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     

A `three transfer functions' approach for the standstill frequencyresponse test of synchronous machines

A three transfer functions approach for the standstill frequency response (SSFR) test of synchronous machines is proposed. Network theory is employed for the explanation of the three-function approach. The three-function and two-function approaches, as well as the one-function approach, are compared. The verification of the approach is obtained through simulations and an application to an SSFR test on a 3 kVA laboratory microalternator. The accuracy of the determined d-axis model parameters, particularly of the rotor circuits, is improved by the use of the suggested third transfer function (L _afo(s)) together with the two transfer functions ( L_d(s) and G(s)). This requires that the measurements of the three transfer functions be taken in the SSFR test and be involved in the d-axis model fitting instead of the common practice involving only L_d(s) and G(s)     

Shaft signals of salient-pole synchronous machines for eccentricityand shorted-field-coil detections

Relationships between shaft signals and eccentricities of salient-pole synchronous machines are studied. The magnitude and the thickness of shaft-signal loci reflect steady and dynamic eccentricities. Harmonic components of shaft signals closely relate to shorted field coils. Threshold records can be used for indications of changes in eccentricities and shorted field coils. Extensive experimental work agrees with the theoretical predictions     

An algebraic semi-empirical model for evaluating fuel crossover fluxes of a DMFC under various operating conditions

In a fuel cell of low temperature, especially a direct methanol fuel cell (DMFC), fuel crossover phenomenon plays a significant role not only in its performance evaluation and analysis, but also in the optimum control under various operating conditions. A quantitative prediction of the fuel crossover flux thus becomes essential. Generally speaking, the theoretical approaches to the issue will be dramatically complex and less practical. On the other hand, experimental schemes are time-consuming and less capable of further analysis and applications. Consequently, a semi-empirical model that incorporates dominant physical parameters and operating variables is proposed in this paper to adequately evaluate the phenomenon of fuel crossover fluxes. It is stated analytically in the form of an algebraic function, in which the fuel concentration, the current density, and the temperature of the fuel cell are considered. It is therefore more suitable for a variety of in-situ applications. In the proposed model, the methanol concentration gradient in the anode backing layer, the anode catalyst layer, and the membrane are analyzed. The transfer behavior of methanol is modeled on the basis of diffusion and electro-osmosis mechanisms. By means of the proposed model, one can obtain a better prediction and a clearer picture of the effects of operating variables and physical parameters on methanol crossover fluxes.     

An approach to sensorless operation of the permanent-magnet synchronous motor using diagonally recurrent neural networks

Due to the drawbacks associated with the use of rotor position sensors in permanent-magnet synchronous motor (PMSM) drives, there has been significant interest in the so-called rotor position sensorless drive. Rotor position sensorless control of the PMSM typically requires knowledge of the PMSM structure and parameters, which in some situations are not readily available or may be difficult to obtain. Due to this limitation, an alternative approach to rotor position sensorless control of the PMSM using a diagonally recurrent neural network (DRNN) is considered. The DRNN, which captures the dynamic behavior of a system, requires fewer neurons and converges quickly compared to feedforward and fully recurrent neural networks. This makes the DRNN an ideal choice for implementation in a real-time PMSM drive system. A DRNN-based neural observer, whose architecture is based on a successful model-based approach, is designed to perform the rotor position estimation on the PMSM. The advantages of this approach are discussed and experimental results of the proposed system are presented.     

An efficient and accurate method of representing magneticsaturation in physical-variable models of synchronous machines

When analyzing machine/converter systems it is convenient to represent the stator variables in physical (abc) form. It has been shown that a physical-variable voltage-behind-reactance form of the synchronous machine model can be derived which is numerically more efficient than existing physical-variable models. In this research, a new voltage-behind-reactance model is derived which incorporates the effects of magnetic saturation. This model is shown to have the same numerical efficiency of the unsaturated model and is readily implemented in either circuit-based or differential-equation based simulation languages. An example system is provided which demonstrates the accuracy and efficiency of this model for a wide-range in operating conditions     

A field reconstruction method for optimal excitation of permanent magnet synchronous machines

Vibration caused by torque ripple and radial force harmonics is a concern in many applications of permanent magnet synchronous machines (PMSMs). Alternative methods of machine design and/or stator excitation to minimize torque ripple have received considerable attention in recent years. Comparatively, methods to minimize radial force harmonics have received less attention. In this paper, a field reconstruction (FR) method is derived that provides a designer with the capability to rapidly determine the radial and tangential components of force under arbitrary stator excitation. Using the field reconstruction method, stator current waveforms that minimize the ripple of both torque and radial force are derived subject to the constraint of maintaining a satisfactory level of torque density.     

A new model of saturated synchronous machines for power systemtransient stability simulations

This paper presents a new method to express the main flux saturation in synchronous machines. In the new method, the saturation is expressed by auxiliary currents and unsaturated magnetizing inductances instead of the saturated inductances. The new model using the currents contains only constant coefficients defined in terms of the unsaturated magnetizing inductances     

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     

An equivalent circuit approach to analysis of synchronous machineswith saliency and saturation

An equivalent circuit approach is presented for synchronous machine analysis. The approach provides a detailed treatment of magnetic nonlinearity without excessive computational complexity. The number of parameters is typically in the range of 30 to 100, in contrast with 13 to 19 in conventional linear models and with 2000 to 10000 for finite-element analysis. The models developed are intended for use with digital simulation software in the solution of transient, asynchronous, and synchronous steady-state situations     

Modelling of synchronous machines for dynamic studies withdifferent mutual couplings between direct axis windings

Synchronous machine models commonly utilized for transient performance calculations normally disregard such effects as induction of currents on slot walls and rotor surfaces during canceling or blocking of field current and the difference of mutual coupling between direct axis windings. It is shown how such different couplings can easily be incorporated into the synchronous machine conventional modeling and how the characteristic reactance to be used in this representation can be determined through the reactive power rejection test with zero active power. The air gap flux saturation representation, when considered together with the inclusion of the effect of the difference of mutual couplings between direct axis windings, is also discussed     

串联型集成光伏组件光伏系统的运行电压优化

在串联分布式光伏系统中,若母线电压为固定值,易使分布式最大功率跟踪控制(Distributed Maximum Power Point Tracking,DMPPT)失效,造成功率的损失。通过逆变器可调节母线电压,但调节能力受电网电压的限制。因此,提出了一种新型的电路结构,在光伏组件串联侧和逆变器之间添加全局功率优化器(Global Power Optimizer,GPO),构建串联运行总线,消除了逆变器对原母线电压范围的影响。并在此拓扑基础上,提出了一种变串联运行电压功率优化算法,通过对串联运行电压进行周期性调节,提升了最大功率跟踪控制的性能。仿真结果表明,在光照严重不均的情况下,系统依然快速、稳定的工作在最大功率点处。     

Methods for determining the q-axis saturation characteristics of salient-pole synchronous machines from the measured d-axis characteristics

For the accurate analysis of salient-pole synchronous machines using the two-axis frame models, the direct- (d-) and quadrature-axis (q-axis) saturation characteristics are needed. Usually, the d-axis saturation characteristics can be obtained easily by the conventional open-circuit test with the machines excited from their field winding. On the other hand, the q-axis saturation characteristics of synchronous machines cannot be measured applying simple, conventional methods, and thus, they are usually not available. In this paper, four different methods for calculating the q-axis saturation characteristics of salient-pole synchronous machines from the measured d-axis saturation characteristics are explored. In these methods, the q-axis saturation characteristics can be calculated from the readily available test data, namely the d-axis saturation characteristics and the d- and q-axis unsaturated magnetizing reactances. Finally, a comparison between these methods is made.     

Steady-state analysis of self-excited induction generators using genetic algorithm approach under different operating modes

The main focus of this paper is the steady-state analysis of self-excited induction generators (SEIGs). It employs the genetic algorithm approach (GAA) to estimate the steady-state performance of these machines. Further, the GAA is used for the solution of problems related to the operation of a number of SEIGs running in parallel. GA-based modelling is found to be effective to determine the generated voltage and frequency. Experimental results validate the proposed methodology.     

Hybrid MPPT approach using Cuckoo Search and Grey Wolf Optimizer for PV systems under variant operating conditions

Photovoltaic(PV) systems are adversely affected by partial shading and non-uniform conditions. Meanwhile, the addition of a bypass shunt diode to each PV module prevents hotspots. It also produces numerous peaks in the PV array’s power-voltage characteristics, thereby trapping conventional maximum power point tracking(MPPT) methods in local peaks.Swarm optimization approaches can be used to address this issue. However, these strategies have an unreasonably long convergence time. The Grey Wolf Op...     

A close-form solution for the maximum-power operating point of a solar cell array

A solar cell array is inherently a nonlinear device consisting of several solar cell modules connected in series-parallel combinations to provide the desired DC voltage and current. At a fixed insolation level, the terminal voltage decreases nonlinearly as the load current increases. Due to this nonlinearity, it is difficult to determine analytically the operating point at which the output power is maximum; a condition required for maximum utilization efficiency of the array. Iterative techniques are normally used to determine this operating condition. These techniques are lengthy, time-consuming, and have to be repeated for any change in the array's parameters, temperature, and insolation level. In this paper, an accurate closed-form solution is derived in terms of the array's parameters and solar insolation level. This solution is useful to the system designer or researcher as a fast and accurate tool for system design and performance analysis.     

Effectiveness of active noise and vibration cancellation for switched reluctance machines operating under alternative control strategies

The effectiveness of active vibration cancellation by inducing antiphase vibration of the stator to reduce the acoustic noise emitted from a switched reluctance machine is evaluated under typical operating modes, viz., single pulse excitation, pulsewidth modulation (PWM) voltage control, and PWM current control. Measurements in both the frequency and time domains are complemented by sound pressure level measurements. It is shown that active vibration cancellation is most effective in reducing a single mode of vibration when a switched reluctance (SR) machine is operated under single-pulse excitation, and becomes less effective when more than one dominant vibration mode exists within the audible frequency range. In general, it also works relatively well when the machine is operated under fixed frequency PWM voltage control, although less effectively than for single pulse control. Further, it is shown that the technique is ineffective when the machine is operated under PWM current control since the duration of the zero voltage period varies significantly from the optimal value due to the random nature of the PWM.     

An analytical approach to non‐linear dynamical model of a permanent magnet synchronous generator

This paper proposes an analytical approach to investigate the transitory dynamic regime of a low‐power permanent magnet synchronous generator that works in an actual wind power station. The governing non‐linear differential equations are solved by means of the optimal homotopy asymptotic method, and explicit analytical solutions are obtained. Four cases are analysed for different moments of inertia and electrical resistances specific to sudden short circuit produced at the generator terminals and sudden change of load. The proposed procedure is highly efficient and controls the convergence of the approximate solutions, ensuring a very fast convergence. Such analytical approach allows modeling and simulating turbine generator systems for real‐time computations, offline applications or stability problems. Numerical investigations are also performed in order to validate the analytical results. Copyright © 2014 John Wiley & Sons, Ltd.     

An adaptive neuro-control system of synchronous generator for powersystem stabilization

This paper proposes a nonlinear adaptive generator control system using neural networks, called an adaptive neuro-control system (ANCS). This system generates supplementary control signals to conventional controllers and works adaptively in response to changes in operating conditions and network configuration. Through digital time simulations for a one-machine infinite bus test power system, the control performance of the ANCS and advanced controllers such as a linear optimal regulator and a self-tuning regulator is evaluated from the viewpoint of stability enhancement. As a result, the proposed ANCS using neural networks with nonlinear characteristics improves system damping more effectively and more adaptively than the other two controllers designed for the linearized model of the power system