Parameter Estimation Algorithm Research Articles

Efficient estimation of PV parameters for existing datasets by using an intelligent algorithm

This paper introduced a newly proposed Meta-heuristic (MH) algorithm for parameter estimation of solar PV, i.e. Bonobo Optimizer (BO) algorithm. This algorithm has been inspired by the social behaviour as well as the reproductive behaviour of the bonobos. Thirteen datasets are investigated for the first time using the Single Diode Model (1-D), Double Diode Model (2-D), and Three Diode Model (3-D): RTC, Photowatt-PWP201, Leibold Solar Module LSM 20, Amorphous Silicon aSi:H, Amorphous Silicon aSi:H, Monocrystalline Commercial 3 × 3, Leybold Solar Module STE 4/100, Leybold 664 431, Kyocera KC200GT, STP-120/36, PVM 752GaAs thin film, SS2018, STM6–40/36, Sharp ND-R250A5 PV panels. This algorithm is applied to extract the parameters in all three models, 1-D, 2-D, and 3-D. According to the results, the BO optimization techniques outperform the existing MH optimization algorithms in terms of Root Mean Square Error (RMSE). However, to increase the accuracy of the output solutions, the BO algorithm is combined with the Newton-Raphson approach in this paper.

Hierarchical iterative identification of output nonlinear Box-Jenkins Wiener model with ARMA noise

This paper focuses on development of a three-stage iterative identification algorithm for parameter estimation of a Wiener model with autoregressive moving average (ARMA) noise entering between linear and nonlinear part. The proposed three-stage algorithm is developed based on generalized extended gradient iterative (GEGI) algorithm to increase the convergence rate at a low number of iterations. To increase the convergence rate, a second algorithm is derived based on generalized extended least squares algorithm (GELSI). The proposed 3-stage generalized extended least square iterative (3S-GELSI) algorithm will also decrease the computational burden compared to the single-stage algorithm. Finally, to show that the discussed methods can identify the considered system effectively, simulation results of three examples are provided. The results demonstrate that the 3-stage GEGI (3S-GEGI) algorithm outperforms its basic single-stage counterpart in terms of convergence rate. Moreover, the proposed 3S-GELSI algorithm exhibits even greater superiority in terms of convergence rate compared to other existing methods. Additionally, it will be demonstrated that the computational burden decreases in the 3S-GELSI algorithm, as indicated by the reduced number of total flops.

A Modified Gradient Search Rule Based on the Quasi-Newton Method and a New Local Search Technique to Improve the Gradient-Based Algorithm: Solar Photovoltaic Parameter Extraction

Harnessing solar energy efficiently via photovoltaic (PV) technology is pivotal for future sustainable energy. Accurate modeling of PV cells entails an optimization problem due to the multimodal and nonlinear characteristics of the cells. This study introduces the Multi-strategy Gradient-Based Algorithm (MAGBO) for the precise parameter estimation of solar PV systems. MAGBO incorporates a modified gradient search rule (MGSR) inspired by the quasi-Newton approach, a novel refresh operator (NRO) for improved solution quality, and a crossover mechanism balancing exploration and exploitation. Validated through CEC2021 test functions, MAGBO excelled in global optimization. To further validate and underscore the reliability of MAGBO, we utilized data from the PVM 752 GaAs thin-film cell and the STP6-40/36 module. The simulation parameters were discerned using 44 I-V pairs from the PVM 752 cell and diverse data from the STP6-40/36 module tested under different conditions. Consistency between simulated and observed I-V and P-V curves for the STM6-40/36 and PVM 752 models validated MAGBO’s accuracy. In application, MAGBO attained an RMSE of 9.8 × 10−4 for double-diode and single-diode modules. For Photowatt-PWP, STM6-40/36, and PVM 752 models, RMSEs were 2.4 × 10−3, 1.7 × 10−3, and 1.7 × 10−3, respectively. Against prevalent methods, MAGBO exhibited unparalleled precision and reliability, advocating its superior utility for intricate PV data analysis.

A parameter estimation method based on discontinuous unscented Kalman filter for non-smooth gap systems

Internal parameters of non-smooth systems, such as hysteresis and gap systems, are unable to be measured when the system is enclosed. For a non-smooth gap system, the actual system state is difficult to be evaluated due to state transitions and the coupled internal mechanical and geometric parameters. Moreover, the system state evaluation directly affects the parameter estimation result, which, in turn, will affect the evaluation of the system state at the next time instant. The interdependence between the estimated parameters and the subsequent state evaluation creates a large challenge for the traditional filter method. Based on the idea of estimating system parameters at different states from the discontinuous extended Kalman filter (DEKF) and the discontinuous unscented Kalman filter (DUKF), a parameter estimation algorithm, namely DBAA-DUKF, is proposed herein by introducing a dynamic boundary approximation algorithm (DBAA) to address this contradiction. Firstly, three evaluation conditions are presented for the system state identification. Then, DBAA-DUKF adjusts the state transition parameters and their upper and lower limits according to the difference between the evaluation results of the system state and filtering results. The parameter estimation accuracy of DBAA can be improved by adjusting the upper and lower limits of state transition parameters to constrain them in a narrow range. Finally, a single-sided nonlinear impact system with a significant gap and a double-sided gap system with small gaps are used to verify the effectiveness and feasibility of the algorithm. The results show that DBAA-DUKF can effectively solve the parameter estimation problem in the uncertain state transition conditions of non-smooth gap systems and can obtain better estimation results than DEKF and DUKF.

COSMOGLOBE: Towards end-to-end CMB cosmological parameter estimation without likelihood approximations

We implement support for a cosmological parameter estimation algorithm in Commander and quantify its computational efficiency and cost. For a semi-realistic simulation similar to Planck LFI 70 GHz, we find that the computational cost of producing one single sample is about 20 CPU-hours and that the typical Markov chain correlation length is ∼100 samples. The net effective cost per independent sample is ∼2000 CPU-hours, in comparison with all low-level processing costs of 812 CPU-hours for Planck LFI and WMAP in COSMOGLOBE Data Release 1. Thus, although technically possible to run already in its current state, future work should aim to reduce the effective cost per independent sample by one order of magnitude to avoid excessive runtimes, for instance through multi-grid preconditioners and/or derivative-based Markov chain sampling schemes. This work demonstrates the computational feasibility of true Bayesian cosmological parameter estimation with end-to-end error propagation for high-precision CMB experiments without likelihood approximations, but it also highlights the need for additional optimizations before it is ready for full production-level analysis.

Relaxed Adaptive Lasso for Classification on High-Dimensional Sparse Data with Multicollinearity

High-dimensional sparse data with multicollinearity is frequently found in medical data. This problem can lead to poor predictive accuracy when applied to a new data set. The Least Absolute Shrinkage and Selection Operator (Lasso) is a popular machine-learning algorithm for variable selection and parameter estimation. Additionally, the adaptive Lasso method was developed using the adaptive weight on the l1-norm penalty. This adaptive weight is related to the power order of the estimators. Thus, we focus on 1) the power of adaptive weight on the penalty function, and 2) the two-stage variable selection method. This study aimed to propose the relaxed adaptive Lasso sparse logistic regression. Moreover, we compared the performances of the different penalty functions by using the mean of the predicted mean squared error (MPMSE) for the simulation study and the accuracy of classification for a real-data application. The results showed that the proposed method performed best on high-dimensional sparse data with multicollinearity. Along with, for classifier with the support vector machine, this proposed method was also the best option for the variable selection process.

Distributed sub-Nyquist sampling and parameters measurement of common frequency support MEDS signals

Parameters measurement of multiple exponentially damped sinusoids (MEDS) signals is of great significance in many areas. However, recent technologies are most based on Nyquist sampling theory and thus require very high sampling rate for wide band signals. In this paper, a distributed sub-Nyquist sampling and parameters measurement method is proposed for common frequency support MEDS signals. The proposed method can measure the parameters of P common frequency support MEDS signals by P+1 distributed sub-Nyquist sampling channels. A subspace based parameters estimation algorithm is proposed to estimate the unknown amplitudes, frequencies and damping factors of the measured MEDS signals. It can be determined that K(P+1)+P⌈K/P⌉ samples are sufficient for parameters measuring of P MEDS signals with K common frequency components, which is much smaller than previous works. Finally, we design a hardware prototype to verify the effectiveness of the proposed method. Simulation and hardware experiments have demonstrated the effectiveness and noise robustness of the proposed method.

An extended Markov-switching model approach to latent heterogeneity in departmentalized manpower systems

In recent works in manpower planning interest has been awakened in modeling manpower systems in departmentalized framework. This, as a form of disaggregation, may solve the problem of observable heterogeneity but not latent heterogeneity; it rather opens up other aspects of latent heterogeneity hitherto unaccounted for in classical (non departmentalized) manpower models. In this article, a multinomial Markov-switching model is formulated for investigating latent heterogeneity in intra-departmental and interdepartmental transitions in departmentalized manpower systems. The formulation incorporates extensions of the mover-stayer principle resulting in several competing models. The best manpower model is chosen based on the optimum number of hidden states established by the use of Expectation-Maximization iterative algorithm for estimation of the model parameters and a search procedure for assessing model performance against one another. The illustration establishes the usefulness of the model formulation in highlighting hidden disparities in personnel transitions in a departmentalized manpower system and in avoiding wrong model specification.

A Novel Adaptive Model Predictive Control Strategy for DFIG Wind Turbine With Parameter Variations in Complex Power Systems

In this paper, a novel adaptive model predictive control (MPC) strategy is proposed for doubly fed induction generator (DFIG) wind turbine (WT), which is integrated into a complex power system, in order to improve the power output tracking precision and dynamic performance. Considering the existence of parameter variations in DFIG, an adaptive parameter estimation method is firstly designed. By analysis of stability, the convergence of the parameter estimation algorithm is rigorously proved. Furthermore, the parameter estimation algorithm is effectively integrated into MPC to realize real-time optimal control of DFIG with the adaptive model. To achieve the rotor side converter (RSC) design based on adaptive MPC, the DFIG model is linearized. In addition, a virtual output compensation (VOC) strategy is adopted to alleviate the impact of model linearization errors on the MPC, especially the variation of the model parameter. The newly proposed adaptive MPC-based RSC is capable of greatly improving the tracking performance, meanwhile taking into account the realistic constraints under various operation conditions. The simulation results demonstrate the effectiveness and superiority of the proposed control method.

UAV-Aided Radio Map Construction Exploiting Environment Semantics

This paper constructs a full dimensional (6D) radio map to predict the channel gain between any transmitter location and any receiver location based on received signal strength (RSS) measurements between low-altitude aerial nodes and ground nodes. The main challenge is how to describe the signal strength attenuation due to the blockage from the environment. Conventional interpolation-type approaches fail to exploit the close relation between the radio map and the geometry of the environment. This paper proposes to construct radio maps by first estimating and constructing a multi-class 3D virtual obstacle map that describes the geometry of the environment with radio semantics. Mathematically, a least-squares problem is formulated to jointly estimate the virtual obstacle map and the propagation parameters. This problem is found to have a partial quasiconvexity that leads to the development of an efficient parameter estimation and radio map construction algorithm. Numerical experiments confirm that the proposed method substantially reduces the amount of measurement required for achieving the same radio map accuracy. It is also demonstrated that in a unmanned aerial vehicle (UAV)-aided relay communication scenario, a radio map assisted approach for UAV placement can achieve more than 50% capacity gain.

Health Observation of the Capital Airport South Expressway Based on Improved MT-InSAR Technology

Objectives: The Airport South Expressway in China is built in a soft soil area, which may induce great hidden danger to airport traffic safety operations. Technology or Method: A new method to improve Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology, based on a novel time-series InSAR deformation model and an improved parameter estimation algorithm, is proposed for soft soil expressways monitoring. The functional relationship between the deformation and the creep parameters (viscosity and elastic modulus) based on the Maxwell model in one-dimensional linear rheology replaces the traditional InSAR linear model, and the creep physical parameters can be solved simultaneously in the solution process. The least-squares method with inequality constraints (LSIC) is induced to solve the unknown parameters. In total, 19 TerraSAR-X radar satellite images covering the South Expressway were utilized to validate the proposed method. The creep parameters for each pixel along the expressways and the time series deformation sequences from January 2012 to July 2014 were obtained. Results: As the results showed, the maximum settlement along the expressway was up to 125 mm, and the accuracy verification results showed that the modeling accuracy was 1.6 mm, with an improvement of 36.0% compared to the traditional linear model; the internal accuracy of the deformation results was 11.9 mm, accounting for 1.5% of the maximum deformation. Clinical or Biological Impact: Our method can provide data support and a reference for long-term health monitoring and early warning of infrastructure and traffic operation management in poor soil regions.

(Keynote) Inferring Degradation Modes from Battery Cycling Data

Battery research and development efforts necessarily involve large-scale experimental studies where cells are cycled under different conditions. Physics based models (PBM) of the type pioneered by John Newman, Ralph White, and others can play a key role in interpreting the resulting cycling data, and of course can be used to improve performance by modifications of cell design to optimize for a specific application. Long-term studies that characterize degradation in cell performance are an important type of cycling experiment that can be used to estimate useful battery life in a real-world application. The establishment of correlations between duty cycle and life is valuable and can inform battery management systems with a goal to extend life. During the course of such experiments, large data sets are created and we explore how a PBM might be used to infer the modes of cathode degradation. From degradation modes, mechanisms of degradations might be hypothesized, and this may lead to insights to further improve design and operation.Here, two PBM are reformulated to determine how cell parameters such as the amount of active cathode material appears to change with cycle number. Parameter estimation is applied to cycling data, and parameter changes are tracked for cycling data at different C-rates and different cutoff voltages. Parameter estimates are challenged with complementary experimental data for NMC and for a second cathode material that has been characterized extensively. The comparisons suggest that the methodology is effective and can provide physical insights into degradation mechanisms. To facilitate the “workflow,” inverse surrogate machine-learning techniques, in which simulation data are used for training neural networks to rapidly estimate parameters from experimental data, were utilized. To allow for both imperfect PBM and for non-ideal experimental data, data augmentation methods were introduced to improve the accuracy of parameter estimation algorithms. Examples are shown, with an emphasis on the physics over the mathematics and with an eye towards next steps.

Real-time parameter estimation of polymer electrolyte membrane fuel cell in absence of excitation

Parameter estimation is crucial for polymer electrolyte membrane fuel cell monitoring and control. Nonetheless, most parameter estimation algorithms rely on a persistence of excitation condition, which is rarely satisfied and not convenient in fuel cell systems. For this reason, this work presents and compares three algorithms to estimate in real-time some critical PEMFC parameters in the voltage equation: the ohmic resistance, the charge transfer coefficient and the oxygen activity of a proton exchange fuel cell. The first algorithm is a standard gradient descent, while the other two are based on a set of pre-preprocessing dynamics. It is shown that, while the gradient descent requires the persistence of excitation condition, the addition of the pre-processing dynamics ensures reliable estimation under significantly weaker excitation assumptions. Moreover, it is shown that the pre-processing dynamics improves the transient behaviour and noise performance of the estimators. The results are validated through a set of numerical simulations and in an experimental prototype, where sensor noise and unmodelled disturbances are considered.

Ambient oscillatory mode assessment in power system using an advanced signal processing method

Estimation of electromechanical mode properties is crucial in modern power systems for providing the operators with an adequate indication of the stress in the system. Measurement-based approaches use signal processing algorithms for mode identification and parameter estimation. This paper presents a novel framework for the assessment of low-frequency oscillation modes using real-world synchrophasor data with minimum computational effort. A nonstationary approach known as Time-Varying Filter based Empirical Mode Decomposition (TVF-EMD) technique is used to identify the dominant low-frequency modes present in the ambient PMU data. The combination of TVF-EMD with Teager Kaiser Energy Operator (TKEO) precisely estimates the instantaneous mode parameters, such as frequency, amplitude, and damping ratio. The efficacy of the proposed approach is demonstrated by applying it in a synthetic signal, simulated data of a standard IEEE test system, and in real-world PMU data of the Indian power grid. The proposed method is compared with the existing methodologies and the observations reveal that the proposed method has robust performance in estimating the instantaneous mode features in the power system with less computational complexities.

Modulatory dynamics of periodic and aperiodic activity in respiration-brain coupling

Bodily rhythms such as respiration are increasingly acknowledged to modulate neural oscillations underlying human action, perception, and cognition. Conversely, the link between respiration and aperiodic brain activity – a non-oscillatory reflection of excitation-inhibition (E:I) balance – has remained unstudied. Aiming to disentangle potential respiration-related dynamics of periodic and aperiodic activity, we applied recently developed algorithms of time-resolved parameter estimation to resting-state MEG and EEG data from two labs (total N = 78 participants). We provide evidence that fluctuations of aperiodic brain activity (1/f slope) are phase-locked to the respiratory cycle, which suggests that spontaneous state shifts of excitation-inhibition balance are at least partly influenced by peripheral bodily signals. Moreover, differential temporal dynamics in their coupling to non-oscillatory and oscillatory activity raise the possibility of a functional distinction in the way each component is related to respiration. Our findings highlight the role of respiration as a physiological influence on brain signalling.

Optimal model parameter estimation and performance analysis of PEM electrolyzer using modified honey badger algorithm

The electrochemical model of a proton exchange membrane (PEM) electrolyzer consists of a set of different unknown parameters, and identification of these parameters is needed. However, obtaining the optimum values of these parameters is critical, and this involves a complex, multi-variate, non-linear, and multimodal optimisation problem. In this article, a novel model parameter estimation problem for a PEM electrolyzer with eight unknown model parameters is formulated and solved using modified honey badger algorithm (MHBA). A mean squared error-based objective function is formulated and considered as the mean squared error (MSE) between the experimental voltage and the estimated voltage using MHBA algorithm. The accuracy of the proposed model is validated using polarization curves (J-V curve). It is found that the MHBA outperforms with a good mapping between experimental and estimated voltage. Also, the reliability and robustness for parameter estimation algorithm is validated considering two case studies with four different operating conditions. Additionally, the results obtained using MHBA are compared to those obtained with other competing algorithms. A statistical study including different statistical indices is also carried out to prove the robustness for the proposed approach. The results show that the minimum obtained values of MSE for different operating conditions are 8.73E-06, 8.75E-06, 6.17E-05 and 6.44E-05. Box plot study and convergence curves have also been drawn to check the effectiveness of the algorithm. Furthermore, to prove the superiority of the proposed approach, the PEM electrolyzer's performance is explored at different operating conditions, such as operating temperatures and output hydrogen pressures.

Neural Partial Differentiation for Parameter Estimation of Flexible Aircraft Dynamics

Although the parameter estimation methodologies had been successfully applied for the rigid aircraft dynamics, the aircraft with high degree of flexibility poses challenges to determine the appropriate aeroelastic models for inclusion in the parameter estimation algorithms. Application of the neural networks for the aerodynamic modeling and parameter estimation of an aeroelastic aircraft (or flexible aircraft) is addressed in this paper. A neural network with the scaled conjugate gradient learning algorithm is used for modeling lift and pitching moment coefficients. The partial differentiation of the neural network output is carried out for the estimation of equivalent parameters of an aeroelastic aircraft. It is demonstrated that a reasonable model and estimates of the equivalent parameters for the aeroelastic aircraft are possible with the neural partial differential method by using the measured motion and control variables, without any need for postulating the coupled equations of motion or initialization of the parameters to be estimated. The proposed approach reduces the large number of parameters for aeroelastic aircraft dynamics into fewer equivalent parameters, and this methodology does not require the measurements of the elastic deflections or their derivatives. The neural partial differential method is applied to the simulated flight data of an aeroelastic aircraft for the longitudinal dynamics. The performance of the neural partial differential approach is compared with the estimates obtained from the other methods like classical estimation methods, analytical computation, and values given in the literature for rigid body modeling. The performance is also validated by comparing the measured lift force and pitching moment coefficients with the neural predicted and neural reconstructed coefficients using the estimates obtained through the neural partial differential approach.

Implementing a novel fault prognosis technique based on nonlinear fault observer and online parameters estimation

<span lang="EN-US">This research presents a methodology for predicting errors of parameters, as the algorithm tries to monitor the parameters in order to maintain or replace them when needed to avoid excessive expenses. The presented implementation mechanism is based on monitoring parameters according to a specific number of batches and each batch consists of a number of iterations, which in turn are a number of samples. The proposed algorithm involves designing a new nonlinear observer and writing a secondary algorithm for parameter estimation based on the online nonlinear recursive least squares algorithm associated with the observer states. In addition, the algorithm presents an attempt to find a relationship between the error states and the state of the parameters by creating a new function to determine the weight of the error according to four components; parameter changes, output residuals, output errors and the error diagnosed by the new observer. The algorithm also includes introducing the probability form of the weights using the kernel density function for the average and maximum weights for each batch. Finally, relying on the results, it is possible to take the appropriate decision to maintain or change the parameters as shown a non-linear direct current motor model case study.</span>

Aircraft Parameter Estimation Using Gaussian Sum Filter With Lyapunov Stability Analysis

The use of Gaussian Sum Filter (GSF) as a state/parameter estimation tool for aerodynamic modelling is relatively unexplored in the literature. In this paper GSF has been employed for aircraft parameter estimation. The stability analysis of GSF has also been discussed using the normalized Lyapunov energy functional and the conditions for the convergence of the GSF are derived by using the proposed nonlinear observer which is a novel approach. An approach to prune the weights of GSF based on Pearson coefficient is considered. The performances of the three recursive parameter estimation algorithms: Extended Kalman Filter (EKF), the simplified version of the GSF and the improved version of the GSF are evaluated for aircraft parameter estimation using simulated flight data in the MATLAB implementations. The numerical results show that pruning the weights reduces the computational load by nearly 75%, and still provides good performance compared to the conventional GSF.

Tension Control in Unwinding System Based on Nonlinear Parameter Estimation

In this paper, two representative nonlinearities in the unwinding system are modelled. One is the rotation-disturbance caused by the rotational change of unwinding roll position; another is the time-varying radius of unwinding roll. Based on the nonlinear mechanism model, the parameters of rotation-disturbance are obtained by the Gauss-Newton algorithm, and the time-varying radius is estimated online by the Kalman filter. Then the feedforward and time-varying feedback controller are designed based on the estimated parameters to compensate for nonlinear effect in unwinding system. The experiment results show that the parameter estimation algorithm can accurately estimate parameters. And the use of estimated parameters to control can effectively solve the problem of tension fluctuation and tension deviation which caused by the nonlinearity of unwinding system.