In this work, we consider the consensus problem of the multi-agent system over matrix-weighted networks using an event-triggered mechanism. An event-triggered coordination strategy is proposed to steer this generalize...
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Power systems dominated by converter-interfaced distributed energy resources (DERs) typically exhibit weaker damping capabilities and lower inertia, compromising system stability. Although individual DER controllers a...
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Distributed generation based on photo-voltaic (PV) modules play a vital role to provide continuous power supply for consumers. Power quality (PQ) events caused by integration of distributed energy sources in microgrid...
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ISBN:
(数字)9798350348637
ISBN:
(纸本)9798350348644
Distributed generation based on photo-voltaic (PV) modules play a vital role to provide continuous power supply for consumers. Power quality (PQ) events caused by integration of distributed energy sources in microgrids (MG) deteriorate the operation of distribution and utility of power supply. In this work, a new CNN-LSTM based deep learning approach is proposed to identify power quality disturbances (PQDs) in PV-integrated microgrid. In a proposed framework, full closed-loop neural architecture containing different layers to facilitate automatic feature extraction and long-time short memory (LSTM) network provide temporal feature for intelligent classifier to improve the training speed. Moreover, comparison with other deep neural networks proves that proposed approach can simplify the procedure of PQ problems with the conventional signal analysis and feature selection methods. A typical PV-integrated microgid is simulated on PSCAD software to prove the robustness of classifier for classification of PQDs in renewable energy integrated distribution networks.
Hysteresis is a nonlinear characteristic ubiquitously exhibited by smart material sensors and actuators, such as piezoelectric actuators and shape memory alloys. The Prandtl- Ishlinskii (PI) operator is widely used to...
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Mn-Zn ferrite is commonly used in inductive power transfer (IPT) systems to enhance magnetic coupling and reduce flux leakage. However, ferrite is not ideal for high power transfer due to the large volume of material ...
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ISBN:
(数字)9798350349139
ISBN:
(纸本)9798350349146
Mn-Zn ferrite is commonly used in inductive power transfer (IPT) systems to enhance magnetic coupling and reduce flux leakage. However, ferrite is not ideal for high power transfer due to the large volume of material needed for its low saturation flux density, brittleness, and high core loss leading to thermal issues. Nanocrystalline alloys are potential alternatives to replace ferrite in IPT pads to improve magnetic performance. This paper analyses the core loss and equivalent series resistance (ESR) of ferrite and nanocrystalline cores in high-power IPT pads based on the measurement of magnetic properties using the partial cancellation method. The experimental findings show that the ESR of IPT pads using nanocrystalline cores decreases with higher excitation current in the pads, contrary to the typical behaviour of IPT pads using ferrite.
Material identification is a technology that can help to identify the type of target *** approaches depend on expensive instruments,complicated pre-treatments and professional *** is difficult to find a substantial ye...
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Material identification is a technology that can help to identify the type of target *** approaches depend on expensive instruments,complicated pre-treatments and professional *** is difficult to find a substantial yet effective material identification method to meet the daily use *** this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level *** practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi *** that,the amplitude ratios and phase differences can be more stably utilized to classify the *** implement our system and evaluate its ability to identify materials in indoor *** result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.
How to construct an effective ANFIS (Adaptive Network-based Fuzzy Inference System) with insufficient (sparse) training data is a challenging problem, as the rule base of such an ANFIS model will be sparse. Fuzzy rule...
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ISBN:
(数字)9798350319545
ISBN:
(纸本)9798350319552
How to construct an effective ANFIS (Adaptive Network-based Fuzzy Inference System) with insufficient (sparse) training data is a challenging problem, as the rule base of such an ANFIS model will be sparse. Fuzzy rule interpolation technique enables fuzzy inference to be performed over a sparse rule base, so it is natural to introduce FRI to support the ANFIS construction. In this work, a new clustering guided rule interpolation approach is proposed for the ANFIS construction problem. Different with most existing FRI based ANFIS construction methods that commonly conduct rule interpolation at individual rule level, the proposed method makes the interpolation to be performed on a cluster level. It adopts a clustering strategy to guide the rule selection and rule weights calculation processes, ensuring the rule similarity and diversity at the same time. Particularly, the proposed approach firstly generates a rule dictionary and divides it into several clusters. Following that a cluster guided method is designed for automated selection of relevant rules from each cluster to be included in subsequent interpolation process. Then the weight for each selected rule is calculated by considering both the cluster size and the cluster distance. Experimental results against benchmark regression datasets indicate the effectiveness of the proposed approach.
The earth observation satellite scheduling has always been critical for the maximum use of limited satellite resources, which basically includes scheduling ground target observation and observation data downloading. D...
The earth observation satellite scheduling has always been critical for the maximum use of limited satellite resources, which basically includes scheduling ground target observation and observation data downloading. Due to the complexity of the mathematical model and its NP-hardness, current research usually splits it into two problems to simplify the model. In this paper, we propose a model of the integrated scheduling problem of satellite observation and data downloading (ISP-SODD) based on the decentralized partially observable Markov decision process (Dec-POMDP), treating each satellite as an agent with autonomous decision-making capability. A multi-agent deep reinforcement learning-based (MADRL-based) algorithm is proposed to solve it. A series of simulation experiments of different scales validate the effectiveness of the method and its advantages compared with the traditional heuristic algorithms. To the best of our knowledge, this study is the first attempt to apply multi-agent reinforcement learning to a multi-satellite scheduling scenario with downloading considerations.
There has been a significant increase in the use of renewable energy solutions such as Photovoltaic (PV) power plant projects to decrease the dependency on fossil fuels while still meeting the global energy demands. H...
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In this paper, we propose a secure-by-construction scheme for synthesizing controllers to enforce safety and security properties simultaneously over control systems. As a key insight, we establish a bridge between the...
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In this paper, we propose a secure-by-construction scheme for synthesizing controllers to enforce safety and security properties simultaneously over control systems. As a key insight, we establish a bridge between the desired safety and security properties by leveraging notions of (augmented) control barrier functions. Based on these functions, we show that one can synthesize secure-by-construction controllers for control systems with continuous state and input sets. Additionally, we provide sum-of-square (SOS) conditions under which the desired (augmented) control barrier functions can be constructed. Finally, we demonstrate the applicability of our results on a case study.
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