The importance of the continuous service of power transformers and their role in the reliability of the power delivery has resulted in the need for various monitoring systems which utilise different electrical, mechanical, optical, chemical, and acoustic sensors to monitor the vital characteristics of the power transformer. A brief review of these sensors is attempted. First, the parameters that are normally monitored in a transformer are explained to clarify the types of sensors that are needed and their duties. Then, each sensor type is described in summary while giving proper references for further reading. Accordingly, a fast guide for practitioners researching the sensors utilised for power transformer monitoring is presented.

Partial discharge (PD) monitoring in high-voltage equipment is one of the effective methods for assessment of its insulation strength. To do this, noise reduction is one of the essential processes on measured PD signals. One of the most popular tools employed for PD de-noising is wavelet transform. To exploit this transformation, three main parameters, including ‘mother wavelet’, ‘number of decomposition level’, and ‘thresholding procedure’, should be assigned. In this study, a novel and also more accurate method for the determination of required decomposition level is suggested. The proposed method employs a decision tree which takes the pattern of energy spectral density of PD signals in the frequency domain and delivers the optimum decomposition level for de-noising by wavelet transform. The results, as compared with others, show the superiority of the proposed method in noise reduction of PD signals, both for simulations and field measured signals.

A low-voltage ultra-low-power two-quadrant current squarer based-true RMS to DC converter circuit is presented in this study. The proposed current squarer is based on two translinear loops with matched NMOS transistors operating in the weak inversion region. Ultra-low-power dissipation of 1.4 nW, multi-decade frequency range of 0.5 Hz–55 kHz and large input dynamic range of 72 pA–1 nA (with error <3%) are the main achievements of the proposed design while constructed of only ten transistors. Post-layout simulations with 0.8-V supply voltage using HSPICE software in 0.18 μm TSMC CMOS process (level-49 parameters) verified the good functionality of the proposed circuit. Moreover, Monte Carlo analysis is performed to validate the satisfactory PVT robustness and reliability of the design's performance.

Inter-laminar faults (ILFs) have major impacts on the overall performance of the electrical machines and power transformers, among which extra power loss and hence lower efficiency could be highlighted. This study presents an in depth analysis on energy loss and energy loss components of stacks of grain-oriented electrical steels subjected to different kinds of ILFs, under sinusoidal and non-sinusoidal inductions. Practical methods are developed to monitor quality of the magnetic cores, based on the measured static and dynamic hysteresis loops. The experimental results showed that, ILFs have a significant impact on the dynamic performance and dynamic energy loss of the cores, while their impact on the hysteresis loss is negligible. Furthermore, they become more destructive under non-sinusoidal inductions, and hence condition monitoring of the magnetic cores is more important for these applications.

A robust technique based on maximum correntropy criterion (MCC) is proposed for frequency estimation in an unbalanced power system using error in phase angle. This is achieved by replacing the magnitude error used in the standard cost function of the MCC with the phase error and by extending the concept of the augmented (widely linear) complex statistics for the processing of unbalanced voltages and other abnormal system conditions. The idea emanates from the facts that: (i) useful information is primarily conveyed over the phase in power system frequency estimation, and (ii) the performance degradation of the well-known minimum mean squared error criterion based adaptive filters in impulsive noise environments can be overcome by the MCC due to the non-quadratic and higher order moments imbedded in its cost function. The proposed phase error-based MCC method also utilises all the second-order information within the complex valued system voltage through the Clarke's transformation. Simulation studies conducted using both synthetic and experimental data verify that the proposed algorithm provides better estimations compared to the considered algorithms.

The authors propose and experimentally demonstrate a calibration process unit for mitigating the effects of nonlinearity and dependence on the ambient temperature for the fibre Fabry–Perot (FFP) filter. Additionally, the performance limits of the peak detection process unit based on the maximum detection algorithm according to the noise and the characteristics of the fibre Bragg grating (FBG) and FFP filter are presented. The experimental results obtained without and with the calibration process unit exhibited an average absolute error of <93.0 and 7.34 pm, respectively. The repeatability test results for the FBG interrogator showed an absolute error <2.19 pm. To investigate the performance limits of the peak detection process unit, the Bragg wavelength with variations in the noise of both the FBG reflection spectrum and the FFP filter transmission spectrum and with different characteristics of the FBG and the FFP filter were calculated. The calculated results for the given parameters show that the error of the mean value and the standard deviation increase with decreasing signal-to-noise ratios of both the FBG and FFP filter. Additionally, it was confirmed that the errors increase with the increase of the full-widths at half-maximum of both the FFP filter and the FBG and with the decrease of the Bragg wavelength intervals.

Various types of insulation models with time-invariant parameters are available in the literature. Depending on the aging sensitive performance parameters to be evaluated, different models need to be employed (e.g. XY model for oil and paper-conductivity, conventional Debye model (CDM) for paper-moisture and tanδ). While the XY model cannot be used for estimating paper-moisture directly, analysis based on CDM parameter becomes dependent on its branch parameters, which are non-unique. These factors lead to either incomplete or ambiguous insulation diagnosis. These problems are resolved using the proposed new insulation model containing unique time-varying branch parameters. Another major advantage of the proposed model is that it can be used to evaluate a host of performance parameters (like paper-conductivity, oil and paper-moisture, dielectric loss) thus giving a complete picture of the insulation concerned. The application of the proposed model is also tested on data collected from several real-life power transformers.

Condition monitoring has been widely used to detect electrical faults of gas-insulated switchgear (GIS), but little attention has been paid to diagnosing the mechanical failures. Owing to the fact that the operating energy of circuit breaker assembled in GIS is large enough to stimulate the whole GIS to vibrate, in this study, non-invasive techniques for evaluating the mechanical condition of GIS by considering the vibration signals excited by the operation of circuit breaker are implemented. Vibration signatures of five typical mechanical failures sated on both static mechanisms and drive mechanisms were extracted from a 126 kV GIS test system. In addition, new algorithms for signal processing and decision-making techniques based on the combination of energy equipartition S-transform and particle swarm optimisation support vector machine classifier were proposed. A practical diagnostic instrument was developed and applicated on line for identifying the GIS mechanical condition. Experimental and field demonstration verified the effectiveness of the proposed GIS mechanical fault-diagnosis technology.

This study presents an application of the reassigned Gabor time–frequency transform to the inductance inrush current transient analysis. Measurement setup is constructed and the measurement methodology is presented to obtain fully controlled transformer inrush current measurements. The measured data is analysed by the reassigned Gabor time–frequency transform. The resulting time–frequency distribution of the measured signal shows how inrush DC component and harmonics change with time for different initial conditions. By extracting the stationary points from the reassigned Gabor transform, an algorithm for computing the time duration of the inrush is presented and applied to 88 measured transients.

In-line inspection (ILI) robot has been considered as an inevitable requirement to perform non-destructive testing methods efficiently and economically. The detection of flaws that could lead to leakages in buried concrete pipes has been an area of great concern to the oil and gas industry and water resource-based industry. The major problem is the difficulty in modelling the detection of cracks as a result of its irregularity and randomness that cannot be easily detected. This work covers the study of defect detection of non-destructive testing methods using fusion inspect sensors, light detection and ranging, and optic sensors. Studies on ILI robots are also reviewed to construct an efficient gauge. Nevertheless, the study on current support vector machine (SVM) technique is centred as the main classification engine for the combined sensory data. The prototype robot has been designed and successfully operated in a lab-scale environment. Intuitively, the obtained data described the status of inspected pipe clearly. Sensors from the proposed list also well integrated as attaching to the robot. The experiments with SVM techniques to evaluate the feasibility of defects detection also achieved remarkable results.

This study presents a dynamic arc model of a polluted insulator based on the formulation of the Obenaus model and the Hampton criterion for discharge propagation. An experiment was conducted on a practical glass insulator installed in the Algerian network for the purpose of measuring the flashover voltage and estimating the arc parameters (A and N) using a genetic optimisation algorithm. The parameters used in the dynamic model were the arc current, arc length, and arc resistance calculated using MATLAB. Moreover, an attempt was made with finite element software to calculate the voltage and electric-field throughout the insulator surface with and without the presence of contaminants. Finally, the obtained simulation and experimental results showed high performances under operational conditions and respect IEC 60060-1 standard recommendations.

This study proposes an alternative methodology for predicting salt contamination in rotating blade of wind turbine under lightning strike using fuzzy c-means (FCM) and k-means (KM) clustering approaches. The salt contamination states of wind turbine blades are classified with four different levels of equivalent salt deposit density (ESDD) classes. The lightning strike experiments are set up for simulating the condition when the blades are struck by lightning with four rotational speeds under various ESDD classes. Then, the absolute peak value of the measured current signals in grounding line using the high-frequency current transformer and the average power are used to represent the input vectors of FCM and KM to predict the class of the salt contamination. The experimental results validated that the proposed approach can effectively classify the measured current signal and accurately predict the ESDD class on lightning strike occurrence.

To diagnose the running state of bushing and avoid serious power grid accidents, online multi-parameter monitoring equipment for dissolved hydrogen, oil pressure and temperature in bushing oil was developed. The equipment includes three units: signal acquisition, control cabinet and data analysis. A new hydrogen sensor without oil–gas separation membrane and based on palladium alloy film technology was developed. A three-in-one sensor for hydrogen, oil temperature and pressure monitoring has been developed, which has the advantages of miniaturisation, lightweight and easy installation. Test results show the hydrogen measurement range of the sensor is 0–5000 ppm, and the accuracy can reach 10% or 15 ppm (with larger values). The pressure measurement range is 0–1.0 MPa, the resolution is 0.1 kPa, and the accuracy can reach 0.25 grade. The temperature measurement range is −40 to 105°C, and the accuracy is ±1°C. The measurement performance of the device fully meets the requirement of online monitoring of transformer bushing. The equipment has been put into operation in a 330 kV substation, which can monitor the bushing status online and help eliminate the early latent fault of bushing in the weak budding state.

This study proposes a high-accuracy method of localising partial discharge (PD) for transformer fault diagnosis. This study aims to solve the problem of high-accuracy estimation of PD in transformers by detecting the acoustic signals. First, by combining the advantages of the differential evolution (DE) algorithm and the particle swarm optimisation (PSO) algorithm, the authors describe a hybrid DE-PSO algorithm that can maintain great diversity even at the later stage of calculation. For further accuracy, a cooperative localisation differential evolution-particle swarm optimization-correction-Newton's method (DPCN) algorithm based on the DE-PSO algorithm and Newton's method with consideration of corrected time difference of arrival values is proposed. The results of simulations and experiments show that the proposed algorithm has excellent performance with high accuracy and strong robustness, and it can meet the needs of field applications.

Various unwanted phenomena that are taken place in the transformer may occasionally mal-operate selected fault classification based protective schemes. Hence, it is necessary to discriminate internal fault from external abnormal conditions for unit protection of power transformer. This study presents a new hierarchical ensemble extreme learning machine (HE-ELM) based classifier technique to identify faults in & out of transformer. The component extreme learning machine (ELM) is structured hierarchically to improve its fault data classification accuracy. The developed algorithm is evaluated by simulating multiple disorders on 100 MVA, 132/220 kV transformer with the help of PSCAD software. DWT is used to extract features from acquired current signals from transformer. The feature vector formed after extraction process is fed to the HE-ELM algorithm for data classification. The fault discrimination accuracy of HE-ELM technique is 99.91%. This shows its effectiveness with respect to other classifier techniques. Moreover, the developed algorithm is successfully tested on hardware prototype in laboratory environment under various inrush and fault conditions using Cortex M4 microcontroller (STM32F407) with maximum identification time of 27 ms. The proposed HE-ELM technique is compared with existing support vector machine, probabilistic neural network and ELM techniques for identical fault data. Results demonstrate that HE-ELM outperforms than existing schemes in cross-domain recognition task.

The research into the development of discharge under positive polarity voltage across the short air gap is of great significance to improve insulation of electrical equipment. By establishing a photoelectric joint detection system, this study observed phenomena in short air gaps before, and the period of breakdown. By utilising a photomultiplier tube and a Rogowski coil, the optical and current signals were obtained. Moreover, through z-type Schlieren system and high-speed digital camera, relaxation images of ion jet formed across cone–sphere electrodes were obtained. Through comparison and analysis, the discharge of short air gap was divided into three stages: initial corona, strong corona and streamer breakdown. On the basis of this, the relationship between optical and current signals was compared by assessing characteristics of the ion jet in each stage. Also, the relaxation characteristics of an ion jet were analysed. The results showed that ion jets appeared in the initial corona stage, while relaxation channel of the ion jet was formed and the channel fluctuated in the strong corona stage. In the streamer breakdown stage, the process of expansion, constant fluctuation and breakdown appeared; the amplitudes of corona current signals and optical signals in these three stages constantly increased.

To develop an accurate and efficient depth of anaesthesia (DoA) assessment technique that could help anaesthesiologists to trace the patient's anaesthetic state during surgery, a new automated DoA approach was proposed. It applied wavelet-Fourier analysis (WFA) to extract the statistical characteristics from an anaesthetic electroencephalogram (EEG) signal and to design a new DoA index. In this proposed method, firstly, the wavelet transform was applied to a denoised EEG signal, and a fast Fourier transform was then applied to the wavelet detail coefficient D3. Ten statistical features were extracted and analysed, and from these, five features were selected for designing a new index for the DoA assessment. Finally, a new DoA () was developed and compared with the most popular bispectral index (BIS) monitor. The results from the testing set showed that there were very high correlations between the and the BIS index during the awake, light and deep anaesthetic stages. In the case of poor signal quality, the BIS index and the were also tested, and the obtained results demonstrated that the could indicate the DoA values, while the BIS failed to show valid outputs for those situations.