Volume 67 (2016): Issue 4 (July 2016)

In this paper short-term planning algorithm for hybrid power system consist of different types of cascade hydropower plants (run-of-the river, pumped storage, conventional), thermal power plants (coal-fired power plants, combined cycle gas-fired power plants) and wind farms is presented. The optimization process provides a joint bid of the hybrid system, and thus making the operation schedule of hydro and thermal power plants, the operation condition of pumped-storage hydropower plants with the aim of maximizing profits on day ahead market, according to expected hourly electricity prices, the expected local water inflow in certain hydropower plants, and the expected production of electrical energy from the wind farm, taking into account previously contracted bilateral agreement for electricity generation. Optimization process is formulated as hourly-discretized mixed integer linear optimization problem. Optimization model is applied on the case study in order to show general features of the developed model.

This paper proposes a hybrid method for calculating lightning performance of overhead lines caused by direct strokes by combining Lattice diagram together with the Monte Carlo method. In order to go through this, firstly, the proper analytical relations for overvoltages calculation are established based on Lattice diagram. Then, the Monte Carlo procedure is applied to the obtained analytical relations. The aim of the presented method that will be called ‘ML method’ is simply estimation of the lightning performance of the overhead lines and performing the risk analysis of power apparatus with retaining the acceptable accuracy. To confirm the accuracy, the calculated results of the presented ML method are compared with those calculated by the EMTP/ATP simulation.

Paper proposes mathematical model of single phase shaded pole motor suitable for analysis of motor dynamic behavior. Derived mathematical model from d–q reference frame theory is applied at motor simulation model. Derived simulation model enables analysis of transient performance characteristics of motor currents, speed and electromagnetic torque under different operating regimes. Obtained results from the simulation are compared with data from analytical calculations based on method of symmetrical components and data from experiment for the purpose of verification of the simulation model. Simulation model is useful for studying the effect of parameters on motor starting and running characteristics at different types of loads.

This paper presents the simulations results in comparison with the measured results of the practical realization of the multifunctional second order frequency filter with a Digitally Adjustable Current Amplifier (DACA) and two Dual-Output Controllable Current Conveyors (CCCII +/−). This filter is designed for use in current mode. The filter was designed of the single input multiple outputs (SIMO) type, therefore it has only one input and three outputs with individual filtering functions. DACA element used in a newly proposed circuit is present in form of an integrated chip and the current conveyors are implemented using the Universal Current Conveyor (UCC) chip with designation UCC-N1B. Proposed frequency filter enables independent control of the pole frequency using parameters of two current conveyors and also independent control of the quality factor by change of a current gain of DACA.

This article discusses the possibilities of obtaining new information related to human behavior, namely the changes or progressive development of pilots’ abilities during training. The main assumption is that a pilot’s ability can be evaluated based on a corresponding behavioral model whose parameters are estimated using mathematical identification procedures. The mean values of the identified parameters are obtained via statistical methods. These parameters are then monitored and their changes evaluated. In this context, the paper introduces and examines relevant mathematical models of human (pilot) behavior, the pilot-aircraft interaction, and an example of the mathematical analysis.

Method of Adaptive testing (AT) is described, as a way of optimization of experimental evaluation of degraded materials, which are measured nondestructively, indirectly via a convenient physical process. The general AT is illustrated by a well-explored special case of magnetic adaptive testing (MAT) of strained steel. Degradation curves obtained from AT are regularly more sensitive and experimentally friendly than traditional descriptors of the used physical process itself. Any of the traditional descriptors of the physical process, being optimized for description of the process itself, is very probably not optimized for description of the investigated material degradation.

The paper deals with the parameter identification of cascade nonlinear dynamic systems with noninvertible piecewise linear input nonlinearities and backlash output nonlinearities. Application of the key term separation principle provides special expressions for the corresponding nonlinear model description that are linear in parameters. A least squares based iterative technique allows estimation of all the model parameters based on measured input/output data. Simulation studies illustrate the feasibility of proposed identification method.

This work is focused on microwave treatment of sewage sludge. The aim of our experiments was to investigate the impact of microwave radiation upon different sewage sludge parameters such as concentration of nitrates and nitrites, phosphates, COD (Chemical Oxygen Demand), SVI (Sludge Volume Index) and the microscopic structure of sludge. The experiments with microwave irradiation of sewage sludge indicate that moderate microwave power causes visible effects on the chemical, physical and biological properties of the sludge. The calculation of profitability and energy efficiency is also presented.

This work deals with the statistical characterization of real-time digital measurement of the amplitude of harmonics affected by frequency instability. In fact, in modern power systems both the presence of harmonics and frequency instability are well-known and widespread phenomena mainly due to nonlinear loads and distributed generation, respectively. As a result, real-time monitoring of voltage/current frequency spectra is of paramount importance as far as power quality issues are addressed. Within this framework, a key point is that in many cases real-time continuous monitoring prevents the application of sophisticated algorithms to extract all the information from the digitized waveforms because of the required computational burden. In those cases only simple evaluations such as peak search of discrete Fourier transform are implemented. It is well known, however, that a slight change in waveform frequency results in lack of sampling synchronism and uncertainty in amplitude estimate. Of course the impact of this phenomenon increases with the order of the harmonic to be measured. In this paper an approximate analytical approach is proposed in order to describe the statistical properties of the measured magnitude of harmonics affected by frequency instability. By providing a simplified description of the frequency behavior of the windows used against spectral leakage, analytical expressions for mean value, variance, cumulative distribution function, and probability density function of the measured harmonics magnitude are derived in closed form as functions of waveform frequency treated as a random variable.

Wireless indoor localization has generated recent research interest due to its numerous applications. This work investigates Wi-Fi based indoor localization using two variants of the fingerprinting approach. Specifically, we study the application of an artificial neural network (ANN) for implementing the fingerprinting approach and compare its localization performance with a probabilistic fingerprinting method that is based on maximum likelihood estimation (MLE) of the user location. We incorporate spatial correlation of fading into our investigations, which is often neglected in simulation studies and leads to erroneous location estimates. The localization performance is quantified in terms of accuracy, precision, robustness, and complexity. Multiple methods for handling the case of missing APs in online stage are investigated. Our results indicate that ANN-based fingerprinting outperforms the probabilistic approach for all performance metrics considered in this work.

The ⁵⁷Fe Mössbauer spectroscopy method was used to investigate iron-containing compounds in town Košice meteorite fallen on the territory of Slovakia in February 2010. The results showed that the Mössbauer spectra consisted of magnetic and non-magnetic components related to different iron-bearing phases. The non-magnetic phase includes olivine, pyroxene and traces of Fe³⁺ phase and the magnetic component comprises troilite (FeS) and iron-rich Fe-Ni alloy with hyperfine magnetic field typical for kamacite. Samples from meteorite were obtained in powder from different depths to inspect its heterogeneous composition. The content of kamacite increases to the detriment of troilite from the surface toward the centre of the sample. Measurements at liquid nitrogen temperature confirmed phase composition of investigated meteorite. Main constituent elements of studied samples were also determined by X-ray fluorescence analysis.