Adaptivni estimator brzine za bezsenzorsko vektorsko upravljanje asinkronim motorom zasnovan na umjetnoj neuronskoj mreži

This paper presents an adaptive speed observer for an induction motor using an artificial neural network with a direct field-oriented control drive. The speed and rotor flux are estimated with the only assumption that from stator voltages and currents are measurable. The estimation algorithm uses a state observer combined with an intelligent adaptive mechanism based on a recurrent neural network (RNN) to estimate rotor speed. The stator and rotor resistances are estimated by a simple Proportional-Integrator (PI) controller, which reduces sensitivity to variations, due essentially to the influence of temperature. The proposed sensorless control scheme is tested for various operating conditions of the induction motor drive. Experimental results demonstrate a good robustness against load torque disturbances, the estimated fluxes and rotor speed converge to their true values, which guarantees that a precise trajectory tracking with the prescribed dynamics.Ovaj članak opisuje adaptivni estimator brzine temeljen na umjetnoj neuronskoj mreži, koji se primijenjuje na asinkroni motor pogonjen izravnim vektorskim upravljanjem. Brzina i magnetski tok rotora estimiraju se uz pretpostavku dostupnosti mjerenja napona i struja statora. Algoritam koristi estimator stanja u kombinaciji s inteligentnim adaptivnim mehanizmom temeljenim na povratnoj neuronskoj mreži (RNN) kako bi se estimirala brzina rotora. Otpori statora i rotora estimiraju se jednostavnim Proporcionalno-Integralnim (PI) regulatorom, čime se smanjuje osjetljivost na varijacije uzrokovane utjecajem temperature. Predložena bezsenzorska upravljačka shema testirana je za različite radne uvjete asinkronog motora. Eksperimentalni rezultati pokazuju visoki stupanj robusnosti s obzirom na poremećaj momenta tereta, a estimirani tokovi i brzina rotora konvergiraju prema stvarnim vrijednostima što garantira precizno praćenje trajektorija uz zahtijevanu dinamiku

Experimental study on self-excited induction generator for small-scale isolated rural electricity applications

Induction generators have been gaining popularity since the last few decades for the small -scale off-grid power generation renewable energy applications due to many inherent advantages. The IG is not a self-started generator in off-grid mode of operation to generate the required voltage because of isolation from the grid to supply the required amount of reactive power to the generator. In this context, the self-excitation process in the induction generator mainly depends on the amount of reactive power, then speed of the rotor, and load on the system. In this paper, the effect of these three parameters on the performance of the self-excited induction generator is experimentally studied. The focus of the paper is to identify the best configuration of generator operation to carry out the maximum loading capacity and economical operation. Further, the proposed study is extended for an extra initial excitation approach and verified the performance under the same source and load condition and derived some key aspects for utilization of the generator at its maximum loading capacity. A micro-hydro source driven turbine emulations used as an input source to the SEIG in this experimental work

Design, control and performance comparison of PI and ANFIS controllers for BLDC motor driven electric vehicles

The research and usage of electric vehicles (EVs), including two and four-wheeler vehicles, are rapidly increasing worldwide as alternatives to oil/gas-based vehicles. Brushless direct current (BLDC) motors are popular for industrial and traction applications due to their inherent advantages. In EVs, achieving low error in steady-state and transient responses is crucial for smooth acceleration at the wheel. This paper presents the design and control of a BLDC motor for speed control during acceleration and deceleration, considering error as a key factor in the MATLAB/Simulink environment. Proportional-integral (PI) and fuzzy controllers are commonly used for motor control to improve steady-state and transient performance, thereby reducing error. In this study, the PI and adaptive neuro-fuzzy inference system (ANFIS) controllers are designed and compared for a 5-kW, 48-V, and 100-Amp BLDC motor in EV applications. The results demonstrate that the ANFIS controller enhances the dynamic performance of the BLDC motor and improves other operating characteristics such as rise time, settling time, peak overshoot percentage and the vehicle response in terms of speed and distance

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Application of a multivariable feedback linearization scheme for STATCOM control

This paper investigates application of a multivariable control technique to the multi-input multi-output (MIMO) nonlinear model of a static synchronous compensator (STATCOM). The proposed controller design is based on a feedback linearization scheme. Its prime goal is the coordinated control of ac and dc voltage for a STATCOM installed in a power distribution system. First, the nonlinear mathematical model of STATCOM along with the distribution system is derived. Then, by using input–output feedback linearization, a state feedback control law is obtained by pole placement. The efficacy of the control strategy is evaluated by digital computer simulations on the complete system for various types of loads and/or disturbances. The comparative study of these results with those obtained in a conventional cascade control architecture establishes the elegance of this new control scheme