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]

According to the United Nation’s World Water Development Report, by 2050 more than 50% of the world’s population will be under high water scarcity. To avoid water stress, water resources are needed to be managed more securely. Smart water technology (SWT) has evolved for proper management and saving of water resources. Smart water system (SWS) uses sensor, information, and communication technology (ICT) to provide real-time monitoring of data such as pressure, water ow, water quality, moisture, etc. with the capability to detect any abnormalities such as non-revenue water (NRW) losses, water contamination in the water distribution system (WDS). It makes water and energy utilization more efficient in the water treatment plant and agriculture. In addition, the standardization of data format i.e., use of Water Mark UP language 2.0 has made data exchange easier for between different water authorities. This review research exhibits the current state-of-the-art of the on-going SWT along with... [more]

In this paper, remote error monitoring techniques for electricity meters are overviewed suggesting their utilization for in-service surveillance assistance. It is discussed that in-service error observation could provide valuable input, contributing to the timely detection of batches of meters reaching nonconformance status. The payback period analysis of the deployment of a remote error monitoring solution is considered. However, it is pointed out that such an analysis lacks input information describing the relationship between the remote monitoring system’s performance and its ability to detect nonconformance of the batch. It is also noticed that there is no published methodology for grading the status of an entire batch of meters referring to error estimates of a subset of the meters, when the uncertainty of estimation is rather high.

The installation of smart meters at customer premises provides opportunities for the monitoring of distribution grids. This paper addresses the problem of improving the observability of low-voltage distribution grids using smart metering infrastructure. In particular, this paper deals with the application of state estimation algorithm using smart meter measurements for near-real-time monitoring of low-voltage distribution grids. This application is proposed to use a nonlinear weighted least squares method-based algorithm for estimating the node voltages from minimum number of smart meter measurements. This paper mainly deals with sensitivity analysis of the state estimation algorithm with respect to multiple uncertainties for, e.g., measurements errors, line parameter errors, and pseudo-measurements. Simulation studies are conducted to estimate the accuracy of the DSSE under various operating scenarios of a real-life low-voltage grid, and cost-effective ways to improve the accuracy of... [more]

The sustainable development of an organisation requires a holistic approach to the evaluation of an enterprise’s goals and activities. The essential means enabling an organisation to achieve goals are business processes. Properly managed, business processes are a source of revenue and become an implementation of business strategy. The critical elements in process management in an enterprise are process monitoring and control. It is therefore essential to identify the Key Performance Indicators (KPIs) that are relevant to the analysed processes. Process monitoring can be performed at various levels of management, as well as from different perspectives: operational, financial, security, or maintenance. Some of the indicators known from other fields (such as personnel management, finance, or lean manufacturing) can be used in mining. However, the operational mining processes require a definition of specific indicators, especially in the context of increasing the productivity of mining mac... [more]

This research work proposes a novel approach to estimate probabilities of availability states of the energy transfer network in marine energy conversion subsystems, using Bayesian Networks (BNs). The logical interrelationships between units at different level in this network can be understood through qualitative system analysis, which then can be modeled by the fault tree (FT). The FT can be mapped to a corresponding BN, and the condition probabilities of nodes can be determined based on the logic structure. A case study was performed to demonstrate how the mapping is implemented, and the probabilities of availability states were estimated. The results give the probability of each availability state as a function of time, which serves as a basis for choosing the optimal design solution.

In high-voltage equipment, it is vital to detect any failure in advance. To do this, a determination of the partial discharges occurring at different voltage types as well as at different electrode configurations is essential for observing the oil condition. In this study, an experimental setup consisting of a needle−semi-sphere electrode configuration immersed in mineral oil is prepared for laboratory experiment. In such a way, a non-uniform electric field is created and the leakage currents are monitored from the grounded electrode. A total of six different electrode configurations are analyzed during the tests by the use of hemispheres of different diameters as grounded electrodes and copper and steel pointed (medical) needle high-voltage electrodes. In the experiments, the partial discharges occurring at four different voltage levels between 5.4 and 10.8 kV are measured and recorded. The effect of the different electrode configurations and voltage levels on the harmonic distortion... [more]

Partial shading conditions of photovoltaic (PV) modules often occurs in urban areas leading to losses in electricity power generation of the PV power plant. The purpose of this study is to present how the PV power plant with high value of inverter power sizing factor (Kinv) can achieve high performance and power production under partial shading conditions with high shading losses. In this paper the results of long-term monitoring, performance analysis and experimental results are presented, while the results are compared to the estimated values calculated using PVsyst software. The study focused on the PV power plant at the Faculty of Technical Sciences (FTS) in Novi Sad, Republic of Serbia, for the period between the years 2012 and 2019. It has been shown that the values of PV power plant performance parameters are better than expected (very high), and resemble the power plants operating without shading. The high value of the inverter power sizing factor may lead to occasional saturat... [more]

The objective of this research was to explore correlates and predictors that play a role in the process of adopting and withdrawing from using a smart metering information platform (SMP). The SMP supports energy monitoring behaviors of the electricity consumers. The literature review shows, however, that not every customer is ready to the same extent to adopt novel solutions. Adoption requires going through stages of readiness to monitor energy consumption in a household. In a longitudinal field experiment on Polish residential consumers, we aimed to see whether messages congruent with the stage of readiness in which participants declared to be at a given moment will be more effective in prompting participants to progress to the next stage than a general message or a passive control condition. We also tested the effect of attitude and knowledge about energy monitoring on phase changes. Our study reveals that what affects the phase change is the participation in the study. The longer th... [more]

Already installed cables are aging and the cable network is growing rapidly. Improved condition monitoring methods are required for greater visibility of insulation defects in the cable networks. One of the critical challenges for continuous monitoring is the large amount of partial discharge (PD) data that poses constraints on the diagnostic capabilities. This paper presents the performance comparison of two data acquisition techniques based on phase resolved partial discharge (PRPD) and pulse acquisition (PA). The major contribution of this work is to provide an in-depth understanding of these techniques considering the perspective of randomness of the PD mechanism and improvements in the reliability of diagnostics. Experimental study is performed on the medium voltage (MV) cables in the laboratory environment. It has been observed that PRPD based acquisition not only requires a significantly larger amount of data but is also susceptible to losing the important information especially... [more]

The existing fault location methods for all-parallel autotransformer railway systems (AARS) have limitations because they are generally designed for several given feeding conditions. In alternate feeding conditions, the existing fault location methods do not work well and may have large errors. To solve this problem, we have proposed a generalized fault location scheme for AARS in this paper. After analyzing the fault characteristics of AARS, we classified the feeding conditions of the faulted section of AARS into three types and introduced the corresponding fault location methods. In order to identify the faulted section and its feeding condition, we first formed a switch state matrix based on the adjacency matrix and mapped the fault current distribution into a current state matrix, then we unified the two matrices into a fault state matrix to reflect the fault state of the AARS. Finally, a generalized fault location scheme was proposed based on a fault state matrix. The proposed sch... [more]

The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only a few contributions to current-based pump diagnosis. In this paper, two current-based methods for the detection of bearing defects, impeller clogging, and cracked impellers are presented. The first approach, load point-dependent fault indicator analysis (LoPoFIA), is an approach that was derived from motor current signature analysis (MCSA). Compared to MCSA, the novelty of LoPoFIA is that only amplitudes at typical fault frequencies in the current spectrum are considered as a function of the hydraulic load point. The second approach is advanced transient current signature analysis (ATCSA), which represents a time-frequency analysis of a current signal during start-up. According to the literature, ATCSA is mainl... [more]

In this paper, we present a low-cost health assessment system for oil-immersed service transformers using a monitoring device to measure energy in real time. By assessing the important level of transformer components, three indicators, top oil temperature, vibration, and transformer load, were selected as main indicators to investigate the service transformer’s condition. An evaluation system using Fuzzy logic method is also presented in the paper to support monitor transformer health without adding the extra cost of installing expensive sensors. Different testing scenarios with different case studies were carried out on a simulated 50 kVA oil-immersed service transformer to express the feasibility and effectiveness of this low-cost, fast response health assessment system.

Energy monitoring is a vast field of research [...]

Borehole cataloguing is an important task in geological drilling. Traditional manual cataloguing provides the stratification of underground boreholes based on changes in core lithology. This paper proposes a novel borehole cataloguing method using a drilling process monitoring (DPM) system. This DPM cataloguing method stratifies a borehole according to the drilling speed through the rock. A 102 m borehole was drilled and cored in Baota district, Yan’an city, Shaanxi Province, China. The rock-breaking response parameters of the drill bit displacement, drill rod rotation speed and inlet pipe and outlet pipe oil pressures were monitored throughout the drilling process, and the drilling depth-penetration rate curve during the net drilling process was obtained. The changes in drilling speed show that the DPM cataloguing can identify the depths of the layer interfaces of the borehole and describe the stratification. The interface depth values obtained by DPM have little difference from the i... [more]

This special issue is devoted to new developments in measurement technologies for upstream and downstream bioprocessing [...]

Partial least squares (PLS) and linear regression methods are widely utilized for quality-related fault detection in industrial processes. Standard PLS decomposes the process variables into principal and residual parts. However, as the principal part still contains many components unrelated to quality, if these components were not removed it could cause many false alarms. Besides, although these components do not affect product quality, they have a great impact on process safety and information about other faults. Removing and discarding these components will lead to a reduction in the detection rate of faults, unrelated to quality. To overcome the drawbacks of Standard PLS, a novel method, MI-PLS (mutual information PLS), is proposed in this paper. The proposed MI-PLS algorithm utilizes mutual information to divide the process variables into selected and residual components, and then uses singular value decomposition (SVD) to further decompose the selected part into quality-related an... [more]