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.

Compared with the normal Gaussian filter that measurements are acquired in real-time and process and measurement noises are independent, the authors study a class of non-linear state estimation problems with one-step randomly delayed measurements and correlated noises. Using a novel alternative framework, where the process and measurement noises are as the augment of the state vector, the author can deduce the related formula and get a novel filtering algorithm. In order to explain the feasibility of the proposed algorithm, this study demonstrates that the relevant recursive formula satisfies the Gaussian distribution. In order to explain the rationality of the proposed algorithm, the relevant union estimation form is given in Section 1. For the sake of facilitating computer simulation, the sub-optimal realisation of the proposed algorithm on the basis of the spherical-radial rule is given, which is alternative framework Gaussian filter with the correlated noise and delayed measurements. The superiority of the algorithm presented, compared with the other algorithm, is shown in the end of this study.

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.

In this study, the unstructured transmission line modelling (UTLM) method based on a tetrahedral mesh is applied to model the electromagnetic coupling into wire looms and bundles with multiple cores that are typical of an aircraft system, when they are exposed to plane wave illuminations. The impact on the electromagnetic coupling into wires of both bundle configuration and the positioning of the bundle relative to simple structures are investigated using the UTLM method with explicit meshing of the wires. The work not only confirms that UTLM method as a powerful tool for dealing with wire looms and bundles but provides invaluable information on the margins to be expected in key experimental waveform parameters such as peak amplitude and frequency response.

In the traditional observability analysis methods, much attention is paid to the accuracy of the condition number. However, the authors find the fact that besides the accuracy, the structure of condition numbers is important, such as their range and sequence. A structured condition number method is proposed, which includes the double-reciprocal condition number and the compensation based on the continuous period. As the condition number is in non-linear relation with the navigation error, the mean of condition numbers does not reflect the navigation performance accurately. To solve this problem, the double-reciprocal condition number is proposed, where the impact of a large condition number is small. Considering the fact that the longer the continuous period is, the worse the navigation performance is, the authors develop the compensation method based on the continuous period. Amending the double-reciprocal condition number with the continuous period-based compensation, the authors propose the structured condition number which possesses the advantages of them, and apply it into the celestial direction measurement-based integrated navigation systems which have sharply variable observability degree. The simulation results demonstrate that the structured condition number can reflect the navigation performance and select a proper navigation star effectively.

The convection heat transfer coefficient (CHTC) distribution of surfaces of end metal parts directly affects the temperature distribution of end metal parts. In this study, a large turbogenerator is analysed. To study the CHTC distribution of surfaces of end metal parts in detail, a three-dimensional (3D) fluid and thermal analysis model of whole turbogenerator end region is established. The losses of end metal parts from 3D transient electromagnetic field calculation are provided to end metal parts as heat sources. Pressure values of end-region outlets and fluid velocity of fan outlet from flow network calculation are provided to end region as boundary conditions in the 3D fluid and thermal analysis model. CHTC distribution laws of the surfaces of the clamping plate, finger plate, and copper screen are studied in detail by the finite volume method. The fluid flow and temperature distribution of end metal parts are determined in the end region of the turbogenerator. The calculated results are compared with measured data.

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.

In this study, determining the location of coating defects and severities is evaluated using measurements along the Jordanian buried gas pipeline. Induced alternating current (AC) voltages and current densities along the pipeline are investigated using analytical and practical data under normal operation of the overhead transmission line. Since the induced AC voltages depend on the load currents, it is worthy to determine the actual load current during the lifetime of the pipeline. Accordingly, the forecasting of pipeline-induced AC voltages is investigated using the least square method. The investigation is carried out in order to expect the future effects on the gas pipeline. The mitigation of induced AC voltage based on inserting polarisation cells (PCs) along the pipeline is evaluated. The results show that an agreement between the measured and calculated induced AC voltages is achieved with and without PCs. Based on the forecasting of induced AC voltages and current density along the pipeline, determining the number of PCs and allocations is suggested for the future years. This study increases the lifetime of the pipeline as well as decreases the maintenance cost.

Intercomparison among six terrestrial laser scanner systems focused on the measurement of small elements ( < 0.5 m) is performed. Phase shift (PS) and time of flight (ToF) scanners are considered. Two standard artefacts containing three-dimensional printing spheres and steps of variable height are used for the experiment. Results show errors between −4.5 and 3.5 mm in the measurement of distances between step planes. The most stable systems for measuring small elements seem the Leica C10, Faro Photon and Riegl LMS Z390i. The quality of the results is linked to the overall quality of the system rather than the specific technology used for range measurement (PS or ToF) which does not appear to be a determining factor.

The magnetic design of switched reluctance (SR) motors is inherently a hierarchical process. The design cycle progresses through distinct stages where the accuracy improves but computing times increase greatly, thus it often becomes impractical to furnish extensive multi-objective optimisation required to accomplish the optimal design. In order to enable rapid and accurate optimisation of SR motors, an improved reduced-order computational method of flux tubes is implemented to complement and practically replace the time-consuming 2D finite-element-based magnetic analysis. This study demonstrates how the use of the improved flux tubes approach to evaluate objective functions results in substantially faster while still accurate multi-objective optimisation of SR motors.

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.

Fast and precise location of faults in power transmission lines is essentially important for fast line restoration. Widely used fault location methods based on impedance measurement and travelling wave analysis have some drawbacks leaving a lot of room for further research. This study proposes a novel method for estimation of the location of the asymmetrical phase-to-ground faults that exist during line energisation. The method is based on the empirical mode decomposition and its application relies on knowing the fundamental system parameters and frequency estimation of the analysed signal. It is simple to use while providing highly accurate results for overhead lines up to 80 km length.

Frequency domain dielectric spectroscopy (FDS) is a non-destructive method widely used to evaluate the state of dielectric materials. Traditional FDS measurement is conducted in frequency sweep mode between 1 kHz and 1 mHz. However, this method requires long measurement time when sweeping in low frequencies (1 mHz–1 Hz), thereby limiting its field applications. Hammerstein model is selected as the mathematical model for insulation systems according to known property of dielectric and established based on system identification theory. An equivalent mathematical model fitting >97% with the insulation system is obtained. The equivalent model can be used to calculate dielectric response parameters and construct an FDS. The proposed method requires 10 min to obtain dielectric response parameters under any frequency excitation in the low-frequency section. The measurement time decreases by up to 90% compared with that of traditional FDS. Comparison experiments are conducted between system identification method and traditional FDS measurement applied to different insulation systems, such as insulation pressboards, testing transformers, and field power transformers. Results show that frequency responses are consistent between the equivalent model and insulation system, ensuring the accuracy of FDS curve obtained by the proposed method.

In this study, a four-way power combiner using four equally designed 2-way power combiners is introduced. The physical structure of the combiner is such that the components and connections are symmetrically arranged. In the final section of the power combiner, two 2-way combiners are connected in parallel, which helps for easier circuit alignment and better electrical properties. The 2-way power combiner is the building block of the 4-way power combiner. The 2-way power combiner is analysed by even and odd method and its performance is verified by measurements. The analysis method can be extended to 4-way or N-way power combiners. The proposed 4-way power combiner is fabricated and its performance is measured. The amplitude imbalance of the 4-way power combiner is <0.2 dB in the path between the input ports and the summing port. The attenuation above the theoretical 6 dB insertion loss is 0.5 dB from 30 to 500 MHz. The 4-way power combiner has the same performance as the 2-way power combiner, regarding the added insertion loss above the theoretical ideal value. Ease of fabrication, simple circuit connections and use of equally constructed 2-way power combiners result in repeatability and reduce electrical mismatch.

This study proposes an adaptive fault line detection method based on the improved singular value decomposition (SVD) de-noising and ideal clustering curve for distribution networks. First, the improved SVD algorithm is introduced to obtain the pure transient zero sequence current. Then, the fast Fourier transformation is employed to analyse the baseband signal and calculate the phase differences. After that, if the difference values are larger than the set threshold, the detection method based on the improved SVD and ideal clustering curve of baseband components is proposed; if not, the 1/4 cycle damping non-periodic components are rearranged, then based on the first half waveform extreme values and the rearranging damping non-periodic components, the detection method based on the ideal clustering curve of damping components is introduced. The simulation results prove the correctness of proposed selection method. After comparing with the existing methods, the advantages of proposed method are confirmed.

In view of the importance of rotation scheme for parameter estimation in continuous self-calibration of the inertial platform, a design method based on sensitivity analysis is proposed. The error models of continuous self-calibration are given firstly, which are time-varying and non-linear. Lie-derivative is employed to analyse the global observability of models to ensure the system is observable. For the fast convergence of filter and decreasing calibration time, the observability of each parameter in error models is considered, which is represented by output sensitivity of each parameter. Based on the principle of maximum output sensitivity, the design of optimal rotation schemes for each parameter is converted to an ergodic optimisation problem. Then the global optimal rotation scheme is obtained by combining all the optimal rotations of each parameter, which can generate a suitable input for continuous self-calibration and excite all the parameters efficiently. Finally, the proposed approach is demonstrated by an illustrative example. The results show that the rotation scheme designed by the proposed method can excite all the parameters efficiently with high calibration precision and convenient operation.

With M sampling operational amplifiers (op-amps) in negative feedback, the fast readout zero potential circuit (FRZPC) can simultaneously access M elements in the M × N resistive sensor array with the shared row–column fashion but it suffered from the crosstalk problem caused by the switch-on resistance of the column multiplexers, the non-scanned elements and the size of the array. For suppressing the crosstalk problem, the authors proposed an improved FRZPC, called IFRZPC, by introducing one more op-amp in negative feedback to sample the current in the ground to the array direction. Then, in the FRZPC and the IFRZPC, the effects of different parameters of the resistive sensor array and the readout circuit on the measurement accuracy of the elements being tested were simulated using National Instrument Multisim. Simulations and comparative experiments were performed and demonstrated that IFRZPC, though requiring M + 1 op-amps, M + 1 resistors, and M + 1 sampling channels, could simultaneously access M elements being tested on the same column in the M × N resistive sensor array with a better accuracy.

Partial discharge (PD) measurement is an efficient method for monitoring the integrity of the high-voltage equipment. However, extensive interferences can compromise the measured PD signals. After the acquisition, a post-processing algorithm is required for the effective extraction of PD signals from noise. The wavelet shrinkage is one of the most popular tools used in such application. The wavelet processing involves the choice of parameters that will determine the performance of denoising. With the purpose of finding suitable parameters, optimisation techniques, such as genetic algorithms (GAs), can be used in conjunction with a fitness function to evaluate how good a solution is. This work proposes a new fitness function, based on a composition of local variables and statistics usually employed for the evaluation of PD denoising algorithms. The proposed function was applied to a set of PD signals and proved effective in the identification of optimal parameter values by a GA, thereby producing better PD denoising.