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Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters

ENERGIES 2024年第7期17卷 1562页

作者： Bento, Murilo Eduardo Casteroba Univ Fed Rio de Janeiro Dept Elect Engn BR-21941909 Rio De Janeiro Brazil

Challenges in the operation of power systems arise from several factors such as the interconnection of large power systems, integration of new energy sources and the increase in electrical energy demand. These challenges have required the development of fast and reliable tools for evaluating the operation of power systems. The load margin (LM) is an important index in evaluating the stability of power systems, but traditional methods for determining the LM consist of solving a set of differential-algebraic equations whose information may not always be available. Data-Driven techniques such as Artificial Neural Networks were developed to calculate and monitor LM, but may present unsatisfactory performance due to difficulty in generalization. Therefore, this article proposes a design method for Physics-Informed Neural Networks whose parameters will be tuned by bio-inspired algorithms in an optimization model. Physical knowledge regarding the operation of power systems is incorporated into the PINN training process. Case studies were carried out and discussed in the IEEE 68-bus system considering the N-1 criterion for disconnection of transmission lines. The PINN load margin results obtained by the proposed method showed lower error values for the Root Mean Square Error (RMSE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) indices than the traditional training Levenberg-Marquard method.

关键词： power systems power system stability smart grids voltage stability small-signal stability load margin Physics-Informed Neural Network Phasor Measurement Unit Particle Swarm optimization Coati optimization algorithm pelican optimization algorithm

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Fault diagnosis of transformer winding short circuit based on WKPCA-WM and IPOA-CNN

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Pei, Xiping Han, Songtao Bao, Yanyan Chen, Wei Li, Hengjie Lanzhou Univ Technol Sch Elect Engn & Informat Engn Lanzhou Peoples R China

When the winding of the power transformer is short-circuited, the winding will experience constant vibration, which will cause axial instability of the winding, and then lead to winding looseness, deformation, bulge, etc., therefore, a diagnosis method based on the Improved pelican optimization algorithm and Convolutional Neural Network (IPOA-CNN) for short-circuit voiceprint signal of transformer windings is proposed. At the same time, considering the input parameter dimension of deep learning cannot be too high, a new feature parameter selection method is constructed for this model. Firstly, the frequency characteristics of winding acoustic vibration signals are analyzed, and then the characteristic parameters of transformer acoustic signals are extracted by Wavelet Packet Energy Spectrum (WPES) and Mel Frequency Cepstrum Coefficient (MFCC), respectively. Then, the two methods are combined to construct the WM feature extraction algorithm, and the Weighted Kernel Principal Component Analysis (WKPCA) is used to reduce the dimension of the feature to obtain the feature parameters with accurate feature information and low redundancy;Finally, combined with Sobol sequence to optimize the initial population of pelican optimization algorithm (POA), the convolution kernel of Convolutional neural network (CNN) was optimized by IPOA, and the optimal convolution kernel was obtained. The transformer winding short-circuits voiceprint diagnosis models of WKPCA-WM and IPOA-CNN were constructed, which realized the accurate diagnosis of winding short-circuit voiceprint. The validity and feasibility of the method are verified by the acoustic signal data collected in the laboratory.

关键词： voiceprint signal feature selection WKPCA-WM pelican optimization algorithm Sobol sequence IPOA-CNN

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A Rolling Bearing Fault Feature Extraction algorithm Based on IPOA-VMD and MOMEDA

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SENSORS 2023年第20期23卷 8620-8620页

作者： Yi, Kang Cai, Changxin Tang, Wentao Dai, Xin Wang, Fulin Wen, Fangqing Yangtze Univ Sch Elect Informat Jingzhou 434023 Peoples R China Hubei Key Lab Drilling & Prod Engn Oil & Gas Wuhan 430100 Peoples R China Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China China Three Gorges Univ Elect & Commun Inst Yichang 443002 Peoples R China Hubei Univ Automot Technol Inst Vehicle Informat Control & Network Technol Shiyan 442002 Peoples R China

Since the rolling bearing fault signal captured by a vibration sensor contains a large amount of background noise, fault features cannot be accurately extracted. To address this problem, a rolling bearing fault feature extraction algorithm based on improved pelican optimization algorithm (IPOA)-variable modal decomposition (VMD) and multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) methods is proposed. Firstly, the pelican optimization algorithm (POA) was improved using a reverse learning strategy for dimensional-by-dimensional lens imaging and circle mapping, and the optimization performance of IPOA was verified. Secondly, the kurtosis-square envelope Gini coefficient criterion was used to select the optimal modal components from the decomposed components of the signal, and MOMEDA was used to process the optimal modal components in order to obtain the optimal deconvolution signal. Finally, the Teager energy operator (TEO) was employed to demodulate and analyze the optimally deconvoluted signal in order to enhance the transient shock component of the original fault signal. The effectiveness of the proposed method was verified using simulated and actual signals. The results showed that the proposed method can accurately extract failure characteristics in the presence of strong background noise interference.

关键词： vibration sensor bearing fault pelican optimization algorithm variational modal decomposition Teager energy operator

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Task grouping and optimized deep learning based VM sizing for hosting containers as a service

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2023年第1期12卷 65-65页

作者： Patra, Manoj Kumar Sahoo, Bibhudatta Turuk, Ashok Kumar Misra, Sanjay Natl Inst Technol Dept Comp Sci & Engn Rourkela 769008 India Ostfold Univ Coll Dept Comp Sci & Commun Halden Norway Inst Energy Technol Dept Appl Data Sci Halden Norway

Containers as a service (CaaS) are a kind of services that permits the organization to handle the containers more effectively. Containers are lightweight, require less computing resources, portable, and facilitate better support for microservices. In the CaaS model, containers are deployed in virtual machines, and the virtual machine runs on the physical machine. The objective of this paper is to estimate the resource required by a VM to execute a number of containers. VM sizing is a directorial process where the system administrators have to optimize the allocated resources within the permitted virtualized space. In this work, the VM sizing is carried out using the Deep Convolutional Long Short Term Memory Network (Deep-ConvLSTM), where the weights are updated by Fractional pelican optimization (FPO) algorithm. Here, the FPO is configured by hybridizing the concept of Fractional Calculus (FC) within the updated location of the pelican optimization algorithm (POA). Moreover, the task grouping is done with Deep Embedded Clustering (DEC), where the grouping is established with respect to the various task parameters, such as task length, submission rate, scheduling class, priority, resource usage, task latency, and Task Rejection Rate (TRR). In addition, the performance analysis of VM sizing is done by taking 100, 200, 300, and 400 tasks. We got the best resource utilization of 0.104 with 300 tasks, a response time of 262ms with 100 tasks, and a TRR of 0.156 with 100 tasks and makespan of 0.5788 with 100 tasks.

关键词： Cloud computing Container as a service Long short term memory pelican optimization algorithm Fractional calculus Deep embedded clustering Convolutional LSTM network

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Prediction of Dielectric Loss Factor of Wood in Radio Frequency Heating and Drying Based on IPOA-BP Modeling

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FORESTS 2024年第7期15卷 1187-1187页

作者： Gao, Jingying Wang, Wei Qu, Zening Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China

In this paper, an Improved pelican optimization algorithm (IPOA) was proposed to optimize a BP neural network model to predict the dielectric loss factor of wood in the RF heating and drying process. The neural network model was trained and optimized using MATLAB 2022b software, and the prediction results of the BP neural network with POA-BP and IPOA-BP models were compared. The results show that the IPOA-optimized BP neural network model is more accurate than the traditional BP neural network model. After the BP neural network model with IPOA optimization was used to predict the dielectric loss factor of wood, the value increased by 4.3%, the MAE decreased by 68%, and the RMSE decreased by 67%. The results provided by the study using the IPOA-BP model show that the prediction of the dielectric loss factor of wood under different macroscopic conditions in radio frequency heating and drying of wood can be realized without the need for highly costly and prolonged experimental studies.

关键词： pelican optimization algorithm BP neural network model dielectric loss factor radio frequency wood drying

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Blockchain with secure data transactions and energy trading model over the internet of electric vehicles

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SCIENTIFIC REPORTS 2024年第1期14卷 1-25页

作者： Al-Shehari, Taher Kadrie, Mohammed Alfakih, Taha Alsalman, Hussain Kuntavai, T. Vidhya, R. G. Dhanamjayulu, C. Shukla, Shubhi Khan, Baseem King Saud Univ Dept Self Dev Skill Comp Skills Common Year Deanship 1 Riyadh 11362 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11543 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11543 Saudi Arabia Adhi Parasakthi Engn Coll Dept EEE Chengalpattu India HKBK Coll Engn Dept ECE Bangalore India Vellore Inst Technol Sch Elect Engn Vellore India Hawassa Univ Dept Elect & Comp Engn POB 05 Hawassa Ethiopia Zhejiang Univ Ctr Renewable Energy & Micro Huanjiang Lab Zhuji 311816 Zhejiang Peoples R China Western Caspian Univ Dept Tech Sci Baku Azerbaijan

The rise of Electric Vehicles (EVs) has introduced significant advancement and evolution in the electricity market. In smart transportation, the EVs have earned more popularity because of its numerous benefits including lower carbon footprints, higher performance, and sophisticated energy trading mechanisms. These potential benefits have resulted in widespread EV adoption across the world. Despite its benefits, energy management remains the biggest challenge in EVs and it is mainly because of the lack of Charging Stations (CSs) near EVs. This creates a demand for an effective, secure and reliable energy management framework for EVs. This study presents a secure data and energy trade paradigm based on Blockchain (BC) in the Internet of EVs (IoEV). BC technology prepares for the high volume of EV integration that serves as the foundation for the next generation, and to assist in developing unique privacy-protected BC-based D-Trading and storage Models. Entities evaluated for the proposed model include Trusted Authority (TA), Vehicles, Smart Meters, Roadside Units (RSU), BC, and Inter-Planetary File System (IPFS). In addition, E-trading involves several phases, including the acquiring E-trading demand requests, E-trading response requests, request matching and token assignment. Moreover, account mapping is performed using a Mayfly pelican optimization algorithm (MPOA), which is created by merging the Mayfly algorithm (MA) and pelican optimization algorithm (POA). Various security features are used to protect data and energy trade in IoEV, including encryption, hashing, polynomials, and others. The testing results revealed that the MPOA outperformed the state-of-the-art results regarding memory consumption, trading rate, transaction cost, and trading energy volume with values of 4.605 MB, 91%, 0.654, and 90 kW, respectively.

关键词： Internet of electric vehicles pelican optimization algorithm Blockchain Inter-planetary file system Energy trading

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Student Performance Estimation Through Innovative Classification Techniques in Education

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第3期15卷 831-844页

作者： Fan, Hui Zhu, Guoping Zhan, Jianhua Guangzhou Xinhua Univ Sch Marxism Guangzhou 510520 Guangdong Peoples R China Wannan Med Coll Sch Marxism Wuhu 241002 Anhui Peoples R China South China Univ Technol Sch Marxism Guangzhou 510641 Guangdong Peoples R China Shanghai Int Studies Univ Sch Japanese Studies Shanghai 200083 Peoples R China

In the current era of intense educational competition, institutions must effectively classify individuals based on their abilities, proactively forecast student performance, and work towards enhancing their forthcoming examination outcomes. Providing early guidance to students is crucial in helping them focus their efforts on specific areas to boost their academic achievements. This analytical approach supports educational institutions in mitigating failure rates by utilizing students' previous performance in relevant courses to predict their outcomes in a specific program. Data mining encompasses a variety of techniques used to reveal hidden patterns within vast datasets. In the context of educational data mining, these methods are applied within the educational sphere, with a specific emphasis on analyzing data from both students and educators. These patterns can offer significant value for predictive and analytical objectives. In this study, Gaussian Process Classification (GPC) was employed for the prediction of student performance. To improve the model's accuracy, two cutting-edge optimizers, namely the Golden Eagle Optimizer (GEO) and the pelican optimization algorithm (POA), were incorporated. When assessing the model's performance, four widely used metrics were utilized: Accuracy, Precision, Recall, and F1-score. The results of this study underscore the effectiveness of both the POA and GEO optimizers in enhancing GPC performance. Specifically, GPC+GEO demonstrated remarkable effectiveness in the Poor grade, while GPC+POA excelled in the Acceptable and Excellent category. This highlights the positive impact of these optimization techniques on the model's predictive capabilities.

关键词： Student performance Gaussian Process Classification Golden Eagle Optimizer pelican optimization algorithm

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Optimizing Student Performance Prediction: A Data Mining Approach with MLPC Model and Metaheuristic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第4期15卷 55-71页

作者： Hai, Qing Wang, Changshou Hetao Coll Dept Water Resources & Civil Engn Bayan Nur 01500 Inner Mongolia Peoples R China Hetao Coll Dept Agr Bayan Nur 01500 Inner Mongolia Peoples R China

Given the information stored in educational databases, automated achievement of the learner's prediction is essential. The field of educational data mining (EDM) is handling this task. EDM creates techniques for locating data gathered from educational settings. These techniques are applied to comprehend students and the environment in which they learn. Institutions of higher learning are frequently interested in finding how many students will pass or fail required courses. Prior research has shown that many researchers focus only on selecting the right algorithm for classification, ignoring issues that arise throughout the data mining stage, such as classification error, class imbalance, and high dimensionality data, among other issues. These kinds of issues decreased the model's accuracy. This study emphasizes the application of the Multilayer Perceptron Classification (MLPC) for supervised learning to predict student performance, with various popular classification methods being employed in this field. Furthermore, an ensemble technique is utilized to enhance the accuracy of the classifier. The goal of the collaborative approach is to address forecasting and categorization issues. This study demonstrates how crucial it is to do algorithm fine-tuning activities and data pretreatment to address the quality of data concerns. The exploratory dataset utilized in this study comes from the pelican optimization algorithm (POA) and Crystal Structure algorithm (CSA). In this research, a hybrid approach is embraced, integrating the mentioned optimizers to facilitate the development of MLPO and MLCS. Based on the findings, MLPO2 demonstrated superior efficiency compared to the other methods, achieving an impressive 95.78% success rate.

关键词： Educational data mining multilayer perceptron classification pelican optimization algorithm crystal structure algorithm student performance

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Hybrid pelican Komodo algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第6期13卷 46-55页

作者： Kusuma, Purba Daru Dinimaharawati, Ashri Telkom Univ Comp Engn Bandung Indonesia

In this work, a new metaheuristic algorithm, namely the hybrid pelican Komodo algorithm (HPKA), has been proposed. This algorithm is developed by hybridizing two shortcoming metaheuristic algorithms: the pelican optimization algorithm (POA) and Komodo Mlipir algorithm (KMA). Through hybridization, the proposed algorithm is designed to adapt the advantages of both POA and KMA. Several improvisations regarding this proposed algorithm are as follows. First, this proposed algorithm replaces the randomized target with the preferred target in the first phase. Second, four possible movements are selected stochastically in the first phase. Third, in the second phase, the proposed algorithm replaces the agent's current location with the problem space width to control the local problem space. This proposed algorithm is then challenged to tackle theoretical and real-world optimization problems. The result shows that the proposed algorithm is better than grey wolf optimizer (GWO), marine predator algorithm (MPA), KMA, and POA in solving 14, 12, 14, and 18 functions. Meanwhile, the proposed algorithm creates 109%, 46%, 47%, and 1% better total capital gain rather than GWO, MPA, KMA, and POA, respectively in solving the portfolio optimization problem.

关键词： Metaheuristic pelican optimization algorithm Komodo Mlipir algorithm portfolio optimization algorithm LQ45 index

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Deep feature fusion with computer vision driven fall detection approach for enhanced assisted living safety

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Almukadi, Wafa Sulaiman Alrowais, Fadwa Saeed, Muhammad Kashif Yahya, Abdulsamad Ebrahim Mahmud, Ahmed Marzouk, Radwa Univ Jeddah Coll Comp Sci & Engn Dept Software Engn Jeddah Saudi Arabia Princess Nourah Bint Abdulrahman Univ PNU Coll Comp & Informat Sci Comp Sci Dept POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Comp Sci Abha Saudi Arabia Univ Northern Border Coll Comp & Informat Technol Informat Technol Dept Rafha Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11835 Egypt Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia

Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are frequent among older persons and might cause severe injuries and complications. Incorporating computer vision techniques into assisted living environments is revolutionary for these issues. By leveraging cameras and complicated approaches, a computer vision (CV) system can monitor residents' movements continuously and identify any potential fall events in real time. CV, driven by deep learning (DL) techniques, allows continuous surveillance of people through cameras, investigating complicated visual information to detect potential fall risks or any instances of falls quickly. This system can learn from many visual data by leveraging DL, improving its capability to identify falls while minimalizing false alarms precisely. Incorporating CV and DL enhances the efficiency and reliability of fall detection and allows proactive intervention, considerably decreasing response times in emergencies. This study introduces a new Deep Feature Fusion with Computer Vision for Fall Detection and Classification (DFFCV-FDC) technique. The primary purpose of the DFFCV-FDC approach is to employ the CV concept for detecting fall events. Accordingly, the DFFCV-FDC approach uses the Gaussian filtering (GF) approach for noise eradication. Besides, a deep feature fusion process comprising MobileNet, DenseNet, and ResNet models is involved. To improve the performance of the DFFCV-FDC technique, improved pelican optimization algorithm (IPOA) based hyperparameter selection is performed. Finally, the detection of falls is identified using the denoising autoencoder (DAE) model. The performance analysis of the DFFCV-FDC methodology was examined on the benchmark fall database. A widespread comparative study reported the supremacy of the DFFCV-FDC approach with existing techniques.

关键词： Fall detection Computer vision Deep feature fusion pelican optimization algorithm Deep learning Denoising autoencoder

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