52results about How to "Reduce fault response time" patented technology

The invention discloses an intelligent online monitoring device for a transformer, and relates to the technical field of transformers. The intelligent on-line monitoring device for the transformer comprises a front-end acquisition layer, a data transmission layer, a data processing and analysis layer, a cloud monitoring and management layer and an emergency response layer, through the deployment of the high-precision sensor network and the edge computing nodes, the system can accurately collect and process the operation data of the transformer in real time, and the monitoring accuracy and efficiency are improved. And secondly, the data transmission layer adopts a self-adaptive communication protocol and a data encryption technology, so that real-time and reliable transmission and security protection of the data are ensured, and meanwhile, traceability and tampering prevention of the data are realized by utilizing a block chain technology. In the data processing and analysis layer, the system realizes intelligent early warning and continuous optimization of transformer data through an integrated learning and deep learning model, and improves the accuracy and reliability of early warning. And the emergency response layer is arranged, so that the system can quickly respond to faults.

The invention is suitable for the field of electric power safety, and provides a fault prediction and maintenance method and system for safe operation of electric power equipment, and the system comprises a data collection module, a data analysis and abnormity identification module, a dynamic alarm and response module, a geographic information system integration module, and a maintenance path optimization module. The system adopts a non-contact dynamic electric field monitoring system to monitor power equipment, can identify potential insulation failures or electrical faults in real time, and significantly reduces the risk of contact between operation and maintenance personnel and high-voltage equipment. And in combination with an intelligent alarm system, real-time data are analyzed through machine learning, an alarm is automatically triggered, and the accuracy and response speed of fault identification are improved. In addition, the monitoring system is integrated with a geographic information system, automatic optimization of an optimal maintenance response path is achieved, and it is ensured that faults are rapidly processed. According to the comprehensive method, the safety of the power equipment is improved, the fault risk is reduced, the maintenance efficiency is optimized, and the comprehensive method has important practical value.

The invention relates to the technical field of power electronics and energy management. The invention provides an AC / DC power supply system monitoring device and method. The method comprises the following steps: constructing a historical data set; preprocessing the historical data set to obtain a preprocessed data set; based on the preprocessed data set and a machine learning algorithm, constructing a system monitoring model, and presetting a voltage anomaly judgment threshold value of the system monitoring model; acquiring real-time power data of the AC / DC power supply system and real-time operation state and load change data of the power grid, inputting the data into the system monitoring model, and identifying the mode, trend and potential abnormity of voltage fluctuation of the AC / DC power supply system; and dynamically adjusting a preset voltage abnormity judgment threshold value, and giving a corresponding AC / DC power supply system adjustment strategy and early warning information. The problems that in the prior art, an alternating-current and direct-current power supply system monitoring method is low in efficiency, frequent in false alarm and missing alarm conditions, difficult to adapt to a complex and changeable power grid environment and lack of intelligent analysis and early warning functions are solved.

The invention discloses a fault identification early warning method and system for a power transmission line, and the method comprises the steps: receiving a fault flashover signal, determining a generation region of the fault flashover signal, and obtaining a target image; determining whether the power transmission line in the occurrence area has abnormal discharge or not; if yes, performing image recognition to obtain a to-be-detected fault source; obtaining a tower gradient, an electromagnetic field and a radar change of a tower area in the occurrence area; determining a motion path of a current fault source, determining the type of the fault source through image snapshot, and determining a target fault source; obtaining meteorological data through a micro-meteorological sensor and network meteorological weather, and obtaining future and real-time galloping swing and galloping frequency of the power transmission line; and starting image snapshot to determine a target probability and an image of a secondary fault caused by a target fault source on the power transmission line by combining a galloping amplitude, a galloping frequency, a tree barrier, an ultrahigh engineering vehicle, a floating object, a lightning stroke and a value greater than an early warning value, determining a high-risk interval section, and sending an early warning picture and a video to a background in real time.

The invention belongs to the technical field of power equipment, and particularly relates to a multi-modal deep learning edge computing platform for power equipment. The edge computing platform comprises a data acquisition module, a data fusion module, a dynamic model updating module, an edge computing optimization module, an intelligent alarm and prediction module, an upstream and downstream data link integration module, an edge device resource management module, a security enhancement module and a simulation and emulation module. And the data fusion module performs splicing, weighted fusion or PCA mathematical transformation on the features of different modalities in the feature extraction stage to form a comprehensive feature vector, and performs fusion according to model output results of different modalities in the model decision-making layer, including use of a voting mechanism, weighted scoring or logistic regression. According to the invention, by integrating a multi-modal deep learning technology and an edge computing architecture, comprehensive performance improvement and cost saving are brought to power equipment monitoring and management.

The invention discloses a fault reason analysis method and device for an IT operation and maintenance scene. A server side method comprises the steps that a question request sent by a client side is received; the question request carries a to-be-analyzed operation and maintenance question; converting the problem into high-dimensional vector representation as a target vector; calculating the similarity between the target vector and a vectorized object of each entity stored in a preset dynamic vector knowledge base, and taking the entity of which the similarity is greater than a preset similarity threshold as a target entity; querying a target vectorization object and a target field attribute corresponding to each target entity from a preset dynamic knowledge graph; according to the target vectorization object and the target field attribute, constructing a plurality of prompt words used for guiding a large language model to carry out fault reason analysis; and inputting the plurality of prompt words into a pre-fine-tuned large language model, outputting a fault reason corresponding to the operation and maintenance problem, and sending the fault reason to the client. Therefore, by adopting the method and the device, the fault reason of the system can be quickly positioned, so that the stability of the system is improved.

The invention relates to the technical field of power system diagnosis, in particular to a power distribution network single-phase earth fault multilateral collaborative diagnosis and positioning method, which comprises the following steps: S1, collecting current and voltage signals in a power distribution network in real time through a sensor network; s2, preprocessing the current and voltage signals; s3, analyzing the unbalance degree of the current and the abrupt change point of the voltage, and identifying whether a single-phase earth fault occurs or not; s4, calculating the propagation time difference of the fault signal by using a time difference of arrival method, and preliminarily estimating the position of the fault; s5, correcting a fault position to locate a specific fault point; s6, generating a fault positioning report; and S7, according to a fault positioning result, triggering a processing measure to send a maintenance instruction to related personnel. According to the invention, through combination of multi-data source analysis and a power grid topological structure, an accurate fault positioning and automatic processing flow is provided, the fault detection precision and the processing efficiency are effectively improved, and stable operation of a power grid is ensured.

The invention discloses a wind turbine generator variable pitch communication fault monitoring system and device, and relates to the technical field of monitoring systems.The wind turbine generator variable pitch communication fault monitoring system comprises an on-site monitoring system which comprises a wind turbine generator variable pitch control unit, an on-site monitoring module, a data acquisition module and a communication module and is used for acquiring, processing and transmitting monitoring data of a unit in real time; and the central monitoring system comprises a data receiving module, a data storage library, a state judgment module and a fault signal sending module, and is used for receiving, storing and analyzing data and early warning faults. According to the method, equipment damage and casualty risks caused by fault upgrading are avoided, powerful support is provided for safe and stable operation of the wind generating set, and the shutdown risk and the maintenance difficulty are remarkably reduced.

The invention relates to the technical field of power distribution systems, in particular to a power distribution system operation fault monitoring and predicting system which comprises a data acquisition module used for acquiring operation data of a power distribution system in real time; the data processing module is used for preprocessing, storing and transmitting the data acquired by the data acquisition module; the fault detection and prediction module is used for monitoring the operation state of the power distribution system in real time, detecting abnormal conditions and predicting potential faults; the user interface is used for man-machine interaction and displaying real-time data, historical data, alarm information and maintenance suggestions; the maintenance management module is used for making a management maintenance plan, recording maintenance information and evaluating a maintenance effect; early detection and prediction of faults are realized, and reliability and operation efficiency of a power distribution system are improved.

The present invention relates to the technical field of power plant production inspection. The present invention provides a power plant intelligent inspection system based on a large model. The present invention realizes data transmission and processing through communication connection between the server side and multiple inspection devices and the inspected equipment. The present invention includes multiple units such as sensor fault detection, drone inspection optimization, data fusion analysis, and data acquisition fault repair, aiming to solve the inspection distortion problem caused by sensor failure or data transmission interference, and at the same time improve the inspection stability and accuracy of drones under harsh conditions. The present invention uses a random forest algorithm to construct a sensor data model, dynamically adjusts the drone inspection path, and performs data fusion analysis through deep learning to generate accurate inspection conclusions and disposal plans. The present invention not only improves the accuracy and efficiency of inspections, but also further enhances the intelligence and adaptability of the system.

The invention discloses a power grid fault diagnosis method, which belongs to the field of power grid diagnosis and combines a topological structure and equipment configuration of a power grid, constructs a dynamic fault tree model and dynamically adjusts node states and logic relations in the model so as to realize rapid detection and accurate positioning of faults. The method mainly comprises the following steps: data acquisition: collecting real-time operation data from each monitoring point of a power grid; data preprocessing: cleaning and standardizing the collected data; constructing a static fault tree: constructing a static fault tree model according to the topological structure and equipment configuration of the power grid; dynamically updating the fault tree: dynamically adjusting a fault tree model in combination with real-time operation data; fault mode identification: identifying a potential fault mode through logical operation by using the updated fault tree model; fault positioning and diagnosis: based on the identified fault mode, determining the position and the type of the fault; and fault response: according to the diagnosis result. The method is mainly used for power grid fault diagnosis.

The invention discloses a power distribution network fault positioning method and system based on multi-source sensing fusion, and relates to the technical field of intelligent power distribution, and the method comprises the following steps: collecting high-frequency fluctuation signals corresponding to a plurality of sensing points around the transient high-frequency disturbance generated at the initial stage of a power distribution network fault, expanding each high-frequency fluctuation signal under a unified time reference, and obtaining a plurality of high-frequency fluctuation signals; and extracting starting and ending moments of protruding segments in the high-frequency fluctuation signal, and forming a local amplification fingerprint set based on the protruding segments. According to the invention, by introducing local amplification fingerprint identification and propagation consistency constraint, the pseudo propagation influence is eliminated, so that the fault trace is more consistent with the real propagation law, and the positioning accuracy and stability are improved; and meanwhile, through bandwidth convergence type peak clipping and energy boundary retention, in combination with time and space anchoring of a main fault trace, accurate locking and dynamic isolation are realized, and the fault response speed and the system reliability are improved.

The present invention discloses an RPA business data anomaly detection method and detection system integrating an AI model, and belongs to the field of data processing technology. Aiming at the abnormal challenges in the RPA business data processing of enterprises, the present invention constructs a set of efficient and collaborative anomaly detection mechanisms. After receiving the anomaly detection request, the mechanism can quickly extract data from the securely connected target database, thanks to the pre-cross-certification processing to ensure data security. Advanced algorithms are used to deeply analyze data, accurately generate abnormal labels, provide intuitive instructions for operation and maintenance, and drive the automatic troubleshooting and updating of processes, significantly shortening the fault response time, reducing the risk of business interruption, and ensuring the continuous and stable operation of enterprises. Automated and intelligent processing methods reduce the manual burden, improve the operation and maintenance efficiency, provide technical support for the digital transformation of enterprises, effectively solve the problems of complex data processing, slow abnormal response, and heavy manual troubleshooting in RPA applications, and improve the safety and efficiency of enterprise operations.

The invention provides a machine room monitoring system based on zabbix. The system comprises a zabbix proxy client deployed on monitored equipment in a machine room, a zabbix service center connected with the zabbix proxy client, a database connected with the zabbix service center and a zabbix user front end connected with the database. The zabbix proxy client side is used for collecting monitoring data of monitored equipment where the zabbix proxy client side is located; the zabbix service center is used for acquiring the monitoring data, storing the monitoring data to a database, and performing matching operation on the monitoring data and each preset triggering condition to determine whether the triggering condition matched with the monitoring data exists or not; if yes, executing an action corresponding to the matched triggering condition; regularly generating an analysis report, and storing the analysis report in a database; and the zabbix user front end is used for acquiring the monitoring data and / or the analysis report from the database and displaying the monitoring data and / or the analysis report on an interface. The method does not need a large amount of human resources, is not liable to cause omissions, and is more suitable for monitoring a large-scale machine room.

The invention discloses an interface monitoring method and device, equipment, a storage medium and a product, and relates to the technical field of computers.The interface monitoring method comprises the steps that an interface monitoring task is received, and an interface arrangement script is determined according to the interface monitoring task; generating an execution command script based on the interface monitoring task, and performing parameter configuration on the interface arrangement script according to the execution command script to obtain a target interface arrangement script; and executing the target interface arrangement script according to a preset remote configuration tool to obtain an interface monitoring result. Compared with an existing mode that the log is checked manually and regularly for interface monitoring, the method has the advantages that the interface monitoring can be automatically carried out, the fault response time is greatly shortened, the operation and maintenance efficiency and stability of a large-scale cluster are effectively improved, and the resource waste and the operation and maintenance cost are reduced.

This invention provides a campus hardware and software integrated management method and system supporting remote operation via mobile devices. It collects status parameters and business data from heterogeneous devices in the campus teaching, security, and logistics domains, synchronizes them with a hybrid clock, and pushes them to a campus digital twin virtual model incorporating geographic information, achieving full-domain resource virtual-real mapping and 3D visualization. A sliding time window combined with a business weight correction mechanism is used to determine adaptive warning thresholds and cross-device linkage strategies based on device operating time and business scenarios. Mobile devices access the system via an end-to-end encrypted link, enabling remote hierarchical handling based on dynamic hierarchical permissions. Hierarchical warnings are generated when a single parameter exceeds limits or multi-dimensional correlation anomalies are detected. If remote handling is ineffective, maintenance work orders are automatically generated and resources are intelligently scheduled, forming a closed-loop management process. This solution breaks down campus hardware and software data silos, enabling deep mobile control and intelligent operation and maintenance, significantly improving campus management efficiency and emergency response capabilities.

The invention provides an intelligent maintenance, inspection, evaluation and management system for a wind power plant, and relates to the technical field of environmental protection energy power plant management, and the system comprises a central control subsystem which is used for providing terminal control and facilitating the observation and control of each index of the wind power plant by a worker, and the central control subsystem comprises a control terminal. According to the system, environment and equipment data of a wind power plant are comprehensively monitored by integrating various sensors, analysis is performed by using real-time monitoring, power prediction and fault alarm programs, accurate overall condition judgment is provided, the operation and maintenance efficiency is improved, and a scientific basis is provided for power dispatching and maintenance plans. Meanwhile, automatic inspection equipment and an intelligent management module are adopted, the inspection frequency and the coverage range are remarkably improved, and through defect, work order and safety management functions, equipment defects are recorded and tracked in time, signals to be overhauled are automatically sent, the fault response time is shortened, the shutdown risk is reduced, and the overall operation efficiency and safety are improved.

The invention discloses a production inspection method for an online environment and a related device, relates to the technical field of software, and determines an online fault of the online environment through an operation result of a target production inspection task. Business inspection warning information, business processing progress information and processing completion information can be respectively generated according to the processing progress of the online fault. The business inspection warning information, the business processing progress information and the processing completion information can be displayed in the same notification group, and the processing progress can be displayed. Therefore, others in the same notification group can see the processing progress of the online fault. According to the invention, the alarm processing flow is standardized and automated. And online faults are distributed through the principal identifier, so that the alarm processing becomes efficient. According to the invention, the operation and maintenance personnel can know the occurrence of the fault at the first time, thereby reducing the fault response time.

The application discloses a kind of protection system and method of IGBT drive signal based on DSP and FPGA, belong to power electronics and power semiconductor control technical field.System includes signal acquisition unit, DSP master module, FPGA co-control module, IGBT drive circuit and IGBT power module;Signal acquisition unit acquires the operating state information of IGBT power module;DSP master module receives the control instruction and initial drive parameter of external system and carries out optimization to the parameter, handles fault signal and generates fault response instruction, sends control instruction, fault response instruction and optimized drive parameter to FPGA co-control module;FPGA co-control module generates PWM signal, generates fault signal and protection instruction according to the operating state information of IGBT power module when fault occurs;IGBT drive circuit receives PWM signal and protection instruction, generates IGBT drive signal;IGBT power module receives drive signal and responds, generates operating state information.The application can realize the real-time generation of IGBT drive signal, fault protection and dynamic optimization.

The invention relates to the technical field of major activity safety guarantee, and particularly discloses a major activity safety guarantee risk management system and method based on risk portray.The system comprises a data footstone layer, an analysis, study and judgment layer, a decision action layer and a verification optimization layer, and the data footstone layer constructs a dynamic hidden danger library following the four-dimensional principle; multi-source data are gathered through a standardized model, risks are quantified based on a weighted RPN formula, and a data injection and dynamic updating mechanism is matched. And the analysis, research and judgment layer generates a risk portrait report containing a structured list through scene modeling and multi-dimensional aggregation analysis. The decision action layer converts the portraits into tactical actions such as special plans, resource presetting and special drilling. And the verification optimization layer realizes risk monitoring regulation and knowledge precipitation through closed-loop management. According to the invention, the conversion from passive response to data-driven active defense is realized, the risk identification and pre-judgment precision is improved, the resource configuration and emergency efficiency are optimized, the system self-learning evolution is driven, and the risk recurrence rate of the same kind is reduced.

The invention provides a current-limiting fault ride-through method and system for a network construction type converter and a new energy unit, and belongs to the technical field of converters and new energy units. The method comprises the steps of collecting multi-source heterogeneous data, and performing data preprocessing to obtain standardized data; coupling analysis is carried out on the fault risk of the new energy and the power grid and the safety redundancy capability of the device, and pre-adaptive parameters are initialized and set; continuously monitoring the state of the power grid, and judging and triggering fault occurrence; constructing a multi-parameter coupling analysis model, calculating an optimal current limiting target and adjusting core current limiting parameters; continuously monitoring a fault recovery state by a control core of the network-building type converter, and switching an operation mode of the network-building type converter; and constructing a multi-scene correlation analysis model, carrying out model and parameter optimization calculation, and updating the parameters after application optimization. The fault response time is shortened, different fault scenes are matched, the service life of a power device is prolonged, the whole life cycle of the fault ride-through capability is improved, and diversified operation requirements of a new energy power grid are met.

The invention provides an electrical equipment operation and maintenance management system based on a power supply and distribution project, and relates to the technical field of electrical equipment management. The electrical equipment operation and maintenance management system based on the power supply and distribution project comprises a data acquisition layer which acquires operation data, including related parameters of current, voltage, temperature and humidity, of electrical equipment in real time through a sensor and an intelligent instrument; the data transmission layer is used for transmitting the acquired data to a central server by utilizing a wireless communication technology; according to the data processing layer, the central server stores, analyzes and processes the received data, and fault prediction and health state evaluation are carried out by adopting big data analysis and a machine learning algorithm. By using a machine learning algorithm, analyzing historical data and establishing an equipment fault prediction model, potential faults can be identified in advance, a fault diagnosis report can be provided, maintenance personnel can be guided to maintain, the reliability of equipment is improved, and the service life of the equipment is prolonged.

The application discloses a kind of mechanical arm wireless energy transmission method and system with direction neutralization strategy, it is related to wireless energy transmission technical field.Passive sensing module receives magnetic field signal and combines node feedback, extract node three-dimensional coordinate, direction and energy state, construct three-dimensional space layout diagram;Low-power intelligent control module judges energy supply scene accordingly, single-source scene calculates the optimal alignment posture of coil, multi-source scene divides energy supply area by clustering, calculates geometric center and adjusts direction according to node energy, power weight, avoids barrier scene and selects suboptimal path without shelter;Control module issues instruction to mechanical arm servo system and coil driving module, adjusts coil to target posture and structure;Compare data with optimal value, low-power node is preferentially stored energy, and energy sufficient node is promptly corrected posture.The method improves transmission efficiency and energy supply reliability by passive sensing, direction neutralization strategy and dynamic correction, adapts to the demand of multi-scene wireless energy transmission.

The invention provides a power wire breakage fault detection device and a use method, and relates to the technical field, through distributed high-precision acquisition and strong electromagnetic interference resistance and vibration resistance design of a multi-mode sensing module, a dynamic environment noise model is constructed and a background interference intensity factor is generated by combining an environment noise modeling module and utilizing machine learning; complex environment noise is effectively filtered out, the false alarm rate is remarkably reduced, and the real broken line signal capturing capacity is improved; a risk assessment unit of a system adaptive optimization module associates a fault confidence index with a background interference intensity factor, and fits a broken line risk assessment index, so that adaptive detection sensitivity adjustment based on real-time environmental noise is realized; and the strategy optimization unit carries out multi-level risk assessment and grading treatment through an assessment threshold interval, so that the fault response time is shortened, the capturing and early warning efficiency of incomplete or initial weak signals is improved, and the safety of a railway power supply system is effectively guaranteed.

The invention discloses an equipment inspection and management method based on image recognition, and the method comprises the steps: carrying out the digital filing of a target region and equipment in the region, obtaining the basic information, maintenance information and position information of the equipment, and setting a corresponding id for each piece of equipment; establishing a digital twinborn model for the target area and equipment in the area; obtaining the image and position information of the fault equipment uploaded by the inspection equipment, and determining the id of the fault equipment based on image recognition and / or the digital twinborn model; and generating a work order according to the fault equipment id, and generating a corresponding fault description. The invention further discloses equipment inspection and management equipment based on digital twinning and image recognition and a storage medium. According to the equipment inspection and management method based on image recognition, rapid recognition and fault detection of the equipment are realized, the fault response time is shortened, and the loss caused by the equipment fault is reduced.

The invention provides an energy scheduling and MPPT (Maximum Power Point Tracking) control system and method suitable for multi-path photovoltaic. The system comprises a plurality of photovoltaic panels, a photovoltaic conversion module, a battery bus, a battery management module and a main control module, wherein the main control module integrates a sampling unit, an energy scheduling unit, an MPPT control unit and a comparator. The sampling unit obtains voltages and currents of all paths of photovoltaic and battery buses, the energy dispatching unit distributes reference power under the constraint of target charging power, the MPPT control unit drives all photovoltaic conversion modules to track the maximum power point under the constraint of the reference power, and the comparator compares a sampling electrical parameter with a safety threshold value. And the corresponding photovoltaic conversion module is turned off through the independent hardware access during overrun. According to the scheme, matching of multi-path photovoltaic power and battery requirements is achieved, the photovoltaic energy utilization rate is improved, fluctuation of a battery bus is reduced, and the fault response speed and the system safety are improved.

The invention provides a vehicle-mounted wireless intelligent monitoring and real-time interference early warning method, and belongs to the technical field of rail transit wireless communication, and the method comprises the steps: synchronously collecting multi-band wireless signals in a rail-mounted interval through a vehicle-mounted sensing terminal; analyzing the collected signals by using multiple parallel demodulation channels to obtain signal parameters; based on the analyzed signal parameters, constructing a signal quality thermodynamic diagram of the rail travel interval in real time, and performing state evaluation by using a dynamic interference early warning model; and when the state evaluation result meets a preset alarm threshold condition, interference early warning information is automatically generated and reported. According to the invention, comprehensive monitoring and accurate early warning of a rail transit wireless communication environment can be realized, the accuracy and real-time performance of interference identification are effectively improved, and stable operation of a CBTC system is guaranteed.

The application discloses a low-voltage area distribution network fault diagnosis method based on light storage equipment trusted access, and belongs to the technical field of power grid fault diagnosis. The steps comprise the following: in a trusted computing environment constructed by a security chip, a protocol deep analysis is performed on a data packet of a light storage equipment to obtain trusted standardized data; a minimum feature subset required for fault diagnosis is dynamically selected based on a maximum correlation minimum redundancy algorithm, and encrypted transmission is collected to obtain efficient sensing data; the efficient sensing data is fused with historical fault cases to construct an edge fault diagnosis knowledge unit; when a fault occurs, the edge fault diagnosis knowledge unit is used to perform parallel execution of fault type rapid identification and power supply recovery potential pre-evaluation. The application adopts protocol deep analysis and a trusted computing environment, can ensure the integrity and security of non-standard access data, and dynamically optimizes the dimension of measurement data through a maximum correlation minimum redundancy algorithm, thereby improving the real-time performance and efficiency of fault type rapid identification and power supply recovery potential pre-evaluation.

This utility model discloses an energy storage cabinet that is easy to maintain, relating to the field of energy storage cabinets. The key technical features are: a cabinet body with a front door and a rear door; the front door is used for operating and viewing the internal equipment, and the rear door is used for maintenance; the cabinet body houses electrical components and battery modules, both of which adopt a drawer-type installation structure and can only be pulled out when the rear door is open. This design significantly reduces the workload of staff by eliminating the need for additional complex fixing structures, allowing maintenance personnel to directly inspect faulty components, thus achieving convenient maintenance. Furthermore, the separation of functions between the front and rear doors ensures both the safety of daily operations and the efficiency of maintenance work.

The utility model discloses a compact high -voltage outgoing line cabinet relates to transformer manufacturing technical field, aims at solving the low space utilization of existing equipment, poor heat dissipation, insufficient reliability and other problems. It includes cabinet body, top cover and base, and the cabinet body is divided into upper, middle, lower layer space through mounting panel, and the lower layer is equipped with lower disconnecting switch, and the contact blade is inner circular arc shape, and is matched with the linkage plate and lock catch structure, reduces the space occupation and promotes the operation accuracy, and the top cover is split type, and forms efficient heat dissipation system with the base cavity, and can switch heat dissipation mode according to temperature, and the integrated control panel, observation window and operating lever are convenient for operation and maintenance. The utility model improves space utilization, power transmission efficiency and equipment stability through the modularization stratification, structural innovation and heat dissipation optimization, and reduces the cost.