bluuebunny/crossref_metadata_2025 huggingface dataset:
Continuous Tip Widening Technique for Roll-to-Roll Fabrication of Dry Adhesives
Authors: Lee Sung Ho , Yi Hoon , Park Cheol Woo , Jeong Hoon Eui , Kwak Moonkyu
Published: October 2018

In this study, we reported continuous partial curing and tip-shaped modification methods for continuous production of dry adhesive with microscale mushroom-shaped structures. Typical fabrication methods of dry adhesive with mushroom-shaped structures are less productive due to the failure of large tips on pillar during demolding. To solve this problem, a typical pillar structure was fabricated through partial curing, and tip widening was realized through applying the proper pressure. Polyurethane acrylate was used in making the mushroom structure using two-step UV-assisted capillary force lithography (CFL). To make the mushroom structure, partial curing was performed on the micropillar, followed by tip widening. Dry adhesives with properties similar to those of typical mushroom-shaped dry adhesives were fabricated with reasonable adhesion force using the two-step UV-assisted CFL. This production technology was applied to the roll-to-roll process to improve productivity, thereby realizing continuous production without any defects. Such a technology is expected to be applied to various fields by achieving the productivity improvement of dry adhesives, which is essential for various applications.
Superhydrophobic Fabrics with Mechanical Durability Prepared by a Two-Step Plasma Processing Method
Authors: Ellinas Kosmas , Tserepi Angeliki , Gogolides Evangelos
Published: October 2018

Most studies on superhydrophobic fabrics focus on their realization using additive manufacturing (bottom-up) techniques. Here we present the direct modification of three different fabrics using a plasma-based method to obtain anti-adhesive and self-cleaning properties. A two-step plasma processing method is used: (a) for the creation of micro-nanoscale features on the fabric surface (plasma texturing step) and (b) the minimization of the fabric surface energy (by a short plasma deposition step of a very thin, low surface energy layer). The entire process takes only 14 min and all fabrics after processing exhibit high water static contact angles (WSCA > 150°), low contact angle hysteresis (CAH < 7°) and advantageous mechanical durability against hand-rumpling. The method is simple and generic, and it can be therefore expanded to other polymeric fabrics (i.e., acrylic) in addition to polyester, without any limitation rising from the weaving characteristics of the fabric or the starting nature of the material (i.e., hydrophobic or hydrophilic).
Hidden Metals in Several Brands of Lipstick and Face Powder Present on Polish Market
Authors: Łodyga-Chruścińska Elżbieta , Sykuła Anna , Więdłocha Marzenna
Published: October 2018

Cosmetics still retain their brilliant effect, but public concern about their toxicity has become a hot issue. Trace amounts of toxic heavy metals can be either intentionally added to cosmetics or present as impurities in the raw materials. We therefore assessed the levels of lead, nickel, copper, zinc and iron in six brands of lipstick and six brands of cosmetic powder that are widely available in local Polish markets. The cosmetics were digested and analyzed for the metals using flame atomic absorption spectrophotometry. Lead and nickel were found in some powders, but none in lipstick samples. This study revealed that the levels of these metals were higher than the specifications reported in the literature data. On the other hand, the copper content was determined at the level of 435 mg/kg in one sample of powder and at 75.92 mg/kg in one lipstick. Iron levels ranged from 0 to 12,168.57 mg/kg depending on the brand of powder or lipstick, and were generally higher in powders. Zinc was detected in the range of 1.73–488.31 mg/kg in all 12 samples. The results lead to the conclusion that constant control of metallic content in lipsticks, powders and other facial cosmetics should be seriously considered.
Agro-Industrial By-Products and Their Bioactive Compounds—An Ally against Oxidative Stress and Skin Aging
Authors: Simitzis Panagiotis E.
Published: October 2018

The increased consumer awareness towards hazards related with sun exposure has given a boost in the cosmetics industry and particularly the sun care market. Human skin is continually being threatened by the UV irradiation present in sunlight and acute UV exposure leads to skin photoaging. Cosmetic and/or dermatological applications include several bioactive compounds that contribute to the regulation of epidermal homeostasis by providing protection against solar radiation and improving the antioxidant activity of epidermis. Plant extracts are sources of active ingredients with intense therapeutic properties, and the topical application or oral intake of these compounds could ameliorate skin condition. Nowadays, there is a growing demand for the application of the bioactive agents contained in agro-industrial byproducts in sun care products, since many of them have shown promising properties as skin photoprotectants. However, well-conducted clinical studies are required to prove their safety and efficacy before they could be regularly used. Environmentally friendly extraction and sustainable techniques are therefore under examination for recovering such compounds from agro-industrial byproducts and converting them into innovative high-value natural ingredients used in cosmetic formulations.
A Crystallographic Study on the Growth of Partially Faceted MnSn2 Phase during Solidification Process
Authors: Li Lei , Bi Yuantong , Ban Chunyan , Zhang Haitao , Liu Tie , Wang Xiangjie , Esling Claude , Cui Jianzhong
Published: October 2018

The growth of MnSn2 phase during the solidification process of Sn-Mn alloy was crystallographically investigated. The results show that the non-faced spherical caps of the MnSn2 crystals follow the continuous growth mechanism to grow rapidly along the <001> direction, while the side surfaces the two-dimensional nucleation mechanism to form the low index {100} and {110} facets. An interface structure analysis indicates that the atom planes within the {100} interplanar spacing period (IPSP) has a lower average reticular density than those within the {011} IPSP. This leads to the faster growth rates and thus the shortening and disappearance of the {100} side facets. As a consequence, the partially faceted (i.e., non-faceted spherical caps and faceted side surfaces) MnSn2 crystals follow an octagonal-base/spherical-cap geometric model (few crystals possess square bases) in three dimensions.
Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections
Authors: Jafari Mohammad , Xu Hao
Published: October 2018

In this paper, a biologically-inspired distributed intelligent control methodology is proposed to overcome the challenges, i.e., networked imperfections and uncertainty from the environment and system, in networked multi-Unmanned Aircraft Systems (UAS) flocking. The proposed method is adopted based on the emotional learning phenomenon in the mammalian limbic system, considering the limited computational ability in the practical onboard controller. The learning capability and low computational complexity of the proposed technique make it a propitious tool for implementing in real-time networked multi-UAS flocking considering the network imperfection and uncertainty from environment and system. Computer-aid numerical results of the implementation of the proposed methodology demonstrate the effectiveness of this algorithm for distributed intelligent flocking control of networked multi-UAS.
Sparse Optimistic Based on Lasso-LSQR and Minimum Entropy De-Convolution with FARIMA for the Remaining Useful Life Prediction of Machinery
Authors: Wu Bo , Gao Yangde , Feng Songlin , Chanwimalueang Theerasak
Published: October 2018

To reduce the maintenance cost and safeguard machinery operation, remaining useful life (RUL) prediction is very important for long term health monitoring. In this paper, we introduce a novel hybrid method to deal with the RUL prediction for health management. Firstly, the sparse reconstruction algorithm of the optimized Lasso and the Least Square QR-factorization (Lasso-LSQR) is applied to compressed sensing (CS), which can realize the sparse optimization for long term health monitoring data. After the sparse signal is reconstructed, the minimum entropy de-convolution (MED) is used to identify the fault characteristics and to obtain significant fault information from the machinery operation. Health indicators with Skip-over, sample entropy and approximate entropy are then performed to track the degradation of the machinery process. The performance analysis of the Skip-over is superior to other indicators. Finally, Fractal Autoregressive Integrated Moving Average model (FARIMA) is employed to predict the Skip-over using the R/S method. The analysis results evidence that the novel hybrid method yields a good performance, and such method can achieve highly accurate RUL prediction and safeguard machinery operation for long term monitoring.
Video Summarization for Sign Languages Using the Median of Entropy of Mean Frames Method
Authors: Saqib Shazia , Kazmi Syed Asad Raza
Published: October 2018

Multimedia information requires large repositories of audio-video data. Retrieval and delivery of video content is a very time-consuming process and is a great challenge for researchers. An efficient approach for faster browsing of large video collections and more efficient content indexing and access is video summarization. Compression of data through extraction of keyframes is a solution to these challenges. A keyframe is a representative frame of the salient features of the video. The output frames must represent the original video in temporal order. The proposed research presents a method of keyframe extraction using the mean of consecutive k frames of video data. A sliding window of size k / 2 is employed to select the frame that matches the median entropy value of the sliding window. This is called the Median of Entropy of Mean Frames (MME) method. MME is mean-based keyframes selection using the median of the entropy of the sliding window. The method was tested for more than 500 videos of sign language gestures and showed satisfactory results.
Zipf’s, Heaps’ and Taylor’s Laws are Determined by the Expansion into the Adjacent Possible
Authors: Tria Francesca , Loreto Vittorio , Servedio Vito D. P.
Published: October 2018

Zipf’s, Heaps’ and Taylor’s laws are ubiquitous in many different systems where innovation processes are at play. Together, they represent a compelling set of stylized facts regarding the overall statistics, the innovation rate and the scaling of fluctuations for systems as diverse as written texts and cities, ecological systems and stock markets. Many modeling schemes have been proposed in literature to explain those laws, but only recently a modeling framework has been introduced that accounts for the emergence of those laws without deducing the emergence of one of the laws from the others or without ad hoc assumptions. This modeling framework is based on the concept of adjacent possible space and its key feature of being dynamically restructured while its boundaries get explored, i.e., conditional to the occurrence of novel events. Here, we illustrate this approach and show how this simple modeling framework, instantiated through a modified Pólya’s urn model, is able to reproduce Zipf’s, Heaps’ and Taylor’s laws within a unique self-consistent scheme. In addition, the same modeling scheme embraces other less common evolutionary laws (Hoppe’s model and Dirichlet processes) as particular cases.
A Complexity View into the Physics of the Accelerating Seismic Release Hypothesis: Theoretical Principles
Authors: Vallianatos Filippos , Chatzopoulos Georgios
Published: October 2018

Observational indications support the hypothesis that many large earthquakes are preceded by accelerating-decelerating seismic release rates which are described by a power law time to failure relation. In the present work, a unified theoretical framework is discussed based on the ideas of non-extensive statistical physics along with fundamental principles of physics such as the energy conservation in a faulted crustal volume undergoing stress loading. We define a generalized Benioff strain function Ω ξ ( t ) = ∑ i = 1 n ( t ) E i ξ ( t ) , where Ei is the earthquake energy, 0 ≤ ξ ≤ 1 . and a time-to-failure power-law of Ω ξ ( t ) derived for a fault system that obeys a hierarchical distribution law extracted from Tsallis entropy. In the time-to-failure power-law followed by Ω ξ ( t ) the existence of a common exponent mξ which is a function of the non-extensive entropic parameter q is demonstrated. An analytic expression that connects mξ with the Tsallis entropic parameter q and the b value of Gutenberg—Richter law is derived. In addition the range of q and b values that could drive the system into an accelerating stage and to failure is discussed, along with precursory variations of mξ resulting from the precursory b-value anomaly. Finally our calculations based on Tsallis entropy and the energy conservation give a new view on the empirical laws derived in the literature, the associated average generalized Benioff strain rate during accelerating period with the background rate and connecting model parameters with the expected magnitude of the main shock.
Creation of Numerical Constants in Robust Gene Expression Programming
Authors: Fajfar Iztok , Tuma Tadej
Published: October 2018

The problem of the creation of numerical constants has haunted the Genetic Programming (GP) community for a long time and is still considered one of the principal open research issues. Many problems tackled by GP include finding mathematical formulas, which often contain numerical constants. It is, however, a great challenge for GP to create highly accurate constants as their values are normally continuous, while GP is intrinsically suited for combinatorial optimization. The prevailing attempts to resolve this issue either employ separate real-valued local optimizers or special numeric mutations. While the former yield better accuracy than the latter, they add to implementation complexity and significantly increase computational cost. In this paper, we propose a special numeric crossover operator for use with Robust Gene Expression Programming (RGEP). RGEP is a type of genotype/phenotype evolutionary algorithm closely related to GP, but employing linear chromosomes. Using normalized least squares error as a fitness measure, we show that the proposed operator is significantly better in finding highly accurate solutions than the existing numeric mutation operators on several symbolic regression problems. Another two important advantages of the proposed operator are that it is extremely simple to implement, and it comes at no additional computational cost. The latter is true because the operator is integrated into an existing crossover operator and does not call for an additional cost function evaluation.
The Adaptive Behavior of a Soccer Team: An Entropy-Based Analysis
Authors: Neuman Yair , Israeli Navot , Vilenchik Dan , Cohen Yochai
Published: October 2018

To optimize its performance, a competitive team, such as a soccer team, must maintain a delicate balance between organization and disorganization. On the one hand, the team should maintain organized patterns of behavior to maximize the cooperation between its members. On the other hand, the team’s behavior should be disordered enough to mislead its opponent and to maintain enough degrees of freedom. In this paper, we have analyzed this dynamic in the context of soccer games and examined whether it is correlated with the team’s performance. We measured the organization associated with the behavior of a soccer team through the Tsallis entropy of ball passes between the players. Analyzing data taken from the English Premier League (2015/2016), we show that the team’s position at the end of the season is correlated with the team’s entropy as measured with a super-additive entropy index. Moreover, the entropy score of a team significantly contributes to the prediction of the team’s position at the end of the season beyond the prediction gained by the team’s position at the end of the previous season.
Are We Aware of What Is Going on in a Student’s Mind? Understanding Wrong Answers about Plant Tropisms and Connection between Student’s Conceptions and Metacognition in Teacher and Learner Minds
Authors: Sobieszczuk-Nowicka Ewa , Rybska Eliza , Jarmużek Joanna , Adamiec Małgorzata , Chyleńska Zofia
Published: October 2018

Problems with understanding concepts and mechanisms connected to plant movements have been diagnosed among biology students. Alternative conceptions in understanding these phenomena are marginally studied. The diagnosis was based on a sample survey of university students and their lecturers, which was quantitatively and qualitatively exploratory in nature (via a questionnaire). The research was performed in two stages, before and after the lectures and laboratory on plant movements. We diagnosed eight alternative conceptions before the academic training started. After the classes, most were not been verified, and in addition, 12 new conceptions were diagnosed. Additionally, we report that teachers are not aware of students’ possible misunderstandings. They do not perceive students’ troubles with switching between levels of representations, nor their alternative conceptions. A case of “curse of knowledge” was observed and academic teacher training is recommended. Additionally, the need for metacognition as a crucial element in laboratory activities seems supported by our presented results. Such metacognition refers to students as well as teachers, which leads to the conclusion that teachers should be aware of students’ way of thinking and the development of knowledge in one’s own mind.
Low-Harmonic DC Ice-Melting Device Capable of Simultaneous Reactive Power Compensation
Authors: Lu Jiazheng , Zhu Siguo , Li Bo , Tan Yanjun , Zhou Xiudong , Huang Qinjun , Zhu Yuan , Mao Xinguo
Published: October 2018

As a result of the high efficiency of ice-melting and the small power supply capacity, DC ice-melting devices are widely used in relation to transmission lines in the power grid. However, it needs to consume reactive power when ice-melting, and voltage fluctuation of the substation may be caused when the demand for reactive power is large. It also generates a large number of 5th and 7th harmonics when ice-melting. In this paper, combined with the demand for ice-melting for transmission lines and the dynamic reactive power of substations, a low-harmonic DC ice-melting device capable of simultaneous reactive power compensation is studied. The function of ice-melting and reactive power compensation can be operated simultaneously and the rectifier’s main harmonics can be eliminated. The simulation and experimental research on the device was carried out in the 500 kV Chuanshan substation. The actual ice melting was carried out on the 500 kV Chuansu I line and took only 68 min to melt the ice. The 500 kV bus voltage had no negative deviation, and the positive deviation decreased from +3.09% to +1.57% within 24 h of testing. The results prove the feasibility of the proposed DC ice-melting device in this paper.
PIDR Sliding Mode Current Control with Online Inductance Estimator for VSC-MVDC System Converter Stations under Unbalanced Grid Voltage Conditions
Authors: Yang Weipeng , Zhang Hang , Li Jungang , Zhang Aimin , Zhou Yunhong , Wang Jianhua
Published: October 2018

This study aims to present a novel proportional-integral-derivative-resonant law-based sliding mode current control strategy with online inductance estimator (PIDR-SMCC-OIE) for voltage source converter medium voltage direct current (VSC-MVDC) system converter stations under unbalanced grid voltage conditions. A generalized current reference calculation method, by which the ratio of the amplitude of the active power ripple to that of the reactive power ripple can be continuously controlled without current distortion is presented. A dynamic model of the current control errors in the positive sequence synchronous reference frame is developed, and a PIDR law-based sliding mode current controller is designed, where derivatives of the current references are obtained by simple algebraic operations. An OIE adopting the dynamic filtering method and gradient algorithm is proposed to further improve system robustness. In this OIE, the converter pole voltages are obtained by computation utilizing the gate signals of the switching devices and the DC bus voltage, so that no additional voltage sensors are needed. To verify effectiveness of the PIDR-SMCC-OIE strategy, simulation studies on a two-terminal VSC-MVDC system are conducted in PSCAD/EMTDC. The results show it can provide satisfactory performance over a wide range of operating conditions.
The Effect of Electronic Scavenger Additives on the AC Dielectric Strength of Transformer Mineral Oil
Authors: Khaled Usama , Beroual Abderrahmane
Published: October 2018

This paper is devoted to the influence of two types of electronic scavenger additives/compounds, namely, carbon tetrachloride (CCl4) and methyl iodide, which is also called iodomethane (CH3I), on the dielectric strength of transformer mineral oil. The tests are achieved in a sphere-sphere electrodes arrangement under AC voltage according to the IEC 60156 standard. The investigated additive concentrations range from 0 to 600 ppm. The verification of the conformity of the experimental results with normal and Weibull probabilistic distributions as well as the estimation of the breakdown voltage with risk probabilities of 1%, 10%, and 50% are also performed. It is shown that there is an optimum concentration of each type of electronic scavenger compound at which the dielectric strength of the mineral oil is significantly improved (i.e., it reaches a maximum value). This improvement is of 98% with 500 ppm of CH3I and 93% with 200 ppm of CCl4. It is also shown that the breakdown voltage values of all of the investigated samples with and without additives conform to a Weibull distribution but not to a normal distribution. The obtained results are discussed with regard to the possible mechanisms that may be responsible, particularly the two phases of inception and propagation of the streamers.
An Enhanced Control Strategy for Doubly-Fed Induction Generators Based on a Virtual Harmonic Resistor and Capacitor under Nonlinear Load Conditions
Authors: Xie Zhen , Niu Lifan , Zhang Xing
Published: October 2018

Harmonic amplification for doubly-fed induction generator wind turbine systems (DFIG WTSs) will occur due to the existence of non-linear loads and reactive power compensation installation, and grid voltage and total grid current at the point of common coupling (PCC) will be distorted. An impedance model is established to analyze the interaction between DFIG WTS, non-linear loads and weak grids. Harmonic current impact factor and harmonic voltage impact factor is proposed to analyze the impact of harmonic current source on total grid current and voltage at the PCC with different control strategies. A virtual harmonic resistor and capacitor method is adopted to reduce the harmonic voltage. An impedance-based analysis method is adopted to analyze the stability of the DFIG system. To achieve optimal control of harmonic voltage and harmonic current, a coordination factor is proposed to adjust the dynamic allocation for harmonic voltage and harmonic current at PCC. The experimental results demonstrate the effectiveness of the proposed control strategy.
Assessing the Effect of Incentive Policies on Residential PV Investments in Colombia
Authors: Castaneda Monica , Zapata Sebastian , Aristizabal Andres
Published: October 2018

As the cost of solar photovoltaic (PV) falls, their potential for transforming modern electricity generation increases. Solar PV provides a simpler way of producing clean and affordable energy, which makes it an attractive investment. Great investments in solar PV have occurred in industrialized countries, but government efforts to promote this technology have not been effective in nonindustrialized countries. Despite this, some of these countries may have a high solar PV potential, such as Colombia, where policies to encourage solar PV are only just starting to take place. Therefore, this paper proposes a simulation model to assess different policies—feed-in tariff, net metering, and capital subsidy—to promote solar PV investments in the Colombian residential sector. Policies are assessed considering the criteria of efficiency and effectiveness. Simulation results suggest that (i) net metering is the most efficient policy with a cost indicator of 20,298 USD/MW; (ii) feed-in tariff is the most effective policy as it reaches the highest level of avoided CO2 emissions—4,792,823 million tons of CO2—and a meaningful PV installed capacity of 7522 MW; (iii) capital subsidy is the least efficient policy as it has the highest cost indicator of 509,616 USD/MW.
Designing Localized MPPT for PV Systems Using Fuzzy-Weighted Extreme Learning Machine
Authors: Du Yang , Yan Ke , Ren Zixiao , Xiao Weidong
Published: October 2018

A maximum power point tracker (MPPT) should be designed to deal with various weather conditions, which are different from region to region. Customization is an important step for achieving the highest solar energy harvest. The latest development of modern machine learning provides the possibility to classify the weather types automatically and, consequently, assist localized MPPT design. In this study, a localized MPPT algorithm is developed, which is supported by a supervised weather-type classification system. Two classical machine learning technologies are employed and compared, namely, the support vector machine (SVM) and extreme learning machine (ELM). The simulation results show the outperformance of the proposed method in comparison with the traditional MPPT design.
A Novel Phase Current Reconstruction Method for a Three-Level Neutral Point Clamped Inverter (NPCI) with a Neutral Shunt Resistor
Authors: Son Yungdeug , Kim Jangmok
Published: October 2018

This paper presents three phase current reconstruction methods for a three-level neutral point clamped inverter (NPCI) by measuring the voltage of a shunt resistor placed in the neutral point of the inverter. In order to accurately acquire the phase currents from the shunt resister, the dwell time of the active voltage vectors need to exceed the minimum time. On the other hand, if the time of active voltage is shorter than the minimum time, the current measurement becomes impossible. In this paper, unmeasurable regions for current are classified into three areas. Area 1 is a region in which both phase currents can be measure. Therefore, it is not necessary to restore the current. In Area 2, only one phase current can be measured. Thus, an estimation or restoration method is needed to measure another phase current. In this paper, the current estimation method using an electrical model of the motor is proposed. Area 3 is the region in which both phase currents can not be measured. In this case, it is necessary to move the voltage vector to the current measurable area by injecting the voltage. In this paper, Area 3 is divided into 36 sectors to inject optimal voltage. The proposed methods have the advantages of high current measurement accuracy and low THD (total harmonic distortion). The effectiveness of the proposed methods are verified through experimental results.
A Bilateral Zero-Voltage Switching Bidirectional DC-DC Converter with Low Switching Noise
Authors: Huang Chien-Chun , Tsai Tsung-Lin , Hsieh Yao-Ching , Chiu Huang-Jen
Published: October 2018

This paper proposes a novel bilateral zero-voltage switching (ZVS) bidirectional converter with synchronous rectification. By controlling the direction and timing of excessive current injection, the main power switches can achieve bilateral ZVS under various loads and output voltages. Compared with the common soft-switching power converter with only zero-voltage turn-on, the proposed bilateral ZVS bidirectional converter can achieve both zero-voltage switching on and off in every switching cycle. This feature can alleviate the output switching noise due to the controlled rising and falling slope of the switch voltage. Furthermore, the voltage slopes almost remain unchanged over a wide range of output voltages and load levels. The most important feature of bilateral ZVS is to reduce the output switching noise. Experimental results based on a 1 kW prototype are presented to demonstrate the performance of the proposed converter. From experimental results on the proposed scheme, the switching noise reduction is about 75%.
Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain
Authors: Pîrjan Alexandru , Căruțașu George , Petroșanu Dana-Mihaela
Published: October 2018

Accurate forecasting of the produced and consumed electricity from wind farms is an essential aspect for wind power plant operators. In this context, our research addresses small-scale wind farms situated on hilly terrain, with the main purpose of overcoming the low accuracy limitations arising from the wind deflection, caused by the quite complex hilly terrain. A specific aspect of our devised forecasting method consists of incorporating advantages of recurrent long short-term memory (LSTM) neural networks, benefiting from their long-term dependencies, learning capabilities, and the advantages of feed-forward function fitting neural networks (FITNETs) that have the ability to map between a dataset of numeric inputs and a set of numeric targets. Another specific element of our approach consists of improving forecasting accuracy by means of refining the accuracy of the weather data input parameters within the same weather forecast resolution area. The developed method has power plant operators as main beneficiaries, but it can also be successfully applied in order to assess the energy potential of hilly areas with deflected wind, being useful for potential investors who want to build this type of wind farms. The method can be compiled and incorporated in the development of a wide range of customized applications targeting electricity forecasting for small wind farms situated on hilly terrain with deflected wind. The experimental results, the implementation of the developed method in a real production environment, its validation, and the comparison between our proposed method and other ones from the literature, confirm that the developed forecasting method represents an accurate, useful, and viable tool that addresses a gap in the current state of knowledge regarding the necessity for an accurate forecasting method that is able to predict with a high degree of accuracy both the produced and consumed electricity for small wind power plants situated on quite complex hilly terrain with deflected wind.
Probabilistic Power Flow Methodology for Large-Scale Power Systems Incorporating Renewable Energy Sources
Authors: Huynh Van , Ngo Van , Le Dinh , Nguyen Nhi
Published: October 2018

In this paper, we propose a new scheme for probabilistic power flow in networks with renewable power generation by making use of a data clustering technique. The proposed clustering technique is based on the combination of Principal Component Analysis and Differential Evolution clustering algorithm to deal with input random variables in probabilistic power flow. Extensive testing on the modified IEEE-118 bus test system shows good performance of the proposed approach in terms of significant reduction of computation time compared to the traditional Monte Carlo simulation, while maintaining an appropriate level of accuracy.
Data-Driven Optimization Control for Dynamic Reconfiguration of Distribution Network
Authors: Yang Dechang , Liao Wenlong , Wang Yusen , Zeng Keqing , Chen Qiuyue , Li Dingqian
Published: October 2018

To improve the reliability and reduce power loss of distribution network, the dynamic reconfiguration is widely used. It is employed to find an optimal topology for each time interval while satisfying all the physical constraints. Dynamic reconfiguration is a non-deterministic polynomial problem, which is difficult to find the optimal control strategy in a short time. The conventional methods solved complex model of dynamic reconfiguration in different ways, but only local optimal solutions can be found. In this paper, a data-driven optimization control for dynamic reconfiguration of distribution network is proposed. Through two stages that include rough matching and fine matching, the historical cases which are similar to current case are chosen as candidate cases. The optimal control strategy suitable for the current case is selected according to dynamic time warping (DTW) distances which evaluate the similarity between the candidate cases and the current case. The advantage of the proposed approach is that it does not need to solve complex model of dynamic reconfiguration, and only uses historical data to obtain the optimal control strategy for the current case. The cases study shows that the optimization results and the computation time of the proposed approach are superior to conventional methods.
Cost-Energy Optimum Pathway for the UK Food Manufacturing Industry to Meet the UK National Emission Targets
Authors: Gowreesunker Baboo Lesh , Tassou Savvas , Atuonwu James
Published: October 2018

This paper investigates and outlines a cost-energy optimised pathway for the UK food manufacturing industry to attain the national Greenhouse Gas (GHG) emission reduction target of 80%, relative to 1990 levels, by 2050. The paper employs the linear programming platform TIMES, and it models the current and future technology mix of the UK food manufacturing industry. The model considers parameters such as capital costs, operating costs, efficiency and the lifetime of technologies to determine the cheapest pathway to achieve the GHG emission constraints. The model also enables future parametric analyses and can predict the influence of different economic, trade and dietary preferences and the impact of technological investments and policies on emissions. The study showed that for the food manufacturing industry to meet the emission reduction targets by 2050 the use of natural gas as the dominant source of energy in the industry at present, will have to be replaced by decarbonised grid electricity and biogas. This will require investments in Anaerobic Digestion (AD), Combined Heat and Power (CHP) plants driven by biogas and heat pumps powered by decarbonised electricity.
Asymptotic Output Tracked Artificial Immunity Controller for Eco-Maximum Power Point Tracking of Wind Turbine Driven by Doubly Fed Induction Generator
Authors: Hassan Marwa , Balbaa Alsnosy , Issa Hanady H. , El-Amary Noha H.
Published: October 2018

This paper aims to design a controller for a Doubly Fed Induction Generator (DFIG) targeting the Eco-Maximum Power Point Tracking (EMPPT) for environmental aspects. The proposed controller consists of two clusters, which are the novel Artificial Immunity sensorless Eco-Maximum Power Point Tracking (AI EMPPT) and the asymptotic non-linear control techniques. The main target of the AI EMPPT is to reduce the carbon dioxide emission by generating the maximum possible power from the renewable electrical energy resource, which is wind electrical power generation to replace the fossil-fuel conventional generation. To build the AI EMPPT, an Artificial Immunity System Estimator (AISE) based on artificial immunity technique and a Model Reference Adaptive System (MRAS) are used to estimate the DFIG rotor speed. Then, the AI EMPPT is applied to provide the reference electromagnetic torque signal. Subsequently, the reference electromagnetic torque interacts with the estimated generator speed, determined by the wind mechanical power, to supply the wind electrical power. The second cluster is the asymptotic non-linear control technique which proposes the reference signal tracking of the rotor direct and quadratic current, respectively. Thus, assigning specific zeros through feedback ensures the reproduction of an output that converges asymptotically to a required reference rotor current. For online operation, the Artificial Immunity Technique (AIT) is utilized to deal with the generated control reference signal. A proposal hardware implementation on Field Programmed Gate Array (FPGA) is also presented. The introduced approach was applied to a wind turbine generator driving a 3.7 kW load. MATLAB program was used to simulate and test the performance of the proposed control methods. The results to show the effectiveness of the proposed technique. The reduction in CO2 emission was calculated.
Continuous and Discontinuous Streamer Leader Propagation Phenomena under Slow Front Impulse Voltages in a 10-meter Rod-Plane Air Gap
Authors: Shah Wahab Ali , He Hengxin , He Junjia , Yang Yongchao
Published: October 2018

Investigation of positive streamer-leader propagation under slow front impulse voltages can play an important role in the quantitative research of positive upward lightning. In this work, we performed a large-scale investigation into leader development in a 10-m rod–plane gap under a long front positive impulse. To describe the leader propagation under slow front impulse voltages, we recorded the leader propagation with a high-speed charge coupled device (CCD) camera. It is important to figure out this phenomenon to deepen our understanding of leader discharge. The observation results showed that the leader mechanism is a very complex physical phenomenon; it could be categorized into two types of leader process, namely, continuous and the discontinuous leader streamer-leader propagation. Furthermore, we studied the continuous leader development parameters, including two-dimensional (2-D) leader length, injected charge, and final jump stage, as well as leader velocity for rod–plane configuration. We observed that the discontinuous leader makes an important contribution to the appearance of channel re-illuminations of the positive leader. To clarify the above doubts under long front cases, we carried out extensive experiments in this study. The comparative study shows better results in terms of standard switch impulse and long front positive impulse. Finally, the results are presented with a view toward improving our understanding of propagation mechanisms related to restrike phenomena, which are rarely reported.
Distributed Control Strategy for DC Microgrids of Photovoltaic Energy Storage Systems in Off-Grid Operation
Authors: Chen Mingxuan , Ma Suliang , Wan Haiyong , Wu Jianwen , Jiang Yuan
Published: October 2018

DC microgrid systems that integrate energy distribution, energy storage, and load units can be viewed as examples of reliable and efficient power systems. However, the isolated operation of DC microgrids, in the case of a power-grid failure, is a key factor limiting their development. In this paper, we analyze the six typical operation modes of an off-grid DC microgrid based on a photovoltaic energy storage system (PV-ESS), as well as the operational characteristics of the different units that comprise the microgrid, from the perspective of power balance. We also analyze the key distributed control techniques for mode transformation, based on the demands of the different modes of operation. Possible reasons for the failure of PV systems under the control of a voltage stabilizer are also explored, according to the characteristics of the PV output. Based on this information, we propose a novel control scheme for the seamless transition of the PV generation units between the maximum PV power tracking and steady voltage control processes, to avoid power and voltage oscillations. Adaptive drooping and stabilization control of the state of charge of the energy storage units are also considered, for the protection of the ESS and for reducing the possibilities of overcharging and/or over-discharging. Finally, various operation conditions are simulated using MATLAB/Simulink, to validate the performance of the proposed control strategy.
FPGA-Based Controller for a Permanent-Magnet Synchronous Motor Drive Based on a Four-Level Active-Clamped DC-AC Converter
Authors: Nicolas-Apruzzese Joan , Lupon Emili , Busquets-Monge Sergio , Conesa Alfonso , Bordonau Josep , García-Rojas Gabriel
Published: October 2018

This paper proposes a closed-loop control implementation fully-embedded into an FPGA for a permanent-magnet synchronous motor (PMSM) drive based on a four-level active-clamped converter. The proposed FPGA controller comprises a field-oriented control to drive the PMSM, a DC-link voltage balancing closed-loop control (VBC), and a virtual-vector-based modulator for a four-level active-clamped converter. The VBC and the modulator operate in consonance to preserve the DC-link capacitor voltages balanced. The FPGA design methodology is carefully described and the main aspects to achieve an optimal FPGA implementation using low resources are discussed. Experimental results under different operating conditions are presented to demonstrate the good performance and the feasibility of the proposed controller for motor-drive applications.
A New Bridgeless High Step-up Voltage Gain PFC Converter with Reduced Conduction Losses and Low Voltage Stress
Authors: Lin Xiang , Wang Faqiang , Iu Herbert H. C.
Published: October 2018

Bridgeless power factor correction (PFC) converters have a reduced number of semiconductors in the current flowing path, contributing to low conduction losses. In this paper, a new bridgeless high step-up voltage gain PFC converter is proposed, analyzed and validated for high voltage applications. Compared to its conventional counterpart, the input rectifier bridge in the proposed bridgeless PFC converter is completely eliminated. As a result, its conduction losses are reduced. Also, the current flowing through the power switches in the proposed bridgeless PFC converter is only half of the current flowing through the rectifier diodes in its conventional counterpart, therefore, the conduction losses can be further improved. Moreover, in the proposed bridgeless PFC converter, not only the voltage stress of power switches is lower than the output voltage, but the voltage stress of the output diodes is lower than the conventional counterpart. In addition, this proposed bridgeless PFC converter features a simple circuit structure and high PFC performance. Finally, the proposed bridgeless PFC converter is analyzed and designed in the discontinuous conduction mode (DCM). The simulation results are presented to verify the effectiveness of the proposed bridgeless PFC converter.
Mapping Burn Severity of Forest Fires in Small Sample Size Scenarios
Authors: Zheng Zhong , Zeng Yongnian , Li Songnian , Huang Wei
Published: October 2018

Mapping burn severity of forest fires can contribute significantly to understanding, quantifying and monitoring of forest fire severity and its impacts on ecosystems. In recent years, several remote sensing-based methods for mapping burn severity have been reported in the literature, of which the implementations are mainly dependent on several field plots. Therefore, it is a challenge to develop alternative method of mapping burn severity using limited number of field plots. In this study, we proposed a support vector regression based method using multi-temporal satellite data to map the burn severity, evaluated its performance by calculating correlations between the predicted and the observed Composite Burn Index, and compared the performance with that of the regression analysis method (based on dNBR). The results show that the performance of support vector regression based mapping method is more accurate (RMSE = 0.46–0.57) than that of regression analysis method (RMSE = 0.53–0.68). Even with fewer training sets, it can map the detailed distribution of burn severity of forest fires and can achieve relatively better generalization, compared to regression analysis burn severity mapping methods. It could be concluded that the proposed support vector regression based mapping method is an alternative to the regression analysis method in small sample size scenarios. This method with excellent generalization performance should be recommended for future studies on burn severity of forest fires.
Applying Image Processing to the Textile Grading of Fleece Based on Pilling Assessment
Authors: Huang Mei-Ling , Fu Chien-Chang
Published: October 2018

Textile pilling causes an undesirable appearance on the surface of garments, which is a long-standing problem. In this study, textile grading of fleece based on pilling assessment was performed using image processing and machine learning methods. Two image processing methods were used. The first method involved using the discrete Fourier transform combined with Gaussian filtering, and the second method involved using the Daubechies wavelet. Furthermore, binarization was used to segment the textile pilling from the background. Morphological and topological image processing methods were applied to extract the essential characteristics of textile image information to establish a database for the textile. Finally, machine learning methods, namely the artificial neural network (ANN) and the support vector machine (SVM), were used to objectively solve the textile grading problem. When the Fourier-Gaussian method was used, the classification accuracies of the ANN and SVM were 96.6% and 95.3%, and the overall accuracies of the Daubechies wavelet were 96.3% and 90.9%, respectively.
Geomechanical Response of Fractured Reservoirs
Authors: Zareidarmiyan Ahmad , Salarirad Hossein , Vilarrasa Victor , De Simone Silvia , Olivella Sebastia
Published: October 2018

Geologic carbon storage will most likely be feasible only if carbon dioxide (CO2) is utilized for improved oil recovery (IOR). The majority of carbonate reservoirs that bear hydrocarbons are fractured. Thus, the geomechanical response of the reservoir and caprock to IOR operations is controlled by pre-existing fractures. However, given the complexity of including fractures in numerical models, they are usually neglected and incorporated into an equivalent porous media. In this paper, we perform fully coupled thermo-hydro-mechanical numerical simulations of fluid injection and production into a naturally fractured carbonate reservoir. Simulation results show that fluid pressure propagates through the fractures much faster than the reservoir matrix as a result of their permeability contrast. Nevertheless, pressure diffusion propagates through the matrix blocks within days, reaching equilibrium with the fluid pressure in the fractures. In contrast, the cooling front remains within the fractures because it advances much faster by advection through the fractures than by conduction towards the matrix blocks. Moreover, the total stresses change proportionally to pressure changes and inversely proportional to temperature changes, with the maximum change occurring in the longitudinal direction of the fracture and the minimum in the direction normal to it. We find that shear failure is more likely to occur in the fractures and reservoir matrix that undergo cooling than in the region that is only affected by pressure changes. We also find that stability changes in the caprock are small and its integrity is maintained. We conclude that explicitly including fractures into numerical models permits identifying fracture instability that may be otherwise neglected.