Sungkyunkwan University · Engineering
Professor Arif Hussain's research lab specializes in smart grid technologies, with a focus on islanding detection in distribution networks incorporating distributed generation (DG) systems, including both synchronous and inverter-based sources. The lab develops advanced data-driven and machine learning-based solutions—such as LSTM, Bi-LSTM, and ensemble models—for real-time anomaly detection and fault diagnosis in power systems. Research also extends to cyber-physical security in electric vehicle charging stations and the performance analysis of wireless communication in power system environments. The lab emphasizes communication-less, intelligent, and reliable protection schemes to enhance the resilience and stability of modern power networks.
Figures are computed from collected data and may differ slightly.
Detection of unintentional islanding, defined as inadvertently separation of distributed generators (DGs) from the utility grid, is a major challenging issue for modern distribution networks. Islanding detection becomes problematic especially when the local generation matches or closely matches the local load. Therefore, there are strict requirements for accurate, fast, and reliable islanding detection of renewables and DG-based systems. Various islanding schemes have been proposed in the litera
Abstract One of the crucial challenges of the distribution network is the unintentionally isolated section of electricity from the power network, called unintentional islanding. Unintentional islanding detection is severed when the local generation is equal to or closely matches the load requirement. In this paper, both ensemble learning and canonical methods are implemented for the islanding detection technique of synchronous machine‐based distributed generation. The ensemble learning models fo
With the increasing integration of electric vehicles (EVs) into the distributed energy resources (DER) system, the security of EV charging stations (EVCS) from cyber-attacks is paramount. Utilizing deep learning and recurrent neural networks (RNNs) presents promising advantages in anomaly detection within power systems. Bi-directional long-short-term memory (Bi-LSTM) emerges as a viable choice for anomaly detection, offering distinct advantages that learn from both the forward and backward seque
Unintentional islanding is a problem in electrical distribution networks; it happens when the central utility is unintentionally separated from the rest of the distributed power system. The islanding detection problem becomes severe in non-detection zones. We propose an intelligent islanding detection technique with zero non-detection zone for a hybrid distributed generation system. It is based on the computation of frequency spectrum variations over time using short-term Fourier transform and c
The proposed scheme in this research paper is a communication-less islanding detection system based on recurrent neural network (RNN) for hybrid distributed generator (DG) systems that include both synchronous and inverter-based DG devices. The scheme consists of three stages: time-domain feature extraction (FE) from the three-phase voltage signal at the point of common coupling (PCC), feature selection using the wrapper method, and detection using a long short-term memory (LSTM) RNN algorithm.
The paper shows experimental and theoretical study of over-the-air (OTA) throughput of LTE wireless devices for different system bandwidths and coherence bandwidths in rich isotropic multipath (RIMP) environment. The theory models the effect of the frequency diversity obtained in OFDM system and shows good agreement with the measurements for the different system bandwidths and coherence bandwidths.
The long-term forecasting of electricity demand at regional level has recently gained significance due to competitiveness of decentralized energy system. For sustainable development of any region, energy demand modeling plays a key role. This paper is based on LEAP (Long-range Energy Alternative Planning) to calculate the total demand of Gilgit-Balitistan for the base year 2016 to the end year 2040. Gilgit is an area of massive strategic and economic-importance, located in the northern most regi
With the emergence of the smart grid, the distribution network is facing various problems, such as power limitations, voltage uncertainty, and many others. Apart from the power sector, the growth of electric vehicles (EVs) is leading to a rising power demand. These problems can potentially lead to blackouts. This paper presents three meta-heuristic techniques: grey wolf optimization (GWO), whale optimization algorithm (WOA), and dandelion optimizer (DO) for optimal allocation (sitting and sizing
Grid-connected PV inverters require sophisticated control procedures for smooth integration with the modern electrical grid. The ability of FCS-MPC to manage the discrete character of power electronic devices is highly acknowledged, since it enables direct manipulation of switching states without requiring modulation techniques. This review discusses the latest approaches in FCS-MPC methods for PV-based grid-connected inverter systems. It also classifies these methods according to control object
As a country, Pakistan is mostly dependent on fossil fuels for fulfilling its energy demand, which is expensive, as well as being environmentally unfriendly. It is high time that the country decides to shift from fossil fuels to renewable energy resources like geothermal, wind, solar, etc., to cater for global warming issues. Pakistan has a lot of potential geothermal sites, as the location of Pakistan lies on several fault lines and hot springs, thus making it very easy to extract the temperatu
The paper studies the over-the-air (OTA) performance of a mobile terminal. A practical two-port mobile terminal model on the left side and the right side of the head for both standard cheek position and standard tilt position is used to study the diversity gains. The diversity gain has been determined by measurements in a reverberation chamber as well as by simulations using the far field patterns from CST Microwave Studio, and then exposing these patterns to rich isotropic multipath (RIMP) envi
This research presents a federated learning (FL) framework and employs a lightweight Simple Neural Network (SimpleNN) model to identify cyber-attacks in electric vehicle charging stations (EVCS). Federated learning is instrumental in this scenario because it protects data privacy by storing local data on edge devices while allowing collaborative model training across scattered EVCS. The proposed technique is tested on the IEEE 123-bus system, which has four EVCS dispersed over various buses. Key
There is a paradigm shift to hybrid (AC/DC) networks that integrate both AC and DC to meet growing energy demands, mitigate global warming, and interconnect distributed energy sources (DERs). However, the unique characteristics of AC/DC faults, the mutual interaction of hybrid lines, the harmonic components of converters/inverters, multiple directions of energy flow, and varying current levels have challenged the existing protection algorithms. Therefore, this paper presents a data-driven coordi
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