The subsynchronous oscillations(SSOs)related to renewable generation seriously affect the stability and safety of the power *** realize the dynamic monitoring of SSOs by utilizing the high computational efficiency and...
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The subsynchronous oscillations(SSOs)related to renewable generation seriously affect the stability and safety of the power *** realize the dynamic monitoring of SSOs by utilizing the high computational efficiency and noise-resilient features of the matrix pencil method(MPM),this paper propos es an improved MPM-based parameter identification with syn *** MPM is enhanced by the angular frequency fitting equations based on the characteristic polynomial coeffi cients of the matrix pencil to ensure the accuracy of the identi fied parameters,since the existing eigenvalue solution of the MPM ignores the angular frequency conjugation constraints of the two fundamental modes and two oscillation ***,the identification and recovery of bad data are proposed by uti lizing the difference in temporal continuity of the synchropha sors before and after noise *** proposed parameter identification is verified with synthetic,simulated,and actual measured phase measurement unit(PMU)*** with the existing MPM,the improved MPM achieves better accuracy for parameter identification of each component in SSOs,better real-time performance,and significantly reduces the effect of bad data.
Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the *** computing services are available through common internet protocols and netwo...
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Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the *** computing services are available through common internet protocols and network standards.n addition to the unique benefits of cloud computing,insecure communication and attacks on cloud networks cannot be *** are several techniques for dealing with network *** this end,network anomaly detection systems are widely used as an effective countermeasure against network *** anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of *** anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning *** paper presents an efficient model based on autoencoders for anomaly detection in cloud computing *** autoencoder learns a basic representation of the normal data and its reconstruction with minimum ***,the reconstruction error is used as an anomaly or classification *** addition,to detecting anomaly data from normal data,the classification of anomaly types has also been *** have proposed a new approach by examining an autoencoder's anomaly detection method based on data reconstruction *** the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value,we assume that the reconstruction error is a *** enables our model to use the reconstruction error of every input feature as an anomaly or classification *** further propose a multi-class classification structure to classify the *** use the CIDDS-001 dataset as a commonly accepted dataset in the *** evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy,recall,false-positive rate,and F1-score
This study investigates the combined berth allocation problem (BAP) and quay crane allocation problem (QCAP) while considering a multi-quay setting. First, a mixed integer linear programming mathematical model is deve...
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This study investigates a safe reinforcement learning algorithm for grid-forming(GFM)inverter based frequency *** guarantee the stability of the inverter-based resource(IBR)system under the learned control policy,a mo...
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This study investigates a safe reinforcement learning algorithm for grid-forming(GFM)inverter based frequency *** guarantee the stability of the inverter-based resource(IBR)system under the learned control policy,a modelbased reinforcement learning(MBRL)algorithm is combined with Lyapunov approach,which determines the safe region of states and *** obtain near optimal control policy,the control performance is safely improved by approximate dynamic programming(ADP)using data sampled from the region of attraction(ROA).Moreover,to enhance the control robustness against parameter uncertainty in the inverter,a Gaussian process(GP)model is adopted by the proposed algorithm to effectively learn system dynamics from *** simulations validate the effectiveness of the proposed algorithm.
Surgical tool tip localization and tracking are essential components of surgical and interventional procedures. The cross sections of tool tips can be considered as acoustic point sources to achieve these tasks with d...
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Piezoelectric accelerometers excel in vibration *** the emerging trend of fully organic electronic microsystems,polymeric piezoelectric accelerometers can be used as vital front-end components to capture dynamic signa...
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Piezoelectric accelerometers excel in vibration *** the emerging trend of fully organic electronic microsystems,polymeric piezoelectric accelerometers can be used as vital front-end components to capture dynamic signals,such as vocal vibrations in wearable speaking assistants for those with speaking ***,high-performance polymeric piezoelectric accelerometers suitable for such applications are *** organic compounds such as PVDF have inferior properties to their inorganic counterparts such as ***,most existing polymeric piezoelectric accelerometers have very unbalanced performance *** often sacrifice resonance frequency and bandwidth for a flat-band sensitivity comparable to those of PZT-based accelerometers,leading to increased noise density and limited application *** this study,a new polymeric piezoelectric accelerometer design to overcome the material limitations of PVDF is *** new design aims to simultaneously achieve high sensitivity,broad bandwidth,and low *** samples were manufactured and characterized,demonstrating an average sensitivity of 29.45 pC/g within a±10 g input range,a 5%flat band of 160 Hz,and an in-band noise density of 1.4μg/√*** results surpass those of many PZT-based piezoelectric accelerometers,showing the feasibility of achieving comprehensively high performance in polymeric piezoelectric accelerometers to increase their potential in novel applications such as organic microsystems.
As renewable energy is becoming the major re-source in future power grids,the weather and climate can have a higher impact on grid *** expansion planning(TEP)has the potential to reinforce the power trans-fer capabili...
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As renewable energy is becoming the major re-source in future power grids,the weather and climate can have a higher impact on grid *** expansion planning(TEP)has the potential to reinforce the power trans-fer capability of a transmission network for climate-impacted power *** this paper,we propose a systematic TEP proce-dure for renewable-energy-dominated power grids considering climate impact(CI).Particularly,this paper develops an im-proved model for TEP considering climate impact(TEP-CI)and evaluates the reliability of power grid with the obtained transmission investment ***,we create climate-impact-ed spatio-temporal future power grid data to facilitate the study of TEP-CI,which include the future climate-dependent re-newable power generation as well as the dynamic line rating profiles of the Texas 123-bus backbone transmission(TX-123BT)***,the TEP-CI model is proposed,which considers the variation in renewable power generation and dy-namic line rating,and the investment plan for future TX-123BT system is ***,a customized security-con-strained unit commitment(SCUC)is presented specifically for climate-impacted power *** reliability of future power grid in various investment scenarios is analyzed based on the daily operation conditions from SCUC *** whole procedure presented in this paper enables numerical studies on power grid planning considering climate *** can also serve as a benchmark for other studies of the TEP-CI model and its performance evaluation.
This paper proposes a novel fault location method for overhead feeders,which is based on the direct load flow *** method is developed in the phase domain to effectively deal with unbalanced network conditions,while it...
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This paper proposes a novel fault location method for overhead feeders,which is based on the direct load flow *** method is developed in the phase domain to effectively deal with unbalanced network conditions,while it can also handle any type of distributed generation(DG)units without requiring equivalent *** utilizing the line series parameters and synchronized or unsynchronized voltage and current phasor measurements taken from the sources,the method reliably identifies the most probable faulty *** the aid of an index,the exact faulty section among the multiple candidates is *** simulation studies for the IEEE 123-bus test feeder demonstrate that the proposed method accu-rately estimates the fault position under numerous short-circuit conditions with varying prefault system loading conditions,fault resistances,and measurement *** proposed method is promising for practical applications due to the limited number of required measurement devices as well as the short computation time.
Electric vehicles(EVs)are becoming more popular worldwide due to environmental concerns,fuel security,and price *** performance of EVs relies on the energy stored in their batteries,which can be charged using either A...
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Electric vehicles(EVs)are becoming more popular worldwide due to environmental concerns,fuel security,and price *** performance of EVs relies on the energy stored in their batteries,which can be charged using either AC(slow)or DC(fast)***,EVs can also be used as mobile power storage devices using vehicle-to-grid(V2G)*** electronic converters(PECs)have a constructive role in EV applications,both in charging EVs and in ***,this paper comprehensively investigates the state of the art of EV charging topologies and PEC solutions for EV *** examines PECs from the point of view of their classifications,configurations,control approaches,and future research prospects and their impacts on power *** can be classified into various topologies:DC-DC converters,AC-DC converters,DC-AC converters,and AC-AC *** address the limitations of traditional DC-DC converters such as switching losses,size,and high-electromagnetic interference(EMI),resonant converters and multiport converters are being used in high-voltage EV ***,power-train converters have been modified for high-efficiency and reliability in EV *** paper offers an overview of charging topologies,PECs,challenges with solutions,and future trends in the field of the EV charging station applications.
Diffusion models, a powerful and universal generative artificial intelligence technology, have achieved tremendous success and opened up new possibilities in diverse applications. In these applications, diffusion mode...
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Diffusion models, a powerful and universal generative artificial intelligence technology, have achieved tremendous success and opened up new possibilities in diverse applications. In these applications, diffusion models provide flexible high-dimensional data modeling, and act as a sampler for generating new samples under active control towards task-desired properties. Despite the significant empirical success, theoretical underpinnings of diffusion models are very limited, potentially slowing down principled methodological innovations for further harnessing and improving diffusion models. In this paper, we review emerging applications of diffusion models to highlight their sample generation capabilities under various control goals. At the same time, we dive into the unique working flow of diffusion models through the lens of stochastic processes. We identify theoretical challenges in analyzing diffusion models, owing to their complicated training procedure and interaction with the underlying data distribution. To address these challenges, we overview several promising advances, demonstrating diffusion models as an efficient distribution learner and a sampler. Furthermore, we introduce a new avenue in high-dimensional structured optimization through diffusion models, where searching for solutions is reformulated as a conditional sampling problem and solved by diffusion models. Lastly, we discuss future directions about diffusion models. The purpose of this paper is to provide a well-rounded exposure for stimulating forward-looking theories and methods of diffusion models.
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