Trip coil signature analysis and interpretation for distribution circuit breaker condition assessment and diagnosis

A Real-Time Data-Driven Algorithm for Health Diagnosis and Prognosis of a Circuit Breaker Trip Assembly

“…Content may change prior to final publication. The algorithm further predicts mechanical problems for trip events - 6,7,9,10,11,13,14,15,16,17,19,20,21,22,23 and 24 for the next 5 trip events after each of these events respectively. It can be seen that except 1 of these predictions (i.e.…”

“…However, automated or online CB TC assembly health monitoring is a relatively new concept that is now possible because of the developments in the field of electronic sensors and data acquisition equipment. This has led to the development of a few online trip coil health-monitoring algorithms [11][12][13][14][15]. In [11], the analysis server takes knowledge derived from inspection of the data set and applies it to the setting of thresholds defining acceptable operating times associated with each of the five breaker test featureslatch time, buffer time, main contact time, auxiliary contact time, and end time.…”

“…Here, the solution is implemented using advanced wavelet transforms for waveform feature extraction and an expert system for decision making. Authors in [14] presented a data driven approach to the condition assessment of distribution circuit breakers, operating on trip signature data captured from a population of in-service circuit breakers during routine trip testing. This allows analysts and maintenance experts to identify and establish correlations between signature shapes and specific breaker conditions.…”

“…In [15], 6 different transitional points are identified in the trip coil current signature and their individual deviations from the expected normal condition values are treated as indicators of trip coil health condition. But, these circuit breaker health monitoring algorithms [11][12][13][14][15] are mostly focused on identifying the mechanical health problems of the trip coil rather than those caused due to electrical problems. These algorithms in general utilize the operating time of the different components of the trip coil arrangement, and are not focused at the varying magnitude of the current flowing through the trip coil.…”

Practical Applications of Data Mining in Plant Monitoring and Diagnostics

“…These features subsequently form a feature vector characterizing the operating sequence of the breaker [5] B. Data Segmentation and Visualization The application of a suitable clustering algorithm enabled segmentation of the trip signature data into distinct clusters where specific trip signature shapes characterized by these clusters can be considered indicative of distinct breaker conditions/behavior ( Figure 3).…”