Abstract
Power system health prognosis is a key process of condition-based maintenance. For the problem of large error in the residual lifetime prognosis of a power system, a novel residual lifetime prognosis model based on a high-order hidden semi-Markov model (HOHSMM) is proposed. First, HOHSMM is developed based on the hidden semi-Markov model (HSMM). An order reduction method and a composite node mechanism of HOHSMM based on permutation are proposed. The health state transition matrix and observation matrix are improved accordingly. The high-order model is transformed into the corresponding first-order model, and more node dependency information is stored in the parameter group to be estimated. Secondly, in order to estimate the parameters and optimize the structure of the proposed model, an intelligent optimization algorithm group is used instead of the expectation–maximization (EM) algorithm. Thus, the simplification of the topology of the high-order model by the intelligent optimization algorithm can be realized. Then, the state duration variables in the high-order model are defined and deduced. The prognosis method based on polynomial fitting is used to predict the residual lifetime of the power system when the prior distribution is unknown. Finally, the intelligent optimization algorithm is used to solve the proposed model, and experiments are performed based on a set of power system data sets to evaluate the performance of the proposed model. Compared with HSMM, the proposed model has better performance on the power system health prognosis problem and can get a relatively good solution in a short computation time.
Highlights
With the development of technology, the operation of power systems has an increasing demand for higher reliability, lower environmental risks, and higher human safety
The results showed that the model can effectively capture the characteristics of temperature data under various conditions Polynomial fitting was a simple tool for nonlinear fitting, which has efficient data processing ability [23]
In the whole life cycle of a high-voltage circuit breaker system, it is in normal state most of the time, and the number of normal samples in the monitored signal will be more than the number of fault samples, resulting in the imbalance of training data and unknown distribution
Summary
With the development of technology, the operation of power systems has an increasing demand for higher reliability, lower environmental risks, and higher human safety. Power system failures usually come at the cost of high maintenance costs and uncertain downtime. It is difficult to obtain accurate health status and predict failures of a power system in time. Power system health prognosis is an important topic in reliability and maintenance engineering, which determines how to properly integrate positioning degradation data into power system fault detection and failure prevention [1]. Health prognosis involves evaluating the current state, classifying the current state of several failure modes, and predicting the residual lifetime of the power system. Residual lifetime refers to the remaining life from the current health status to the functional failure of the system
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
