling frequency of once per second. S2. Install current sensors, voltage sensors, temperature sensors and humidity sensors at key nodes of transformers, switch cabinets and lines of the power distribution network. The current sensor is used to monitor the current in the line in real time, the voltage sensor is used to monitor the voltage value, the temperature sensor is used to monitor the operating temperature of the equipment, and the humidity sensor is used to monitor the humidity; different sensors collect the operating parameters of the power distribution network at a sampling frequency of once per second; S3. Analyze the preprocessed data using a data analysis algorithm, and classify and predict the fault type by using a support vector machine learning algorithm; clean the data, remove duplicate data, extract features and analyze the cleaned data, build a normal operating state model, and then compare the collected data model with the normal state model to calculate the deviation value. When it is found that the distribution network operating parameters exceed the normal range or show abnormal trends, a fault message is issued; S4. Send alarm information to maintenance personnel and the monitoring platform based on the fault information sent, informing them of the location of the fault point, the temperature of the faulty equipment and the possible fault type. Set different early warning methods according to the severity and type of the fault. For minor faults, display the alarm information on the monitoring platform. For serious faults, in addition to displaying it on the monitoring platform, send emergency SMS notifications to the operation and maintenance personnel and trigger the sound and light alarm of the on-site equipment. S5. By viewing the operating status of the power distribution network in real time on the interface, the maintenance personnel manage the sensors and intelligent devices in the power distribution network through the monitoring management unit. The monitoring management unit displays the voltage, current, temperature and humidity parameters of each node in the power distribution network, and displays the fault information, so as to facilitate real-time monitoring of each node and facilitate maintenance personnel to quickly reach the fault point; S6. Develop a manual inspection plan based on the scale, equipment type, importance and inspection route of the distribution network, and adjust the manual inspection plan based on the system's evaluation results of the equipment's operating status; for important distribution network segments, conduct a comprehensive inspection at least once a week, and for ordinary distribution network segments, develop a comprehensive inspection once a month. When developing the inspection route, consider the distribution of equipment to determine the inspection time, route and key inspection content; In S6, an expert system platform is built to achieve rapid location and preliminary diagnosis of equipment faults. At the same time, the professional knowledge of experts in the power field is used to formulate distribution network maintenance and operation training content, and patrol personnel are trained in power equipment knowledge, safe operating procedures, and fault identification skills. Patrol personnel can master the internal structure of the transformer and know how to check the oil level and oil temperature parameters of the transformer. Patrol personnel understand the working principles, structural composition and common fault types of different equipment, ensure that patrol personnel can comply with safety regulations during the inspection process, wear insulating protective equipment correctly, and maintain a safe distance when working near high-voltage equipment. By displaying real-time cases of the appearance, sound, smell and other characteristics of the equipment under different fault conditions, the ability of patrol personnel to identify faults is improved, allowing patrol personnel to quickly determine whether there is a fault in the equipment.

[0029] The main functions achieved by the present invention are: Internet of Things monitoring can obtain a large amount of equipment operation data in real time, and manual inspection can make intuitive inspections and empirical judgments on the equipment. By combining the two, faults can be analyzed and judged from multiple angles, which can reduce misjudgments and missed judgments, and more accurately judge whether there are hidden fault hazards; Through IoT monitoring, the operating status of the equipment can be evaluated in real time. For equipment in good operating condition, the frequency of manual inspections can be appropriately reduced to save labor costs. At the same time, manual inspections can improve resource utilization efficiency by focusing on weak links in IoT monitoring.

[0030] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent power distribution network fault early warning system based on the Internet of Things, characterized in that: It includes a sensing unit, a data processing unit, a data analysis unit, a fault warning unit, a monitoring management unit and a personnel inspection subsystem unit; The sensing unit is used to install current sensors, voltage sensors, temperature sensors and humidity sensors at key nodes of transformers, switch cabinets and lines in the power distribution network. The current sensor is used to monitor the current size in the line in real time, the voltage sensor is used to monitor the voltage value, the temperature sensor is used to monitor the operating temperature of the equipment, and the humidity sensor is used to monitor the humidity; The data processing unit is used to pre-process the data collected by the sensor by filtering, amplifying and converting the collected data information into analog-to-digital data; The data analysis unit uses a data analysis algorithm to analyze the pre-processed data, and classifies and predicts the fault type by adopting a support vector machine learning algorithm; The fault warning unit sends an alarm message to the maintenance personnel and the supervision platform based on the fault information sent by the data analysis unit, informing them of the location of the fault point, the temperature of the faulty equipment and the possible fault type; The monitoring management unit is used to provide maintenance personnel with an intuitive monitoring interface, through which they can view the operating status of the power distribution network in real time, and the maintenance personnel can manage the sensors and intelligent devices in the power distribution network through the monitoring management unit; The manual inspection subsystem unit is used to formulate manual inspection plans based on the scale, equipment type, importance and inspection routes of the distribution network. At the same time, the manual inspection plan is adjusted according to the system's evaluation results of the equipment operating status. For important distribution network segments, a comprehensive inspection is carried out at least once a week. For ordinary distribution network segments, a comprehensive inspection is formulated once a month. When formulating the inspection route, the distribution of equipment is taken into consideration to determine the inspection time, route and key inspection content.

2. The intelligent power distribution network fault early warning system based on the Internet of Things as claimed in claim 1, characterized in that: The personnel inspection subsystem unit also includes an expert fault diagnosis unit, a training unit, and an inspection equipment management unit; The expert fault diagnosis unit uses the professional knowledge of experts in the power field to build an expert system platform to achieve rapid location and preliminary diagnosis of equipment faults. At the same time, it uses the professional knowledge of experts in the power field to formulate distribution network maintenance and operation training content; The training unit is used to train inspection personnel on power equipment knowledge, safe operating procedures, and fault identification skills using the established distribution network maintenance and operation training content; Enable the inspectors to understand the internal structure of the transformer, know how to check the oil level and oil temperature parameters of the transformer, understand the working principles, structural composition and common fault types of different equipment, ensure that the inspectors can comply with safety regulations during the inspection process, wear insulating protective equipment correctly, and keep a safe distance when working near high-voltage equipment. By showing real-time cases of the appearance, sound, smell and other characteristics of the equipment under different fault conditions, the inspectors' ability to identify faults is improved, allowing the inspectors to quickly determine whether the equipment has faults; The inspection equipment management unit is used to equip the inspection personnel with necessary inspection tools and equipment, improve the inspection efficiency and ensure the inspection personnel's work safety; The inspection coordination unit is used to collect the equipment name, location, fault phenomenon, and preliminary judgment of fault factors recorded by the inspectors during the inspection process, and then send the information to the monitoring management unit. The maintenance personnel of the monitoring management unit integrate and compare the information results of manual inspections with the data monitored by the Internet of Things. When both show that a certain equipment has a fault or a risk of fault, or one party finds an abnormality, the data of the other party can provide further explanation or verification, so as to more accurately determine the cause of the fault and take effective maintenance measures. For problems that cannot be solved, they are reported to the expert fault diagnosis unit, and the fault is further eliminated after discussion by experts.

3. The intelligent power distribution network fault early warning system based on the Internet of Things as claimed in claim 1, characterized in that: The personnel inspection subsystem unit also includes a feedback optimization unit and an inspection auxiliary unit; The feedback optimization unit is used to collect the equipment operation status during the manual inspection process and feed back the inspection information to the monitoring management unit. Through manual inspection, the actual operation status of the equipment can be intuitively understood. When the inspectors find potential safety hazards in the equipment, the inspectors will feedback this information. After that, the monitoring management unit will re-evaluate the risk level of the equipment in the area based on the information fed back from the site, and adjust and optimize the subsequent inspection plan. The inspection auxiliary unit is used to use system data to assist inspectors in on-site inspections. Before the start of manual inspections, inspectors access the IoT data in the intelligent distribution network fault warning system through mobile terminals to view the recent voltage and current fluctuation curves of the equipment, understand the historical fault records of the equipment, and conduct targeted inspections on equipment or parameters that may have problems, thereby improving inspection efficiency.

4. The intelligent power distribution network fault early warning system based on the Internet of Things as claimed in claim 2, characterized in that: The inspection equipment management unit also includes an equipment monitoring unit; The equipment monitoring unit is used to collect and record the data results sent by the inspection equipment. During the manual inspection process, the inspectors use various tools to detect the equipment. The inspection results of the tools are recorded and uploaded to the perception unit. When the sensor of the perception unit cannot accurately obtain the data of a certain part of the equipment due to the installation position or its own accuracy problems, the data of the area is accurately measured through manual inspection. The manual inspection data supplements the deficiencies of IoT monitoring and makes the system's judgment on the operating status of the equipment more comprehensive and accurate.

5. The intelligent power distribution network fault early warning system based on the Internet of Things as claimed in claim 1, characterized in that: Also includes a network transmission unit; The network transmission unit packages the data collected by the sensing unit according to a predefined data transmission protocol, and then uses the Internet of Things communication to transmit the packaged data to the data processing unit. During the data transmission process, encryption technology is used to improve data security.

6. The intelligent power distribution network fault early warning system based on the Internet of Things as claimed in claim 1, characterized in that: Also included is a data storage unit; A large database is established to store the massive amount of data collected. The database adopts a combination of relational database and non-relational database. The relational database is used to store structured data such as basic information of equipment and historical fault records, while the non-relational database is used to store unstructured data such as real-time operation data collected by sensors.

7. The intelligent power distribution network fault early warning system based on the Internet of Things as claimed in claim 1, characterized in that: The personnel inspection subsystem unit also includes a sharing unit and a scheduling unit; The sharing unit is used to create a network sharing platform. The data analysis unit, experts and inspection personnel can publish the latest fault types, fault phenomena and fault solutions on the platform, so that fault information can be communicated and shared on the platform, promoting the knowledge update and skill improvement of personnel; The dispatch unit is used to reasonably allocate inspection positions for personnel. When the data analysis unit detects abnormal conditions in equipment in a certain area, it quickly allocates appropriate inspection personnel to the area based on the current inspection personnel's location, working status and the importance of the area. At the same time, based on the probability of equipment failure in different time periods, the number of inspection personnel allocated near important equipment is increased during peak power consumption periods.

8. The intelligent power distribution network fault early warning system based on the Internet of Things as claimed in claim 1, characterized in that: Also included is a maintenance advice unit; The maintenance recommendation unit is used to regularly evaluate the health of power equipment in the distribution network. By analyzing and processing the data collected by sensors, it evaluates the operating health status and service life of the equipment and makes maintenance recommendations to improve the safety of equipment operation.

9. A fault warning method for intelligent power distribution network based on the Internet of Things, characterized in that: The following steps are involved: S1. Install current sensors, voltage sensors, temperature sensors and humidity sensors at key nodes of transformers, switch cabinets and lines of the power distribution network. The current sensor is used to monitor the current in the line in real time, the voltage sensor is used to monitor the voltage value, the temperature sensor is used to monitor the operating temperature of the equipment, and the humidity sensor is used to monitor the humidity. Different sensors collect the operating parameters of the power distribution network at a sampling frequency of once per second. S2. Install current sensors, voltage sensors, temperature sensors and humidity sensors at key nodes of transformers, switch cabinets and lines of the power distribution network. The current sensor is used to monitor the current in the line in real time, the voltage sensor is used to monitor the voltage value, the temperature sensor is used to monitor the operating temperature of the equipment, and the humidity sensor is used to monitor the humidity; different sensors collect the operating parameters of the power distribution network at a sampling frequency of once per second; S3, using data analysis algorithms to analyze the preprocessed data, and using support vector machine learning algorithms to classify and predict fault types; By cleaning the data and removing duplicate data, extracting features and analyzing the cleaned data, a model of normal operation status is constructed, and then the collected data model is compared with the normal status model to calculate the deviation value. When it is found that the distribution network operation parameters exceed the normal range or show abnormal trends, fault information is issued; S4. Send alarm information to maintenance personnel and the monitoring platform based on the fault information sent, informing them of the location of the fault point, the temperature of the faulty equipment and the possible fault type. Set different early warning methods according to the severity and type of the fault. For minor faults, display the alarm information on the monitoring platform. For serious faults, in addition to displaying it on the monitoring platform, send emergency SMS notifications to the operation and maintenance personnel and trigger the sound and light alarm of the on-site equipment. S5. By viewing the operating status of the power distribution network in real time on the interface, the maintenance personnel manage the sensors and intelligent devices in the power distribution network through the monitoring management unit. The monitoring management unit displays the voltage, current, temperature and humidity parameters of each node in the power distribution network, and displays the fault information, so as to facilitate real-time monitoring of each node and facilitate maintenance personnel to quickly reach the fault point; S6. Develop a manual inspection plan based on the scale, equipment type, importance and inspection routes of the distribution network. Adjust the manual inspection plan based on the system's evaluation of the equipment's operating status. For important distribution network segments, conduct a comprehensive inspection at least once a week. For ordinary distribution network segments, plan a comprehensive inspection once a month. When developing the inspection route, consider the distribution of equipment to determine the time, route and key inspection content.

10. The method for early warning of faults in an intelligent power distribution network based on the Internet of Things according to claim 9, characterized in that: In S6, an expert system platform is built to achieve rapid location and preliminary diagnosis of equipment faults. At the same time, the professional knowledge of experts in the power field is used to formulate distribution network maintenance and operation training content, and patrol personnel are trained in power equipment knowledge, safe operating procedures, and fault identification skills. Patrol personnel can master the internal structure of the transformer and know how to check the oil level and oil temperature parameters of the transformer. Patrol personnel understand the working principles, structural composition and common fault types of different equipment, ensure that patrol personnel can comply with safety regulations during the inspection process, wear insulating protective equipment correctly, and maintain a safe distance when working near high-voltage equipment. By displaying real-time cases of the appearance, sound, smell and other characteristics of the equipment under different fault conditions, the ability of patrol personnel to identify faults is improved, allowing patrol personnel to quickly determine whether there is a fault in the equipment.

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