A methodology for predicting the performance of algorithm in terms of fidelity parameters is introduced. Algorithm performance is measured in terms of space and time complexities required to solve a particular problem. The proposed approach correlates the performance with input data and output results. Initial theory of performance model generation is based on polynomial regressing and curve fitting of least mean square (LMS) algorithm for adaptive filtering. Testing and validation of the generated model has been done through the coefficient of determination parameters calculated over interpolation and extrapolation results. Other measures like adjusted R ², sum of square error and mean square error are also reported. Overall study is summarised in form of algorithm, and is tested with LMS and recursive LMS algorithm of adaptive filtering applied over electroencephalography/event related potentials (EEG/ERP) noise removal to build up relation between input signal-to-noise ratio (SNR) and output fidelity parameters (like output SNR and correlation (r)).

In this study, a substrate integrated waveguide (SIW) combined with complementary split-ring resonator (CSRR) structure is proposed for high-sensitivity measurement of complex permittivity of low loss material at 2.45 GHz. The simulated results demonstrated that the small changes can be sensed through loading the CSRR structure on both sides of SIW. Then, the experiments are performed on various dielectric samples to validate the proposed structure by measuring the particulate samples with different moisture contents and solution samples in different mixed volume ratio. The measured results show that the sensitivity of the proposed SIW with CSRR structure is always higher than the structure without CSRR. Furthermore, neural network based on actual experimental scattering parameters is used for obtaining the permittivity of samples under test. The experimental values agree well with the theoretical values, and the relative errors of and are <5 and 10%.

The aim of this study is to improve the performance of a wire fault diagnosis approach based on time-domain reflectometry (TDR). In this approach, the TDR response collected from measurements for a given (faulty) coaxial cable network is used as a reference for the cable network model response computed by an accurate analytical transmission line (ATL) method. The ATL model is iteratively tuned according to the outcome of the optimisation algorithm proposed. The advantage of the proposed ATL method over the more classic finite difference time-domain method is the extremely faster computational time which represents a significant factor for the diagnosis of faults in wiring networks. The proposed ATL method accurately provides the features of the resistance–inductance–conductance–capacitance-based model of a RG-58 coaxial cable employed as a device under test. The ATL model has been validated by comparison with measurement based and circuit simulations. Furthermore, seven experiments have been investigated in order to evaluate the performance of the ATL method for the diagnosis of wiring networks. The obtained results reveal that the ATL method together with the efficient optimisation algorithm is a reliable, efficient and fast method for the diagnosis of wiring networks.

This paper proposes a new machine learning approach in the presence of missing measurements using surrogate-splits ensembles for real-time voltage stability assessment comprising of the classification of the operating state of the power system, and the prediction of the power system's margin to voltage collapse. The proposed approach applied the Boosting and Bagging machine learning methods in the design of the ensemble models required for the classification and regression tasks, respectively, based on the feature attributes obtained from synchrophasor measurements. The performance of the trained classifiers and regressors was evaluated for the case of missing phasor measurement unit (PMU) measurements. Robust classifier and regressor models immune to missing PMU measurements were afterwards developed using the surrogate-splits technique. The validity of the proposed approach was tested using the New England 39-bus test system. The experimental results obtained validate the effectiveness of the proposed method for various operating scenarios and contingencies even in the presence of missing PMU measurements.

The current distribution, ampacity and magnetic field of parallel power cables are calculated. The calculations are performed in both sinusoidal and harmonic current profiles. The simulations are conducted on the underground medium voltage cables with metallic sheaths. The effects of single-point bonding and solid bonding of sheaths grounding system are evaluated. In this study, cables are installed in flat formation with three cables in each phase and the effect of different distances between cables on the parallel cables parameters is studied. Computations are conducted for all possible arrangements of parallel cables and the worst and the best arrangements from the point of balancing current division, maximum ampacity and minimum magnetic field are presented. Furthermore, the effects of placing parallel cables in one row or two rows above each other are investigated. Calculations of current distribution and magnetic field are performed using the sub-conductors method that considers the skin and proximity effects. Finite element method thermal simulations are excited to determine the power cable ampacity in different configurations. Experimental tests on parallel cables in both single-point bonding and solid bonding systems are carried out and the results verify the accuracy of simulations.

Scope of the current study is to examine the electrical characteristics of photovoltaic (PV) modules after being subjected to high impulse voltages, which are usually generated by lightning or switching surges. Tests were conducted at the High Voltage Laboratory of the University of Patras in Greece on PV modules of 5 and 195 W_p derived from different manufacturers, by performing various stress scenarios. The I-V and P-V characteristic curves of the examined modules, before and after the impulse voltage stress tests, are compared with each other considering the modules' ability to maintain their nominal electrical characteristics. After certain periods of time, the electrical performances of the stressed modules are examined again in order to ensure whether they regain some or all of their properties in case of prior differentiation. The experimental results are compared with theoretical estimations, so as to confirm the rightness of the presented procedure.

Candidate regions extraction is a crucial problem in object detection. In this study, a new algorithm is proposed for extracting candidate regions of intruder airplane to assist with vision-based sense and avoid system. A key consideration is that the algorithm works well under complex background. The algorithm contains three parts: roughly determining the area of the intruder airplane on the image, segmenting the detected area into image patches and finally extracting candidate regions from the segmented patches. The cycle-frequency filter is adopted to roughly determine the area of the intruder on the image. The graph-based image segmentation method is used to further separate the determined area into the image patches of the intruder and the image patches of the background. The sliding window technique is employed to obtain the regions of interest (ROIs), and the candidate regions of the intruder airplane are finally extracted by merging the overlapped ROIs. The performance of the algorithm is analysed using a number of aerial video sequences with complex background and different angles of view. The results of the experiments indicate the superiority of the proposed algorithm.

Partial discharges (PDs) are a cause of degradation of insulation system and its permanent activity monitoring is used as a tool for insulation condition assessment in power transformers. Information on PD position can be used by plant monitors to diagnose the cause of PD and can help to identify type and severity of an insulation fault for maintenance strategies. The acoustic PD detection system, with great advantages relative to other methods, contains inaccuracies in PD localisation, such as the multipath interferences due to reflections within transformer tank, improper acoustic coupling between sensor and tank surface, mechanical vibrations and inherent sensor inaccuracies, which can severely limit the accuracy of the positioning system and make locating the exact positions of PD difficult. In this study, a method has been proposed for calculation of position measurement sensitivity for different PD origins within transformer tank. Different possible cases have been considered for placement of acoustic emission (AE) sensors. For each case, sensitivity of PD location to the time measurement errors has been calculated. The optimum arrangement of AE sensors has been chosen as the configuration yielding lowest error in the PD localisation. A test setup has been organised and the experimental results have justified the theoretical analysis.

A novel precise position tracking controller is presented for permanent magnet synchronous motors (PMSMs). The proposed controller consisting of non-linear extended state observer (ESO), tracking differentiator (TD) and dynamic surface control (DSC) can reject disturbance and can prevent PMSMs from chaos with parameters falling into some special areas. Compared with the conventional DSC approach, the TD of the proposed controller is designed to remove the explosion problem. Further, non-linear ESO is designed to estimate the unmeasurable states and uncertainties of system, which together with TD significantly ameliorate the tracking performance and improve the disturbance rejection ability of system output in the presence of uncertainties and unmodelled dynamics of PMSMs. Different from the available control methods of PMSMs in reference, both the transient and steady-state stability of the closed-loop control system is proved by the Lyapunov method. Finally, comparative simulations are completed to demonstrate that the precise trajectory tracking performance, chaos-suppressing performance and disturbance rejection performance of the proposed controller can be guaranteed.

In this work, the authors improve the magnetic anomaly detector by using Karhunen–Loeve expansion, for expanding the detection distance of visually obscured ferromagnetic object when the target signal is contaminated by coloured noise. In the detector, the orthonormal basis functions are modified by the autocorrelation function of geomagnetic noise, and the anomaly is detected by matched filtering. Compared with the traditional detector, simulation results show that the modified detector has better performance with coloured Gaussian noise; especially, the improvement is more evident when the noise exponent is bigger. The real geomagnetic noise is proved to be coloured and obey approximate Gaussian distribution by the measurements in experiment. Tests with this noise indicate that the proposed detector achieves an incremental detection probability of 0.1 – 0.2 in the case of low signal-to-noise ratio, which makes it more attractive in practice.

Regular preventive maintenance scheduling (PMS) of generating units is required in order to prolong their life expectancy so as to ensure safe operating conditions, and most importantly to reduce the risk of unplanned outages due to failures. The decision making of PMS is related to the measures of economic dispatch and other planning activities that necessitate an exact mathematical PMS model. Traditionally, generator PMS problem is based on levelling the spinning reserve. However, in practical, levelling the reserve is not alone good enough. Including a probabilistic model along with the deterministic model is a welcome perspective. In this study, a new multi-objective model combining deterministic/probabilistic models is proposed. Furthermore, the generating unit failures have been incorporated in PMS to realise their impacts on reliability objectives. These aspects will increase further the complexity of the solution procedure. The advantages of modern bio-inspired algorithm, namely ant lion optimiser (ALO) attracts to use it as a main optimisation tool. The fuzzy decision mechanism is incorporated in the ALO to extract the best compromise solution in the multi-objective solution space. The standard test systems are used for implementation. Numerical simulation results indicate that the intended method has the capability of obtaining best compromised solution.

For online monitoring of complex temperature distributions, an acoustic tomography system based on sound travel-time measurement is developed. For coping with the reverberation phenomenon in actual measurement, an anti-reflection interference measurement method for sound travel-time is proposed based on cross-correlation and peak recognition. In view of the fact the pixel number provided by commonly used reconstruction algorithms, such as least-squares methods, is very limited, radial basis function approximation and regularisation technique are used, which leads to the pixel number of the reconstruction image increasing dramatically. Mixed programming with LabVIEW and MATLAB is used. Queue structure is adopted to make data acquiring program and data processing program parallel. Test results show that the system can run normally as designed. The measurement values of sound travel-time acquired by the proposed method are stable even in strong reflection interference. Test results show that the system can run normally as designed. The measurement values of sound travel-time acquired by the proposed method are stable even in strong reflection interference. The temperature fields reconstructed are coincided with actual fields, and correctly reflect the temperature changes in the measured area with the heater number, heater location and heating time.

Dressing is an important operation for the grinding process. Its goal is to recondition the wheel tool to re-establish its cutting characteristics, owing to the wear produced after successive passes. Monitoring systems that use acoustic emission (AE) have been studied to correlate the signals with several tool conditions. This study brings a new approach of processing AE signals with the purpose of identifying the correct moment to stop the dressing, which is essential in an automatic control system. From the AE signals collected in dressing tests with aluminium oxide grinding wheel and single-point dresser, spectral analysis was made through power spectral density, selecting frequencies bands that best characterise the process. The statistical parameter ‘counts’ was applied to the raw signal unfiltered and filtered in the selected bands in order to identify the tool condition and, in turn, towards a monitoring system implementation. Results showed an expressive relation between tool cutting conditions and processed signals in the selected bands. There was a great disparity of the filtered signals in the selected bands and signals unfiltered, reflecting that the filtered ones were more efficient in terms of process automation.

Owing to the importance of rolling bearings in mechanical systems, many meaningful works have been presented for bearing degradation assessment. However, most of contributions fail to take into account the influence of input features on the performance of a degradation model. As a potential multivariable process control method considering serial correlations, canonical variate analysis (CVA) is introduced to detect abnormal behaviours of rolling bearings. This study combines the CVA and support vector data description (SVDD) to develop a robust approach to weaken or remove the negative impact of input data on bearing degradation estimation. First, the time-domain features extracted from healthy vibration signals are fused by CVA to generate projection data for SVDD training. Second, the distance of new variables to the centre of the hypersphere is calculated as an indicator to describe the degradation of rolling bearings. To demonstrate the effectiveness and robustness of the proposed method, two different experiments on bearing run-to-failure tests are conducted. Experimental results validate that the proposed work is capable of detecting bearing failure under various working conditions. Besides, comparison of the proposed work with the original CVA is implemented. The robustness of the proposed work is enhanced obviously compared with the original CVA.

The oil-paper compound insulation plays a vital role in the insulation structure of power transformer. To obtain the characteristics of the transformer's insulation system, it is of great importance to study the dielectric response of oil-paper compound insulation. In this study, fractional calculus is applied to model the oil-paper compound insulation. Both low-frequency and broadband high-frequency models are proposed and then they are verified by fitting the measured data of different insulation papers. Finally they are applied to two different cases: (i) the low-frequency model for fitting polarity reversal property; (ii) the broadband high-frequency model for studying the influence of the parameters variations on the frequency domain spectroscopy (FDS). The results demonstrate the advantages of the proposed models when compared with traditional ones.

This study is based on International Electrotechnical Commission (IEC) 61967-4 standards for measuring electromagnetic emissions and interferences at the chip level. Conventional measurement methods using fixed SMA connectors were replaced with an improved radio frequency current probe featuring semi-rigid coaxial cables, making it capable of taking mobile measurements, enhancing the flexibility and convenient probing usage. To effectively assess the practical value of the improved probe, the authors have referred to the standard designs provided in IEC 61967-1 to manufacture a four-layer digital circuit board that controls the screen of an on-board navigational screen for automobiles. Circuit board electromagnetic interference (EMI) noise frequency and level readings from the improved probe were also compared with three other measurement methods, namely magnetic field probes available in the market, TEM cells, and the 10-m far-field measurement method. The results demonstrated the capability of the improved probe to effectively identify sources of noise at the chip-level during the IC design phases, helping with root cause analysis and allowing EMI noises experienced by the product to be solved promptly during the process of electromagnetic compatibility design optimisation and analysis.

The transit-time ultrasonic flow meters often show non-zero-flow rate when the flow is stationary due to the variations between transducers, which results in a zero-flow error. Moreover, the zero-flow error may change along with the measurement environment which causes the zero-drift error. The zero-flow error and zero-drift error will lead to false flow detection and influence the measurement stability at low velocities. Reciprocal operation theory has been proven to be an effective technique to eliminate zero-flow error. Therefore, reciprocal electronic circuits for gas ultrasonic flow meter are designed in this study. The circuits are first modelled in PSpice to validate its effectiveness. It is shown that both the zero-flow error and the zero drift are reduced significantly with the reciprocal circuits. Besides, the optimum matched resistor is determined by simulation. Second, three experiments are carried out to test the reciprocal circuits including transducers paired experiment, temperature test and flow calibration. Finally, it is verified that the reciprocal circuits can improve the measurement accuracy and stability for gas ultrasonic flow meter and the calibration results show that the gas ultrasonic flow meter (USM) with reciprocal circuits can achieve 1.0% measurement accuracy in 0.3–30 m/s.

Static synchronous compensator (STATCOM), a shunt type flexible AC transmission system device, is used to control the voltage at different locations within a power system by absorbing/injecting reactive power. The dynamic control action associated with the operation of STATCOM in both capacitive and inductive modes is the reason for varying system condition and failure of different relaying schemes. Directional relaying is affected by the reactive current contributed by STATCOM in wide range of inductive to capacitive mode of operation. Further, single pole tripping (SPT) condition during STATCOM operation leads to mal-function of the directional element due to phase change in sequence components of voltage and current. In this study, directional relaying is addressed for different types of faults in the presence of STATCOM connected at different locations during single phase auto reclosing period. Proposed directional relaying technique is based on the phase angle between positive sequence fault and pre-fault apparent powers. Performance of the proposed scheme has been investigated for different types of faults for single and double circuit lines during SPT using simulated data of different power networks using PSCAD/EMTDC software. A comparative study is also carried out to verify the performance of the proposed scheme for different STATCOM operating conditions.