This paper deals with the problem of inserting a temperature sensor in the neighbourhood of a chip to monitor the junction temperature. If the sensor is not in the middle of the heat source, the recorded temperature can be quite different from the chip temperature we are mainly interested in. For the steady state temperature, it is rather easy to introduce a correction factor. For the transient behaviour of the temperature, there is a tremendous difference between the chip and the sensor temperature, which cannot be neglected if the temperature is used as a parameter to change, for example, the clock frequency in order to improve the throughput.

This article presents a study of the sampling rate effect on electrical power measurements whose definitions are based on the Conservative Power Theory (CPT). The definitions of active power and reactive power of the CPT were applied in the MATLAB® software by varying the sampling rate and using a digital power meter as a reference. The measurements were performed in scenarios with linear and non-linear loads. Due to the usage of an integral in the CPT calculus, an error was verified associated with the reactive power being inversely proportional to the sampling rate. From the present study, it is possible to conclude that depending on the sample rate, the errors associated with the reactive power measurements are unacceptable and make the CPT implementation unfeasible. The results also presented effective information about the minimal sampling rate needed to make these errors neglected and to assist in choosing suitable microprocessors for the digital implementation of the CPT. It is... [more]

In a nuclear power plant environment, low-voltage cables experience different stresses during their service life which challenge their integrity. A non-destructive and reliable condition monitoring technique is desired to determine the state of these low-voltage cables during service and for the life extension of nuclear power plants. Hence, in this research work, an EPR/CSPE-based low-voltage cable was exposed to γ-rays for five different absorbed doses. The overall behavior of the cable under stress was characterized by frequency and time domain electrical measurements (capacitance, tan δ, and Extended Voltage Response) and a mechanical measurement (elongation at break). Significant variations in the electrical parameters were observed, as was a decline in the elongation at break values. A strong correlation between the measurement methods was observed, showing the ability of the electrical methods to be adopted as a non-destructive condition monitoring technique.

Advancements in wind turbine condition monitoring systems over the last decade have made it possible to optimise operational performance and reduce costs associated with component failure and other unplanned maintenance activities. While much research focuses on providing more automated and accurate fault diagnostics and prognostics in relation to predictive maintenance, efforts to quantify the impact of such strategies have to date been comparatively limited. Through time-based simulation of wind farm operation, this paper quantifies the cost benefits associated with predictive and condition-based maintenance strategies, taking into consideration both direct O&M costs and lost production. Predictive and condition-based strategies have been modelled by adjusting known component failure and repair rates associated with a more reactive approach to maintenance. Results indicate that up to 8% of direct O&M costs can be saved through early intervention along with up to 11% reduction in lost... [more]

The emergence of smart sensors has had a significant impact on the utility industry. In particular, it has made the planning and implementation of demand-side management (DSM) programmes easier. Nevertheless, for various reasons, some users may not implement smart meters for load monitoring. This paper addresses such cases, particularly large-scale industrial users, which, despite heavy electrical loads coming from many different processes, implement only simple energy measuring equipment for billing purposes. This necessitates the utilisation of novel methodologies for load disaggregation, often referred to as nonintrusive load monitoring (NILM). The availability of such tools can create multifold benefits for industrial park management, utility service providers, regulators, and policymakers. Here, we introduce an optimisation algorithm for nonintrusive load disaggregation that is low-cost, speedy, and acceptably accurate. As a case study, we used real network data of three industria... [more]

Because the scope of risk assessments at nuclear power plants (NPPs) is being extended both spatially and temporally, conventional, or static fault trees might not be able to express failure mechanisms, or they could be unnecessarily conservative in their expression. Therefore, realistic assessment techniques are needed to adequately capture accident scenarios. In multi-unit probabilistic safety assessment (PSA), fault trees naturally become more complex as the number of units increases. In particular, when considering a shared facility between units of the electric power system (EPS), static fault trees (SFTs) that prioritize a specific unit are limited in implementing interactions between units. However, dynamic fault trees (DFTs) can be available without this limitation by using dynamic gates. Therefore, this study implements SFTs and DFTs for an EPS of two virtual NPPs and compares their results. In addition, to demonstrate the dynamic characteristics of the shared facilities, a st... [more]

The possibility to monitor and evaluate power system stability in real-time is in growing demand. Whilst most stability-related studies focus on long-term voltage stability and frequency stability, very little attention is given to the issue of short-term (voltage) instability. In this paper, the most common evaluation methods present in the literature are summarized, with a focus on their applicability to modern power systems with a large amount of renewable energy integration. The paper presents a first-of-a-kind structured review of this topic. We find that all existing methods have noteworthy limitations that necessitate further improvements. Additionally, the need of having an inclusive short-term instability prediction method is demonstrated, due to strong interactions between various short-term instability mechanisms. These findings provide a good foundation for further research and advancement in the field of real-time stability monitoring.

A wide range of multimedia applications must be supported by the modern fifth generation (5G) wireless communication systems for realizing the diverse applications in smart cities. The diverse applications such as real-time monitoring of roads, smart homes, smart industries, etc., for a sustainable smart city emphasizes a robust and efficient image transmission. In this paper, the influence of maximal ratio combining (MRC) on the reception of images with different orthogonal frequency division multiplexing (OFDM) versions is studied. The different OFDM versions considered here are the fast Fourier transform (FFT) based OFDM and discrete cosine transform (DCT) based OFDM. A comparison between diverse modulation levels for the images transmitted through different OFDM methodologies, along with variation in a number of receiving antennas for MRC, is proposed for additive white gaussian noise (AWGN) and Rayleigh fading channels. The diverse modulation levels used are binary phase shift key... [more]

Home energy monitoring has recently become a very important issue and a means to reduce energy consumption in the residential sector. Sensors and control systems are deployed at various locations in a house and an intelligent system is used to efficiently manage the consumed energy. Low power communication systems are used to provide low power consumption from a smart meter. Several of these systems are battery operated. Other systems use AC/DC adapters to supply power to sensors and communication systems. However, even using low-power technology, such as ZigBee, the power consumption of a router can be high because it must always be powered on. In this work, to evaluate power consumption, a system for monitoring energy usage and indoor air quality was developed. A technique is proposed to efficiently supply power to the components of the system. All sensor nodes are battery operated, and relays are used to control the battery charging process. In addition, an energy harvesting system... [more]

Cities and nations worldwide are pledging to energy and carbon neutral objectives that imply a huge contribution from buildings. High-performance targets, either zero energy or zero carbon, are typically difficult to be reached by single buildings, but groups of properly-managed buildings might reach these ambitious goals. For this purpose we need tools and experiences to model, monitor, manage and optimize buildings and their neighborhood-level systems. The paper describes the activities pursued for the deployment of an advanced energy management system for a multi-carrier energy grid of an existing neighborhood in the area of Milan. The activities included: (i) development of a detailed monitoring plan, (ii) deployment of the monitoring plan, (iii) development of a virtual model of the neighborhood and simulation of the energy performance. Comparisons against early-stage energy monitoring data proved promising and the generation system showed high efficiency (EER equal to 5.84), to b... [more]

In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate several sources of distortions often observed in PRPDs obtained from fully operational hydroelectric generators. The capabilities of the denoising algorithm are the automatic removal of sparse noise and the suppression of non-dominant discharges, including those due to crosstalk. The input features are functions of PD distributions along amplitude and phase, which are calculated in a novel way to mitigate random effects inherent to PD measurements. The impact of the proposed contributions was statistically evaluated and compared to classification performance obtained using formerly published approaches. Higher recognition rates and reduced variances were obtained using... [more]

It is popular that correlated seismic failures spread over the fault tree of a seismic probabilistic safety assessment (PSA) for a nuclear power plant (NPP). To avoid the calculational difficulty of core damage frequency (CDF), the fault tree has been simplified by replacing correlated seismic failures with one typical seismic failure by assuming a full correlation among the correlated seismic failures. Then, the approximate seismic CDF of a seismic single-unit PSA (SUPSA) has been calculated for decades with this simplified SUPSA fault tree. Furthermore, current seismic multi-unit PSAs (MUPSAs) have been performed with imperfect seismic MUPSA models that were generated by combining such imperfect seismic SUPSA fault trees. The authors of this study recently developed a method that can calculate an accurate seismic CDF by converting correlated seismic failures into seismic common cause failures (CCFs). In this study, accurate and imperfect MUPSA models were created and their seismic CD... [more]

A non-intrusive load monitoring (NILM) process is intended to allow for the separation of individual appliances from an aggregated energy reading in order to estimate the operation of individual loads. In the past, electricity meters specified only active power readings, for billing purposes, thus limiting NILM capabilities. Recent progress in smart metering technology has introduced cost-effective, household-consumer-grade metering products, which can produce multiple features with high accuracy. In this paper, a new method is proposed for applying a BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm as part of a multi-dimensional load disaggregation solution based on the extraction of multiple features from a smart meter. The method uses low-frequency meter reading and constructs a multi-dimensional feature space with adaption to smart meter parameters and is useful for type I as well as type II loads with the addition of timers. This new method is describ... [more]

At present, the non-intrusive load decomposition method for low-frequency sampling data is as yet insufficient within the context of generalization performance, failing to meet the decomposition accuracy requirements when applied to novel scenarios. To address this issue, a non-intrusive load decomposition method based on instance-batch normalization network is proposed. This method uses an encoder-decoder structure with attention mechanism, in which skip connections are introduced at the corresponding layers of the encoder and decoder. In this way, the decoder can reconstruct a more accurate power sequence of the target. The proposed model was tested on two public datasets, REDD and UKDALE, and the performance was compared with mainstream algorithms. The results show that the F1 score was higher by an average of 18.4 when compared with mainstream algorithms. Additionally, the mean absolute error reduced by an average of 25%, and the root mean square error was reduced by an average of... [more]

A critical survey has been conducted on high energy-efficient bidirectional converters, various topologies that effectively meet the automated vehicle requirements, and 24 GHz/77 GHz low-profile antennas (for automotive radar applications). The present survey has been identified into two parts on the current topic of study as perspectives and challenges. Part 1 of this survey covers energy-efficient power electronic convertor topologies and condition monitoring aspects of convertors to enhance the lifespan and improve performance. Condition-monitoring issues concerning the abnormalities of electrical components, high switching frequencies, electromagnetic interference, leakage currents, and unwanted joint ruptures have also been emphasized. It is observed that composite converters are proficient for automated hybrid electric vehicles due to fast dynamic response and reduced component count. Importantly, electrical component failures in power electronic converters are most common and ne... [more]

A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell transform. The extracted features aim to describe the shape of the current transient signal by applying an energy measure on the fundamental and the harmonic frequency voices. In order to validate the proposed methodology, classical machine learning tools are applied (k-NN and decision tree classifiers) on two existing datasets (Controlled On/Off Loads Library (COOLL) and Home Equipment Laboratory Dataset (HELD1)). The classification rates achieved are clearly higher than that for other related studies in the literature, with 99.52% and 96.92% classification rates for the COOLL and HELD1 datasets, respectively.

Integration of a large number of renewable systems produces line congestions, resulting in a problem for distribution companies, since the lines are not capable of transporting all the energy that is generated. Both environmental and economic constraints do not allow the building new lines to manage the energy from renewable sources, so the efforts have to focus on the existing facilities. Dynamic Rating Management (DRM) of power lines is one of the best options to achieve an increase in the capacity of the lines. The practical application of DRM, based on standards IEEE (Std.738, 2012) and CIGRE TB601 (Technical Brochure 601, 2014) , allows to find several deficiencies related to errors in estimations. These errors encourage the design of a procedure to obtain high accuracy ampacity values. In the case of this paper, two methodologies have been tested to reduce estimation errors. Both methodologies use the variation of the weather inputs. It is demonstrated that a reduction of the con... [more]

The probabilistic safety assessment (PSA) of a nuclear power plant (NPP) under single and multiple hazards is one of the most important tasks for disaster risk management of nuclear facilities. To date, various approaches—including the direct quantification of the fault tree using the Monte Carlo simulation (DQFM) method—have been employed to quantify single- and multi-hazard risks to nuclear facilities. The major advantage of the DQFM method is its applicability to a partially correlated system. Other methods can represent only an independent or a fully correlated system, but DQFM can quantify the risk of partially correlated system components by the sampling process. However, as a sampling-based approach, DQFM involves computational costs which increase as the size of the system and the number of hazards increase. Therefore, to improve the computational efficiency of the conventional DQFM, a two-stage DQFM method is proposed in this paper. By assigning enough samples to each hazard p... [more]

Monte Carlo simulations are widely used for uncertainty analysis in the probabilistic safety assessment of nuclear power plants. Despite many advantages, such as its general applicability, a Monte Carlo simulation has inherent limitations as a simulation-based approach. This study provides a mathematical formulation and analytic solutions for the uncertainty analysis in a probabilistic safety assessment (PSA). Starting from the definitions of variables, mathematical equations are derived for synthesizing probability density functions for logical AND, logical OR, and logical OR with rare event approximation of two independent events. The equations can be applied consecutively when there exist more than two events. For fail-to-run failures, the probability density function for the unavailability has the same probability distribution as the probability density function (PDF) for the failure rate under specified conditions. The effectiveness of the analytic solutions is demonstrated by app... [more]

In order to reduce the electricity consumption in our homes, a first step is to make the user aware of it. Raising such awareness, however, demands to pinpoint users of specific appliances that unnecessarily consume electricity. A retrofittable and scalable way to provide appliance-specific consumption is provided by Non-Intrusive Load Monitoring methods. These methods use a single electricity meter to record the aggregated consumption of all appliances and disaggregate it into the consumption of each individual appliance using advanced algorithms usually utilizing machine-learning approaches. Since these approaches are often supervised, labelled ground-truth data need to be collected in advance. Labeling on-phases of devices is already a tedious process, but, if further information about internal device states is required (e.g., intensity of an HVAC), manual post-processing quickly becomes infeasible. We propose a novel data collection and labeling framework for Non-Intrusive Load Mon... [more]

This paper consists of two parts: an overview of existing open datasets of electricity consumption and a description of the Moroccan Buildings’ Electricity Consumption Dataset, a first of its kind, coined as MORED. The new dataset comprises electricity consumption data of various Moroccan premises. Unlike existing datasets, MORED provides three main data components: whole premises (WP) electricity consumption, individual load (IL) ground-truth consumption, and fully labeled IL signatures, from affluent and disadvantaged neighborhoods. The WP consumption data were acquired at low rates (1/5 or 1/10 samples/s) from 12 households; the IL ground-truth data were acquired at similar rates from five households for extended durations; and IL signature data were acquired at high and low rates (50 k and 4 samples/s) from 37 different residential and industrial loads. In addition, the dataset encompasses non-intrusive load monitoring (NILM) metadata.

The monitoring of drilling processes is a well-known topic in the mining industry. It is widely used for rock mass characterization, bit wear monitoring and drilling process assessment. However on-board monitoring systems used for this purpose are installed only on a limited number of machines, and breakdowns are possible. There is a need for a data acquisition system that can be used on different drilling rigs and for an automatic data analysis procedure. In this paper, we focused on the automatic detection of drilling cycles, presenting a simple yet reliable system to be universally installed on drilling rigs. The proposed solution covers hardware and software. It is based on the measurement of electric current and acoustic signals. The signal processing methods include threshold-based segmentation, a short-time envelope spectrum and a spectrum for the representation of results. The results of the research have been verified on a real drilling rig within the testing site of its manuf... [more]

In this study, a new monitoring method was developed, titled infrared thermal imaging technology, which can effectively evaluate the thermal effect of the charge-discharge test in the vanadium/iodine redox flow battery (V/I RFB). The results show that the all-vanadium redox flow battery (all-V RFB) has a greater molar reaction Gibbs free energy change than that of the V/I RFB, representing a large thermal effect of the all-V RFB than the V/I RFB. The charge-discharge parameters, flow rate and current density, are important factors for inducing the thermal effect, because of the concentration polarization and the ohmic resistor. The new membrane (HS-SO3H) shows a high ion exchange capacity and a good ions crossover inhibitory for the V/I RFB system, and has a high coulomb efficiency that reaches 96%. The voltage efficiency was enhanced from 61% to 86% using the C-TiO2-Pd composite electrode as a cathode with the serpentine-type flow field for the V/I RFB. By adopting the high-resolution... [more]

High voltage direct current (HVDC) transmission systems are suitable for power transfer to meet the increasing demands of bulk energy and encourage interconnected power systems to incorporate renewable energy sources without any fear of loss of synchronism, reliability, and efficiency. The main challenge associated with DC grid protection is the timely diagnosis of DC faults because of its rapid built up, resulting in failures of power electronic circuitries. Therefore, the demolition of HVDC systems is evaded by identification, classification, and location of DC faults within milliseconds (ms). In this research, the support vector machine (SVM)-based protection algorithm is developed so that DC faults could be identified, classified, and located in multi-terminal high voltage direct current (MT-HVDC) systems. A four-terminal HVDC system is developed in Matlab/Simulink for the analysis of DC voltages and currents. Pole to ground and pole to pole faults are applied at different location... [more]

Measuring the void fraction of different multiphase flows in various fields such as gas, oil, chemical, and petrochemical industries is very important. Various methods exist for this purpose. Among these methods, the capacitive sensor has been widely used. The thing that affects the performance of capacitance sensors is fluid properties. For instance, density, pressure, and temperature can cause vast errors in the measurement of the void fraction. A routine calibration, which is very grueling, is one approach to tackling this issue. In the present investigation, an artificial neural network (ANN) was modeled to measure the gas percentage of a two-phase flow regardless of the liquid phase type and changes, without having to recalibrate. For this goal, a new combined capacitance-based sensor was designed. This combined sensor was simulated with COMSOL Multiphysics software. Five different liquids were simulated: oil, gasoil, gasoline, crude oil, and water. To estimate the gas percentage... [more]