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Power Electronic System Maintenance for Enhanced Reliability

  • Contents

Overview: The article discusses the importance of maintenance in ensuring the reliability and safety of power electronic systems. It outlines the steps involved in maintenance, including condition observation, anomaly identification, defect diagnosis, and remaining useful life prediction.

 

Power electronic systems are subject to a variety of risks, including catastrophic failures, despite the careful consideration of dependability characteristics during design and control. This is because of the complex and demanding operating settings of power electronic systems. For field applications, power electronic components, converters, and systems must be extremely reliable and safe.

 

What are the steps in maintenance to make the power electronic system more reliable?

Preventive maintenance systems are useful ways to guarantee that planned functions are carried out as intended. The steps in maintenance of power electronic system includes

  • Condition observation
  • Identification of anomalies
  • Diagnosing defects
  • Remaining Use Life (RUL) prediction

The above actions coincide with the IEEE standard framework of prognostics and health management for electronic systems.

 

Condition Observation

Power electronics condition observation consists of

  • Identification of system parameters
  • Preprocessing data
  • Mining features

The data from the condition observation is used to discover informative and hidden patterns that form the foundation for the prognostic and health management applications that follow.

 

Identification of System Parameters

Identification of system parameters involves the gathering of data for important components.

Characteristics of power electronic systems includes

  • Extremely small space inside a power module
  • Extremely fast switching frequency
  • Relatively insignificant parameter changes in terms of aging, etc.

Because of these characteristics, developing specific hardware for parameter identification is quite a challenging task.

A noninvasive approach that uses existing physical signals to indirectly get information or estimate relevant information without the need for additional hardware implementation is one of the more promising methods.

Therefore, a sensorless and cost-effective option can be used for condition monitoring, which is good for people who work in industry. In general, there are two types of methods for identifying system parameters:

  • Model-free
  • Model-based.

 

Preprocessing data and Mining features

The goal of data preprocessing and feature mining is to improve the quality of the raw data so that it can be used for applications like problem diagnostics.

Improving the quality of data involves the following steps to make it more organized. The steps are as follows

  • Data cleaning to minimize noise
  • Data clustering is used to find groups of related data points
  • Density estimation is used to determine the distribution of the data
  • Data compression to reduce the number of features by projecting large-sized data to small-sized data
  • Data fusion to combine various information sources, and more

When data preparation and feature mining are done correctly, the performance of the ensuing prognostics and health management applications—such as diagnostic accuracy—can usually be greatly enhanced.

 

Identification of Anomalies and Diagnosing Defects

The anomaly detection process focuses on identifying unusual patterns and making a binary decision. When the nominal parameters or rated system characteristics exceed the predetermined safety range, it gives an indication.

The fault diagnosis finds and identifies the specific failure modes after the unusual changes happen.

The classification, regression, or clustering tasks are essentially anomaly detection and fault diagnosis. When a new fault signature arrives, it identifies the fault label based on the learned relationship from the training stage.

Anomaly detection and fault diagnosis techniques fall into two categories:

  • Supervised learning
  • Unsupervised learning

 

Remaining Useful Life (RUL) Prediction

In the design phase, lifetime prediction serves to support the characteristics of a population of units known as the ‘Design for Reliability’. It is one of the crucial components of prognostics and health management.

The purpose of the estimation of RUL is not to accurately predict the lifespan of a population of units. Based on condition monitoring data, it predicts the remaining lifespan of each single unit in operation. For applications where availability, safety, or reliability are crucial, RUL prediction is used as an extra tool to lower uncertainty.

The lifetime estimate is subject to several challenges, such as

  • Inaccuracies in model calibration
  • Manufacturing tolerances
  • Differences in operational environments and workload

When a particular unit is operated in the field, these uncertainties lead to inaccurate reliability estimations. The following areas require greater attention in order to improve the practicality of AI-based RUL prediction techniques for field applications.

 

Quantification of uncertainty

All of these uncertainties—due to population heterogeneity, measurement noise, various operating settings, etc.—should be considered in a workable practical solution. Quantifying the uncertainty using AI algorithms is quite difficult.

A few practical options are

  • The use of particle filters in neural networks (NNs)
  • Bayesian-based artificial intelligence techniques (e.g., Gaussian process, RVM)
  • Monte Carlo methods
  • Stochastic data-driven

Stochastic, data-driven approaches are an interesting option to explore. These approaches can naturally yield the probability density function of the RUL for the purpose of computing the confidence interval.

 

Adaptive capability

Power electronics have difficulties because the operational conditions of the training dataset, which is often acquired through accelerated testing trials, differ significantly from those of the in-situ system (i.e., the test data). Most of the research makes the assumption that the in-situ system's operational parameters are the same as those of the training dataset, which could not be the case in real-world applications.

Therefore, the AI-based RUL prediction method's adaptability is essential for bridging the gap between research in academia and practical implementations in industry.

Detailed mapping relationship derivations and transfer learning of degradation characteristics under different operating settings (temperature, voltage, humidity, etc.) are also interesting ways to tune model parameters. This means that system models need to be studied in great detail.

Fig. 1 shows a methodical flowchart of power electronic system maintenance tasks. It typically comprises the three elements listed below.

           

 

Summarizing the Key Points

  • Maintenance of power electronic systems involves condition observation, anomaly identification, defect diagnosis, and remaining useful life prediction to ensure reliability and safety.
  • The IEEE standard framework for prognostics and health management is applicable to power electronic systems, emphasizing the importance of a comprehensive maintenance approach.
  • Data preprocessing and feature mining are crucial for improving the quality of raw data, enhancing the performance of prognostics and health management applications.
  • AI-based remaining use life prediction techniques face challenges in real-world applications, requiring quantification of uncertainty and adaptability.
  • Power electronic systems require an adaptive maintenance strategy to bridge the gap between research and practical implementation in industry, addressing operational parameter variations.

 

Reference

Zhao, Shuai, Frede Blaabjerg, and Huai Wang. “An Overview of Artificial Intelligence Applications for Power Electronics.” IEEE Transactions on Power Electronics 36, no. 4 (April 2021): 4633–58. https://doi.org/10.1109/tpel.2020.3024914.

Rakesh Kumar, Ph.D.

Rakesh Kumar holds a Ph.D. in electrical engineering, specializing in power electronics. He is a Senior Member of the IEEE Power Electronics Society, Class of 2021. He writes high-quality, long-form technical articles for global B2B semiconductor brands. Feel free to reach out to him at rakesh.a@ieee.org! Checkout his complete portfolio @muckrack.com/rakesh-kumar-phd | @linkedin.com/in/rakesh-kumar-phd

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