A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids

Mirshekali, Hamid; Santos, Athila Q.; Shaker, Hamid Reza

doi:10.3390/en16176332

Cited by 11 publications

(1 citation statement)

References 152 publications

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“…probability distributions [5,6]), time series methods (e.g. Auto-Regressive Integrated Moving Average model (ARIMA) [7]), machine learning techniques (e.g. Minimum Redundancy Maximum Relevance (MRMR) [8], Support Vector Regression (SVR) [9], Random Forest Regression (RFR) [10]), deep learning methods (e.g.…”

Section: A Backgroundmentioning

confidence: 99%

Fuzzy Few-Shot Learning for Predictive Maintenance and Cost-Benefit Analysis in Semiconductor Manufacturing: A Case Study on Photomask Haze

Huang,

Huang

2024

IEEE Access

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Predictive Maintenance (PdM) plays a key role in production management by extending the lifespan of components and reducing maintenance costs. In the manufacture of semiconductors, the scarcity of data related to haze defects makes it difficult to draw correlations between environmental factors and haze formation. The uncertainty associated with a reliance on indirect evidence (i.e., cumulative time in the environment) has seldom been explored in the literature. Therefore, we developed a PdM framework based on fuzzy few-shot learning to deal with photomask haze in the semiconductor industry. The robustness of the model was evaluated in a three-month pilot study conducted in a wafer foundry. This paper also provides cost-benefit analysis of model implementation. The results demonstrate that the proposed haze photomask detection model outperforms existing models in terms of hit rates and false alarm rates. It also proved effective in lowering labor costs and power consumption, as evidenced by the fact that the number of haze candidates was well below the daily inspection cap, which allowed the decommissioning of one photomask inspection device, reducing the photomask inspection volume by 21%, which is equivalent to an annual labor cost reduction of USD 18,780. These results should help to promote the adoption of predictive maintenance applications, even in situations with small sample sizes.

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Section: A Backgroundmentioning

confidence: 99%

Fuzzy Few-Shot Learning for Predictive Maintenance and Cost-Benefit Analysis in Semiconductor Manufacturing: A Case Study on Photomask Haze

Huang,

Huang

2024

IEEE Access

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Overload Alarm Prediction in Power Distribution Transformers

Rafati,

Mirshekali,

Shaker

2024

Smart Grids and Energy

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The growing demand for electricity puts more strain on the grid, requiring automated and proactive strategies such as overload prediction to improve grid maintenance. However, the intermittent nature of power distribution loads makes the prediction more challenging. This paper proposes a novel framework for overload alarm prediction in distribution transformers, aimed at enhancing the reliability and efficiency of grid operations. Leveraging real-world smart meter data and machine learning techniques, the proposed system develops a classification model to predict overloads for distribution transformers. Due to resource constraints, a new strategy is adopted to assess the significance of alarms based on expert observations. Subsequently, a new approach is developed to imitate the experts, leading to an automated decision-making process using random forest. Ultimately, the transfer learning strategy is utilized to predict overload alarms for distribution transformers facing data scarcity in real-world applications. The proposed system demonstrates high accuracy of overload alarm predictions, paving the way for developing more proactive grid maintenance strategies.

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Reliability-aware multi-objective approach for predictive asset management: A Danish distribution grid case study

Mirshekali,

Mortensen,

Shaker

2024

Applied Energy

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A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids

Cited by 11 publications

References 152 publications

Fuzzy Few-Shot Learning for Predictive Maintenance and Cost-Benefit Analysis in Semiconductor Manufacturing: A Case Study on Photomask Haze

Fuzzy Few-Shot Learning for Predictive Maintenance and Cost-Benefit Analysis in Semiconductor Manufacturing: A Case Study on Photomask Haze

Overload Alarm Prediction in Power Distribution Transformers

Reliability-aware multi-objective approach for predictive asset management: A Danish distribution grid case study

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