In this paper an artificial neural network, which realizes a nonlinear adaptive control algorithm, has been applied in a control system of variable speed generating system. The speed is adjusted automatically as a function of load power demand. The controller employs a single layer neural network to estimate the unknown plant nonlinearities online. Optimization of the controller is difficult because the plant is nonlinear and no stationary. Furthermore, it deals with the situation where the plant becomes uncontrollable without any restrictive assumptions. In contrast to previous work [1] on the same subject, the number of neural networks has been reduced to only one network. The number of the neurons in a network structure as well as choosing certain design parameters was specified a priori. The computer test results have been presented to show performance of proposed neural controller.

Permanent magnet motors are more and more frequently used in various applications. In this group motors with a trapezoidal EMF deserve a special attention. They are characterized by a simple construction, high efficiency and high torque overload. A certain drawback of BLDC motors are difficulties with an operation at a speed above the nominal value. The article presents the results of investigations into the variablestructure electronic commutator designed for the drive of a small electric vehicle equipped with BLDC motors. Such a solution allows extending the standard range of the drive's speed. The considerations contained in the article focus on the possibilities and effects of regeneration mode in the proposed topology of converter. A theoretical analysis has been presented as well as computer simulations carried out by means of Matlab- Simulink, which were then verified at a laboratory. The tests were finished with trias conducted using a small electric vehicle Elipsa.

The paper presents the method of control of an induction squirrel-cage machine supplied by a voltage source converter. The presented idea is based on an innovative method of the voltage source converter control, consisting in direct joining of the motor control system with the voltage source rectifier control system. The combined control system gives good dc-bus voltage stabilization. In the applied control system the limits of the reference variables have been introduced. A correction of the estimated machine load torque is proposed. The new proposed solutions are confirmed by mathematical dependences, simulation and experimental results.

The Low Temperature Joining Technique (LTJT) using silver compounds enables to significantly increase the thermal conductivity between joined elements, which is much higher than for soldered joints. However, it also makes difficult to measure the thermal conductivity of the joint. The Laser Flash Analysis (LFA) is a non-intrusive method of measuring the temperature rise of one surface of a specimen after excitation with a laser pulse of its other surface. The main limitation of the LFA method is its standard computer software, which assumes the dimensions of a bonded component to be similar to those of the substrate, because it uses the standard Parker’s formula dedicated for one-dimensional heat flow. In the paper a special design of measured specimen was proposed, consisting of two copper plates of different size joined with the sintered silver layer. It was shown that heat properties of these specimens can also be measured after modifying the LFA method. The authors adapted these specimens by masking the false heat signal sourced from the uncovered plate area. Another adaptation was introducing a correcting factor of the heat travel distance, which was calculated with heat-flow simulations and placed into the Parker’s formula. The heat-flow simulated data were compared with the real LFA measurement results, which enabled estimation of the joint properties, e.g. its porosity.

The article presents the analysis of the simulation test results for three variants of the power electronics used as interface between the power network and superconducting magnetic energy storage (SMES) with the following parameters: power of 250 kW, current of 500 A DC and voltage of 500 V DC. Three interface topologies were analyzed: two-level AC-DC and DC-DC converters; three-level systems and mixed systems combining a three-level active rectifier and a two-level DC-DC converter. The following criteria were considered: input and output current and voltage distortions, determined as THDi and THDu, power losses in power electronics components; cost of the semiconductor components for each topology and total cost of the interface. Results of the analysis showed that for high-power low-voltage and high-current power electronics systems, the most advantageous solution from a technical and economical perspective is a two-level interface configuration in relation to both AC-DC and DC-DC converters.

Fault detection and location are important and front-end tasks in assuring the reliability of power electronic circuits. In essence, both tasks can be considered as the classification problem. This paper presents a fast fault classification method for power electronic circuits by using the support vector machine (SVM) as a classifier and the wavelet transform as a feature extraction technique. Using one-against-rest SVM and one-against-one SVM are two general approaches to fault classification in power electronic circuits. However, these methods have a high computational complexity, therefore in this design we employ a directed acyclic graph (DAG) SVM to implement the fault classification. The DAG SVM is close to the one-against-one SVM regarding its classification performance, but it is much faster. Moreover, in the presented approach, the DAG SVM is improved by introducing the method of Knearest neighbours to reduce some computations, so that the classification time can be further reduced. A rectifier and an inverter are demonstrated to prove effectiveness of the presented design.

A simple analog circuit is presented which can play a neuron role in static-model-based neural networks implemented in the form of an integrated circuit. Operating in a transresistance mode it is suited to cooperate with transconductance synapses. As a result, its input signal is a current which is a sum of currents coming from the synapses. Summation of the currents is realized in a node at the neuron input. The circuit has two outputs and provides a step function signal at one output and a linear function one at the other. Activation threshold of the step output can be conveniently controlled by means of a voltage. Having two outputs, the neuron is attractive to be used in networks taking advantage of fuzzy logic. It is built of only five MOS transistors, can operate with very low supply voltages, consumes a very low power when processing the input signals, and no power in the absence of input signals. Simulation as well as experimental results are shown to be in a good agreement with theoretical predictions. The presented results concern a 0.35 1m CMOS process and a prototype fabricated in the framework of Europractice.

Gapped magnetic components are inherent to applications where conversion of power would force magnetic flux density beyond the saturation point of magnetic materials. A physical discontinuity in a magnetic path, which an air gap represents, signifies a drastic change in its reluctance to magnetic flux. This gives rise to a phenomenon referred to as the fringing effect, which impacts the performance of magnetic components. The fringing flux also affects the physical properties of magnetic components, such as magnetic reluctance and inductance. Since inductance of gapped magnetic components is a function of the size of the air gap, a relatively simple change to the configuration of the air gap or splitting a single gap into a plurality of gaps entails, frequently, a radical change to the magnetic circuit of the component. This paper examines the way the air-gap configuration affects the distribution of the fringing flux and, by extension, magnetic reluctance and inductance. A method to aid the design of multigap inductors is presented based on 3-D electromagnetic modelling as well as measurements. An analytic expression, which closely approximates the required length of quasi-distributed gaps substituting a single gap, is developed.

Due to recent developments in the field of high-power and medium-voltage, the multilevel inverter has raised to such an extent owing to some of its amazing facts regarding harmonic spectrum, ease in control, reduced electromagnetic interference (EMI), filterless circuit, stress on power switches, common-mode voltage. This paper well describes a novel architecture of a single-phase multilevel inverter using a lesser number of overall components, especially the power switches. The proposed topology is generalized in the structure that can generate any number of voltage steps. A 7-level structure of the proposed topology is explained and is elaborately discussed. Simulation is carried out in MATLAB and corresponding experimental results verify the existence of the proposed multilevel inverter. The real-time experimental results were presented and are well verified by the simulation results for 7-level as well for 13-level across RL-Load. The nature of load current is also indicated as per the nature of load voltage. Nevertheless, the topology is further compared with some of the recent literature and found superior in each respect.