On-line estimation of variable parameters of synchronous machinesusing a novel adaptive algorithm-principles and procedures

A novel adaptive algorithm is presented for the online estimation of the variable parameters of a synchronous machine (SM) as a function of the operating conditions. The concept of a synthesized information factor (SIF) is proposed as the core of the novel adaptive algorithm. For a continuous process, the SIF optimally combines information from the past with that at the present. Adaptive principles based on the SIF are discussed and adaptive estimation procedures are developed. Computer simulation results are given to highlight the advantages of the novel adaptive algorithm over conventional least mean square and recurrence least square algorithms     

On-line estimation of variable parameters of synchronous machinesusing a novel adaptive algorithm. Estimation and experimentalverification

The online estimation of the variable parameters of synchronous machines based on a novel adaptive algorithm is presented. The estimation process involves instantaneous parameter tracking and subsequent data processing. The variable parameters are ultimately expressed as nonlinear functions of operating conditions. The online estimation process is applied to a 100 MVA turbogenerator     

Identification of physical parameters of a synchronous Generator from online measurements

A new method to determine physical parameters of a synchronous generator based on an online measurement of the electrical power, terminal voltage, and field voltage, following a small perturbation of the field voltage, and the rotor angle at the same steady-state operating condition is described in this paper. A multivariable linear transfer function, identified using the sampled input-output data, is converted to the parameters of the Heffron-Phillips model. Using the relations of the Heffron-Phillips parameters with the physical parameters, the physical parameters are estimated. These estimated parameters are then used in a nonlinear structure to model the synchronous generator. Experimental results with the proposed method applied to a micromachine show good accuracy of the model and also show that the identified nonlinear model is valid at other operating conditions. At dramatically different operating conditions, however, to include the effects of unstructured nonlinearities such as magnetic saturation, the parameters of the nonlinear structure can be slightly adjusted for better performance.     

Identification of synchronous machines parameters using broadbandexcitations

This paper describes a newly developed measurement and identification technique for synchronous machine parameters. The first part presents a technique to generate periodic broadband power signals with a controllable amplitude spectrum using a classic rectifier bridge. These signals are well suited to measure the frequency response function of large power systems using the fast Fourier transform. The broadband measurements are compared with the classical mono frequency measurements. The second part presents a frequency domain identification method taking into account the uncertainty on all the current and voltage measurements, and giving a confidence interval on the estimated synchronous machine parameters. As an illustration the d- and q-axis model of a 1.905 kVA/220 V micro-machine is identified     

Identification of synchronous machine parameters using a multipleinput multiple output approach

This paper presents an alternative method for the identification of the d-axis parameters of a synchronous machine. The first part of the paper describes a multiple input multiple output (MIMO) broadband excitation and measurement method which is more time efficient than the standard standstill frequency response (SSFR) method. The second part describes a MIMO frequency domain identification procedure which estimates the d-axis parameters in 3 steps. The proposed identification procedure is self starting. It does not require starting values or other prior information. The measurement method and the identification procedure are tested on a 20 kVA salient pole synchronous machine     

Determination of synchronous machine parameters using networksynthesis techniques

This paper deals with the problem of the determination of synchronous machine parameters starting from two-port information. Network synthesis techniques are used to show that no unique solution can be found for models containing more than one damper winding. Only a limited number of parameters can be determined in a unique way from two-port information     

Identification of synchronous machine parameters using loadrejection test data

This work shows a computational methodology for the determination of synchronous machines parameters using load rejection test data. By machine modeling one can obtain the quadrature parameters through a load rejection under an arbitrary reference, reducing the present difficulties. The proposed method is applied to a real machine     

Finite-element calculation of leakage inductances of a saturated salient-pole synchronous machine with damper circuits

It is important to understand the relationship between leakage flux distributions and machine characteristics for better design of synchronous machines. This paper presents a calculation method for leakage inductances of saturated salient-pole machines with damper circuits. All leakage inductances are divided into the self-leakage, gap leakage, and winding-differential leakage inductances. The leakage inductances correspond directly to the leakage flux distributions in the machines. Cross-magnetizing inductances are also calculated. The method is applied to a 300-MVA class generator. The relationship between the winding distributions, flux distributions, saturation, and inductances is discussed. Weak magnetic influence of the damper circuits on the armature in the d-axis is quantitatively illustrated through values of the winding-differential leakage inductances. The cross-magnetizing inductances, except for the d-axis damper circuits, are relatively large. Variations of the armature self-leakage inductances with saturation are small, and variations of the field and damper self-leakage inductances are larger.     

Determination of damper winding and rotor iron currents inconvertor- and line-fed synchronous machines

The damper winding currents of a synchronous machine with a solid iron rotor are analyzed. Based on the self and mutual inductance modelling technique, the damper currents are described by a detailed damper model taking the damper cage and the rotor iron into account. The model's component elements are determined by the finite element method. Theoretical results are verified by measuring damper bar currents of a six-phase synchronous machine fed by two six-pulse converters. For a number of different operating modes the model is used to calculate the damper current distribution of a six-phase machine     

Synchronous machine parameters estimation using a new genetic‐based algorithm

A new genetic‐based algorithm (GA) for estimating synchronous machine parameters from frequency tests is presented in this paper. GAs are general search techniques based on biological concepts and are very suitable for solving optimization problems. The proposed method uses a set of digital measurements for the direct axis impedance magnitude and phase as functions of frequency for estimating both the d‐ and q‐axis parameters, such as direct reactance and time constants. The problem is formulated as an optimization problem and solved using the proposed method. Two different models along with different fitness functions are suggested to be used with the genetic algorithm. A practical example from the literature is used to test the proposed algorithm. The results obtained are compared with those given in the literature using other methods. The results and comparison show that the new algorithm is very applicable and highly accurate. Copyright © 1999 John Wiley and Sons, Ltd.     

Use of a permeance model to predict force harmonic components and damper winding effects in salient-pole synchronous machines

This paper presents a combined finite-element and analytical modeling technique for the prediction of force-density harmonics in salient-pole synchronous machines. The model calculates the induced currents in the damper winding cage and includes their effect on force-density components in the solution. Use of a combined analytical and finite-element approach considerably reduces simulation times compared to full time-stepping finite-element solutions, while including the effects of design changes on air-gap force harmonics. Results of the model predictions are presented together with measured data from two different machines.     

Nonlinear control of a permanent magnet synchronous motor withdisturbance torque estimation

This paper introduces a sensorless nonlinear control scheme for controlling the speed of a permanent magnet synchronous motor (PMSM) driving an unknown load torque. The states of the motor and disturbance torque are estimated via an extended nonlinear observer avoiding the use of mechanical sensors. The control strategy is an exact feedback linearization law, with trajectory tracking evaluated on estimated values of the PMSM states and the disturbance torque. The system performance is evaluated by simulations     

On-line evaluation of a round rotor synchronous machine parameterset estimated from standstill time-domain data

This paper presents a method for identifying the best linear circuit model parameters of a three-phase, round rotor synchronous machine. Standstill time-domain test data and the maximum likelihood estimator are used to identify the values for the equivalent circuit models. The estimated models are validated against standstill data and an online test. A steady-state error adjustment procedure is introduced and the results are analyzed. The final dand q-axis model selections are based on the minimization of the cost function, the concept of parsimony and how well the models predict the online dynamics of the machine. Issues related to the necessity of the L_fld differential leakage inductance and the necessity of the Z_fe eddy current branch are also discussed     

Accurate estimation of cost function parameters for thermal power plants using a novel optimization approach

Accurate parameter estimation of the input-output characteristics in thermal power plants is an important issue in power system because these characteristics directly affect the economic dispatch calculations. Parameter estimation is an optimization problem in which the optimal values of the unknown parameters should be estimated by an optimization technique. By considering the valve-point effect, the parameter estimation will be more difficult since the fitness function of this optimization problem turns into a non-smooth and non-convex function in which finding the global optimal is a challenging task. In this paper, a recently proposed metaheuristic approach, crow search algorithm (CSA), is proposed for accurate estimation of the input-output characteristics of thermal power plants with and without valve-point effect. Simulation results show that CSA finds more promising results than least squares method (LSM), particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC).     

某型柴油机减振器可靠性分析及其维修改进

对某型柴油机减振器只有一个失效数据,在寿命服从正态分布时,将现场维修人员的主观先验信息与现场数据融合,给出了该型柴油机减振器的各检测时刻可靠度估计值,最后对该型减振器的维修周期进行调整。     

Robust experiment design for the estimation of thermophysical parameters using stochastic algorithms

This numerical study deals with the design of experiments. It aims at optimizing thermocouple positions in order to reduce uncertainties in the estimation of thermophysical properties when an inverse method is applied. The 2D system under investigation is a squared sample of orthotropic material submitted to a constant heat flux on left and bottom edges. The temperature response is given by three sensors. The unknown parameters are the volumetric thermal capacity and the conductivities in the two principal directions. The experiment design is based on an original optimality criterion. Two stochastic algorithms are used to find its minimum and their efficiency is compared to a pure random search algorithm. To deal with the fact that the experiment design depends on the unknowns, a robust optimization approach is proposed.     

Improvement of estimation of surge arrester parameters by using Modified Particle Swarm Optimization

Metal Oxide Surge Arrester (MOSA) accurate modeling and its parameter identification are very important aspects for arrester allocation, system reliability determination and insulation coordination studies. In this paper, Modified Particle Swarm Optimization (MPSO) algorithm is used to estimate the parameters of surge arrester models. The convergence to the local optima is often a drawback of the Particle Swarm Optimization (PSO). To overcome this demerit and improve the global search capability, Ant Colony Optimization (ACO) algorithm is combined with PSO algorithm in the proposed algorithm. The suggested algorithm selects optimum parameters for the arrester model by minimizing the error among simulated peak residual voltage values given by the manufacturer. The proposed algorithm is applied to a 120 kV MOSA. The validity and the accuracy of estimated parameters are assessed by comparing the predicted residual voltage with experimental results.     

On-line estimation of inlet and outlet composition in catalytic partial oxidation

An estimation strategy is presented for determining inlet and outlet composition of catalytic partial oxidation (CPOX) of methane over rhodium catalyst using simple, fast measurements: temperature, and thermal conductivity. A 1-D high fidelity simulation model for CPOX studied in Ref. [1] for a portable fuel cell application is developed and enhanced for transient experiments. Process dynamics are analysed to demonstrate how solid temperatures along the axes of the reactor reflect the endothermic/exothermic interplay of reactions during a process upset. Model reduction is then used to obtain a low complexity model suitable for use in a moving horizon estimator with update rates faster than 0.02 s. System theoretic observability analysis is then conducted to predict the suitability of different measurement designs and the best locations for temperature measurements for estimating both inlet and outlet gas mole fractions for all species. Finally, a Moving Horizon estimator is implemented and simulation experiments are conducted to verify the accuracy of the estimator.     

Genetic‐based algorithm for identification of synchronous machine parameters using short‐circuit tests

In this paper a new method for the digital identification of synchronous machine parameters from short‐circuit tests is presented. The method is based on the genetic algorithm optimization technique. Genetic algorithms (GAs) are adaptive search procedures based on the mechanism of natural selection and genetics. This kind of algorithm can search for a global solution using multiple paths. The proposed method uses a digital set of measurements for the short‐circuit current for estimating direct axis reactance and time constant. A set of over‐determined system of equations is constructed using the digital current samples. The identification problem is then solved using the proposed GAs‐based method. Different fitness functions that evaluate the solutions are suggested. A practical case study is presented in this work to evaluate the proposed method. Results are reported and conclusions are drawn. Copyright © 2000 John Wiley & Sons, Ltd.