A protection strategy for distribution networks using IoT technology and support vector machine
TL;DR
The findings demonstrate that the proposed intelligent protection framework provides accurate and reliable fault detection, classification, and location through optimized DWT-based feature extraction and SVM-based decision making under the investigated simulation scenarios.
Abstract
This paper presents an intelligent protection framework for fault detection, classification, and location in power distribution networks by combining Discrete Wavelet Transform (DWT)-based feature extraction, Support Vector Machine (SVM)-based decision making, and Internet of Things (IoT)-enabled cloud monitoring. An IEEE 16-bus distribution system is modeled in MATLAB/Simulink, where transient current signals are processed using DWT to extract discriminative time–frequency features. A comparative evaluation of different mother wavelets and decomposition levels is performed to identify the most effective feature extraction configuration in terms of accuracy and computational efficiency. The extracted features are processed locally by SVM-based models for fault detection, classification, and location, while selected fault-related features are simultaneously transmitted to the ThingSpeak cloud platform for cloud-assisted monitoring and remote accessibility. The proposed framework is evaluated under a wide range of operating conditions, including different fault types, overload events, load switching, capacitor switching, and scenarios with integrated photovoltaic and wind generation. The results demonstrate 100% fault classification accuracy and fault-location accuracies ranging from 97.95% to 99.88% within the investigated simulation scenarios. Furthermore, the proposed approach effectively distinguishes faults from non-fault disturbances, thereby reducing the likelihood of false fault indications. The findings demonstrate that the proposed intelligent protection framework provides accurate and reliable fault detection, classification, and location through optimized DWT-based feature extraction and SVM-based decision making under the investigated simulation scenarios. Nevertheless, additional validation using noisy measurements and hardware-based experimental platforms is required to further assess the robustness and practical applicability of the proposed protection methodology under real operating conditions.