Feature extraction method of HPLC communication signal based on genetic algorithm

Abstract

Communication quality is a key factor affecting the effectiveness of distribution network operation status management. Therefore, it is required that the communication performance of the distribution network management system be good, and the communication signal can directly reflect the communication quality. Therefore, a genetic algorithm (GA) based HPLC communication signal feature extraction method is proposed. The process of constructing reference samples, training recognition models, constructing noise samples, self‐coding denoising processing, and denoising sample recognition has completed the identification of power line channel transmission characteristics. The state feature quantity of the topological line is based on the positioning results of the diagnostic function, and the calculated value of the reflection coefficient calculation model at the head end of the topological line is compared with the measured value. By optimizing the feature quantities containing topological line states through GAs, abnormal feature quantities close to the true state of topological lines are obtained. Experimental analysis shows that this method can correctly identify the position of reactive power compensation in the transmission line, and can also correctly identify the extraction of topological anomaly features. Therefore, the research method is of great significance in improving the safe operation of power systems and extending the service life of topology lines.

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

“…Powerline communication has transcended its conventional applications in the smart grid (SG) and found extensive use in various domains [146,146]. Originally employed for two-way SG communication, advanced metering infrastructure (AMI) applications, demand response, and power system control, PLC now serves diverse purposes.…”

Specific Emitter Identification Based on Attractor Feature Space of System under Blind Equalization

“…In the literature [4], the authors applied deep learning to electromagnetic fingerprint extraction, and constructed a pre-transformation network suitable for electromagnetic signals for the collected addressing signals between civil aircraft and airport towers, converting one-dimensional signals into two-dimensional feature maps, and then inputting the feature maps into the neural network for classification, but this method is also accomplished in a relatively ideal situation. In the literature of [5], the authors proposed a feature extraction method for HPLC communication signals based on a genetic algorithm, the feature quantity containing the topological line state is optimized, and the feature quantity which is closer to the real state of the topological line is obtained, which improves the effect of the final recognition and localization; however, this method is only suitable for the HPLC signal. The literature of [6] proposes an SEI algorithm based on signal track images.…”