Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Economic Dispatch Considering Demand Response Based on Reinforcement Learning

With the explosive growth of various participants and information in smart grid, data-driven methods such as reinforcement learning are getting increasing attention for solving problems concerning power system operation and management. In this paper, a dynamic economic dispatch method based on deep deterministic policy gradient (DDPG) algorithm is designed to minimize total operation cost of multi-period economic dispatch. The model for multi-period economic dispatch considering demand response is firstly established. To transform it into a reinforcement learning problem, the model is then reconstructed as a sequential decision-making process, with state, action and reward defined accordingly. A modified DDPG algorithm is introduced to solve the decision-making problem. Finally, case study based on a modified IEEE 14-bus system validates that the proposed method can obtain a satisfactory dispatch schedule which can approximate the effect of optimization solvers near real-time with robustness.
A Review of Deep Reinforcement Learning Applications in Power System Parameter Estimation

New type of power system has become the emerging topics to meet the China’s carbon emission target at 2030 and 2060. Among many key technology innovations within the new type of power system domain, one critical challenge to face is the coordination among the generation, network infrastructures, load and energy storage systems (ESS). To manage and operate these grid resources in an economic, secure and reliable manner, the corresponding static and dynamic model parameters need to be estimated accurately and effectively. Deep reinforcement learning (DRL) is one of the most trending artificial intelligence techniques in recent years and has led to many advanced applications in power systems, especially in control related topics. Since DRL itself is also a powerful tool in optimization, it has shown promising results in parameter estimation problem. Therefore, in this paper, we systematically review the DRL methods adopted for the power system model parameter estimation, including load models and generator models. To further promote the applications of DRL in the related areas for academic and industry researchers, future research directions are also highlighted.
Robust Partitioning Scheme for Accelerating SQL Database

SQL Databases have shown tremendous performance in the last four decades. Data consistency, isolation, and durability are the main strengths of SQL databases. However, in relational databases, computing joins are time-consuming at run time. To address this issue, we propose to utilize the Oracle partitioning technique to enhance specific query performance. Physical database tuning approaches of SQL databases are being used to speed up each SQL statement such as partitioning and indexing. With the partitioning technique, the Oracle 11g RDBMS efficiency performance improves by approximately absolute 50%. Additionally, we compare its performance with the state-of-the-art NoSQL graph database (Neo4j).
Identification of the Oscillation Source in Multiple Grid-connected Converters Systems

The increasing penetration of the renewable generation, using converters as the interfaces, is challenging the secure operation of power grids, such as bringing the small signal instability issues. To ensure the stability of power grids, it is important to research on the identification of the oscillation source in a multiple-converter system (MCS). To this end, this paper proposes an identification method of the oscillation source in a MCS based on the modal power theory, which reflects the participation of converters to the system’s oscillation mode. Firstly, the admittance model of the MCS is derived for small signal stability analysis. Secondly, the concept of modal power, which is the previous research work for quantifying the participation of the converter’s admittance element to the system’s oscillation mode in a single-converter system, is extended to the MCS for identifying the oscillation source. The validity of the proposed method is verified on a 9-converter 39-node system.
Endpoint Detection & Response: A Malware Identification Solution

Malicious hackers breach security perimeters, cause infrastructure disruptions as well as steal proprietary information, financial data, and violate consumers’ privacy. Protection of the whole organization by using the firm's security officers can be besieged with faulty warnings. Engineers must shift from console to console to put together investigative clues as a result of today's fragmented security technologies that cause frustratingly sluggish investigations. Endpoint Detection and Response (EDR) solutions adds an extra layer of protection to prevent an endpoint action into a breach. EDR is the region's foremost detection and response tool that combines endpoint and network data to recognize and respond to sophisticated threats. Offering unrivaled security and operational effectiveness, it integrates prevention, investigation, detection, and responding in a single platform. EDR provides enterprise coverage and uninterrupted defense with its continuous monitoring and response to threats. We have presented a comprehensive review of existing EDRs through various security layers that includes detection, response and management capabilities which enables security teams to have unified end-to-end corporate accessibility, powerful analytics along with additional features such as web threat scan, external device scan and automatic reaction across the whole technological tower.
Reactive Power Dynamic Allocation Considering Multiple Wind Farms Voltage Regulation Diversity

In the secondary voltage control, the wind farm realizes voltage control by adjusting the reactive power. Within an adjusting cycle, the wind farm substation changes its reactive power output through droop control. Adjusting cycles of substations are different, thus the adjustments of substations is out of sync. In the dynamic process of substation voltage adjustment, the variation of reactive power output of one substation will affect the voltage of other substations, thereby affecting the reactive power output of other substations. It is generally required that the substations in the area decide the reactive power output value according to the electrical distance between them so as to achieve an optimal balance of reactive power with minimal cost. This paper proposes two indexes to evaluate the imbalance of reactive power allocation and then explores the elements that affect the balance of regional reactive power through simulation. Results show that the different regulating cycle of substations will lead to the imbalance of reactive power allocation. Substations with short voltage regulating cycle need to output more reactive power. And the imbalance level of reactive power allocation over substations can be mitigated by reducing the droop coefficient.
Modeling of High Voltage DC Power Supply System for More Electric Aircraft Based on Co-simulation of Saber-Simulink

With the development of advanced power electronic technology and power conversion technology, the establishment of high voltage DC system of More Electric Aircraft becomes possible. In this paper, a 270 V HVDC power supply system is established based on co-simulation of Saber-Simulink. The reliability and advantages of the co-simulation are verified by comparing with the Saber simulation model. The simulation results show that the system can stably output 270 V HVDC, and co-simulation has a better performance in dynamic response.
DCGAN: Deep Convolutional GAN with Attention Module for Remote View Classification

In recent times, the development of Deep Learning Techniques for Image Classification has increased. The deep learning module uses an unsupervised learning technique. The supervised learning requires an adequate and outsized dataset with labels to train a machine. This paper proposes a unique Unsupervised Deep Feature Learning Method called Deep Convolutional GAN (DCGAN) with Attention Module for Remote Scene Classification. The Attention module is integrated with DCGAN to optimize the power of feature extraction. To extract the contextual information, a feature fusion architecture is proposed and it is integrated with Discriminator. The proposed module optimizes the discriminator and generator losses. The extracted features are given as input to SVM for the classification. The proposed module DCGAN with Attention module is implemented with publicly available remote sensing scene UC-Merced dataset which have 21 different scene classes and the RSSCN7 dataset which have 7 different scene classes. The experimental results obtained by the proposed model DCGAN with Attention module are better than the state-of-art machines or methods results. The proposed model achieves an accuracy of 91.67% and 84.29 % for the RSSCN7 and UC-Merced datasets respectively.
Detection of Eye State using Brain Signal Classification with EBPTA and KNN Algorithm

Electroencephalograms (EEG) signals generally vary rapidly with time. These signals also have effect on the biotic lives which have brain. As the signals are rapid, their categorization becomes difficult. Most of the works regarding EEG signal categorization are highly domain specific. It requires a lot of time for the sub processes which are processing of signals and feature extraction. As EEG signals are analog, rapid changing, they have negligible Signal to Noise Ratio (SNR) value and hence this makes them vulnerable to any small disturbances or discrepancies. The proposed work is hence non-domain specific and has a comparison about the accuracies when the objective is achieved using Evolutionary Back Propagation Training Algorithm (EBPTA) and k-nearest neighbor (K-NN) algorithms. Convolutional operation is used for effective changing of specific data that can reveal the implicit spatial dependence of the Electroencephalography signals distribution. A dataset of 14 features and information about eye status is taken, and is tested, validated after training accordingly with two algorithms that is EBPTA and K-NN algorithms differently. The trails done prove that our work outperforms few other algorithms and results in accuracies of 98.94% and 96.99% for EBPTA and K-NN algorithms respectively, on chosen dataset with considerable resilience and time which makes them suitable for various range of problems which may be encountered in future.
CO2 electrochemical reduction to C2H4 by anion exchange membrane electrode assemble device with high selectivity and productivity

We invented anion exchange membrane electrode assemble (AEM MEA) CO<inf>2</inf> electrochemical reduction device composed with electrodeposit-spray two step synthesized Cu<inf>x</inf>O catalyst on cathode. The Faraday Efficiency of C<inf>2</inf>H<inf>4</inf> reached up to 50% and C<inf>2</inf>H<inf>4</inf> yield reached 14.5mmol/h at 4V. The performance and stability of electrodeposit-spray Cu<inf>x</inf>O catalyst are superior than electrodeposited compare sample and commercial Cu-Cu<inf>2</inf>O sample. Therefore a competitive method of Cu<inf>x</inf>O catalyst synthesis and device fabrication for CO<inf>2</inf> utilization was demonstrated.
Double Edge-Triggered Tristate Flip-Flop Physical Unclonable Function for Secure IoT Ecosystem

The sequential circuit in the clock distribution consumes a substantial amount of power in a digital system. The digital circuitry in the security devices requires low power and energy with increased throughput. In this paper, the security of the IoT devices have been realized using Physical Unclonable Functions (PUF). The proposed Double Edge-Triggered Tristate Flip-Flop PUF (DETTFF PUF) demonstrates significantly enhanced PUF metrics and robustness. The reduced power and energy of the proposed architecture can make DETTFF PUF a preferable choice in the IoT ecosystem. Comparison against the conventional double edge-triggered flip-flops to validate the structure. The design is also compared with conventional double edge-triggered flip-flops to elucidate the significance of the proposed architecture.
Utilization of Electrochemical Energy Storage System with External Characteristics of Voltage Source for Independent Voltage Support in Power Systems

The increasing penetration of renewable energies poses a threat to the voltage stability of power system. Energy storage technology can be utilized for voltage support in the power system with high proportion of renewables. The external characteristic of traditional energy storage system (ESS) performs as a current source, which only passively responds to the variations in power system after disturbance. In this paper, the model of electrochemical energy storage system with external characteristics of the voltage source is proposed. The control strategies of ESS are established based on the virtual synchronous generator (VSG) algorithm, in order to emulate the electromotive force and inertia of traditional synchronous generator. Application results of a practical power system in Northwest China show that the proposed model can provide independently voltage support, and effectively improve the consumption and delivery capacity of renewable energies.
A Binocular Distance Measurement Method with ORB Features in Region of Interest

Traditional binocular distance measurement techniques require high-precise cameras and have large complexities. In order to improve the accuracy with images taken by normal cameras, we propose a binocular distance measurement method to effective oriented fast and rotated brief features in region of interest (BRI _ ORB). In the method, the binocular camera takes Zhang Zhengyou calibration method to calibrate. Then, the feature points of the region of Interest image are extracted by the ORB features extraction method, and takes RANSAC (Random Sample Consensus) eliminate mismatches. Finally, a nonlinear camera model is established to optimize the measurement distance value. Experimental results show that our method outperforms than the traditional techniques.
An Efficient Physically Unclonable Function based Authentication Scheme for V2G Network

With the advancement of ICT, the Electrical vehicles (EVs) are connected to the smart grid and this type of network known as Vehicle to Grid (V2G). During the Energy trading process, EV consumers also receives an economic benefit where they buy energy at low cost during slack hours and sell same to grid during higher traffic. However, the V2G network faces various security challenges like hardware security, integrity, identity preservation, mutual authentication, etc. Since EVs and CSs (charging stations) are generally unmanned hence physical security is also an important concern. In this paper, we proposed a secure, lightweight, and hardware-based key agreement scheme using Physical Unclonable Function (PUF). The proposed scheme uses the PUF concept to perform mutual authentication (MA) among EV, CS, and the GS. The formal security analysis has been performed using AVISPA tool. Further, the performance evaluation results show that overhead costs in communication and computation are less compared to the existing schemes.
Analysis of power abandon characteristics in Offshore Wind Farms with DC Series-Parallel Architectures

With the distance between offshore wind farms and coastline increasing, as one of the important alternatives of traditional AC wind farm, DC wind farm has broad development prospects. However, the DC series-parallel architecture will result in the power abandon, whose ultimate cause is that the operation states of wind turbines are different under different wind speeds. In order to analyze the correlation between wind speed characteristics and the power abandon in offshore wind farms with series-parallel architectures, a detailed analysis is presented in this paper. Moreover, the severity of power abandon is calculated through the case of a real wind farm, which will provide reference for the design and planning of offshore wind farms with series-parallel architectures in the future.
Effect Comparison of Rectified and Inverter-fed Power Control Converters on Sub-synchronous Torsional Stability in Practical ±500kV Lu-Gao-Zhao Three-Terminal DC Project

Lu-Gao-Zhao three-terminal DC project is the first ±500kV multi-terminal line-commutated converter-based high voltage direct current (LCC-HVDC) transmission project in China. Different with the two-terminal LCC-HVDC situation that only the rectifier can operate in power control mode, Gaopo converter station of the Lu-Gao-Zhao three-terminal DC project can operate in power/dc-current control mode for both the rectified and inverter-fed conditions. Sub-synchronous torsional interactions between thermal power plants and the rectified power control converter have been well researched, but the effects of the inverter-fed power control converter on sub-synchronous torsional stability have seldom been analyzed. Based on the concepts of damping and synchronizing coefficients, this paper presents a small-signal model of the SG connected to HVDC converter system to compare the effects of rectified and inverter-fed power control converters on sub-synchronous torsional stability, and reveals the mechanism of the effect differences. The results have been adopted to the practical Lu-Gao-Zhao three-terminal DC project, and verified by the DPT test and actual operation.
Automatic Classification of Lung Sounds Using Machine Learning Algorithms

Lung sounds provide substantial information about the state of respiratory system. These sounds are frequently influenced by noise from heart and muscles which complicate accurate diagnosis. This research concerns development of an efficient framework for automatically classifying lung auscultation sounds. Two well-known publically available lung sound datasets are utilized in this work. A total of 280 lung sounds of a varying duration of 3 seconds to 1 minute with sampling rates of 4k, 10k, and 44.1k Hz were used. The raw signals were first pre-processed by resampling to 4 kHz and zero-padding for uniformity and fixed-length duration, and then segmented. Next, Mel-Frequency Cepstral Coefficients (MFCCs) and Short-Time Fourier Transform (STFT) were computed. Then 3,299,341 combined extracted features were used to train (70%) and validate (30%) models including Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF). The best results were obtained with STFT+MFCC-ANN combination with an accuracy of 98.61%, 98% F1-score, 98% recall, and 99% precision.
Phase Impairment Estimation for mmWave MIMO Systems

Most millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems are negatively affected by phase noise and carrier frequency offset due to non-ideal transceiver hardware components. If not compensated, the phase impairments significantly reduce the data rates of such systems. Much prior work either ignored these impairments or proposed high-complexity estimation algorithms. In this paper, we consider a mmWave MIMO system model that takes into account many practical hardware impairments. Our main contributions are a problem formulation of phase impairments and a low-complexity estimation method to solve the problem. Numerical results are provided to evaluate the performance of the proposed algorithm. In particular, it works quite well in the wide range of phase noise variances.
Optimized Economic Dispatching Approach for PEM Electrolyzer Cell Combined with Coal-fired Power Units to Participate in Frequency Regulation Service

By coupling flexible energy facilities such as the PEM electrolyzer cell with coal-fired power units, the flexibility of the coal-fired power platform can be significantly improved, which is the key solution for the challenges of system frequency regulation caused by the high penetration of renewable energy in power system. In order to dispatch resources properly and improve the flexibility of power system while ensuring the optimal economy, this paper proposes an optimized economic dispatching approach for PEM electrolyzer cell combined with coal-fired power units to participate in frequency regulation service. First, we analyze the revenue of frequency regulation auxiliary service and establish the revenue model. Then, the frequency regulation characteristics of PEM electrolyzer cell and coal-fired power units are elaborated and compared. For dispatching single frequency regulation signal properly, we take the economy as the optimized objective and consider constraints related to the units ramping rate, capacity and signal amplitude, etc., and proposed an optimized economic dispatching approach for the PEM electrolyzer cell combined with a coal-fired power units in a single frequency regulation. When the frequency regulation service stress high requirements for the response time, the economic dispatching table of the integration system under the single regulation signal can be formulated before by the proposed approach before service. A calculation case is carried out, and the results show that the proper dispatching frequency regulation signal among different units by the proposed approach can improve the flexibility of units in frequency regulation while ensuring the optimal economy, which provide a reference for coupling operation between flexible energy facilities and coal-fired power units.
Areal Precipitation Coverage Ratio for Enhanced AI Modelling of Monthly Runoff: A New Satellite Data-Driven Scheme for Semi-Arid Mountainous Climate

Satellite remote sensing provides useful gridded data for the conceptual modelling of hydrological processes such as precipitation–runoff relationship. Structurally flexible and computationally advanced AI-assisted data-driven (DD) models foster these applications. However, without linking concepts between variables from many grids, the DD models can be too large to be calibrated efficiently. Therefore, effectively formulized, collective input variables and robust verification of the calibrated models are desired to leverage satellite data for the strategic DD modelling of catchment runoff. This study formulates new satellite-based input variables, namely, catchment- and event-specific areal precipitation coverage ratios (CCOVs and ECOVs, respectively) from the Global Precipitation Mission (GPM) and evaluates their usefulness for monthly runoff modelling from five mountainous Karkheh sub-catchments of 5000–43,000 km2 size in west Iran. Accordingly, 12 different input combinations from GPM and MODIS products were introduced to a generalized deep learning scheme using artificial neural networks (ANNs). Using an adjusted five-fold cross-validation process, 420 different ANN configurations per fold choice and 10 different random initial parameterizations per configuration were tested. Runoff estimates from five hybrid models, each an average of six top-ranked ANNs based on six statistical criteria in calibration, indicated obvious improvements for all sub-catchments using the new variables. Particularly, ECOVs were most efficient for the most challenging sub-catchment, Kashkan, having the highest spacetime precipitation variability. However, better performance criteria were found for sub-catchments with lower precipitation variability. The modelling performance for Kashkan indicated a higher dependency on data partitioning, suggesting that long-term data representativity is important for modelling reliability.