Diagnosis and classification of disturbances in the power distribution network by phasor measurement unit based on fuzzy intelligent system

• Published in: Journal of engineering (Stevenage, England) Vol. 2024; no. 1
• Main Authors: Khosravi, Marzieh, Trik, Mohammad, Ansari, Alireza
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01.01.2024; Wiley
• Subjects: Accuracy; Adaptive systems; Alternative energy sources; Artificial neural networks; Classification; Component reliability; Distributed generation; distribution networks; Disturbances; Electric power distribution; Electricity distribution; Error analysis; Estimates; Fault detection; Fuzzy control; Fuzzy logic; Fuzzy sets; Fuzzy systems; Inference; Measuring instruments; Network reliability; Parameter estimation; phasor measurement units (PMU); Phasors; Reliability analysis; Renewable resources; Signal processing; Simulation; Software reliability; Structural reliability
• ISSN: 2051-3305; 2051-3305
• DOI: 10.1049/tje2.12322

The dynamic nature of distribution networks raises fresh issues with how such electrical systems function. These networks have some characteristics that indicate the need for better monitoring and control capabilities, including dispersed generation employing renewable resources, changing load profiles, and rising reliability requirements. Phasor measurement units (PMUs) offer simultaneous voltage and current phasor measurements at various places and offer a variety of options for gauging the condition and health of the power distribution network. In this regard, a cost‐optimized PMU with some unique features for distribution systems is presented in this work. These features include a fuzzy inference system to determine the root cause of potential electrical disturbances and methods to estimate electrical parameters through measured field data, which are necessities. This study takes into account the modelling of PMUs, utilizing a process for fault detection and classification with a fuzzy inference network. The 9‐bus distribution network's dependability model is built once the components and their functions are first outlined. The proposed model is then used to calculate the availability of the presented model, which has been examined to provide an analogous reliability model for PMUs. Depending on the specific manufacturer, the PMU's design and specs will change. To extract phase and size measurement features for the proposed model adaptive neural‐fuzzy inference system network's training, two PMU structures and associated reliability models are described here. When merging input data for fuzzy neural network prediction using MATLAB software, fuzzy sets are taken into account for error classification analysis. The creation of a low‐cost computing platform capable of running the usual PMU procedures, like measuring three‐phase voltage and performing phasor calculations simultaneously, and making that data immediately available on the Internet. The creation of a fuzzy inference‐based system for the probable detection and categorization of electrical disturbances. Building frameworks for estimating parameters.