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The rapidly growing dispersion of distributed generation systems into the utility grid needs appropriate control techniques to stay interconnected even under abnormal and distorted grid conditions to ensure the overall grid stability. To avoid the loss of renewable energy sources (RES) based power generation, the disintegration of RES with respect to synchronization issues must be prohibited for efficient operation. RES control highly relies on the synchronization technique as it is faster and accurate enough to detect the utility grid variables in terms of amplitude, phase, and frequency. Mostly, the phase-locked loop (PLL) synchronization schemes are utilized for control of RES and monitoring of grid voltage. The dynamics of the grid side converter (GSC) is directly influenced by the performance and design criteria of the PLLs. This paper proposes an overall review of the performances of three phase PLL based grid synchronization methods under diverse weak grid conditions. [ABSTRACT FROM AUTHOR]

A Field Programmable Gate Array (FPGA) is a semiconductor device based around a Configurable Logic Blocks (CLB) matrix connected by programmable interconnects. FPGA has numerous applications in biomedical signal processing due to its flexible programming and low power consumption. An Electrocardiogram (ECG) is a medical test used to determine heart rates, cardiac activity, and classify arrhythmias. All pass filters have Low Pass Filter (LPF), High Pass Filter (HPF), Band Stop Filter (BSF), and Band Pass Filter (BPF) to produce the exact amplitude in peak detection. However, the separate coefficient for individual filters increases the area in traditional all-pass filters. To overcome this issue, a Reconfigurable All-Pass Filter (RAPF) which considers a single coefficient for all filters and minimizes memory usage, consuming less area and power is employed. The LPF coefficient is utilized to perform the LPF operation, and then the two's complement of this LPF coefficient is computed to produce the HPF coefficient. Next, the two's complement is fed into the Hilbert Transform (HT) to produce the BSF coefficient and determine BSF operations. A RAPF is designed to perform these operations effectively. The RAPF performance is determined using Register, Look Up Table (LUT), Global Buffer (BUFG), Digital Signal Processing (DSP), Power, and Flip Flop (FF). RAPF consumes lesser power of 34 mW for Artix 7 XC7A200TFBG676-2 FPGA device, as opposed to the existing technique, Single Node Reservoir Computing (SNRC) using cumulative mean filter. [ABSTRACT FROM AUTHOR]

Vannamei shrimp is a high-value, economically important brackish water aquaculture commodity that is easy to cultivate. Optimal growth of Vannamei shrimp can be achieved by monitoring water quality parameters such as pH and turbidity. The pH levels can be measured using a pH sensor, with a pH meter as a reference. Turbidity levels are measured with a turbidity sensor in NTU units, with a turbidity stick serving as a reference. Testing of these sensors was conducted from morning to noon over three days in the brackish water ponds of IBAP Banjar Kemuning, Sidoarjo, recording 100 data samples. The performance of both sensors fluctuated due to disturbances around the pond, prompting the use of the Moving Average (MA) filter method to improve accuracy. Applying MA with varying window sizes (wz) resulted in a performance increase of 0.24% in the morning and 0.1% at noon. Additionally, turbidity sensor testing indicated that the pond conditions were consistent with the turbidity measurements obtained using the turbidity stick.

With the rapid development of the Internet of Things (IoT), the positioning accuracy requirement of the IoT is increasing, especially for those applications without Global Navigation Satellite System (GNSS) signals. Ultra-Wideband (UWB) is treated as a high-accuracy positioning method that can be utilized in GNSS-blocked environments. However, UWB's performance is still limited when it is applied in practical applications due to errors such as Non-Line-of-Sight (NLOS) errors, multipath errors, and systematic errors in UWB range values. To constrain the impacts of these mentioned errors on UWB positioning accuracy, this work proposes a novel UWB positioning model by introducing a UWB ranging offset calibration algorithm and a moving average filter into a robust extended Kalman filter. In such a UWB positioning model, the ranging offset calibration algorithm is employed to limit the infuence of UWB systematic errors, and the prior residual-based IGG-III weighting model is used to restrain the impacts of NLOS and multipath errors. The moving average filter is to further decrease the impact of the measuring noise on UWB positioning parameter estimation. To investigate the effectiveness of this proposed method, three sets of UWB experiments are arranged in three classic user environments. The experimental results show that (1) after applying the UWB ranging offset calibration algorithm, UWB positioning accuracies in classic environments, namely indoor condition, outdoor condition, and transition area are increased by 50.3%, 20.2%, and 46.9%, respectively; (2) the moving average filter can effectively improve the smoothness of UWB positioning results in terms of standard deviation; (3) the prior residual-based robust theory brings about 49.4% and 25.2% positioning improvements to horizontal and vertical components under poor measurement quality conditions, but such improvements are rather slight when there are good-quality measurements; and (4) after applying the ranging offset calibration algorithm and moving average filter to the robust EKF together, the elevation accuracy of UWB positioning is increased by 67.1%, 22.2%, and 50.5%, respectively, in the three classic user environments. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) signals are vital for diagnosing cardiac abnormalities, but they are corrupted by powerline interference noise, which can obscure critical features and compromise diagnostic accuracy. This paper investigates various filtration techniques aimed at effectively removing powerline interference noise from ECG signals. Different approaches, including average filters and moving average filters, are explored and compared to determine their efficacy in noise reduction while preserving important signal characteristics. Experimental evaluations are conducted using synthetic ECG signals contaminated with simulated powerline interference noise and real-world ECG recordings corrupted by actual powerline interference. The performance of each filtration technique is assessed based on metrics such as Root Mean Square Error (RMSE) and Percentage Root Mean Square Difference (PRD). The trade-offs between noise reduction effectiveness and preservation of ECG signal fidelity are analyzed to identify the most suitable approach for clinical applications. This paper provides valuable insights into the selection and implementation of filtration techniques for mitigating powerline interference noise in ECG signals. The findings contribute to the development of robust signal processing methods for improving the reliability and accuracy of ECG-based diagnostic systems. [ABSTRACT FROM AUTHOR]

The challenge of achieving a balanced capacitor voltage is one of the factors affecting the efficient operation of modular multilevel converters (MMC). This paper investigates this challenge through a proposed method that utilizes a high carrier frequency phase-shifted pulse width modulation (PS-PWM) scheme. This method aims to achieve natural balancing without the need for any additional control mechanisms. Moreover, the number of output voltage levels is affected by the phase shift between the carriers of the upper and lower arms. When there is no phase shift, N+1 discrete levels are achieved, but when there is a phase shift, the number of discrete levels increases to 2N+1. The proportional-resonant (PR) controller and moving average filter (MAF) are employed to decrease the capacitor voltage ripples by suppressing the fourth and second harmonics in the circulating currents. The MMC inverter structure is modeled and simulated in the PLECS and MATLAB/Simulink environments to evaluate the impact of this control scheme on the converter's performance. [ABSTRACT FROM AUTHOR]

This paper has proposed an improved single phase software phase lock loop (SPLL) with the ability of grid voltage DC and harmonics components suppression. An orthogonal signal generating method by frequency adaptive time delaying has been proposed to eliminate the grid voltage DC component, and frequency adaptive moving average filter (MAF) has been adopted to suppress the grid voltage harmonics. The loop transfer function has been deduced and the control parameter design of the MAF and SPLL controller has been detailed to balance between stability, overshoot and response time. Simulation experiments have shown that the proposed SPLL can correctly lock the fundamental frequency and phase even if there are large amounts of DC and high order harmonics components in the grid voltage.

Introduction. The problem of primary information low quality of crane operation parameters recorders is analyzed. The accepted direction to improve the quality of information about the mass of the cargo is to improve the algorithm of weight measurement. Shortcomings of algorithms of serially produced parameter recorders are described. The design of the algorithm based on the moving average filter is proposed.Materials and Methods. The principle of operation of the proposed weight measurement algorithm is described. The algorithm consists of preliminary (determination of the dynamic process section suitable for weight measurement, calculation of the smoothing window length) and main stages (application of filter, calculation of mass and measurement error). Two methods for determining the length of the smoothing window are given. The parameters of the overhead crane, load cell and motor speed sensor used in the experimental part are given.Results. Graphs of lifting mechanism force and motor rotation speed at the moment of weight measurement are given. The work of the steady-state motion control function is shown. The results of algorithm operation at fixed measurement accuracy of 3% with determination of algorithm operation time and fixed algorithm operation time of 10 seconds with determination of measurement accuracy are given.Discussion. The time of weight measurement with the accuracy required for parameter recorders was 2...5 seconds. Increase in speed of weight measurement is achieved due to adjustable parameters of the algorithm at each switching on of the lifting mechanism. For cranes with duration of lifting time continuously more than 10 seconds the algorithm allows to calculate the mass of the load with an accuracy of 1%. This ensures the accuracy of the crane intensity indicators with an accuracy of 3%.Conclusion. Evaluation of accuracy and time parameters showed the applicability of the developed algorithm of weight measurement in the task of determining the mass of the load to recorders of crane operation parameters. The advantage of the developed algorithm is the guaranteed accuracy of weight measurement. The algorithm of weight measurement allows to adapt the parameter recorder to the technological process of crane operation and effectively use the areas of the dynamic process suitable for measurement.

This paper proposes a global positioning system (GPS) based relative navigation algorithm for CubeSats that perform autonomous rendezvous and docking missions. To realize precise relative navigation using only GPS, an algorithm is developed to improve the differential GPS (DGPS) performance by reducing the integer ambiguity search space of carrier-phase DGPS. To this end, a Hatch filter is used to improve the pseudorange noise performance, and range-domain DGPS-based single-frequency relative navigation is realized. Because GPS measurements are transmitted intermittently using an inter-satellite link, orbit propagation is performed using the Hill–Clohessy–Wiltshire equation. Moreover, to improve the performance of the propagation error accumulated over time, an in-orbit velocity moving average filter is incorporated. Because the rate change of relative motion in the local-vertical-local-horizontal coordinate system on the orbit is small, the noise level for the relative velocity and overall relative navigation system performance are improved. To demonstrate the usefulness of the proposed method, software-in-the-loop simulation and processor-in-the-loop simulation-based real-time implementation is realized on the onboard computer of a reference CubeSat (SNUGLITE-III A, target) and thruster-equipped CubeSat (SNUGLITE-III B, chaser), and their performances are evaluated. [ABSTRACT FROM AUTHOR]

Supervisory control and data acquisition (SCADA) systems are widely utilized in power equipment for condition monitoring. For the collected data, there generally exists a problem—missing data of different types and patterns. This leads to the poor quality and utilization difficulties of the collected data. To address this problem, this paper customizes methodology that combines an asymmetric denoising autoencoder (ADAE) and moving average filter (MAF) to perform accurate missing data imputation. First, convolution and gated recurrent unit (GRU) are applied to the encoder of the ADAE, while the decoder still utilizes the fully connected layers to form an asymmetric network structure. The ADAE extracts the local periodic and temporal features from monitoring data and then decodes the features to realize the imputation of the multi-type missing. On this basis, according to the continuity of power data in the time domain, the MAF is utilized to fuse the prior knowledge of the neighborhood of missing data to secondarily optimize the imputed data. Case studies reveal that the developed method achieves greater accuracy compared to existing models. This paper adopts experiments under different scenarios to justify that the MAF-ADAE method applies to actual power equipment monitoring data imputation. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT), the positioning accuracy requirement of the IoT is increasing, especially for those applications without Global Navigation Satellite System (GNSS) signals. Ultra-Wideband (UWB) is treated as a high-accuracy positioning method that can be utilized in GNSS-blocked environments. However, UWB’s performance is still limited when it is applied in practical applications due to errors such as Non-Line-of-Sight (NLOS) errors, multipath errors, and systematic errors in UWB range values. To constrain the impacts of these mentioned errors on UWB positioning accuracy, this work proposes a novel UWB positioning model by introducing a UWB ranging offset calibration algorithm and a moving average filter into a robust extended Kalman filter. In such a UWB positioning model, the ranging offset calibration algorithm is employed to limit the infuence of UWB systematic errors, and the prior residual-based IGG-III weighting model is used to restrain the impacts of NLOS and multipath errors. The moving average filter is to further decrease the impact of the measuring noise on UWB positioning parameter estimation. To investigate the effectiveness of this proposed method, three sets of UWB experiments are arranged in three classic user environments. The experimental results show that (1) after applying the UWB ranging offset calibration algorithm, UWB positioning accuracies in classic environments, namely indoor condition, outdoor condition, and transition area are increased by 50.3%, 20.2%, and 46.9%, respectively; (2) the moving average filter can effectively improve the smoothness of UWB positioning results in terms of standard deviation; (3) the prior residual-based robust theory brings about 49.4% and 25.2% positioning improvements to horizontal and vertical components under poor measurement quality conditions, but such improvements are rather slight when there are good-quality measurements; and (4) after applying the ranging offset calibration algorithm and moving average filter to the robust EKF together, the elevation accuracy of UWB positioning is increased by 67.1%, 22.2%, and 50.5%, respectively, in the three classic user environments.

This paper presents the development of advanced adaptive controller for dynamic voltage restorer (DVR) to regulate the power quality connected to a distribution system. Various voltage quality issues have been generated as distorted grid conditions are responsible for abnormal operation of the consumer electronics and domestic household appliances. Therefore dynamic voltage restorer (DVR) is employed for elimination of voltage quality issues in the distribution lines. To improve the performance and efficiency of DVR, an improved robust SRF (IR-SRF) controller has been developed in this paper for the mitigation of voltage quality issues under weak grid conditions. In the developed IR-SRF controller, moving average filter (MAF) has been utilized for better extraction of ripples from the sensed voltage. For better grid voltage signal synchronization with the proposed controller under highly dynamic condition, an enhanced Phase Locked Loop (PLL) scheme has been utilized instead of conventional PLL. For the regulation of DC link voltage under dynamic environmental conditions, MAF based fuzzy tuned PI controller has been utilized with the ability to adapt the situation. The developed controller has been simulated and results for various grid voltage conditions have been analyzed. The obtained simulation results are compared with conventional control algorithm to validate the efficacy. The efficiency of the proposed controller has been validated using the experimental results. [ABSTRACT FROM AUTHOR]

Synchronization is a critical aspect of integrating renewable energy sources and inverter-based power plants into the electrical grid. Phase-Locked Loops (PLLs) are widely used for this purpose, providing rapid and accurate phase and frequency estimation. In PLLs, the Moving Average Filter (MAF) is commonly employed to extract the fundamental grid voltage component, particularly in the presence of harmonic distortions. Traditional PLLs with a full-cycle time-window MAF perform well in grids with sinusoidal voltage waveforms and DC offsets. However, this approach sacrifices the speed of dynamic response due to the extended time window. In this paper, we introduce a novel approach to address this trade-off. Our method involves reducing the MAF’s time window to one cycle by incorporating a delay operator, effectively reducing model complexity and runtime by 50%. Through comprehensive simulations and experimental scenarios, we demonstrate the practical advantages of the proposed method. Comparison of the proposed approach is provided with existing algorithms in the literature, which illustrate its effectiveness in terms of mitigating PLL oscillations in the presence of DC offsets and other non-ideal grid conditions while achieving a 50% improvement in the execution speed. Therefore, the contribution of this paper is in the field of grid synchronization by providing a balanced solution that enhances dynamic response without compromising DC-offset rejection. The proposed method can improve the stability and efficiency of grid-connected systems involving renewable energy sources and inverter-based power plants.

The performance of a modular multilevel converter (MMC) is highly related to the three-phase phase-locked loop (PLL). The presence of the DC component, the harmonic component, and the negative sequence component results in the poor dynamic and steady-state performance of the PLL, such as fundamental frequency oscillations in the phase estimated by the PLL. In order to suppress the influence of parameters variation and disturbance of the three-phase grid voltage, an improved PLL with a moving average filter with two degrees of freedom PID (2DoFs-PID) is proposed. Moreover, to improve the phase margin and gain margin, an extra compensator with a pole and a zero is introduced into the 2DoFs-PLL. The improved 2DoFs-PLL can track the frequency and phase with superior accuracy, stability, and rapidity. Furthermore, the methodology for the parameters design of the 2DoFs PID controller is introduced, based on the index of integrated time and absolute error. A simulated model and experimental platform are established to verify the performance of the improved 2DoFs-PLL. The results show that the improved 2DoFs-PLL can meet the needs of both steady-state performance and dynamic performance by employing the 2DoFs-PID controllers.

The empirical envelope (EE) method based on the amplitude-modulation and frequency-modulation (AMFM) decomposition is effective for identifying the modal parameters of time-varying and weakly nonlinear systems. However, the identification accuracy of the EE method is sensitive to noises which often exist in vibration measurements of real structures. In this study, an improved empirical envelope (IEE) method is proposed to achieve robust modal parameter identification from noisy measurements. Specifically, the idea of sliding window threshold denoising is introduced to reduce the error in the instantaneous envelope induced by abnormal extreme points, and a moving average filter is utilized to reduce the error in the instantaneous frequency induced by high-frequency noises. Two numerical examples and an experimental example of a full-bridge aeroelastic model are analyzed to validate the accuracy of the IEE method and highlight the superiority of the IEE method relative to the original EE method. It is concluded that the IEE method is robust to measurement noises (the considered signal-to-noise ratios include 5–90 dB) and that the IEE method is more accurate than the EE method. Hence, the IEE method serves as a promising alternative for modal parameter identification of time-varying and weakly nonlinear systems. [ABSTRACT FROM AUTHOR]

This work proposes a structural enhancement and a new technique to design the loop filter (LF) of a third-order phase-locked loop (PLL) to enhance the PLL dynamic performance under abnormal grid conditions. The proposed PLL combines a moving average filter (MAF) and an arbitrarily delayed signal cancelation (ADSC) for structural enhancement to achieve DC-offset rejection and harmonics elimination. The window length of the MAF is selected to be one-sixth of the fundamental grid period to remove non-triple odd harmonics and speed up the PLL dynamic response. The triple harmonics are eliminated, adopting the line-to-line voltage concept, while the ADSC operator rejects the DC offset. The LF design is based on a modified third-order polynomial tuned using stochastic optimization to minimize the settling time of the frequency deviation, offering better dynamic performance over the symmetrical optimum method (SOM) and achieving synchronization within one grid cycle. The PLL mathematical model, small-signal model, and LF design based on the modified polynomial are discussed. Finally, the proposed PLL performance is verified numerically and experimentally with comparisons with other PLLs to demonstrate the effectiveness of the proposed work. [ABSTRACT FROM AUTHOR]

Supervisory control and data acquisition (SCADA) systems are widely utilized in power equipment for condition monitoring. For the collected data, there generally exists a problem—missing data of different types and patterns. This leads to the poor quality and utilization difficulties of the collected data. To address this problem, this paper customizes methodology that combines an asymmetric denoising autoencoder (ADAE) and moving average filter (MAF) to perform accurate missing data imputation. First, convolution and gated recurrent unit (GRU) are applied to the encoder of the ADAE, while the decoder still utilizes the fully connected layers to form an asymmetric network structure. The ADAE extracts the local periodic and temporal features from monitoring data and then decodes the features to realize the imputation of the multi-type missing. On this basis, according to the continuity of power data in the time domain, the MAF is utilized to fuse the prior knowledge of the neighborhood of missing data to secondarily optimize the imputed data. Case studies reveal that the developed method achieves greater accuracy compared to existing models. This paper adopts experiments under different scenarios to justify that the MAF-ADAE method applies to actual power equipment monitoring data imputation.

As one of the state-of-the-art grid voltage synchronization techniques, Quasi-type-1 structure based phase locked loop (QT1-PLL) achieves an excellent detection performance when only odd-order harmonics needs to be eliminated by Moving average filter (MAF). Nonetheless, when it is necessary for MAF to eliminate the fundamental negative sequence component, more delay will be introduced to the PLL, reducing its stability. The transient response speed of QT1-PLL needs to be reduced to ensure stability, which may be unacceptable for grid-connected power converters. To deal with this situation, this paper proposes a cascaded filtering stage with small delay. The suggested filtering stage is composed of second-order lead compensator, notch filter, first-order lead compensator and MAF, which is integrated in QT1-PLL. Compared with the traditional QT1-PLL, the proposed PLL has faster transient response speed and higher detection accuracy. The simulation and experimental results verify the effectiveness of the suggested filtering stage.

After comparing the economic advantages of different methods for energy storage system capacity configuration and hybrid energy storage system (HESS) over single energy storage system, a method based on improved moving average and ensemble empirical mode decomposition (EEMD) to smooth wind power fluctuations is proposed aiming at the optimal capacity configuration of HESS. Firstly, EEMD was used to decompose the HESS reference power which was derived by improved moving average filtering, and then several intrinsic mode functions (IMFs) were obtained. Each IMF instantaneous frequency-time profile was processed by Hilbert transform, the so- call gap frequency was identified, and then reconstruct the high and low frequency components to obtain the HESS reference power. Subsequently, taken the energy storage system charge-discharge efficiency and state of charge (SOC) into account, the rated power and capacities of each scheme was determined. Finally, based on Life Cycle Cost (LCC) theory, an energy storage system economic cost calculation model was established to compare the costs of each scheme and select the optimal one.

Precise and fast estimation of system frequency during distorted voltage has become the main challenge for phase locked loop (PLL) for wind energy conversion system (WECS). In this paper, moving average filter (MAF)-based PLL is proposed to solve this issue for permanent magnet synchronous generator (PMSG)-based WECS. MAF allows suitable measurement of frequency and eliminates dc offset, harmonics, and unbalance in voltage. Also scheme is able to perform harmonics reduction. Model of the proposed system for simulation is prepared in MATLAB/Simulink and its performance analysis is carried through a comparative simulation study with conventional synchronous-reference-frame (SRF) PLL, adaptive-frequency-loop PLL, type-1 PLL, and type-3 PLL. Simulation results verify that the proposed controller extracts the maximum-power from wind and stabilizes the system voltage and frequency during any variable/distorted working conditions. System performs considerably excellent during dynamic and steady-state condition. [ABSTRACT FROM AUTHOR]

Out of various moving average filter (MAF)-based phase-locked-loop (PLL), quasi type-1 PLL (QT1-PLL) is widely adopted due to its fast dynamic performance, implementation simplicity, and harmonics rejection abilities. However, the performance of QT1-PLL deteriorates in the presence of an off-nominal frequency unbalanced grid voltage component. Moreover, the sensitivity towards the fundamental frequency negative sequence (FFNS) component is high. Hence, this paper proposes a novel enhanced QT1-PLL solution that is insensitive to unbalance in the grid voltage signal during off-nominal frequency conditions. The proposed adaptive phase detector makes it possible to estimate both the fundamental frequency positive sequence (FFPS) and FFNS components with a high degree of immunity against harmonics. Notably, the pre-loop separation of the FFPS and the FFNS components helps suppress the second harmonic oscillations for improving the parameter estimation accuracy. The loop-filter design of QT1-PLL remains unaffected and requires a proportional gain to estimate the fundamental phase and frequency information. To address the DC offset issue, a modified delayed signal cancellation method is also proposed, which can theoretically eliminate the DC offset for any arbitrary delay length. A small-signal model of the proposed PLL is developed for the sake of stability analysis. Comparative numerical simulation and experimental results are provided with various variants of QT1-PLLs to demonstrate the performance improvement achieved with the proposed technique. [ABSTRACT FROM AUTHOR]

In three-phase grid synchronization applications, the performance of traditional phase-locked loops will decrease under non-ideal grid voltage conditions. In order to solve the above problem, moving average filters (MAF) has been widely studied and utilized in PLLs in recently published papers with the expense of slower transient speed. This paper proposes a paralleled filtering stage to improve the dynamic performance of PLL. The suggested filter with smaller time delay is based on MAF and delay signal cancellation (DSC) operator, which is integrated in quasi-type-1 structure based PLL (QT1-PLL). Compared with differential MAF-PLL (DMAF-PLL) and QT1-PLL, the proposed filter can obtain a faster transient response without degrading phase estimation accuracy. The experimental results demonstrate that the proposed method can provide satisfactory performance.

The surge in energy demand needs to be met with environmentally pleasant resources to reduce the production of greenhouse gases. Solar Photovoltaic (PV) power is a widespread choice as it is accessible in plenty and is comparatively inexpensive. However, the large-scale penetration of intermittent PV power causes multiple variabilities in the grid such as frequency issues and voltage deviations. To counteract these instabilities, Battery Energy Storage System (BESS) is integrated into the grid as it reduces the PV fluctuations and promotes optimal operation. Nevertheless, storage systems are expensive, and thus smoothing filters are coupled with the BESS for cost reduction and power smoothing. Formerly, traditional filters such as Low Pass Filters (LPF), Moving Average (MA), and Moving Median (MM) filters have been proposed for power smoothing. However, these filters have inadequate power tracking capabilities particularly with longer window sizes (W.S) and time constants, which subsequently depreciates the storage system performance. To compensate for the delayed power tracking, larger energy storage systems are required which in turn adds to the overall operational costs. This paper proposes the Moving Regression (MR) filter combined with state of charge (SoC) feedback control for solar PV variability reduction, reduced time delay, decreased battery charging/discharging power, and ramp rate. Simulation results attest that the MR filter achieves better solar power smoothing without increasing the BESS capacity. Also, the performance of the MR filter is less affected with the increase in window sizes. Additionally, the execution of the introduced smoothing filter was found to be superior when assessed against the LPF, MA, MM, Savitsky-Golay (SG), and the Gaussian filter. In comparison to the SG (W.S = 53), the MR (W.S = 45) filter reduces the battery charging/discharging power by approximately 30.48% and as opposed to the LPF (T.C = 48), the peak SoC is reduced by 19.1%.

In the harsh working environment of mines, the traditional cable reeling method cannot guarantee the power supply of electric shovel for a long time, and there are hidden safety hazards in the process of power supply. A new cable reel car which follows the shovel is proposed to address the above problems. In order to realize the autonomous following of electric shovel by cable reel car and ensure the long time power supply of electric shovel in mining environment, the positioning algorithm of electric mine shovel based on ultra wide-band (UWB) technology is proposed and time difference of arrival (TDOA) algorithm is used to construct the positioning model of electric mine shovel. Based on the TDOA ranging algorithm, the distance from each base station to the target electric shovel position is measured and the difference is calculated. The distance difference information obtained is moving average filtered to suppress the noise generated in the ranging process and achieve smooth data. The tag position is calculated according to the distance difference after filtering correction. The target electric shovel position is tracked with the strong tracking extended Kalman filter (STFEKF) algorithm to further eliminate noise and improve the positioning accuracy of the target electric shovel during movement. The simulation results show that under the influence of different observation noises, the error of the moving filter + STFEKF positioning method is smaller than that of the traditional EKF algorithm. This method effectively solves the problem of positioning error increasing with the distance increasing or the sudden change of shovel movement. The positioning mean square deviation is reduced by more than 70% compared with the traditional EKF algorithm, and the positioning trajectory is closer to the real movement trajectory of the target with good performance of positioning tracking and noise suppression.

This work proposes a structural enhancement and a new technique to design the loop filter (LF) of a third-order phase-locked loop (PLL) to enhance the PLL dynamic performance under abnormal grid conditions. The proposed PLL combines a moving average filter (MAF) and an arbitrarily delayed signal cancelation (ADSC) for structural enhancement to achieve DC-offset rejection and harmonics elimination. The window length of the MAF is selected to be one-sixth of the fundamental grid period to remove non-triple odd harmonics and speed up the PLL dynamic response. The triple harmonics are eliminated, adopting the line-to-line voltage concept, while the ADSC operator rejects the DC offset. The LF design is based on a modified third-order polynomial tuned using stochastic optimization to minimize the settling time of the frequency deviation, offering better dynamic performance over the symmetrical optimum method (SOM) and achieving synchronization within one grid cycle. The PLL mathematical model, small-signal model, and LF design based on the modified polynomial are discussed. Finally, the proposed PLL performance is verified numerically and experimentally with comparisons with other PLLs to demonstrate the effectiveness of the proposed work.

A subsampling PLL is characterized by systematic low in-band phase noise but spurious performance may be degraded. In this article, its fractional spurious performance is investigated. A moving average filter (MAF) is used in the loop in two cases—forward path and feedback path—to reduce these spurs. The MAF in forward path suppresses the variations in the control voltage and generates a clean perturbation-free signal for the voltage-controlled oscillator. The MAF in feedback path nullifies the errors in phase differences between the phases from the oscillator. Simulation results are done in 180 nm technology with process, temperature and supply variation for the two cases. For the first case, the worst case spurs are reduced from - 43.3 to - 119.6 dBc. For the second case, the worst case spurs are reduced to - 50.6 dBc. Monte Carlo simulations with process variations and device mismatches are also performed. Worst case spurs for the MAF in forward path are reduced from - 31.8 to - 123.4 dBc. For the case with the MAF in feedback path the spurs are reduced to - 59.4 dBc. [ABSTRACT FROM AUTHOR]

Indirect bridge health monitoring is conducted by running an instrumented vehicle over a bridge, where the vehicle serves as a source of excitation and as a signal receiver; however, it is also important to investigate the response of the instrumented vehicle while it is in a stationary position while the bridge is excited by other source of excitation. In this paper, a numerical model of a stationary vehicle parked on a bridge excited by another moving vehicle is developed. Both stationary and moving vehicles are modeled as spring–mass single-degree-of-freedom systems. The bridges are simply supported and are modeled as 1D beam elements. It is known that the stationary vehicle response is different from the true bridge response at the same location. This paper investigates the effectiveness of contact-point response in reflecting the true response of the bridge. The stationary vehicle response is obtained from the numerical model, and its contact-point response is calculated by MATLAB. The contact-point response of the stationary vehicle is investigated under various conditions. These conditions include different vehicle frequencies, damped and undamped conditions, different locations of the stationary vehicle, road roughness effects, different moving vehicle speeds and masses, and a longer span for the bridge. In the time domain, the discrepancy of the stationary vehicle response with the true bridge response is clear, while the contact-point response agrees well with the true bridge response. The contact-point response could detect the first, second, and third modes of frequency clearly, unlike the stationary vehicle response spectra. [ABSTRACT FROM AUTHOR]

The moving-average-filter-based quasi-type-1 phase-locked loop (MAF-QT1 PLL) can provide zero steady-state phase error in the presence of frequency drifts, achieving a high filtering capability. However, in the presence of an MAF and frequency drift, MAF-QT1 PLL cannot achieve a fast transient response and frequency-dependent characteristic under adverse grid conditions. To overcome this drawback of the MAF-QT1 PLL, this paper proposes a frequency-adaptive improved moving-average-filter-based quasi-type-1 PLL (FAIMAF-QT1 PLL). Aiming at the shortcoming of the slow dynamic response of the MAF-QT-1 PLL, a correction link is introduced, and an improved moving-average-filter-based quasi-type-1 PLL (IMAF-QT1 PLL) is obtained. To improve the anti-interference ability of the IMAF-QT1 PLL in response to grid frequency changes, a FAIMAF-QT1 PLL and a corresponding digital implementation scheme are proposed. The regulator parameter setting method is proposed based on the small-signal model of the FAIMAF-QT1 PLL. Simulation and experimental results show that the FAIMAF-QT1 PLL can accurately track the grid voltage phase in the presence of power grid frequency fluctuations, phase angle jumps, distorted harmonic injections and unbalanced voltage drops and has good steady-state and dynamic performance.

A simple and efficient method to remove white Gaussian noises and physiological noises from electrocardiogram (ECG) signals is presented. It is based on simple tools usually used in digital signal processing like moving average filter, median filter, baseline drift removal and peak detection. We show by several simulations that the proposed algorithm outperforms significantly conventional median filter and moving average filter and can be considered as a valid concurrent to the standard wavelet-based method.

The interdecadal variability of precipitation over sub-Saharan Africa (SSA) and central North China (CNC) is examined and compared in this study. Previous studies have found that the two regions have similar interdecadal dry–wet evolution in the past 100 years. The results show obvious decadal precipitation fluctuations in the two regions. In CNC, a persistent negative precipitation anomaly is detected from the early 1970s to the 2000s. In SSA, a negative precipitation anomaly is apparent since the late 1970s, while a distinct upward trend is found since the 1990s although the precipitation anomaly is still negative. Significant correlation is found between the decadal precipitation anomalies in SSA and the SST modes (Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO)), as well as the North Atlantic Oscillation (NAO), while in CNC the decadal precipitation is influenced by the NAO and PDO. The EOF results show that the total explained variance of the first four EOFs in SSA is smaller than that of CNC. The fourth and third modes in SSA are significantly associated with the AMO and PDO respectively, while the first, third, and fourth modes are significantly associated with the NAO. The first mode in CNC is significantly associated with the NAO. The first mode of the precipitation anomaly in SSA fluctuates out of phase with that in CNC, while in-phase changes are apparent among the third and the fourth modes.

Due to the unlawful manipulation and image processing attacks, the copyright protection of digital image is paid more and more attention. This paper proposes a digital image watermarking technique to protect image authentication based on discrete wavelet transform (DWT), entropy and neural network. Firstly, the DWT is used to divide the host image and the watermark image into frequency sub-bands. Then, the entropy of each frequency sub-band is calculated to find the maximum entropy sub-band in order to embed the highest entropy sub-band of the watermark image into the highest entropy sub-band of the host image. Finally, the neural network is used for determining the relationship between the pixel values of the host image and the watermarked image which is used later in the watermark extraction process. Moreover, a moving average filter is used prior to the extraction process for reducing the image processing attacks. Performance of the proposed method is tested against different image processing attacks (such as Filtering, Gaussian noise, Rotation, and Cropping). Experimental results demonstrate that this proposed method not only has the superior robustness and imperceptibility but also has improved the anti-attack capability in comparison with recent works in this field.

In recent years, nano-impact electrochemistry (NIE) has attracted widespread attention as a new electroanalytical approach for the analysis and characterization of single nanoparticles in solution. The accurate analysis of the large volume of the experimental data is of great significance in improving the reliability of this method. Unfortunately, the commonly used data analysis approaches, mainly based on manual processing, are often time-consuming and subjective. Herein, we propose a spike detection algorithm for automatically processing the data from the direct oxidation of sliver nanoparticles (AgNPs) in NIE experiments, including baseline extraction, spike identification and spike area integration. The resulting size distribution of AgNPs is found to agree very well with that from transmission electron microscopy (TEM), showing that the current algorithm is promising for automated analysis of NIE data with high efficiency and accuracy.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we propose an efficient two-stage decimator structure based on Kaiser Hamming sharpening (KHS) of moving average filter. The proposed KHS decimator divides the overall decimation factor M = M 1 M 2 , such that both M 1 , M 2 ∈ Z + . The first stage uses KHS with first and second order of tangency at unity and zero, respectively. The second stage uses the simplest KHS sharpening, i.e. first-order tangency at both unity and zero. The architecture of the proposed structure is designed to match the existing decimator to have nearly equal number of integrator sections operating at high sampling frequency of f s . The pass-band droop at the normalized pass-band cut-off frequency of 1/2M in the proposed design is 13.8 % less as compared to the existing KHS decimator, with a compromise of 3 % in alias rejection. Further, as the normalized pass-band cut-off frequency is reduced to 1/4M, the proposed design in comparison to existing decimator has 34.5 % less droop with only 2.3 % compromise in alias rejection. [ABSTRACT FROM AUTHOR]

ABSTRAK Aspek utama yang membedakan sensor satu dengan yang lainnya adalah tingkat akurasinya. Pada penelitian ini, dibuat sistem untuk menurunkan tingkat deviasi untuk meminimalisir kesalahan hasil pengukuran pada sensor berbiaya murah. Sensor yang digunakan adalah sensor tekanan udara BMP180. Sensor tersebut digunakan untuk mengukur kedalaman berdasarkan tekanan udara dalam air. Moving Average Filter (MAF) digunakan untuk membuang pencilan data, sehingga didapatkan data yang lebih relevan yang kemudian digunakan untuk melakukan curve fitting. Kemudian dilakukan analisis regresi linear untuk menghasilkan persamaan yang berfungsi sebagai pengoreksi data terekam dari sensor tersebut. Pengujian sistem dilakukan melalui beberapa skenario lalu diambil persamaan yang menghasilkan nilai Mean Square Error (MSE) yang paling kecil. Berdasarkan hasil penelitian, diperoleh kesimpulan bahwa MAF mampu meningkatkan akurasi data hingga mencapai 99.12%. Kata kunci: sensor BMP180, koreksi kesalahan, regresi linear, moving average filter, mean square error ABSTRACT The main aspect that distinguishes sensors from one another is the level of accuracy. In this study, a system was developed to reduce the level of deviation to minimize the measurement error on low-cost sensors. The sensor used is the air pressure sensor, BMP180. Then this sensor is used to measure water depth based on air pressure in water. The Moving Average Filter (MAF) method is used to get rid of outliers of data, to obtain more relevant data for curve fitting. Then a linear regression analysis is performed to produce a function as a correction of recorded data from the sensor. System testing is carried out through a number of scenarios and then the equation is chosen with the smallest Mean Square Error (MSE). Based on this research, MAF increases data accuracy up to 99.12%. Keywords: sensor BMP180, error correction, linear regression, moving average filter, mean square error

A compound energy management strategy is proposed to improve the fuel cell’s durability and the economy of fuel cell hybrid electric vehicles (FCHEV). A control strategy that combines fuzzy control and switching control is proposed, taking into account factors that affect the fuel cell’s durability and the supercapacitor park’s safety. To smooth the output power of fuel cells under frequent variable load conditions, a moving average filtering algorithm has been added. Finally, co-simulation using Advisor and Matlab/Simulink under the World Light Vehicle Test Cycle (WLTC) compares the proposed strategy with fuzzy control and power following strategies. The experimental results show that the proposed strategy ensures the safety of the supercapacitor park and improves the durability of the fuel cell while improving the economy of the whole vehicle.

This paper fully studies the relation between the velocity of ultrasonic wave and the concentration and temperature of urea solution obtained from vehicle exhaust, based on the principle of ultrasonic wave transmitted with different speeds in different concentration solutions.The experiment results show that the velocity of ultrasonic wave transmitted in the urea solution is higher when the concentration of urea solution increases, and indicate that their relationship is obviously linear. Furtherly, experiment results also reveals that the temperature of urea solution has a certain influence on the propagation speed of ultrasonic wave. By using the ultrasonic time-difference method, the temperature compensation, moving average filter and linear regression method, the real-time detection for the concentration of urea solution is achieved, and the error of this detection method is less than 0.5%. The system sends data to the main equipment of Selective Catalytic Reduction(SCR) system through CAN bus, which provides an efficient guidance on vehicle exhaust treatment.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)