A kind of device based on pressure differential theory is introduced to measure the physiological parameters of people's lungs. This device possesses the advantages of small size, low power consumption and easy handling. It is not only able to overcome the effect of humidity caused by thermal airflow sensor, but also able to conveniently realise the simultaneous measurement of airflow and pressure with high precision. For this purpose, a four-way tube is designed and fabricated. Detailed theoretical analysis and simulation are used to verify the correctness of the structure. The digital signal processing and complex programmable logic device system is used and corresponding algorithms for the lung function parameters are programmed. Then, the measuring error of system is analysed by the error theory. The accomplished device is calibrated in the National Institute of Metrology of China, and the maximum relative error between the standard and measured results is about 2.84%. Clinical trials indicate that when the vital capacity is 5800, the relative error between the standard system and this device is just 0.6%, which suggests that this device can measure the lung function parameters at a higher level.

In this study, an unconditionally stable finite difference time domain algorithm for time domain analysis of the active multiconductor transmission lines is presented. The results of the proposed algorithm are compared with the results of the conventional algorithm. While the results of the proposed algorithm have a very good accuracy, the CPU time of the proposed algorithm is less than (about 90%) that of the conventional one. The amplification matrixes of the proposed and conventional algorithms are extracted and numerical stability of them are investigated. The spectral radius of the presented algorithm is smaller than one for any temporal step sizes and the proposed algorithm is unconditionally stable.

In a power system, transmission lines are prone to faults of different nature, which challenge the system stability and reliability. Thus system performance analysis under such fault conditions has drawn attention of researchers. Particularly, the advent of fast and efficient data acquisition using higher sampling rates combined with high speed digital signal processors has paved the way for efficient digital real-time simulations. Though sustained efforts have been made by different researchers to develop some good real-time digital simulators, this study is an attempt to implement a laboratory prototype model of a 20 V, 200 km transmission line, representing a 400 kV extra-high voltage transmission line, so as to improve the real time performance. In addition, efficient National Instruments based data acquisition system in conjunction with LabVIEW has been incorporated to acquire the best possible representative data with commensurate characterisation and transmission with fidelity. The unique contributions of this real-time fault analysis laboratory hybrid model are accurate fault detection and classification using a frequency-domain approach having immunity to fault impedance and fault inception angle, which affect the time-domain analyses severely. It is also equipped with visual displays so that even non-experts can use it for planning and decision-making purposes.

The primary problem of tracking filtering algorithms is the tracking stability and effectiveness of target states. Based on the particle filter, an adaptive strong tracking particle filter algorithm is proposed in this study. According to the residual between actual measurement values and predicted measurement values of every moment, adjustment of the forgetting factor and the weakening factor is adaptively conducted. Then, by calculating the fading factor, transfer covariance matrix and filter gain of the system are obtained to estimate the particles state value. Updating the importance density function can alleviate the degradation phenomenon of particle filter, and it contributes to effective estimation for the optimal state value of a target. The simulation results demonstrate that the proposed algorithm provides a better tracking precision. In addition, when the target states make mutations, the proposed algorithm can track the mutation states of moving targets effectively and improve the stability of the system.

In recent years, significant attention has been paid to biodegradable vegetable oils as an alternative to mineral insulating oils. This study presents the influence of electrical discharges and thermal stress on the variation of the properties of uninhibited olive oil. This latter constitutes a serious environmentally friendly and renewable substitute for currently used mineral oils. The investigated properties are: dielectric loss factor and permittivity, resistivity, acid number, cinematic viscosity, water content, breakdown voltage, flash point and colour index. To analyse the effect of thermal stress, the uninhibited olive oil was aged in sealed bottles at 110°C for durations up to 716 h. As concerns the electrical stress, series of 200 up to 1600 low energy discharges are applied under 50 Hz alternating divergent electric field. Frequency domain spectroscopy has also been performed in order to assess the oil behaviour in a large frequency range (20 Hz–1 MHz). The real part of the complex dielectric permittivity of oil tends to increase with ageing. The obtained results show an acceptable stability with regard to thermal ageing and even an improvement of the oil key properties when submitted to discharges of controlled low energy level.

The design and implementation of a novel room temperature charge-coupled device (CCD) spectrometer is presented. The timing generator and data acquisition circuits driving a 16,020 photodiode linear tricolour CCD detector capable of 24 dots per millimetre are described. Linear detection of ultraviolet, infrared and visible wavelengths over total wavelength range of 300 to 1000 nm of total wavelength range is possible by using three-colour array multiplexing. The field programmable gate array logic processing realises a low-cost, high-speed digital spectrometer with USB interface. A finite state automaton is used to generate low-skew, high-speed complex clocks to drive the system. The output of the instrument is 8 bit array intensity versus wavelength array acquired using semi-flash, 20 Mb/s analogue-to-digital converter, realising a 1.23 Gb/s per colour readout rate on the CCD. The resulting spectrometer is capable of USB 2.0 communication with high-level processing programmes.

For nuclear power turbo-generators, the transient thermal variation of its winding and its insulation is one of the important performance indices. For a long time, there have been some occasional accidents at nuclear power stations such as hydrogen and fuel rod leakage. These accidents affect the economy and the safety of people. In this study, a transient temperature variation mechanism of stator winding strands and insulation with a stator water brake has been researched. On account of the stator slot being an open structure, the resistance and reactance are different along the radial direction of the slot. It results in the heat source being different for different strands in the stator slot. The resistance enhancement coefficient of the stator strands is calculated based on the analysis of the eddy-current of magnetic field. A transient fluid-thermal coupled model of a 1000 MW nuclear power turbo-generator stator is established regarding the resistance enhancement coefficient. Calculation results of the insulation temperature are compared with the test data of the turbo-generator that is used at a nuclear power plant.

Both the feature extraction method and pattern recognition method are of great importance to assess the health condition of a power transformer. Since the partial discharge (PD) signals of the transformer are non-stationary and non-linear, and the existing pattern recognition methods fail to capitalise on the inter-relations between the extracted features of the signals, a novel pattern recognition method, namely variable predictive model based class discrimination (VPMCD) is introduced for the PD pattern recognition in this research. However, the parameters of VPMCD are estimated by using least squares (LS) regression which is sensitive to multiple correlations between independent variables. Fortunately, partial LS (PLS) regression is usable even if features are highly correlated or the number of trained samples is very small. It is novelly adopted to overcome the defects of LS regression. Therefore, an automatic PD source classifier based on PLS and VPMCD, i.e. PLS-VPMCD, is put forward in this study. PD signal features from either pulse shape characterisations or phase-resolved PD are extracted. Then, the features are used as the input vectors of PLS-VPMCD classifier. PD signals sampled from four artificial defect models are adopted for the algorithms testing. Compared with the original VPMCD and back propagation recognition methods, PLS-VPMCD has much higher recognition accuracy.

In this study, a transformer model is proposed to simulate the behaviour of a real transformer, under presence of different types of defects which contribute to partial discharge (PD) generation, as closely as possible. Five different types of defects (scratch on winding insulation, bubble in oil, moisture in insulation paper, very small free metal particle in transformer tank and fixed sharp metal point on transformer tank) are implemented artificially into these transformer models to investigate the resultant PD current signal magnitude and characteristics. Time-domain PD current waveforms are recorded on those transformer models which have one type of those defects. The resultant statistical PD current wave shapes and texture features are extracted from these captured PD current signals. The principal component analysis (PCA) is used to reduce the dimension of feature spaces which are required to develop the inputs for the classifier. The principal components obtained through PCA are applied to the support vector machine classifier, as an input. The classification results indicate that the extracted texture features (using grey-level covariance matrix) preserve the best characteristics for separation of the related patterns of those five defect models, accurately.

This study presents a method using inverse hyperbolic functions to calculate the mutual and self-inductance inductance of coaxial circular filamentary coils, thin-wall solenoids, disk coils, and coils with rectangular cross-section in air. A generalised expression is given for any combination of these coils, which only requires numerical integration with one variable. Singularities are fully analysed and methods to resolve the singularities are presented in this study. When calculating the self-inductance of a disk coil or a thin-wall solenoid, the proposed method involves an improper integral, and the accuracy of the proposed method is limited unless a quadrature rule applicable to improper integrals is applied. For other situations, results by the proposed method were compared with those in the literature, and they were in excellent agreement.

This study presents a transmission line fault classification algorithm using integrated moving sum (IMSUM) technique. The method is based on current signal only. In this method, the decision of moving sum approach-based fault detection unit is used in integration with the proposed method to obtain accurate fault class. Rules are set for all ten types of fault. Considering half cycle computed data from the instant of fault inception classification is possible. High-resistance far-end fault, close-in fault, fault at different inception angle and fault considering variable prefault loading conditions can be easily classified using IMSUM technique. Severe current transformer saturation and weak infeed conditions are also discussed. The method can also be applied to a series compensated line. Any variation in fault and system condition will not alter the decision period of IMSUM technique which is an exceptional attribute for a protective function.

The present work is devoted to the analysis of currents and emitted lights associated to electrical spark discharges in mineral oil in a point–plane electrode system under high-voltage direct current stress. A systematic survey has been led on currents and emitted lights that characterise both positive and negative events through large applied field levels. On the other hand, interrelationships have been established between the currents and their corresponding emitted lights for all types of discharges. A particular attention will be accorded to the inter-correlation function between the recorded currents and light emissions and between their corresponding fast Fourier transforms. In spite of the similar shapes, major differences have been found between the spectral behaviours between currents corresponding to low-energy level discharges and those corresponding to high-energy level ones. On the other hand, photonic signals are found to be more accurate and expressive than the recorded current ones to investigate the physicochemical processes involved in the degradation of the insulating properties of the oil. The total energies of all types of discharges are of comparable magnitudes for both polarities. Their derivatives, i.e. powers, dissipated along the propagation are more expressive of the nature of discharge.

In current study, in order to find the control of general uncertain nonlinear systems, a new optimal hybrid control approach called Optimal General Type II Fuzzy Sliding Mode (OGT2FSM) is presented. In order to estimate unknown nonlinear activities in monitoring dynamic uncertainties, the benefits of general type-2 fuzzy logic systems (GT2FLS) is utilized by the suggested controller. The suggested controller not only warranties the constancy and hardiness against uncertainties of the lumped resulted by external disturbances and un-modeled dynamics, but also considerably decreases the control chattering inherent in traditional sliding mode control. Moreover, the Modified Backtracking Search Algorithm (MBSA), which is a new heuristic algorithm, is used in the offered controller. A comparison is performed between the results of current study and the results of some of the most recent studies on the same topic, which are an Adaptive Interval Type-2 Fuzzy Logic Controller (AGT2FLC) and Conventional Sliding Mode Controller (CSMC), to assess the efficiency of the suggested controller. The suggested control scheme is finally used to the Electric Vehicles type as a case study. Results of simulation confirm the good performance the suggested controller.

It is well known that interharmonics close to the fundamental or harmonics may cause significant flicker. Although International Electrotechnical Commission (IEC) standard is adopted for interharmonic measurement, the interharmonic frequencies may not be obtained accurately under this framework. Based on the frequency resolution at 5 Hz recommended by IEC, this study develops a dispersed energy distribution (DED) algorithm to measure those interharmonics that may cause flicker due to a low frequency fluctuation phenomenon. The relation between the frequency and dispersed leakage from discrete Fourier transform is induced, and the mathematical model is thus established to find the interharmonic frequency. However, the dispersed energy can be collected to retrieve its original amplitude. The performance results based on the periodically varying load verify that the proposed scheme can achieve reliable, rapid and accurate outcome.

The detection method of partial discharge (PD) ultrasonic array signals is a new method that applies array sensors and array signals process technique to the PD detection. Moreover, the technique has high signal-to-noise ratio and high reliability. Compared with a full array sensor, degree reduction array sensor is a sparse array detection system, which has advantages of simple structure, low cost, and meeting most needs of engineering. However, these existed degree reduction algorithms of sparse optimal design are relatively complicated, affecting the success rate and the accuracy of detection. Therefore, this study proposes a method, which is the optimal design of the PD ultrasonic array sensor based on genetic algorithm dimension reduction technique. First, a two-dimensional (2D) plane circular array search is changed into a corresponding 1D linear search, and the genetic algorithm performs the linear search. Then, a circular array structure is restored to calculate the array performance. Finally, an optimal sparse structure of the circular PD ultrasonic array sensor is obtained under multiple constraints. The simulated results and the experimental studies are carried out to verify the validity and effectiveness of the proposed approach.

Partial discharge (PD) detection of the gas-insulated switchgear (GIS) is an effective method for substation security. The GIS with three-phase construction is widely used for substations. The propagation characteristics of PD-induced ultra-high-frequency (UHF) signal in three-phase GIS is investigated in this study by time–frequency analysis method. The variation of peak-to-peak value (V _pp) and cumulative energy, and especially the energy density in the time–frequency plane are used to represent the feature of the UHF signal. Furthermore, the different situation of the insulation defects and the detection point positions are compared, and the reason for the difference is explained as well. As a result, this study identified a novel method to determine the PD source position in the GIS with three-phase construction.

Recent studies have shown that wavelet transform can effectively be used for noise reduction in the context of partial discharge (PD) signal detection and classification. Several thresholding approaches for wavelet denoising have been reported in the literature. In this study, a novel wavelet threshold estimation method, named energy conservation-based thresholding (ECBT), is introduced. The proposed thresholding function is capable of conserving a significant portion of the original signal energy, while the threshold value is determined based on the relative difference between the original and noisy signal energies. The proposed method is first applied to PD signals contaminated with different levels of simulated noise. Results show that ECBT produces a denoised PD signal with higher signal-to-noise ratio (SNR) and less distortion than PDs produced by the existing wavelet methods. Then, ECBT is modified to address actual PD signals corrupted with real noise, where a robust SNR estimation method is derived to estimate the noise level embedded in the measured PD signals. The denoised PD signals indicate that the proposed method yields higher reduction in noise levels than other methods.

In this study technical diagnostic tests and economical lifetime assessment of transformers are investigated to evaluate the overall health condition of working transformers. Two artificial intelligence models including artificial neural network and adaptive neuro-fuzzy inference system models are presented to determine the health index for transformers. The technical and economical parameters are used as input parameters to develop the models. Technical parameters are extracted from oil characteristics and dissolved gas analysis of different transformers. Economical parameters are constructed with transformer capital investments, maintenance and operating costs. The models are developed using 226 experimental field datasets of transformers technical and economical parameters. The models are trained using 80% of the experimental datasets. The remaining 20% is used to evaluate the performance and applicability of the models. The results prove that the models can be used to determine the health condition of transformers with high accuracy.