Showing papers in "Iet Science Measurement & Technology in 2019"

Application of C6F12O/CO2 mixture in 10 kV medium-voltage switchgear

Abstract: Due to the high global warming potential (GWP) of SF 6 , the use of new gases in electrical equipment has drawn increasing attention. Here, the application of environmental-friendly gas dodecafluoro-2-methylpentan-3-one (C 6 F 12 O) and CO 2 mixture in 10 kV medium-voltage switchgear is studied. First, the basic physical and chemical properties of C 6 F 12 O were discussed, and the toxicity and safety of its application in equipment was analysed. Then, the breakdown voltage was measured in the laboratory. The power-frequency breakdown test was carried out with the mixing ratio of 2, 4 and 6% at 0.1~0.2 MPa, and the influence of utilisation coefficient of electric field on the breakdown performance was analysed. Finally, the appropriate mixing ratio was determined and tested in the load switch cabinet and the circuit breaker switch cabinet, including the power frequency withstand voltage test and the lightning impulse test. The test results show that 4% C 6 F 12 O and CO 2 meet the standard value tested at a pressure of >0.14 MPa, which has the potential to be used as an insulating gas in a 10 kV switchgear.

Exploring Hermite transformation in brain signal analysis for the detection of epileptic seizure

Abstract: Automatic detection of epileptic seizure from brain signal data (e.g. electroencephalogram (EEG)) is very crucial due to dynamic and complex nature of EEG signal (e.g. non-stationarity, aperiodic and chaotic). Owing to these natures, manual interpretation and detection of epileptic seizure is not reliable and efficient process. Hence, this study is intended to develop a new computer-aided detection system that can automatically and efficiently identify epileptic seizure from huge amount EEG data. In this study, Hermite Transform is introduced for extracting discriminating information from EEG data for the detection of epileptic seizure. The analysis is performed in three stages: EEG signal transformation into a new form by Hermite Transform; computation of three types of features, namely permutation entropy, histogram feature and statistical feature; and classification of obtained features by least square support vector machine. The classification outcomes reveal the presence of epileptic seizure. The proposed method is evaluated on a benchmark Epileptic EEG database (Bonn University data) and the performance of this method is compared with several state-of-art algorithms for the same database. The experimental results demonstrate that the proposed scheme has the ability to efficiently detect epileptic seizure from EEG data outperforming competing techniques in terms of overall classification accuracy.

Development of industrial scale PVC nanocomposites with comprehensive enhancement in dielectric properties

Abstract: This study aims to develop large-scale polyvinyl chloride (PVC) nanocomposites for industrial application with power cables. To achieve this goal, PVC/silicon dioxide and PVC/titanium dioxide nanocomposites were fabricated with two different loadings of nanoparticles: 0.3 and 0.6 wt.%, in the presence of a suitable coupling agent that was used to reduce the agglomeration of nanoparticles and enhance the compatibility with polymer matrix. The coupling agent used in this study was the amino silane, and the process followed in the nanocomposites preparation was the melt blending method. The dielectric properties of these synthesised nanocomposites were studied by measuring the AC dielectric breakdown strength under the uniform field, then compared with the simulation results. The relative permittivity ( ɛ r), loss tangent (tan δ ), and DC electrical conductivity ( σ ) were also measured under frequencies ranging from 20 Hz to 1 MHz. In addition, the internal discharge measurements are performed using the traditional needle-plane configuration with the help of phase-resolved partial discharge (PD) analyser. This technique is used to analyse the PDs activity with respect to the phase angle of the applied voltage. It was found that the dielectric breakdown strength and PD resistance of the prepared samples are increased higher than that of the neat PVC; however, the ɛ r, tan δ , and σ at 50 Hz are decreased.

Emotion classification using EEG signals based on tunable-Q wavelet transform

Abstract: Emotion is a most instinctive feeling of a human. Emotion classification finds application in brain-computer interface systems for the assistance of disabled persons. To recognise the emotional state, electroencephalogram (EEG) signal plays a vital role because it provides immediate response to every state of change in the human brain. Here, the utility of tunable- Q wavelet transform (TQWT) is explored for the classification of different emotions EEG signals. TQWT decomposes EEG signal into subbands and time-domain features are extracted from subbands. The extracted features are used as an input to extreme learning machine classifier for the classification of happy, fear, sad, and relax emotions. Experimental results of the proposed method show better four emotions classification performance when compared with the other existing methods.

Multiple nanoparticles for improvement of thermal and dielectric properties of oil nanofluids

Abstract: To improve the performance and increase the lifetime of oil-filled transformers, the thermal and dielectric properties of the transformer oil should be enhanced. Recently, nanotechnology was used as an effective science in the field of transformer oil development. In this study, barium titanate (BT) nanoparticles were inserted into the base transformer oil by a concentration of 0.005 g/L as an individual nanofluid sample (INFS). This insertion enhances the heat transfer coefficient by 33% but the breakdown voltage (BDV) was decreased by >10%. To overcome this problem of dielectric properties degradation, other three hybrid nanofluid samples (HNFS) were prepared using three different types of metal oxide (MO) nanoparticles; titania, alumina, and silica. These samples were prepared by adding a concentration 0.01 g/L of MO nanoparticles together with 0.005 g/L of BT nanoparticles into the oil. The thermal and dielectric properties of HNFS were measured to study the behaviour of nanoparticles hybridisation on transformer oil properties. HNFS using titania nanoparticles provided the best composition regarding either BDV or heat transfer coefficient. Dynamic light scattering (DLS) technique was used to evaluate the particle size distribution of hybrid nanoparticles and to clarify the corresponding physical mechanisms behind the obtained enhancement.

DC offset estimation-based fault detection in transmission line during power swing using ensemble of decision tree

Abstract: The nuisance tripping of distance relay during power swing has been identified as one of the major causes for power system blackouts. Avoiding the risk of maloperation of distance relay and hence improving resilience during power swing, demands the development of a scheme capable of generating blocking signal during power swing and detecting a fault occurring with power swing from the measurement of current signal. In this regard, a protection scheme based on the hybrid framework of an ensemble of the tree, least square, and Adaline algorithm to perform the task of fault detection/classification, zone identification, location estimation, and power swing detection is proposed. The proposed scheme is based on the online estimation of DC offset information along with the fundamental component of the current signal. The performance of the proposed scheme has been analysed for diverse power swing and fault scenarios for two-machine system and Western System Coordinating Council 9-bus system. The simulation results reflect the effectiveness of the developed scheme in terms of sensitivity and reliability. Furthermore, to authenticate the effectiveness of the proposed scheme for practical scenarios, implementation, and validation of the proposed scheme has been carried out in a real-time environment using the OPAL-RT digital simulator.

Electrostatic discharge algorithm: a novel nature-inspired optimisation algorithm and its application to worst-case tolerance analysis of an EMC filter

Abstract: Because of their several advantages like simplicity, flexibility and adaptability, nature-inspired (NI) optimisation algorithms have attracted significant attention for solving complex optimisation problems. Source of inspiration for NI are multiple. This study aims to propose a new NI optimisation algorithm inspired by the electrostatic discharge (ESD) event. Tested on a large set of benchmarks and compared with several well-known optimisation algorithms the ESD algorithm (ESDA) is found to be a very competitive algorithm. Moreover, the ESDA has been applied for the determination of wort-case scenarios for electromagnetic compatibility (EMC) filter.

Diagnostic challenges in dielectric loss assessment and interpretation: a review

Abstract: In any sustainable electrical systems, various high-voltage (HV) power apparatuses such as electrical cable, transformer etc. play a pivotal role to transfer electrical energy from the generation end to the consumer end. The degradation due to electrical stress is very unlikely to avoid in operation. Prominently, the insulation system is mostly expected to be strong enough, and its degradation always provides a constant threat to the reliable operation. Moreover, the continual deterioration can gradually lead to the complete breakdown of HV equipment. To assess the overall dielectric properties, the regular monitoring/diagnosing of electrical insulation has a paramount importance in power systems. One of the potential tools for diagnostic assessment is the tan-delta (tan δ ) measurement. Considering different aspects at various frequencies, this study elucidates a comprehensive review of tan δ measurement. First, a detailed review and explanation of multiple challenges associated with dielectric loss measurement are presented. Various dielectric losses, their contribution to tan δ , measurement techniques and their comparisons are also discussed. This study further reviews the analogy of different physical dipolar theories, modelling and a brief analysis of commercially available measurement instruments.

Identification of power quality events: selection of optimum base wavelet and machine learning algorithm

Abstract: This study comprehensively investigates power quality (PQ) identification problem and proposes the optimum combination of base wavelet and machine learning algorithm (MLA) which would yield the highest classification accuracy. Although this problem has been studied by various researchers in the recent past, the selection of appropriate base wavelet and MLA, which would give better classification accuracy, have received comparatively less attention. This study bridges this gap by investigating the classification performance of 110 wavelets and 7 well-known MLAs across various noise levels using over 3500 PQ events generated as per IEEE Standard 1159. The results of this investigation demonstrate that the choice of base wavelet does significantly affect the classification performance. Further, it was observed that a single base wavelet does not provide optimum performance across all MLAs at various noise levels. In contrast, each MLA gives the maximum accuracy with a distinct base wavelet. The robustness of MLA against noise is studied which establishes that the simple MLAs, such as decision tree and Naive-Bayes, are more robust against noise compared to the other intricate MLAs. Finally, several recommendations are drawn for the selection of base wavelet and MLA which yields the best possible accuracy.

Multiple incipient fault classification approach for enhancing the accuracy of dissolved gas analysis (DGA)

Abstract: Multiple incipient faults are practically known to exist in transformers. They tend to produce suddenly changing ratio limits in ratio-based methods or oscillation of fault location in graphical methods. In consequence, the energy associated with them lies in-between low and high severity single faults. Hence multiple fault detection needs to be addressed appropriately which may otherwise pose the serious constraints during transformer condition monitoring. In this study, novel and intelligent classification approach is proposed to upgrade the classical dissolved gas analysis (DGA) technique to cater the requirement of multiple fault diagnosis. This consists of Duval-triangle-based optimised fuzzy inference system and neural network models sensitive to both single and multiple incipient faults. Both models have been rigorously trained and tested using dataset credited to field and literatures to achieve high fault recognition and isolation rates, alternatively low false detection and no-detection rates. Both parameters are combined into single index to determine the accuracy in terms of F 1 score which is evaluated to be >97%. The diagnostic ability of the scheme is highly promising and can improve reliability of transformer fault forecasting by DGA.

Evaluation of strand-to-strand capacitance and dissipation factor in thermally aged enamelled coils for low-voltage electrical machines

Abstract: The dissipation factor (i.e. tanδ) and insulation capacitance (IC) measurements are conventional monitoring methods for assessing the aging level of insulation systems. These quantities provide an invaluable indication of the dielectric losses within the insulating materials. However, how these values are affected by the aging processes due to thermal stresses have until today never been investigated fully. Thus, this study exhibits the influence of thermal aging on tanδ and IC of windings for electrical machines (EMs). The work is performed for class 200, round enamelled magnet wire specimens. The study aims at improving the design process of EMs for short duty cycle applications; hence, its outcome might be included at the design stage for enhancing reliability and lifetime. Random wound coils are chosen in the performed study, because they are the most common winding arrangement for low-voltage EMs, which are employed in a wide range of applications (e.g. from home appliances to aerospace motors). Based on the collected data, considerations regarding the impact of relative humidity on both the dissipation factor and IC are presented. Finally, the correlation between the partial discharge inception voltage and the diagnostic measurements is experimentally verified.

Winding turn-to-turn short-circuit diagnosis using FRA method: sensitivity of measurement configuration

Abstract: Frequency response analysis (FRA) is increasingly being accepted as an effective technique to diagnose transformer faults. Transformer electric parameters are affected by such faults in a complex manner, and there is yet no standard approach for interpretation of FRA results. Most studies have focused on diagnosing winding and core deformations, but subtle defects in the winding insulation and turn-to-turn short circuits can develop into a more serious fault, and their early diagnosis is equally important. Furthermore, there are several test configurations which have different sensitivities to different faults. This study reports a study where the winding input impedance is measured to diagnose turn-to-turn short circuits using different measurement configurations. A comparison is made between the sensitivities of each measurement configuration to faults of increasing severity. It is found that this fault is detected in the low- and mid-frequency regions as significant reduction in impedance and a shift in resonance peaks towards high frequencies. The results, analysed using different statistical parameters, indicate differences in sensitivities with different levels of short circuits. Marginal variations were found between the sensitivities of statistical parameters in different frequency regions. The study provides useful contribution into interpretation of FRA signatures for turn-to-turn short-circuit diagnosis of transformers.

Hardware design and software development of a motion control and driving system for transradial prosthesis based on a wireless myoelectric armband

Abstract: This research work presents the design of the electronics modules of Adam's Hand, a transradial myoelectric prosthesis based on an innovative mechanism which can actuate five three-phalanges fingers (15 degrees of freedom) with just one motor, instead of the five/six motors conventionally used in other prosthetic devices; moreover, the prosthesis uses two servomotors to actuate the wrist movements. Adam's Hand fingertips are provided with temperature and pressure sensors, while the user myoelectric signals are acquired wirelessly by means of the Myo armband, a wearable device provided with eight electromyography electrodes, a nine-axis inertial measurement unit, and a transmission module. These data are received through an HM-11 BLE module, connected to Adam's Hand custom PCB, which features an Arduino Micro board. This board processes all the data and drives the actuators by means of properly chosen drivers. A Raspberry Pi 3 board manages a touchscreen display - which can be used to visualise the gathered data - and sends them to a dedicate cloud platform, so that the orthopaedic technicians who take care of Adam's Hand users can monitor them in real time, thus improving their recovery during the rehabilitation period.

Machine-learning approach for fault detection in brushless synchronous generator using vibration signals

Abstract: In order to maintain continuous production and to avoid the maintenance cost increment in power plants, it is important to monitor the condition of equipment, especially the generator. Regarding the impossibility of direct access to rotating diodes in brushless synchronous generators, the condition monitoring of these elements is very important. Here, a novel fault detection method is proposed for the diode rectifier of brushless synchronous generator. At the first stage of this method, the vibration signals are recorded and feature extraction is performed by calculating the relative energy of discrete wavelet transform components. Multiclass support vector machine (MSVM) is used for classification, and the best mother wavelet and number of decomposition level are chosen based on classification performance. To enhance the performance of the classification, a modified sequential forward subset selection approach is included by which the best statistical features are selected. In this approach, besides selecting the best subset of statistical features, the classification parameter is tuned according to the selected subset to achieve the best performance. The result of the proposed method is eventually compared with those results of classification performance using conventional subset selection. Experimental results show that the proposed method can detect rectifier faults effectively.

Performance analysis of vegetable oil-based nanofluids used in transformers

Abstract: Nanofluids (NFs) are an emerging technology in the field of dielectrics. These oils are used in transformers and other power apparatus for the purpose of insulation and cooling. The vegetable oil (VO) proposed in this study is the Pongamia pinnata oil (PPO), commonly known as karanji oil. The molecular structure of PPO is changed by the transesterification process and converted to Pongamia oil methyl ester (POME) as the crude oil is not suitable for direct use because of its high viscosity, high pour point, and acid number. For preparing the NF, 0.01 wt.% of exfoliated hexagonal boron nitride (Eh-BN) is dispersed in mineral oil (MO) and POME to prepare MO-based NF (MO-NF) and POME-based NF (POME-NF), respectively. This study explores the various thermophysical and electrical properties of four types of oils - MO, MO-NF, POME, and POME-NF. Thermophysical properties such as thermal conductivity, interfacial tension, flash point, and pour point, and electrical properties such as dielectric constant, dielectric dissipation factor, and dielectric strength are measured and a comparative analysis is carried out among all the four types of oils. The charging dynamics study has also been investigated to understand the phenomenon underlying the enhanced breakdown voltages of VO-based NFs.

Fault detection and isolation based on fuzzy-integral fusion approach

Abstract: In this study, the bearing, as well as the electric faults of an induction motor, are diagnosed using the fuzzy-integral data-fusion method in feature level with high reliability. Time domain of various features is computed using the induction motor three-phase current and voltage measurements. Appropriate features are extracted by means of the proposed method and then classified by the fuzzy C -means algorithm. The fuzzy membership functions show the relation between a feature set and a fault to establish the mappings between the features and the given faults. Finally, different features are fused using the fuzzy-integral method to produce diagnostic results. The technique is validated experimentally on an induction motor coupled with a centrifugal pump. The capability of the proposed technique is also evaluated in the presence of disturbances and simultaneous occurrence of different faults. The results indicate an increase in the reliability in fault detection and isolation.

Range-based localisation of a wireless sensor network using Jaya algorithm

Abstract: Node localisation plays a significant role in wireless sensor networks, as most of the applications require exact location of sensor nodes. To obtain the exact location of sensor nodes, a range-based localisation method based on Jaya optimisation using received signal strength indicator (RSSI) is proposed. The distance between the target node and the reference nodes is obtained from the measured RSSIs using regression-based log normal shadowing model. Further, improvement in the localisation accuracy is accomplished by formulating the location of target node as an optimisation problem. Jaya optimisation algorithm is adopted, as it is parameter-free and efficient. The Jaya algorithm is used for estimating the distance as well as the coordinates of the target node. The proposal is compared with particle swarm optimisation and validated through simulation and hardware experiments. The maximum localisation error using Jaya and particle swarm optimisation through simulation is observed to be 0.08 and 0.37 m, respectively. Real-time experiments using Jaya algorithm exhibited the maximum localisation error of about 0.14 m for indoor environment and 0.3 m for outdoor environment. The results of the proposed methodology show significant improvement in terms of accuracy.

Hybrid approach for immunisation of DFT-based phasor estimation method against decaying DC components

Abstract: In addition to high-order harmonic and noise components, the fault current may contain decaying DC components which highly affect the estimated fundamental frequency phasor. This study presents a new hybrid method which aims to calculate and remove the unwanted effect of decaying DC components on fundamental phasor estimation. The main novelty of the proposed method is that the proposed method is independent of the dynamic behaviour of the current transformer (CT). In other words, unlike previously published papers, this method analytically calculates the CT time constant. To such aim, utilising frequency modulation, fault current signal is shifted. Utilising simple but comprehensive formulations based on integration and Prony-complex theory, the magnitude and time constant of decaying DC components are calculated in one cycle. After that, utilising a modified discrete Fourier transform the fundamental phasor component is calculated. Several test signals and also practical fault signals are applied to the proposed method. The simulation results illustrate the proposed method simply but comprehensively remove the unwanted effects of the decaying DC components. Also, it has quick speed as well as a high-precision response in the estimation of fundamental phasor under different simulation scenarios.

Feature extraction of analogue circuit fault signals via cross-wavelet transform and variational Bayesian matrix factorisation

Abstract: Analogue circuits are one of the most commonly used components in industrial equipment, but circuit failure may lead to significant causalities and even enormous financial losses. To address this issue, in this work the authors propose a new feature extraction scheme based on cross-wavelet transform (XWT) and variational Bayesian matrix factorisation (VBMF). Primarily, fault signals acquired from defect circuits are collected and processed by using XWT to obtain the joint time-frequency representation. VBMF is utilised to fetch the time-frequency information of the fault signal. A nine-dimensional feature vector is then constructed. Finally, a support vector machine optimised by a flower pollination algorithm is introduced to locate faults. Results show that the proposed approach can effectively locate the different kinds of defection while achieving a higher accuracy.

Particle swarm approach to the optimisation of trenched cantilever-based MEMS piezoelectric energy harvesters

Abstract: A micro-electro-mechanical system (MEMS) trenched piezoelectric energy harvester based on a cantilever structure has been proposed. The trenched piezoelectric layer has increased the output voltage and the generated power. It also provides three additional design parameters such as the trench position, depth and length. A particle swarm approach has been used for optimisation of the piezoelectric energy harvester geometry with the aim of finding the optimum design which transfers the maximum harvested power to a definite load. The optimisations and comparisons have been made for unimorph, bimorph, trenched and non-trenched cantilever beams. The results are quite revealing that the generated power for a trenched bimorph energy harvester is much larger than other structures. The optimum design found by particle swarm optimisation algorithm has asymmetric trenches in the top and bottom piezoelectric layers and can generate much more power than the unoptimised structure.

Diagnostic quality assured ECG signal compression with selection of appropriate mother wavelet for minimal distortion

Abstract: This study presents a diagnostic quality assured electrocardiogram (ECG) signal compression algorithm which uses discrete wavelet transform with the selection of appropriate mother wavelet. Since distortion of reconstructed ECG signal depends on the type of mother wavelet used for decomposition. Therefore, appropriate mother wavelet is selected first which produces minimal distortion. Small valued wavelet transform coefficients are discarded using dead-zone quantisation. Further integer conversion of coefficients is performed to improve compression at the cost of very less error. The processed transform coefficients obtained at this stage contain approximate coefficients and detail coefficients, where approximate coefficients consist very less repetition of data instances while detail coefficients are much repetitive. The repetition of detail coefficients is exploited by run-length encoding which represents the data as run and length. Compression performance of the proposed algorithm is evaluated using single-channel ECG records taken from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH ECG compression test database and Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database. Compression results exhibit better performance than other existing techniques. Subjective evaluation of reconstructed ECG signals is also performed which ensures effective working of the proposed algorithm on the different morphology of ECG signals.

Transformer oil diagnostic by using an optical fibre system: a review

Abstract: High-voltage transformers are essential for power systems, particularly in the transmission and distribution sectors. The reliability of this equipment typically depends on their insulation condition. Thus, considerable attention must be accorded to the development of diagnostic and monitoring systems for predicting the condition of transformer oil. At present, several electrical, physical and chemical diagnostic techniques have been applied to transformer condition monitoring. This study presents a review of the application of optical sensors to transformer condition monitoring. Optical-sensor-based monitoring of various parameters of transformer oil as part of transformer condition monitoring is discussed. The classification and sensing principles of optical sensors are also reviewed.

Study on heating mechanism of GRP rod in a composite insulator

Abstract: An abnormal temperature rise of the composite insulator, which was reported as the early stage of decay-like fracture of the composite insulator, has drawn the close attention of the operation and maintenance department of the power grid. In this study, the heating mechanism of GRP rod in composite insulator was studied. At first, the current, partial discharge and temperature rise of 30 mm-length short GRP rods were tested in high and low humidity environment. According to the experimental results, the simulation model for the temperature rise of the GRP rod was established. It was proved that the temperature rise was caused by the volume current. At last, the circuit model of the GRP rod was set up and the contribution of polarisation loss and conduction loss to the temperature rise was discussed. It could be concluded that the power of temperature rise of the GRP rods was mainly supplied by polarisation loss and the invasion of moisture was the main reason for the increase of polarisation loss in high humidity. In low humidity environment, both polarisation loss and conduction loss would play an important role in temperature rise and the deterioration of the material was the main reason for the increase of the dielectric loss.

Experimental and simulation study on space charge characteristics of epoxy resin filled with graphene oxide

Abstract: Graphene has attracted much attention due to its advanced properties. In previous work, the relationship between temperature and the conductivity of epoxy-based nanocomposite filled with graphene oxide has been experimentally studied. To understand the charge transport behaviour, the space charge characteristics at different temperatures and field strengths are measured with an improved pulsed electroacoustic measurement system, which is designed for high-temperature space charge measurement. From the measurement results, epoxy resin (ER) filled with multi-layer graphene oxide (MGO/ER) shows suppressed charge migration while ER filled with single-layer graphene oxide (SGO/ER) shows a hetero charge accumulation, which is assumed to be caused by the partial reduction of graphene oxide to graphene. Compared to pure ER, the apparent mobility of the nanocomposites is smaller while the trap depth and number of trapped charges are larger, which is probably caused by the introduction of traps. In the simulation, the relationship between mobility, trap depth, trap density and charge distribution are firstly studied. Then the calculated values of mobility and trap depth are given to the parameters to compare the simulated charge profiles with experiment results. The simulation results agree well qualitatively with the experimental results, giving some support for the proposed explanations.

Extended aeromagnetic compensation modelling including non-manoeuvring interferences

Abstract: Aeromagnetic compensation, elimination of magnetic interferences produced by the aircraft platform, plays a vital role in aeromagnetic applications. Conventional compensation methods are based on the Tolles-Lawson (T-L) model, which accounts for only the manoeuvring interferences. However, magnetic interferences due to other factors, such as currents from electrical systems and movable parts, are found significant in practice. In this study, the authors take these interference factors into consideration, including the strobe light and beacon light, the rudder and elevator. The magnetic interference from constant current is equivalent to the `permanent magnetisation' and has been contained in the T-L model. The interferences due to the strobe and beacon lights are modelled to be proportional to the currents and their temporal variations. The model coefficients can be estimated in advance in a ground experiment. The interferences by the pivoting rudder and elevator are still characterised by the T-L model but in the local rudder or elevator coordinate systems, and the compensated coefficients can be obtained on the ground or in the sky. Onsite experiments both on the ground and in the sky are conducted to illustrate the proposed modelling, and improved compensation results are obtained.

Auxiliary Prony‐based algorithm for performance improvement of DFT phasor estimator against transient of CCVT

Abstract: The measured fault voltage signal through coupling capacitor voltage transformer (CCVT) contains transient components which highly affect the estimated fundamental frequency phasor. This paper presents a new hybrid method which aims to calculate and remove the unwanted effect of CCVT transient on the fundamental phasor estimation. The main novelty of the proposed method is that proposed method is independent from the CCVT parameters involved in the transient response. In other words, unlike previously published papers, this method calculates the CCVT characteristics involved in the transient response. To such aim, by utilizing frequency modulation, fault voltage signal is shifted. Utilizing simple but comprehensive formulations based on integration and Prony-complex theory, the CCVT characteristics are calculated in the first cycle after fault inception. After that, by utilizing modified discrete Fourier transform (MDFT), the fundamental phasor component is calculated in one cycle. Several simulated test signals are applied to the proposed method. The simulation results illustrate that the proposed method removes the unwanted effects of CCVT. Also, it is fast and has a good precision in estimating the fundamental phasor component of fault voltage signal under different simulation scenarios.

Accurate phasor and frequency estimation during power system oscillations using least squares

Abstract: Accurate estimation of a phasor, frequency, and rate of change of frequency during changing power system condition is important to initiate precise control action for reliable power system operation. Conventional phasor estimation techniques cannot estimate the phasor accurately during dynamic conditions of power system. Furthermore, the phasor-based frequency estimation algorithms are being affected under such conditions. In this study, both angle and amplitude modulation parameters are considered in the signal model to improve the accuracy of the phasor estimation. The method uses Taylor's series expansion to linearise the signal model and computes the signal parameters using least squares. The method first computes the phasor using estimated parameters of the signals, subsequently calculates the frequency and rate of change of frequency. The performance of the proposed method is evaluated for standard test signals. Real signals recorded in numerical relay are used to assess the performance of the proposed method. The results are compared with the available method and found that the method can supplement the existing methods used in phasor measurement units and intelligent electronic devices.

Analysis of the spectrum of electromagnetic radiation generated by electrical discharges

Abstract: The stidy presents research results considering measurement and detection of electromagnetic radiation (EMR) generated by partial discharges (PDs) in the air and during breakdown. The key objective of the works was to develop a methodology of detection of ionising radiation generated by electrical discharges. This includes design of experimental setup for simulation of discharges under laboratory conditions and selection of appropriate measuring devices. For measurements of visible light and UV radiation, a HR-4000 spectrophotometer was chosen. The high energy ionising radiation was detected by means of a scintillation counter by own production. The experimental studies involved registration of EMR emitted by both PD and electrical breakdowns of the measuring system for various point-point electrodes arrangements. Then, characteristic spectra were defined in bands from visible light, through UV to the X-ray radiation frequencies. The use of high-energy radiation detection for determining basic forms of discharges and the potential possibility of the detailed specification of their power balance are curtail achievements of the presented article. The other contributions lie in the simultaneously application of two independent discharge detection methods.

Performance enhancement of the machine-fault diagnosis system using feature mapping, normalisation and decision fusion

Abstract: A unified fault modelling approach for machines with varying operating speeds is of interest in automating production facilities. Building a unified fault model is challenging due to the presence of speed-specific attributes in the features derived. In this work, it is shown how feature selection, mapping, normalisation and fusion of heterogeneous systems can help enhance the performance of speed-independent (SI) machine-fault diagnosis systems. Statistical features, obtained after applying feature selection, are used as input to a support vector machine (SVM) back-end classifier as the baseline system. Entropy-based feature selection algorithm is proposed to improve the performance of the fault diagnosis system. Furthermore, to make the fault diagnosis system independent of speed, locality constrained linear coding (LLC), Fisher vector encoding (FVE) and mean and variance normalisation (MVN) are used. The LLC-MVN system and FVE-MVN system map the input features in terms of SI basis vectors to make the features robust to speed-specific variations. Finally, the decision scores of the time-domain LLC-MVN-SVM, frequency-domain LLC-MVN-SVM systems and variational mode decomposition-based FVE-MVN-SVM system were fused with appropriate weighting factors. The detection error trade-off curve is also used as a performance measure for intelligent fault diagnosis systems.

3D approach for fault identification within power transformers using frequency response analysis

Abstract: With the growing pool of aged power transformers, application of the sweep frequency response analysis (SFRA) to assess power transformers' mechanical integrity has been given much attention. One of the research gaps in this field is the lack of reliable and automated techniques to interpret SFRA signatures. Conventional interpretation technique relies on visual inspection and personnel level of expertise, which may lead to inconsistent interpretation for the same signature. Furthermore, current SFRA technique fails in detecting transformer incipient mechanical deformations of low levels. To overcome these limitations, this paper presents a new three-dimensional (3D)-SFRA signature that comprises frequency, magnitude and phase angle in one plot. In contrary to the current interpretation practice that relies only on the magnitude plot, the proposed 3D signature exhibits more features, which can improve the SFRA identification accuracy. To automate and standardise the fault identification process, a digital image processing code is developed to extract some unique features from the proposed signature. The proposed technique is validated through finite element simulation analysis to detect short-circuit turns, axial displacement and radial deformation of a three-phase 40 MVA transformer and practical feasibility is assessed through its application to detect short-circuit turns of a three-phase 45 MVA transformer.