AI-based fault recognition and classification in the IEEE 9-bus system interconnected to PV systems

PV(photovoltaic) systems have been deployed more in the recent years to support green energy generation. In the recent era, grid tied PV has been sparked by the emergence of the electricity market and offers an alternative solution to traditional fossil fuel-based electricity generation. A few of the potential impacts of solar PV on grid are false tripping of feeders, unwanted tripping, unnecessary islanding, and blind protection. In this research paper, different artificial intelligence techniques are tested in order to overcome the protection challenges. The PSCAD/EMTDC software package is used to analyze a section of the power system, and an algorithm has been constructed using Python v3.8 and MATLAB 2018. On an IEEE-9 BUS power system connected to a PV source, the Artificial Neural Network, Naive Bayes, Support Vector Machine, Random Forest, and Convolution Neural Network (CNN) algorithms are implemented to classify the faults. The suggested methods are proven effective for both in-zone and out-of-zone problems on power lines interconnected with solar park. The proposed techniques have been validated using total 16,320 internal and external fault cases with a wide range of system parameters alteration. In the proposed system, the effectiveness of several machine learning and deep learning techniques is compared. The obtained results demonstrate that CNN provides greater accuracy in the presence of a PV source, but at the same time, it is appropriate for a large number of data sets. The fault classification accuracy acquired is adequate and demonstrates the adaptability of the proposed approach.光伏（photovoltaic，PV）系统近年来部署规模持续扩大，以助力绿色能源发电。近年来，随着电力市场的兴起，并网光伏得以快速发展，可为传统化石燃料发电提供替代方案。太阳能光伏对电网存在若干潜在影响，包括馈线误跳闸、非预期跳闸、不必要的孤岛效应以及盲区保护问题。本文针对上述保护难题，测试了多种人工智能技术。研究采用PSCAD/EMTDC软件包对部分电力系统进行建模分析，并基于Python 3.8与MATLAB 2018开发了相关算法。在接入光伏电源的IEEE 9节点电力系统上，研究人员实现了人工神经网络、朴素贝叶斯、支持向量机、随机森林以及卷积神经网络（Convolution Neural Network，CNN）算法以完成故障分类。所提方法在光伏电站互联线路的区内与区外故障问题中均展现出良好效果。研究共采用16320组内外故障案例，结合多种系统参数变化场景对所提技术进行了验证。在所提系统框架下，研究对比了多种机器学习与深度学习技术的应用效果。实验结果表明，在接入光伏电源的场景下，卷积神经网络的分类准确率更高，且其适配大规模数据集的能力出色。所获得的故障分类准确率满足应用要求，同时验证了所提方法的适应性。