Development of a Fast Thermal Model for Calculating the Temperature of the Interior PMSM

A 40 kW–4000 rpm interior permanent magnet synchronous machine (IPMSM) applied to an electric vehicle (EV) is introduced as the study object in this paper. The main work of this paper is theoretical derivation and validation of the first-order and multi-order transient lumped-parameter thermal network (LPTN) for the development of a fast thermal model. Based on the first-order LPTN built, the study finds that the heat transfer coefficient of fluid and thickness of the air gap layer are the main influencing factors for the final temperature and time of reaching the steady state. The larger the heat transfer coefficient of fluid is, the lower the steady node temperature is. The smaller the air layer thickness is, the lower the steady node temperature is. The multi-order LPTN theory is further deduced based on the extension of the first-order LPTN. For the constant load and rectangular periodic load, transient node temperatures of the IPMSM are obtained by modeling and solving the first order inhomogeneous differential equations. Temperature rise curves and efficiency maps of the IPMSM under load conditions are realized on a dynamometer platform. The FLUKE infrared-thermal imager and the thermocouple PTC100 are used to validate the mentioned method. The experiment shows that the LPTN of the IPMSM can accurately predict the node temperature.

NUMERICAL INVESTIGATION OF UNSTEADY COMBUSTOR TURBINE INTERACTION FOR FLEXIBLE POWER GENERATION

In the present study low-frequency disturbances introduced by a periodic load variation have been simulated and superimposed to the inhomogeneous, unsteady flow entering a 3-stage, high-pressure industrial gas turbine fed by a can-type combustion chamber comprising 6 silo-burners. The effects of the unsteadiness realized at the combustor exit have been investigated by means of Detached Eddy Simulations, whereby a density-based solution approach with detailed thermodynamics has been employed. The periodic disturbances at the turbine inlet have been obtained by means of an artificially generated, unsteady field, resulting from a two-dimensional snapshot of the flow field at the combustor exit. Also, a combustor failure has been mimicked by reducing (respectively increasing) the mean temperature in some of the turbine inlet regions corresponding to the outlet of two burners. The propagation and amplitude changes of temperature fluctuations have been analyzed in the frequency domain. Tracking of the temperature fluctuations' maxima at the lowest frequencies revealed characteristic migration patterns indicating that the corresponding fluctuations persist with a non-negligible amplitude up to the last rows. A distinct footprint could also be observed at the same locations when a combustor failure was simulated, showing that, in principle, the early detection of combustor failures is indeed possible.

Dynamic Stability of Orthotropic Viscoelastic Rectangular Plate of an Arbitrarily Varying Thickness

The research object of this work is an orthotropic viscoelastic plate with an arbitrarily varying thickness. The plate was subjected to dynamic periodic load. Within the Kirchhoff–Love hypothesis framework, a mathematical model was built in a geometrically nonlinear formulation, taking into account the tangential forces of inertia. The Bubnov–Galerkin method, based on a polynomial approximation of the deflection and displacement, was used. The problem was reduced to solving systems of nonlinear integrodifferential equations. The solution of the system was obtained for an arbitrarily varying thickness of the plate. With a weakly singular Koltunov–Rzhanitsyn kernel with variable coefficients, the resulting system was solved by a numerical method based on quadrature formulas. The computational algorithm was developed and implemented in the Delphi algorithmic language. The plate’s dynamic stability was investigated depending on the plate’s geometric parameters and viscoelastic and inhomogeneous material properties. It was found that the results of the viscoelastic problem obtained using the exponential relaxation kernel almost coincide with the results of the elastic problem. Using the Koltunov–Rzhanitsyn kernel, the differences between elastic and viscoelastic problems are significant and amount to more than 40%. The proposed method can be used for various viscoelastic thin-walled structures such as plates, panels, and shells of variable thickness.

Numerical Investigation of Unsteady Combustor Turbine Interaction For Flexible Power Generation

With the growing importance of regenerative power generation and especially of a hydrogen-based economy, the full potential of gas turbines of the smaller output class (< 10 MW) can be ideally exploited to provide peak coverage of the energy need whilst stabilising the electric grids in the mid- and low-voltage range. Such machines can be typically started in a relatively short time (similarly to aero engines) and are capable, at the same time, of delivering dispatchable power-on-demand. A safe, stable and profitable operation under highly unsteady conditions poses renewed challenges for an optimal thermal management (especially in the HP stages) as well as control and surveillance of the machines. The understanding and hence predictability of the propagation of the temperature inhomogeneities originating at the combustor outlet remains hence a primary objective of current research, as persistent distortion patterns could be adopted at the turbine exhaust as diagnostic indications of a malfunction of the combustor, for example. In the present study low-frequency disturbances introduced by a periodic load variation have been simulated and superimposed to the inhomogeneous, unsteady flow entering a 3-stage, high-pressure industrial gas turbine fed by a can-type combustion chamber comprising 6 silo-burners. The effects of the unsteadiness realized at the combustor exit have been investigated by means of Detached Eddy Simulations, whereby a density-based solution approach with detailed thermodynamics has been employed. The periodic disturbances at the turbine inlet have been obtained by means of an artificially generated, unsteady field, resulting from a two-dimensional snapshot of the flow field at the combustor exit. Also, a combustor failure has been mimicked by reducing (respectively increasing) the mean temperature in some of the turbine inlet regions corresponding to the outlet of two burners. The propagation and amplitude changes of temperature fluctuations have been analyzed in the frequency domain. Tracking of the temperature fluctuations’ maxima at the lowest frequencies revealed characteristic migration patterns indicating that the corresponding fluctuations persist with a non-negligible amplitude up to the last rows. A distinct footprint could also be observed at the same locations when a combustor failure was simulated, showing that, in principle, the early detection of combustor failures is indeed possible.

Fractional-Order Hyperbolic Tangent Sliding Mode Control for Chaotic Oscillation in Power System

Chaotic oscillation will occur in power system when there exist periodic load disturbances. In order to analyze the chaotic oscillation characteristics and suppression method, this paper establishes the simplified mathematical model of the interconnected two-machine power system and analyzes the nonlinear dynamic behaviors, such as phase diagram, dissipation, bifurcation map, power spectrum, and Lyapunov exponents. Based on fractional calculus and sliding mode control theory, the fractional-order hyperbolic tangent sliding mode control is proposed to realize the chaotic oscillation control of the power system. Numerical simulation results show that the proposed method can not only suppresses the chaotic oscillation but also reduce the convergence time and suppress the chattering phenomenon and has strong robustness.

Sensorless PMSM Drive Inductance Estimation Based on a Data-Driven Approach

In the permanent magnet synchronous motor (PMSM) sensorless drive method, motor inductance is a decisive parameter for rotor position estimation. Due to core magnetic saturation, the motor current easily invokes inductance variation and degrades rotor position estimation accuracy. For a constant load torque, saturated inductance and inductance error in the sensorless drive method are constant. Inductance error results in constant rotor position estimation error and minor degradations, such as less optimal torque current, but no speed estimation error. For a periodic load torque, the inductance parameter error periodically fluctuates and, as a result, the position estimation error and speed error also periodically fluctuate. Periodic speed error makes speed regulation and load torque compensation especially difficult. This paper presents an inductance parameter estimator based on polynomial neural network (PNN) machine learning for PMSM sensorless drive with a period load torque compensator. By applying an inductance estimator, we also proposed a magnetic saturation compensation method to minimize periodic speed fluctuation. Simulation and experiments were conducted to validate the proposed method by confirming improved position and speed estimation accuracy and reduced system vibration against periodic load torque.