تشخیص خطا برای قطع کننده های مدار بر اساس خوشه بندی
Abstract
1- Introduction
2- CB trip/close CC signals and CB operation
3- Theoretical basis of proposed method
4- Optimized AP clustering algorithm
5- Fault diagnosis method
6- Simulation results
7- Conclusion
References
Abstract
Online condition monitoring and fault diagnosis of circuit breakers (CBs) is a significant method to effectively improve the stability and reliability of the power system. However, the currently used fault diagnosis method still have certain defects including the inability to identify unknown faults for training samples. Therefore, this paper proposes an evolving method for fast and accurate online fault diagnosis of CBs. On the basis of collecting samples of CB trip/close coil current (CC) features, an optimized affinity propagation (AP) clustering algorithm to accurately extract the sample clustering exemplars is presented. Additionally, operating state identification and fault diagnosis of CBs is carried out by calculating the similarity coefficient between the new sample and exemplars online. Diagnosis of unknown faults is also achieved by introducing the threshold and comparing it with similarity coefficient results. Simulation results prove that the proposed method can precisely identify various known CBs faults and has the ability to recognizes unknown CBs fault samples even when the number of training samples is small, providing a foundation for CB fault location and condition-based maintenance.
Introduction
Circuit breakers (CBs) are recognized as one of the most crucial components to power equipment. They are the key to isolating faulty components driven by protection devices, and play a dual role in the protection and control of the power system [1]. Incorrect operation of CBs can cause a power grid accident or expand the scope of the accident. In severe cases of CB failure, the power system may collapse and cause major economic losses. Therefore, online monitoring and fault diagnosis of CBs have practical significance for enhancing the reliability and stability of the electric power system. According to the CIGRE surveys, more than 80 per cent of the fault of CBs is caused by operating mechanism and auxiliary control circuits failures. The trip and close coil current (CC) signal is an accessible and noninvasive parameter in CB online condition monitoring. Previous studies have determined that analysis of the CC characteristics can identify effectively many signs of various faults type occurring in control circuits and operating mechanism [2–4]. In recent years, increasingly advanced data-analytics algorithms have been applied to implement the fault diagnosis of various kinds of power equipment [5,6], and also provide a new approach in the fault diagnosis of CBs [7–10]. In Ref. [11], a fault diagnosis method was proposed which has high detection accuracy, and utilizes back-propagation neural network (BPNN) technique. BPNN requires a large number of training samples to ensure an accurate diagnosis. However, it is usually quite difficult to obtain general and sufficient CC fault samples in practical applications, which is because the operating frequency and average failure rate of CBs are relatively low, and different types of the CB may have different CC waveform. In addition, Ref. [12] proposed a fault diagnosis method combining CC characteristics and support vector machine algorithm, which can obtain better diagnostic results compared with the BPNN method in the small training sample case.