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.     

A Single-Phase DG Generation Unit With Shunt Active Power Filter Capability by Adaptive Neural Filtering

This paper deals with a single-phase distributed generation (DG) system with active power filtering (APF) capability, devised for utility current harmonic compensation. The idea is to integrate the DG unit functions with shunt APF capabilities, because the DG is connected in parallel to the grid. With the proposed approach, control of the DG unit is performed by injecting into the grid a current with the same phase and frequency of the grid voltage and with an amplitude depending on the power available from renewable sources. On the other hand, load harmonic current compensation is performed by injecting into the alternating current system harmonic currents like those of the load but with an opposite phase, thus keeping the line current almost sinusoidal. Both detection of the grid voltage fundamental and computation of the load harmonic compensation current have been performed by two neural adaptive filters with the same structure, one in a configuration ldquonotchrdquo and the other in the complementary configuration ldquoband.rdquo The ldquonotchrdquo filter has been used to compute the compensation current by eliminating only the contribution of the fundamental of the load current, whereas the ldquobandrdquo configuration is able to extract the fundamental of the coupling point voltage. Furthermore, because the active power generation and the APF features require current control of components at different frequencies, respectively, a multiresonant current controller has been adopted. The methodology has been tested successfully both in numerical simulation and experimentally on a suitably devised test setup. The stability analysis of the proposed control approach has been performed in the discrete domain.     

1,2,4-Oxadiazole Topsentin Analogs with Antiproliferative Activity against Pancreatic Cancer Cells,Targeting GSK3β Kinase

A new series of topsentin analogs, in which the central imidazole ring of the natural lead was replaced by a 1,2,4-oxadiazole moiety, was efficiently synthesized. All derivatives were pre-screened for antiproliferative activity against the National Cancer Institute (NCI-60) cell lines panel. The five most potent compounds were further investigated in various pancreatic ductal adenocarcinoma (PDAC) cell lines, including SUIT-2, Capan-1, and Panc-1 cells, eliciting EC₅₀ values in the micromolar and sub-micromolar range, associated with significant reduction of cell migration. These remarkable results might be explained by the effects of these new topsentin analogues on epithelial-to-mesenchymal transition markers, including SNAIL-1/2 and metalloproteinase-9. Moreover, flow cytometric analysis after Annexin V-FITC and propidium iodide staining demonstrated that these derivatives enhanced apoptosis of PDAC cells. Keeping with these data, the PathScan intracellular signaling and ELISA array revealed cleavage of caspase-3 and PARP and a significant inhibition of GSK3β phosphorylation, suggesting this kinase as a potential downstream target of our novel compounds. This was further supported by a specific assay for the evaluation of GSK3β activity, showing IC₅₀ values for the most active compounds against this enzyme in the micromolar range.     

A new analytical and dynamical model of a scroll compressor with experimental validation

This paper deals with the scroll compressor, which is a machine used for compressing air or refrigerant. By using a novel reference frame, it proposes an original way of describing the geometry of the scroll wraps (represented as circle involutes) in which the symmetries are exploited in order to establish a thermodynamic model of the scroll compressor. This approach allows the chamber volumes to be analytically described without any special assumption and takes into account the discharge as a non-symmetrical process.The proposed geometric model is aimed to be coupled with the thermodynamic model by using the standardized VHDL-AMS language and should be then considered as preliminary to the scroll overall simulation and design of a functional virtual prototype.Simulations and experiments have shown good agreement.     

Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor

Wind energy evaluation is an important goal in the conversion of energy systems to more environmentally friendly solutions. In this paper, we present a novel approach to wind speed spatial estimation on the isle of Sicily (Italy): an incremental self-organizing neural network (Generalized Mapping Regressor – GMR) is coupled with exploratory data analysis techniques in order to obtain a map of the spatial distribution of the average wind speed over the entire region.     

Surrogate modelling of compressor characteristics for fuel-cell applications

The compressor is an important auxiliary for fuel-cell (FC) operation. Growing fuel-cell system efficiency involves an optimal fuel cell energy management and the air management is a key issue. Thus, a good modelling for static and dynamic operation of all components of the FC system, and in particular of the compressor, is required. The difficulties, due to a lack of information about the performance of compressors, demand predictive and modern approximation methods to be used for compressor modelling. To overcome these issues, the paper proposes and presents a moving least squares (MLS) algorithm for obtaining a surrogate model of the centrifugal compressor. The experimental data provided by manufacturers are used for this task. The results can be used for the development of an off-design model or the overall dynamic simulation of the behaviour of a FC system.     

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.     

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