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Stability improvement of the PSS-connected power system network with ensemble machine learning tool

ENERGY REPORTS 2022年 8卷 11122-11138页

作者： Shahriar, M. S. Shafiullah, M. Pathan, M. I. H. Sha'aban, Y. A. Bouchekara, Houssem R. E. H. Ramli, Makbul A. M. Rahman, M. M. Univ Hafr Al Batin Dept Elect Engn Hafar al Batin 31991 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Renewable Energy & Power Dhahran 31261 Saudi Arabia Hajee Mohammad Danesh Sci & Technol Univ Dept Elect & Elect Engn Dinajpur Bangladesh King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Hamad Bin Khalifa Univ Coll Sci & Engn Informat & Comp Technol Div Doha Qatar

Stability is a primary requirement of the electrical power system for its flawless, secure, and economical operation. Low-frequency oscillations (LFOs), commonly seen in interconnected power systems, initiate the possibility of instability and, therefore, require sophisticated care to deal with. This paper proposes an original approach to tuning the parameters of the power system stabilizer (PSS), which plays a crucial role in the power system networks to dampen unwanted oscillations. The ensemble method combines multiple machine learning techniques and has been used for tuning the PSS parameters in real-time for two PSS-connected power system networks. The first system is a single-machine infinite bus power system, while the second is a unified power flow controller (UPFC) device. The backtracking search algorithm (BSA) based proposed ensemble model is formed by combining three machine learning (ML) techniques, namely the extreme learning machine (ELM), neurogenetic (NG) system, and multi-gene genetic programming (MGGP). To validate the stability of the network, Eigenvalues, well-recognized statistical parameters, and minimum damping ratios were analyzed, besides the time-domain simulation results. Furthermore, results for various loading conditions were prepared to check the robustness of the proposed model. A comparative study of the proposed approach with NG, ELM, MGGP models, and two reference cases along with the conventional method will validate the superiority of the employed ML approach. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Artificial intelligence backtracking search algorithm Ensemble method Extreme learning machine Genetic programming Low-frequency oscillation Neurogenetic Power system stability Real-time

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Islanding detection and load shedding scheme for radial distribution systems integrated with dispersed generations

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IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第15期9卷 2261-2275页

作者： Khamis, Aziah Shareef, Hussain Mohamed, Azah Univ Tekn Malaysia Melaka Fac Elect Engn Durian Tunggal Melaka Malaysia United Arab Emirates Univ Dept Elect Engn Al Ain U Arab Emirates Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Bangi 43600 Malaysia

This study presents a novel islanding detection and optimal load shedding scheme for radial distribution systems integrated with dispersed generation (DG). A simple and effective passive islanding detection method is developed based on the phase-space technique and using a probabilistic neural network-based classifier. After the system detects unintentional islanding state, it utilises an optimal load shedding scheme to prevent system collapse due to load-generation mismatch and voltage instability encountered in the islanded part of the system. The optimal load shedding scheme is developed based on the backtracking search algorithm. Extensive tests conducted with the DigSilent Power Factory((R)) software and MATLAB((R)) software on the IEEE 33-bus system with four DG units considering several scenarios of islanding condition were utilised to evaluate the effectiveness of the proposed islanding detection method and the optimal load shedding scheme. The results showed that the proposed method can effectively detect the islanding events and stabilise the islanded part of the system with minimum load curtailment.

关键词： power distribution faults load shedding probability neural nets distributed power generation voltage regulators search problems control engineering computing pattern classification power engineering computing passive islanding detection method optimal load shedding scheme radial distribution systems dispersed generation phase-space technique probabilistic neural network-based classifier unintentional islanding state detection system collapse prevention load-generation mismatch voltage instability backtracking search algorithm DigSilent Power Factory software MATLAB software IEEE 33-bus system minimum load curtailment

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Effect of face and ocular multimodal biometric systems on gender classification

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IET BIOMETRICS 2019年第4期8卷 243-248页

作者： Eskandari, Maryam Sharifi, Omid Toros Univ Dept Comp & Software Engn Mersin Turkey

This study is concerned with analysing face-ocular multimodal biometric systems for a person gender prediction. Particularly, this is the first study considering fusion of face and ocular biometrics to predict gender of a person via a hybrid multimodal scheme. The authors aim to investigate the effect of multimodal biometric systems at score and feature-level fusion on gender classification. The implementation of uniform local binary pattern (ULBP) feature extractor is taken into account to extract the face and ocular texture information. This paper proposes to select the efficient feature sets of both modalities using a novel evolutionary algorithm called backtracking search algorithm (BSA). On the other hand, support vector machine (SVM) is applied for classification purpose using the fused face and ocular features and scores. The proposed scheme is validated using CASIA-Iris-Distance and MBGC multimodal biometric databases with consideration of a subject-disjoint training and testing evaluation. The achieved gender recognition demonstrates the superiority of the hybrid multimodal face-ocular scheme over unimodal face and ocular schemes implemented in this study for a subject gender prediction.

关键词： support vector machines image texture image classification search problems evolutionary computation image fusion face recognition iris recognition feature extraction biometrics (access control) gender issues gender classification backtracking search algorithm MBGC multimodal biometric databases gender recognition person gender prediction feature-level fusion ULBP feature extractor face fusion face-ocular multimodal biometric systems uniform local binary pattern feature extractor support vector machine SVM CASIA-Iris-Distance databases evolutionary algorithm

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Optimization-Based Fuzzy Energy Management Strategy for PEM Fuel Cell/Battery/Supercapacitor Hybrid Construction Excavator

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY 2021年第4期8卷 1267-1285页

作者： Dao, Hoang Vu To, Xuan Dinh Truong, Hoai Vu Anh Do, Tri Cuong Ho, Cong Minh Dang, Tri Dung Ahn, Kyoung Kwan Univ Ulsan Grad Sch Mech Engn 93 Daehak Ro Ulsan 680749 South Korea Univ Ulsan Sch Mech Engn 93 Daehak Ro Ulsan 680749 South Korea Ho Chi Minh City Univ Technol & Educ Dept Mechatron Ho Chi Minh City 700000 Vietnam

Fuel cell hybrid electric construction equipment (FCHECE) is known as a promising solution to achieve the goal of energy saving and environment protection. Energy management strategy is a key technology of FCHECE, which splits the energy flow between power sources. This paper presents a novel optimal energy management strategy for a hybrid electric-powered hydraulic excavator system to enhance power performance, power sources lifespan, and fuel economy. As for the proposed powertrain configuration, fuel cell serves as a primary energy source, and supercapacitor and battery are considered as energy storages. The integration of supercapacitor and battery in fuel cell vehicle has advantages of improving power performance and storing the regenerative energy for future usage. An energy management strategy based on fuzzy logic control and a rule-based algorithm is proposed to effectively distribute the power between the three sources and reuse the regenerative energy. Furthermore, the parameters of the fuzzy logic system are optimized using the combination of a backtracking search algorithm which provides a good direction to the global optimal region and sequential dynamic programming as a local search method to fine-tune the optimal solution in order to reduce the hydrogen consumption and prolong the lifetime of the power sources. Simulation results show that the proposed energy management strategy enhances the vehicle performance, improves fuel economy of the FCHECE by 10.919%, increase battery and supercapacitor charge-sustaining capability as well as efficiency of the fuel cell system.

关键词： PEM fuel cell Energy management strategy Construction excavator Fuzzy logic backtracking search algorithm Sequential quadratic programming

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Feature selection for the fusion of face and palmprint biometrics

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SIGNAL IMAGE AND VIDEO PROCESSING 2016年第5期10卷 951-958页

作者： Farmanbar, Mina Toygar, Onsen Eastern Mediterranean Univ Dept Comp Engn Northern Cyprus 10 Mersin Turkey

Multimodal biometric systems aim to improve the recognition accuracy by minimizing the limitations of unimodal systems. In this paper, different fusion schemes based on feature-level and match score-level fusion are employed to provide a robust recognition system. The proposed method presents a multimodal approach based on face-palmprint biometric systems by match score-level fusion technique. Local binary patterns are performed as local feature extractor to obtain efficient texture descriptor. Feature selection is performed using backtracking search algorithm to select an optimal subset of face and palmprint extracted features. Hence, computation time and feature dimension are considerably reduced while obtaining the higher level of performance. Then, match score-level fusion is performed to show the effectiveness and accuracy of the proposed method. In score-level fusion, face and palmprint scores are normalized using tanh normalization and matching scores of individual classifiers are fused using sum rule method. The experimental results are tested on a developed virtual multimodal database combining FERET face and PolyU palmprint databases. The results demonstrate a significant improvement compared with unimodal identifiers, and the proposed approach significantly outperforms other face-palmprint multimodal systems with a recognition accuracy of 99.17 %. Additionally, the proposed approach is compared with the state-of-the-art methods.

关键词： Multimodal biometrics Face recognition Palmprint recognition Feature-level fusion Match score-level fusion backtracking search algorithm

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Virtual Power Plant and Microgrids controller for Energy Management based on optimization techniques

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JOURNAL OF ELECTRICAL SYSTEMS 2017年第2期13卷 285-294页

作者： Abdolrasol, Maher G. M. Mohamed, Azah Hannan, M. A. Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Univ Tenaga Nas Coll Engn Dept Elect Power Engn Bandar Baru Bangi 43650 Selangor Malaysia

This paper discuss virtual power plant (VPP) and Microgrid controller for energy management system (EMS) based on optimization techniques by using two optimization techniques namely backtracking search algorithm (BSA) and particle swarm optimization algorithm (PSO). The research proposes use of multi Microgrid in the distribution networks to aggregate the power form distribution generation and form it into single Microgrid and let these Microgrid deal directly with the central organizer called virtual power plant. VPP duties are price forecast, demand forecast, weather forecast, production forecast, shedding loads, make intelligent decision and for aggregate & optimizes the data. This huge system has been tested and simulated by using Matlab simulink. These paper shows optimizations of two methods were really significant in the results. But BSA is better than PSO to search for better parameters which could make more power saving as in the results and the discussion.

关键词： Virtual Power Plant Microgrid Energy Management System backtracking search algorithm particle swarm optimization algorithm

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Improved Particle Swarm Optimization for the Mutual Inductance of an Implantable Biomedical Application 5

Improved Particle Swarm Optimization for the Mutual Inductan...

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International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS)

作者： Houcine, Marouani Amin, Sallem Mondher, Chaoui Nouri, Masmoudi Univ Sfax LETI Sfax Tunisia

ISBN: (纸本)9781538678503

Particle Swarm Optimization (PSO) and Differential Evolution (DE) are two dominant metaheuristic optimization techniques with different special features. These specialties are combined together in an improved particle swarm optimization (DPSO) algorithm is estimated. The proposed idea consists of incorporating the concept of the DE algorithm when updating velocity equation of the PSO algorithm in sort to guarantee the global optimal. Also, the objective is to bias the solution, the difference between the selected randomly and the target particle, with an adjust parameter. The improved algorithm is simulated on several test functions and compared between three references metaheuristics and one variant most used in the literature. Then, DPSO has been used to optimize the mutual inductance of an implantable biomedical problem.

关键词： Improved DPSO Particle Swarm Optimization Differential Evolution backtracking search algorithm Gravitational search algorithm Mutual inductance Archimedes coil Solenoid coil

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Fuzzy based BSA optimization for maximum power point tracking controller performance evaluation

Fuzzy based BSA optimization for maximum power point trackin...

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Annual Meeting of the IEEE-Industry-Applications-Society (IAS)

作者： Abu Sayem, M. Nadzirah, Ainna Al-Shetwi, Ali Q. Hannan, M. A. Ker, Pin Jern Rahman, S. A. Muttaqi, K. M. Univ Tenaga Nas COE Dept Elect Power Engn Kajang 43000 Malaysia Fahad Bin Sultan Univ Dept Elect Engn Tabuk 47721 Saudi Arabia Univ Tenaga Nas Inst Sustainable Energy Kajang 43000 Malaysia Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia

ISBN: (纸本)9781728164014

The world transportation sector is transitioning from traditional fuel vehicles to electric vehicles (EV) in order to minimize carbon emissions from vehicles. Furthermore, for a cleaner environment, it is better to charge the EV using clean power such as solar photovoltaic (PV) systems. However, PV generating an insignificant sum of power which is not sufficient to apply in a specific application. To solve this issue, it is essential to improve the energy conversion efficiency by the Maximum Power Point Tracking (MPPT). In the literature, various algorithms have been used to enhance the MPPT efficiency which still have different limitations. Therefore, this study is developed to improve the MPPT for solar car system charge using Fuzzy Logic Controller (FLC) as the input of the voltage and current of PV systems is variable. Moreover, to overcome the FLC limitations and enhance the performance of the controller, the FLC-based backtracking search algorithm (BSA) optimization method is developed for harvesting maximum power from the solar panel. The power and voltage output of MPPT with and without the optimization is compared to show the optimized controller efficiency. In comparison to the controller with the optimization method, the efficiency is increased significantly (10.36% at 10s for power) than the non-optimized method. In sum, the results have shown the ability of FLC based-BSA optimization method to enhance the efficiency of MPPT for solar-powered cars linking to the swift alteration of insolation due to the dynamic motion of the vehicle.

关键词： Fuzzy Logic Control Maximum Power Point Tracking backtracking search algorithm

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Comparative Study of Some Optimization Techniques Applied to Jacketed CSTR Control 4

Comparative Study of Some Optimization Techniques Applied to...

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4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO)

作者： Gupta, Pradeep Kumar, Vineet Rana, K. P. S. Mishra, Puneet Netaji Subhas Inst Technol Div Instrumentat & Control Engn Sect 3 New Delhi 110078 India

ISBN: (纸本)9781467372312

In this paper, the performance of four optimization techniques i.e. Grey Wolf Optimizer (GWO), backtracking search algorithm (BSA), Differential Evolution (DE), and Bat algorithm (BA) have been investigated for optimizing the scaling factors of fuzzy proportional-integral controller (FPIC). Jacketed continuous stirred tank reactor (CSTR) has been considered for step set-point and trajectory tracking of reactor temperature. The present work has been simulated in LabVIEW (TM). The performance of aforementioned algorithms has been evaluated by comparing the cost function Integral of Absolute Error for step set-point and trajectory tracking. On the basis of simulation results, it can be inferred that, GWO outperformed other optimization algorithms for all considered cases.

关键词： Controller Tuning Fuzzy Logic Controller Grey Wolf Optimizer backtracking search algorithm Bat algorithm Differential Evolution

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Optimal Neural Network Approach for Estimating State of Energy of Lithium-ion Battery Using Heuristic Optimization Techniques 6

Optimal Neural Network Approach for Estimating State of Ener...

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6th International Conference on Electrical Engineering and Informatics (ICEEI) - Sustainable Society Through Digital Innovation

作者： Lipu, M. S. Hossain Hussain, Aini Saad, M. H. M. Hannan, M. A. Univ Kebangsaan Malaysia Fac Engn & Built Environm Bangi 43600 Selangor Malaysia Univ Tenaga Nas Dept Elect Power Engn Kajang 43000 Selangor Malaysia

ISBN: (纸本)9781538604755

This paper presents an optimal state of energy (SOE) estimation strategy of a lithium-ion battery using the back-propagation neural network (BPNN). Two heuristic optmization techniques named backtracketing search algorithm (BSA) and particle swarm optimization (PSO) algorithm are applied to improve the accuracy of BPNN model. Optimization algorithms are developed to determine the optimal value of hidden layer neurons and learning rate of BPNN model. Three most influencing factors including current, voltage and temperature are considered as the inputs to the optimal BPNN model. Federal Urban Driving Schedule (FUDS) is used to check the model robustness at 0 degrees C, 25 degrees C and 45 degrees C. The model performance is evaluated based on the root mean square error (RMSE) and mean absolute error (MAE). The results show that the proposed model obtains good accuracy with an absolute error of +/- 5%. The BPNN based BSA model improves the SOE estimation accuracy by reducing RMSE and MAE by 2.8% and 4.4% compared to BPNN based PSO model at 25 degrees C.

关键词： state of energy back-propagation neural network backtracking search algorithm particle swarm optimization Federal Urban Driving Schedule

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