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Wireless Multiplexing Control Based on Magnetic Coupling Resonance and Its Applications in Robot

JOURNAL OF MECHANISMS AND ROBOTICS-TRANSACTIONS OF THE ASME 2022年第1期14卷 011009页

作者： Wang, Jue Chen, Genliang Zhang, Zhuang Suo, Jiaqi Wang, Hao Shanghai Jiao Tong Univ Shanghai Key Lab Digital Manufacture Thin Walled Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Gensler Baltimore MD 21202 USA

Nowadays, more and more researchers are pursuing miniaturized and lightweight structure of robots. However, robots with multiple actuators require large control systems if each actuator needs to be controlled independently. In addition, the cables and circuits for control and power supply are the obstacles in reducing size and weight. In this article, a wireless multiplexing control system based on magnetic coupling resonance (MCR) is proposed. The control system can realize wireless energy transmission and control simultaneously. By decomposing a composite signal, it can control multiple actuators with only one input signal. However, in previous researches, their applications are primary and simple due to the switch control without feedback and the lack of systematic design method for robot application. Thus, based on the discrete form of composite signal, the closed-loop of wireless multiplexing control is presented, which makes this promising method a step closer to the practical application. Besides, based on the theoretical model of load power and transmission efficiency, five parameters to be optimized are extracted in accordance with the actual design requirements. The optimization algorithm for load power is proposed using particle swarm optimization (PSO). As for its applications in robots, a Delta robot with flexible linkage and an untethered multidrive pipe robot for sampling operation are designed to demonstrate the proposed control method. The experiment results of the Delta robot show the reliability and accuracy of the system, while the results of the pipe robot prove its potential use in the untethered robot system.

关键词： magnetic coupling resonance multiplexing control optimization algorithm parallel robot pipe robot

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A Software Defined Networking Architecture for DDoS-Attack in the Storage of Multimicrogrids

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IEEE ACCESS 2022年 10卷 83802-83812页

作者： Taherian-Fard, Elaheh Niknam, Taher Sahebi, Ramin Javidsharifi, Mahshid Kavousi-Fard, Abdollah Aghaei, Jamshid Shiraz Univ Technol Dept Elect & Elect Engn Shiraz *** Iran Aalborg Univ Dept AAU Energy DK-9100 Aalborg Denmark Lappeenranta Lahti Univ Technol Sch Energy Syst Dept Elect Engn Lappeenranta 83851 Finland

Multi-microgrid systems can improve the resiliency and reliability of the power system network. Secure communication for multi-microgrid operation is a crucial issue that needs to be investigated. This paper proposes a multi-controller software defined networking (SDN) architecture based on fog servers in multi-microgrids to improve the electricity grid security, monitoring and controlling. The proposed architecture defines the support vector machine (SVM) to detect the distributed denial of service (DDoS) attack in the storage of microgrids. The information of local SDN controllers on fog servers is managed and supervised by the master controller placed in the application plane properly. Based on the results of attack detection, the power scheduling problem is solved and send a command to change the status of tie and sectionalize switches. The optimization application on the cloud server implements the modified imperialist competitive algorithm (MICA) to solve this stochastic mixed-integer nonlinear problem. The effective performance of the proposed approach using an SDN-based architecture is evaluated through applying it on a multi-microgrid based on IEEE 33-bus radial distribution system with three microgrids in simulation results.

关键词： Microgrids Computer architecture Servers Support vector machines Reliability Denial-of-service attack Software defined networking Cloud computing Edge computing optimization Multi-microgrid software defined networking cloud-fog computing distributed denial of service attack (DDoS) optimization algorithm

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Real time power management strategy for an all-electric ship using a predictive control model

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第9期16卷 1808-1821页

作者： Wang, Bo Peng, Xiuyan Zhang, Lanyong Su, Peng Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China China Ship Dev & Design Ctr Wuhan Peoples R China

This study proposes a power management strategy (PMS) that can efficiently and accurately address the nonlinear dynamics of ship power systems for all-electric ships (AESs). The design of a PMS can be considered naturally in a model predictive control framework. However, the control error caused by a model mismatch is a challenge of the PMS for physical system implementation. The proposed PMS is based on a complex system-level nonlinear model that ensures a minimum mismatch. In addition, an efficient optimization algorithm is proposed to efficiently apply the PMS. The proposed method is verified using real-time simulations with physical systems in various scenarios. The experimental results demonstrate the superior efficiency of the proposed optimization algorithm and the accuracy of the developed PMS for physical system implementation.

关键词： real time power management strategy optimisation power system management energy management systems real-time simulations predictive control ship power system complex system-level nonlinear model physical system implementation nonlinear dynamics optimization algorithm nonlinear dynamical systems power system control marine power systems PMS design AES ships model predictive control all-electric ship control error electric vehicles

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Predicting Wind Power Generation Using Hybrid Deep Learning With optimization

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2021年第8期31卷 1-5页

作者： Hossain, Md Alamgir Chakrabortty, Ripon K. Elsawah, Sondoss Gray, Evan Mac A. Ryan, Michael J. Griffith Univ Queensland Micro & Nanotechnol Ctr Nathan Qld 4111 Australia Univ New South Wales Canberra Capabil Syst Ctr Sch Engn & Informat Technol Canberra ACT 2612 Australia

Accurate prediction of wind power generation is complex due to stochastic component, but can play a significant role in minimizing operating costs, and improving reliability and security of a power system. This paper proposes a hybrid deep learning model to accurately forecast the very-short-term (5-min and 10-min) wind power generation of the Boco Rock Wind Farm in Australia. The model consists of a convolutional neural network, gated recurrent units (GRU) and a fully connected neural network. To improve performance, the hyper-parameters of the model are tuned using the Harris Hawks optimization algorithm. The effectiveness of the proposedmodel is evaluated against other advanced models, including multilayer feedforward neural network (NN), recurrent neural network (RNN), long short-termmemory(LSTM) and GRU. The forecasting model demonstrates around 38% and 24% higher accuracy as compared to the 5- and 10-min forecasting of the NN model, respectively.

关键词： Very-short term prediction wind power generation hybrid deep learning optimization algorithm Boco Rock Wind Farm

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Research on Short - Term Power Load Forecasting Based on Improved-SCSO-TCN

Research on Short - Term Power Load Forecasting Based on Imp...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Han, Haotong Peng, Jishen Ma, Jun Li, Mengjiao Faculty of Electrical and Control Enginerring Liaoning Technical University Huludao125000 China CHN Energy Xiong An Energy Co. Ltd Xiongan071701 China

ISBN: (纸本)9789819618552

With the increasing demand for power loads, accurate forecasting of power loads is of great significance for power system planning and generation operation scheduling. Improving the accuracy of short-term power load forecasting can significantly enhance the efficiency of power generation and dispatching, ensuring the efficient operation of the power system. To this end, this paper proposes a short-term power load forecasting model based on Sand Cat Swarm optimization (SCSO) and Temporal Convolutional Network (TCN). To address the issue of poor initial population quality in the SCSO algorithm, this paper improves SCSO and uses it to optimize the hyperparameters of TCN, thereby significantly enhancing the performance of the neural network by finding the best combination of hyperparameters. The experimental results show that ISCSO-TCN achieves 11.72, 14.21, and 98.49% on MAPE, RMSE, and R2, respectively. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Deep learning Electrical engineering Load forecasting optimization algorithm

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Constraint estimation in three-diode solar photovoltaic model using Gaussian and Cauchy mutation-based hunger games search optimizer and enhanced Newton-Raphson method

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IET RENEWABLE POWER GENERATION 2022年第8期16卷 1733-1772页

作者： Premkumar, Manoharan Jangir, Pradeep Kumar, Chandrasekaran Jebaseelan, Somasundaram David Thanasingh Sundarsingh Alhelou, Hassan Haes Elavarasan, Rajvikram Madurai Chen, Huiling Dayananda Sagar Coll Engn Dept Elect & Elect Engn Bengaluru 560078 Karnataka India Rajasthan Rajya Vidyut Prasaran Nigam Ltd Power & Energy Sect Sikar 332025 Rajasthan India M Kumarasamy Coll Engn Dept Elect & Elect Engn Karur 639113 Tamil Nadu India Sathyabama Inst Sci & Technol Dept Elect & Elect Engn Chennai 600119 Tamil Nadu India Tishreen Univ Dept Elect Power Engn Latakia 2230 Syria Thiagarajar Coll Engn Dept Elect & Elect Engn Madurai 625015 Tamil Nadu India Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

The reliability of the photovoltaic models is strongly reliant on their parameters, which are primarily determined by the optimization algorithm and the objective function. As a result, obtaining the parameters under different environmental conditions is critical for increasing their performance, reliability and significantly lowering cost. Many optimization techniques are reported to address this problem based on the complexity. As a result, an enhanced version of the recently reported Hunger Games Search Optimizer (HGSO) method called Gaussian and Cauchy Mutation-based HGSO (GCMHGSO) algorithm for defining the requirements of the Three-Diode equivalent Model (TDeM) by utilizing multiple representations in the algorithm along with an efficient objective function. The Cauchy mutation increases the exploration ability, and Gaussian mutation increases the exploitation ability of the basic HGSO. Furthermore, an Enhanced Newton-Raphson Method (ENRM) is presented to effectively solve the behaviour of the current-voltage relation of the TDeM. The robust optimization is also considered to demonstrate the impact of the measurement error. Comparing the GCMHGSO-ENRM to other competitors reveals that the proposed GCMHGSO-ENRM can accurately find the best solution, and its effectiveness is verified in many statistical parameters. It is found that the GCMHGSO-ENRM algorithm is stable and robust compared to other competitors.

关键词： constraint estimation photovoltaic power systems Gaussian mutation efficient objective function Three-Diode equivalent Model recently reported Hunger Games Search Optimizer method optimisation Enhanced Newton-Raphson Method robust optimization TDeM Cauchy Mutation Cauchy mutation metaheuristics optimization techniques Newton-Raphson method enhanced Newton-Raphson method statistical parameters evolutionary computation Gaussian processes photovoltaic models search problems GCMHGSO-ENRM algorithm three-diode solar photovoltaic model basic HGSO optimization algorithm

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Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads

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WATER RESOURCES MANAGEMENT 2022年第11期36卷 4313-4342页

作者： Ghanbari-Adivi, Elham Ehteram, Mohammad Farrokhi, Alireza Khozani, Zohreh Sheikh Shahrekord Univ Dept Water Sci Engn Shahrekord Iran Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Alaodoleh Semnani Inst Higher Educ ASIHE Dept Civil & Environm Engn Semnan Semnan Province Iran Bauhaus Univ Weimar Inst Struct Mech D-99423 Weimar Germany

An important issue in water engineering is predicting suspended sediment load (SSL). For the Telar River and its tributaries, this study employs an inclusive multiple model (IMM) to predict SSL. Telar River branches into two main branches: Telar and Kasilian. The modeling process consisted of two levels: 1) creating hybrid models and 2) creating ensemble models. At the first level, the Honeybadger optimization algorithm (HBOA), salp swarm algorithm (SSA), and particle swarm optimization (PSO) were applied to set the parameters of the radial basis function neural network (RBFNN) models. The IMM model was used to integrate the outputs of the RBFNN-HBOA, RBFNN-SSA, RBFNN-PSO, and RBFNN models into the RBFNN model at the second level. Inputs to the models included lagged rainfall, discharge, and SSL. Several new ideas have been introduced in the current paper, including hybrid RBFNN models, a gamma test for selecting optimal input combinations, an analysis of output uncertainty, and an advanced IMM for SSL prediction. Various performance evaluation criteria, including root mean square error (RMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), and percentage bias (PBIAS), were used to evaluate the models. The comparative results indicated high accuracy of IMM with an MAE of 0.983, NSE of 0.254, PBIAS of 0.991 at Telar station. The training MAE of the IMM model was 4.4%, 4.8%, 6.7%, 52%, and 9.2% lower than that of the RBFNN-HBOA, RBFNN-SSA, RBFNN-PSO, and RBFNN models at Kasilian station. The study results revealed that the IMM and RBFNN-HBOA provided lower uncertainty than the other RBFNN models. Thus, the IMM model represents the most accurate estimation of SSL.

关键词： Suspended sediment load Telar River optimization algorithm Inclusive multiple model Radial basis function neural network

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Optimal economic operation of microgrids considering combined heat and power unit, reserve unit, and demand-side management using developed adolescent identity search algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第90期47卷 38295-38310页

作者： Su, Chang Yang, Chao Tian, Cong Hu, Haiting Dehan, Sim Guangdong Univ Technol Sch Adv Mfg Jieyang Campus Jieyang 522000 Guangdong Peoples R China Heilongjiang Univ Technol Sch Elect & Informat Engn Jixi 158100 Heilongjiang Peoples R China Arian Co EED Yerevan Armenia

In the present study, a method is proposed to solve the problem of economic load distribution in MGs, meet the challenges arising from the use of renewable sources periodically, ensure the stable performance of MGs, and minimize the operating cost of MGs considering combined heat and power (CHP) units and reserve system. Moreover, demand-side management (DSM) as a tool is employed to reduce the operating cost of the power system. Therefore, the proposed model for optimal operation of MGs using DSM is formulated as an optimization problem. Load shifting is considered as an effective solution in DSM. Minimizing the total operating cost of the system is considered as the objective function of this problem. Problem constraints include operating and executive constraints for load shifting. Finally, the model is solved using the developed adolescent identity search algorithm

关键词： Microgrid (MG) Generation scheduling Demand response (DR) Stochastic scheduling optimization algorithm

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Low-Scattering-Cross Section Thinned Phased Array Antenna Based on Active Cancellation Technique

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2022年第7期70卷 5481-5490页

作者： Li, Peng-Fa Qu, Shi-Wei Yang, Shiwen Hu, Jun Univ Elect Sci & Technol China UESTC Sch Elect Sci & Engn Chengdu 611731 Peoples R China

In this article, a novel approach to simultaneously reduce the scattering cross section (SCS) and lower sidelobe level (SLL) as well as the cost of a phased array is proposed, which is a combination of active cancellation and array thinning techniques. Two 10 x 10 planar microstrip phased arrays are designed to validate the proposed concept, including a full array and a thinned array with 20 passive elements. In the thinned array, the passive elements are made use of and terminated at an optimized position with short-circuited loads to generate scattering fields with the required magnitude and phase for cancellation of the scattering fields from the active elements. Furthermore, the positions of the passive elements are optimized by a multiple dimensional discrete particle swarm optimization (MDDPSO) algorithm to achieve a low SLL. It is theoretically predicted that the thinned array features a low SLL below -18 dB in a scan range of +/- 45 degrees, and SCS reductions of 21 and 9.1 dB in 7.3-10.1 GHz compared to an equal-sized metallic plate and a full array, respectively, which are further verified in simulations and measurements. It is also remarkable that the array cost is reduced due to array thinning, at the cost of an approximately 1 dB gain drop.

关键词： Active cancellation technique optimization algorithm phased array scattering cross section reduction (SCSR) thinned arrays

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An Optical Tool to Optimize the Output of a Photonic Integrated Chip Architecture

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2022年第3期12卷 694-702页

作者： Gemma, Luca Bernard, Martino Brunelli, Davide Univ Trento Dept Ind Engn I-38123 Trento Italy Fdn Bruno Kessler Ctr Sensors & Devices I-38123 Trento Italy

Photonic Integrated Chips (PICs) are an up-and-coming solution for base building blocks of quantum computers. The ready accessibility of photonics integrated systems and the fact that they allow for direct monolithic integration with electronics in a compact architecture have pushed the academic community to inspect their capabilities further. Still, at this stage, their characterization, control, and fine-tuning is an open issue for which this work aims to provide a viable solution. In this paper we present a method for an easy setup and an efficient architecture to simultaneously optimize multiple outputs of a PIC. The proposed solution can be embedded in an automation tool for tuning and verifying output on- and off-chip detectors. We fabricated a Silicon Oxynitride (SiON)-based PIC featuring a network of Mach-Zehnder interferometers (MZI) arranged in Clements architecture driven by Titanium-Titanium Nitride (TiTiN) thermistors. We interfaced the chip electronically through commercial driver modules and optically through optical guides and controlled it through a Python optimization algorithm. Finally, we retrieved the feedback of the control both by sensing it through integrated silicon photodetectors and by an image processing algorithm capable of inspecting the light intensity scattered by the waveguides in a custom region.

关键词： Photonics Optical waveguides Computer architecture Optical device fabrication Integrated optics Optical imaging Detectors Quantum circuits PIC image processing optimization algorithm

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