Moving Average Filter Research Articles

Ship Detection in SAR Images Based on Steady CFAR Detector and Knowledge-Oriented GBDT Classifier

Ship detection is a significant issue in remote sensing based on Synthetic Aperture Radar (SAR). This paper combines the advantages of a steady constant false alarm rate (CFAR) detector and a knowledge-oriented Gradient Boosting Decision Tree (GBDT) classifier to achieve the location and the classification of ship candidates. The steady CFAR detector smooths the image by a moving-average filter and models the probability distribution of the smoothed clutter as a Gaussian distribution. The mean and the standard deviation of the Gaussian distribution are estimated according to the left half of the histogram to remove the effect of land, ships, and other targets. From the Gaussian distribution and a preset constant false alarm rate, a threshold is obtained to segment land, ships, and other targets from the clutter. Then, a series of morphological operations are introduced to eliminate land and extract ships and other targets, and an active contour algorithm is utilized to refine ships and other targets. Finally, ships are recognized from other targets by a knowledge-oriented GBDT classifier. Based on the brain-like ship-recognition process, we change the way of the decision-tree generation and achieve a higher classification performance than the original GBDT. The results on the AIRSARShip-1.0 dataset demonstrate that this scheme has a competitive performance against deep learning, especially in the detection of offshore ships.

A 0.055 mm2 Total Area Triple-Loop Wideband Fractional-N All-Digital Phase-Locked Loop Architecture for 1.9–6.1 GHz Frequency Tuning

This paper presents a wideband fractional-N all-digital phase-locked loop (WBPLL) architecture featuring a triple-loop configuration capable of tuning frequencies from 1.9 to 6.1 GHz. The first and second loops, automatic frequency control (AFC) and counter-assisted phase-locked loop (CAPLL), respectively, perform coarse locking, while the third loop employs a digital sub-sampling architecture without a frequency divider for fine locking. In this third loop, fractional-N frequency synthesis is achieved using a delta-sigma modulator (DSM) and digital-to-time converter (DTC). To minimize area, digital modules such as counters, comparators, and differentiators used in the AFC and CAPLL loops are reused. Furthermore, a moving average filter (MAF) is employed to reduce the frequency overlap ratio of the digitally controlled oscillator (DCO) between the second and third loops, ensuring stable loop switching. The total power consumption of the WBPLL varies with the frequency range, consuming between 8.8 mW at the WBPLL minimum output frequency of 1.9 GHz and 12.8 mW at the WBPLL maximum output frequency of 6.1 GHz, all at a 1.0 V supply. Implemented in a 28 nm CMOS process, the WBPLL occupies an area of 0.055 mm2.

Grid synchronization method based on three novel types of third-order generalized integrators

Phase-locked loop (PLL) is widely used to estimate synchronous information such as amplitude and phase angle of grid voltage. It plays a crucial role in distributed renewable energy grid-connected power generation. However, the presence of grid harmonics and DC offset voltage (DCOV) can affect the accurate extraction of the fundamental voltage component, leading to phase-locked deviation. This paper introduces three novel or improved third-order generalized integrators (TOGI) with DCOV rejecting function to address this issue. A unified dual third-order generalized integrator (DTOGI) structure is designed based on the three TOGI structures. This DTOGI structure is further combined with a moving average filter (MAF) to propose three innovative three-phase PLLs. Unlike other PLL filtering methods that inadequately consider DCOV, the proposed PLL methods not only reject DCOV but also eliminate the fundamental voltage negative sequence component (FVNSC) and each order harmonic component. They demonstrate strong robustness and excellent filtering performance even under abnormal grid conditions. Among the three methods, the DII-TOGI based PLL exhibits superior dynamic performance compared to the other two methods, it can improve dynamic characteristics by about 10 % compared to the other two methods. The effectiveness of the proposed methods is verified through extensive Matlab/Simulink simulations and experiments.

Analysis of Phase-Locked Loop Filter Delay on Transient Stability of Grid-Following Converters

To ensure precise phase estimation within the q-axis of the phase-locked loop (PLL), integrating a filter into the q-axis loop is essential to mitigate grid-voltage harmonics. Nevertheless, the intrinsic delay characteristics of this filter impede PLL synchronization during significant grid disturbances. This study begins by developing mathematical models for three types of filters—moving-average filter (MAF) for eliminating odd harmonic components, dq-frame cascaded delayed signal cancellation (dqCDSC) filter, and notch filter (NF). Following the reduction in filter orders, a third-order nonlinear large-signal model of the PLL, incorporating an additional q-axis internal filter, is formulated. Using phase plane analysis, this study investigates the transient synchronism of the grid-following converter (GFL) and explores the influence of delay time constants from the three PLL filters on its behavior while delineating the boundaries of their basins of attraction. Theoretical findings indicate that, relative to the traditional SRF-PLL, incorporating an internal filter into the PLL compromises the transient synchronous stability of GFL. Specifically, greater filter delay time constants exacerbate the GFL’s vulnerability to transient instability amid substantial grid disturbances. Hence, careful consideration is essential when using MAF-PLL and NF-PLL in situations demanding high synchronization stability. The theoretical analyses are validated using Matlab/Simulink to verify their accuracy.

Circulating current suppression and natural voltage balancing using phase-shifted modulation for modular multilevel converter

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<sup>+1</sup> discrete levels are achieved, but when there is a phase shift, the number of discrete levels increases to 2<sup>N+1</sup>. 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.

Hybrid energy storage system control strategy to smooth power fluctuations in microgrids containing photovoltaics

Abstract The use of a hybrid energy storage system (HESS) consisting of lithium-ion batteries and supercapacitors (SCs) to smooth the power imbalance between the photovoltaics and the load is a widespread solution, and a reasonable probabilistic allocation of the batteries and SCs affects the performance of the HESS. This paper focuses on developing a simple and effective power allocation strategy for the HESS. The moving average filtering (MAF) is considered to effectively decompose the low-frequency components as well as the high-frequency components in the signal and is simple to implement, and we spoil the low-frequency components decomposed by MAF several times to obtain more excellent low-frequency components; moreover, during HESS charging, when the state of charge (SOC) of the SC is high, we reduce the SC’s charging power by decreasing the number of times of MAF usage while ensuring the performance. During the HESS discharging, when the SOC of the SC is low, this strategy is also used to reduce the SC’s discharging power to ensure the SC’s safe operation. The proposed approach is fully validated, and it has excellent performance.

Research on a New Synchronous Phase-Locked Method for Renewable Energy Grid-connected Converters

Background:: The phase-locked loop (PLL) is widely used to estimate synchronization information such as magnitude and phase angle of the grid voltage, which is essential for gridconnected power generation from distributed renewable energy sources. However, the harmonics in the grid often affect the accurate extraction of fundamental positive sequence, resulting in phase deviations. Methods:: A dual extended novel third-order generalized integrator (DENTOGI) is proposed and placed into the control outer loop of the PLL. This approach can be applied in the condition of the grid with DC offset voltage (DCOV). Additionally, the PLL is proposed by introducing moving average filter (MAF) and eliminating the proportional controller of the integration link to suppress high-frequency harmonics and speed up the response. Results:: A comparative experiment using Matlab/Simulink is conducted to evaluate the proposed PLL against the conventional PLLs. The experimental results confirm that the proposed PLL exhibits superior filtering performance for DC offset and harmonic components when compared to the conventional PLL. Additionally, the proposed PLL demonstrates better dynamic adjustment capabilities. Conclusion:: The main contribution of this paper is to propose a phase-locked method that can eliminate DCOV and harmonic components, which can be applied in non-ideal power grid conditions. The proposed PLL based on DENTOGI and MAF can lock the grid phase stably without errors under the grid fault involving DCOV and harmonic disturbance, it also exhibits the characteristics of fast response.

Multi-Type Missing Imputation of Time-Series Power Equipment Monitoring Data Based on Moving Average Filter-Asymmetric Denoising Autoencoder.

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.

An improved single phase SPLL with DC and harmonics suppression

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.

Research on Soil Moisture Estimation of Multiple-Track-GNSS Dual-Frequency Combination Observations Considering the Detection and Correction of Phase Outliers.

Soil moisture (SM), as one of the crucial environmental factors, has traditionally been estimated using global navigation satellite system interferometric reflectometry (GNSS-IR) microwave remote sensing technology. This approach relies on the signal-to-noise ratio (SNR) reflection component, and its accuracy hinges on the successful separation of the reflection component from the direct component. In contrast, the presence of carrier phase and pseudorange multipath errors enables soil moisture retrieval without the requirement for separating the direct component of the signal. To acquire high-quality combined multipath errors and diversify GNSS-IR data sources, this study establishes the dual-frequency pseudorange combination (DFPC) and dual-frequency carrier phase combination (L4) that exclude geometrical factors, ionospheric delay, and tropospheric delay. Simultaneously, we propose two methods for estimating soil moisture: the DFPC method and the L4 method. Initially, the equal-weight least squares method is employed to calculate the initial delay phase. Subsequently, anomalous delay phases are detected and corrected through a combination of the minimum covariance determinant robust estimation (MCD) and the moving average filter (MAF). Finally, we utilize the multivariate linear regression (MLR) and extreme learning machine (ELM) to construct multi-satellite linear regression models (MSLRs) and multi-satellite nonlinear regression models (MSNRs) for soil moisture prediction, and compare the accuracy of each model. To validate the feasibility of these methods, data from site P031 of the Plate Boundary Observatory (PBO) H2O project are utilized. Experimental results demonstrate that combining MCD and MAF can effectively detect and correct outliers, yielding single-satellite delay phase sequences with a high quality. This improvement contributes to varying degrees of enhanced correlation between the single-satellite delay phase and soil moisture. When fusing the corrected delay phases from multiple satellite orbits using the DFPC method for soil moisture estimation, the correlations between the true soil moisture values and the predicted values obtained through MLR and ELM reach 0.81 and 0.88, respectively, while the correlations of the L4 method can reach 0.84 and 0.90, respectively. These findings indicate a substantial achievement in high-precision soil moisture estimation within a small satellite-elevation angle range.

RUMP: Resource Usage Multi-Step Prediction in Extreme Edge Computing

Extreme Edge Computing that leverages the copious yet underutilized computational resources of Extreme Edge Devices (EEDs) has gained significant momentum lately. Estimating the computational capabilities of EEDs can be strenuously challenging since EEDs are user-owned devices, and are thus subject to a highly dynamic user access behavior (i.e., dynamic resource usage). In this paper, we propose the Resource Usage Multi-step Prediction (RUMP) scheme. RUMP is the first scheme that strives to enable multistep-ahead prediction of the dynamic resource usage of EEDs (i.e., workers) in a computationally efficient way, while providing a relatively high prediction accuracy. Towards that end, RUMP exploits the use of the Hierarchical Dirichlet Process-Hidden Semi-Markov Model (HDP-HSMM). In addition, RUMP uses the Simple and Exponential Moving Average (SMA&EMA) and Savitzky-Golay (SG) filters to improve the prediction accuracy. We scrupulously study the trade-off between computational efficiency, prediction accuracy, practicality, and adaptability of the underlying prediction model by conducting complexity analysis, performing various experiments on a testbed of heterogeneous workers, and comparing the HDP-HSMM model used in RUMP to three other prominent prediction models in different dynamic resource usage scenarios. Extensive evaluations show that RUMP achieves a 91% categorical multi-step prediction accuracy and renders a small performance gap of 6% on average in terms of the Mean Absolute Error (MAE) compared to representatives of state-of-the-art prediction models, while yielding a low computational complexity.

Adaptive single-phase auto-reclosing scheme based on the moving average filter-quadrature signal generator for transmission lines with shunt reactors

An adaptive single-phase auto-reclosing method based on the moving average filter (MAF)-quadrature signal generator (QSG) is developed to determine secondary arc extinction time for high-voltage transmission lines with shunt reactors. Firstly, the difference between permanent and transient faults is derived and analyzed by the equivalent circuit model. Then, MAF-QSG is utilized to extract these features from the fault voltage. During the second arc period, for transient fault, the fundamental component and subharmonic component with around 50 Hz are reserved by MAF-QSG; however, in the case of permanent fault, only the fundamental component is retained. Finally, the identification index and its threshold are designed above the extraction fault features. The performance of the proposed method is evaluated by simulation and field data studies, accounting for different transmission line compensation levels, transition resistances, and fault location conditions.

A two-stage online remaining useful life prediction framework for supercapacitors based on the fusion of deep learning network and state estimation algorithm

Supercapacitors have been widely used in many fields. The safe and stable operation of supercapacitors requires accurate remaining useful life (RUL) prediction. This paper proposes a two-stage online RUL prediction framework based on the bidirectional long short-term memory (BiLSTM) network and the H∞ observer. In Stage 1, the BiLSTM network as well as the Bayesian Optimization algorithm is used to estimate capacitance. The Bayesian Optimization (BO) algorithm aims to optimize the hyperparameters of the BiLSTM network. In Stage 2, the estimated capacitance is handled by the moving average filter (MAF) to alleviate short-term fluctuations. The double exponential model is employed to describe the degradation trajectory, and the model is iteratively updated by the H∞ observer with the estimated capacitance as the measurement. Experiments are implemented to verify the validation of the proposed framework. The results indicate that the BiLSTM network can explain more than 99% variation of capacitance. The deviation of RUL prediction is less than 6% in most cases, and it is even less than 2.5% in late phase. The proposed framework takes the advantages of online deployment, and achieves competitive prediction accuracy when compared with offline methods.

Determination of the coefficient of thermal expansion of ultra-low-expansion glass using anultrasonic immersion testing method.

The coefficient of thermal expansion (CTE) of ultra-low-expansion (ULE) glass is critical to the development of precision optical systems. Herein, an ultrasonic immersion pulse-reflection method is proposed to characterize the CTE of ULE glass. The ultrasonic longitudinal wave velocity of ULE-glass samples with significantly different CTE values was measured using a correlation algorithm combined with moving-average filtering, which can achieve 0.2m/s precision with a contribution to the ultrasonic CTE measurement uncertainty of 0.47ppb/°C. Furthermore, the established ultrasonic CTE measurement model predicted the 5°C-35°C mean CTE with a root-mean-square error of 0.9ppb/°C. Notably, a complete uncertainty analysis methodology was established in this paper, which can provide directional guidance for the subsequent development of higher-performance measurement devices and the improvement of relevant signal processing procedures.

Enhancing PDC Functional Connectivity Analysis for Subjects with Dyslexia Using Artifact Cancellation Techniques

The neurobiological origin of dyslexia allows the study of this disorder by examining functional connectivity between regions of the brain. During rest-state or at task completion, Electroencephalograms (EEG) are used to observe brain signals. By using Partial Directed Coherence (PDC) analysis, the correct analysis of functional connectivity was assessed. In spite of that, the estimation of functional connectivity can be inaccurate due to the presence of artifacts. Several methods have been employed by researchers to remove artifacts, including Moving Average Filters (MAF), Wiener Filters (WF), Wavelet Transforms (WT), and hybrid filters. Despite this, no research has been conducted on the effects of artifact removal methods on functional connectivity. Consequently, Artifact Cancellation (AC) algorithms are developed to reduce the effects of eye blinks, eye movements, and muscle movements on functional connectivity estimation. In this work, the denoising filters discussed earlier are utilized as part of the AC algorithm. Additionally, a comparison was conducted to determine the effectiveness of the filters. According to the results, AC-MAF removed all artifacts with the least computational complexity after improving the MAF. In order to test its efficacy in real-world conditions, it was applied to the real signals recorded while children with dyslexia were participating in rapid automatized naming activities. Utilizing the PDC approach, the developed algorithm accurately assessed functional connectivity.

Enhancing the Filtering Capability and the Dynamic Performance of a Third-Order Phase-Locked Loop under Distorted Grid Conditions

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.

Self-Correcting Algorithm for Estimated Time of Arrival of Emergency Responders on the Highway

Edge computing is one of the key features of the 5G technology-scape that is realizing new and enhanced automotive use cases for improving road safety and emergency response management. Back Situation Awareness (BSA) is such a use case that provides an advance notification to the vehicles of an arriving emergency vehicle (EmV). This paper presents an algorithm for enhancing the accuracy of the advanced Estimated Time of Arrival (ETA) notification of an approaching EmV towards the other vehicles on the highway. The notification is expected to ensure timely reaction by the vehicles to create a clear corridor for the EmV to pass through unhindered, thereby saving critical time to reach the emergency event in a safe manner. The main features of the presented solution are i) the self-correcting algorithm, ii) adaptive and dynamic dissemination areas size allocation, as a response to traffic changes, and iii) the evaluation of the ETA estimation accuracy. We have used the real travel time data measurements collected on the E313 highway (Antwerp, Belgium), to evaluate the performance of the algorithm. The performance is evaluated and compared in terms of accuracy and run-time complexity, using different methods such as Kalman filter, Filter-less method, Moving Average, and Exponential Moving Average filters. It is observed that the Kalman filter provides better accuracy on the ETA estimation, thereby reducing the estimation error by around 14% on average.

Two-level optimization model of hybrid energy storage system in wind/solar distributed generation system

Hybrid energy storage system (HESS) composed of battery and supercapacitor can effectively compensate the power deviation between supply and demand in distributed generation system (DGs). However, the popularization application of which is limited by the high cost of capacity configuration and subsequent maintenance. Hence, considering dynamic change of frequency characteristics of power deviation, a two-level optimization model for power programming and capacity configuration of HESS is established. In the outer layer, aiming at minimizing the allocation of the capacity, power allocation strategy of HESS based on adaptive filtering is proposed, which adaptively adjusts the sliding window of moving average filter (MAF), and decomposes the supply–demand power deviation sequence into the scope of battery and supercapacitor. In the inner layer, considering grid-connected power fluctuations, a full life cycle HESS capacity optimization model is proposed. The model aims at the total cost of HESS and the renewable energy utilization (REU), and uses non-dominated sorting genetic algorithm II (NSGA-II) to solve the model. Furthermore, a case is introduced to verify the feasibility of the proposed strategy and the effectiveness of the model solution.

Extended State-Based OSG Configurations for SOGI PLL With an Enhanced Disturbance Rejection Capability

Changes in the orthogonal signal generator (OSG) configuration of the standard second-order generalized integrator-based phase-locked loop (S-SOGI PLL) have shown to offer an improved low-voltage ride-through capability. However, the resulting PLLs: highpass SOGI (H-SOGI) and lowpass SOGI (L-SOGI) PLLs have no dc-offset estimation capability and lack robustness to disturbances. Therefore, this article focuses on maximizing the merits of H-SOGI and L-SOGI PLLs for grid and industrial applications by incorporating dc-offset estimation and enhancing their disturbance rejection capability. Accordingly, two varieties of SOGI PLLs, namely: extended state highpass SOGI (ES-H-SOGI) and extended state lowpass SOGI (ES-L-SOGI) PLLs are proposed. In the proposed PLLs, states of the OSGs are extended to achieve dc-offset estimation and rejection, while frequency-adaptive implementation of the moving average filter (MAF) is developed to achieve complete harmonic rejection at off-nominal frequencies of the input voltage. To alleviate the undesirable effect of the MAF in terms of the PLL's slow transient response, frequency-adaptive implementation of a phase-lead compensator is introduced. In this article, design guideline that achieves an optimal tradeoff between disturbance rejection and dynamic performance in the ES-H-SOGI and ES-L-SOGI PLLs is presented. Finally, experimental comparison between the proposed ES-H-SOGI and ES-L-SOGI PLLs and their ES-S-SOGI PLL counterpart is carried to show that the proposed PLLs outperform the ES-S-SOGI PLL in terms of dynamic performance while achieving enhanced disturbance rejection capability.

Selection of pulse-like ground motions with strong velocity-pulses using moving-average filtering

Pulse-like ground motions with strong velocity-pulses are of special concern for engineers when designing or assessing structures built adjacent to active tectonic faults. This study intends to propose a new approach that makes use of the moving-average filtering (MAF) for selecting ground motions with strong velocity-pulses. Specifically, MAF is employed to filter out the high-frequency content of ground motions, so that relatively smooth waveforms could be extracted and the velocity pulse is easily captured by the peak point method. Then, the pulse-energy relative to the total energy of the ground motion is used as the pulse indicator (PI) for selecting pulse-like records. A training set containing 954 components with PGV> 20 cm/s is compiled to determine the PI threshold. The effectiveness of the proposed algorithm is verified by comparisons with previous methods. Finally, by using the proposed method it is shown that pulse-like features can be observed in a couple of orientations, and the identified velocity pulses are seen occurring at sites in the propagation direction of the fault rupture front.