Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency Compensation Control Strategy of Energy Storage in the Wind-energy storage Hybrid System for Improving Frequency Response Performance

With the increasing penetration of renewable energies into power system, the lower inertia problem has been the shackle to the system operation. For improving the system transient stability, enabling wind turbine generators (WGs) to provide temporarily frequency support has been required by grid operators. However, due to the mechanical characteristic, the frequency response performance of WG is not satisfactory. Considering the fast response characteristic of the energy storage (ES), Using ES to help in improving the frequency support of WGs is a new prospective application. Limited to the energy storage capacity, existing frequency supporting control strategies that using the ES to improve wind turbine frequency response cannot fully compensate in whole frequency response process. Enlarging the capacity of ES may significantly increase the investment cost of Wind-ES hybrid system. To overcome these problems, this paper investigates the possibility and financial benefits of large-scale ES configuration in the Wind-ES hybrid system. The analysis results demonstrates that using the ES into electric market as frequency response resource could make large-scale ES configuration possible. Based on potential large-scale ES configuration scenarios, a frequency compensation control strategy for ES of Wind-ES hybrid system is proposed in this paper to compensate whole frequency response process of WGs. The case study shows that the proposed strategies could significantly improve the frequency response performance of WG.
A Feasibility Study on Upgrading the Static TLC Infrastructure to Adaptive TLC

This paper evaluates the feasibility of upgrading the static traffic light control to a local adaptive traffic light control, for a road network carrying less-lane-disciplined, heterogeneous traffic. We analyze the performance of a few deep learning based object detection algorithms (e.g., SSD, RCNN), with respect to the computation requirements, and accuracy for computing the Passenger Car Unit (PCU) count, under heterogeneous traffic condition. We propose an algorithm for a local adaptive TLC, leveraging the existing infrastructure. This algorithm efficiently computes the phase duration, based on round-robin scheduling, considering real-time traffic information. Simulations are carried out to analyze the effect of varying error rates in PCU count on the performance of adaptive TLCs. Further, the performance of the proposed TLC is compared with the conventional static TLC and the recently proposed micro auction based adaptive TLC algorithms. The simulation results suggest that the proposed TLC algorithm can tolerate 20% error in the PCU count without degrading the performance. Also, this work demonstrates that the traffic information with the required accuracy can be processed in real time using the available platforms (e.g., Raspberry Pi). The proposed work establishes the feasibility of upgrading the existing static TLC to a local adaptive TLC with minimal infrastructure requirement.
Overvoltage Mitigation of Medium Voltage Electric Drives with Long Cables using Multilevel-Converters and Passive Filters

Variable-speed drives with long cables between the electrical machine and the power converters suffer from overvoltages at the connection terminals of the cables. These overvoltages which are caused by reflections of the PWM voltage pulses to the two ends of the cable are investigated in this paper. Suitable passive filters are proposed in order to mitigate the overvoltages to an acceptable level, which is not harmful for the devices of the system. Additionally, the use of multilevel power converters can help to minimize the size of the passive filters. The filter design for drives with up to 7-level converters is described in this paper and simulations results on LTspice are presented in order to validate the mathematical analysis.
Output Filter with Adaptive Damping for Interleaved Converters with Low Ripple and High Dynamics

Interleaved DC-DC converters are used in a wide range of applications offering a high current rating combined with ripple cancellation. In these systems, the design of the output stage is of crucial importance since it not only influences the output ripple at steady state but also affects the dynamic performance of the system. In particular, by reducing the output filter, a higher dynamic performance can be achieved, making interleaved converters especially interesting for high performance sources, used for example in accelerators. In this paper, an adaptive output filter with variable impedance, suitable for a wide range of inductive loads (e.g. magnets), is proposed for interleaved converters with low ripple and high dynamic specifications. The proposed solution is compared with a conventional filter topology and its superiority is exhibited through analytical calculations and simulations.
Robust Load Frequency Control for Smart Power Grid Over Open Distributed Communication Network with Uncertainty

This work develops delay dependent load frequency control scheme for multi-area smart power grid over open communication networks with the presence of uncertainty and unsymmetrical time varying delays. First, the design employs direct method using differential inequalities and matrix measures to derive stability conditions for power grid network systems. The design assumed that the uncertainty associated with modeling errors and external fault disturbances are bounded. The stability conditions are given together with the upper bound of the delays and the convergence rate of the solution trajectory of the closed loop system. Second, we introduce Lyapunov based indirect method to establish stability criterion for LFC systems. The stability condition is established for both symmetrical and unsymmetrical time varying delays in measurement and control channel. The design analyzes the upper bound of the delay and solution trajectory in the presence of uncertainty varying with the state and constant. Compared with the existing designs, the proposed design and analysis uses time varying delays both in measurement channel from RTU to control center and control channel from control center to power generation unit. Unlike the existing LFC schemes, the design employs the uncertainty appearing into multi-area power system networks from the modeling errors, variation of loads and other external disturbances.
Modelling Interactions in Rescue Operations

In rescue operations, situation awareness is very critical to decision-makers and responders in order to reduce the bad consequences. Furthermore, a good collaboration between services and organizations is vital to achieve successful operations. Therefore, a good communication and information exchange between actors is crucial to meet these requirements. This work aims to enhance situation awareness in rescue operations by supporting communication and interaction between participating actors. To do that, we start by studying interactions, communication, and information flow. After that, we look forward to design and implement a communication system that will be used by rescuers and first responders. In this paper, we show our preliminary results in which we model the interactions.
Hardware Tripartite Synapse Architecture based on Stochastic Computing

Research showed that the tripartite synapse has the capability of self-repairing in the spiking neural networks (SNNs), where the interactions between astrocyte, neuron and synapse underpin this mechanism. It has been used for the hardware electronic systems to enhance the fault-tolerant abilities, especially for the critical task applications. Due to the complex models of the tripartite synapse, its efficient hardware architecture and scalability are the research challenges. In this paper, an efficient hardware tripartite synapse architecture is proposed which is based on the Stochastic Computing (SC) technique. The SC is used to replace the conventional computing components such as DSPs in the hardware devices, and the extended stochastic logics are used to scale the data range during the calculation process. Results show that the proposed hardware architecture has the same output behaviours as the software simulations and has a low hardware resource consumption (with a reduction rate of >85% compared to state-of-the-art approach) which can maintain the system scalability for large SNNs.
Does acute response to safe dose of carvedilol vary across different child class of liver disease: A large cohort hemodynamic study

Background: Carvedilol is a nonselective beta-blocker with an additional alpha 1 adrenoceptor blocking action, causing a much greater decrease in portal pressure as compared to propranolol. Materials and Methods: One hundred two consecutive patients of cirrhosis of the liver with significant portal hypertension were included, and hepatic venous pressure gradient (HVPG) was measured at the baseline and after 90 min of administration of 12.5 mg carvedilol. Results: A total of 102 patients with mean age of 58.3 ± 6.6 years were included. A total of 42.2%, 31.9%, and 26.6% patients had child Class A, child Class B, and Child Class C cirrhosis, respectively. Mean baseline HVPG was 16.75 ± 2.12 mmHg that dropped to 13.07 ± 2.32 mmHg, after 90 min of administration of 12.5 mg of carvedilol. The mean drop of HVPG was 4.5 ± 2.2 mmHg and 2.4 ± 1.9 mmHg between responders and nonresponders, respectively. Overall, 52 patients (51%) showed acute response while 50 (49%) were nonresponders. Baseline low cardiac output (CO) and high mean arterial pressure (MAP) were significant predictors of acute response on univariate analysis. On multivariate analysis, low baseline CO was found as an independent predictor. Conclusion: Carvedilol is a drug of choice among beta-blockers for primary prophylaxis of variceal bleed. Hemodynamic parameters like baseline low CO high MAP are significantly predicting acute response while as etiology, child class, and variceal size are not significantly associated with acute response to a safe dose of carvedilol.
A Through-Hole Capacitive Micromachined Ultrasonic Transdcuer with High Perfromance

Generally, CMUTs are composed of numbers of independent cells, whose edges are clamped on the posts. Such structure is characterized with high resonant frequency and high sensitivity. However, too much parasitic capacitance is often induced for the CMUTs because of the posts between cells, causing a low electromechanical coupling coefficient. At the same time, the collapse voltage is too high and does not have low power consumption characteristics. A through-hole CMUTs (TH-CMUTs) structure is proposed, which is clamped at the four corners of membrane. It has many advantages on the premise of ensuring the ultrasonic emission power and output current. Since the stiffness of TH-CMUTs structure is reduced, its collapse voltage can be greatly reduced. The original membrane on the post is suspended, then the original parasitic capacitance is transformed into the effective capacitance to increase the electromechanical coupling coefficient. Finally, the structure also has the characteristics of high filling ratio. All the advantages of proposed TH-CMUTs structure are simulated and validated.
Optimal Ultra-Reliable Low-Latency Multi-Hop Wireless Networks

We present algorithms for optimal routing in generic multi-hop wireless networks. Our proposal leverages optimization procedures with latency-reliability constraints that reflect the stringent requirements of cyber-physical systems. Taking as input the latency-reliability constraints for a given application and simple channel quality indicators for the individual links connecting neighboring nodes, the proposed routing algorithms are able to find the optimal multi-hop path connecting source and destination together with the optimal modulation and coding, node cooperation and network coding schemes, and beamforming patterns along the optimal path. Numerical results show how our algorithms can be tuned to satisfy different communication requirements, where the gains in terms of reliability and latency due to node cooperation and network coding, respectively, are highlighted.
A Comprehensive Analysis of Gate Drive Delay in CLLC Converters and Its Compensation Method

In this paper, the influences of gate drive signal delay caused by driver delay and power device parameters for CLLC converter are analyzed. This paper points out that the influences are equivalent to a phase shift and a change of duty cycle of PWM signal, and eventually can be demonstrated as four potential conditions. In some conditions, the operating process of the converter remains unchanged and the efficiency merely decreases slightly. However, in other conditions, the operating process changes a lot, where the circulating component of the secondary current is significant, and the ZCS feature of secondary power devices is lost. As a result, the efficiency decreases significantly. A compensation method is proposed and verified by experiments. By adopting proposed compensation method, the circulating current is suppressed, and the highest increase of efficiency is 8.66%.
Briefing Note 1

1 Alkire, 2002 2 Alkire, 2002; Clark, 2002; Crocker, 2005 projects. The best known participatory approach in the current development scene, Participatory Rural Appraisal (PRA), was elaborated in the late 1980s in India and Kenya. According to Chambers (1997) “PRA is a growing family of approaches and methods to enable local people to share, enhance and analyse their knowledge of life and conditions, and to plan, act, monitor and evaluate”.
A Review of Disturbance Detection in Islanded Microgrids

Microgrids can connect and disconnect from the grid to enable them to operate in both grid-connected and islanded modes. Stability issues are often a concern in islanded microgrids due to a decrease in system inertia. In addition, the quick response of power electronic inverters in the system creates a high rate of change in frequency, voltage and current. Thus, the system should have the capability to detect any disturbance within a short period of time. Real-time disturbance detection is challenging in islanded microgrids. Advanced signal processing techniques have the ability to detect all kinds of transients occurring in the system parameters either in the time or frequency domain. This paper provides an in-depth analysis of existing disturbance detection methods in islanded microgrids. The drawbacks of the existing methods are identified with an IEC islanded microgrid modeled in MATLAB/SIMULINK. Real system measurements from the IEEE DataPort are used to extract samples of real system noise. This noise is added to the simulated measurements to better test the existing methods.
A Highly-Integrated SiC Power Module for Fast Switching DC-DC Converters

This paper addresses design elements of highly-integrated silicon carbide power modules allowing fast switching transients and, consequently, elevated PWM frequencies for dc-dc converters with high power densities. However, a minimal stray inductance is crucial for applications at high PWM frequencies. It is realized in this work using two key features: an electrical layout is proposed that creates an optimized commutation loop with minimized self-inductance on the basis of direct-bonded copper substrates. Furthermore, the dc-link capacitor is integrated onto the substrate such that typical connection elements are eliminated. The electrical design is verified with double-pulse measurements. To realize a high power density, the power module is cooled by an integrated micro-channel cooling structure. The cooling structure enables heat dissipation that most state-of-the-art power converters achieve only with double-sided cooling techniques. The addressed design elements are combined with an optimized inductor resulting in a synchronous boost dc-dc converter operated at a switching frequency of 333 kHz. Experimental results show that a maximum efficiency of 98.12 % at a maximum output power of 23.4 kW is achieved. This results in a power density of 70.9 kW/dm3.
Online Switch Open-Circuit Fault Diagnosis Using Reconfigurable Scheduler for Modular Multilevel Converter with Parallel Connectivity

The modular multilevel series-parallel converter (MMSPC) was shown to provide advantages in several industrial applications, but the many individual switches may challenge its reliability. This paper proposes an effective fault diagnosis method (FDM) based on temporary hot-swapping of the look-up table of the scheduler and the use of additional interconnection states to detect and locate transistor open-circuit faults. First, the feature table of all possible switch open-circuit faults is collected via temporarily reconfiguring the scheduler. The converter can intermittently switch to the fault-diagnosis mode for only a few cycles. Features as simple as the square of the error between the output and reference in the fault-diagnosis mode indicate a switch open-circuit fault. Finally, the output voltage characteristics in the fault diagnosis mode are checked and compared with those in the aforesaid characteristic tables to locate the open switch. Validation results reveal that this proposed fault diagnosis method is efficient, accurate, and fast.
Improved Self-Sensing Estimation Accuracy and System Bandwidth via Negative Sequence Image Tracking

Image tracking self-sensing can improve estimation accuracy by considering all system level effects and increase system bandwidths by reducing the number of filters required in demodulation. In state-of-the-art heterodyning demodulation low-pass filters are used to remove noise from the position estimate and present a known limitation to achievable system bandwidths. In contrast, the time series of the position dependent negative sequence current response to a high frequency rotating voltage in the stationary frame can be registered with a known current response template to accurately estimate the rotor position directly. By tracking multiple current samples within an image, the need for low pass filtering the results is removed and higher system bandwidths are achievable. This method is applicable to all salient motor types and has been developed for servo motors which require low cogging and ripple torque and as a result, have low saliency ratios. The resulting dynamic performance is evaluated in a full system, is compared to heterodyning demodulation, and demonstrates improvements to estimation accuracy as well as achievable system bandwidths.
Electrolytic Capacitor Degradation Monitoring using an On-line Parameter Estimation Scheme involving Sliding Mode Differentiators and a Kalman Filter

An indication of electrolytic capacitor gradual degradation may be obtained by observing an increase in the equivalent series resistance (ESR) and a decrease in capacitance over time. On-line condition monitoring of these two electrical parameters allows for early detection of the capacitor ageing that can reduce catastrophic failure and their costly consequences. In this paper, a parameter estimation scheme consisting of sliding mode differentiators and a Kalman filter is proposed for electrolytic capacitor degradation monitoring. Satisfactory on-line estimation of the ESR and capacitance under noisy conditions is achieved with the proposed approach as demonstrated through computer simulation results.
Spiking Neurons with Differential Evolution Algorithm for Pattern Classification

Recently deep learning has revolutionized the field of machine learning, for pattern recognition in particular. A deep neural network (DNN) requires a large number of labeled training samples, and the recognition accuracy is truly impressive, sometimes outperforming humans. Neurons in an artificial neural network (ANN) are modeled by a single, static, continuous-valued activation function. However, biological neurons employ discrete spikes to compute and transmit information. The information can be encoded by the spike times, spike rates, and spike phase, etc. As the third generation artificial neural networks, spiking neural networks (SNNs) are more closely mimic natural neural networks. SNNs can achieve the same goals as ANNs, and it has the ability to build a large-scale network structure (i.e. deep spiking neural network) to accomplish complex tasks. In this paper, a state-of-the-art manner, differential evolving spiking neural network (DESNN), is proposed for pattern classification. The XOR task, Iris data, and hand-written digits classification task on MNIST are used to validate the proposed training method. The experimental results show that the algorithm used in this work applies the fewer neurons and it is effective for pattern classification tasks.
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks

In cloud and edge computing models, it is important that compute devices at the edge be as power efficient as possible. Long short-term memory (LSTM) neural networks have been widely used for natural language processing, time series prediction and many other sequential data tasks. Thus, for these applications there is increasing need for low-power accelerators for LSTM model inference at the edge. In order to reduce power dissipation due to data transfers within inference devices, there has been significant interest in accelerating vector-matrix multiplication (VMM) operations using non-volatile memory (NVM) weight arrays. In NVM array-based hardware, reduced bit-widths also significantly increases the power efficiency. In this paper, we focus on the application of quantization-aware training algorithm to LSTM models, and the benefits these models bring in terms of resilience against both quantization error and analog device noise. We have shown that only 4-bit NVM weights and 4-bit ADC/DACs are needed to produce equivalent LSTM network performance as floating-point baseline. Reasonable levels of ADC quantization noise and weight noise can be naturally tolerated within our NVM-based quantized LSTM network. Benchmark analysis of our proposed LSTM accelerator for inference has shown at least 2.4× better computing efficiency and 40× higher area efficiency than traditional digital approaches (GPU, FPGA, and ASIC). Some other novel approaches based on NVM promise to deliver higher computing efficiency (up to ×4.7) but require larger arrays with potential higher error rates.
Evaluation service quality of crowding metros using structural equation modelling - A case study in Beijing*

In this study, a structural equation modelling (SEM) is used to evaluate the service quality of crowded urban railway. 8,011 sample questioners were collected using both online and face-to- face survey methods. First, service components that influence the service quality of crowded metros are investigated, and 7 service components and 27 factors are collected from the perspective of metro daily operation. Second, a SEM is proposed to establish the relationship between service quality and these service components and factors. We found that security check and transfer have the greatest effect on the overall satisfaction. And some practical suggestions (e.g., reducing the height of security check equipment, and theme station) are proposed to improve the service quality of daily metro operation.