Search Results (21)

Traditional received signal strength indicator (RSSI)-based and angle of arrival (AoA)-based positioning methods are highly susceptible to multipath effects, signal attenuation, and noise interference in complex indoor environments, which significantly degrade positioning accuracy. To mitigate the impact of the above deterioration, we propose a deep learning-based Bluetooth indoor positioning system, which employs a Kalman filter (KF) to reduce the angular error in AoA measurements and utilizes a median filter (MF) and moving average filter (MAF) to mitigate fluctuations in RSSI-based distance measurements. In the deep learning network architecture, we propose a convolutional neural network (CNN)–multi-head attention (MHA) model. During the training process, the backpropagation (BP) algorithm is used to compute the gradient of the loss function and update the parameters of the entire network, gradually optimizing the model’s performance. Experimental results demonstrate that our proposed indoor positioning method achieves an average error of 0.29 m, which represents a significant improvement compared to traditional RSSI and AoA methods. Additionally, it displays superior positioning accuracy when contrasted with numerous emerging indoor positioning methodologies. Full article

Grid-connected inverter (GCI) plays a crucial role in facilitating stable and efficient power delivery, especially under severe and complex grid conditions. Harmonic distortions and imbalance of the grid voltages may degrade the grid-injected current quality. Moreover, inductive-capacitance (LC) grid impedance and the grid frequency fluctuation also degrade the current control performance or stability. In order to overcome such an issue, this study presents a frequency-adaptive current control strategy of a GCI based on incomplete state observation under severe grid conditions. When LC grid impedance exists, it introduces additional states in a GCI system model. However, since the state for the grid inductance current is unmeasurable, it yields a limitation in the state feedback control design. To overcome such a limitation, this study adopts a state feedback control approach based on incomplete state observation by designing the controller only with the available states. The proposed control strategy incorporates feedback controllers with ten states, an integral controller, and resonant controllers for the robustness of the inverter operation. To reduce the reliance on additional sensing devices, a discrete-time full-state current observer is utilized. Particularly, with the aim of avoiding the grid frequency dependency of the system model, as well as the complex online discretization process, observer design is developed in the stationary reference frame. Additionally, a moving average filter (MAF)-based phase-locked loop (PLL) is incorporated for accurate frequency detection against distortions of grid voltages. For evaluating the performance of the designed control strategy, simulations and experiments are executed with severe grid conditions, including grid frequency changes, unbalanced grid voltage, harmonic distortion, and LC grid impedance. Full article

The maglev gyroscope torque feedback orientation measurement system, equipped with abundant sampling data and high directional accuracy, plays a crucial role in underground engineering construction. However, when subjected to external instantaneous vibration interference, the gyroscope rotor signal frequently exhibits abnormal jumps, leading to significant errors in azimuth measurement results. To solve this problem, we propose a novel noise reduction algorithm that integrates Moving Average Filtering with Autoregressive Integrated Moving Average (MAF-ARIMA), based on the noise characteristics of the rotor jump signal. This algorithm initially adaptively decomposes the rotor signal, subsequently extracting the effective components of the north-seeking torque with precision and applying MAF processing to effectively filter out noise interference. Furthermore, we utilize the stable sampling trend data of the rotor signal as sample data, employing the ARIMA model to accurately predict the missing abnormal jump trend data, thereby ensuring the completeness and coherence of the rotor signal trend information. Experimental results demonstrate that, compared to the original rotor signal, the reconstructed signal processed by the MAF-ARIMA algorithm exhibits an average reduction of 70.58% in standard deviation and an average decrease of 47.31% in the absolute error of azimuth measurement results. These findings fully underscore the high efficiency and stability of the MAF-ARIMA algorithm in processing gyroscope rotor jump signals. Full article

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. Full article

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. Full article

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. Full article

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. Full article

It is of great significance to explore the spatial and temporal evolution of soil and rockfill dam deformation, ensuring the safety of people’s lives and healthy economic development. The spatial and temporal evolution patterns of deformation of 17 large soil and rockfill dams in Henan Province were analyzed by using the PS-InSAR technique and 55 Sentinel-1A images from March 2017 to September 2021. Based on factors such as reservoir water level and rainfall, a series of analyses were conducted on the Xiaolangdi soil and rockfill dam, which has the highest dam height and the most prominent deformation problem. The monitoring results show that all the soil and rockfill dams in Henan Province have different degrees of deformation, and there is a close relationship between dam height and deformation. In addition, the deformation rate of the Xiaolangdi soil and rockfill dam in the past five years presents a “Stepped Shape” deformation trend from the top to the bottom of the dam. The deformation of the upper, middle, and lower parts of the dam body reaches 80 mm, 40 mm, and 20 mm, respectively, among which the middle part of the dam crest has the largest deformation. Furthermore, the time series prediction model for sparrow search algorithm Long Short-Term Memory considering the moving average filter (MAF-SSA-LSTM) is proposed to predict and accurately analyze the future deformation of Xiaolangdi soil and rockfill dam with RMSE of 1.526 mm, MAE of 1.447 mm, and MAPE of 2.22%, which proved that the model has high prediction accuracy. It can truly reflect the overall deformation trend of the dam body. The results provide a theoretical basis and decision basis for the census of reservoir safety conditions and deformation history retrieval in Henan Province. Full article

The moving average filter-based phase-locked loop (MAF-PLL) can obtain grid synchronization signals accurately under adverse grid conditions with a large amount of harmonics due to the high filtering capability of the MAF. However, MAF-PLL cannot achieve a fast dynamic response in the case of frequency drift, phase angle steps, and unbalanced voltage sag. MAF is essentially an FIR filter, and its filtering performance is hard to be adjusted. To address this issue, this paper proposes an alternative to MAF consisting of a set of cascading second-order IIR filters (CIIRF). Based on MAF, CIIRF introduces multiple zeros and poles from the zero–pole replacement perspective, and by changing the position of the poles, the filter performance can be adjusted. To improve the anti-interference ability of PLL based on CIIRF (CIIRF-PLL) in the presence of grid frequency drift, a frequency-adaptive scheme is also proposed. Simulation and experimental results show that CIIRF-PLL can accurately track the grid voltage phase in the case of frequency steps, phase angle jumps, harmonics injection, and unbalanced voltage sag and has good steady-state and dynamic performance. Full article

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. Full article

Today, and especially with the growing interest in distributed renewable energy sources (DRESs), modern electric power systems are becoming more and more complex. In order to increase DRES penetration, grid side converter (GSC) control techniques require appropriate synchronization algorithms that are able to detect the grid voltage status as fast and accurately as possible. The drawbacks of the published synchronization phase-locked loop (PLL) techniques were structured mainly around the slow dynamic responses, the inaccuracy of extracting the fundamental components of the grid voltages when they contain a DC offset, and the worsening of the imbalance rejection ability facing significant frequency changing. This paper proposes a new synchronization PLL technique ensuring efficient and reliable integration of DRESs under normal, abnormal, and harmonically distorted grid conditions. The proposed PLL uses the mixed second- and third-order generalized integrator (MSTOGI) in the prefiltering stage through its adaptability to power quality and numerous grid conditions and its low sensitivity to input DC and inter-harmonics. Moreover, a modified quasi type-1 PLL (MQT1-PLL), which integrates two compensation blocks for phase and amplitude errors, respectively, has been used in the control loop. The discussion of sizing requirements and the effectiveness of the so-called MSTOGI-MQT1-PLL are tested under grid voltage imbalances and distortions and confirmed through simulation results compared to double second-order generalized integrator PLL (DSOGI-PLL), cascaded delayed signal cancellation PLL (CDSC-PLL), and multiple delayed-signal cancellation PLL (MDSC-PLL). Full article

Recent healthcare systems based on human body communication (HBC) require human interaction sensors. Due to the conductive properties of the human body, capacitive sensors are most widely known and are applied to many electronic gadgets for communication. Capacitance fluctuations due to the fact of human interaction are typically converted to voltage levels using some analog circuits, and then analog-to-digital converters (ADCs) are used to convert analog voltages into digital codes for further processing. However, signals detected by human touch naturally contain large noise, and an active analog filter that consumes a lot of power is required. In addition, the inclusion of ADCs causes the system to use a large area and amount of power. The proposed structure adopts a digital-based moving average filter (MAF) that can effectively operate as a low-pass filter (LPF) instead of a large-area and high-power consumption analog filter. In addition, the proposed ∆C detection algorithm can distinguish between human interaction and object interaction. As a result, two individual digital signals of touch/release and movement can be generated, and the type and strength of the touch can be effectively expressed without the help of an ADC. The prototype chip of the proposed capacitive sensing circuit was fabricated with commercial 65 nm CMOS process technology, and its functionality was fully verified through testing and measurement. The prototype core occupies an active area of 0.0067 mm², consumes 7.5 uW of power, and has a conversion time of 105 ms. Full article

This paper aims at presenting and describing a dimensioning methodology for energy storage systems (ESS), in particular for one based on flywheels, applied for the specific application of reducing power oscillation in a wave energy conversion (WEC) plant. The dimensioning methodology takes into account the efficiency maps of the storage technology as well as the effect of the filtering control techniques. The methodology is applied to the case study of a WEC plant in operation in Spain, using real power generation profiles delivered into the electric grid. The paper firstly describes the calculation procedure of the efficiency maps for the particular technology of flywheels, although it could be extended to other storage technologies. Then, the influence of using future data values in the dimensioning process and the control of the ESS operation is analysed in depth. A moving average filter (MAF) is defined to compensate for power oscillations, studying the difference between considering prediction and not doing so. It is concluded that a filtering control using future values based on a number of samples equivalent to a 4-min time order provides an important reduction in the energy storage requirements for a power generation plant. Finally, and based on the selection of storage modules previously defined, the efficiency maps, and the MAF used for delivering the power into the grid, an optimal operation strategy is suggested for the storage modules, based on a stepped switching technique. The selection of four flywheel energy storage system (FESS) modules achieves a reduction of 50% in power oscillations, covering 85% of the frequency excursions at the grid. Full article

The interconnection of new generating and storing devices to the power grid imposes the necessity of synchronizing, so the power flow can be manipulated and distributed. In the presence of an increasingly perturbed electric grid, many proposals of novel and modified synchronization techniques attained enough robustness to deal with known perturbations. However, such proposals exhibit drawbacks on their own, leaving open enhancement opportunities, mostly over their discrete implementation—e.g., sampling issues and not-considered inter/harmonics—and their inherent complexity—e.g., the need for frequency adaptability. In this work, three traditional synchronous reference frame (SRF) phase-locked loops (PLL) are modified to implement discrete filtering, such as the well-known proposals based on moving average filters (MAFs), to avoid the problems mentioned above, known for affecting the MAF’s performance. This proposal makes use of discrete, efficient units modularly assembled to yield a signal’s average, based on elliptic half-band filters. The proposed PLLs were tested and exhibited clear advantages—robustness against frequency disturbances—over MAF-based equivalents at standardized tests over a typical simulation environment, setting through this work an initial milestone for its verification and further incorporation in more complex synchronization topologies. Full article

With the increasing penetration of renewable energy, a weak grid with declining inertia and distorted voltage conditions becomes a significant problem for wind and solar energy integration. Grid frequency is prone to deviate from its nominal value. Grid voltages become more easily polluted by unbalanced and harmonic components. Grid synchronization technique, as a significant method used in wind and solar energy grid-connected converters, can easily become ineffective. As probably the most widespread grid synchronization technique, phase-locked loop (PLL) is required to detect the grid frequency and phase rapidly and precisely even under such undesired conditions. While the amount of filtering techniques can remove disturbances, they also deteriorate the dynamic performance of PLL, which may not meet the standard requirements of grid codes. The objective of this paper is to propose an effective PLL to tackle this challenge. The proposed PLL is based on quasi-type-1 PLL (QT1-PLL), which provides a good filtering capability by using a moving average filter (MAF). To accelerate the transient behavior when disturbance occurs, a modified delay signal cancellation (DSC) operator is proposed and incorporated into the filtering stage of QT1-PLL. By using modified DSCs and MAFs in a cascaded way, the settling time of the proposed method is reduced to around one cycle of grid fundamental frequency without degrading any disturbance rejection capability. To verify the performance, several test cases, which usually happen in high renewable penetrated power systems, are carried out to demonstrate the effectiveness of the proposed PLL. Full article