In the operation of industrial automation and power systems, AC motors are core power equipment, and the performance of their AC motor capacitors directly affects the stable operation of the motor. Once AC motor capacitors fail, it may cause difficulty in starting the motor, reduce efficiency, or even shut down. Therefore, building efficient online monitoring technology and fault warning systems, grasping the operating status of AC motor capacitors in real time, and discovering potential problems in advance have become key links to ensure the safe and reliable operation of power systems.
Online monitoring of AC motor capacitors requires focus on a number of key parameters. Capacitance value is the core indicator reflecting the performance of AC motor capacitors. Abnormal changes in capacitance value often indicate problems such as internal dielectric aging and plate damage; working voltage and current parameters reflect the load state of AC motor capacitors. Overvoltage and overcurrent will accelerate the aging of AC motor capacitors and even cause breakdown failures; dielectric loss factor (tanδ) can intuitively reflect the degree of loss of the internal dielectric of AC motor capacitors. An increase in its value indicates that the dielectric performance is reduced and there is a potential risk of failure. In addition, the operating temperature of AC motor capacitors is also an important monitoring object. Excessive temperature may be caused by abnormal internal heating or poor heat dissipation. Continuous high temperature will seriously shorten the service life of AC motor capacitors.
In order to accurately obtain the monitoring parameters, it is necessary to select appropriate sensor technology. Capacitive sensors can directly measure the capacitance value of ac motor capacitors, and monitor the capacitance changes in real time through non-contact induction or direct access to the circuit; voltage and current sensors use electromagnetic induction or Hall effect principles to convert high voltage and large current into measurable electrical signals to achieve accurate acquisition of working voltage and current; for the measurement of dielectric loss factor, a special measuring device based on the bridge principle is often used, which can quickly and accurately calculate the tanδ value. Temperature sensors are mostly thermocouples or thermal resistors, which can be installed on the surface or key internal parts of ac motor capacitors to monitor temperature changes in real time. These sensors are reasonably laid out and installed to ensure the accuracy and reliability of the collected data.
The collected monitoring data needs to be analyzed with the help of a reliable data transmission and processing system. Wired or wireless communication technology is used to transmit the data collected by the sensor to the data processing center. Wired communications such as Ethernet and RS-485 have the characteristics of stable transmission and strong anti-interference ability; wireless communications such as Bluetooth, Zigbee, 4G/5G, etc. are suitable for difficult wiring or remote monitoring scenarios. In terms of data processing, digital signal processing (DSP) technology and data analysis algorithms are used to filter, reduce noise, extract features, etc. on the collected data, remove interference signals, extract key features reflecting the operating status of ac motor capacitors, and provide accurate data support for fault diagnosis.
Based on the processed data, a scientific fault diagnosis model needs to be established. Machine learning algorithms, such as support vector machines (SVM) and artificial neural networks (ANN), are used to train a large amount of monitoring data under normal operation and fault conditions to learn the mapping relationship between different fault types and characteristic parameters of ac motor capacitors. By setting reasonable thresholds and judgment rules, when the monitoring data exceeds the normal range, the model can quickly identify the fault type, such as a decrease in capacitance value may indicate dielectric breakdown, and an increase in dielectric loss factor may mean dielectric aging, thereby achieving accurate diagnosis of ac motor capacitors faults.
The fault warning system is an important extension of online monitoring. According to the fault diagnosis results, combined with the importance of ac motor capacitors and the level of fault hazard, a hierarchical warning mechanism is designed. When the monitoring data approaches or reaches the warning threshold, the system will promptly send an alarm to the operation and maintenance personnel through various means such as sound and light alarm, SMS push, and email notification to inform the fault type, severity, and location. At the same time, the early warning system can also be linked with the motor control system to automatically take protective measures such as load reduction and shutdown when the fault is serious to prevent the fault from expanding and ensure the safety of equipment and personnel.
The online monitoring and fault warning system needs to be maintained and optimized regularly. Calibrate and check the sensor to ensure the accuracy of data collection; check the data transmission line and communication module to ensure the stability of data transmission; update and optimize the fault diagnosis model, introduce new algorithms and data, and improve the accuracy of fault diagnosis. In addition, collect fault cases and monitoring data in actual operation, analyze the deficiencies of the system, improve the system function and performance in a targeted manner, and continuously improve the reliability and practicality of the system.
With the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, the online monitoring and fault warning system of AC motor capacitors will usher in new development opportunities. In the future, the system can be deeply integrated with the industrial Internet platform to achieve data sharing and collaborative analysis of multiple devices and multiple sites; use big data technology to deeply mine massive monitoring data, predict the remaining service life of AC motor capacitors, and realize predictive maintenance; combine artificial intelligence technology to further optimize the fault diagnosis model, improve the intelligence level of fault diagnosis, and provide strong support for the intelligent operation and maintenance of the power system.
