Fast Estimation Algorithm Research Articles

Cooley-Tukey FFT Algorithm based on GDFT for Phasor Estimation by PMU under Power Quality Disturbances

Due to the high penetration of renewable energy sources, such as photovoltaic panels and wind turbines, in addition to the use of different electric power supplies in the power grid, there are major disturbances in the forms of electric waves. These variations and disturbances must be monitored and controlled for the efficient management of transmission and distribution of electrical energy, safety, and electrical protection systems. Nowadays, Phasor Measurement Unit (PMU) technology is an essential tool to develop the supervision, protection, and control of the electrical power grid. PMUs measure the amplitude and angle of current and voltage waveforms on a Coordinated Universal Time (UTC) time scale and speedily measure the fundamental frequencies and their rates of change using fast and accurate estimation algorithms. This paper presents a phasor estimation using a Modified Cooley-Tukey Fast Fourier Transform algorithm based on the Generalized Discrete Fourier Transform (DFT) used in PMUs, using simulations in MATLAB. This algorithm was utilized to accelerate and simplify the computation of DFTs. To validate the performance under waveform disturbances, several tests with different waveforms and disturbances were simulated and interpreted according to the standard and compared with DFT.

Evaluation of the Fast Synchrophasors Estimation Algorithm Based on Physical Signals

The goal of this study is to evaluate the performance of the fast algorithm for synchrophasor estimation proposed on the basis of a physical system. The test system is represented by a physical model of a power system with four synchronous generators (15 and 5 kVA). Three synchronous machines represent steam turbine generators, while the fourth machine represents a hydro generator. The proposed method of accuracy assessment is based on comparison of the original and the recovered signals, using values of amplitude and phase angle. The experiments conducted in the study include three-phase faults, two-phase faults and single-phase faults at various buses of the test model. Functional dependencies of initial signal standard deviation from the recovered signal are obtained, as well as those for sampling rate and window width. Based on the results, the following requirements for measurement system and window width are formulated: sampling rate of analog-to-digital converter should be 10 kHz; and window width should start from 5 ms. In addition, the fast algorithm of synchrophasor estimation was tested on event recorder signals. The sampling rate of these signals was 2 kHz. Acceptable window width for event recorder signals is 8 ms. The algorithm was implemented using programming language Python 3 for the testing purposes. The proposed fast algorithm of synchrophasor estimation can be applied in methods for emergency control and equipment state monitoring with short time response.

Deep learning method for rapidly estimating pig body size

Context During pig breeding, a change in a pig’s body size is an important indicator that reflects its health. However, it is difficult to extract the necessary features from images to estimate pig body size without contact. Aims It is crucial to develop a fast and accurate body size estimation algorithm to meet the practical needs of farms, i.e., numerous body size detections. Methods This report presents a rapid pig body size estimation technique based on deep learning. The YOLOv5 model is enhanced by integrating MobilenetV3, and a lightweight object detection network is introduced as the feature extraction network. An attention mechanism is also added to this system. Following these improvements, the proposed YOLOv5_Mobilenet_SE model is more suitable for the small-target detection of key parts of live pigs. A depth camera was used at a fixed height to capture the pig’s back information, which enables calculations of the critical height, i.e., the body height, of live pigs. Other key measuring points on the pig are generated according to the detection frame of the key parts located by the model. A gradient boosting regression algorithm is used to establish the body size prediction model based on the Euclidean distance between the key measuring points and the actual body size data. Key results The upgraded YOLOv5_Mobilenet_SE model achieves a mean average precision of 3.9%, which is higher than that obtained using the original YOLOv5 model. The model size is reduced from 91.2 to 10.2 M, and the average detection time for each image is 4.4 ms. The mean absolute percent errors in terms of body size, body width, and body height are 2.02%, 1.95%, and 1.84%, respectively, relative to manual measurements. Conclusions This method greatly reduces the model size and detection time while ensuring accuracy, and therefore, this method can cut costs for farms performing pig body size measurements. Implications The results of this study can provide technical support for automated and digital monitoring in the pig breeding industry.

A fast TDOA and VDOA estimation method of the wideband frequency hopping signal

In the mobile multi-station passive positioning scenario, the carrier frequency hopping of the wideband frequency hopping communication signal will produce a changing Doppler frequency shift. Therefore, it is not possible to directly estimate the Doppler frequency difference of the observed signal containing multiple pulses. In this case, only the time difference and frequency difference of the single-hop signal can be estimated, but the accuracy is low. The existing methods often need the carrier frequency of each FH pulse signal as a priori information, and the high computational complexity of the two-dimensional search method of the time difference and frequency difference is difficult to overcome. Aiming at the above problems, a fast and high-precision estimation algorithm for the time difference of arrival and velocity difference of wideband frequency hopping signals is proposed in this paper. Firstly, the wideband frequency hopping signal is segmented in the time domain with multiple pulses as the unit, the signal model of time-varying time difference is established, and the time difference of each short-time segment is estimated by the generalized cross-correlation algorithm. The estimated values of the initial time difference and Doppler velocity difference can be worked out by least squares fitting of the time difference sequence obtained from the single pulse estimation. Finally, taking the Link16 data link signal as an example, the simulation experiment of the proposed method is conducted, which verifies the correctness and performance superiority of the algorithm.

Solution of the emergency control of synchronous generator modes based on the local measurements to ensure the dynamic stability

Stochastic renewable sources of energy have been causing changes in the structure and operation of power systems. High penetration of this type of generation results in decreased inertia of a power system, increased active power fluctuations, and a higher probability of false tripping of emergency control devices. Traditional algorithms of emergency control are not adaptable and flexible enough for systems with high penetration of renewables and flexible alternating current transmission systems. Integration and development of phasor measurement units make it possible to create adaptable emergency control systems, which would require minimal pre-defined data. The purpose of this study is to develop an adaptable algorithm for turbine fast valving control synthesis and transient stability estimation for a generator. The suggested algorithm is based on the equal area criterion in the domain synchronous generator “torque–load angle”. The measurements of the generator operation under consideration are used as the input data for the steam turbine fast valving control synthesis. Thus, the algorithm becomes adaptable because no pre-defined parameters of a power system model are required. The suggested algorithm was tested on the power system model NE39bus using Matlab/Simulink. The efficiency of the suggested algorithm is verified and demonstrated.

Robust factored principal component analysis for matrix-valued outlier accommodation and detection

Principal component analysis (PCA) is a popular dimension reduction technique for vector data. Factored PCA (FPCA) is a probabilistic extension of PCA for matrix data, which can substantially reduce the number of parameters in PCA while yield satisfactory performance. However, FPCA is based on the Gaussian assumption and thereby susceptible to outliers. Although the multivariate t distribution as a robust modeling tool for vector data has a very long history, its application to matrix data is very limited. The main reason is that the dimension of the vectorized matrix data is often very high, and the higher the dimension, the lower the breakdown point that measures the robustness. To solve the robustness problem suffered by FPCA and make it applicable to matrix data, in this paper a robust extension of FPCA (RFPCA) is proposed, which is built upon a t-type distribution called matrix-variate t distribution. Like the multivariate t distribution, the matrix-variate t distribution can adaptively down-weight outliers and yield robust estimates. A fast EM-type algorithm for parameter estimation is developed. Experiments on synthetic and real-world datasets reveal that (i) RFPCA is compared favorably with several closely related methods. Importantly, RFPCA has a significantly higher breakdown point than its vector-based cousin multivariate t PCA (tPCA), which makes RFPCA more applicable to matrix data; (ii) the expected latent weights of RFPCA can be readily used for outlier detection, and they are much more reliable than those by tPCA. Such detection is rarely available with existing matrix-based methods, especially for gross matrix-valued outliers.

Fast algorithm for time decay estimation with applications to electrostatic ion beam traps

The ability of peptide trapping in an electrostatic ion beam trap (EIBT) is used for the measurement of renin substrate lifetime dependence from the pressure. The time decay estimation is traditionally obtained by optimization of nonlinear curve-fitting in the least-squares sense. This paper presents a novel algorithm to address this problem, using a numerical differentiation method as the basis for lifetime estimation. Simulations results show that the proposed method provides results similar to those obtained with the classical approach, but is faster by about two orders of magnitude. An experimental result is detailed, which shows the adequacy of this algorithm for the real-life monitoring of decay measurements, not only for EIBT, but also for other processes such as luminescence where exponential decay is involved.

Joint fast time-varying channel estimation with noise elimination and ICI cancellation for LTE-V2X systems

Vehicle-to-everything (V2X) communication is essential for intelligent transportation systems (ITS) and critical technology to ensure traffic safety. Aiming at the problems of noise interference, time-varying, and inter-carrier interference (ICI) in the LTE-V2X channel, a joint fast time-varying channel estimation with noise elimination and ICI cancellation is proposed in this paper. Firstly, using the autocorrelation characteristics of the Zadoff Chu (ZC) pilot sequence, a modified discrete Fourier transform (M-DFT) channel estimation algorithm is proposed to eliminate the noise in cyclic prefix (CP). Secondly, a joint iterative direct decision (IDD) and time-varying channel fitting (CF), called IDD-CF channel estimation algorithm, is proposed to track the rapid changes of channels on different symbols and eliminate ICI. The system simulation results show that the proposed joint fast time-varying channel estimation algorithm can effectively eliminate the noise and ICI, improve the performance of channel estimation, and have better robustness under different Doppler frequency shifts than the representative channel estimation algorithm.

Fast Terminal Sliding Mode Fault-Tolerant Control for Markov Jump Nonlinear Systems Based on an Adaptive Observer

In this paper, a new adaptive observer is proposed to estimate the actuator fault and disturbance of a quadrotor UAV system with actuator failure and disturbance. Based on this, a nonsingular fast terminal sliding mode controller is designed. Firstly, according to the randomness of faults and disturbances, the UAV system under faults and disturbances is regarded as one of the Markov jump nonlinear systems (MJNSs). Secondly, an adaptive observer is designed to simultaneously observe the system state, fault, and disturbance. In order to improve the precision, the fast adaptive fault estimation (FAFE) algorithm is adopted in the adaptive observer. In addition, a quasi-one-sided Lipschitz condition is used to deal with the nonlinear term, which relaxes the condition and contains more nonlinear information. Finally, a nonsingular fast terminal sliding mode controller is designed for fault-tolerant control of the system. The simulation results show that the faults and disturbances can be observed successfully, and that the system is stochastic stable.

Advanced direction of arrival estimation using step‐learnt iterative soft‐thresholding for frequency‐modulated continuous wave multiple‐input multiple‐output radar

The number of antennas in automotive frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar systems is increasing. Existing greedy or subspace-based methods cannot quickly and accurately estimate the direction of arrival (DoA) of the target. Therefore, we propose a fast and accurate DoA estimation algorithm for the automotive FMCW MIMO radar. To achieve both fastness and accuracy, we exploit the group sparsity in DoA estimation by defining the problem as a multiple measurement vector (MMV) compressive sensing and extend the step-learnt iterative soft-thresholding algorithm (SLISTA) to the MMV problem. To apply the extended SLISTA, we train the network in an unsupervised manner and normalise the input. We conduct experiments to evaluate the performance of the proposed method. Compared to the algorithms such as ISTA/FISTA/MFOCUSS that solve the same optimisation problem, the extended SLISTA exhibits the most accurate DoA estimation results for actual targets, with less execution time than a subspace-based method. Moreover, the results show that the extended SLISTA prevents false detections, whereas greedy and subspace-based methods do not.

Nowcasting GDP with a pool of factor models and a fast estimation algorithm

We propose a novel mixed-frequency dynamic factor model with time-varying parameters and stochastic volatility for macroeconomic nowcasting and develop a fast estimation algorithm. This enables us to generate forecast densities based on a large space of factor models. We apply our framework to nowcast US GDP growth in real time. Our results reveal that stochastic volatility seems to improve the accuracy of point forecasts the most, compared to the constant-parameter factor model. These gains are most prominent during unstable periods such as the Covid-19 pandemic. Finally, we highlight indicators driving the US GDP growth forecasts and associated downside risks in real time.

Fast joint estimation for time delay and angle of arrival based on smooth preprocessing with orthogonal frequency division multiplexing

In order to solve the problems of high complexity and absence of adaptability with the joint estimation for time delay (TD) and angle of arrival (AOA) in coherent multipath environments, a fast estimation algorithm based on orthogonal frequency division multiplexing (OFDM) technique is proposed. First, we combine OFDM signal characteristics with array features to obtain extended arrays. Then, we obtain the channel frequency domain response covariance matrices for TD and AOA estimation separately by smoothing preprocessing in the spatial and frequency domains, respectively. Finally, we estimate the TD values by a one-dimensional (1-D) spectral peak search, as well as determine the closed-form solution for AOA by Unitary-ESPRIT. In comparison with previous work, the proposed algorithm not only improves the adaptability in correlated or mixed multipath environments but also significantly reduces the complexity. The simulation results show the effectiveness and robustness of the proposed joint estimation algorithm.

Fast state estimation under sensor attacks: A sensor categorization approach

In a sensor network, some sensors usually provide the same or equivalent measurement information, which is not taken into account by the existing secure state estimation methods against sparse sensor attacks such that the computational efficiency of these methods needs to be further improved. In this paper, by considering the observation equivalence of sensor measurement, a concept of analytic sensor types is introduced based on the equivalence class to develop a fast state estimation algorithm. By verifying the similarity degree of measurement data of a sensor type, the attack location information can be extracted to exclude some mismatching search candidates, without loss of estimation correctness. This confirms high speed performance of the proposed algorithm, since the number of sensor types is usually far less than the number of sensors.

Inductive Power Transfer System With Maximum Efficiency Tracking Control and Real-Time Mutual Inductance Estimation

To design simple and efficient inductive power transfer (IPT) systems, a minimum number of conversion stages and an effective maximum efficiency tracking (MET) are the primary design criteria. The MET control uses a fast mutual inductance estimation (MIE) algorithm to achieve fast tracking of the maximum efficiency point, especially when the variation of the gap distance and misalignment of the magnetic coupler are significant. The feedback control of the MET-MIE IPT system should be designed without a wireless communication channel to improve stability and robustness. In this article, a novel MET-MIE control strategy is proposed for achieving simplicity and high efficiency in IPT systems. Verifications are provided by simulations and experiments. It is shown that maximum efficiency can be tracked with less than 4% error in MIE under significant variation of the gap distance and misalignment of the magnetic coupler.

Reduction of the Rank Calculation of a High-Dimensional Sparse Matrix Based on Network Controllability Theory

Numerical computing of the rank of a matrix is a fundamental problem in scientific computation. The datasets generated by the Internet often correspond to the analysis of high-dimensional sparse matrices. Notwithstanding recent advances in the promotion of traditional singular value decomposition (SVD), an efficient estimation algorithm for the rank of a high-dimensional sparse matrix is still lacking. Inspired by the controllability theory of complex networks, we converted the rank of a matrix into maximum matching computing. Then, we established a fast rank estimation algorithm by using the cavity method, a powerful approximate technique for computing the maximum matching, to estimate the rank of a sparse matrix. In the merit of the natural low complexity of the cavity method, we showed that the rank of a high-dimensional sparse matrix can be estimated in a much faster way than SVD with high accuracy. Our method offers an efficient pathway to quickly estimate the rank of the high-dimensional sparse matrix when the time cost of computing the rank by SVD is unacceptable.

Spatially varying coefficient models using reduced-rank thin-plate splines

Spatially varying coefficient (SVC) regression models are concerned about regression for spatial data, where regression coefficients may vary in space. This paper proposes a new approach for SVC modeling by representing regression coefficients using a class of multiresolution spline basis functions in a generalized-linear model framework. The proposed method provides flexible and parsimonious representations for regression coefficients. It enables commonly used (generalized) linear-regression packages for estimation, testing, and constructing confidence levels. We develop a fast estimation algorithm that simultaneously performs variable selection, detects spatial heterogeneity for each variable, and determines its complexity. We provide numerical examples and an application to a real estate dataset to demonstrate the proposed method’s effectiveness.

Sparse matrix linear models for structured high-throughput data

Recent technological advancements have led to the rapid generation of high-throughput biological data which can be used to address novel scientific questions in broad areas of research. These data can be thought of as a large matrix with covariates annotating both its rows and columns. Matrix linear models provide a convenient way for modeling such data. In many situations, sparse estimation of these models is desired. We present fast, general methods for fitting sparse matrix linear models to structured high-throughput data. We induce model sparsity using an L1 penalty and consider the case when the response matrix and the covariate matrices are large. Due to data size, standard methods for estimation of these penalized regression models fail if the problem is converted to the corresponding univariate regression scenario. By leveraging matrix properties in the structure of our model, we develop several fast estimation algorithms (coordinate descent, FISTA and ADMM) and discuss their trade-offs. We evaluate our method’s performance on simulated data, E. coli chemical genetic screening data and two Arabidopsis genetic datasets with multivariate responses. Our algorithms have been implemented in the Julia programming language and are available at https://github.com/senresearch/MatrixLMnet.jl.

Fast Active Fault-Tolerant Control for a Quadrotor UAV Against Multiple Actuator Faults

This paper proposes a fast adaptive fault estimator-based active fault-tolerant control strategy for a quadrotor UAV against multiple actuator faults. A fast adaptive fault estimation algorithm is designed to estimate the unknown actuator fault parameters. By synthesizing the fast adaptive fault estimator with the embedded control law, an active fault-tolerant control mechanism is established to compensate the adverse effects of multiple actuator faults. The effectiveness of the proposed strategy is validated through both numerical simulations and experimental tests.

A fast frequency estimation algorithm and its application in transformer terminal unit

To accurately obtain the frequency of voltage and current signals and form the basis for the analysis of power quality in the Transformer Terminal Unit (TTU), a Hanning window and interpolation algorithm is proposed to estimate the signal frequency. The principle of the algorithm is introduced, and the computer program is written. At the same time, the voltage signals with different frequencies are numerically generated and the real voltage is measured, and the signal frequency is estimated by using the algorithm. The results reveal that the proposed method can accurately estimate the signal frequency for TTU, and the maximum error of frequency estimation is only 2 mHz when the signal frequency varies in the range of 49.8-50.2 Hz. However, the maximum error estimated by the FFT algorithm is up to 0.2 Hz. The computation time of the Hanning window and interpolation algorithm is only 1.29 times that of the Fast Fourier Transform algorithm. The work lays the foundation for accurate and rapid estimation of voltage and current signal frequency in TTU.

Fast Active Aerial Exploration for Traversable Path Finding of Ground Robots in Unknown Environments

This article proposes an autonomous aerial exploration framework for traversable path finding of ground robots in unknown environments to achieve a fast response in search and rescue (SAR) missions. Different from existing works, our method provides a task-oriented active exploration strategy that makes the aerial robot fully autonomous during the exploration and does not require human intervention. To identify the candidate regions which ground robots can go through, a fast incremental traversability estimation algorithm is presented, which can evaluate the traversability of the region of interest (ROI) efficiently. Based on the traversability map, we propose a highly efficient frontier detection algorithm that can guarantee both reachability and safety of the extracted frontiers. To reduce the total response time of the whole robot system, a new cost function considering both the time costs of the aerial and ground robots is designed that aims to avoid back-and-forth motion behavior during aerial exploration. The performance of our approach is evaluated in both simulation and real-world experiments. The results show that the proposed method outperforms existing methods in terms of traversability estimation and frontier detection time (about one order of magnitude faster) as well as the response time (at least 13% shorter on average).