A Novel Remaining Useful Life Prediction Approach for Superbuck Converter Circuits Based on Modified Grey Wolf Optimizer-Support Vector Regression

Abstract

Abstract:The reliability of power packs is very important for the performance of electronic equipment and ensuring the reliability of power electronic circuits is especially vital for equipment security. An alteration in the converter component parameter can lead to the decline of the power supply quality. In order to effectively prevent failure and estimate the remaining useful life (RUL) of superbuck converters, a circuit failure prognostics framework is proposed in this paper. We employ the average value and ripple value of circuit output voltage as a feature set to calculate the Mahalanobis distance (MD) in order to reflect the health status of the circuit. Time varying MD sets form the circuit state time series. According to the working condition time series that have been obtained, we can predict the later situation with support vector regression (SVR). SVR has been improved by a modified grey wolf optimizer (MGWO) algorithm before estimating the RUL. This is the first attempt to apply the modified version of the grey wolf optimizer (GWO) to circuit prognostics and system health management (PHM). Subsequently, benchmark functions have been used to validate the performance of the MGWO. Finally, the simulation results of comparative experiments demonstrate that MGWO-SVR can predict the RUL of circuits with smaller error and higher prediction precision.

An Overview of Artificial Intelligence Applications for Power Electronics

“…They are developed and improved with different biological inspirations. In addition to the above widely applied metaheuristic methods, several other emerging approaches have been applied in a limited scale, e.g., biogeography-based optimization [72], crow search algorithm [73], grey wolf optimization [74], firefly optimization algorithm [16], bee algorithm [75], colonial competitive algorithm [76], teaching-learning-based optimization [77], etc. It is worth mentioning that the selection of the best method is not a simple task, which is application-dependent [12].…”

“…The diagrammatic representation implies the conditional dependence relationship between the decision variables. The underlying relationship in the model is formulated in the Bayesian framework [1] and can be inferred in Design [79], [80], Control [4], [51], [81]- [85], Maintenance [86]- [97] Radial Control [121], [122], Maintenance [7], [74], [96], [118], [123]- [126] Relevance vector machine (RVM)…”

“…21 that the error bar of the evolution of ∆V ce,on is explicitly derived, which can be further utilized for the calculation of the confidence interval of RUL. Another example of kernel method for RUL prediction can be found in [74], where a support vector machine is applied to the degradation modeling of a buck converter.…”

“…It is challenging for power electronics since operational settings of the insitu system (i.e., the test data) are quite different from that of the training dataset, which is generally obtained with accelerated testing experiments. The majority of the research [74], [114], [119] assumes that the operational settings of the in-situ system are identical to the training dataset (e.g., accelerated aging experiments), which may not be the case in field applications. Thus, the adaptive capability of the AI-based RUL prediction method is critical to bridge academic research and industrial applications.…”

Detection and Classification of Recessive Weakness in Superbuck Converter Based on WPD-PCA and Probabilistic Neural Network

“…More than 90 percent of spacecrafts domestically and overseas adopt such power systems for energy conversion. A fault in the circuit is unexpected and unavoidable, which is caused by many reasons, so it is of great significance to fault detection and diagnosis [3]. The recessive weakness of component parameters in a circuit is even more complicated to identify and detect, presenting unsteady inner system performance without a dominant output character difference from the normal operating situation.…”