Electrical shorts in transformers on load can be diagnosed by frequency response analysis (FRA) using impulse or sinusoidal sweep signal. At HV laboratory of SASTRA, a low-voltage impulse based on-line on-load FRA (OLOL FRA) was carried out on a single phase 50 Hz, 1 kVA, 240 V/240 V small laboratory transformer while delivering power to lamp load, and the effectiveness in electrical short diagnosis was analysed. For this purpose, some artificial faults were created within one of the windings of the transformer investigated, and the responses of the winding to different frequency components of the impulse voltages were observed before and after introducing these faults. Comparison of the frequency response (impedance) of the winding under normal condition with frequency response under fault condition was made, which clearly indicated that faults had altered the winding impedance for some frequency components of the impulse. This reveals that fault within the winding can be identified and located, by the OLOL FRA technique, through careful comparisons of frequency response. Statistical analysis of transfer function magnitudes with correlation coefficient, standard deviation and absolute sum of logarithmic error has also validated the same.

Partial discharge (PD) is the symptom of insulation breakdown in power equipment. The high rising steepness of PD current radiates electromagnetic waves which can be coupled with antenna. In this study, an antenna array is applied to PD detection and localisation in air-insulated substations, in which the locations of PD sources are found with time difference of arrival localisation algorithm. The influences of antenna deployment including the size, aspect ratio and shape of antenna array on localisation accuracy are systematically investigated. Three approaches including Cramér–Rao lower bound, numerical simulation of root of mean square error and experiments are adopted to evaluate the localisation accuracy. Three kinds of antenna arrays with diamond, rectangle and Y shapes are designed for locating the coordinates of PD sources. Their localisation performances are compared by these evaluation approaches, and the optimum antenna configurations for coordinate estimation are derived. The results indicate that the optimum array is dependent on aspect ratio of array size. If the aspect ratio is higher than 0.5, the rectangle array gives minimum localisation error and wide bearing ranges with low error. For array with aspect ratio smaller than 0.25, the diamond array is optimum among the three shapes.

The study presents a novel method, based, on one hand on the transmission line model and on the other hand on boundary element method to calculate the per unit length conductances of a pair of conductively coupled rails by taking into account the presence of important elements of the track, such as sleepers and ballast. Basic premise to this study is that the rails can be represented as a distributed electrical circuit equivalent to a multi-conductor transmission line carrying the transverse electric and magnetic mode. The analysis is done under DC hypothesis but it can be applied also to power frequencies. The results of the proposed calculation method have been compared with the data available in literature obtaining a fair agreement.

In this study, a new multi-criterion based fuzzy logic model has been proposed to determine the overall health index of transformers. The method relies on the concentrations of individual dissolved gasses, significant diagnostic test results of transformer oil and paper insulation. Real field data of 200 working transformers of a state owned power utility have been tested to validate the accuracy and reliability of the proposed fuzzy logic condition assessment model of transformers. Results obtained from the present proposed model have been compared with the previously proposed condition monitoring models. Comparison of the results shows that the output of present proposed model is more reliable and accurate. Integration of multi-criterion analysis in the fuzzy logic model has overcome the shortcomings of the previous fuzzy models requiring higher number of inputs and large set of rules. The proposed fuzzy model is flexible, accurate and easy to implement for determining the overall health index of the working transformers. It shall prove to be very useful to the utility managers and power utilities.

The wind and hydro technologies express a significant part of the electricity generation section. This study presents an optimal coordinated bidding strategy of wind, cascaded hydro generation, and pumped-storage (PS) units. One of the chief purposes of this study is maximisation the profit of the wind and hydro plants by participating in the day-ahead energy and ancillary service markets. The regulation and spinning reserve markets are regarded as ancillary services. Thanks to the inherent variability and uncertainty of wind power, it does not participate in the ancillary service market. Hydro company is constructed of several cascaded hydro units which design alongside a river basin as well as a PS unit. In this study, the risk is modelled by using conditional value at risk. To reach the optimum solution, a new improved clonal selection algorithm is applied which shows the effectiveness of the proposed method for optimising a generation companies (GENCOs) profit.

Chaotic whale optimisation algorithm (CWOA) is a metaheuristic real-parameter optimisation algorithm. This study appears to be well capable of providing solution to the transient stability constrained optimal power flow (OPF) problem of power system. Basically, transient stability constrained OPF (TSCOPF) problem is the extended study of conventional OPF problem while additionally, considering transient stability constraints along with other previously considered equality and inequality constraints of the conventional OPF problem. Here, CWOA algorithm is validated by choosing two test power systems viz. (a) New England 10-generator, 39-bus and (b) 17-generator, 162-bus test systems. Considering multiple contingency cases, the main objective of the proposed algorithm is minimisation of the total fuel cost of these two test systems. Simulation test results, as obtained from the proposed CWOA, are compared to the results offered by some other evolutionary optimisation techniques surfaced in the recent state-of-the-art literature. The results presented in this study indicate that the proposed algorithm shows its efficacy over other recently originated popular optimisation techniques (including basic WOA) in terms of extending potential of offering higher quality solutions, effectiveness and faster convergence speed.

In this contribution, a method to segregate electroencephalogram (EEG) signals into focal (F) and non-focal (NF) groups has been proposed, employing a novel multifractal detrended fluctuation analysis (MFDFA)-based feature sets. Manifestations in the fractal behaviour occurring due to the subtle morphological changes in F and NF EEG signals, can serve as an essential presurgical intervention for automated detection of structural epileptogenic area within the human brain. Considering the above-said fact, in the present approach, EEG signals acquired from a publicly available database, are analysed using multifractal parameters to investigate the complex, non-linear and stochastic fluctuations. Based on MFDFA of EEG signals, four statistically significant, new set of features have been extracted, which are eventually being used as inputs to a support vector machines and k-nearest-neighbour classifiers for the purpose of classification of EEG signals. It has been observed that the proposed MFDFA aided feature extraction method delivers quite commensurable and even better results in discriminating F and NF EEG signals, compared with the existing methods studied on the similar database.

The coal mining research technology is gaining popularity in terms of thickness measurement of coal mining horizon. The main challenge is to improve a robust sensing method for estimating the coal layer thickness left on mine-haulage way roofs for mine safety. This study addresses this challenge by step frequency continuous wave ground penetrating radar (GPR), whose resolution is dependent on bandwidth. To improve the time resolution of GPR signal, this study adopted four high-resolution algorithms, which are also known as subspace methods, namely, estimation of signal parameter via rotational invariance techniques, multiple-signal classification (MUSIC) algorithm, polynomial version of MUSIC, i.e. root-MUSIC and root-Min-Norm. The performance of all these algorithms is compared with synthetic data generated by the plane wave model and full wave model. The results are presented in terms of resolution power as well as relative root-mean-square error on the estimated thickness.

Decaying DC component has considerable effect on both accuracy and speed of convergence in numerical and digital relays for estimating fundamental phasor. Basically, this component causes unwanted and undesired oscillations in the time response of the fundamental phasor. As a result, it extremely affects decision making of the protective relay, which operates according to fundamental phasor component. In this study, a novel hybrid algorithm based on integration and half-cycle discrete Fourier transform (HDFT) is presented. Proposed method of this study is able to efficiently estimate fundamental frequency component by taking different parameters, including DC offset in the fault current, DC offset in a current transformer secondary side and off-nominal frequency condition into account. Moreover, in this study, modified DFT-based method for phasor estimation that is called combined integration and HDFT (CIHDFT) is put forward, which accurately estimates the original value of fundamental frequency phasor in half-cycle by considering the presence of harmonics in the waveforms. The proposed method is successfully applied to different test cases, followed by discussion on the results. Analysing the simulation results, it can be concluded that CIHDFT algorithm has remarkable performance in comparison with previous phasor estimation algorithms.

In this study, the authors propose a method to estimate information on impedance mismatches in coaxial cable networks. This information can help maintenance crews to locate and repair these mismatches. The information is estimated under the framework of DOCSIS preventive maintenance, and is based solely on equaliser coefficients already known by the system. Thus, it does not require any change to the system or the standard. The authors will show that, in contrast to the current DOCSIS approach, the proposed method provides a finer estimate, and allows for the estimation of information even when more than one micro-reflection is present.

The characteristics of coil are obtained through the following measurement: the root mean square (RMS) coil current, as a function of RMS coil voltage, and the coil loss, as a function of RMS coil voltage. These values were then transformed, using Dommel's method, into instantaneous characteristics of non-linear coil, in a form of a non-linear inductance, representing saturation effect; in parallel with the non-linear resistance representing coil loss. The instantaneous characteristics are composed of peak values of resistance current and voltage, and inductance current and flux. The uncertainties of the characteristics are defined through the uncertainty of the peak values. The uncertainty of instantaneous characteristics is calculated on an example of a coil realised as unloaded iron-cored toroid transformer with a strip-wounded core made of oriented transformer sheets (M5-type) using the adaptive Monte-Carlo method (MCM) and uncertainty framework of ‘Guide to the expression of uncertainty in measurement’ (GUM). Results reveal the peculiarities of both methods and the impact of the uncertainty of measured values on the uncertainty of instantaneous characteristics.

Plug-in electric vehicles (PEVs) can produce active, reactive and distorted power as well as pollution reduction. This study proposes an optimisation framework to allocate the PEVs capacity to generate each power component considering to grid and vehicle constraints, technical concerns and market price. In the proposed framework, PEVs compete with active power-line conditioners (APLCs) to generate distorted power and with generators to produce active and reactive power. An objective function is defined which includes distribution system operator (DSO) payment for each market participant. This function is minimised based on a hybrid optimisation algorithm (HOA) combining artificial bee colony (ABC) and differential evolution (DE) algorithms subject to grid and vehicles constraints. In the presented algorithm, a novel self-adaptive modification phase is proposed to improve overall ability of the algorithm for optimisation applications. The effectiveness and efficiency of the method is demonstrated on a low-voltage network with 134 customers as a case study.

This study deals with a hybrid method for the estimation of the remaining useful life (RUL) that does not require prior knowledge on the degradation phenomena, as the lack of information about the process of degradation is one of the main issues in the theory of fault prognosis of dynamic systems. The proposed method uses a dynamic model for the generation of fault indicators based on the principle of analytical redundancy, and the generation of a database by simulating the normal and faulty operations. Then, a data-driven method is used to identify a cluster for each operating state. Taking into account uncertainties allows the generation of normal operation thresholds, which govern the launch of a kinematic model based on the calculation of the Euclidean distance for RUL estimation. The developed method is applied with three of the most used methods in literature, to a mechanical transmission system for the estimation of the remaining time before the breakage of the drive belt. The obtained results are discussed and evaluated with appropriate metrics in order to demonstrate the effectiveness of the proposed approach.

Signal demodulation method of a frequency output resonant gyroscope is studied in this study. It has been a major problem of frequency micro gyroscopes. The detection elements of frequency gyroscopes are two double-ended tuning forks (DETF). The vibratory response of DETF is a periodic signal modulated by both amplitude and frequency. According to the characteristic that the amplitude and frequency of the gyro signal vary continuously with time, the signal demodulation algorithm is designed based on instantaneous frequency analysis. Hilbert transform is used to solve the instantaneous frequency. The algorithm is programmed in digital signal processor (DSP) and a test circuit board is made. Experimental test demonstrates that the demodulation circuit has a high precision and linearity, and the correlation coefficient between input signals and demodulation results achieves as much as 0.99999998. The demodulation method is proved to be feasible. This study provides a new train of thought for the study of the signal demodulation method of frequency micro gyroscopes and brings a new direction for the signal analysis of resonant sensors.

This study presents the design of a supervisory software algorithm that can detect and classify different types of electromechanical faults and determine the fault source in the reaction wheel. Unlike conventional methods, which are generally based on the modelling of the entire control system, the fault occurrences and all critical points are predicted at the module level. For this purpose, a new model of the wheel is proposed in which all internal interactions are considered. Based on this model, a method of parameter estimation is proposed to classify stator and bearing faults. Furthermore, an innovative approach based on the spectral analysis of the stator current signal is applied to detect and classify faults caused by damage to the permanent magnet or flywheel. These strategies are combined to ensure that all of the electromechanical faults that are likely to occur in the wheel will be detected. Simulation and experimental results obtained from a hardware-in-a-loop test bed and xPC Target toolbox demonstrate the applicability of the proposed algorithms in actual systems.

Owing to the ferromagnetism and stray fields of inertial navigation system, component compensation of magnetic field distortion is significant for geomagnetic field vector measurement system. However, traditional scalar compensation methods cannot be used for component compensation of magnetic field distortion, so some improvement works have been researched for component compensation in the study, the major of which is estimating compensation parameters using the Lagrange multiplier method. Experiment results show that the performance of the component compensation method is much better than traditional scalar compensation method. After compensation, the maximal measurement errors of north, vertical, east components and total intensity caused by magnetic field distortion are reduced to 10.4 nT (0.91% of the raw error), 32.5 nT (5.20%), 12.9 nT (1.27%), and 18.4 nT (2.02%), respectively. In addition, compared with previous component compensation method, the proposed method has three advantages: (i) simplified equipment, (ii) easier operation process, and (iii) better generality.

This study proposes a novel method for sleep disorder monitoring based on a finite-state machine (FSM) from various bio-signals, namely electroencephalography (EEG), electro-oculography (EOG) and electromyography (EMG) signals. The sleep signals have been obtained from physionet sleep repository, which includes horizontal EOG, submental-EMG and EEG sampled at 100 Hz sampling frequency. Inputs given to the FSM-based module are the processed signals from EMG, EEG and EOG signals. The FSM module for sleep analysis is composed of different states and the conditions to flow from one state to another state. In this study, two FSM modules are designed, one for sleep wave and another for sleep stage identification. Based on the outputs obtained from the above two FSM modules, the sleep disorder can be monitored. The accuracy of the proposed method has been calculated with percentage accuracy, false acceptance rate and false rejection ratio. The average classification accuracy of the finite-state automaton (FSA)-based method is up to 99% for all the tested fault cases. The proposed FSA method suggests a novel method and can be put to effective use in the rural areas for primary analysis.

This paper develops a novel method for feature extraction of steady communication signals based on the distribution of bispectrum amplitude and phase (BAP) to solve the identification problem of the same type of emitters. First, a propagation model of communication signals is modeled by analyzing the mechanism of emitter fingerprint, and the irrelevance between bispectrum amplitude and phase is demonstrated. Then, onthe basis of estimating the signal bispectrum, bispectrum symmetry is used to adopt different methods to extract features according to their own characteristics of amplitude and phase spectrum. Finally, the simulated and actual communication signals from the same type of emitters are used for the experiment. The identification performance of BAP method is compared with another three methods in terms of antinoise performance, influence of the numberof training samples, and feature distribution. Theoretical analysis and experimental results show that the BAP method overcomes the shortcomings of SIB and SB methods, and the extracted features have good clustering and interclass separability, solvingthe identification problem of the same type of emitters under low SNR and small number of training samples.