Three-level T-type inverters have lower total harmonic distortion in output voltage, higher power density and lower voltage stress of power switches compared with conventional two-level inverters and have been widely used in applications with a wide-power range. Reliability improvement is particularly important for the T-type inverters because of the increased number of power switches and high system complexity. This paper proposes a fault-tolerant topology, which is constructed by adding a redundant leg including halfbridge switches and neutral-point switches connected between the DC bus capacitors and the DC-link midpoint of the conventional T-type inverter. In addition, an after-fault control strategy is proposed based on the results of a fault diagnosis method using bridge voltage. The fault-tolerant control of the open-circuit fault of the power switches and the phase-leg fault can both be achieved by the proposed method. Experimental results are given to verify that the proposed fault-tolerant three-level T-type inverter can output the full voltage level and power during the fault-tolerant operation based on the proposed control strategy.

Neotectonic studies in Poland concern mainly manifestations of those tectonic movements that have been active in Late Neogene and Quaternary times, as well as geodetically measured recent vertical and horizontal crustal motions. Among problems of particular interest, the following should be listed: periodicity of neotectonic (mostly Quaternary) activity, estimation of the parametres of the neotectonic stress field, amplitudes and rates of Quaternary and recent movements, development of neotectonic troughs and young faults, mutual relationships among photolineaments, geological structures and recent seismicity, as well as the role of tectonic reactivation of fault zones due to human activity. Neotectonic faults in Poland have developed in Neogene and Quaternary times due to reactivation of Laramian or older structures, or in the Quaternary due to reactivation of Neogene faults. The size of throw of Quaternary faults changes from 40-50 m and >100 m in the Sudetes and the Lublin Upland, to several - several tens of metres in the Carpathians. The average rate of faulting during Quaternary times has been 0.02 to 0.05 mm/yr, what enables one to include these structures into the domains of inactive or low-activity faults. A similar conclusion can be drawn from the results of repeated precise levellings and GPS campaigns. Strike-slip displacements have been postulated for some of these faults. Isolated faults in Central Poland have shown middle Quaternary thrusting of the order of 40-50 m, and some of the Outer Carpathian overthrusts tend to reveal young Quaternary activity, as indicated, i.a., by concentrations of fractured pebbles within the thrust zones. Episodes of increased intensity of faulting took place in the early Quaternary, in the Mazovian (Holsteinian) Interglacial, and during or shortly after the Odranian (Drenthe) glacial stage. Some of the faults have also been active in Holocene times. Recent seismic activity is often related to strike-slip faults, which in the Carpathians trend ENE-WSW and NE-SW, whereas outside the Carpathians they are oriented parallel to the margin of the East-European Platform and the Sudetic Marginal Fault. Future investigations should put more emphasis on palaeoseismotectonic phenomena and practical application of neotectonic research.

This paper is devoted to multiple soft fault diagnosis of analog nonlinear circuits. A two-stage algorithm is offered enabling us to locate the faulty circuit components and evaluate their values, considering the component tolerances. At first a preliminary diagnostic procedure is performed, under the assumption that the non-faulty components have nominal values, leading to approximate and tentative results. Then, they are corrected, taking into account the fact that the non-faulty components can assume arbitrary values within their tolerance ranges. This stage of the algorithm is carried out using the linear programming method. As a result some ranges are obtained including possible values of the faulty components. The proposed approach is illustrated with two numerical examples.

The paper presents an analogue circuit testing method that engages the analysis of the time response to a non-periodic stimulus specialized for the verification of selected specifications. The decision about the current circuit diagnostic state depends on an amplitude spectrum decomposition of the time response measured during the test. A shape of the test excitation spectrum is optimized with the use of a differential evolution algorithm and it allows for achieving maximum fault coverage and the optimal conditions for fault isolation. Genotypes of the evolutionary system encode the amplitude spectrum of candidates for testing stimuli by means of rectangle frequency windows with amplitudes determined evolutionarily.

This paper deals with multiple soft fault diagnosis of nonlinear analog circuits comprising bipolar transistors characterized by the Ebers-Moll model. Resistances of the circuit and beta forward factor of a transistor are considered as potentially faulty parameters. The proposed diagnostic method exploits a strongly nonlinear set of algebraic type equations, which may possess multiple solutions, and is capable of finding different sets of the parameters values which meet the diagnostic test. The equations are written on the basis of node analysis and include DC voltages measured at accessible nodes, as well as some measured currents. The unknown variables are node voltages and the parameters which are considered as potentially faulty. The number of these parameters is larger than the number of the accessible nodes. To solve the set of equations the block relaxation method is used with different assignments of the variables to the blocks. Next, the solutions are corrected using the Newton-Raphson algorithm. As a result, one or more sets of the parameters values which satisfy the diagnostic test are obtained. The proposed approach is illustrated with a numerical example.

The paper deals with a multiple fault diagnosis of DC transistor circuits with limited accessible terminals for measurements. An algorithm for identifying faulty elements and evaluating their parameters is proposed. The method belongs to the category of simulation before test methods. The dictionary is generated on the basis of the families of characteristics expressing voltages at test nodes in terms of circuit parameters. To build the fault dictionary the n-dimensional surfaces are approximated by means of section-wise piecewise-linear functions (SPLF). The faulty parameters are identified using the patterns stored in the fault dictionary, the measured voltages at the test nodes and simple computations. The approach is described in detail for a double and triple fault diagnosis. Two numerical examples illustrate the proposed method.

The present article investigates the dynamic behavior of a fully assembled turbogenerator system influenced by misalignment. In the past, most of the researchers have neglected the foundation flexibility in the turbogenerator systems in their study, to overcome this modelling error a more realistic model of a turbogenerator system has been attempted by considering flexible shafts, flexible coupling, flexible bearings and flexible foundation. Equations of motion for fully assembled turbogenerator system including flexible foundations have been derived by using finite element method. The methodology developed based on least squares technique requires forced response information to quantify the bearing–coupling–foundation dynamic parameters of the system associated with different faults along with residual unbalances. The proposed methodology is tested for the various level of measurement noise and modelling error in the system parameters, i.e., 5% deviation in E (modulus of elasticity) and ρ (density), respectively, for robustness of the algorithm. In a practical sense, the condition analyzed in the present article relates to the identification of misalignment and other dynamic parameters viz. bearing and residual unbalance in a rotor integrated with flexible foundation.

This study offers two Support Vector Machine (SVM) models for fault detection and fault classification, respectively. Different short circuit events were generated using a 154 kV transmission line modeled in MATLAB/Simulink software. Discrete Wavelet Transform (DWT) is performed to the measured single terminal current signals before fault detection stage. Three level wavelet energies obtained for each of three-phase currents were used as input features for the detector. After fault detection, half cycle (10 ms) of three-phase current signals was recorded by 20 kHz sampling rate. The recorded currents signals were used as input parameters for the multi class SVM classifier. The results of the validation tests have demonstrated that a quite reliable, fault detection and classification system can be developed using SVM. Generated faults were used to training and testing of the SVM classifiers. SVM based classification and detection model was fully implemented in MATLAB software. These models were comprehensively tested under different conditions. The effects of the fault impedance, fault inception angle, mother wavelet, and fault location were investigated. Finally, simulation results verify that the offered study can be used for fault detection and classification on the transmission line.

When a single line-to-ground fault occurs in the ungrounded distribution system, the steady-state fault current is relatively small for fault analysis and the transient fault current is observable, which can be used for faulted feeder identification and location. The principal frequency component retains most of the characteristics of the transient current. The principal frequency is related to the distance from the fault point to the substation and can be used for fault location. This paper analyzes the sequence network model of a single line-to-ground fault in the distribution network, and gives a method for principal frequency calculation. Depending on the characteristics of the maximum amplitude of the principal frequency component of the faulted feeder, the method of faulted feeder identification is given. Based on the complementary characteristics of the phase angle of the principal frequency component of the fault current and the phase angle at the substation bus, the faulted section location is carried out. MATLAB simulation is used to verify the effectiveness of the faulted feeder identification and location method.

Multilevel inverters have been widely used in various occasions due to their advantages such as lowharmonic content of the outputwaveform. However, because multilevel inverters use a large number of devices, the possibility of circuit failure is also higher than that of traditional inverters. A T-type three-level inverter is taken as the research object, and a diagnostic study is performed on the open-circuit fault of insulated gate bipolar transistor (IGBT) devices in the inverter. Firstly, the change of the current path in the inverter when an open-circuit fault of the device occurred, and the effect on the circuit switching states and the bridge voltages were analyzed. Then comprehensively considered the bridge voltages, and proposed a fault diagnosis method for a T-type three-level inverter based on specific fault diagnosis signals. Finally, the simulation verification was performed. The simulation results prove that the proposed method can accurately locate the open-circuit fault of the inverter device, and has the advantage of being easy to implement.

The Bulletin of the Polish Academy of Sciences: Technical Sciences (Bull.Pol. Ac.: Tech.) is published bimonthly by the Division IV Engineering Sciences of the Polish Academy of Sciences, since the beginning of the existence of the PAS in 1952. The journal is peer‐reviewed and is published both in printed and electronic form. It is established for the publication of original high quality papers from multidisciplinary Engineering sciences with the following topics preferred: Artificial and Computational Intelligence, Biomedical Engineering and Biotechnology, Civil Engineering, Control, Informatics and Robotics, Electronics, Telecommunication and Optoelectronics, Mechanical and Aeronautical Engineering, Thermodynamics, Material Science and Nanotechnology, Power Systems and Power Electronics.

Journal Metrics: JCR Impact Factor 2018: 1.361, 5 Year Impact Factor: 1.323, SCImago Journal Rank (SJR) 2017: 0.319, Source Normalized Impact per Paper (SNIP) 2017: 1.005, CiteScore 2017: 1.27, The Polish Ministry of Science and Higher Education 2017: 25 points.

Abbreviations/Acronym: Journal citation: Bull. Pol. Ac.: Tech., ISO: Bull. Pol. Acad. Sci.-Tech. Sci., JCR Abbrev: B POL ACAD SCI-TECH Acronym in the Editorial System: BPASTS.

The paper presents a methodology for parametric fault clustering in analog electronic circuits with the use of a self-organizing artificial neural network. The method proposed here allows fast and efficient circuit diagnosis on the basis of time and/or frequency response which may lead to higher production yield. A self-organizing map (SOM) has been applied in order to cluster all circuit states into possible separate groups. So, it works as a feature selector and classifier. SOM can be fed by raw data (data comes from the time or frequency response) or some pre-processing is done at first. The author proposes conversion of a circuit response with the use of e.g. gradient and differentiation. The main goal of the SOM is to distribute all single faults on a two-dimensional map without state overlapping. The method is aimed for the development stage because the tolerances of elements are not taken into account, however single but parametric faults are considered. Efficiency analyses of fault clustering have been made on several examples e.g. a Sallen-Key BPF and an ECG amplifier. Testing procedure is performed in time and frequency domains for the Sallen-Key BPF with limited number of test points i.e. it is assumed that only input and output pins are available. A similar procedure has been applied to a real ECG amplifier in the frequency domain. Results prove a high efficiency in acceptable time which makes the method very convenient (easy and quick) as a first test in the development stage.

In rotating machineries, misalignment is considered as the second most major cause of failure after unbalance. In this article, model-based multiple fault identification technique is presented to estimate speed-dependent coupling misalignment and bearing dynamic parameters in addition with speed independent residual unbalances. For brevity in analysis, a simple coupled rotor bearing system is considered and analytical approach is used to develop the identification algorithm. Equations of motion ingeneralized co-ordinates are derived with the help of Lagrange’s equation and least squares fitting approach is used to estimate the speed-dependent fault parameters. Present identification algorithm requires independent sets of forced response data which are generated with the help of different sets of trial unbalances. To avoid/suppress the ill-conditioning of regression equation, independent sets of forced response data are obtained by rotating the rotor in clock-wise and counter clock-wise directions, alternatively. Robustness of algorithm is checked for different levels of measurement noise.

This article concerns numerical modeling of the impact of mining operations on fault behavior, carried out on the basis of a calculation program based on the finite element method. It was assumed that the fault is a single discontinuity in the form of a vertically-oriented plane, and the conditions in which surfaces merge are defined by the right of the Coulomb friction. On the one hand, the calculations are related to the fault’s response to additional weight resulting from mining operations, and on the other, they are related to the impact that occurrences in the fault’s plane had on the immediate surroundings of the extraction center. The behavior of the fault was analyzed based on distributions in the plane of shear stress and slip, together with their range and energy dissipated due to friction. In turn, the impact of the fault on its immediate environment was analyzed based on variations in the total energy density of elasticity. The results of numerical modeling made it possible to draw conclusions concerning mining operation in the proximity of tectonic dislocations in the context of seismic hazard’s levels.

An intelligent boundary switch is a three-phase outdoor power distribution device equipped with a controller. It is installed at the boundary point on the medium voltage overhead distribution lines. It can automatically remove the single-phase-to-ground fault and isolation phase-to-phase short-circuit fault. Firstly, the structure of an intelligent boundary switch is studied, and then the fault detection principle is also investigated. The single-phase-to-ground fault and phase-to-phase short-circuit fault are studied respectively. A method using overcurrent to judge the short-circuit fault is presented. The characteristics of the single-phase-to-ground fault on an ungrounded distribution system and compositional grounded distribution system are analyzed. Based on these characteristics, a method using zero sequence current to detect the single-phase-to-ground fault is proposed. The research results of this paper give a reference for the specification and use of intelligent boundary switches.

As a result of the development of modern vehicles, even higher accuracy standards are demanded. As known, Inertial Navigation Systems have an intrinsic increasing error which is the main reason of using integrating navigation systems, where some other sources of measurements are utilized, such as barometric altimeter due to its high accuracy in short times of interval. Using a Robust Kalman Filter (RKF), error measurements are absorbed when a Fault Tolerant Altimeter is implemented. During simulations, in order to test the Nonlinear RKF algorithm, two kind of measurement malfunction scenarios have been taken into consideration; continuous bias and measurement noise increment. Under the light of the results, some recommendations are proposed when integrated altimeters are used.

Three phase induction motors are widely used in industrial processes and condition monitoring of these motors is especially important. Broken rotor bars, eccentricity and bearing faults are the most common types of faults of induction motors. Stator current and/or vibration signals are mostly preferred for the monitoring and detection of these faults. Fourier Transform (FT) based detection methods analyse the characteristic harmonic components of stator current and vibration signals for feature extraction. Several types of simultaneous faults of induction motors may produce characteristic harmonic components at the same frequency (with varying amplitudes). Therefore, detection of multiple faults is more difficult than detection of a single fault with FT based diagnosis methods. This paper proposes an alternative approach to detect simultaneous multiple faults including broken rotor bars, static eccentricity and outer/inner-race bearing faults by analysing stator current and vibration signals. The proposed method uses Hilbert envelope analysis with a Normalized Least Mean Square (NLSM) adaptive filter. The results are experimentally verified under 25%, 50%, 75%, 100% load conditions.

Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are low-cost. Changes in the acoustic signal are often observed for faults in induction motors. In this paper, the authors present a fault diagnosis technique for three-phase induction motors (TPIM) using acoustic analysis. The authors analyzed acoustic signals for three conditions of the TPIM: healthy TPIM, TPIM with two broken bars, and TPIM with a faulty ring of the squirrel cage. Acoustic analysis was performed using fast Fourier transform (FFT), a new feature extraction method called MoD-7 (maxima of differences between the conditions), and deep neural networks: GoogLeNet, and ResNet-50. The results of the analysis of acoustic signals were equal to 100% for the three analyzed conditions. The proposed technique is excellent for acoustic signals. The described technique can be used for electric motor fault diagnosis applications.

This paper presents a fault-tolerant control scheme for a 2 DOF helicopter. The 2 DOF helicopter is a higher-order multi-input multi-output system featuring non-linearity, cross-coupling, and unstable behaviour. The impact of sensor, actuator, and component faults on such highly complex systems is enormous. This work employs sliding mode control, which is based on reaching and super-twisting laws, to handle the problem of fault control. Simulation tests are carried out to show the effectiveness of the algorithms. Various performance metrics are analyzed and the results show SMC based on super-twisting law provides better control with less chattering. The stability of the closed-loop system is mathematically assured, in the presence of faults, which is a key contribution of this research.

This paper presents a novel fault detection algorithm for a three-phase interleaved DC–DC boost converter integrated in a photovoltaic system. Interleaved DC–DC converters have been used widely due to their advantages in terms of efficiency, ripple reductions, modularity and small filter components. The fault detection algorithm depends on the input current waveform as a fault indicator and does not require any additional sensors in the system. To guarantee service continuity, a fault tolerant topology is achieved by connecting a redundant switch to the interleaved converter. The proposed fault detection algorithm is validated under different scenarios by the obtained results.

The neutral point clamped (NPC) three-level grid-tied converter is the key equipment connecting renewable energy and power grids. The current sensor fault caused by harsh environment may lead to the split of renewable energy. The existing sensor fault-tolerant methods will reduce the modulation ratio index of the converter system. To ensure continuous operation of the converter system and improve the modulation index, a model predictive control method based on reconstructed current is proposed in this paper. According to the relationship between fault phase current and a voltage vector, the original voltage vector is combined and classified. To maintain the stable operation of the converter and improve the utilization rate of DC voltage, two kinds of fault phase current are reconstructed with DC current, normal phase current and predicted current, respectively. Based on reconstructed three-phase current, a current predictive control model is designed, and a model predictive control method is proposed. The proposed method selects the optimal voltage vector with the cost function and reduces time delay with the current reconstruction sector. The simulation and experimental results showthat the proposed strategy can keep the NPC converter running stably with one AC sensor, and the modulation index is increased from 57.7% to 100%.

This paper proposes a new fault location method in radial medium voltage distribution networks. The proposed method only uses the measurement data at the feeder beginning to approximate the characteristic equation showing the dependence between the positive-sequence voltage and phase angle at the monitoring point with the distance to the fault location for each fault type on each line segment. To determine these characteristic equation coefficients, the entire distribution network will be modeled and simulated by four types of faults at different locations along the lines to build the initial database. Based on this database, the mathematical functions in MATLAB software are applied to approximate these coefficients corresponding to each type of fault for each line segment in the network. Then, from the current and voltage measurement data at the feeder beginning, the algorithms of global search, comparison, and fault ranking are used to find out where the fault occurs on the distribution network. Two types of distribution network with and without branches are studied and simulated in this paper to verify and evaluate the effectiveness of the proposed method.

As the capacity and scale of distribution networks continue to expand, and distributed generation technology is increasingly mature, the traditional fault location is no longer applicable to an active distribution network and "two-way" power flow structure. In this paper, a fault location method based on Karrenbauer transform and support vector machine regression (SVR) is proposed. Firstly, according to the influence of Karrenbauer transformation on phase angle difference before and after section fault in a low-voltage active distribution network, the fault regions and types are inferred preliminarily. Then, in the feature extraction stage, combined with the characteristics of distribution network fault mechanism, the fault feature sample set is established by using the phase angle difference of the Karrenbauer current. Finally, the fault category prediction model based on SVR was established to solve the problem of a single-phase mode transformation modulus and the indistinct identification of two-phase short circuits, then more accurate fault segments and categories were obtained. The proposed fault location method is simulated and verified by building a distribution network system model. The results show that compared with other methods in the field of fault detection, the fault location accuracy of the proposed method can reach 98.56%, which can enhance the robustness of rapid fault location.