Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach

Abstract

Nowadays, due to the increase in the demand for electrical energy and the development of technology, the electrical devices have a more complex structure. This situation has increased the importance of concept of the power quality in the electrical power system. This paper presents a deep learning-based system to recognize the power quality disturbances (PQDs) in the solar photovoltaic (SPV) plant integrated with power system networks. The PQDs are analyzed using continuous wavelet transform (CWT) and image files are obtained from scalograms of CWT. Then, these image files are used to recognize PQDs with the help of a hybrid deep learning approach based on convolutional neural network (CNN), neighbor component analysis (NCA), and support vector machine (SVM). In this hybrid deep learning approach, the image files are given as input to AlexNet and GoogLeNet. The NCA is applied to the features obtained from the last dropout layer of each architecture. The distinctive features obtained from the NCA process are classified using the SVM algorithm. In order to evaluate the proposed approach, PQD data are obtained from a modified IEEE 13-bus test system including the SPV system. Several analyses and comparisons are carried out to verify the success of the proposed approach. It has been found that the proposed hybrid deep learning approach has the ability to accurately recognize the PQDs even if the SPV plant integrated power system has a negative effect on power quality.

Classification of multiple power quality disturbances based on continuous wavelet transform and lightweight convolutional neural network

“…Apart from test accuracy, a comprehensive evaluation of the proposed model incorporates model size, parameter count, and average test time. Moreover, other four CNN models including AlexNet, 34 GoogleNet, 35 ResNet-50, 36,37 and VGG-16 38 are selected as the contrast. These experiments were conducted on a computing platform comprising an Intel Xeon Gold 5220 CPU and NVIDIA GeForce RTX 2080 Ti GPU, utilizing Matlab2022a as the programming environment.…”

Backstepping Controller Design for Power Quality Improvement in a Two-Stage Grid-Connected Photovoltaic Systems with LCL Filter

“…In the gridconnected PV systems, solar energy source is connected to the grid through inverters and these are used to fll the energy defcit of grids [15,16]. During the coordination and operation of the grid-connected photovoltaic systems, undesirable efects on both the grid and end-user equipment can arise due to power quality disruptions due to the variations in meteorological conditions and the presence of nonlinear loads into the grid [17]. One of the main and worst power quality disturbances is the injection of harmonic distortions of the current by the solar photovoltaic systems and nonlinear loads into the grid [9,18,19], especially in low irradiance conditions [20,21].…”

Utilization of Stockwell Transform and Random Forest Algorithm for Efficient Detection and Classification of Power Quality Disturbances

“…Feature extraction and classifcation are the two key components of these strategies. Te frst stage is to use signal processing methods to glean useful information from the PQ noise [5]. Tis step is crucial because it helps distinguish between diferent kinds of disruptions, which is necessary for later classifcation.…”