High Sampling Frequency Research Articles

Review of Discrete Fourier Transform During Dynamic Phasor Estimation and the Design of Synchrophasor Units

In a large and complex interconnected power system, the measurement of synchronized bus voltage and line current plays a vital role in the monitoring and precise control of various sophisticated electrical equipment for secure and reliable operation. Phasor measurement units (PMUs) are incorporated into a wide area of the power system to extract the different signals of synchronized phasors. In this paper, the capacity of the PMU phasor estimation algorithm is explored based on discrete Fourier transform (DFT) under different sampling frequency rates during various dynamic scenarios in accordance with the IEEE C37.118.1a-2014 standard. Furthermore, the performance of the DFT algorithm varies according to the phase angle and dynamic parameters such as frequency, frequency ramp rates, modulation frequency, harmonic levels, step change, decaying dc, and noise levels. The simulation results reveal that accuracy of the phasor estimation algorithm based on DFT can be achieved at high sampling frequency rates. Furthermore, the results of DFT-based phasor estimation are compared with Shank’s estimation method (SEM) and the least-squares estimation method (LEM). The presented method is best suited to PMU algorithms development based on DFT for better visualization of the smart electric grid.

An Integrated GNSS/MEMS Accelerometer System for Dynamic Structural Response Monitoring under Thunder Loading

Dynamic response monitoring is of great significance for large engineering structural anomaly diagnosis and early warning. Although the global navigation satellite system (GNSS) has been widely used to measure the dynamic structural response, it has the limitation of a relatively low sampling rate. The micro-electro-mechanical system (MEMS) accelerometer has a high sampling frequency, but it belongs to the approaches of acceleration measurements as the absolute position is unavailable. Hence, in this paper, an integrated vibration monitoring system that includes a GNSS receiver and 3-axis MEMS accelerometers was developed to obtain the dynamic responses under the thunder loading. First, a new denoising algorithm for thunderstorm-induced vibration data was proposed based on variational mode decomposition (VMD) and the characteristics of white noise, and the low-frequency disturbance was separated from the GNSS displacement time series. Then, a power spectral density (PSD) analysis using data collected by the integrated system was carried out to extract low/high natural frequencies. Finally, field monitoring data collected at Huanghuacheng, Hefangkou, and Qilianguan in Beijing’s Huairou District were used to validate the effectiveness of the integrated system and processing scheme. According to the results, the proposed integrated GNSS/MEMS accelerometer system can not only be used to detect thunder loading events, but also completely extract the natural frequency based on PSD analysis. The high natural frequencies detected from the accelerometer data of the four Great Wall monitoring stations excited by the thunderstorms are 42.12 Hz, 12.94 Hz, 12.58 Hz, and 5.95 Hz, respectively, while the low natural frequencies detected from the GNSS are 0.02 Hz, 0.019 Hz, 0.016 Hz, and 0.014 Hz, respectively. Moreover, thunderstorms can cause the Great Wall to vibrate with a maximum displacement of 14.3 cm.

Combined Effect of Climate and Anthropopressure on River Water Quality

This study was a continuation of our investigation of the spatio-temporal variability of the Bzura River's water chemistry. Our research is of particular importance in the context of the recent ecological disaster on the Oder River and concerns the international problem of surface water contamination. The study area was a 120 km section of the Bzura River. We tested more measurement points and with a higher sampling frequency than those used in the national monitoring of river water quality. During two hydrological years, 360 water samples were collected. The selected parameters: electrical conductivity, temperature, dissolved oxygen, dissolved organic carbon, nitrates, phosphates, bicarbonates, chlorides, sodium, potassium, calcium, and magnesium were determined. Numerous results exceeded the Polish threshold limits. Spatio-temporal variability and water quality were assessed using principal component analysis (PCA), cluster analysis (CA), and water quality index (WQI) approaches. Many point sources of pollution related to urbanization, agriculture, and industry were detected. Moreover, due to the changing climatic conditions, a significant difference between temporal variability in both years was observed. Our results indicated that it is necessary to increase the number of measurement stations for surface water monitoring; it will allow for a faster detection of the threat.

Determination of subpicogram levels of airborne polycyclic aromatic hydrocarbons for personal exposure monitoring assessment

A method based on the use of GC coupled to Q-exactive Orbitrap mass spectrometry (GC-Orbitrap-MS) has been developed for the analysis of polycyclic aromatic hydrocarbons (PAHs) at sub-picogram levels. Outdoor ambient air particulate matter (PM2.5) and standard reference materials (SRM2260a) were analyzed in full scan mode showing low instrumental uncertainties (1–22%) and high linearity over a wide concentration range (0.5 pg and 500 pg/μL). Good reproducibility was obtained compared to the use of conventional single quadrupole GC–MS of PM samples. The quantification limit of the GC-Orbitrap-MS method for full scan analysis of PAHs in outdoor ambient air PM samples was 0.5 pg/μL. This low limit allowed the analysis of PAHs in samples collected with low volumes (< 0.5 m3), such as punch samples from whole filters or filter strips from personal exposure monitoring equipment. PAHs were successfully analyzed in filter strips from real-time Aethalometer AE51 equivalent black carbon (eBC) analyzers used in urban and rural sites, and in personal exposure monitors of firefighters during prescribed burns. The correlations between PAHs and eBC in these analyses were very strong (r2 ≥ 0.93). However, the equations obtained reflected the dominance of different emission sources, such as traffic in urban areas, wood burning for domestic heating, or wildfires. The method reported here affords the analyses of PAHs in high precision studies of atmospheric PM samples, e.g., high frequency sampling of low volumes, affording personal exposure monitoring assessments.

Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis

Multiple Sclerosis (MS) is a progressive demyelinating disease of the central nervous system characterised by a wide range of motor and non-motor symptoms. The level of disability of people with MS (pwMS) is based on a wide range of clinical measures, though their frequency of evaluation and inaccuracies coming from objective and self-reported evaluations limits these assessments. Alternatively, remote health monitoring through devices can offer a cost-efficient solution to gather more reliable, objective measures continuously. Measuring smartphone keyboard interactions is a promising tool since typing and, thus, keystroke dynamics are likely influenced by symptoms that pwMS can experience. Therefore, this paper aims to investigate whether keyboard interactions gathered on a person’s smartphone can provide insight into the clinical status of pwMS leveraging machine learning techniques. In total, 24 Healthy Controls (HC) and 102 pwMS were followed for one year. Next to continuous data generated via smartphone interactions, clinical outcome measures were collected and used as targets to train four independent multivariate binary classification pipelines in discerning pwMS versus HC and estimating the level of disease severity, manual dexterity and cognitive capabilities. The final models yielded an AUC-ROC in the hold-out set above 0.7, with the highest performance obtained in estimating the level of fine motor skills (AUC-ROC=0.753). These findings show that keyboard interactions combined with machine learning techniques can be used as an unobtrusive monitoring tool to estimate various levels of clinical disability in pwMS from daily activities and with a high frequency of sampling without increasing patient burden.

The AugerPrime upgrade of the Pierre Auger Observatory

Operating since 2004, the Pierre Auger Observatory has yielded several important results. The suppression of the flux around 5×1019 eV is now confirmed without any doubt, a large-scale dipole anisotropy has been found for energies above 8×1018 eV, as well as an indication for some intermediate-scale anisotropy at the highest energies. Furthermore, strong limits have been placed on ultra-high-energy photons and neutrinos. In order to elucidate the origin of the flux suppression at the highest energies and search for composition-enhanced anisotropies, the Auger Collaboration is currently upgrading the Observatory. In the framework of the upgrade, called AugerPrime, the array of 1660 water-Cherenkov detectors is equipped with plastic scintillators, allowing us to enhance the composition sensitivity. The station electronics is also upgraded, including better timing with up-to-date GPS receivers, higher sampling frequency, and increased dynamic range. Currently, more than 40% of the surface detectors have been upgraded, and the commissioning studies are well advanced. In this paper, the design of the AugerPrime surface detectors will be presented, and the performance obtained from the analysis of the first data will be discussed.

Evaluation of a GNSS for wave measurement and directional wave spectrum analysis

Wave buoys are important devices to monitor and analyze wave data for ocean and coastal engineering. A GNSS wave buoy is briefly introduced in the paper, which has high resolution to measure the buoy motion by vertical, north-south and west-east displacements and independent velocities in above three directions. Based on the displacements and velocities, statistical results, frequency spectra and directional spectra are analyzed, and results based on the displacements are compared with that from Waverider with a distance less than 6m deployed in the special sea water with the GNSS buoy. Wave profiles comparison show that GNSS buoy presented slightly large significant wave height and mean wave height due to its high sampling frequency, and resulted in smaller mean wave period than that from Waverider. Statisticaly, between the analyzing result of the GNSS and wave rider, the maximum error of wave height is about 5.5%; and the maximum difference of wave period is about 0.5s, when sampling frequency is similar. The energy spectra were basically consistent from these two devices. The peaks of directional spectra were similar but the spreading angle was smaller from GNSS. Results mean the GNSS device presents almost similar wave information to that from Waverider.

Importance of hydraulic travel time for the evaluation of organic compounds removal in bank filtration

The growing global demand for drinking water is driving both the diversification of water supply sources and their sustainability. River bank filtration (RBF) is an excellent option since it strongly reduces the extent of treatment steps compared to direct usage of surface water. Organic micropollutants (e.g. pharmaceuticals) are widely recognized as a hazard in drinking water production from surface water. Due to their potentially high mobility, stability, bioaccumulation and persistency, these substances can pass through RBF-systems. Scientific studies on compound removal and attenuation efficiency of RBF rely on the knowledge of travel time to compare concentrations in the river to the ones in the bank filtrate since water quality in rivers can change rapidly. However, bank filtrate samples represent a mixture of water with different travel times as the flow paths vary. This has not yet been considered in studies of bank filtration removal efficiency for organic micro pollutants.Here we present a method that considers the residence-time distribution of the bank filtrate sample obtained by groundwater modelling to evaluate the removal efficiency of RBF for organic micropollutants. The method was tested in a comprehensive study with 50 samples taken over a one-year-period at a river bank filtration site in Vienna (Austria). Our findings revealed that better coverage of varying river water quality (higher sampling frequency during the period of infiltration) resulted not only in a higher number of compounds considered as removed but also significantly reduced the number of compounds considered to have formed during the RBF process. The application of the presented method indicated that RBF is very effective in removing organic micropollutants. Considering different travel times will provide better models and a better understanding of the potential of RBF for pollutant removal and thus supports its safe application as a solution to the growing demand for drinking water.

A Novel Knowledge Sharing Method for Rolling Bearing Fault Detection Against Impact of Different Signal Sampling Frequencies

Accurate and efficient bearing fault diagnosis method plays an important role in modern industrial system. In practical applications, a large number of data are employed for fault analysis. However, the performance of the fault diagnosis model varies with the sampling frequency of the training data. Theoretically, higher sampling frequency (HSF) data contains much richer fault information, the model trained with HSF data has better performance compared with that of lower sampling frequency (LSF). However, most of the intelligent methods only focused on the bearing fault diagnosis at a specific data sampling frequency. They did not take into account the impact of different sampling frequencies on fault diagnosis performance. To this end, a novel knowledge sharing multi-task (KSMT) model is proposed in this paper. The KSMT model consists of two task branches, which can automatically share useful features from the task with HSF input (HSF-task) to the task with LSF input (LSF-task) by a feature selection and fusion module (FSF-module). The effectiveness of the proposed model is verified on a rolling bearing dataset. Experimental results show that the performance of the LSF-task can be improved by the proposed knowledge sharing mechanism. Compared with other state-of-the-art methods, the KSMT model has a better performance and the results can effectively demonstrate that the KMST model can overcome the limitation of less information on fault diagnosis performance.

Data Augmentation Strategies for High-Frequency NILM Datasets

The Nonintrusive Load Monitoring (NILM) process aims to monitor the different appliances connected to a power grid. It accomplishes this by analyzing one or multiple signals measured at the main breaker level of the power grid. In order for it return a response with high fidelity, it needs to be trained and tested using data pools of individual and aggregated appliance signals. Although, some datasets are publicly available, they lack a sufficient quantity of labeled and time stamped aggregated signals, and a sufficient quantity of individual signals per appliance. Although, recent works have worked on this topic and introduced synthetic datasets. However, they revolve around low sampling frequencies, which does not allow us to extract high frequency signatures from the signal waveform and in particular from the transient phases. In this paper, we aim to solve the two problems mentioned above. We solve the first one using an algorithm that takes any number of individual appliance signals and construct a fully labeled and time stamped aggregated signal. Next, we solve the second problem by introducing an approach that allows us to construct a large number of synthetic single appliance signals using a relatively smaller number of real single appliance signals. In addition, we perform all of this work on signals with high sampling frequencies, which will allow the extraction of the high frequency features for later use.

AugerPrime status and prospects

The Pierre Auger Collaboration started a few years ago the AugerPrime project to increase the Surface Detector (SD) performance of the Pierre Auger Observatory. It aims to address the still open questions on the origin and composition of the highest energy cosmic rays by allowing a better identification of the nature of the primaries. The key element of this major upgrade is the capability of measuring the different components of extensive air showers, which will be significantly improved by the addition of a Surface Scintillator Detector (SSD) on each water-Cherenkov detector (WCD) constituting the SD. Moreover, the dynamic range of measurement is extended through an additional small photomultiplier tube inside the WCD. New electronics is processing the signals from the WCD and the SSD with higher sampling frequency and enhanced resolution. The scintillator module deployment started in 2019, and the new electronics in December 2020. The collected data allow for the evaluation of the first performances of the upgraded array and to adapt the whole data acquisition chain necessary for an efficient and sustainable operation of the Observatory. After the recall of the motivations for the upgrade, the main characteristics of the new detection setup are reviewed, as well as the status of its deployment and commissioning. The expected prospects are also discussed.

Evaluation of Image Quality of Overweight and Obese Patients in CT Using High Data Rate Detectors.

To evaluate the impact of high data rate and sampling frequency detector technology compared to standard scan equipment on the image quality in abdominal computed tomography (CT) of overweight and obese patients. A total of 173 patients were retrospectively included in this study. Objective image quality in abdominal CT was evaluated using comparative analysis with new detector technology prior to market launch and standard CT equipment. Contrast noise ratio (CNR), image noise, volumetric computed tomography dose index (CTDIVol), and figures of merit (Q and Q1) were assessed for all patients. Image quality was superior in the new detector technology for all parameters evaluated. The dose dependent parameters Q and Q1 showed a significant difference (p<0.001). A significant increase in objective image quality could be demonstrated using a new generation detector setup with increased frequency transfer in abdominal CT of overweight patients.

A Universal Multicarrier Multisampling Method for Digitally Controlled Inverters

For reducing the control delays introduced by the digital control of dc/ac converters, multisampling technology applying high sampling frequency of exact multiples of the switching frequency becomes attractive. In this way, higher control bandwidth and stability margin are achievable. However, multiupdate (>2) of the modulation signal within one switching cycle is not usually allowed on the digital signal processors. Besides, multiupdate within one switching cycle leads to an abnormal phenomenon referred to as vertical crossing when the modulation signal intersects the carrier signal vertically. The vertical crossing further causes the deadband effect of the pulsewidth-modulation signal and distortion of the output waveform. The abovementioned phenomenon greatly restricts the application of multisampling technology. In order to break the aforementioned barriers, a novel multicarrier-based multisampling method by dividing the triangular carrier signal into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> equal sawtooth segments is proposed in this article. Moreover, the problem of vertical crossings and the resulting deadband effect in the multiupdate mode is solved. Simulation and experimental results on a single-phase dc–ac inverter are provided to validate the effectiveness of the proposed approach.

Investigation on Characteristic Parameters Identification and Evolution of Supercapacitor Energy Storage System From Sparse and Fragmented Monitoring Data

Supercapacitors with advantages of high-power density, fast charging speed and long cycle life, have very promising application prospects in many fields such as transportation and energy storage. Usually, the onboard operation profiles will be recorded and sent to remote monitoring terminal for further study. As the working condition of onboard supercapacitor is complex and fluctuating, it requires high sampling frequency, accuracy, and continuous recording. However, the remote monitoring data usually has the problems of low sampling frequency and fragmented recording, making it difficult to extract the characteristic parameters by traditional parameter identification methods. To solve the problem, this paper makes an extensive investigation on the long-term remote monitoring data of a supercapacitor tram and proposes a set of data processing method that can extract the characteristic parameters of supercapacitors from the sparse and fragmented data. Firstly, a group of proper data segments considering thermal stability of supercapacitor system was selected as effective data from the fragmented daily monitoring data. Secondly, the parameters which can be used to study the aging indicators are extracted by interior point method. With this method, the simulated voltage error is within 0.3% with a sampling frequency of 0.1 Hz. Through analyzing 3.5 years of remote monitoring data, it is found that the characteristic parameters exhibit the feature of seasonal fluctuations, which is highly related to temperature. To further extract the aging trend, a linear fitting model which eliminates the effect of seasonal fluctuations is proposed which can be used for analyzing the evolution characteristic of the studied supercapacitor system.

A Three-Dimensional Vibration Data Compression Method for Rolling Bearing Condition Monitoring

In condition monitoring for rolling bearings, it has achieved good diagnostic performance and clear mechanistic interpretation based on vibration data. The high sampling frequency of data collection preserves fault characteristics but brings the problem of big data. An effective way to reduce this problem is to apply data compression. However, in order not to affect the diagnostic performance of data, it is difficult to improve the compression ratio further. Inspired by the binarization method, the compression dimension of the bit cost of a single sample point is first introduced into the fault-mechanism-based method in this article. On this basis, a three-dimensional data compression method is proposed, and it is subsequently validated with two real-bearing datasets. Two performance metrics, including a newly defined one, are utilized to compare the proposed method with the five existing methods. The comparison results show that the proposed method significantly improves the compression ratio of data but maintains good diagnostic performance.

Traveling Wave-Based Primary Protection and Fault Localization Scheme for MTDC Grid Considering IEC 61869-9 Measurement Standard

Traveling wave (TW) based protection and fault localization requires high sampling frequency measurements (500 kHz - 1 MHz) and are non-compliant to the IEC 61869-9 measurement standards, which allows maximum sampling frequency of 96 kHz for DC application. This work proposes a novel low sampling frequency (50 kHz) TW based primary protection and real-time DC fault localization technique for multi terminal high voltage DC (MTDC) grid which is compliant with the aforementioned measurement standard. In the proposed MTDC protection scheme, a TW arrival time (TWAT) is estimated under low sampling frequency measurement using cascaded combination of modified mathematical morphology gradient (MMG), sobel operator and linear regression tool. Subsequently, the TWAT information at both DC link terminals is communicated via dedicated communication channel compliant to IEC-61850-9-2 standard. And if the TWAT difference between DC terminals is less than the TW propagation threshold for the protected DC line, then a DC trip signal is issued to the associated hybrid DC circuit breaker (HDCCB). For fast power system restoration, the same TWAT information is also used for real-time fault localization in the faulted DC line, which is also compliant with IEC 61869-9 measurement standards. Finally, the practical feasibility of the proposed algorithm is demonstrated through control hardware-in-loop testing with real-time digital simulation setup and TI TMS320F28379d digital signal processor card.

Investigation of Wind Data Resolution for Small Wind Turbine Performance Study

&#x0D; Small wind turbine sites, in general, use a 0.5Hz sampling interval and a 10-minute averaging interval for a feasibility study or turbine testing. Studies have established that the calculated performance variation of small wind turbines when averaging at large time intervals. The performance variation is larger for sites with high wind variability. However, these studies are often based on low sampling frequency and high averaging intervals. &#x0D; &#x0D; &#x0D; In the present study, wind speed data has been measured at a high sampling frequency of 20Hz with an ultrasonic sensor. A dynamic model of a 50W Rutland wind turbine is used to analyse the simulated performance using wind speed data at a range of sampling intervals and some averaging intervals. The wind turbine and the anemometer are installed in a residential area of high wind variability. &#x0D; &#x0D; &#x0D; The energy is calculated and compared directly using the wind turbine model and using the IEC recommended method of bins. The direct method results show that the rise in instantaneous sampling intervals up to 20 seconds has an insignificant effect on the energy output. Whereas, for 2-seconds sampled wind data averaged over 10-minutes, energy overestimates of 19% is observed. However, where only 10-minute interval averaged wind data are available, there is a significant underestimate in energy by 45%. The energy calculated using the method of bins overestimates the energy by 19% for high resolution wind data and underestimates by 22% for 10-minute average data.&#x0D;

Heart Rate Variability in Elite International ITF Junior Davis Cup Tennis Players.

The aim of the present study was to investigate the evolution of the HRV during an international team tennis tournament in elite junior tennis players. A total of 11 tennis players, with a mean age of 15.36 (0.67) from six nationalities, participated in this cross-sectional study. Participants were measured one day before the event, before the first match, and before the second match of the ITF Junior Davis Cup event. Each of the registers lasted 5 min and the time domain, frequency domain and non-linear variables were extracted. The tournament took place in Valencia (Spain). Results showed that there was not a significant decrease in the heart rate variability (HRV) induced by precompetitive stress. However, significant differences (p-value < 0.05) were found between baseline and second match pre-competition HRV values in low frequency (LFNu) (p-value = 0.008) and high frequency (HFNu) power (p-value = 0.008), LF/HF ratio (p-value = 0.008) and Sample Entropy (SampEn) (p-value = 0.033) variables. Furthermore, cognitive anxiety (measured by CSAI-2r) (p-value = 0.017) and anxiety (measured by STAI-S) (p-value = 0.042) were higher before the first match than before the second match. Coaches and physical trainers could use HRV to control and manage the precompetitive anxiety of junior tennis players. The experience in international tournaments and the familiarization with the environment in the second match could explain these results.

On the availability bias in narwhal abundance estimates

Abundance estimation of narwhals is usually done with either visual or photographic aerial surveys. The basic estimation for both methods is detection of whales at the surface, and to obtain fully corrected abundance estimates, the at-surface detections need to be corrected for the proportion of whales that, at any given time, is available to be detected at the surface. The surfacing time or ‘availability correction factor’ is obtained from whales instrumented with dive recorders, that either relay concatenated information on the proportion of time spent at different depth intervals to satellites, or from recovered instruments that collect complete dive profiles, measured at high frequency. Concatenated data binned in depth histograms from Satellite-Linked-Time-Depth-Recorders (SLTDR) falls in two categories, where those that correct the zero depth values with information from the saltwater switch provide larger and apparently more accurate surfacing times, than those collected from instruments that does not correct the zero depth readings. The erroneous detection of near-surface pressure values is likely due to slow response of pressure transducers made from temperature sensitive materials. The high frequency sampling from AcousondeTM recorders documents erroneous surface detections, and adjustments of the dive profiles are needed to obtain realistic near-surface values. Any reconstruction of dive profiles and near-surface values apparently involves some level of corrections and it is recommended, for development of availability correction factors for aerial surveys, that data from zero-adjusted SLTDRs or TDR instruments are used. The mean estimate of surface time from 7 SLTDRs was 29% (CV=0.05). One SLTDR, with steel pressure transducer and zero-adjustments, that was retrieved from the whale, provided a particular long-record (83 days) of reliable high-resolution data. The surface time for this sample was 31%, when calculated as the sum of all depth readings at or above 2 m. The mean of 144 hourly depth readings during 06:00-18:00, and including dives above 3m, was 27.36% (CV=0.8) for 12 days overlapping with the usual timing of aerial surveys. Accurate estimation of smaller depth bins (e.g. 0-1 m) should, even with high resolution instruments, be used with caution when estimating availability bias.

Sliding Mode Input Current Control of the Synchronous DC-DC Buck Converter for Electro-Mechanical Actuator Emulation in More Electric Aircrafts

The main challenges of the input current control in synchronous DC-DC buck converters are the nonlinear model of the system, changes of the operating point in a wide range, and the need to use an input LC filter for current smoothing, which may result in the instability of the closed-loop system. In this paper, a step-by-step approach is developed for the design and improvement of a PI-feedforward closed-loop controller. It is shown that a linear PI controller cannot stabilize the closed-loop system properly during wide changes in model parameters, e.g., an equivalent series resistance of the input filter. To cope with the stability issues, a fixed-frequency sliding mode controller (SMC) has been developed in this paper for the implementation of an electro-mechanical actuator (EMA) emulator. Moreover, a systematic approach is proposed for controller tuning and the selection of the SMC’s gains. To achieve high power efficiency, high-frequency GaN switches are used for the practical implementation of the DC-DC converter. Despite large changes in the load current, the designed nonlinear controller can track the input current reference satisfactorily. Steady-state and dynamic responses of the proposed SMC are compared with conventional linear controllers. Considering the Lyapunov stability theorem, it is proved that the designed SMC can stabilize the closed-loop system in the entire utilizable domain. The proposed nonlinear SMC controller enjoys a very simple control law. Hence, despite having very high switching and sampling frequencies, it can be easily implemented. The experimental response of the designed synchronous DC-DC buck converter is evaluated experimentally by implementing the control strategy in a TMS320F28335PGFA DSP from Texas Instrument. Moreover, the comprehensive comparison of the proposed SMC controller and a PI-feedforward controller proved the superior performance of the developed closed-loop system, in terms of the transient time response, robustness, and stability of the EMA emulator.