Article
Green & Sustainable Science & Technology
Surendra Singh, Avdhesh Sharma, Akhil Ranjan Garg, Om Prakash Mahela, Baseem Khan, Ilyes Boulkaibet, Bilel Neji, Ahmed Ali, Julien Brito Ballester
Summary: This paper presents a power quality detection and categorization algorithm that combines multiple signal processing techniques and a rule-based decision tree. The algorithm aims to accurately identify PQ events of simple nature and higher order multiplicity with less computational time. It utilizes the Stockwell transform and Hilbert transform to compute PQ detection indices and the Discrete Wavelet transform for classification feature indices. A combined PQ detection index is calculated by combining these indices. The algorithm successfully detects and categorizes various PQ events and is also tested on a practical distribution utility network. The algorithm's performance is evaluated and compared with a DWT-based technique in terms of accuracy, computational time, and multiplicity of PQ events. Simulation is performed using MATLAB software.
Article
Engineering, Electrical & Electronic
Nagendra Kumar Swarnkar, Om Prakash Mahela, Mahendra Lalwani
Summary: This paper presents a multi-variable power quality disturbance identification algorithm (MPQDIA) using the Stockwell transform (ST), Hilbert transform (HT), and rule-based decision tree (RBDT). The algorithm effectively identifies, locates, and classifies various types of power quality disturbances (PQDs) by processing voltage signals with PQDs using the ST and HT and extracting features for classification with the RBDT. The performance of MPQDIA is compared with other algorithms and it is demonstrated that MPQDIA effectively recognizes PQDs in real-time power system networks.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Kiron Nandi, Arup Kumar Das, Riddhi Ghosh, Sovan Dalai, Biswendu Chatterjee
Summary: This study developed a deep neural network for feature extraction and classification of power quality disturbances in electrical power system network, demonstrating high accuracy in classifying various PQ events. The framework proposed is practical for power quality monitoring in electrical power systems.
IEEE SENSORS JOURNAL
(2021)
Article
Energy & Fuels
Noman Shabbir, Lauri Kutt, Bilal Asad, Muhammad Jawad, Muhammad Naveed Iqbal, Kamran Daniel
Summary: This article discusses research on power quality issues in modern power systems, focusing on improving power factor to mitigate the adverse effects of inductive loads on the system. Through the analysis of real-time data from a frequency converter, a hybrid solution based on wavelet transform and Fourier transform is proposed for diagnosing the causes of motor failure in ventilation systems.
Article
Automation & Control Systems
Chengbin Liang, Zhaosheng Teng, Jianmin Li, Wenxuan Yao, Shiyan Hu, Yan Yang, Qing He
Summary: The proposed Kaiser window-based S-transform (KST) accurately detects power quality disturbances in power systems and achieves significant advantages in time-frequency analysis.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Muhammad Abubakar, Arfan Ali Nagra, Muhammad Faheem, Muhammad Mudassar, Muhammad Sohail
Summary: This paper proposes a new algorithm that combines the discrete orthogonal S-transform (DOST) with compressive sensing (CS) and a deep stacking network (DSN) for the automatic classification of power quality disturbances (PQDs). The algorithm compresses the extracted feature matrix to minimize computational process and provide more diverse features. Experimental results show that the algorithm achieves high precision in identifying multiple power quality events.
Article
Green & Sustainable Science & Technology
Dan Su, Kaicheng Li, Nian Shi
Summary: In order to improve power quality analysis, it is necessary to select the optimal window function to accurately locate the time frequency. A modified S-transform method is proposed in this paper, introducing an improved window function to optimize the shape of each window function. By determining the parameters of the Gaussian window, this method aims to maximize the product of energy concentration in a time-frequency domain within a given interval, thus improving energy concentration compared to the standard S-transform.
Article
Engineering, Electrical & Electronic
Lei Fu, Xi Deng, Haoqi Chai, Zepeng Ma, Fang Xu, Tiantian Zhu
Summary: This article proposes a hybrid approach called PQEventCog for assessing power quality disturbances (PQDs). The approach utilizes an improved variational mode decomposition for signal reconstruction and combines S-transform with singular value decomposition to enhance features of the time-series signal. Differential Training is applied to reduce label noises of training sets, and well-labeled samples are fed into a CNN model for evaluation.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Om Prakash Mahela, Mayank Parihar, Akhil Ranjan Garg, Baseem Khan, Salah Kamel
Summary: This paper proposes an algorithm based on the Stockwell transform and Hilbert transform to classify complex power quality disturbances. The algorithm achieves high accuracy and low computational time, and has been validated on a practical distribution network.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Yulong Liu, Ding Yuan, Zheng Gong, Tao Jin, Mohamed A. Mohamed
Summary: In this paper, an adaptive spectral trend-based optimized empirical wavelet transform (EWT) method is proposed to analyze Multiple Power Quality Disturbances (MPQDs). By fitting the upper and lower envelopes of the signal frequency spectrum and calculating the main frequency components, the spectrum can be accurately segmented and disturbance parameters can be monitored with high precision. The algorithm is computationally simple and its effectiveness is verified.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Energy & Fuels
Jinsong Li, Hao Liu, Dengke Wang, Tianshu Bi
Summary: The study proposed a power quality disturbance signal classification method based on S-transform and Convolutional Neural Network, which extracts features using S-transform and performs secondary feature extraction and classification using CNN, achieving accurate classification of single and composite disturbance signals. This method has better noise immunity, higher accuracy, and better real-time performance.
FRONTIERS IN ENERGY RESEARCH
(2021)
Article
Engineering, Civil
Ping-Ping Yuan, Jian Zhang, Jia-Qi Feng, Hang-Hang Wang, Wei-Xin Ren, Chao Wang
Summary: This paper proposes an improved time-frequency analysis method for accurate identification of structural instantaneous frequency. A new transform method is introduced by optimizing the window function and combining it with a synchroextracting transform. The performance of the proposed method is significantly improved in numerical signals, even in the presence of noise. The effectiveness of the method is verified using different simulation and experimental data.
ENGINEERING STRUCTURES
(2022)
Article
Engineering, Multidisciplinary
Ye Yuan, Francis T. K. Au, Dong Yang, Jing Zhang
Summary: This study presents an active learning-guided online cable force monitoring system based on a modified S-transform approach, which can identify cable tension during non-stationary wind loads and improve accuracy and efficiency. Laboratory validations demonstrate the system's stability and reliability.
Article
Engineering, Electrical & Electronic
Ahmed Amirou, Yanis Amirou, Djaffar Ould-Abdeslam
Summary: A novel method for recognizing power quality (PQ) events is proposed in this paper, which uses features observed in the time frequency plane for classification and applies various techniques for feature extraction. The experimental results demonstrate that the overall accuracy of Support Vector Machines and Random Forest classifiers is 100% even with high levels of additive white Gaussian noise, while the XGboost classifier accurately detects 99.72% of PQ events under the same conditions.
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2022)
Article
Energy & Fuels
Sakthivel Ganesan, Prince Winston David, Praveen Kumar Balachandran, Devakirubakaran Samithas
Summary: The proposed method utilizes power quality data and starting current data to identify broken rotor bars and bearing faults in induction motors. By using discrete wavelet transform (DWT) and neural network classifier (NN), a classification accuracy of up to 96.7% is achieved, making the method suitable for hardware implementation.
Article
Thermodynamics
Achikkulath Prasanthi, Hussain Shareef, Rachid Errouissi, Madathodika Asna, Azah Mohamed
Summary: This paper presents a methodology for controlling a multi-source battery-capacitor hybrid electric vehicle (EV) using a dynamic power splitting strategy, a non-linear state feedback controller (NLSFC), a non-inverted buck-boost H-bridge converter, and an adaptive energy management strategy. Simulation results demonstrate accurate speed and torque control of the EV.
ENERGY CONVERSION AND MANAGEMENT-X
(2022)
Article
Engineering, Multidisciplinary
Rachid Errouissi, Hussain Shareef, Amulya Viswambharan, Addy Wahyudie
Summary: This article proposes a modification to the state model of a DC-DC boost converter to reduce difficulties in designing a stable control for the output voltage. The modified model behaves like a minimum-phase system, allowing for the use of a dynamic compensator that combines linearizing feedback control with a disturbance observer. Experimental tests demonstrate the effectiveness of the proposed controller in achieving good transient and steady-state performances.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2022)
Review
Energy & Fuels
Sophea Elmmydya Damian, Ling Ai Wong, Hussain Shareef, Vigna K. Ramachandaramurthy, C. K. Chan, T. S. Y. Moh, Meng Chung Tiong
Summary: This article presents a comprehensive overview of the challenges associated with implementing BESS in modern commercial ships, as well as various technologies and advantages of BESS. The future development of BESS control and performance is also discussed.
JOURNAL OF ENERGY STORAGE
(2022)
Review
Energy & Fuels
Jahangir Hossain, Aida. F. A. Kadir, Ainain. N. Hanafi, Hussain Shareef, Tamer Khatib, Kyairul. A. Baharin, Mohamad. F. Sulaima
Summary: The rising cost and demand for energy have led to the need for innovative methods of energy monitoring, control, and conservation. Energy management can significantly reduce unnecessary energy consumption. This paper critically reviews and compares energy management in commercial buildings, aiming to improve building energy efficiency and achieve net-zero energy buildings.
Article
Energy & Fuels
Aslam Amir, Hussain Shareef, Falah Awwad
Summary: This paper proposes a dual-stage dispatch strategy using a novel split-horizon approach to enhance energy management in a standalone microgrid. The strategy utilizes a customized PSO algorithm for optimal scheduling and dispatch operation, resulting in a significant reduction in costs.
Article
Engineering, Electrical & Electronic
Md Mainul Islam, Hussain Shareef, Eslam Salah Fayez Al Hassan
Summary: This paper proposes an artificial intelligence-based random forest method to estimate wind speed and solar radiation, and optimizes the number of decision trees for better prediction accuracy. A dynamic Microgrid (MG) system is developed using the best forecasting data, and a novel binary genetic algorithm is proposed to control the system and minimize cost. The impact of energy storage system is also investigated during the simulation.
PRZEGLAD ELEKTROTECHNICZNY
(2023)
Article
Engineering, Chemical
Saleh Masoud Abdallah Altbawi, Saifulnizam Bin Abdul Khalid, Ahmad Safawi Bin Mokhtar, Hussain Shareef, Nusrat Husain, Ashraf Yahya, Syed Aqeel Haider, Lubna Moin, Rayan Hamza Alsisi
Summary: In this paper, a new optimizer called improved gradient-based optimizer (IGBO) is proposed to enhance the performance and accuracy of the algorithm in complex optimization and engineering problems. The proposed IGBO incorporates additional features such as adjusting the best solution with inertia weight, fast convergence rate with modified parameters, and a novel functional operator (G) to avoid local optima. The effectiveness and scalability of IGBO are evaluated through benchmark functions and real-world optimization problems, confirming its competitiveness and superiority in finding optimal solutions.
Article
Engineering, Electrical & Electronic
Suhail Afzal, Hazlie Mokhlis, Hazlee Azil Illias, Abdullah Akram Bajwa, Saad Mekhilef, Marizan Mubin, Munir Azam Muhammad, Hussain Shareef
Summary: In recent decades, flash floods have become a common and substantial risk for many cities worldwide due to climate change. As the power distribution system is crucial infrastructure in urban areas, it is imperative to make it resilient against flash flooding. However, existing research in this area mainly focuses on wind-related events, so this study models and evaluates the effects of a flash flood on the distribution system, considering dynamic load demand, uncertainties in renewable generation, and interdependence among critical loads. The proposed framework demonstrates efficient restoration solutions for the distribution system despite increased complexity caused by varying conditions.
IET GENERATION TRANSMISSION & DISTRIBUTION
(2023)
Article
Chemistry, Analytical
Hussain Shareef, Madathodika Asna, Rachid Errouissi, Achikkulath Prasanthi
Summary: Monitoring electricity energy usage through non-intrusive load monitoring (NILM) technique enables identification of individual load consumption details using current waveform features. The proposed NILM technique, CRuST, utilizes six non-redundant current waveform features for load identification and achieves more than 96% accuracy in performance evaluation. CRuST NILM outperforms other existing NILM techniques and a feed-forward back-propagation network model.
Article
Green & Sustainable Science & Technology
Jahangir Hossain, Aida. F. A. Kadir, Hussain Shareef, Rampelli Manojkumar, Nagham Saeed, Ainain. N. Hanafi
Summary: In this article, the optimal sizing of hybrid solar photovoltaic and battery energy storage systems is evaluated using a proposed rule-based energy management strategy. Optimization modeling is carried out to minimize the total net present cost, considering the inputs of solar irradiance, air temperature, electrical loads, and electricity rates. The results show that an optimal photovoltaic and battery energy storage system can significantly reduce electricity costs and energy consumption, while also reducing peak demand and greenhouse gas emissions.
Article
Engineering, Multidisciplinary
Rachid Errouissi, Amulya Viswambharan, Hussain Shareef
Summary: This paper presents the design and implementation of a composite controller for a grid-tied inverter with LCL filter. The composite controller consists of a state-feedback control law and a high-gain observer. The high-gain observer is used to estimate a variable representing model uncertainties and unknown disturbances, and is canceled by the state-feedback controller. The controller is able to achieve regulation even in the presence of uncertainties and unknown inputs, and is experimentally tested to meet performance specifications and reduce the effect of measurement noise in steady-state.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2023)
Proceedings Paper
Green & Sustainable Science & Technology
Jahangir Hossain, Aida Fazliana Abdul Kadir, Hussain Shareef, Md. Alamgir Hossain
Summary: The paper proposes a PV-BES energy management system for commercial buildings, which uses a hybrid solar photovoltaic and battery energy storage system to reduce peak demand and electricity costs. Real-time load patterns, solar insolation, ambient temperature, Malaysian net energy metering, and the limitation of maximum exporting power to the grid are considered in the system design. The case study shows that the proposed method can reduce monthly electricity bills by 22.27%, annual energy consumption by 22.62%, peak demand by 15.85%, and also generate additional revenue by selling excess electricity to the grid.
2023 IEEE IAS GLOBAL CONFERENCE ON RENEWABLE ENERGY AND HYDROGEN TECHNOLOGIES, GLOBCONHT
(2023)
Article
Computer Science, Information Systems
R. V. Damodaran, Hussain Shareef, K. S. Phani Kiranmai, Rachid Errouissi
Summary: This paper presents a two-switch boost converter (TSBC) which improves the voltage gain and degree of freedom of control (DoFoC) by adding an extra switch-diode pair. The TSBC can replace the conventional boost converter (CBC) and provides more choices for adjusting voltage gain, thereby enhancing DoFoC.
Review
Computer Science, Information Systems
Ganesan V. V. Murugesu, Saiful Nizam Khalid, Hussain Shareef
Summary: This article discusses the voltage reversal phenomenon in microbial fuel cells (MFCs) and its impact on power generation, proposes methods to address this issue and the challenges faced, and provides an overview of the evolution of MFC development and the factors influencing its performance.
Proceedings Paper
Green & Sustainable Science & Technology
Aslam Amir, Hussain Shareef, Falah Awwad
Summary: This paper proposes a method to enhance the usefulness of an energy storage system in a microgrid, aiming to improve the load factor of a section of the network. The proposed method is tested through simulations and includes case studies to determine the most suitable initial state of charge for the battery energy storage system.
3RD INTERNATIONAL CONFERENCE ON SMART GRID AND RENEWABLE ENERGY (SGRE)
(2022)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
