Investigation of the Performance of Resumable Load Data Compression Algorithm (RLDA) for Distribution Voltage and Frequency Monitoring Data

Data acquisition is a crucial for monitoring power system and analysing the system conditions in a precise manner. It is often responsible for producing an enormous volume of information and therefore compression of such information can be useful for reducing the volume of information considerably....

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Vydané v:2023 3rd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET) s. 01 - 06
Hlavní autori: Sarkar, Subhra J., Patro, P. Swati, Paswan, Manish Kumar
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 21.12.2023
Predmet:
Compression ratio (CR)
Data acquisition
Data acquisition system (DAS)
Data compression
Differential Coding
Encoding
Hardware
Lossless data compression
Monitoring
Power systems
Resumable Load Data Compression Algorithm (RLDA)
Voltage measurement
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Popis
Shrnutí:Data acquisition is a crucial for monitoring power system and analysing the system conditions in a precise manner. It is often responsible for producing an enormous volume of information and therefore compression of such information can be useful for reducing the volume of information considerably. Resumable Load Data Compression Algorithm (RLDA) developed originally for compressing load profile data, is a differential coding based lossless algorithm which can be an alternative for compressing the slow varying monitoring data sets. Therefore, the performance of RLDA is investigated for compressing the practical voltage and frequency datasets to check its compatibility with the developed works before attempting its implementation at the hardware level. The respective routines were developed in the MATLAB environment and the analysis is performed with practical voltage and frequency datasets. A voltage measurement system is developed in the laboratory of NIT Sikkim for acquiring the practical distribution voltage datasets at the initial level of the work. From the obtained results, it is clear that the volume of information reduces by more than 60% for the majority of data sets. Several scopes of improvement are identified for the improvement of the work based on which conclusion is drawn.
DOI:10.1109/ICEFEET59656.2023.10452247