An algebraic approach for identifying operating point dependentparameters of synchronous machines using orthogonal series expansions

This paper presents a method for identifying armature and field parameters of synchronous machines from digital fault recorder (DFR) data. The method uses operational properties of orthogonal series expansions such as the Hartley, Walsh and Fourier series to transform a set of differential equations into linear algebraic equations. The algebraic formulation and use of operational calculus reduce the problem of identifying parameters to the manipulation of matrices that may be easily performed in such computational packages as Matlab. The variation of machine parameters with operating point is considered     

Forecasting photovoltaic array power production subject to mismatch losses

The development of photovoltaic (PV) energy throughout the world this last decade has brought to light the presence of module mismatch losses in most PV applications. Such power losses, mainly occasioned by partial shading of arrays and differences in PV modules, can be reduced by changing module interconnections of a solar array. This paper presents a novel method to forecast existing PV array production in diverse environmental conditions. In this approach, field measurement data is used to identify module parameters once and for all. The proposed method simulates PV arrays with adaptable module interconnection schemes in order to reduce mismatch losses. The model has been validated by experimental results taken on a 2.2 kW_p plant, with three different interconnection schemes, which show reliable power production forecast precision in both partially shaded and normal operating conditions. Field measurements show interest in using alternative plant configurations in PV systems for decreasing module mismatch losses.     

Identifying a hydraulic-turbine model from measured field data

An approach is proposed for identifying hydraulic-turbine model parameters from measured response data. The frequency response from the turbine-gate position to the machine power is measured at given steady-state operating conditions, and a structured model is fitted to the response. The fitting process is based on a nonlinear optimization method that provides numerical robustness. This approach allows one to fine tune and validate existing models or to identify a model without initial theoretical analysis. Theoretical development of the approach is discussed and results are presented for a 115-MVA unit at the Mt. Elbert pumped-storage powerplant operated by the Bureau of Reclamation     

Parameter characterization of HTPEMFC using numerical simulation and genetic algorithms

This paper develops a novel approach to the parameterisation of high temperature exchange membrane fuel cells (HTPEMFC) with limited and non-invasive measurements. The proposed method allows an effective identification of electrochemical parameters for three-dimensional fuel cell models by combining computational simulation tools and genetic algorithms. To avoid each evaluation undertaken by the optimisation method involving a complete computational simulation of the 3D model, a strategy has been designed that, thanks to an iterative process, makes it possible to decouple the fluid dynamic resolution from the electrochemistry one.Two electrochemical models have been incorporated into these tools to describe the behaviour of the catalyst layer, Butler-Volmer and spherical aggregate. For each one, a case study has been carried out to validate the results by comparing them with empirical data in the first model and with data generated by numerical simulation in the second. Results show that, from a set of measured operating conditions, it is possible to identify a unique set of electrochemical parameters that fits the 3D model to the target polarisation curve. The extension of this framework can be used to systematically estimate any model parameter in order to reduce the uncertainty in 3D simulation predictions.     

Heat-load modelling for large systems

Control strategies for operating district heating, the utilization of biomass boilers under optimal conditions, estimation of solar production by central solar-heating systems and much more, demand realistic modelling of heat loads/demands for district-heating systems. A dynamic system model is presented: the results from this simulation were then compared with results from alternative methods found in the literature. If knowledge is available for a given case or the load is to be found for a new system, the simulation approach is recommended. The method, however, involves many parameters that can lead to errors and uncertainties. Therefore, if knowledge of case-specific parameters is rare, the degree-day method can lead to realistic results. For low-energy, solar-optimized building area, the energy–signature method leads to reasonable results and if system-wide load data are available, the energy–signature method can even do better than the degree-day method.     

Synchronous generator model identification and parameter estimation from operating data

A novel technique to estimate and model parameters of a 460-MVA large steam turbine generator from operating data is presented. First, data from small excitation disturbances are used to estimate linear model armature circuit and field winding parameters of the machine. Subsequently, for each set of steady state operating data, saturable inductances L/sub ds/ and L/sub qs/ are identified and modeled using nonlinear mapping functions-based estimators. Using the estimates of the armature circuit parameters, for each set of disturbance data collected at different operating conditions, the rotor body parameters of the generator are estimated using an output error method (OEM). The developed nonlinear models are validated with measurements not used in the estimation procedure.     

Identification of armature, field, and saturated parameters of alarge steam turbine-generator from operating data

This paper presents a step by step identification procedure of armature, field and saturated parameters of a large steam turbine-generator from real time operating data. First, data from a small excitation disturbance is utilized to estimate armature circuit parameters of the machine. Subsequently, for each set of steady state operating data, saturable mutual inductances L_ads and L_aqs are estimated. The recursive maximum likelihood estimation technique is employed for identification in these first two stages. An artificial neural network (ANN) based estimator is used to model these saturated inductances based on the generator operating conditions. Finally, using the estimates of the armature circuit parameters, the field winding and some damper winding parameters are estimated using an output error method (OEM) of estimation. The developed models are validated with measurements not used in the training of ANN and with large disturbance responses     

Neural network based modeling of a large steam turbine-generatorrotor body parameters from on-line disturbance data

A novel technique to estimate and model rotor-body parameters of a large steam turbine-generator from real time disturbance data is presented. For each set of disturbance data collected at different operating conditions, the rotor body parameters of the generator are estimated using an output error method (OEM). Artificial neural network (ANN) based estimators are later used to model the nonlinearities in the estimated parameters based on the generator operating conditions. The developed ANN models are then validated with measurements not used in the training procedure. The performance of estimated parameters is also validated with extensive simulations and compared against the manufacturer values     

Comparing rotor plane induction determined from full‐scale measurements and CFD simulations

This paper investigates the flow field in the rotor plane of a full‐scale operating wind turbine using full‐scale light detection and ranging (LiDAR) measurements for the first time. Comparison of the measured flow field with results from large eddy simulations (LES) combined with an actuator line approach is also presented for in‐depth study of the induction field in the rotor plane. The measurements include data from two synchronized LiDAR systems—one scanning the undisturbed upstream inflow field and one measuring in the rotor plane. The standard deviation of the mean of velocity time series are and presented as a measure of reliability. The method for calculating the axial velocity based on the line‐of‐sight velocity is explained and the uncertainty of such method is presented. The process of calculating the yaw misalignment is described. The time‐averaged and phase‐averaged axial velocity and induction factors are presented relative to radius and azimuth, and the general behavior is described relative to the flow regimes around the blades, tower and nacelle. Simulations and measurements are compared with special emphasis on the flow structures in the vicinity of the individual rotor blades. A convincing agreement between measurements and simulations is demonstrated. The uncertainties originated from the imprecise positions and angles of the measurement instruments are shown. The uncertainties are limited to the middle parts of the blades between 15 m to 25 m from the root. In addition, longer selected time series show smaller uncertainties. This proves the reliability of the application of the methodology for even longer time series.     

Identifying the parameters of different configurations of photovoltaic models based on recent artificial ecosystem-based optimization approach

Identifying accurate and precise photovoltaic models' parameters is the primary gate in providing a proper PV system design simulate its real behavior. Therefore, this article proposed a new approach based on a recent meta-heuristic algorithm of artificial ecosystem-based optimization (AEO) to identify the optimal parameters of PV cell and module models. Various PV models are considered in this work as single diode (SD), double diode (DD), and triple diode (TD)-based circuits. The analysis is performed on which are R.T.C. France silicon solar cell, FSM-25 PV module, and Canadian-Solar-(CS6P-240P) multi-crystalline solar panel with the aid of experimental data under different operating conditions. Moreover, Lambert form is employed to validate the constructed model. Furthermore, comparative analysis with Harris hawks optimizer (HHO), gray wolf optimizer (GWO), and salp swarm algorithm (SSA) is performed. Additionally, statistical analysis using the Wilcoxon signed rank test is implemented across the three series of experiments for all employed optimizers. The obtained results confirmed the competence of the proposed approach in identifying the PV cell and modules equivalent circuits' parameters.     

Identification of a PEMFC fractional order model

The present paper addresses the important issue of monitoring the operating state of the Polymer Electrolyte Membrane Fuel Cell systems. The monitoring system takes a model based approach. Its originality lies in adopting a fuel cell fractional order impedance model which permits to provide a better insight into the fuel cell physical phenomena without increasing the number of parameters. This article first validates experimentally the accuracy of the suggested model, using a frequency identification method carried out by nonlinear optimization using single fuel cell experimental impedance spectroscopy data. In a second phase, time series identification is achieved using a least square method specifically designed for fractional order models. The latter method is first verified on registered data which represents a basic tool for offline monitoring. Subsequently it is refined as a recursive tool permitting an online monitoring; it is validated on laboratory test bench.     

Control optimization in experiments for the heat transfer assessment of saturated packed bed regenerators

Many studies about heat transfer characterization of single phase fixed bed matrix regenerators are devoted to the finding of experimental correlations. Despite several deep investigations, the emerged correlations are not well established, indeed the high complexity of the processes involved, the shape of the solid-fluid interface, the complexity of the geometry of the solid matrix, make accurate experimental data difficult to obtain.The aim of the present work pursuit a double objective: (i) to develop and propose an inverse method to identify h, the fluid-matrix heat transfer coefficient, by means of transient simulated experiments, and (ii) to investigate the sensitivity of the h reconstruction process to the variation of the control input parameters and material properties, in order to find the optimal value of the experimental control variables that allows the identification of this unknown coefficient to be performed with “minimum variance”.The reconstruction technique is applied to numerical experiments and it is based on the simulated measurements of oscillating temperatures of the fluid at the inlet and outlet of the regenerator. The identification of h is performed by means of an inverse search technique, driven by the difference between simulated measurements and calculated temperature time histories at the regenerator outlet.At first, experiments in different operating conditions are simulated in order to investigate the ability of the algorithm to identify the correct value of h and its uncertainty. Then a parametric study is performed and the optimal control frequency of the known (imposed) oscillating temperature signal at the inlet is found as a function of the mass flow rate, the geometry and other operating and thermophysical characteristics of the system.     

Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system

This study investigates in details the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS) approach for predicting the performance parameters of a solar thermal energy system. Experiments were conducted on the system under a broad range of operating conditions during different Canadian seasons and weather conditions. The experimental data were used for developing the ANFIS network model. This later was then optimised and applied to predict various performance parameters of the system.The predicted values were found to be in excellent agreement with the experimental data with mean relative errors less than 1% and 9% for the stratification temperatures and the solar fractions, respectively. The results show that ANFIS approach provides high accuracy and reliability for predicting the performance of energy systems.Furthermore, the ANFIS prediction results were compared against the ANNs predictions of Yaïci and Entchev [Appl Therm Eng 2014; 73:1346–57]. Results showed that the ANFIS model performed slightly better than the ANNs one. However, the ANNs method provided more flexibility in terms of model implementation and computing speed capabilities.Finally, this investigation demonstrates that ANFIS is an alternative powerful and reliable method comparable to the ANNs; they can be used with confidence for predicting the performance of complex renewable energy systems.     

Identification of variable frequency induction motor models fromoperating data

The parameters of the induction motor model vary as operating conditions change. Accurate knowledge of these parameters and their dependency on operating conditions is critical for optimal field oriented control. This paper presents a systematic approach to modeling an induction motor considering operating conditions. All parameters are assumed to vary as a function of the operating conditions. The parameters are estimated from transient data using a constrained optimization algorithm. The parameters are mapped to the operating conditions using polynomial functions and artificial neural networks. The model is validated for both steady state and transient conditions     

基于传感器信号和神经网络的汽油机失火状态识别方法

提出以传感器信号作为特征输入向量,建立BP神经网络模型进行失火状态识别的方法。汽油机运行状态可以通过发动机转速、进气歧管绝对压力、节气门开度等主要参数来表征,利用解码器,获取发动机稳定和瞬态空载工况下正常状态和1缸、1、4两缸失火状态的上述传感器信号数据,以此作为训练样本,建立了BP神经网络模型进行失火状态的识别。结果表明,此方法能正确识别出正常状态、一缸和两缸失火状态,并且测试方案更简单,成本更低。     

Quantification of in situ temperature measurements on a PBI-based high temperature PEMFC unit cell

The temperature is a very important operating parameter for all types of fuel cells. In the present work distributed in situ temperature measurements are presented on a polybenzimidazole based high temperature PEM fuel cell (HT-PEM). A total of 16 T-type thermocouples were embedded on both the anode and cathode flow plates. The purpose of this study is to investigate the feasibility of the proposed temperature characterization method and to identify the temperature distribution on an operating HT-PEM in various modes of operation, including a 700 h sensors durability test. The embedded sensors showed minimal influence on cell performance, this difference seen in performance is believed to be caused by different bipolar plate materials. The measurement method is suitable for obtaining detailed data for validation of computational models, moreover the results indicate that the method can be used as a degradation tool, as it is possible to locate areas exposed to degradation, both in plane and between the anode and cathode.     

New multi-physics approach for modelling and design of alkaline electrolyzers

This work presents a multi-physics model used for the design and diagnosis of the alkaline electrolyzers. The model is based on a new approach that allows to choose precisely the design parameters of a new electrolyzer even if it is not commercially available and predicting energy consumption, efficiency and rate of hydrogen production, taking into account to their physical state and various operating conditions. The approach differs from those of conventional models of the following: It allows the characterization of the electrolyzer based on its structural parameters in a relatively short time (few minutes) compared with the conventional approach which need experimental data collected for few weeks (Ulleberg). The approach allows describing a range of alkaline electrolyzers, while semi-empirical models found in literature are inherent to a specific electrolyzer. In addition, the model takes into account the variation of all structural parameters (geometry, materials and their evolution depending on operating conditions) and operational parameters of the electrolyzer (temperature, pressure, concentration, bulk bubbling and recovery rate of electrode surface by the bubble), while the models in the literature involve only the temperature. The developed multi-physics model was programmed in a Matlab Simulink^® environment and an alkaline electrolyzer’s simulation tool was developed. The simulation tool was validated using two industrial (Stuart and Phoebus) electrolyzers with different structures and power rates. Simulation results reproduced experimental data with good accuracy (less than 0.9%). The simulation tool was also used to compare the energy efficiency of two hydrogen production systems. The first one is based on atmospheric electrolyzer with a compressor for hydrogen storage and the second one is a barometric electrolyzer (under pressure) with its auxiliary devices to identify the effective mode of hydrogen production according to the physical state and operating conditions of the electrolyzer. The analysis of results revealed that the second mode of hydrogen production is more efficient and confirms the results of the literature based solely on the thermodynamic approach (K. Onda et al) without the input of the power consumed by power overvoltages.     

Incorporating the Effects of Magnetic Saturation in a Coupled-Circuit Model of a Claw–Pole Alternator

A method of representing the effects of magnetic saturation in a coupled-circuit model of a claw-pole alternator is presented. In the approach considered, the airgap flux density produced by each winding is expressed as a function of magnetic operating point. A challenge in the implementation is that the airgap flux densities consist of several significant harmonics, each of which changes at a distinct rate as iron saturates. Despite this complication, it is shown that relatively simple measurements can be used to determine model parameters. The model is implemented in the analysis of several alternator/rectifier systems using a commercial state-model-based circuit analysis program. Comparisons with experimental results over a wide range of speeds and operating conditions demonstrate its accuracy in predicting both the steady state and transient behavior of the systems.     

Fuel cells static and dynamic characterizations as tools for the estimation of their ageing time

This paper deals with a pattern-recognition-based diagnosis approach, which aim is to estimate the Fuel Cell (FC) operating time, and consequently its remaining duration life. With the method proposed, both static and dynamic information extracted from the stack (i.e. polarization curve records and Electrochemical Impedance Spectroscopy (EIS) measurements) can be used. The complete diagnosis method consists of several steps. First, features are extracted from EIS measurements and polarization curves independently. This enables us to simplify the extracted information without losing relevant information, and to remove noise. For the polarization curves, an empiric model is exploited to ensure the feature extraction. For the impedance spectra, both expert knowledge and parametric modeling are used to extract features. In particular, a latent regression model is used to split automatically the imaginary part of the spectra into several segments that are approximated by polynomials. The next step of the method consists in selecting the most relevant features from the whole set of extracted features. This helps us to estimate the operating time, while adjusting the complexity of the model. The final step of the approach is a linear regression that uses the selected subset of features to estimate the FC operating time. The performances of the proposed approach are evaluated on a dataset made up of EIS measurements and polarization curves extracted from two FC lifetime tests. A mean error of about 2 h over a global operating duration of 1000 h can be obtained. Moreover, the portability of the method is shown by considering another FC ageing test conducted on a different FC stack type.