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An improved non-intrusive load identification using sample shifting and fuzzy rule-based technique.

Imran Islam¹, Pallav Dutta¹, Rumpa Saha¹

¹Electrical Engineering Department, Aliah University, Action Area IIA/27, New Town, Kolkata 700 160, India.

ISA Transactions

|November 4, 2024

Summary

This summary is machine-generated.

This study introduces a novel method for non-intrusive load identification (NILI) using the sample shifting technique (SST) and fuzzy logic. The approach accurately identifies individual and multiple appliances even in noisy conditions with reduced computational load.

Keywords:

Domestic electrical appliances Fuzzy rules Load identification Non-intrusive load monitoring (NILM)Reactive power Sample shifting technique (SST)Total harmonic distortion (THD)

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Area of Science:

Electrical Engineering
Artificial Intelligence
Signal Processing

Background:

Non-intrusive load identification (NILI) is crucial for energy management and smart grids.
Accurate identification of electrical appliances is challenging due to overlapping load signatures.
Existing methods often involve high computational complexity.

Purpose of the Study:

To present a new method for NILI using the sample shifting technique (SST).
To develop a fuzzy rule-based system for identifying electrical equipment.
To evaluate the system's performance in terms of accuracy and computational efficiency.

Main Methods:

Acquisition of voltage and current signals from electrical appliances.
Feature extraction using reactive power and total harmonic distortion for current (THD_I) via SST.
Development of a fuzzy logic-based identification algorithm for individual and simultaneous appliance operation.

Main Results:

The proposed method successfully identifies various electrical equipment.
Satisfactory accuracy was achieved even in noisy environments.
The sample shifting technique (SST) significantly reduced computational burden compared to other methods.

Conclusions:

The developed fuzzy rule-based NILI system utilizing SST is effective and computationally efficient.
The method demonstrates robustness in identifying appliances under adverse conditions.
SST offers a promising approach for simplifying NILI system computations.