The intelligent monitoring system for the box-type substation leverages the deep integration of multi-dimensional data perception, edge computing, IoT communications, and cloud platform analysis to build a fault warning system that covers the entire equipment lifecycle. Its core mechanism can be broken down into four key components: data collection, intelligent analysis, warning issuance, and operational response.
In the data collection phase, the system utilizes comprehensive status sensing equipment deployed within the box-type substation to achieve comprehensive monitoring of electrical parameters, environmental conditions, and equipment status. For electrical parameter monitoring, the intelligent monitoring terminal collects core indicators such as voltage, current, and power factor in real time. Combined with technologies such as switchgear busbar temperature measurement and feeder power and temperature monitoring, it accurately detects early signs of abnormal operating conditions such as overloads and short circuits. For example, in high-voltage switchgear, the system uses ultra-high frequency (UHF) sensors and ultrasonic sensors to detect electromagnetic pulses and mechanical vibration waves generated by partial discharge, thereby proactively identifying potential insulation degradation risks. Environmental status monitoring utilizes temperature and humidity sensors, smoke alarms, and water leak detection devices to continuously track temperature, humidity, gas concentration, and water intrusion within the box around the clock, effectively preventing equipment failures caused by environmental deterioration. Furthermore, the integration of smart cameras with the access control system enables real-time monitoring of security incidents such as equipment damage and unauthorized intrusion, further expanding the coverage of fault warnings.
Intelligent analysis is the core of the system's fault warning capabilities. Based on collected multi-source, heterogeneous data, the system utilizes edge computing technology to perform data cleaning, feature extraction, and preliminary diagnosis locally. For example, the edge computing gateway filters and de-noises the raw signals uploaded by the sensors, extracting key characteristic parameters such as the amplitude, frequency, and phase of the discharge pulses. This information is then combined with historical equipment operating data to construct a health model. For complex faults, the system uploads data to a cloud platform via the Internet of Things (IoT) for in-depth analysis using machine learning algorithms. Cloud platform application software extracts time-frequency features from the discharge data and, combined with deep learning models such as convolutional neural networks (CNNs), automatically identifies typical defect patterns such as internal air gap discharge and surface contamination discharge, significantly improving diagnostic accuracy.
The design of the warning issuance mechanism fully considers the timeliness and accuracy of operation and maintenance responses. The system supports a multi-level early warning strategy, automatically matching notification methods to fault severity. For general anomalies, the system alerts operations and maintenance personnel through platform alerts and SMS notifications. For critical faults, the system initiates intervention measures such as phone alerts and audio and visual notifications to ensure timely information delivery. Furthermore, the system's built-in Beidou positioning function accurately maps the location of faulty box-type substations and, combined with electronic maps, generates optimal operation and maintenance routes, significantly reducing on-site response time. Furthermore, early warning information includes fault type, possible cause, and recommended remedial measures, providing full-process decision support for operations and maintenance personnel.
A closed-loop management mechanism ensures the effective implementation of early warning functions within the operations and maintenance response process. The system supports cloud-based storage of equipment files, allowing operations and maintenance personnel to access historical equipment operation records, maintenance logs, and other information at any time, providing data support for root cause analysis. The intelligent inspection module dynamically adjusts inspection cycles based on equipment status, implementing frequent monitoring of high-risk box-type substations to ensure early detection and resolution of potential hazards. The fault repair module supports automatic work order generation and dispatch. Operations and maintenance personnel can provide real-time feedback on the repair progress via a mobile app. The system then verifies the repair results and updates the equipment health profile. Through this closed-loop management mechanism of "monitoring-warning-action-verification," the system continuously optimizes fault warning functions and gradually improves operations and maintenance efficiency.
