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Impact of Different AC Voltage control Modes of Wind-farm-side MMC on Stability of MMC-HVDC with Offshore Wind Farms

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Journal of Modern Power systems and Clean Energy 2023年第5期11卷 1687-1699页

作者： Hongfei Lin Tao Xue Jing Lyu Xu Cai Key Laboratory of Control of Power Transmission and Conversion Ministry of EducationDepartment of Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Electrical Engineering Shanghai Jiao Tong UniversityShanghaiChina Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina

Wind-farm-side modular multilevel converters(WFMMCs) used in modular multilevel converter based highvoltage direct current(MMC-HVDC) transmission systems must be able to control the AC grid voltage in offshore wind farms. Different AC voltage control strategies can significantly affect the dynamic characteristics of WFMMCs. However, existing studies have not provided a general methodology of controller parameter design, and few comparative studies have been conducted on control performance under varying operating conditions as well as the effects of different AC voltage control modes(AVCMs) on the stability of MMC-HVDCs with offshore wind farms. This paper provides a controller parameter design method for AVCMs, which is tested in various operating scenarios. Sequence impedance models of offshore wind farms and WFMMCs under different AVCMs are then developed. The effects of AVCMs on the small-signal stability of the interconnected system are then analyzed and compared using the impedance-based method. Finally, case studies are conducted on a practical MMC-HVDC system with offshore wind farms to verify the theoretical analysis.

关键词： Offshore wind farm modular multilevel converter based high-voltage direct current(MMC-HVDC) AC voltage control stability dynamic response

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An adaptive fractional controller design for automatic voltage regulator system: sigmoid-based fractional-order PID controller

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Neural Computing and Applications 2024年第23期36卷 14409-14431页

作者： Sahin, Ali Kivanc Cavdar, Bora Ayas, Mustafa Sinasi Department of Electrical and Electronics Engineering Erzurum Technical University Erzurum25050 Turkey Department of Energy Systems Engineering Karadeniz Technical University Trabzon61080 Turkey Department of Electrical and Electronics Engineering Karadeniz Technical University Trabzon61080 Turkey

The primary objective of a power system is to provide safe and reliable electrical energy to consumers. This objective is achieved by maintaining the stability of the power system, a multifaceted concept that can be divided into three distinct classes. The focus of this study is on one of these classes, voltage stability. A critical component in maintaining voltage stability is the automatic voltage regulator (AVR) system of synchronous generators. In this paper, a novel control method, the sigmoid-based fractional-order PID (SFOPID), is introduced with the aim of improving the dynamic response and the robustness of the AVR system. The dandelion optimizer (DO), a successful optimization algorithm, is used to optimize the parameters of the proposed SFOPID control strategy. The optimization process for the DO-SFOPID control strategy includes a variety of objective functions, including error-based metrics such as integral of absolute error, integral of squared error, integral of time absolute error, and integral of time squared error, in addition to the user-defined Zwee Lee Gaing’s metric. The effectiveness of the DO-SFOPID control technique on the AVR system has been rigorously investigated through a series of tests and analyses, including aspects such as time domain, robustness, frequency domain, and evaluation of nonlinearity effects. The simulation results are compared between the proposed DO-SFOPID control technique and the fractional-order PID (FOPID) and sigmoid-based PID (SPID) control techniques, both of which have been tuned using different metaheuristic algorithms that have gained significant recognition in recent years. As a result of these comparative analyses, the superiority of the DO-SFOPID control technique is confirmed as it shows an improved performance with respect to the other control techniques. Furthermore, the performance of the proposed DO-SFOPID control technique is validated within an experimental setup for the AVR system. The simulation res

关键词： Three term control systems

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Enhanced Tube-Based Event-Triggered Stochastic Model Predictive control With Additive Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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Single-Sensor based CSPO Algorithm for Maximum Power Point Tracking under Dynamic Shading Conditions

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Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 841-854页

作者： Bashista, Prateek Mishra, Anurag Kumar, Bhavnesh Tyagi, Arjun Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India Department of Electrical Engineering Faculty of Technology University of Delhi Delhi India

One of the most significant and difficult challenges in solar systems is determining the maximum power point. This process becomes more difficult when the array of solar modules is subjected to non-uniform shade. A unique hybrid maximum power point tracking (MPPT) approach is presented in this research. The suggested approach, cuckoo search–perturb observe, is created by combining the traditional perturb & observe (P&O) and cuckoo search algorithms (CSPO). A hybrid algorithm has been developed to capitalize on the benefits of both algorithms while overcoming their respective disadvantages. The proposed algorithm attempts to incorporate the most prominent aspects of both algorithms while also attempting to address the drawbacks of both algorithms and improving the accuracy of the new approach by resolving the issue of P&O getting stuck on local maxima during non-uniform irradiance and reducing the resource-intensive approach of cuckoo search by limiting its number of iterations. Furthermore, the suggested approach locates the maximum power point using just voltage sensors, decreasing sensor requirements. The simulation findings for maximum power point tracking performance are also presented. © The Institution of Engineers (India) 2024.

关键词： Optimization

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Joint Fuel-optimal control of the Velocity and Power-split of Hybrid Electric Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-12页

作者： Borsboom, Olaf Salazar, Mauro Keulen, Thijs van Control Systems Technology section Department of Mechanical Engineering Eindhoven University of Technology Eindhoven MB The Netherlands

This paper explores the eco-driving problem of parallel hybrid electric vehicles, intended to drive a certain distance within a limited amount of time, where the longitudinal vehicle velocity and powertrain controls are optimized to minimize the fuel consumption. In particular, we incorporate Pontryagin's Minimum Principle (PMP) and singular control theory in an optimization framework to find the fuel-optimal velocity and power-split control policy for the prime mover and the electric machine with global optimality guarantees. In addition, we present reformulations and derivations, so that the same problem can be solved jointly using another framework based on convex optimization, with the same global optimality properties, employing methods originally derived for timeoptimal control of race cars. Thereby, we formally show the equivalence between the eco-driving and the racing problem. We showcase both our frameworks with numerical solutions, drawing three comparisons: First, we solve the velocity and power-split problem, both sequentially and jointly, using the PMP framework. We show that the latter can improve the fuel consumption by 2.6 %. Second, we benchmark the PMP and the convex framework by solving the joint problem with both methods and observe a discrepancy of 0.14% in terms of the resulting fuel energy consumption. Finally, in a numerical study addressing the performance of both methods individually, we observe that the efficiency of the PMP and the convex framework are strongly dependent on the stopping criteria and the discretization step size, respectively. Authors

关键词： Convex optimization

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Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

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Computer Modeling in engineering & Sciences 2024年第2期138卷 1341-1364页

作者： Mohamed Zarouan Ibrahim M.Mehedi Shaikh Abdul Latif Md.Masud Rana Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Nuclear Engineering King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Electronic Engineering Rajshahi University of Engineering and TechnologyRajshahiBangladesh

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry ***, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of *** diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel *** experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

关键词： Fault detection Industry 4.0 gradient optimizer algorithm deep learning rotating machineries artificial intelligence

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Designing an Optimal Integrated Energy System Including Sustainable Resources for Real-Time electrical and Thermal Loads Supplying, Considering Grid Interaction 28

Designing an Optimal Integrated Energy System Including Sust...

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28th International electrical Power Distribution Conference, EPDC 2024

作者： Kiani, Hossein Nazari, Mohammad Hassan Khorsandi, Amir Abyaneh, Hossein Askarian Hosseinian, Seyed Hossein Amirkabir University of Technology Department of Electrical Engineering Tehran Iran Smart Control Systems Department Tehran Iran

ISBN: (纸本)9798350385502

Recently, there has been significant attention directed towards renewable energy sources, driven by advancements in power electronics, cost reductions, and escalating environmental concerns. The utilization of entirely renewable sources for fulfilling energy requirements presents advantages such as reduced energy costs, sustainable expenses, and local accessibility. This research assesses various scenarios encompassing diverse configurations to minimize greenhouse gas (GHG) emissions, cost of energy (COE), and annual net present costs (NPC). The evaluation considers factors like investment, replacement, decommissioning, operation, and maintenance costs. The study conducts an optimal power planning and techno-economic-environmental analysis of a grid-connected hybrid renewable energy system, incorporating elements such as wind turbines (WT), photovoltaic systems (PVs), diesel generators (DG), boilers, converters, thermal load controllers (TLC), and battery energy storage systems (BESS). The system is designed to cater to both electrical and thermal loads concurrently. The proposed methodology is tested using HOMER software on a small-scale system situated in Manjil, Iran, with a focus on its efficacy in meeting economic and sustainable electrical and thermal requirements within a hybrid renewable energy framework. © 2024 IEEE.

关键词： Renewable energy

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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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Examining the Quality Metrics of a Communication Network with Distributed Software-Defined Networking Architecture

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Computer Modeling in engineering & Sciences 2024年第11期141卷 1673-1708页

作者： Khawaja Tahir Mehmood Shahid Atiq Intisar Ali Sajjad Muhammad Majid Hussain Malik M.Abdul Basit Department of Electrical Engineering Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Telecommunication Systems Bahauddin Zakariya UniversityMultan60000Pakistan Department of Electrical Engineering University of Engineering and TechnologyTaxila47050Pakistan School of Engineering and Physical Sciences Heriot-Watt UniversityEdinburghEH144ASUK

Software-Defined Networking(SDN),with segregated data and control planes,provides faster data routing,stability,and enhanced quality metrics,such as throughput(Th),maximum available bandwidth(Bd(max)),data transfer(DTransfer),and reduction in end-to-end delay(D(E-E)).This paper explores the critical work of deploying SDN in large-scale Data Center Networks(DCNs)to enhance its Quality of Service(QoS)parameters,using logically distributed control *** is a noticeable increase in Delay(E-E)when adopting SDN with a unified(single)control structure in big DCNs to handle Hypertext Transfer Protocol(HTTP)requests causing a reduction in network quality parameters(Bd(max),Th,DTransfer,D(E-E),etc.).This article examines the network performance in terms of quality matrices(bandwidth,throughput,data transfer,etc.),by establishing a large-scale SDN-based virtual network in the Mininet *** SDN network is simulated in three stages:(1)An SDN network with unitary controller-POX to manage the data traffic flow of the network without the server load management algorithm.(2)An SDN network with only one controller to manage the data traffic flow of the network with a server load management algorithm.(3)Deployment of SDN in proposed control arrangement(logically distributed controlled framework)with multiple controllers managing data traffic flow under the proposed Intelligent Sensing Server Load Management(ISSLM)*** a result of this approach,the network quality parameters in large-scale networks are enhanced.

关键词： Software defined networking quality of service hypertext transfer protocol data transfer rate latency maximum available bandwidth server load management

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An Innovative PID controller Optimization Algorithm for Two-area Power System Frequency control and Monitoring 9

An Innovative PID Controller Optimization Algorithm for Two-...

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9th International Conference on Technology and Energy Management, ICTEM 2024

作者： Golshaeian, Sajad Nazari, Mohammad Hassan Hosseinian, Seyed Hossein Amirkabir University of Technology Department of Electrical Engineering Tehran Iran Smart Control Systems Department Tehran Iran

ISBN: (纸本)9798350329797

In this article, the frequency of a Two-Area power system is controlled using a proportional-integral-derivative (PID) controller that is adjusted with optimization algorithms. Classical controllers may not perform optimally under different conditions due to the natural variability of renewable energies and their uncertainties. Multiple algorithms are utilized to enhance the nominal value of the PID controller. parameters in these controllers, including particle swarm optimization (PSO), shuffled frog leaping algorithms (SFLA), and genetic algorithms (GA). By comparing their results, the most efficient method will be determined Using the integral time-absolute error (ITAE) method. MATLAB is employed for simulating the frequency control model of a Two-Area power system. By evaluating the results, it can be seen that strong and appropriate performance, in a wide range of system parameter changes and load changes, can be obtained with the proposed SFLA optimization method in system nonlinearity. Examining the proposed enhanced method in the field of frequency monitoring and control will show a significant improvement compared to the previous algorithms used. © 2024 IEEE.

关键词： Three term control systems

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