Average Power Values Research Articles

Solar-Based Smartphone Charging Stations with Voltage, Current, and Power Monitoring

Renewable energy sources continue to be developed as alternative energy sources to reduce the use of fossil energy sources. One of them is a solar power plant that uses a light source from sunlight. For the public, electrical energy is useful to support work and communication activities such as using smartphones, but not many chargers are found in public places to charge smartphone batteries. It has designed and implemented a smartphone charging station to charge smartphone batteries using solar power. The smartphone battery charging on this smartphone charging station can display voltage, current, and power when charging the battery;this tool is equipped with an INA219 sensor, ATmega328 microcontroller, and solar power to make this tool look smart. The purpose of making this tool is to find out the working principle, voltage, current, and power and compare the charging time of the smartphone battery between the smartphone charging station and the manufacturer's charger. The working principle is that when this tool charges the smartphone battery, the INA219 sensor receives output value data in the form of current and voltage. Furthermore, the sensor sends a signal to the ATmega328 to be converted and displayed as data on the LCD so that users can see the output value. The test results of the tool, when charging a smartphone battery using Micro USB, showed the average values of voltage, current, and power, respectively, of 11.7 volts, 0.48 amperes, and 5.98 watts. USB Type C shows the average voltage, current, and power values of 11.33 volts, 0.71 amperes, and 8.37 watts, respectively. The comparison of the duration of battery charging time on the manufacturer's Micro USB with the smartphone charging station has a difference of 27 minutes; the comparison of the duration of the smartphone battery charging time using the manufacturer's USB Type C with the smartphone charging station has a difference of 22 minutes.

Perbandingan Nilai Daya Luaran Panel Surya Kapasitas 50Wp Terhadap Posisi Reflektor Cermin Datar

Solar panel systems are widely used to meet energy needs at this time. Solar panels have optimal power for 4 hours, namely 10:00 to 14:00. but this is still said to be not optimal if the angle of the static solar panels does not follow the movement of the sun. This can also be caused by weather factors that are covered in clouds. Previous research conducted by Tri Wahyu Ardianto used mirror reflectors but was not equipped with sensors and data storage. Based on this, to optimize solar panel energy, a flat mirror reflector design for solar panels has been made, equipped with four flat mirror reflectors on each side to form a large square. The solar panel is placed in the middle between the four flat mirrors. In this study, the measurement of electric power was carried out by carrying 3 variable angles for reflector positions, namely 0°, 60°, and 70°. The decision of the three reflector positions is made to find out the ratio of the maximum output power values. This system is also equipped with current, voltage, and light intensity sensors. The results show that the maximum output power of solar panels is shown at the reflector position of 60° with an average power output of 19.70 Watt followed by a reflector position of 70° with an average output power of 19.18 Watt and finally the reflector position is 0° with an average output power value of 14.87 Watt.

Analysis and study of power consumption of objects in the industrial zone of Adra-Syria

Industrial cities in Syria help the state in developing factories and strengthening the main industries, as they provide the right infrastructure for these industries and provide them with advantages that may not be available abroad. The article explores one of these industrial areas in the city of Adra-Syria.PURPUSE. Conduct a study of the design power of loads of residential electrical consumers and compare with the experimental power consumption in the city of Adra.METHODS. Methods for processing statistical data and methods for determining the parameters of loads and graphs of loads of electrical consumers at the facilities of Adra are used.RESULTS. The data on the parameters of the load curves of the electric consumers of the objects of Adra were determined: The average value of the total load power (Saver), the maximum value of the total load power (Smax), the effective value (Seffective), the standard deviation of the total load power (σSng), the value of the load variation (γSng). The data of the load schedule fill factor, the correlation coefficient (Rkj) was calculated and analyzed, and the values of the statistical indicators of the total schedules (Stot) of the electrical consumers of the Kabun-1 substation in Adra, the statistical data of the total apparent power of the electrical consumers of the transformers of the 33/11 kV substation were obtained. "Kabun-1", the values of correlation coefficients of loads of industrial electrical consumers, the values of the total power of the load of electrical consumers of the new sector of Adra.CONCLUSION. As a result of the studies carried out, the possibility of effective implementation of measures to regulate the maximum total power of the loads of the main electrical consumers in the city of Adra has been shown.

CHARACTERIZATION OF THE AVERAGE POWER, FREQUENCY AND AMPLITUDE OF ACOUSTIC SIGNAL PEAKS OVER THE LUNGS IN CHILDREN WITH COMMUNITY-ACQUIRED PNEUMONIA USING THE NEW DEVICE "TREMBITA-CORONA"

Background. Community-acquired pneumonia (CАР) is an urgent problem today and a significant cause of morbidity and mortality in children. Diagnosis of pneumonia is complex and requires a comprehensive study. At present, respiratory acoustics is a promising method of diagnosis.
 Aim: to investigate the features of average power values, frequencies and amplitudes of acoustic signal peaks over the lungs in children with community-acquired pneumonia using a new acoustic diagnostic device "Trembita-Corona".
 Materials and methods. 120 children aged from 1 month to 18 years who were treated in pediatric departments were studied. The children were divided into two groups: Group I - 60 patients with CAP, Group II - 60 healthy children. The children from the 1st group underwent complex examinations, which required CAP. All children were also examined using the "Trembita-Corona" acoustic monitoring device for the diagnosis of breathing sounds and localization of lung damage zones.
 The study was conducted in accordance with the international principles of conducting clinical studies GCP, GLP, the protocol was approved at the meeting of the Commission on Bioethical Expertise at the National Medical University named after O.O. Bogomolets (protocol No. 138 of November 10, 2020). Informed consent of parents/guardians was obtained for conducting the study, which was approved at the same meeting of the Commission on Bioethical Expertise at Bogomolets National Medical University (protocol № 138 of November 10, 2020).
 Mathematical processing was carried out on specialized software developed in the Python language in the Google Codelabs environment. Further statistical processing of the obtained results was carried out in specialized programs Medstart, EZR (R-Statistics) and "Matlab".
 Results. In children, the clinical picture of CAP consisted of pulmonary (respiratory) complaints, symptoms of intoxication, RF, and local physical changes. All patients were examined for respiratory lung sounds in the frequency range from 0.1 Hz to 30 kHz using the "Trembita-Corona" device and the corresponding original software. The most promising are the studies of breathing noises in the ranges of 0, 1, 2, 3, 4, 5, 6 octaves. Also, significant differences in average signal power between children with CAP and healthy children in 0,1,2,3,4,5,6 octaves were found. Using the "Trembita-Corona" acoustic monitoring device, reliable differences in peak frequency between children with CAP and healthy children in 0 and 5 octaves and reliable differences in peak amplitude between children with CAP and healthy children in 0, 1, 2, 3 were found. ,4,5,6 octaves. Therefore, the use of the "Trembita-Corona" acoustic monitoring device and the use of the developed specialized software in the Python language in the Google Codelabs environment together make it possible to hear specific acoustic signals over the entire surface of the lungs in children with CAP.
 Conclusion. The "Trembita-Corona" acoustic monitoring device is a new and promising acoustic method for determining the location of the pathological process in the lungs. Significant differences were found: in the average signal strength between children with CAP and healthy children in 0,1,2,3,4,5,6 octaves; on the frequency of peaks between children with CAP and healthy children in 0 and 5 octaves; by the amplitude of peaks between children with CAP and healthy children in 0,1,2,3,4,5,6 octaves.

Study of the processing conditions for stainless steel additive manufacturing using femtosecond laser

The use of ultrashort-pulsed (USP) lasers in Additive Manufacturing (AM) enables the processing of different materials and has the potential to reduce the sizes and shapes manufactured with this technology. This work confirms that USP lasers are a viable alternative for Laser Powder Bed Fusion (LPBF) when higher precision is required to manufacture certain critical parts. Promising results were obtained using tailored and own-produced stainless steel powder particles, manufacturing consistent square layers with a series of optimized processing parameters. The critical role of processing parameters is confirmed when using this type of lasers, as a slight deviation of any of them results in an absence of melting. For the first time, melting has been achieved at low pulse repetition (500 kHz) and using low average laser power values (0.5–1 W), by generating heat accumulation at reduced scanning speeds. This opens up the possibility of further reducing the minimum size of parts when using USP lasers for AM.

Estimating Average Power of Welding Process With Emitted Noises Based on Adaptive Neuro Fuzzy Inference System

In this study, the average power consumption of an electrode welding machine during the welding process was estimated using the features of the sound emitted during welding. First, the instantaneous values of electrode current and voltage and the sound emitted during the welding process were recorded simultaneously. The minimum, maximum, average, root mean square (RMS), and energy values of the sound data were found and feature extraction was performed, and the instantaneous power and average power values were calculated using the instantaneous current and voltage values. Three Adaptive Neuro-Fuzzy Inference Systems (ANFIS) using the sound features as inputs and average power values as outputs were created, and their results were compared. The average power values consumed during the welding process have been successfully estimated at a rate of 87-95%.

Rooted Tree Optimization for Wind Turbine Optimum Control Based on Energy Storage System

The integration of wind turbines (WTs) in variable speed drive systems belongs to the main factors causing low stability in electrical networks. Therefore, in order to avoid this issue, WTs hybridization with a storage system is a mandatory. This paper investigates WT system operating at variable speed. The system contains of a permanent magnet synchronous generator (PMSG) supported by a battery storage system (BSS). To enhance the quality of active and reactive power injected into the network, direct power control (DPC) scheme utilizing space-vector modulation (SVM) technique based on proportional-integral (PI) control is proposed. Meanwhile, to improve the rendition of this method (DPC-SVM-PI), the rooted tree optimization technique (RTO) algorithm-based controller parameter identification is used to achieve PI optimal gains. To compare the performance of RTO-based controllers, they were implemented and tested along with some other popular controllers under different working conditions. The obtained results have shown the supremacy of the suggested PIRTO algorithm compared to competing controllers regarding total harmonic distortion (THD), overshoot percentage, settling time, rise time, average active power value, overall efficiency, and active power steady-state error.

The Effect of Different Massage Protocols on Running-Based Anaerobic Sprint Test Performance During and After Ramadan

Purporse: Different methodologies are applied to increase the performance of athletes in sports involving high-intensity activities. Some of these methodologies can be listed as massage applications used before sports activities and listening to music. Listening to music with pre-competition massage treatments can have an impact on performance, but the effect of massage and listening to music during and after Ramadan may arouse curiosity. The aim of this study is to determine the effect of massage and massage + music protocols applied during and after Ramadan on Running-Based Anaerobic Sprint Test Performance (RAST). Methods: Twenty athletes (age, 21.30± 1.49 years; height, 175.10± 2.51 cm; 74.50± 2.79 kg; BMI 24.30±.75) who exercise regularly participated in the study. The study was carried out using four different protocols during the month of Ramadan (massage/massage+music) and after (massage/massage+music) after a 5-minute warm-up at 72-hour intervals. RAST was applied after massage applications and maximum, minimum, average power and FI values were evaluated after RAST. Results: It showed that ARMM protocol values were more effective than RM, ARM and RMM protocols. Conclusions: Massage+listening to music before performing RAST can be recommended to coaches and athletes.

Influence of Fiber Laser (1064 nm) on Shear Bond Strength of Titanium Abutment and Resin Cement.

Introduction: The present study was performed to evaluate the influence of a 1064 nm fiber laser on shear bond strength (SBS) at the interface of titanium and resin cement. Methods: Forty titanium discs of 6 mm × 3 mm (diameter and thickness respectively) were categorized into four groups (n=10): control group without any surface treatment and three groups treated with a fiber laser with 81 ns pulse duration, 30 kHz frequency, 10000 mm/s scanning speed, 0.05 mm spot size, and different average power values (3, 5 and 7 W) depending on the tested group. Titanium disc characterization was performed by the scanning electron microscope (SEM) and surface roughness tester. Phase analysis was achieved using an X-ray diffractometer (XRD). Following these tests, resin cement application to titanium discs was performed. SBS values were determined by the universal testing machine. After debonding, the surface of titanium discs was examined by the stereomicroscope for the determination of failure modes. Data analysis was performed using analysis of variance (ANOVA) and Tukey HSD tests (α=0.05). Results: A higher surface roughness value was observed in the 7 W group followed by the 5 W and 3 W groups, and the lowest surface roughness was in the control group. Additionally, the lowest SBS value was obtained from the control group and the highest SBS value was obtained from the 7 W group followed by the 5 W and 3 W groups. Conclusion: SBS between titanium abutment and resin cement can be significantly enhanced by using a fiber laser as a surface treatment considering tested laser parameters; additionally, a positive association between surface roughness and SBS was noted in the experimental groups.

Comparable Bulk Radar Attenuation Characteristics Across Both Martian Polar Layered Deposits

AbstractThe dust‐laden and predominantly water ice south polar layered deposits (SPLD) exhibit unique radar behavior at Shallow Radar (SHARAD) frequencies that is not seen in the NPLD. A vertically extensive scattering phenomenon leads to diffuse echoes that obscure radar stratigraphy within the SPLD, referred to as “fog.” Here, we use 2‐D and 3‐D SHARAD data to measure the areal extent in which the SPLD basal interface can be identified. We also measure reflected power of SPLD basal reflectors and compare those values to ice thickness, regional geomorphology, and the presence of radar echo scattering, to constrain any influence these factors may have on the propagation of radio waves and detectability of the base. Approximately 33% of the area in which the basal interface is observed in Mars Advanced Radar for Subsurface and Ionosphere Sounding data is also detected in SHARAD data. As expected, basal power shows a negative correlation with ice thickness, but the effects of radar fog are less clear. Areas of “column” radar fog patterns exhibit higher average basal power values than regions without radar fog, and areas with only near‐surface radar fog have the lowest average basal power values. Our study results suggest that these diffuse echoes may not be associated with radio wave attenuation. Average loss tangent for the SPLD (0.0019 ± 0.0012) indicates no distinguishable difference from previously reported NPLD values in terms of bulk loss properties. We speculate that the SPLD's infrequent reflections compared to the NPLD may be due to the former's dust being less densely packed together to facilitate radar detection.

Evaluation of ERIC-PCR method for determining genetic diversity among Escherichia coli isolated from human and retail imported frozen shrimp and beef

There is a global concern and increasing reports regarding foodborne disease infections associated with consuming contaminated vegetables, seafood, meat, and poultry products. Among foodborne bacterial pathogens globally, Salmonella, Escherichia coli, and Shigella were the most frequently implicated in causing food poisoning infections in children and adults. In Saudi Arabia, the consumption rates of imported fresh fruits, vegetables, seafood, and meat products are considered high. Therefore, the development of simple PCR based DNA fingerprinting methods is essential to track the source and route of microbial contamination among imported frozen meat and seafood products. A total of 38 E. coli strains were subtyped using ERIC1R, ERIC2, and a pair combination (ERIC1R + ERIC2) to generate genomic fingerprinting. The three Enterobacterial Repetitive Intergenic Consensus-Polymerase Chain Reaction (ERIC-PCR)-based primers were generated in 26, 24, and 16 different genotypes while using ERIC1R, ERIC2, and ERIC1R + ERIC2, respectively. The Discrimination Index values obtained by ERIC1R, ERIC2, and ERIC1R + ERIC2 were 0.976, 0.965, and 0.903, respectively. ERIC1R and ERIC2 primers had the best discriminatory ability and typeability value and proved suitable for investigating genetic analysis among the population of E. coli strains. At the same time, the ERIC1R + ERIC2 primer pair has average discriminatory power and typeability value for differentiating E. coli strains. These results suggest that subtyping using ERIC1R and ERIC2 primer is a more reliable and rapid typing strategy for E. coli strains.

Performance Comparison of Bayesian Deep Learning Model and Traditional Bayesian Neural Network in Short-Term PV Interval Prediction

The intermittence and fluctuation of renewable energy bring significant uncertainty to the power system, which enormously increases the operational risks of the power system. The development of efficient interval prediction models can provide data support for decision making and help improve the economy and reliability of energy interconnection operation. The performance of Bayesian deep learning models and Bayesian shallow neural networks in short-term interval prediction of photovoltaic power is compared in this study. Specifically, an LSTM Approximate Bayesian Neural Network model (ABNN-I) is built on the basis of the deep learning and Monte Carlo Dropout method. Meanwhile, a Feedforward Bayesian Neural Network (ABNN-II) model is introduced by Feedforward Neural Network and the Markov Chain Monte Carlo method. To better compare and verify the interval prediction capability of the ABNN models, a novel clustering method with three-dimensional features which include the number of peaks and valleys, the average power value, and the non-stationary measurement coefficient is proposed for generating sunny and non-sunny clustering sets, respectively. Results show that the ABNN-I model has an excellent performance in the field of photovoltaic short-term interval forecasting. At a 95% confidence level, the interval coverage from ABNN-I to ABNN-II can be increased by up to 3.1% and the average width of the interval can be reduced by 56%. Therefore, with the help of the high computational capacity of deep learning and the inherent ability to quantify uncertainty of the interval forecast from Bayesian methods, this research provides high-quality interval prediction results for photovoltaic power prediction and solves the problem of difficult modeling for over-fitting that exists in the training process, especially on the non-sunny clustering sets.

Effects of Warm-Up Training on Psychomotor Vigilance and Repeated-Sprint Ability of Professional Soccer Referees: A Pilot Study.

For this research, we analyzed the immediate effects of warm-up condition (WC) or without warm-up condition (WWC) on amateur referees' physical and cognitive functioning. Eight professional soccer referees from the Balearic Committee of Football Referees were the participants of this study. Body composition characteristics were measured and the scores on three tests were recorded: the Yo-Yo intermittent recovery, repeated-sprint ability, and psychomotor vigilance task. Regarding results, the psychomotor vigilance task was performed better after warm-up training (p = .002, η2 = .79) with faster reaction times following WC (M = 318.2, SD = 27.1ms) than WWC (M = 334.9, SD = 26.1). Similarly, the referees' performance was better on the repeated-sprint ability test after WC (p = .002, d = 0.53) than WWC, with minimum and average power values higher after WC (M = 626.77, SD = 112.57) than WWC (M = 562.35, SD = 79.63). We conclude that re-warm-up training may mitigate the vigilance performance changes caused by effects of rest on soccer referees.

PROBABILITY DENSITY FUNCTION OF AVERAGE POWER OF REAL SPEECH SIGNALS

It is known that many real signals, such as speech signals, are non-stationary processes that express theirfeature through changes in parameters over time. By observing speech in shorter time intervals, the property ofstationarity can be notice. This characteristic enables the application of techniques for adaptation to local signalcharacteristics in signal processing algorithms. Many of these algorithms are described by standards, and in additionto intensive development in this area in last decades, there is a constant need for the development of new solutionsand standards. One of the most used parameters of the speech signal for adaptation is the mean (average) signalpower. The change of speech in time results in a wide dynamic range of average power. In addition to the predicteddynamics of average power, in the design of systems with adaptive techniques it is important to include otherparameters, among which is the Probability Density Function (PDF) of the average power. The goal of the researchpresented in this paper is the analysis of the probability distribution of the average power of speech signals, based onwhich the adaptability in the development of algorithms in digital processing would be improved, which wouldensure higher quality and less requirements in data transmission and storage. In addition to the theoreticalconsideration, the research was conducted on real speech signals of different speakers. In modern technical systems,where Internet technologies are prominent, processing, transmission and memorization of speech is executed frameby frame. Therefore, an analysis of the probability density of the average power for different frame lengths wascarried out in the experiment. In the experimental part, for each of the speech signals of the speech corpus, theProbability Density Function that best describes the average power values per frame was determined. Experimentalresearch results indicate that the function that best describes the average power is different for different speakers. Inaddition, when observing one speaker, the Probability Density Function is different for different frame lengths. Itcan be concluded that when it comes to adaptive techniques in the digital processing of the speech signal, it isimportant to consider the characteristics of the average power, among which is the Probability Density Function ofthe average power

Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering

Nonintrusive load identification for industrial users can accurately acquire the operation of each load. However, it is a major challenge in the demand-side response due to the hardship of collection data for modelling, and high precision measuring equipment is required. Aiming at this situation, a nonintrusive load identification method is proposed, combining the least square QR (LSQR) with the sequential leader clustering algorithm. Firstly, regarding accurate depiction of industrial loads, some appropriate load feature indices of steady-state and transient processes are extracted, respectively. For steady-state processes, the active power, the reactive power, and the root mean square (RMS) current value are selected as the feature indices. In the case of transient processes, ten feature indices of three stages are employed: before, during, and after transient events, consisting of the duration of transient events, the RMS current value before and after transient events, the average value of active power before and after transient events, the average value of reactive power before and after transient events, the maximum RMS current value of transient events, etc. On this base, the LSQR algorithm is proposed to decompose unknown composite power to access the operation of various loads at steady-state. The sequential leader clustering algorithm is propounded to classify transient events of typical industrial loads and further identify which kind of loads had switched. Finally, to validate the effectiveness of the presented model, data of industrial loads from a concrete plant are collected, including blender, cement screw, sewage dump, and inclined belt conveyor, and simulation analysis is fulfilled. The results indicate that the model proposed can effectively achieve the nonintrusive industrial load identification, and least unified residue (LUR) is about 10−16, which is much better than the factorial hidden Markov model (FHMM) and the artificial neural network (ANN) model.

APLIKASI GAS HHO PADA SEPEDA MOTOR INJEKSI DENGAN MODIFIKASI ECU AFTERMARKET (TIMING PENGAPIAN)

HHO gas is a gas produced from electrolysis, which is the decomposition of an electrolyte using an electric current which produces Hydrogen Gas and Oxygen Gas / Hydrogen Hydrogen Oxygen. The purpose of this study was to determine the application of HHO gas by increasing ignition timing by 3º, 6º, and 9º using an aftermarket ECU on engine performance and injection motor emissions. The method used in this study is an experiment, testing using a dinotest measuring instrument and a gas analyzer. The results obtained are the highest average power value in all tests obtained on the variable use of HHO Gas without variations in ignition timing using an aftermarket ECU of 5.90 HP at 3500 Rpm engine speed, an increase of 0.13% from the conditions of HHO Gas usage and forward time. . ignition of 3º and 6º, an increase of 0.27% from HHO gas usage conditions and a forward ignition time of 9º. Then the highest average torque value from all tests was obtained on the variable using HHO Gas and variations in ignition timing using an aftermarket ECU with an advance of 3º of 14.39 Nm at 3000 rpm engine speed, an increase of 0.29% from conditions using HHO Gas without using Variation ignition timing using aftermarket ECU.

Variability of wave power production of the M4 machine at two energetic open ocean locations: Off Albany, Western Australia and at EMEC, Orkney, UK

Since intermittent and highly variable power supply is undesirable, quantifying power yield fluctuations of wave energy converters (WECs) aids with assessment of potential deployment sites. This paper presents analysis of 3-hourly, monthly, seasonal, and inter-annual variability of power output of the M4 WEC. We compare expected performance from deployment at two wave energy hotspots: off Albany on the south-western coast of Australia and off the European Marine Energy Centre (EMEC) at Orkney, UK. We use multi-decadal wave hindcast data to predict the power that would have been generated by M4 WEC machines. The M4 machine, as a floating articulated device which extracts energy from flexing motion about a hinge, is sized according to a characteristic wavelength of the local wave climate. Using probability distributions, production duration curves, and coefficients of variation we demonstrate larger variability of the 3-hourly power yield at Orkney compared to Albany. At longer timescales, seasonal trends are highlighted through average monthly power values. From a continuity of supply perspective, we investigate occurrences of low production at three different threshold levels and calculate duration and likelihood of such events. Orkney is found to suffer from more persistent lows, causing a more intermittent power output. We also consider the effect of machine size on its power performance. Smaller machines are found to more effectively smooth out the stochastic nature of the underlying wave resource.

Energy consumption reduction of a center-pivot with the use of a variable-frequency drive

Pressure demand of a center-pivot can be controlled by variable frequency drives during irrigation system operations, leading to a rational use of electrical energy. However, numerous studies encountered problems due to the lack of field data to perform the simulations. The objective of this study was to simulate the reduction of the average value of the active electrical power required to drive the pumping unit of a center-pivot irrigation system by controlling the rotational speed through a frequency inverter. The simulation was conducted considering a complete rotation of the lateral line of a central pivot, installed in an area of 70-ha, in the municipality of Formiga-MG. The simulation demonstrated the possibility of reducing the active electrical power required for the pivot pump by 18%, from an average of 131 to 107 kW. For 1300 pumping hours per year, the investment's payback time would be two and four years for the highest (R$ 0.48 kWh-1) and lowest energy cost (R$ 0.32 kWh-1), respectively. Results suggest that irrigated areas by center-pivot with variations of topographic altitude require technical-economic evaluations for using frequency inverters.

A new approach based on moving least square method for calculating the Weibull coefficients

AbstractIn this study, the graphical method, which is widely used to find the coefficients of the Weibull distribution function (WDF), has been improved by using the moving least squares approximation (MLSA) replace least squares method (LSM). The accuracy of the results obtained by the moving least squares method is shown by two different error analysis tests. In order to implement this method, Osmaniye region, which is rich in wind potential maintenance, has been selected. The root mean square error (RMSE) and mean percentage error (MPE) are used to compare the efficiency of proposed method. In this study, the results of the graphical method developed by the MLSA were compared monthly and yearly with the results obtained according to the least squares method. It was observed that the results of the proposed method in the two error analysis tests were much better. Also, the mean wind speed and average wind power values of the selected region were calculated with the coefficients found by MLSA. These calculated values were compared with actual values.

Effect of coupled platform pitch-surge motions on the aerodynamic characters of a horizontal floating offshore wind turbine

Floating offshore wind turbines (FOWTs) work in a complex natural environment. Under the coupling effect of wind and waves, the platform experiences a six-degree-of-freedom motion, which affects the performance of the wind turbine. In this paper, a computational fluid dynamic method is used to investigate the effect of platform pitch and surge motion coupled at the same frequency as well as at different frequencies with an initial phase difference on the aerodynamic characteristics of FOWTs. The results demonstrate that the platform pitch and surge motion coupling makes the wind turbine operation more unstable. The power and thrust fluctuations are the largest when the two motions are coupled in the same phase, which leads to a dramatic change in the aerodynamic performance of the wind turbine during operation, and can easily cause hazards such as blade fatigue damage. When the initial phase difference does not affect the coupling motion frequency, the effect on the instantaneous power is more significant than that on the instantaneous thrust. However, the effect on the average power and thrust values is weaker. When the initial phase difference leads to reverse coupling of the platform pitch and surge motions, the fluctuation of power and thrust is reduced, and the wind turbine operation is more stable and safer.