A novel method combined with self-calibration and statistical process control for evaluating the state of merging...

Area of Science:

• Electrical Engineering
• Power Systems
• Control Systems

Background:

• Electronic instrument transformers are crucial for data acquisition in smart substations.
• Merging units (MUs) are key interfaces for error adjustment and data transmission.
• Accurate MU state evaluation is vital for reliable power system operation.

Purpose of the Study:

• To propose a novel method for evaluating merging unit (MU) states using self-calibration and MEWMA control charts.
• To analyze the detection capabilities of the MEWMA control chart for MU anomalies.
• To develop an optimized state prediction method for long-term MU operation.

Main Methods:

• Implementation of self-calibration techniques for MU error adjustment.
• Application of Multivariate Exponentially Weighted Moving Average (MEWMA) control charts for state monitoring.
• Development of an optimized multi-feature state prediction model for MUs.
• Comparative analysis of optimized MEWMA charts against traditional methods.

Main Results:

• The proposed MEWMA control chart demonstrates enhanced sensitivity in identifying abnormal MU states.
• Optimized MEWMA charts provide earlier detection of MU state anomalies compared to traditional methods.
• Experimental results align with theoretical predictions, validating the proposed approach.
• The optimized control chart's performance is robust across different operational environments.

Conclusions:

• The combined self-calibration and optimized MEWMA control chart method offers superior MU state evaluation.
• This approach significantly improves the early detection and sensitivity of MU anomalies in smart substations.
• The proposed prediction method enhances the reliability of MUs during long-term operation.