Selective Harmonic Elimination (SHE) shows superior harmonic performance at low switching frequencies to eliminate low-order harmonics and this property decreasing losses is a very effective way to reach higher effici...
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ISBN:
(纸本)9781538663929
Selective Harmonic Elimination (SHE) shows superior harmonic performance at low switching frequencies to eliminate low-order harmonics and this property decreasing losses is a very effective way to reach higher efficiencies in power electronics applications. In this paper, whale optimizer algorithm (WOA) is implemented to decide optimum switching angles for three-phase Voltage Source Inverter (VSI) of eliminating some high order harmonics while providing the required voltage. Besides, Particle Swarm Optimization (PSO) algorithm is also applied and the optimum switching angles are calculated off-line to eliminate the 5th, 7th, 11th, 13th, 17th, and 19th harmonics. The output voltages obtained by the application of WOA and PSO are compared using the total harmonic distortion (THD). By the comparison of these results, we show that WOA gives faster and more accurate results in terms of decreasing THD than PSO algorithm in SHE applications.
Controlling the temperature in chemical reactors, heating furnaces, distillation columns, and reboilers for circulating heating fluids is crucial across all industries. This project is a simulation-based study where b...
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The increasing integration of cyber-physical systems (CPSs) and information and communication technologies (ICT) within the Smart Grid (SG) framework has led to significant advancements in energy systems. However, thi...
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The increasing integration of cyber-physical systems (CPSs) and information and communication technologies (ICT) within the Smart Grid (SG) framework has led to significant advancements in energy systems. However, this integration introduces vulnerabilities, particularly cyberattacks like distributed denial of service (DDoS) attacks. This research presents a new approach to detecting cyberattacks in SG by combining deep learning (DL) techniques with the whale optimization (WOA) and fisher mantis optimization (FMO) algorithm, which forms the WOA-FMO hybrid algorithm. The system utilizes convolutional neural networks (CNN) for feature extraction and long-short-term memory (LSTM) networks to classify network traffic into normal and abnormal categories. The WOA-FMO algorithm optimizes the feature selection process, reducing dimensionality and improving model accuracy, thereby enhancing detection efficiency. Experimental evaluations on the PhishTank, UCI, and Tan datasets demonstrate that the proposed approach outperforms traditional approaches in terms of sensitivity, specificity, accuracy, and precision. A comparison of five optimization algorithms-GOA, ABC, BWO, GWO, and WOA-FMO-reveals that the WOA-FMO hybrid achieves the highest sensitivity (98.86%), accuracy (98.57%), and precision (98.30%), as well as strong specificity (98.28%). These results underscore the effectiveness of WOA-FMO in optimizing feature selection and improving classification performance, offering a robust solution for enhancing the resilience of IoT-based SGs against advanced cyberattacks.
Converters of permanent magnet synchronous generator (PMSG), driven by wind turbines, are controlled by a classical proportional-integral controller. However, many research studies highlighted the challenge in PMSG du...
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Converters of permanent magnet synchronous generator (PMSG), driven by wind turbines, are controlled by a classical proportional-integral controller. However, many research studies highlighted the challenge in PMSG due to the poor performance of the classical proportional-integral controller, especially in the event of faults or wind speed variations. This article proposes a solution for the limitations of the classical proportional-integral controller with PMSG driven by a wind turbine. The proposed solution includes two optimization techniques: gray wolf optimizer and whale optimizer algorithm. To ensure the effectiveness of the proposed techniques, step change and random variation of wind speed are studied. Moreover, fault ride-through capability of the PMSG is studied with gray wolf optimizer and whale optimizer algorithm techniques during the occurrence of a three-phase fault incident. In this case, a braking chopper controlled by a hysteresis controller is connected to the DC-link capacitor. The simulated results show that compared with the classical proportional-integral controller, gray wolf optimizer and whale optimizer algorithm techniques are greatly efficient in improving the dynamic behavior of the PMSG during wind speed variations. Moreover, gray wolf optimizer and whale optimizer algorithm techniques present their effectiveness during the fault incident by suppressing the transient variations of all the PMSG parameters, improving the fault ride-through capability, and decreasing the total harmonic distortion of the current waveforms. All simulations are performed with MATLAB/ Simulink program package.
Permanent Magnet Synchronous Motor (PMSM) is widely used in the industrial applications because of its simple construction and its high efficiency. Unfortunately, if a small change has occurred in the system parameter...
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Permanent Magnet Synchronous Motor (PMSM) is widely used in the industrial applications because of its simple construction and its high efficiency. Unfortunately, if a small change has occurred in the system parameters, the PMSM displays an unexpected performance which is known as a chaotic behavior. To control this undesirable behavior, it is crucial to propose a robust tool to extract the model parameters accurately and rapidly. In this work, novel developed optimization techniques called Chaotic whale Optimization variants (CWOA) are proposed where the standard whale Optimization algorithm (WOA) is integrated with different ten chaos maps to tune some of its parameters. Four CWOA variants are introduced, tested and validated mathematically over CEC 2017. In addition, CWOA variants and the standard WOA version are proposed to estimate the parameters of the chaotic behavior in PMSM at both of the off-line and the on-line operating conditions without and with noise. The results of the proposed algorithms are compared with that of the previous techniques in literature to test their efficiency, reliability, and accuracy. An intensive statistical analysis is accomplished for extra validation of the superiority of the proposed variants. The overall results indicate that the CWOA-II variant with the logistic chaos map offers the best performance among all other variants where it provides lower error values between the estimated and the original system performance, higher convergence speed and shorter execution time. This eventually helps to provide an accurate and fast description of the chaotic region to ensure a fast control of the motor as well as a fast protection from damage. (C) 2018 Elsevier B.V. All rights reserved.
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