Fatma Zohra Dekhandji

The integration of solar and wind energy has increased electricity generation but also introduced power quality disturbances (PQDs) that threaten grid stability. This study examines the detection and classification of five PQD types—voltage sag, swell, interruption, harmonics, and normal conditions—across noisy environments (0, 10, 20, and 30 dB) signal-to-noise ratio (SNR). Traditional methods— support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and 1D convolutional neural networks (1D CNN)—are evaluated on raw signal data, while advanced models—2D CNN and fused 2D CNN-LSTM—utilize time-frequency representations (continuous wavelet transform (CWT) and short-time Fourier transform (STFT)). Results show that deep learning (DL) models achieve high accuracy even in noisy environments, with the fused 2D CNNLSTM using CWT outperforming all other methods. Noise adversely affects feature extraction, with CWT consistently outperforming STFT under low SNR conditions. These findings demonstrate that combining DL models with robust time-frequency analysis and temporal modeling enhances PQD classification and supports dependable monitoring in smart grid environments.

Power quality disturbances (PQDs) such as voltage sags, swells, interruptions, and harmonics significantly impact the performance and stability of electrical distribution networks. With the growing demand for intelligent, embedded monitoring systems in smart grids, edge computing offers a practical approach to real-time disturbance detection. This paper presents a compact and efficient PQD classification system based on a quantized 1D Convolutional Neural Network (CNN), deployed on an ESP32 microcontroller. The model, developed using Edge Impulse, was compressed to approximately 2 KB, allowing deployment on low-power hardware. Key signal features such as root mean square voltage (VRMS) and total harmonic distortion (THD) were extracted to aid in classification. Real-time validation was performed using both live signals and real-world data obtained from the National Company for Electricity and Gas distribution areas, covering various single and combined PQD events. The ESP32 was connected to the Blynk IoT platform for live monitoring, where detected disturbances triggered mobile alerts, virtual LED indicators, and sound notifications. Results confirm the system’s high classification accuracy and responsiveness, demonstrating the potential of lightweight neural models for edge-based PQ monitoring.

Power quality (PQ) plays an important role in ensuring the smooth operation of power systems. PQ disturbances may cause significant consequences, impacting the reliability and efficiency of power networks. This paper presents a comprehensive review of PQ within smart grids and the integration of renewable energy sources. Methods of detecting, classifying, optimizing, and mitigating PQ events are reviewed with providing a brief theoretical explanation for each technique. Related work done in the past two decades, was conducted, presenting a clear view of the development of research in a chronological order serving as a guide for future investigations in this field of study.

This paper deals with the deployment and integration of renewable energies and storage systems. An Energy management system is necessary to achieve this objective. Two energy management techniques are considered in this work. They are termed: “heuristic” and “optimization” methods. Both methods aim to reduce the overall reliance on the conventional energy source from the main grid by maximizing the output of the two other sources (solar energy and storage system) and thus, reducing the overall cost. This study contrasts the two methods in terms of both grid usage and cost. Simulations considering cloudy and clear sky conditions while ensuring the user's satisfaction are carried out.

Environmental concerns have pushed for the replacement of current gasoline- and diesel-powered cars with zero-emission electric vehicles (EVs). As a result of this substitution, more EVs connect to the power grid. As a consequence of such cars, some problems with energy quality arose. This paper examines the issues that electric vehicles pose to power systems. Using an approach termed Vehicle-to-Grid, EVs may serve as both a load and an energy source. This method brings significant characteristics to a microgrid, such as primary frequency regulation. In this paper, a technique known as Decentralized Vehicle-to-Grid Control for Primary Frequency Regulation taking into account Charging Needs is investigated and demonstrated in MATLAB SIMULINK for various scenarios in order to analyze the affect of these EVs on the frequency of the microgrid.

In submitting conference proceedings to Engineering Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors

Multidisciplinary engineering is currently a proliferating area, whereby focus is placed upon an engineering practice which combines several academic disciplines

Power quality problems exist in every power system; the more advanced the system is, the higher the probability for issues to occur is, and the smart grid is no exception. In this paper, a study of a residential area is implemented using the LABVIEW simulation software, in which the power of each house with its own appliances is monitored in real time, meaning the power of the whole system is also monitored, including instantaneous and accumulative power, current, power factor—all elements required for system assessment. In addition, a billing policy of each individual house to see the total price to be paid for the power supplied is also devised. All these factors contribute to the analysis and overview of the effect of power quality problems on the grid, both electrically and economically. The results of the simulation indicate that the pricing policy proposed is indeed effective for both sides of the grid, the supplier and the consumer.

The detection and diagnosis of power quality (PQ) issues are critical topics in the electrical power system’s generation, transmission, and distribution. Nonlinear loads, power electronic converters, system malfunctions, and switching events are the most common causes of PQ issues. The main purpose of this work is to use an artificial intelligence (AI) technique based on automatic feature extraction to discover and identify PQ difficulties. The AI technique consists of a dedicated architecture of the Long Short-Term Memory (LSTM) network, which is a special type of Recurrent Neural Network (RNN).

The detection and diagnosis of power quality (PQ) issues are critical topics in the electrical power system’s generation, transmission, and distribution. Nonlinear loads, power electronic converters, system malfunctions, and switching events are the most common causes of PQ issues. The main purpose of this work is to use an artificial intelligence (AI) technique based on automatic feature extraction to discover and identify PQ difficulties. The AI technique consists of a dedicated architecture of the Long Short-Term Memory (LSTM) network, which is a special type of Recurrent Neural Network (RNN).