Marcello Pucci

Current Harmonic Compensation by a Single-Phase Shunt Active Power Filter Controlled by Adaptive Neural Filtering

This paper presents a single-phase shunt active power filter (APF) for current harmonic compensation based on neural filtering. The shunt active filter, realized by a current-controlled inverter, has been used to compensate a nonlinear current load by receiving its reference from a neural adaptive notch filter. This is a recursive notch filter for the fundamental grid frequency (50 Hz) and is based on the use of a linear adaptive neuron (ADALINE). The filters parameters are made adaptive with respect to the grid frequency fluctuations. A phase-locked loop system is used to extract the fundamental component from the coupling point voltage and to estimate the actual grid frequency. The current control of the inverter has been performed by a multiresonant controller. The estimated grid frequency is fed to the neural adaptive filter and to the multiresonant controller. In this way, the inverter creates a current equal in amplitude and opposite in sign to the load harmonic current, thus producing an almost sinusoidal grid current. An automatic tuning of the multiresonant controller is implemented, which recognizes the largest three harmonics of the load current to be compensated by the APF. The stability analysis of the proposed control system is shown. The methodology has been applied in numerical simulations and experimentally to a properly devised test setup, also in comparison with the classic sinusoidal current control based on the P-Q theory.

Neural MPPT Control of Wind Generators With Induction Machines Without Speed Sensors

This paper presents a maximum power point tracking (MPPT) technique for high-performance wind generators with induction machines based on the growing neural gas (GNG) network. In this paper, a GNG network has been trained offline to learn the turbine-characteristic surface torque versus wind and machine speeds and has been implemented online to obtain the wind tangential speed on the basis of the estimated torque and measured machine speed (surface function inversion). The machine reference speed is then computed on the basis of the optimal tip speed ratio. For the experimental application, a back-to-back configuration with two voltage source converters has been considered, one on the machine side and the other on the grid side. The field-oriented control of the machine has been further integrated with an intelligent sensorless technique; in particular, the so-called total least squares (TLS) EXIN full-order observer has been adopted. Finally, a comparison with a classic perturb-and-observe MPPT has been made on a real wind-speed profile.

An MRAS-based sensorless high-performance induction motor drive with a predictive adaptive model

This paper presents a new model reference adaptive system (MRAS) speed observer for high-performance field-oriented control induction motor drives based on adaptive linear neural networks. It is an evolution and an improvement of an MRAS observer presented in the literature. This new MRAS speed observer uses the current model as an adaptive model discretized with the modified Euler integration method. A linear neural network has been then designed and trained online by means of an ordinary least-squares (OLS) algorithm, differently from that in the literature which employs a nonlinear backpropagation network (BPN) algorithm. Moreover, the neural adaptive model is employed here in prediction mode, and not in simulation mode, as is usually the case in the literature, with a consequent quicker convergence of the speed estimation, no need of filtering the estimated speed, higher bandwidth of the speed loop, lower estimation errors both in transient and steady-state operation, better behavior in zero-speed operation at no load, and stable behavior in field weakening. A theoretical analysis of some stability issues of the proposed observer has also been developed. The OLS MRAS observer has been verified in numerical simulation and experimentally, and in comparison with the BPN MRAS one presented in the literature.

A new adaptive integration methodology for estimating flux in induction machine drives

This paper proposes a simple adaptive notch filter for the elimination of the dc component in the integration of signals used for the flux estimation in high performance ac drives. This integration method is composed of two identical adaptive noise cancellers using a linear neural network with just one bias weight. Its behavior has been investigated in simulation as applied to electrical drives and compared with other four traditional integration algorithms. A test bench has been then developed for its experimentation in a field oriented controlled induction machine. It has been verified that this integration algorithm outperforms the other algorithms in estimating the rotor flux even at low speeds.

Sensorless Control of PMSM Fractional Horsepower Drives by Signal Injection and Neural Adaptive-Band Filtering

This paper presents a sensorless technique for permanent-magnet synchronous motors (PMSMs) based on high-frequency pulsating voltage injection. Starting from a speed estimation scheme well known in the literature, this paper proposes the adoption of a neural network (NN) based adaptive variable-band filter instead of a fixed-bandwidth filter, needed for catching the speed information from the sidebands of the stator current. The proposed NN filter is based on a linear NN adaptive linear neuron (ADALINE), trained with a classic least mean squares (LMS) algorithm, and is twice adaptive. From one side, it is adaptive in the sense that its weights are adapted online recursively. From another side, its bandwidth is made adaptive during the running of the drive, acting directly on the learning rate of the NN filter itself. The immediate consequence of adopting a variable-band structure is the possibility to enlarge significantly the working speed range of the sensorless drive, which can be increased by a factor of five. The proposed observer has been tested experimentally on a fractional horsepower PMSM drive and has been compared also with a fixed-bandwidth structure.

Sensorless Control of Induction Machines by a New Neural Algorithm: The TLS EXIN Neuron

This paper proposes two speed observers for high-performance induction machine drives, both adopting an online adaptation law based on a new total least-squares (TLS) technique: the TLS EXIN neuron. The first is a model reference adaptive system (MRAS) observer with a neural adaptive integrator in the reference model and a neural adaptive model trained online by the TLS EXIN neuron. This observer, presented in a previous article of the authors, has been improved here in two aspects: first, the neural adaptive integrator has been modified to make its learning factor vary according to the reference speed of the drive, second, a neural adaptive model based on the modified Euler integration has been proposed to solve the discretization instability problem in field-weakening. The second observer is a new full-order adaptive one based on the state equations of the induction machine, where the speed is estimated by means of a TLS EXIN adaptation technique. Both these observers have been provided with an inverter nonlinearity compensation algorithm and with techniques for the online estimation of the stator resistance of the machine. Moreover, a thorough theoretical stability analysis has been developed for them both, with particular reference to the field-weakening region behavior for the TLS MRAS observer and to the regenerating mode at low speeds for the TLS adaptive observer. Both speed observers have been verified in numerical simulation and experimentally on a test setup, and have also been compared experimentally with the BPN MRAS observer, the classic adaptive observer and with an open-loop estimator. Results show that both proposed observers outperform all other three observers in every working condition, with the TLS adaptive observer resulting in a better performance than the TLS MRAS observer

Constrained minimization for parameter estimation of induction motors in saturated and unsaturated conditions

This paper presents the analytical solution of the application of the constrained least-squares (LS) minimization to the online parameter estimation of induction machines. This constrained minimization is derived from the classical linear dynamical model of the induction machine, and therefore it is able to estimated the steady-state value of the electrical parameters of the induction motor under different magnetization levels. The methodology has been verified in simulation with a dynamical model which takes into account iron path saturation effects. After a description of the experimental setup and its signal processing systems, the methodology is verified experimentally under saturated and unsaturated working conditions, and the results are discussed and compared to those obtained with a classical unconstrained ordinary LS technique.

MRAS Speed Observer for High-Performance Linear Induction Motor Drives Based on Linear Neural Networks

This paper proposes a neural network (NN) model reference adaptive system (MRAS) speed observer suited for linear induction motor (LIM) drives. The voltage and current flux models of the LIM in the stationary reference frame, taking into consideration the end effects, have been first deduced. Then, the induced part equations have been discretized and rearranged so as to be represented by a linear NN (ADALINE). On this basis, the transport layer security EXIN neuron has been used to compute online, in recursive form, the machine linear speed. The proposed NN MRAS observer has been tested experimentally on suitably developed test set-up. Its performance has been further compared to the classic MRAS and the sliding-mode MRAS speed observers developed for the rotating machines.

Sensorless Control of Induction Motors by Reduced Order Observer With MCA EXIN + Based Adaptive Speed Estimation

This paper presents a sensorless technique for high-performance induction machine drives based on neural networks. It proposes a reduced order speed observer where the speed is estimated with a new generalized least-squares technique based on the minor component analysis (MCA) EXIN + neuron. With this regard, the main original aspects of this work are the development of two original choices of the gain matrix of the observer, one of which guarantees the poles of the observer to be fixed on one point of the negative real semi-axis in spite of rotor speed, and the adoption of a completely new speed estimation law based on the MCA EXIN + neuron. The methodology has been verified experimentally on a rotor flux oriented vector controlled drive and has proven to work at very low operating speed at no-load and rated load (down to 3 rad/s corresponding to 28.6 rpm), to have good estimation accuracy both in speed transient and in steady-state and to work correctly at zero-speed, at no-load, and at medium loads. A comparison with the classic full-order adaptive observer under the same working conditions has proven that the proposed observer exhibits a better performance in terms of lowest working speed and zero-speed operation

Real time simulation of renewable sources by model-based control of DC/DC converters

This paper presents a DC/DC buck converter circuit for real-time laboratory simulation of renewable sources. The DC/DC converter, suitably driven, can accurately describe the current-voltage characteristic of a photovoltaic (PV) array and of a fuel cell (FC). In perspective its hardware structure, if the source modelling is correctly known and implemented, can reproduce any renewable source with rated data compatible with those of the DC/DC converter. The I-V laws of the PV and the FC have been obtained by an appropriate modelling of the considered renewable sources. Particular care has been given to the design of the converter control, to ensure the desired stability and dynamics. It has been verified in numerical simulation and experimentally that the designed DC/DC buck converter is able to reproduce the electrical characteristics of the experimental generator both in steady state and transient conditions, due to either load or parameters variations. The effectiveness of the proposed circuit is verified by laboratory experiments, obtained by implementing the converter control on a low cost DSP board.