Showing papers in "Iet Science Measurement & Technology in 2018"

Time–frequency representation using IEVDHM–HT with application to classification of epileptic EEG signals

Abstract: Time–frequency representation (TFR) is useful for non-stationary signal analysis as it provides information about the time-varying frequency components. This study proposes a novel TFR based on the improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM–HT). In the proposed method, first the authors decompose non-stationary signals using the IEVDHM with suitably defined criterion for eigenvalue selection, requirement of number of iterations, and new component merging criteria. Furthermore, the HT is applied on extracted components in order to obtain the TFR of non-stationary signals. The performance of proposed TFR has been evaluated on synthetic signals in clean and white noise environment with different signal-to-noise ratios. The proposed method gives good performance in terms of Renyi entropy measure in comparison with other existing methods. Application of the proposed TFR is also shown for the classification of epileptic seizure electroencephalogram (EEG) signals. The least-square support vector machine (LS-SVM) with radial basis function kernel is used for classification of seizure and seizure-free EEG signals obtained from the publicly available database by the University of Bonn, Germany. The proposed method has achieved classification accuracy 100% for the studied EEG database.

Feature extraction of power transformer vibration signals based on empirical wavelet transform and multiscale entropy

Abstract: To achieve an effective feature extraction for power transformer vibration signals, the authors propose a method for signal feature extraction based on empirical wavelet transform (EWT) and multiscale entropy (MSE). First, transformer vibration signals are decomposed into several empirical wavelet functions (EWFs) with the method of EWT. Then, the frequency characteristics of signals are demonstrated in the time-frequency representation by applying a Hilbert transform to each EWF component. Finally, in order to quantify the extracted features, the MSEs of components being highly correlated with the original signals are calculated to construct the eigenvectors of transformer vibration signals. Several experiments are presented showing the effectiveness of this method compared with the classic empirical mode decomposition method.

Intelligent prediction of transformer faults and severities based on dissolved gas analysis integrated with thermodynamics theory

Abstract: Most presented dissolved gas analysis (DGA) techniques were interested in determining the fault types (FTs), but few articles discussed the corresponding severity of these faults. Here, the thermodynamic theory is utilised to evaluate the fault severity based on the energy associated with each FT. Therefore, energy weighted DGA is proposed, where the individual gas concentration is multiplied by a relative factor that relates to the enthalpy change of reaction. A fuzzy logic system is built based on the IEC code rules, the transformer condition code that is reported in IEEE Standard C57.104-2008, and the thermodynamic theory. For enhancing the network fault diagnosis of the power transformers all over the distribution network, the proposed fuzzy logic approach is employed for its integration in accordance with the distributed agents of the distribution substations. This smart system facilitates evaluating decisions of the distributed agents as well as providing a higher decision level if needed. That is achieved by sending the important information about transformers attained by the proposed fuzzy approach such as the FT, its severity, the total dissolved combustion gases condition, the recommended action, in addition to the period of incoming action to the primary substation.

High-impedance fault detection in electrical power distribution systems using moving sum approach

Abstract: High-impedance faults (HIFs) in electrical power distribution systems produce a very random, non-linear and low-magnitude fault current. The conventional overcurrent (OC) relaying-based distribution system protection schemes find difficulty in detecting such low-current HIFs. In this study, a simple two criteria-based protection scheme is proposed for detection and isolation of HIFs in multi-feeder radial distribution systems. It utilises one-cycle sum of superimposed components of residual voltage for HIF detection and the maximum value of one-cycle sum of superimposed components of negative-sequence current for faulted feeder identification. The performance of the proposed scheme is evaluated for a wide variety of possible test cases by generating data through power systems computer-aided design/electro-magnetic transient design and control software. Results clearly show that the proposed scheme can assist conventional OC relay for detection and isolation of HIFs in distribution systems with any grounding connections in a more reliable and faster way.

Recognition of multiple partial discharge patterns by multi-class support vector machine using fractal image processing technique

Abstract: Partial discharge (PD) measurement is an efficient method for condition monitoring of insulation in high-voltage (HV) power apparatus. Generally, phase-resolved PD (PRPD) patterns are commonly used to identify the PD sources. It is clearly recognised that there is a correlation between the PD patterns and the insulation quality. However, in the case of multiple PDs, the PRPD patterns partially overlapped in nature, which results in difficult to identify the types of partial discharges. In this proposed methodology, a combined algorithm of different edge detection methods with box-counting fractal image compression technique is used for fractal feature extraction. The extracted features used as the input vector for the classifiers for PD recognition. To evaluate the performance of the proposed methodology, artificially multiple PD sources are simulated in HV laboratory. The result of this proposed work shows better recognition for canny edge detected fractal features implemented with user define kernel multi-class nonlinear support vector machine which can be further used to assess the insulation properties for practical implementation in power industry.

Reliable protection scheme for PV integrated microgrid using an ensemble classifier approach with real-time validation

Abstract: The need for enhancing grid resilience has led to wider acceptance of photo voltaic (PV) integrated microgrid. The variation in fault current during grid connected and islanding operation makes the microgrid protection task challenging. A protection scheme based on ensemble of classifiers has been devised by exploiting the effectiveness of a classifier set coupled with voting strategy. Unlike the existing classifier-based approaches involving single classifier, the ensemble-based approach is insensitive to the biasness of individual classifier and dimension/size of dataset. The proposed scheme is formulated to simultaneously perform the tasks of mode detection, fault detection/classification, section identification and location. The scheme is able to discriminate between faults and power quality disturbances, which avoids unintended false tripping. Along with accuracy, the reliability assessment of proposed scheme has been carried out using two indices, i.e. dependability and security for different faults, operating mode and contingencies. For evaluating the reliability of the fault locator, a stochastic approach (Monte-Carlo simulation) has been adopted. The simulation results justify the applicability of the proposed ensemble classifier-based scheme for complete protection and reliable operation of microgrids with PV penetration. The scheme also has been validated for real-time settings using hardware in loop simulations.

Rhythm-based identification of alcohol EEG signals

Abstract: Alcohol is a severe intoxication substance that changes the functionality of brain by disturbing the neuronal process of the central nervous system, which causes mental and behavioural disorders. These disorders can be diagnosed by automatic classification of normal and alcohol electroencephalogram (EEG) signals, because it provides neurophysiology of alcoholic human brain. In this study, EEG rhythms based features are proposed for automatic identification and analysis of alcohol EEG signals. The instantaneous-frequency computed by analytic representation of intrinsic mode functions (IMFs) is used for separation of different frequency ranges known as EEG rhythms. IMFs are obtained by applying empirical mode decomposition on an EEG signal. The variability and complexity of separated EEG rhythms are measured by features namely: mean absolute deviation, inter quartile range, coefficient of variation, entropy, and neg-entropy. The p-value analysis of these features shows that low-frequency (LF)-rhythms based features are statistically significant for identification and neurological analysis of alcohol EEG signals. The LF-rhythms based features are applied as input to an extreme learning machine (ELM) and least squares support vector machine classifiers for classification of normal and alcohol EEG signals. The proposed method with an ELM classifier provides better classification accuracy (97.92%) as compared with other existing methods.

Effects of non-linear conductivity on charge trapping and de-trapping behaviours in epoxy/SiC composites under DC stress

Abstract: Gas-insulated switchgear (GIS) spacers are made of epoxy resin. However, the surface charge accumulation has been a great concern to the safe operation of GIS, which causes the frequent flashover faults on spacers. In this study, micro-silicon carbide (SiC) particles with non-linear conductivity were added into epoxy matrix and the filler content varied from 0 to 14 vol%. Then, the bulk conductivity and surface potential decay (SPD) tests were conducted. The obtained results showed that the epoxy/SiC composites have obvious non-linear conductivities and the non-linear-conductivity threshold decreases with the increasing filler content. The addition of SiC can effectively resist the rise of surface potential and enhance the surface charge dissipation process. From the trap energy distributions, it can be inferred that the deep traps of ~0.9 eV should be the intrinsic traps of epoxy and the shallow traps of ~0.8 eV are considered to be introduced by SiC. Furthermore, the simulation results confirmed that the sharp increase of carrier mobility in non-linear region significantly reduces the remaining time and possibility of a de-trapped charge being recaptured by traps before reaching the grounded electrode. Therefore, the high conductivity in non-linear region contributes a lot to the increase of SPD rate.

Detecting incipient radial deformations of power transformer windings using polar plot and digital image processing

Abstract: One of the main drawbacks of the frequency response analysis (FRA) technique that is widely accepted as the most reliable tool to detect transformer internal mechanical deformations is the inconsistent interpretation of the measured signature because of its reliance on personal expertise more than standard codes. Moreover, conventional FRA signature has a very low accuracy in detecting incipient and low mechanical fault levels. In order to avoid inconsistent interpretation for the transformer FRA signatures and improve its accuracy to detect minor fault levels, a reliable automated technique has become essential. This study investigates the feasibility of utilising FRA polar plot to detect minor radial deformation levels within two, three-phase power transformers of different ratings and winding configurations simulated using three-dimensional finite-element analysis software. Simulation results are validated through experimental measurements. Results of this study are also compared with the results obtained for other types of transformer winding deformations that are published in the literature in order to identify unique impact for each fault type on the proposed method. Findings reveal the superiority of the proposed approach over existing conventional technique in terms of accurate identification and quantification for minor transformer winding deformations.

Fault location on a series-compensated three-terminal transmission line using deep neural networks

Abstract: In this study, discrete wavelet transform (DWT) and deep neural network (DNN) are utilised for fault location in a series-compensated three-terminal transmission line. The series compensation causes challenges in fault location schemes of the three-terminal transmission lines. The presented fault location method has been extensively tested using the SIMULINK model of a three-terminal transmission line. Features extracted from synchronous measurements of fault currents at the three terminals using DWT are fed to the DNN. Faulted section determination and fault distance calculation are carried out using a single intelligent network simultaneously. Faulted section is determined with 100% accuracy, and the efficiency of algorithm is validated for symmetrical and unsymmetrical faults, and different values of fault resistance, inception angle, and location. The accuracy of the algorithm is acceptable for large fault resistances (above 100 Ω) and fault inception angles near zero. Total mean error for test data is 0.0458% which is much improved with respect to other similar works.

Effect of temperature on AC breakdown voltage of nanofilled transformer oil

Abstract: Studying the effect of adding nanosized ZrO2 to mineral transformer oil on the AC breakdown voltage is presented. The study is carried out considering different concentration levels of nanosized ZrO2. These concentrations are 0, 0.001, 0.002, 0.003 and 0.006%w. The AC breakdown voltage of both nanofilled and base oil is measured according ASTM D1816 standard at room temperature. The evaluation is carried out based on AC breakdown voltage for the nanofilled and base oil considering average and Weibull statistical techniques. Both 50 and 10% breakdown voltage probabilities are obtained and analysed for all samples. Also, the effect of temperature (to take the effect of real operating conditions) on breakdown voltage of base and nanofilled oil is experimentally evaluated. The studied temperatures are 50, 80 and 130°C. The obtained results show that the performance of nanoparticles is significantly affected by increasing the temperature of nanofilled transformer oil. Finally, a proposed mechanism for the effect of temperature on the nanofluids breakdown strength is introduced.

Entropy-based feature extraction and classification of vibroarthographic signal using complete ensemble empirical mode decomposition with adaptive noise

Abstract: Non-invasive methods accomplished by a computer aided diagnosis of knee-joint disorders provide an effective tool. The objective of this study is to analyse vibroarthographic (VAG) signals using non-linear signal processing technique. This study includes different entropy-based feature extraction techniques to attain highly distinguishable features. The authors proposed to use a non-linear method known as complete ensemble empirical mode decomposition with adaptive white noise to decompose the VAG signals into intrinsic mode functions (IMFs). Entropy-based features involving approximate entropy, sample entropy, Shannon entropy, Renyi entropy, Tsallis entropy and permutation entropy (PeEn) are computed from dominant IMFs and reconstructed VAG signals. These extracted features are given as input to the least squares support vector machine as a classifier. The results illustrated that PeEn performed better with respect to other entropies. PeEn gives a classification accuracy of 86.61% and Matthews correlation coefficient of 0.7082. The computational complexity of entropies was also analysed. Results inferred that PeEn has a computational complexity of O ( N ) provided a simple, robust and low computational feature extraction technique. Analysis of VAG signals using non-linear preprocessing and entropy-based features can provide highly distinguishable features for accurate detection of knee-joint disorders.

Induction machine fault detection using smartphone recorded audible noise

Abstract: This study presents induction machine fault detection possibilities using smartphone recorded audible noise. Acoustic and audible noise analysis for fault detection is a well-established technique; however, specialised equipment for diagnostic purposes is often very expensive and difficult to operate. To overcome this obstacle, a simple pre-diagnostic procedure, using hand-held smartphones is proposed. Different faults of the three-phase squirrel cage induction machine such as various numbers of broken rotor bars and dynamic rotor eccentricity are inflicted to the machine and the resulting audible signals are recorded in laboratory circumstances using two widely available commercial smartphones. The analysis is performed on audible noise and compared with the results of mechanical vibrations measurements, recorded by vibration sensors. Rotational speed frequency and twice-line frequency are used as diagnostic indicators of faults. A simple neural network is composed and probabilities of fault detection using such diagnostic measures are presented. The necessity for further study as well as further implementation and method refinement necessity is pointed out.

Study on FDS characteristics of oil-immersed paper insulation bushing under non-uniform moisture content

Abstract: Capacitive high-voltage bushing is one of the typical oil-immersed paper insulation equipment, which is an indispensable external connection component for power transformers. In order to evaluate the effectiveness of frequency domain spectroscopy (FDS) for diagnosing moisture content of oil-immersed paper insulation bushing, this article studied the frequency-domain dielectric characteristics of oil-immersed paper insulation samples and field bushing. At the same time, the basic principle of interfacial polarisation is used to analyse the samples and field bushing with non-uniform moisture content. The results show that the moisture content affects the whole frequency band of the FDS curve. When the distribution of moisture content is non-uniform, the paper with high moisture content has a great influence on the whole FDS curve of oil-immersed paper insulation. In addition, the FDS curves will show a significant loss peak. The frequency of the peak point gradually moves towards the high frequency as the non-uniformity of the moisture content distribution increases. The experimental results show that the variation law of oil-immersed paper insulation can be used to quantitatively evaluate the moisture content and moisture type of oil-paper insulation power equipment.

Determination and localisation of turn-to-turn fault in transformer winding using frequency response analysis

Abstract: An incipient short-circuit fault does not cause a severe condition in the steady-state operation of transformers, but in transient conditions - the lightning or switching - it can result in an extended turn-to-turn short-circuit fault (TSCF) and eventually in transformer outage. This study aims to determine and localise small TSCFs using the frequency response analysis. For this purpose, an appropriate mathematical model is developed, which can accurately simulate the winding transient behaviour. Using this model, turn-to-turn short-circuit patterns are extracted for different turn-to-turn faults in the winding; then, the fault location is determined by comparing the measured faulty signal with mathematical short-circuit patterns. In this study, a heuristic index is proposed to identify small TSCF locations. The presented solutions are implemented on a real winding with a nominal rating of 1600 kVA and a nominal voltage of 20/0.4 kV.

Time–frequency analysis of PD-induced UHF signal in GIS and feature extraction using invariant moments

Abstract: The ultra-high-frequency (UHF) method is efficient in partial discharges (PDs) detection in gas-insulated switchgear (GIS). The features extraction of UHF signals is significant for propagation characteristics analysis and PD pattern classification. The PD-induced UHF signals are acquired by the internal UHF sensors in an actual 252 kV L-shaped GIS. The short-time Fourier transform method is applied to process UHF signals and describe the propagation characteristics in L-shaped GIS. Hu's invariant moments of energy density distribution are extracted as features in time-frequency plane. The features are utilised to discriminate different PD defect patterns in actual GIS model by the support vector machine classifier and achieve good results. Finally, a novel system of features extraction and classification of UHF signals is summarised.

Nanofluid-based transformer oil: effect of ageing on thermal, electrical and physicochemical properties

Abstract: The development of nanofluids is necessary to replace the conventional transformer oil used in power and distribution transformers. A stable nanofluid has been prepared to verify its insulating and heat transfer performance in oxidative ageing environment. Due to the superior thermal and electrical properties of a nanoparticle (NP), its dispersion in mineral oil (MO) is expected to improve the dielectric and cooling properties of the nanofluid. This study presents a comparative assessment of the pre- and post-ageing effects on MO and nanofluids. An open beaker, single temperature oxidative thermal ageing experiment is performed at 115°C for different ageing times, i.e. 164, 328, and 492 h. A concentration of 0.01 wt% of NP for both titanium oxide and exfoliated hexagonal boron nitride (Eh-BN) is dispersed into a base fluid, i.e. MO, to prepare nanofluids. The important thermal, electrical and physicochemical properties of all the three insulating oils are analysed. The superior thermal, electrical and physicochemical performance of Eh-BN/MO-nanofluid is observed to be superior. Higher thermal conductivity, insulation and hydrophobic Eh-BN NPs on dispersion with MO provide better cooling and insulation even after an ageing, thus confirm its usability as a novel nanofluid-based transformer oil.

Multiclass power quality events classification using variational mode decomposition with fast reduced kernel extreme learning machine-based feature selection

Abstract: In this study, a modern adaptive signal processing technique called variational mode decomposition (VMD) has been used for power quality (PQ) events detection. Numerous single, as well as multiple PQ events, are simulated according to IEEE std. 1159-2009 and laboratory experimental signals are collected and passed through the VMD algorithm. VMD decomposes the signal into different modes and from these modes, different features have been extracted. To reduce the dimension of the feature set Fischer linear discriminant analysis (FDA) has been used. As a new contribution to the literature, VMD aided FDA-based feature selection with reduced kernel extreme learning machine technique has been used for detection and classification of multiple PQ disturbances. The performance of the proposed combined technique shows higher classification accuracy while classifying multiple PQ disturbances and the results are comparable with many existing methods.

Modelling and regulation of two mechanical systems

Abstract: In this work, the modelling and regulation of two mechanical systems are studied. The model is employed to describe the system dynamic behaviour. The regulator is employed to force the system states to track constantly wanted signals. The stability of the advised regulator is analysed via the Lyapunov technique. The recommended strategy is applied to a magnetic generator and in a hexarotor.

Transient behaviour of grounding electrodes in uniform and in vertically stratified soil using state space representation

Abstract: This investigation is devoted to study, in time domain, the transient behaviour of vertical and horizontal grounding conductors submitted to impulse current using state space representation-based transmission line method. This study has been carried out with and without incorporating soil ionisation and mutual coupling between conductor elements for soils of low and high resistivities. The results are focused on the time-domain solution of the electrode's transient potential rise and its impulse performance parameters. The results show the importance of including the mutual coupling between segments of the same electrode and soil ionisation. The results are compared with both published theoretical approaches and experimental measurements, and show acceptable agreement. This method has been applied to compute the transient potential of horizontal electrodes buried in vertically stratified soil, a situation which can occur where long horizontal counterpoise is buried in a highly contrasting soil along its length.

Effect of charge accumulated at oil–paper interface on parameters considered for power transformer insulation diagnosis

Abstract: Polarisation and depolarisation current (PDC) measurement and analysis is one of the popular tools for effective diagnosis of power transformer insulation. Normally, it is assumed that polarisation current is the combination of the current due to dipole movement and conduction current. Similarly, the depolarisation current is only due to the relaxation of dipoles. However, it is found that after eliminating the effect of dc conduction from polarisation current the resulting current is not similar to that of measured depolarisation current. This shows some non-linearity is present in the system. This non-linearity occurs due to movement of trapped charge that resides in the interfacial region of oil-paper insulation. This study shows the effect of de-trapping charge on various performance parameters that are used for insulation diagnosis like paper moisture and dielectric dissipation factor (tan δ ).

Radio location of partial discharge sources: a support vector regression approach

Abstract: Partial discharge (PD) can provide a useful forewarning of asset failure in electricity substations. A significant proportion of assets are susceptible to PD due to incipient weakness in their dielectrics. This study examines a low cost approach for uninterrupted monitoring of PD using a network of inexpensive radio sensors to sample the spatial patterns of PD received signal strength. Machine learning techniques are proposed for localisation of PD sources. Specifically, two models based on support vector machines are developed: support vector regression (SVR) and least-squares support vector regression (LSSVR). These models construct an explicit regression surface in a high-dimensional feature space for function estimation. Their performance is compared with that of artificial neural network (ANN) models. The results show that both SVR and LSSVR methods are superior to ANNs in accuracy. LSSVR approach is particularly recommended as practical alternative for PD source localisation due to its low complexity.

Contingency constrained phasor measurement units placement with n − k redundancy criterion: a robust optimisation approach

Abstract: This study presents a novel approach for the contingency constrained phasor measurement units (PMUs) placement. The proposed approach is based on n − k redundancy criterion using robust optimisation. This security criterion ensures observability of the network under any contingency state, containing the loss of up to k PMUs. In the proposed method, the effects of zero injection and power flow measurements as well as possible contingency states (such as branch outage and single or multiple PMU loss) are considered. The non-linear modelling of observability function for measurements is reformulated by linearisation process. The resulting bi-level programming model is solved by its evolution to an equivalent single-level mixed-integer programming problem. The objective function of the optimal PMU placement is aimed at minimising the number of PMUs in a way that guarantees economic goals and the observability of all network buses. The advantage of this model is to significantly reduce the computational burden compared with other methods. The proposed method is tested on modified 7-bus test network, 118- and 2383-bus IEEE test networks. The results of the case studies clearly demonstrate the simplicity and efficiency of the proposed robust optimisation method in different cases.

Understanding the ageing behaviour of transformer oil–paper insulation with ester and mixed dielectric fluids

Abstract: Study of alternate fluids for transformer and their combination with traditional ones is an interesting area of research. The intent of this work is to investigate the thermal ageing performance of ester oils (synthetic and natural), and mixed oil vis-a-vis mineral oil. All the samples were thermally stressed at 110, 140, 160, and 185°C for 2 weeks each. Dissolved decay contents in oil and linear thermal expansion coefficients of solid insulation have been studied using ultraviolet visible infrared spectroscopy and dilatometry analysis, respectively, as per ASTM standards. Change in functional groups of the insulation oils with ageing have been understood by Fourier transform infrared spectroscopy analysis. Additionally, diffusion of oil-moisture mixture into paper with ageing and the corresponding effect on dielectric strength of paper has been also examined using ASTM standards. It is observed that the use of synthetic ester (SE) and addition of SE to mineral insulating oil lead to improved performance of oil-paper insulation system with retarding the rate of degradation of the insulation system.

Sequence-space-aided SVM classifier for disturbance detection in series compensated transmission line

Abstract: Sudden changes in loading or weak constitution of power network causes power swing which may aggravate miss-operation of protective elements. Consequently, it becomes utmost essential to rapidly and accurately distinguish between fault and power swing conditions to prevent instability in smart power grid equipped with compensation. This study demonstrates an effective disturbance classifier scheme for series compensated transmission line (SCTL) for discrimination during disparity in power grid contexts using sequence-space-based support vector machine (SVM) classifier. The test data sets are generated by performing extensive simulations in PSCAD software by varying system and fault context. SVM architecture has been trained and tested by generating feature vector using modified full cycle discrete Fourier transform in MATLAB. After proper extraction of features of the interest at the time of disturbance, a decision about power swing or fault has been carried out using SVM classifiers. Regulation and kernel function parameter have been tuned using ten-fold cross-validation applied on training set. The developed scheme is also evaluated for symmetrical fault detection during power swing and shows remarkable improvement in accuracy and speed for protection of SCTL in comparison to existing schemes.

Interference-rejecting current measurement method with tunnel magnetoresistive magnetic sensor array

Abstract: Innovative methods for wide range measurement of electric current remains an active research problem in modern power systems. Conventional methods based on magnetic field readouts have realized non-contact current measurement by interpreting magnetic flux density into electric current, such as Hall effect and Rogowski Coil arrangement. TMR magnetic sensor has spread its application for current measurement due to its miniaturization, low cost, high response frequency and high sensitivity. However, due to the superposition of unwanted magnetic field, the magnetic fieldunder measurement is strongly affected. In this paper, a four-sensor array design is proposed to solve this problem. Transcendental equations, which can not only calculate the current under measurement but also the interference current at random places, are constructed. Numerical simulations, finite element analysis (FEA) of the field and laboratory experiments were performed to verify the proposed method. It is shown that when targeted current is 100 A and interference is 820 A, the largest simulated error is 3.92 × 10−10; when targeted current is 100 A and interference is 1000 A, error with FEA is 2.3796%, when targeted current and interference are both 500 A, experimental error is 4.14%. This verifies the effectiveness of the proposed method.

Model of ferromagnetic steels for lightning transient analysis

Abstract: This study investigates the model of structural steels used in the lightning current analysis. These steels, magnetic material, can be found in grounded structures, such as towers and buildings. An equivalent electrical circuit is presented first for these magnetic conductors under lighting transient current. Circuit parameters of the steel wires are determined numerically with an equivalent circuit approach. A rational function approximation is incorporated for developing an extended circuit model of frequency-dependent circuits used in time-domain simulations. Experiments are carried out to disclose the transient response of both steel bars and rods when they carry a surge current directly. It is found that these steel wires exhibit weak ferromagnetism, and can be represented with linear magnetic materials. The proposed modelling procedure is applied to analyse surge current sharing in a three-dimensional steel wire/power cable system adopted in radio base stations. Good agreements with the experimental results are observed.

Fault-swing discrimination using Hilbert–Huang transform integrated discrete teager energy operator

Abstract: In this work, a fault detection methodology during power swing is demonstrated. Modern distance relays are embedded with power swing blocking (PSB) function to preserve the security and reliable operation of power system during swing. However, the operation of PSB should be unblocked and let the distance relay allow to operate for any fault during swing. However, sometimes, distance relay unable to make a proper discrimination between swing and fault event leading cascading failures. In order to accomplish the fault detection task, first online empirical mode decomposition is used and processed through Hilbert-Huang transform to compute the amplitude and instantaneous frequency of that signal. Next, discrete teager energy approach is applied to estimate the teager energy. The energy operator functions as a reliable index for fault detection task during power swing. To evaluate the performance of the proposed method, different power system structures are considered and simulated using EMTDC/PSCAD. Results for current transformer saturation, single-pole tripping, and in presence of noise are provided. The response of the proposed method is compared with the conventional and existing methods. Results and comparative assessment reports demonstrate that the method is more efficient and robust in maintaining both selectivity and dependability.

Online detection technology for SF6 decomposition products in electrical equipment: a review

Abstract: The decomposition component analysis method can be used to monitor and diagnose the insulation state of SF6 electrical equipment in power industry. Here, the authors firstly compared the advantages and disadvantages of various SF6 decomposition products detection methods and analysed their feasibility of online monitoring. Then the requirements of SF6 decomposition products online detecting technology according to the actual engineering needs were discussed. The SF6 decomposition products online detection system has been mainly introduced from the aspects of principle, structure, detection effect, and practicability. The research points and development tendency of SF6 decomposition products online detection system is forecasted.

Surface charge distribution patterns of a truncated cone-type spacer for high-voltage direct current gas-insulated metal-enclosed transmission line/gas-insulated metal-enclosed switchgear

Abstract: Surface charge accumulation on spacer is an important factor to cause surface flashover. An experimental set-up is established to measure surface potential over a truncated cone-type spacer under DC voltage in SF 6 gas at atmospheric pressure using a Kelvin vibrating electrostatic probe. The surface charge density is calibrated based on multipoint measurement technique, which takes the influence of electrostatic probe into account. The experimental results show that surface charge distribution patterns can be divided into three classes including centrosymmetric distribution, spot-like distribution, and cloud-like distribution. The symmetrically distributed charges are injected by electrode, which migrate along spacer surface through the surface conductivity. While the charges showing spot-like and cloud-like distribution are generated by the ionisation of SF 6 due to local electrical field concentrated. Besides, the hetero-charges are more easily to be saturated with different charging durations and the polarity of the net charge is the same as that of applied voltage.