Parameter Identification Technique Research Articles

Spatiotemporal adaptive state feedback control of a linear parabolic partial differential equation

AbstractThis article deals with the issue of asymptotic stabilization for a linear parabolic partial differential equation (PDE) with an unknown space‐varying reaction coefficient and multiple local piecewise uniform control. Clearly, the unknown reaction coefficient belongs to a function space. Hence, the fundamental difficulty for such issue lies in the lack of a conceptually simple but effective parameter identification technique in a function space. By the Lyapunov technique combined with a variant of Poincaré‐Wirtinger inequality, an update law is derived for estimate of the unknown reaction coefficient in a function space. Then a spatiotemporal adaptive state feedback control law is constructed such that the estimate of the unknown coefficient is bounded and the closed‐loop PDE is asymptotically stable in the sense of spatial norm if a sufficient condition given in terms of space‐time varying linear matrix inequalities (LMIs) is fulfilled for the estimated coefficient and the control gains. Both analytical and numerical approaches are proposed to construct a feasible solution to the space‐time varying LMI problem. With the aid of the semigroup theory, the well‐posedness and regularity of the closed‐loop PDE is also analyzed. Moreover, two extensions of the proposed adaptive control scheme are discussed: the PDE in ‐D space and the PDE with unknown diffusion and reaction coefficients. Finally, numerical simulation results are presented to support the proposed spatiotemporal adaptive control design.

A surrogate-assisted stochastic optimization inversion algorithm: Parameter identification of dams

Dynamic monitoring data plays an essential role in the structural health monitoring of dams. This study presents a surrogate-assisted stochastic optimization inversion (SASOI) algorithm, a novel technique for static and dynamic parameter identification. This algorithm is based on probabilistic finite element simulations and Bayesian inference theory. It combines the advantages of low computational cost in surrogate models and fast convergence in the Bayesian algorithm. Taking four cases of different complexity, this paper verifies the effectiveness of the SASOI algorithm and validates its practicality for large dams. Surrogate models consider several alternatives, including polynomial chaos expansion (PCE), Kriging, polynomial chaos Kriging, and support vector regression. Implementation of the SASOI algorithm on dams shows that PCE outperforms other techniques. This algorithm improves the accuracy and efficiency of the static parameter identification methods by nearly 27 times compared to the classical inversion methods. Furthermore, the accuracy of dynamic parameter identification is higher than that of static one. The SASOI algorithm is applicable to other large-scale infrastructures.

The role of computational intelligence techniques in the advancements of solar photovoltaic systems for sustainable development: a review

The use of computational intelligence (CI) in solar photovoltaic (SPV) systems has been on the rise due to the increasing computational power, advancements in power electronics and the availability of data generation tools. CI techniques have the potential to reduce energy losses, lower energy costs, and facilitate and accelerate the global adoption of solar energy. In this context, this review paper aims to investigate the role of CI techniques in the advancements of SPV systems. The study includes the involvement of CI techniques for parameter identification of solar cells, PV system sizing, maximum power point tracking (MPPT), forecasting, fault detection and diagnosis, inverter control and solar tracking systems. A performance comparison between CI techniques and conventional methods is also carried out to prove the importance of CI in SPV systems. The findings confirmed the superiority of CI techniques over conventional methods for every application studied and it can be concluded that the continuous improvements and involvement of these techniques can revolutionize the SPV industry and significantly increase the adoption of solar energy.

Optimal parameters identification strategy of a lead acid battery model for photovoltaic applications

AbstractExtracting the parameters of a lead‐acid battery under real‐world operating conditions is a significant part of solar photovoltaic (PV) engineering. Usually, the battery management system handles the battery system based on its model. However, its model's parameters can change due to its electrochemical nature. Hence, enhancing the model parameters' accuracy is required to achieve a reliable and accurate model. This research employs an improved methodology for extracting lead‐acid battery data outdoors. The suggested method combines numerical and analytical formulations of parametric battery models for solar PV energy storage. The Shepherd model, which considers the battery's non‐linear properties, is selected in this paper. Based on a modern meta‐heuristic marine predator algorithm, the parameters of two solar lead‐acid batteries are discovered using an optimal parameter identification technique (MPA). The MPA exhibits its capability in terms of fast convergence and accuracy. The acquired test results are compared to those produced by the salp swarm algorithm, artificial eco‐system optimizer, hunger games search, a new optimization meta‐heuristic method that inspires the behavior of the swarm of birds called COOT, and honey badger algorithm in terms of efficiency, convergence speed, and identification accuracy. The findings demonstrated that the MPA outperformed the other optimizers in identifying ability. This optimizer achieved 99.99% identification efficiency for both Bergan and Banner battery types, making it an excellent battery identification option.

State of charge estimation of lithium-ion battery based on multi-input extreme learning machine using online model parameter identification

The state of charge (SOC) is important for ensuring both battery safety and life. Furthermore, the SOC is required to estimate other battery states such as state of power (SOP) and state of health (SOH). Because the SOC is determined by estimation rather than observation, it is important to establish a proper estimation method. In this paper, an equivalent circuit model (ECM) was first constructed through online parameter extraction. Online parameter identification was based on a recursive least squares (RLS) method to input the various internal information regarding the battery into the extreme learning machine to achieve accurate SOC estimation. Second, to deliver a highly accurate SOC estimation of lithium-ion batteries (LiBs), the multi-input extreme learning machine (MI-ELM) method based on an online model parameter identification technique was applied to the SOC estimation of LiBs. Finally, experiments were conducted under various operating conditions to assess the performance of the proposed method. Compared with other estimation methods such as the extended Kalman filter (EKF), the ordinary extreme learning machine (ELM), the adaptive square root extended Kalman filter (ASREKF), the autoencoder neural network with long short-term memory neural network (AUTOENCOD-LSTM), the artificial neural network and unscented Kalman filter (NN&UKF), and the gravitational search algorithm-based ELM (ELM-GSA) in terms of the mean absolute error (MAE) and the root mean square error (RMSE), the proposed MI-ELM method achieved 60.00 %, 88.83 %, 52.50 %, 80.00 %, 84.55 %, and 79.64 % of the maximum performance improvement, respectively.

Applicability of Current Statutory Rules for Stability Requirements of Bangladesh Inland Passenger Vessels

_ Applicability of current Bangladesh inland vessels stability rule has been investigated for the local passenger vessels. Bangladesh inland vessel’s stability rule is exactly the same as international stability code (2008), despite of the different environmental load in navigational routes. The restricted waterways limit the dimension of the vessels. It has been found that domestic passenger vessels have changed their dimensions considerably from the year 1974 to 2009. But analysis shows, still most of the domestic passenger vessels exceed the dimensional ratio range that is specified in both the righting lever-based criteria and weather criterion of international code. The part of the weather criterion, specifically estimation method for the angle of roll due to wind and wave action has been studied in details. Different nationals have adopted values of governing parameters for extended range of vessel dimensions, which can also successfully cover all the Bangladesh domestic passenger vessels. As per the code 2008, for ships with values outside of the addressed range, the angle of roll may be determined with model experiments of a subject ship as alternative weather criterion. Bangladesh is still lacking both in facility and infrastructure for carrying model tests. This study shows that computer simulation can be used in exchange of experimental setup where the simulation result can be validated by Parameter Identification Technique, which is described as an alternative approach in the interim guideline of International Maritime Organization (IMO). Introduction Passenger vessel accident is a persistent phenomenon in Bangladesh inland waterways. Thousands of people have passed away due to these catastrophic incidents. The major two modes of failure that are responsible for accidents in waterways are identified as structural failure and stability failure (Islam et al. 2015a). Structural failure like collision also leads to stability failure (Damage Stability). Iqbal et al. (2008a, 2008b) analyzed the Bangladesh inland passenger vessels accident data of 25 years and found that in 49% cases, the failure was due to lose of intact stability. Raiyan et al. (2017) have done event tree analysis of marine accidents in Bangladesh taking initial condition as overloading. Literature study (Awal 2006; Awal et al. 2007; Awal et al. 2014; Islam et al. 2015b; Rahman 2017) shows that researchers have proposed many general recommendations from different point of view to prevent the passenger vessel accidents. But still now, no attempt has been made to analyze the currently applicable statutory rules for ensuring the safety in inland waterways, which is mainly Bangladesh Inland Vessels Stability Rule including Weather Criterion (Zulfikar 2005). Stability rules of inland vessels are to be governed by standard localized condition of the subjected parameters. The socioeconomic condition, local environment, as well as infrastructural development like availability of model test facilities need to be considered for smooth and proper application of stability rules which then can only ensure the safety of local vessels. This implies that the individual efforts of concern country in research and analysis are of great importance for the development of practicable stability rules of inland vessels.

Identification of parameters for same-order nonlinear damping terms in polynomial-type vortex-induced force models for bridge decks

Polynomial-type empirical models have been commonly adopted to describe the vortex-induced force (VIF) on bridge decks in the study of vortex-induced vibration. Previous studies chose different nonlinear damping terms like the Van der Pol or Rayleigh type in such models without explaining whether these same-order terms can be chosen arbitrarily. A novel parameter identification technique is developed in this paper to distinguish different same-order nonlinear terms in the VIF model with additional governing equations based on the orthogonality among motion-induced terms. The parameter identification results obtained from the wind-tunnel tests on a central-slotted box deck indicate that the Rayleigh-type damping terms are the only important ones in the vertical VIF, whilst none of the same-order nonlinear damping terms of the torsional VIF should be ignored. The finding is then theoretically explained assuming that the aerodynamic damping nonlinearity of VIF primarily comes from the continuous change of relative attack angle between the oscillating deck and the incoming wind.

Bayesian inference based parametric identification of vortex-excited force using on-site measured vibration data on a long-span bridge

In recent years, vortex-induced vibration (VIV) events have occurred on several long-span suspension bridges around the world. Normally, the VIV of a long-span bridge is investigated in wind tunnel tests or computational fluid dynamics. However, examination of bridge VIV through full-scale field test data has rarely been conducted. Because of the rapid development of high precision sensors and high-frequency data transmission devices, the acquisition of structural modal information utilizing field test data from structural health monitoring systems is emerging as a powerful tool to explore the structural dynamic status and locate potential damage. Therefore, it is possible and necessary to inspect the bridge VEF (vortex-excited force) parameters from full-scale field test data and then to simulate and estimate the structural VIV response based on VEF parameters. Existing VEF parametric identification techniques allow structures (sectional model or full-scale bridges) to be tested under laminar flow in wind tunnel tests with known dynamic properties (inertial frequency and damping ratio), requiring measurement of responsive signals and VEF signals synchronously. However, for the actual field test of the full-scale bridge, the flow field is turbulent, and the structural responsive signal is unavoidably contaminated by measuring noises. Furthermore, it is impractical to synchronously record the aerodynamic force applied on the bridge deck during the field test. In this study, a Bayesian inference approach is introduced for the identification of VEF parameters using field vibration data. Using the fast Fourier transform (FFT) of field vibration data, a frequency domain formulation is proposed focusing on the structural vibration mode excited during VIV events. This method fully considers the influence of random vibration induced by ambient excitation and instrument measurement error on the field vibration data, and only the responsive data are needed without measuring the aerodynamic force information.

A parameter identification technique for traffic speed deflectometer tests of pavements

The structural evaluation of existing pavements forms the basis for formulating cost-effective maintenance and rehabilitation strategies. A promising tool for pavement structural evaluation at network level is the Traffic Speed Deflectometer (TSD) test. However, the application of the TSD test is hindered by the lack of a robust and efficient parameter identification technique. To solve this problem, a theoretical model for the TSD test is first formulated. Then, a minimisation algorithm which works best with the theoretical TSD model for parameter identification is selected. Finally, the performance of this combination in processing field TSD measurements is studied. The results show that the modified Levenberg-Marquardt algorithm using all the 9 detection points is most suitable to be combined with the theoretical TSD model for parameter identification, which gives a promising parameter identification technique for TSD tests of pavements. The presented work contributes to the development of technologies for pavement structural evaluation.

Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries

Battery management systems based on electrochemical models could achieve more accurate state estimations and efficient battery controls with access to cell unmeasurable physical variables. As battery electrochemical models are governed by first-principle partial differential equation sets, model complexity and multiple parameter determination are bottlenecks for their wider applications. This paper gives a systematical review of recent advancements in electrochemical model development and parameterization. Specifically, classic pseudo-two-dimensional model and related model order reduction methodologies are first summarized and analyzed. Given that the homogenization hypothesis of the pseudo-two-dimensional model could lead to significant model mismatch under some operational conditions, enhanced models considering cell internal inhomogeneity with multi-particles, multi-scales, aging and thermal dynamics are examined. To facilitate model portability, parameter identification techniques of these models are classified, and solutions for optimizing the parameterization procedure are explored. Finally, current research gaps in the literature and remaining challenges are discussed and highlighted with some suggestions. This review will therefore inform the engineers of battery management and control engineering, whilst boosting the research, design and operation of control-oriented electrochemical models for smarter battery management at different readiness levels.

The calculation algorithm of oil and gas production enterprise energy efficiency indicators

THE PURPOSE. The main purpose of the study is to develop the calculation algorithm of energy efficiency indicators. To develop an algorithm, it is necessary to consider the features of the power supply system of the oil and gas production enterprise. Then, the power and electricity balance calculation algorithm should be designed to evaluate the share of the technological processes’ consumption, analyze losses, and determine the source of imbalance. The assessment of energy efficiency indicators will require adaptation of the parameter’s identification method for rotating electrical machines in operating modes. To calculate the specific equipment energy, it is necessary to use power tracing in the algorithm. The final stage is to develop a visualization of a software prototype for monitoring and control energy efficiency indicators of an oil and gas production enterprise.METHODS. The equipment parameters identification method based on actual measurements is used for designing a mathematical model. To calculate the steady-state, the nodal-voltage method is used in couple with an addressing matrix for tracing power flows.RESULTS. The paper proposes an algorithm for calculating energy efficiency indicators of equipment for an oil and gas production enterprise. The algorithm is based on the processing and analysis of actual data from the electricity metering devices installed at the enterprise. Analysis of the power consumption information together with the oil production indicators will reveal the most efficient operating mode of the equipment. The calculating algorithm for the specific electricity consumption is considered in the example of a typical hierarchy of metering devices at a field. Further, the paper discusses the possibility of applying the targeting flow distribution principle for reducing power losses. A parameters identification technique is presented for multi equipment nodes to assess energy efficiency and monitor operating parameters.CONCLUSION. The algorithm considered in the paper was introduced into the information system of the production control center at the field as an additional module for assessing energy efficiency. The large deviation of the indicator's values from the specified ones is messaging about the equipment's normal mode violations. It allows creating an optimal schedule of organizational and technical measures to regulate energy consumption and improve the energy efficiency of the enterprise.

Impact of PWM Voltage Waveforms in High-Speed Drives: A Survey on High-Frequency Motor Models and Partial Discharge Phenomenon

The insulation system’s dielectric of the electric motor is very often subjected to severe electrical stress generated by the high dv/dt seen at the machine’s terminals. The electrical stress and high reflected wave transient overvoltage are even more evident in case of high-speed machines fed by high-frequency (HF) converters featuring very fast wide-bandgap devices. They are promoting the occurrence of partial discharges and consequently accelerate ageing. As this is serious issue and the main cause of the drive failure, it is important to analyse and characterise the surges at the motor terminals. Several HF models of motors have been proposed in the literature for this purpose. This article presents a survey on HF motor models, which is crucial in understanding and studying the most critical parameter identification and overvoltage mitigation techniques. Moreover, it offers a comparison of the models’ main features as well as a comparison with the experimental voltage waveform at motor terminals. A general overview of the partial discharge (PD) phenomenon is also provided, as it is favoured by HF operation and together with HF motor modelling provides key insights to the insulation ageing issue. In particular, an analysis of the effects of PWM waveform affecting insulation is given, as well as useful methods for developing strategies for the inspection and maintenance of winding insulation.

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, their configurations, parametric identification techniques, and influential parameters.

Adaptive Hammerstein Filtering via Recursive Non-Convex Projection

Hammerstein model with a static nonlinearity followed by a linear filter is commonly used in numerous applications. This paper focuses on adaptive filtering techniques for parameter identification of Hammerstein systems and output prediction of nonlinear systems. By formulating the underlying filtering problem as a recursive bilinear least-squares optimization with the non-convex feasible region constraint, we develop a recursive non-convex projected least-squares (RncPLS) algorithm based on alternating direction method of multipliers (ADMM). The RncPLS algorithm alternates between implementing ridge regression and projecting on the non-convex feasible set, which successively refines the system parameters. The convergence and accuracy properties of the proposed RncPLS algorithm are theoretically investigated. Moreover, extensive simulation results in the context of system identification, nonlinear predication, and acoustic echo cancellation, are also included to demonstrate the performance characteristics of the proposed algorithm.

Towards effective energy harvesting from stacks of soil microbial fuel cells

The 2050 net-zero carbon target can only be achieved with renewable energy solutions that can drastically reduce carbon emissions. Soil microbial fuel cells (SMFCs) have significant potential as a low-cost and carbon-neutral energy conversion technology. Finding the most practical and energy efficient strategy to operate SMFCs is crucial for transitioning this technology from the lab to field implementations. In this study, an innovative self-sustaining and model-based energy harvesting strategy was developed and tested for the first time on SMFC stacks. The model, based on a first-order equivalent electrical circuit (EEC), enables real-time and continuous maximum power point tracking, without the need for offline analysis of electrochemical parameters. Power extraction from the SMFCs to fully charge a 3.6 V NiMH battery, was carried out for 24 h: the longest test duration reported so far on biological fuel cells for such energy harvesting strategy. A novel second-order EEC was also proposed to better describe the electrical dynamics of the SMFC. Our results provide important advances on both accurate model-based electrochemical parameter identification techniques and maximum power point tracking algorithms, for optimal energy extraction from SMFCs. Consequently, this study paves the way for successful implementations of SMFCs towards viable green energy solutions. • Self-sustaining, model-based energy harvesting strategy for SMFC stacks. • Tailored equivalent electrical circuit for real-time maximum power point tracking. • Performance demonstrated over continuous operation.

A Two-Filter Approach for State Estimation Utilizing Quantized Output Data.

Filtering and smoothing algorithms are key tools to develop decision-making strategies and parameter identification techniques in different areas of research, such as economics, financial data analysis, communications, and control systems. These algorithms are used to obtain an estimation of the system state based on the sequentially available noisy measurements of the system output. In a real-world system, the noisy measurements can suffer a significant loss of information due to (among others): (i) a reduced resolution of cost-effective sensors typically used in practice or (ii) a digitalization process for storing or transmitting the measurements through a communication channel using a minimum amount of resources. Thus, obtaining suitable state estimates in this context is essential. In this paper, Gaussian sum filtering and smoothing algorithms are developed in order to deal with noisy measurements that are also subject to quantization. In this approach, the probability mass function of the quantized output given the state is characterized by an integral equation. This integral was approximated by using a Gauss–Legendre quadrature; hence, a model with a Gaussian mixture structure was obtained. This model was used to develop filtering and smoothing algorithms. The benefits of this proposal, in terms of accuracy of the estimation and computational cost, are illustrated via numerical simulations.

Multi-stage semi-automated methodology for modal parameters estimation adopting parametric system identification algorithms

In recent years, a new research direction in structural condition assessment has been focusing on developing automated or semi-automated procedures to identify a structure’s modal parameters from its response measurements. This is because long-term structural monitoring systems rely on the implementation of system identification methodologies that often involve the intervention of an expert user with an acquired experience in the field.This paper aims to offer a semi-automated methodology for extracting the modal parameters independently of the chosen parametric system identification technique with minimum user involvement in the parameter selection process. Here, the framework is applied to two different parametric system identification algorithms: Data-Driven Stochastic Subspace Identification (DD-SSI) and Output Only Observer Kalman Filter (O/O OKID). The procedure can be represented as a multi-stage strategy where unsupervised tools and three clustering options are offered to the user to reach a reliable estimate of the modal parameters. The proposed procedure is validated with an application in the operational modal analysis of an existing hospital structure located in Italy. The results demonstrated excellent accuracy and robust performance of the methodology, even in the presence of closely spaced modes. The proposed procedure helps to improve the data analysis process in continuous monitoring, where usually, the algorithm’s parameters need to be constantly updated by the user.

Adaptive teaching–learning-based optimization with experience learning to identify photovoltaic cell parameters

Parameter identification of photovoltaic cell and module plays a key role in the simulation, evaluation, control, and optimization of photovoltaic systems. In order to improve the accuracy and reliability of the identified parameters, in this paper, a new adaptive teaching–learning-based optimization with experience learning referred as ELATLBO, is proposed. In ELATLBO, the population is first sorted according to the objective function value, and then it is divided into two parts: fit solutions with good objective function values and inferior solutions with poor objective function values. The fit solution that selects the teacher phase with experience learning is used for local search to improve the exploitation capability of the algorithm. While the inferior solution that selects the learner phase with experience learning is applied for global search to enhance the exploration capability of the algorithm. The performance of ELATLBO is verified by testing the photovoltaic cells and module parameter identification problems, i.e., the single diode model, the double diode model, and the single diode photovoltaic module. The simulated result shows that the proposed ELATLBO exhibits remarkable performance on the accuracy and reliability when compared with other reported parameter identification techniques, especially for the double diode model.

Nondimensionalized Bouc–Wen model with structural degradation for Kalman filter–based real-time monitoring

The Bouc–Wen model is one of the most widely accepted models for describing seismic hysteretic behavior owing to its versatility. However, in structural monitoring applications, implementing the Bouc–Wen model often causes an over-fitting problem because the phenomenological property includes redundant parameters. In this paper, the Bouc–Wen model is reexamined, and its nondimensionalized form is derived. The proposed nondimensionalized model describes hysteretic behavior with yield displacement, and the nondimensional hysteresis displacement is bounded between −1 and 1. In the proposed model, the dissipated energy is almost equivalent to the displacement per cycle and can be interpreted as the dissipation per an equivalent number of cycles. This interpretation enables us to assign a physical meaning to the degradation per cycle. The applicability of the proposed model was experimentally validated based on an unscented Kalman filter–based parameter identification technique. The identification results show that the nondimensionalized Bouc–Wen model can eliminate the over-fitting problem by converting one hysteresis variable into a variable that can be physically specified as the yield displacement. The convergence rate in estimating the strength and stiffness reduction coefficients was increased as a result of the proposed nondimensionalization.

Algebraic Parameter Identification of Nonlinear Vibrating Systems and Non Linearity Quantification Using the Hilbert Transformation

A novel algebraic scheme for parameters’ identification of a class of nonlinear vibrating mechanical systems is introduced. A nonlinearity index based on the Hilbert transformation is applied as an effective criterion to determine whether the system is dominantly linear or nonlinear for a specific operating condition. The online algebraic identification is then performed to compute parameters of mass and damping, as well as linear and nonlinear stiffness. The proposed algebraic parametric identification techniques are based on operational calculus of Mikusiński and differential algebra. In addition, we propose the combination of the introduced algebraic approach with signals approximation via orthogonal functions to get a suitable technique to be applied in embedded systems, as a digital signals’ processing routine based on matrix operations. A satisfactory dynamic performance of the proposed approach is proved and validated by experimental case studies to estimate significant parameters on the mechanical systems. The presented online identification approach can be extended to estimate parameters for a wide class of nonlinear oscillating electric systems that can be mathematically modelled by the Duffing equation.