Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Voltage Regulation of a Doubly-Fed Induction Generator Based Wind Power Plant under Grid Faults

The voltage at all buses in a power system should be kept constant in order to achieve voltage regulation. This manuscript evaluates various voltage regulation control schemes for a wind power plant (WPP) that uses a doubly-fed induction generator (DFIG). These regulation schemes are particularly appropriate in injecting the reactive power to fortify the voltage at the point of interconnection (POI) throughout a disturbance without using external devices such as flexible AC transmission system (FACTS) devices in a WPP. The control schemes employed in the DFIG and WPP controllers are reviewed, and all the control schemes employed in them are also classified. DFIG controllers are operated in the voltage control, reactive power, and reactive current control modes whereas the WPP controller either activates in reactive power control or voltage control modes. Based on the classification, this paper depicts the merits and demerits of each control scheme, compares them, and also shows broad space for further research. The comparative results are likely to be useful to researchers and engineers involved in the field of voltage regulation of DFIG-based WPP.
The Proposal of the Essential Strategies of DataWarehouse Testing

The analysis of relevant standards and guidelines proved the lack of information on actions and activities concerning data warehouse testing. The absence of the complex data warehouse testing methodology seems to be crucial particularly in the phase of the data warehouse implementation. The aim of this article is to suggest essential strategies of data warehouse testing as a part of the DW test methodology. Other important attributes can involve: prerequisites of testing, testing philosophy, traceability metrics, and specifications of test case and test procedure.
Evaluation of Electrocardiogram Biometric Verification Models Based on Short Enrollment Time on Medical and Wearable Recorders

Biometric authentication is nowadays widely used in a multitude of scenarios. Several studies have been conducted on electrocardiogram (ECG) for subject identification or verification among the various modalities. However, none have considered a typical implementation with a mobile device and the necessity for a fast-training model with limited recording time for the signal. This study tackles this issue by exploring various classification models on short recordings and evaluating the performance varying the sample length and the training set size. We run our tests on two public datasets collected from wearable and medical devices and propose a pipeline for ECG authentication with limited data required for competitive usage across applications.
v-Inform - Sistema automático de informação dirigida para o Second Life®

Second Life® is an excellent platform for presenting information and marketing applications, with a large user community, rendering it an enticing marketing channel. In this article, we present a system that allows information/advertising to be displayed to users of the Second Life® virtual world, in a direct and customized way, using a single platform to select the information/advertisement for each person: v-Inform. The system detects the
Human Adaptation to the Web: A Proposal for a Research Programme

This report elaborates on those research areas that the initial background report of the COPE project uncovered. As a general and common idea, we posit that in order to build a model of Web interaction that is able to describe and predict adaptive user behaviour in a more accurate and comprehensive way, it is of utmost importance to consider how the Web environment constraints the interaction. We propose a rationale for a research programme while leaving some doors open for conducting concurrent and medium-term research on some aspects related to the current proposal. The way users interact in constrained Web interaction environments will be observed to build an initial model, laboratory testing will reﬁne such model, longitudinal observations will conﬁrm its validity and unobtrusive measures will help to cross-validate diﬀerent ﬁndings. Interventions to ameliorate the performance of users and bypass situations of stress will be subject to the predictive power of the model.
Combining Flexible Loads with Energy Storage Systems to provide Frequency Control

This paper proposes a method of using flexible loads which switch on and off to provide frequency control based on a probability function. The proposed control strategy combines flexible loads with an energy storage system (ESS) in such a way that the flexible loads are responsible for providing support to larger power imbalances whereas the ESS compensates smaller ones. Simulation results based on the well-known WSCC 9-bus system indicate that the controller improves the overall frequency response of the system while maintaining acceptable voltage profiles, and allows for better use of the capacity of the ESS.
Intelligent Fashion Design Platform of Clothing Colleges in the Guangdong-Hong Kong-Macao Greater Bay Area based on JAVA and SQL

Intelligent fashion design platform of clothing colleges in the Guangdong-Hong Kong-Macao Greater Bay Area based on JAVA and SQL is implemented in this paper. The integration of popular elements themselves should be able to process the elements from changes in form and structure, changes in size, changes in the quantification, and changes in direction. Hence, for the better analysis of the different aspects, the data mining algorithms are adopted, and the SQL system is then integrated for the data storage and analysis. For the smart and intelligent concerns, the JAVA is integrated to implement the system. The proposed model and the platform are simulated using the interactive system. The performance is verified.
Coordinated Optimization of Energy Sharing and Voltage Regulation Service for PV prosumers in the Distribution Network

The access of large-scale photovoltaics and the continuous development of distributed photovoltaic technology have gradually attracted more attention to the energy sharing and auxiliary service market. The significant relationship between active and reactive power in the low-voltage distribution network (DN) makes it difficult to consider energy sharing or voltage regulation in the auxiliary service market independently. This paper proposes a coordinated optimization method of energy sharing and voltage regulation service for PV prosumers in the DN, which uses the Stackelberg game to build a coordination mechanism between distribution network operator (DSO) and prosumers: DSO acts as the leader to determine the price and voltage regulation signals; PV prosumers acts as the follower to make energy management and reactive voltage regulation strategies based on prices and signals. In addition, the relationship between active and reactive power is studied through the amortized coefficients of DN operation cost. The effectiveness of the proposed method is verified in the IEEE 33-bus test system in terms of the prosumers' profits, DN operation cost, and node voltage.
An Active Defense Mechanism for Cyber-Physical Power Systems Against Data Integrity Attack on ADMM-Based Economic Dispatch

As an important power system operation scenario in the future, distributed power generation requires stable and economic operation. Due to development of technologies in physical and cyber systems, as well as the coupling of those mentioned above, cyber-physical-system needs to be modeled and analyzed together. Complex information systems and complex communication environments have introduced a series of more complex security issues. Physical systems rely on information systems to operate, and information systems themselves have fragile characteristics, all of which make them easy to become ideal targets for network attacks. The decomposition-coordination scenarios in distributed power system plays an important role, while the protection or detection on cyberattack is in lack. Therefore, this paper proposes an active detection algorithm on the ADMM-based decomposition-coordination power generation economic dispatch scenario to defend against data integrity attacks, then we test its performance under IEEE standard 9-node system.
A Network Intrusion Detection System Using Ensemble Machine Learning

The type and number of cyber-attacks on data networks have been increasing. As networks grow, the importance of Network Intrusion Detection Systems (NIDS) in monitoring cyber threats has also increased. One of the challenges in NIDS is the high number of alerts the systems generate, and the overwhelming effect that alerts have on security operations. To process alerts efficiently, NIDS can be designed to include Machine Learning (ML) capabilities. In the literature, various NIDS architectures that use ML approaches have been proposed. However, high false alarm rates continue to be challenges to most NID systems. In this paper, we present a NIDS that uses ensemble ML in order to improve the performance of attack detection and to decrease the rate of false alarms. To this end, we combine four ensemble ML classifiers - (Random Forest, AdaBoost, XGBoost and Gradient boosting decision tree) using a soft voting scheme.
Research on Comprehensive Index Evaluation System of Hybrid Ac/Dc Distribution Networks

With the progress of power electronics technology, AC / DC hybrid distribution network has become an effective way to solve future distribution problems. This paper proposes a comprehensive index evaluation system for AC / DC hybrid distribution network based on relevant standards and references in view of the lack of a quantitative evaluation method. Firstly, the typical application scenarios of the AC / DC hybrid distribution network in the future are analyzed, and the corresponding development mode is established. Secondly, considering the constraints of various key technical indicators, a quantitative analysis method of various technical and economic indicators is proposed. A combination evaluation model of GRA + TOPSIS + RID for different application scenarios is constructed to optimize technical and economic indicators comprehensively. Finally, taking the actual data of a regional distribution network as an example, the correctness of the index and the feasibility of the evaluation model are verified. Moreover, the construction priorities in different scenarios are proposed, which can reference the development and construction of AC / DC distribution network in the future.
Electricity Price Prediction Based on Empirical Mode Decomposition and Minimum Gated Memory Network Quantile Regression

In the open electricity market, accurate short-term electricity price prediction is crucial for market participants to formulate market strategies. This paper proposes an electricity price prediction method based on empirical mode decomposition and minimum gated memory network quantile regression. Empirical mode decomposition is used to decompose the electricity price sequence into several modal components, and the quantile regression (QR) and minimum gated memory network (MGM) combined with QR-MGM, a hybrid model, to predict each modal component under different quantiles, and use the kernel density estimation to obtain the probability density function of electricity price for the reconstructed prediction results. Taking the electricity price data of the American ERCOT power market as an example, it shows that the proposed combined forecasting method not only has higher accuracy of short-term electricity price prediction under the same training time, but also obtains a more reliable electricity price probability density function, which provides more effective information for market participants to formulate optimal market strategies.
Load Disaggregation Based on Sequence-to-point Network with Unsupervised Pre-training

It is known that successful load disaggregation via deep learning relies on a large number of labeled data to train the deep neural networks. However, it is hard and expensive to acquire a large amount of appliance-level power data or ON/OFF labels. For overcoming such weakness, in this paper, unsupervised pre-training is applied to the state-of-the-art sequence-to-point (s2p) deep learning approach for NILM, where labeling is not required in pre-training. In the proposed method, the s2p deep neural network is initially pre-trained on unlabeled aggregate power readings for other houses, and then fine-tuned on a small set of aggregate power for the target house labeled by individual appliance monitoring. Finally, the generated network is applied to testing with the aggregate for the target house as input and outputting the power signal for the target load. The proposed method is validated on the UK REFIT dataset1, benchmark with s2p in two popular evaluation metrics. Experimental results show that the proposed unsupervised pre-training effectively improves NILM performance of the deep neural network with a lack of labeled training data.
Revamp and application of exit guard in the seventh stand of the high-speed wire rod

By thoroughly analyzing the characteristics of the damage of the exit guard of the 7th stand of the high speed wire rod mill and its origin the specific improvement measures for reduction of piling up amount of steel and increase of use cycle are put forward and thereupon the separated guard of the 7th stand is changed into the torsion roll set type whole set guard and is applied to the on-site production and thereby the problem of frequently piling up of steel and short cycle of use is solved.In this way not only the availability of the high speed wire rod mill can be raised,but the production cost also saved.
Optimal Dispatch Model considering Flexible Demand Resources and Bidding Uncertainty of Distributed Generation

The increasing proportion of distributed generation (DG) brings intermittency and indeterminacy to the power grid, and the rising amount of the flexible demand resources (FDRs) is re-shaping the way in which electricity markets are operated. This paper aims at the market operation mechanisms considering the uncertain DG and the FDRs. Firstly, the bidding strategies of DG can be formulated according to their risk preferences, and the corresponding bidding strategies based on Pareto front have been constructed. Then, the FDRs are also taken into consideration in the process of market trades and centralized clearance. Lastly, an optimal dispatch model is established where the independent system operator (ISO) will minimize the total costs based on the above risk-based behaviors of DG and the dispatchable FDRs. Numerical results of the simulation case studies are utilized to illustrate the feasibility of the model.
An Intelligent Bidding Strategy based on Social Welfare of Virtual Power Plant considering Carbon Trading

Virtual Power Plant is the concept which enables the Distributed Energy Resources units to take part in the energy market which aims to maximize the profit with the reduction in the operating cost of the system. It also helps to enhance the efficiency and reliability of the existing system. Distributed Energy Resources units are the renewable energy resources which may or may not be dispersed geographically and so able to reduce the carbon emission, which is becoming a major issue in the climate change in the world. This paper presents a strategic bidding model of the VPP units participating in the carbon integrated day ahead electricity market. This paper calculates the carbon credit emitted by the generation from the conventional power plant and Micro Turbine unit with the energy management strategy within the Virtual Power Plant. With the consideration of carbon credit in the electricity market, the presented model will able to reduce the emission and hence improves the environmental quality. In bidding model forecasting of renewable sources has been done with machine learning so that it will results in good accuracy and the penalty can be reduced. The work also considers profit maximization of consumer as well as producer by examining the social welfare of the market presented. The case analysis is done with and without the carbon credit in the model and verified.
Comparison Between Different Air Gaps and Rotor Magnet Geometries of a Scalable Axial Flux Motor with Standard Core Laminations

A novel Scalable Axial Flux Motor with standard core laminations (AxMDM) and with a disc rotor made of glass fiber reinforced plastic (GRP) equipped with NdFeB-Magnets was developed and assembled. This novel machine concept is easy to fabricate, highly scalable, shows a high torque to weight ratio and has an excellent utilization rate of magnet material per torque.However, speed-dependent eddy currents in the rotor magnets significantly influence the magnet temperature, which has to be limited due to its maximum permissible values. Furthermore, the magnet temperature affects torque, maximum speed, power losses and the mechanical stability of the motor. These parameters can be adapted by adjusting the magnet geometry and the air gap, reducing the influence of eddy currents. The motor concept, measurement results and a correlation between air gap and magnet geometry versus magnet temperature and power losses are discussed in this paper.
Direct Thermal Load Control in Active Distribution Networks Based on Deep Reinforcement Learning

With the growth of electrical power consumption in the tertiary industry, the proportion of building thermal load is increasing significantly. On the demand side, load aggregators attempt to realize peak shifting by direct active power control of heating ventilation and air-conditioning (HVAC) devices. However, the difficulty in establishing accurate physical models of HVAC equipment has limited the performance of model-based optimization methods. Recently, deep reinforcement learning (DRL) has been widely studied and trusted to achieve optimal decisions without priori knowledge on the environment. In this paper, a DRL-based building thermal load control method is proposed to provide the auxiliary service of load shifting under the premise of thermal comfort. A resistor-capacitor model is introduced to describe the thermodynamics of exterior protection structure, and the regulation behavior is formulated into a Markov decision process. Then, an augmented algorithm called improved deep deterministic policy gradient (iDDPG) is proposed with high sample efficiency. Comprehensive numerical simulations are conducted to verify the feasibility and superiority of the proposed method. The results have supported the effectiveness of iDDPG in maximizing thermal comfort, optimizing peak shifting effect and reducing network loss.
Improved LSTM Memory Recursive Neural Network Precise Compensation and Prediction for Phase Selective Switching Device

In this paper, a mathematic ideal model of circuit breaker's phase-selecting and switching device is established. It is concluded that the closer the circuit breaker's closing is to the zero crossing, the less influence it has on the AC system.The influence factor model of circuit breaker electric mechanism closing is established, and the influence of ambient temperature, control voltage, wear degree and contact displacement on circuit breaker is qualitatively analyzed. In this paper, based on the principle of sampling as little as possible in the field, a compensation method based on the improved LSTM neural network is proposed to predict the closing delay of circuit breakers. The error between the predicted value and the true value is accurately realized within 0.001s. The MATLAB/Simulink simulation model was established, and the phase-selecting and closing process was carried out on the basis of compensation based on the improved LSTM neural network prediction model. The harmonic content of the AC system could be kept within 2%, 100% meeting the harmonic control requirements of the power industry. Compared with the adaptive prediction model, there are 21.4% probability of harmonic content higher than 2%. In addition, the model presented in this paper can also provide a way of thinking for the intelligent on-line diagnosis of circuit breakers and the setting of optional switching devices.
Long-term Energy Consumption Forecasting for Data Center Industry in China

The number of internet data centers increases rapidly over the past years, which results in a significant incensement on energy demand. In order to achieve a precise forecast of the data center electricity consumption in the next few years, a scale prediction model for data centers based on Bass model is proposed in this paper. We provide a forecast for the long-term energy consumption in the data centers based on the proposed model while accounting the difference between the existing and planning data centers. The simulation results show that data centers in China will consume more than 400 billion kWh of electricity in 2030, accounting for 4% of the national electricity consumption, and the growth rate will slow down at the same time. We further investigate the impact of implementing advanced technologies on cooling systems and servers. The results show that the decrease of PUE values will effectively reduce electricity consumption, and the increase of server utilization ratios will improve energy efficiency for data centers.
Data Collection and Analysis of College Students' Consciousness Information Based on Brain-Computer Connection Modeling

College students are a vital force for the development of the motherland and social progress. Ensuring the safety of college students is an important part of maintaining the stability of the campus and the harmonious society. The safety awareness of college students has become increasingly prominent, and the level of safety awareness of college students will directly affect the harmonious development of themselves, the school and the society. The brain is the most complex organ of the human body, and different brain regions are in charge of different functions of the human body. Once the brain is damaged, it will cause different degrees of damage to the brain nerve function. Based on brain-computer connection modeling, this paper studies the data collection and analysis of college students' consciousness state information.
Time-Critical Data Dissemination Under Flash Crowd Traffic

How to rapidly disseminate a large-sized file to many recipients in flash crowd arrival patterns is a fundamental challenge in many applications, such as distributing multimedia content. To tackle this challenge, we present the Bee, which is a time-critical peer-to-peer data dissemination system aiming at minimizing the maximum dissemination time for all peers to obtain the complete file in flash crowd arrival patterns. Bee is a decentralized system that organizes peers into a randomized mesh-based overlay, and each peer only works with local knowledge. We introduce the slowest peer first strategy to boost the speed of dissemination and present a topology adaptation algorithm that adapts the number of connections based on upload bandwidth capacity of a peer. Bee is designed to support network heterogeneity and deals with the flash crowd arrival pattern without sacrificing the dissemination speed. We also show the lower bound analysis of the data dissemination problem, and present the experimental results to demonstrate that the performance of Bee can roughly approximate the lower bound of the data dissemination problem under flash crowd traffic.