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Publication Date: 2026
Engineering Applications of Artificial Intelligence (09521976)168
As the global transition toward intelligent, resilient energy infrastructures accelerates, integrating artificial intelligence (AI) into power system protection has become a critical enabler of operational efficiency and reliability. A major challenge is the accurate identification and classification of high impedance faults (HIFs) in energy distribution grids (EDGs), where low current levels often cause conventional protection devices to malfunction. Traditional schemes require extensive training data, which is often difficult or impossible to obtain. This study presents a novel protection scheme that combines a pre-trained compact convolutional neural network (SqueezeNet) with Wigner-Ville distribution (WVD) and S-transform-based feature fusion to detect and classify HIFs using minimal data. Leveraging transfer learning, the approach reduces model retraining needs and accelerates deployment. Simulation results on modified IEEE 13-bus and 34-bus EDGs show F 1-scores exceeding 97 %, successful cross-network knowledge transfer without retraining, and rapid detection within 20 msec using only 250 training samples, highlighting its suitability for lightweight, scalable, and real-time smart-grid protection. © 2026 Elsevier Ltd.
Publication Date: 2025
Engineering Applications of Artificial Intelligence (09521976)139
As the primary protection method for transmission lines, distance relays are prone to malfunction during power swings. In fact, the inability of distance relays to differentiate between power swings and short-circuit faults imposes a significant risk to power system stability that can result in blackouts. In recent years, there has been increasing interest in leveraging machine learning techniques to identify various types of faults and power swings in electrical systems. However, previous works mainly focus on fault classification, which is mostly done after a long period from the moment of fault initiation. This is the reason for requiring extensive post-fault data for diagnosis. To address this challenge, this study proposes a predictive protection strategy utilizing deep learning methodologies, specifically a sequence-to-sequence model, to monitor electrical power systems continuously. The objective is to effectively detect power swings from short-circuit faults with minimal reliance on post-fault data and accurately identify short-circuit faults during power swings. In the proposed approach, features are extracted from grid current signals using the Hilbert transform and empirical mode decomposition algorithms. These features are then fed into the sequence-to-sequence model, which issues block/unblock commands upon confirming the presence of a power swing or fault during the power swing. Results from various simulations conducted on an IEEE 39-bus grid in DIgSILENT and MATLAB environments demonstrate that the proposed scheme outperforms baseline methods in the detection of short-circuit faults, power swings, and short-circuit faults occurring during the power swings. The timely and correct operation of the proposed protection scheme contributes to the stability of transmission lines and power systems. © 2024 Elsevier Ltd
Publication Date: 2025
Journal of Supercomputing (15730484)81(5)
The importance of a node, known as centrality, can be defined and measured in various ways. The main challenge of these measurements is their extension to multilayer networks. In multilayer networks, the influence of inter-layer edges compared to intra-layer edges must be considered when calculating centrality measures. Here, the primary purpose is to provide a multilayer network-specific framework to measure the importance of nodes, with special consideration for inter-layer edges and intra-layer ones. First, we considered the different centrality measures offered for multilayer networks, as well as the associated tools and packages. Next, we implemented some more informative measures specific to multilayer networks. The functionality of implemented metrics is provided for some real networks using Python. We assessed these metrics as ranking criteria and then contrasted the ranking results using three methods: intersection similarity, rank differences, and Kendall’s tau. The findings demonstrated that incorporating information from various layers enhances the effectiveness of the criteria. The final product is a publicly available Python package called MultiNetPy, available at https://github.com/Multinetpy/Multinetpy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Publication Date: 2025
Iranian Conference on Electrical Engineering, ICEE (26429527)pp. 56-61
Accurate differentiation between Inrush Current (IC) and internal faults (IFs) is crucial for power transformer differential protection. During transformer energization and IC generation, transformer differential protection may misinterpret IC as an internal fault, leading to a trip command being issued to the breakers. This study proposes an innovative machine learning-based approach named the tree-based pipeline optimization tool (TPOT) to enhance the F-score and efficiency of IC detection in relation to IFs in power transformers. TPOT performs in-depth data analysis and extracts significant features that influence the distinction between IC and transformer internal faults. As a model optimizer, TPOT fine-tunes models by adjusting parameters and structures. Consequently, this approach enables differentiation between IC and IFs in power transformers with high F -score and continuous improvements in detection capability. Simulation results on a real 160 MVA, 230/63 kV transformer in the MATLAB and Python software environments demonstrate the effectiveness of the proposed protection scheme in classifying transformer IC from IFs with an F1-score of 92%. © 2025 IEEE.
Publication Date: 2025
Engineering Applications of Artificial Intelligence (09521976)156
Cellular heterogeneity, even among genetically identical cells, results in variations in their properties and behaviors, making single-cell analysis crucial for obtaining detailed insights. However, isolating single cells from a cell population poses major challenges, as conventional laboratory techniques often risk cell damage and involve complex procedures. Droplet microfluidics has emerged as a promising approach for encapsulating cells, particularly single cells, into individual droplets without causing harm. Despite this, factors like cell sedimentation and aggregation can reduce encapsulation efficiency and lead to deviations from the expected Poisson distribution. To address these challenges, leveraging artificial intelligence and deep learning to monitor, detect, and regulate encapsulation conditions in real-time is critical for enhancing system performance. However, deep learning models require substantial training data, and issues like microfluidic channel clogging and the scarcity of certain cell types often limit data availability. To overcome this limitation, researchers are turning to synthetic data generation to supplement training datasets and address data scarcity challenges effectively. This study emphasizes the potential of integrating synthetic data with cutting-edge deep learning techniques to enhance the accuracy and efficiency of single-cell analysis within droplet microfluidic systems. A diverse dataset integrating synthetic and real images was used to train the YOLOv8s model for automated detection and classification of microfluidic droplets, enhancing accuracy and system performance. The model trained on a combination of real and synthetic data outperformed the one trained using conventional data augmentation methods, achieving an mAP0.5 of 98% due to the increased diversity of training images. It also demonstrated faster and more stable training. Additionally, the YOLOv8 network, with a detection rate of approximately 2338 droplets per second, significantly improved processing speed compared to earlier YOLO versions. © 2025
Publication Date: 2024
IET Generation, Transmission and Distribution (17518687)18(4)pp. 767-778
Power transformers play a critical role in the performance of power systems. This equipment is costly due to significant copper and iron prices and manufacturing costs. Therefore, maintenance and protection of such equipment is essential. Despite its robust performance, maloperation of differential protection (DP) in transformers may cause operational challenges to power system operators. The differential relay may operate incorrectly after the transformer energization leads to an inrush current (IC) and the relay identifies the event as an internal fault, and consequently issues the trip command. The other case of maloperation includes, but not limited to, a moment when the current transformer saturates due to an external fault. In this paper, a novel approach for DP is proposed, that is based on signal processing methods. In this paper, variational mode decomposition (VMD) and the deep neural network are implemented by using the convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) models. The VMD decomposes differential current signal (DCS) to intrinsic mode functions with corresponding narrow-band property frequency spectrums, which provides more detailed information about signal characteristics in different frequency bands. At the next stage, an effective feature for the BiLSTM is extracted by the CNN with the convolutional layers to classify events and proper discrimination. Extensive simulations on a 500 MVA transformer in MATLAB demonstrate the effectiveness of the proposed protection approach to differentiate ICs from internal and external faults with 99.8% accuracy in less than 1/8th of a power cycle. © 2024 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Publication Date: 2024
Alexandria Engineering Journal (11100168)102pp. 327-338
Publication Date: 2024
Iranian Conference on Electrical Engineering, ICEE (26429527)(2024)
High Impedance Arcing Faults (HIAF) have always been considered an influential factor in the protection of electric power distribution networks (EPDNs). Characteristics such as low current levels in these faults causes the malfunction of conventional protection devices because of incorrect detection. Therefore, new methods should be provided that are able to detect the HIAF from other events in the EPDN based on these characteristics. Most of the previous fault detection techniques are dependent on a massive volume of training data to detect and classify the faults and other events, requiring a lot of time for data extraction. Furthermore, in some cases accessibility to these data is too difficult and sometimes impossible. Therefore, this paper proposes a novel protection technique based on a deep-learning algorithm to detect and classify the HIAF from other events, and also to significantly reduce the dependence on a large amount of training data. The proposed technique uses a small amount of data to extend the knowledge of pretrained SqueezeNet architecture to HIAF detection and classification problems, thereby reducing the dependence of the method on a large amount of training data. The simulation results in the presence of renewable energy sources on the modified IEEE 13-bus and 34-bus EPDNs indicate the high accuracy of the proposed technique in categorizing different network events. © 2024 IEEE.
Publication Date: 2024
IET Computer Vision (17519640)18(2)pp. 191-209
The position of vehicles is determined using an algorithm that includes two stages of detection and prediction. The more the number of frames in which the detection network is used, the more accurate the detector is, and the more the prediction network is used, the algorithm is faster. Therefore, the algorithm is very flexible to achieve the required accuracy and speed. YOLO's base detection network is designed to be robust against vehicle scale changes. Also, feature maps are produced in the detector network, which contribute greatly to increasing the accuracy of the detector. In these maps, using differential images and a u-net-based module, image segmentation has been done into two classes: vehicle and background. To increase the accuracy of the recursive predictive network, vehicle manoeuvres are classified. For this purpose, the spatial and temporal information of the vehicles are considered simultaneously. This classifier is much more effective than classifiers that consider spatial and temporal information separately. The Highway and UA-DETRAC datasets demonstrate the performance of the proposed algorithm in urban traffic monitoring systems. © 2023 The Authors. IET Computer Vision published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Publication Date: 2023
pp. 112-116
With the increasing use of mobile phones and messaging services, SMS spam has become a significant issue for users. In this paper, we propose a novel approach1 to tackle this problem by using Sine-Cosine Algorithm (SCA) and Complex Multi-Layer Perceptron (C-MLP). Specifically, we apply the SCA method to reduce the dimensionality of the feature space and CMLP to improve the performance of spam detection. Also, in this paper, we investigate the effectiveness of different classification algorithms, including Support Vector Machines, Random Forests, K-nearest neighbors, Naive Bayes, bagging, and voting approaches. Our experimental results show that the proposed approach achieves high accuracy and outperforms existing methods in terms of both accuracy and F-measure. The proposed approach can be helpful in designing effective SMS spam filters and improving the overall user experience.1All the code used in this research is publicly available on the first author's GitHub repository: https://github.com/seper-sw/SMS-Spam-Detection.git © 2023 IEEE.
Publication Date: 2023
Electric Power Systems Research (03787796)219
Power transformer protection performs an essential role in power systems, ensuring a reliable power supply to the customers. One of the main challenges in differential protection of the transformers is to correctly discriminate inrush currents from internal faults and prevent the maloperation of the differential relay. In this regard, a novel differential protection method is proposed, which decomposes the differential current signal to multiple energy levels through the multi-resolution analysis (MRA) and selects the most useful feature to feed to the bidirectional gated recurrent unit (BIGRU) to classify the events. The use of the BIGRU results in the high accuracy and low implementation complexity of the proposed approach. Various simulations carried out on a 70 MVA transformer demonstrate that the proposed approach has an accuracy of 99.70% in discriminating inrush currents from internal faults in less than one-eighth of the power cycle. © 2023 Elsevier B.V.
Publication Date: 2023
IEEE Systems Journal (19379234)17(2)pp. 3160-3171
The dependence of high-impedance faults (HIFs) detection methods on a large amount of training data has always been a fundamental problem in electrical distribution systems. This article proposes a novel protection system based on the transfer learning technique and GoogleNet architecture to reduce this dependence. The proposed protection system uses a small amount of data to extend the knowledge of pretrained GoogleNet architecture to the HIF detection problem. In this system, a small amount of third harmonic angle data of the current at the measurement point are obtained from the understudy electrical distribution system. Then, the preprocessing phase is performed, and the extracted data are converted to image data using the Wigner-Ville distribution. Afterward, these converted images are fed to the GoogleNet architecture as an input dataset to update the GoogleNet pretrained knowledge. Finally, the process of fault detection and classification is accomplished only by transferring the GoogleNet pretrained knowledge. The simulation results of the modified IEEE 13-bus and 34-bus distribution systems in EMTP-RV and MATLAB indicate the high accuracy of the proposed protection system despite the use of a small amount of input training data. © 2007-2012 IEEE.
Publication Date: 2022
International Journal of Electrical Power and Energy Systems (01420615)141
The operating speed of adaptive single-phase auto-reclosure (ASPAR) is of great importance to maintain power systems stability in high-voltage power transmission lines (PTLs). This paper proposes a two-step protection scheme using the long short-term memory (LSTM) network to enhance the ASPAR performance. The discrimination between transient and permanent faults is made by an LSTM with high accuracy in the first step. If transient faults are detected in the second step, another LSTM is applied to predict the secondary arc extinction time (SAET). To this end, the second LSTM accurately foresees the voltage waveform of the faulty phase a half-power cycle earlier, and predicts the SAET in order to get a successful reclosing. The results obtained from extensive simulations using EMTP-RV and MATLAB software environments indicate that the presented protection scheme outperforms other ones in terms of fault type classification, achieving an F-measure value of 98.90%. Moreover, the results verify that the LSTM can accurately estimate the voltage waveform and the SAET, that ensures a successful reclosing of the faulty phase. © 2022 Elsevier Ltd
Publication Date: 2022
Electric Power Systems Research (03787796)212
Low level current and similarity of High Impedance Faults (HIF) in respect of characteristics to other transient events have posed a critical challenge to the protection of distribution systems. In addition, the dependency of previous methods on large amounts of training data increases the simulation error rate, and preparing this amount of data is time-consuming. In this paper, a novel scheme based on conditional generative adversarial network (CGAN) and convolutional neural network (CNN) classifier techniques is proposed, that reduces this dependency and leads to acceptable classification accuracy. In the proposed method, a small amount of data is extracted from the under-study network as the real data. Then, the third harmonic angle of the current is extracted from the real data by an adaptive linear neuron (ADALINE) as an effective feature. The CGAN is performed to produce a large amount of pseudo data. At last, the fault data is separated from other transient network events via the CNN classifier. Five different scenarios are used to evaluate the proposed method on a 13-bus IEEE network. The simulation results show that the Precision and Recall of distinguishing HIFs from other transient events is greater than 98% in all the scenarios. These results verify that the proposed scheme is very accurate despite the low dependency on input training data. © 2022 Elsevier B.V.
Publication Date: 2022
IEEE Access (21693536)10pp. 120592-120605
Despite significant advances and innovations in deep network-based vehicle detection methods, finding a balance between detector accuracy and speed remains a significant challenge. This study aims to present an algorithm that can manage the speed and accuracy of the detector in real-time vehicle detection while increasing detector speed with accuracy comparable to high-speed detectors. To this end, the Fast-Yolo-Rec algorithm is proposed. The proposed method includes a new Yolo-based detection network and LSTM-based position prediction networks. The proposed semantic attention mechanism in the spatial semantic attention module (SSAM) significantly impacts accuracy and speed on par with the most recent fast detectors. Recurrent position prediction networks, on the other hand, improve the detection speed by estimating the current vehicle position using vehicle position history. The vehicle trajectories are classified, and the LSTM network for the specified trajectory predicts the vehicle positions. The Fast-Yolo-Rec algorithm not only determines the position of the vehicle faster than high-speed detectors but also allows for the speed control of the detection network with acceptable accuracy. The evaluation results on a large Highway dataset show that the proposed scheme outperforms the baseline methods. © 2013 IEEE.
