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Analysis of harmonics propagation in power systems requires information about models of non‐linear loads which are the main source of harmonics. The purpose of the paper is to present a method of parameter determination of non‐linear loads.

A method of parameter determination of a model has been described in the article. This method has been developed assuming that mathematical description of a non‐linear load is known. In order to obtain a harmonic representation of this model for one‐phase loads the theory of harmonic balance method has been used.

Parameters of the current or voltage controlled sources can be expressed as a function of any number of voltage or current harmonics. In the case under consideration the network input current is a function of input voltage and its derivatives. Thus, contrary non‐linear element with inertialess characteristics, the first harmonic of the current depends not only on amplitude but also on frequency of the voltage across terminals of one‐ports.

The method presented in the paper enables relatively simple determination of frequency parameters of non‐linear models of one‐port networks.

Electric arc furnaces are usually modelled using combined models which divide the phenomenon taking place in real objects into a deterministic and a stochastic or chaotic parts. The former is expressed by a nonlinear differential equation. The goal of this paper was to obtain a closed form of the solution to one of the most popular nonlinear differential equations used for the AC electric arc modelling.

The solution has been obtained in the time domain by a sequence of transformations of the original nonlinear equation which lead to a linear equation, for which a closed form solution is known.

The paper provides a set of parameters for which the solution to the nonlinear differential equation describing electric arc can be obtained in a closed form.

There are still some parameter values for which the solution can be found only numerically. Moreover, due to the nature of the phenomena occurring in electric arc furnaces, in order to build a complete model of the arc the deterministic model must be extended using for example stochastic approach.

The obtained results enable determination of exact waveforms of the arc voltage or radius without application of numerical algorithms for ODE solving. The arc model can be used to evaluate the impact of arc furnaces on power quality during the planning stage of new plants. The proposed approach facilitates calculation of the arc characteristic.

The importance of having a closed form of the solution instead of the numerical ones comes from new possible ways of extension of the arc model in order to cover the time‐varying nature of the arc waveforms. So far the equation has been solved only using numerical algorithms.

In most applications the active power filters (APFs) are used to reduce harmonic distortion of a nonlinear load which is located near the APF installation point. This classic approach allows to reduce the distortion introduced to the power system but do not guarantee that the cost of the APFs installation is optimal. The purpose of this paper is to compare the classic approach to harmonic compensation with an optimization method of sizing and placement of the APFs in an existing distributed power network.

An exemplary real-life power system with distributed nonlinear loads was modeled using PCFLO power analysis software. Next, Matlab was used to implement the classic method and the optimization algorithm. Between Matlab and PCFLO a specially written Java middleware was used to provide a seamless workflow integration.

It was shown that the presented optimization method may lead to superior results in comparison with the classic approach. Simulation results clearly showed that the APFs installation cost can be significantly reduced when the optimization algorithm is used. Moreover, the proposed optimization method can overcome some problems connected with the nonlinearity and discontinuity of the APF's price/current function.

There are two main limitations of the presented method. First, the method needs much more computing power then the classic approach. Second, according to the authors’ knowledge, currently there are no commercially available APFs, which allow to directly apply the optimization method in industrial applications.

The presented results showed that the approach, which is the most popular in the industry, is far from being optimal from the cost perspective. As it has been shown in the investigated example, it might be possible to significantly reduce the total cost of APFs installed in the power system.

The optimization method presented in the paper as well as all simulation results are the original authors work. It was shown that the existing harmonic compensation strategies can be significantly upgraded and the proposed optimization method may be a basis and a reference point for future commercial solutions.

A highly accurate method of current spectrum estimation of a nonlinear load is presented in this paper. Using the method makes it possible to evaluate the current injection frequency domain model of a nonlinear load from previously recorded time domain voltage and current waveforms. The paper aims to discuss these issues.

The method incorporates the idea of coherent resampling (resampling synchronously with the base frequency of the signal) followed by the discrete Fourier transform (DFT) to obtain the frequency spectrum. When DFT is applied to a synchronously resampled signal, the spectrum is free of negative DFT effects (the spectrum leakage, for example). However, to resample the signal correctly it is necessary to know its base frequency with high accuracy. To estimate the base frequency, the first-order Prony's frequency estimator was used.

It has been shown that the presented method may lead to superior results in comparison with window interpolated Fourier transform and time-domain quasi-synchronous sampling algorithms.

The method was designed for steady-state analysis in the frequency domain. The voltage and current waveforms across load terminals should be recorded simultaneously to allow correct voltage/current phase shift estimation.

The proposed method can be used in case when the frequency domain model of a nonlinear load is desired and the voltage and current waveforms recorded across load terminals are available. The method leads to correct results even when the voltage/current sampling frequency has not been synchronized with the base frequency of the signal. It can be used for off-line frequency model estimation as well as in real-time DSP systems to restore coherent sampling of the analysed signals.

The method proposed in the paper allows to estimate a nonlinear load frequency domain model from current and voltage waveforms with higher accuracy than other competitive methods, while at the same time its simplicity and computational efficiency is retained.

Poland’s political and economic transformation after 1989 brought the logic of the neoliberal market into the educational system. These changes, however, were far from the real liberal free market and instead relied on bureaucratic and technocratic local-level apparatus as well as supranational supports (the EU). Moreover, instead of enhancing post-socialist education to bring them up to the level of the core territories, this process pushed education out to the (semi?)periphery. The purpose of this paper is to present selected examples of alternative non-mainstream models of education.

Elements analyzed include: non/academic discourses, with particular emphasis on academic texts, media material and public debates concerning the topic in question.

Two related fields and levels ought to be distinguished: the descriptive level, focused on presenting non-mainstream educational institutions and initiatives, within the socioeconomic context of Poland’s post-socialist transformation; the normative level, with recommendations for policymakers, NGOs and educational activists.

Appreciation of systems parallel and alternative to the neoliberal and technocratic mainstream education system in Poland, with a view to encouraging both policymakers to recognise and develop such initiatives, and members of Polish civil society to create and participate in such forms of education.

Most scholars focus on mainstream education, with a number of exceptions, largely those engaged in the parallel models. This neoliberal model of education is accepted or critically examined, but its technocratic base is not recognised. This text is therefore ground-breaking in that it describes the real mechanisms of the Polish educational system in transition and provides a normative account and recommendations.

This study contributes to the pool of knowledge about the impact of monetary policy communication of central banks on financial instruments' prices and assets' value in emerging markets.

Empirical analysis is executed using the National Bank of Poland (NBP) announcements about its monetary policy covering the data from the broad financial market in its three main segments: stock market, foreign exchange market and bonds market. The reactions are measured relative to the changes in the NBP announcements and also with respect to investors' expectations. Autoregressive conditional heteroscedasticity (ARCH) models with dummy variables are used as the main methodological tool.

Bonds market and foreign exchange market are the most sensitive market segments, while interest rate and money supply are the most influential types of announcements. The changes of the revealed new macroeconomic figures had more impact on assets' prices movements than the deviations from their expectations. Moreover, greater diversity of the Monetary Policy Council (MPC) members' opinions on the voted motions, captured in the MPC voting reports, is associated with more cases of statistically significant NBP communication events.

The findings have direct relevance for fund managers, portfolio analysts, investors and also for financial market regulators.

The results provide novel evidence about how the emerging financial market responds to monetary policy announcements. They help understand the nature of the impact of public information on financial assets' valuation and on movements of their prices, analysed comprehensively in three market segments, in the emerging market environment.

In this study, a hybrid rough and grey set-based rule model is designed for diagnosis of one type of blood cancer called multiple myeloma (MM). The grey clustering method is used to combine the same condition attributes and to improve the validity of the final model.

Some tools of the rough set theory (RST) and grey incidence analysis (GIA) are used in this research to analyze the serum protein electrophoresis (SPE) test results. An RST-based rule model is extracted based on the laboratory SPE test results of patients. Also, one decision attribute and 15 condition attributes are used to extract the rules. About four rule models are constructed due to the different algorithms of data complement, discretization, reduction and rule generation. In the following phases, the condition attributes are clustered into seven clusters by using a grey clustering method, the value set of the decision attribute is decreased by using manual discretizing and the number of observations is increased in order to improve the accuracy of the model. Cross-validation is used for evaluation of the model results and finally, the best model is chosen with 5,216 rules and 98% accuracy.

In this paper, a new rule model with high accuracy is extracted based on the combination of the grey clustering method and RST modeling for diagnosis of the MM disease. Also, four primary rule models and four improved rule models have been extracted from different decision tables in order to define the result of SPE test of patients. The maximum average accuracy of improved models is equal to 95% and related to the gamma globulins percentage attribute/object-related reducts (GA/ORR) model.

The total number of observations for rule extraction is 115 and the results can be improved by further samples. To make the designed expert system handy in the laboratory, new computer software is under construction to import data automatically from the electrophoresis machine into the resultant rule model system.

The main originality of this paper is to use the RST and GST together to design and create a hybrid rule model to diagnose MM. Although many studies have been carried out on designing expert systems in medicine and cancer diagnosis, no studies have been found in designing systems to diagnose MM. On the other hand, using the grey clustering method for combining the condition attributes is a novel solution for improving the accuracy of the rule model.