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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Smart grid data compression and reconstruction by wavelet packet transform.

Rakhi Jadhav¹, Anurag Mahajan¹

¹Electronics and Telecommunication Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India.

|August 20, 2024

Summary

This summary is machine-generated.

Smart grids generate vast data, straining networks. This study uses wavelet packet transform for efficient data compression and denoising, improving smart grid performance.

Keywords:

Compression ratio Discrete wavelet transform Reconstruction error Wavelet Packet Transform

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Area of Science:

Electrical Engineering
Computer Science
Data Science

Background:

Smart grids generate massive real-time data, posing challenges for storage and communication networks.
Existing data handling methods can lead to distortion and noise, impacting system reliability.
The increasing data volume necessitates efficient compression and denoising techniques.

Purpose of the Study:

To develop an effective method for compressing and denoising smart grid data.
To minimize the strain on storage and communication networks.
To ensure accurate data regeneration and system state reflection.

Main Methods:

Utilized lower-order different wavelets for data representation.
Applied wavelet packet transform for data compression and reconstruction at level three.
Focused on phasor measurement unit (PMU) current magnitude and voltage sag signals.

Main Results:

Achieved a superior compression ratio compared to existing methods.
Demonstrated low reconstruction error, preserving data integrity.
The proposed design is accessible, systematic, profitable, and time-efficient.

Conclusions:

The wavelet packet transform offers an efficient solution for smart grid data compression and denoising.
The method effectively balances data compression with accurate system representation.
This approach enhances smart grid operational efficiency and network performance.