As the technology is emerging, the process industries are actively migrating to Industry 4.0 to optimize energy, production, profit, and the quality of products. It should be noted that real-time process monitoring is the area where most of the energies are being placed for the sake of optimization and safety. Big data and knowledge-based platforms are receiving much attention to provide a comprehensive decision support system. In this study, the Advanced Advisory system for Anomalies (AAA) is developed to predict and detect the abnormal operation in fired heaters for real-time process safety and optimization in a petrochemical plant. This system predicts and raises an alarm for future problems and detects and diagnoses abnormal conditions using root cause analysis (RCA), using the combination of FMEA (failure mode and effects analysis) and FTA (fault tree analysis) techniques. The developed AAA system has been integrated with databases in a petrochemical plant, and the results have be... [more]

This paper applies a voltage instability monitoring method based on voltage and current measurements from a transmission bus PMU on the Hellenic Interconnected System using both unstable and marginally stable scenarios, derived from the historical 12 July 2004 blackout of the Athens area. The effectiveness, selectivity and reliability of the proposed monitoring method is clearly demonstrated, allowing its integration into a System Protection Scheme with direct load shedding. It is shown that the proposed instability detection and control scheme could have prevented the voltage collapse if applied at the time of the event.

The main goal of this paper is to review and evaluate how we can take advantage of state-of-the-art machine learning techniques and apply them in wind energy operation conditions monitoring and fault diagnosis, boosting wind turbines’ availability. To accomplish this, we focus our work on analysing the current techniques in predictive maintenance, which are aimed at acting before a major failure occurs using condition monitoring. In particular, we start framing the predictive maintenance problem as an ML problem to detect patterns that indicate a fault on turbine generators. Then, we extend the problem to detect future faults. Therefore, this review will consist of analysing techniques to tackle the challenges of each machine learning stage, such as data pre-processing, feature engineering, and the selection of the best-suited model. By using specific evaluation metrics, the expected final result of using these techniques will be an improvement in the early prediction of a future fault... [more]

Power system operators are confronted with a multitude of new forecasting tasks to ensure a constant supply security despite the decreasing number of fully controllable energy producers. With this paper, we aim to facilitate the selection of suitable forecasting approaches for the load forecasting problem. First, we provide a classification of load forecasting cases in two dimensions: temporal and hierarchical. Then, we identify typical features and models for forecasting and compare their applicability in a structured manner depending on six previously defined cases. These models are compared against real data in terms of their computational effort and accuracy during development and testing. From this comparative analysis, we derive a generic guide for the selection of the best prediction models and features per case.

This paper presents a comprehensive approach for performing phasor and frequency estimation from voltage and/or current signals of the modern power system. Undesirable components, such as decaying DC, if present in the input signal, are first attenuated using a complex-gain filter. The initial estimates of phasor and frequency are obtained next using the discrete Fourier transform and an improved estimation of signal parameters via rotational invariance technique, respectively. Finally, the accuracy of phasor and frequency estimates are increased based on the identified system condition. Simulations performed to evaluate the proposed approach confirm that it can do fast and accurate estimation of phasor and frequency under diverse operating conditions, making it ideal for wide-area monitoring, protection, and control applications in power systems.

Spontaneous imbibition is an important mechanism in naturally fractured reservoirs. In our previous studies on the effect of imbibition efficiency of ultra-low permeability reservoirs, we mostly focused on the relationship between macroscopic core recovery rate and influential factors. Additionally, we also mainly focused on the factors that control the final imbibition recovery for ultra-low permeability reservoirs. Through a large number of experiments, it was found that the factors affecting imbibition are different in separate stages. However, the relative importance of those factors in different imbibition stages was hardly studied. In this work, we tested six key factors, i.e., the core length, RQI, salinity, interfacial characteristics, initial oil saturation, and oil viscosity, in natural sandstone samples from Chang 6 in the Zichang area. Based on experimental results, we divided the imbibition process into three stages (i.e., the early stage, the middle stage, and the late st... [more]

A two-fold adaptive dynamic quadrilateral relay is developed in this research for protecting Thyristor-Controlled Series Compensator (TCSC)-compensated transmission lines (TLs). By investigating a new tilt angle and modifying the Takagi method to recognize the fault zone identifier, the proposed relay adapts its reactive reach and resistive reach separately and independently. The investigated tilt angle and identified fault zone use the TCSC reactance to compensate its effect on the TL parameters and system homogeneity. Excessive tests are simulated by MATLAB on the non-homogenous network, IEEE-9 bus system and further tests are carried out on IEEE-39 bus system in order to generalize and validate the efficiency of the proposed approach. The designed trip boundaries are able to detect wide range of resistive faults under all TCSC modes of operations. The proposed approach is easy to implement as there no need for data synchronization or a high level of computation and filtration. Moreo... [more]

A daylighting control system that uses the closed loop proportional algorithm needs to be calibrated both during nighttime and daytime. The selection of the daytime calibration time can affect the behavior of the system and is usually performed when the ratio of the illuminance of the ceiling sensor (SD) to the illuminance at a point on the working plane (ED) is relatively large without sunlight patches in the ceiling photo-sensor’s field of view (FOV). However, this requirement is not associated with a specific value and can be achieved under a wide range of conditions related to the sky luminance distribution. In the present work, four ceiling sensors with different field of views (FOVs) were examined in a typical north-facing office space. The effect of daytime calibration on the system’s performance was estimated through the calculation of lighting energy savings and the overdimming percentage. The results show that the effect of both the FOV of the ceiling sensor and the daytime c... [more]

Two design flows of the Petri net-based cyber-physical systems oriented towards implementation in an FPGA are presented in the paper. The first method is based on the behavioural description of the system. The control part of the cyber-physical system is specified by an interpreted Petri net, and is described directly in the synthesisable Verilog hardware language for further implementation in the programmable device. The second technique involves splitting the design into sequential modules. In particular, adequate decomposition and synchronisation algorithms are proposed. The resulting modules are further modelled within the Verilog language as the composition of sequential automata. The presented design flows are supported by theoretical background, and templates of Verilog codes. The proposed techniques are illustrated by a real-life example of a multi-robot cyber-physical system, where each step of the proposed flows is explained in detail, including modelling, description of the... [more]

The paper analyses how the characteristics of the medium-voltage network with insulated neutral and the conditions under which the single line-to-ground fault occurs (insulation condition and value of the fault resistance) influence the zero-sequence components of the voltage of the transformer station medium-voltage bus bar, the fault current, the currents of the faulted line and the currents of the other non-faulted power lines connected to the transformer station bus bars. Assuming the waveform of the medium voltage bus voltages in the transformer station is known, the influence of the fault resistance on the total distortion coefficient of the fault current is analysed. This establishes the conditions under which a single line-to-ground fault can be detected by controlling the total distortion coefficient of the fault current and the currents of the fault-free lines. It also examines the conditions under which a single line-to-ground fault can be selectively detected by checking th... [more]

Electric motors are used extensively in numerous industries, and their failure can result not only in machine damage but also a slew of other issues, such as financial loss, injuries, etc. As a result, there is a significant scope to use robust fault diagnosis technology. In recent years, interesting research results on fault diagnosis for electric motors have been documented. Deep learning in the fault detection of electric equipment has shown comparatively better results than traditional approaches because of its more powerful and sophisticated feature extraction capabilities. This paper covers four traditional types of deep learning models: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), and recurrent neural networks (RNN), and highlights their use in detecting faults of electric motors. Finally, the issues and obstacles that deep learning encounters in the fault detection mechanism as well as the prospects are discussed and summarized.

High temperature smoke caused by fire is a major cause of casualties. In order to ensure the safety of personnel, it is very important to control the spread of smoke and enable personnel to quickly withdraw from the fire scene. While traditional hard isolation, such as fire doors, may hinder the safe evacuation of people, the use of an air curtain as a flexible isolation has received more and more attention from researchers. In this paper, the influence of jet direction of compound air curtain on the smoke control effect was studied, and six working conditions were designed. The temperature and smoke isolation of the compound air curtain were numerically simulated by using ANSYS FLUENT software. The parameters such as temperature, velocity pressure and velocity streamline were analyzed, and the smoke control effects of six different jet directions were discussed. The simulation results were verified by Pyrosim fire simulation software simulation software. The results show that the dire... [more]

Microgrid (MG) is a novel concept for a future distribution power system that enables renewable energy sources (RES). The intermittent RES, such as wind turbines and photovoltaic generators, can be connected to the MG via a power electronics inverter. However, the inverter interfaced RESs reduce the total inertia and damping properties of the traditional MG. Consequently, the system exhibits steeper frequency nadir and the rate of change of frequency (RoCoF), which may degrade the dynamic performance and cause the severe frequency fluctuation of the system. Smart loads such as inverter air conditioners (IACs) tend to be used for ancillary services in power systems. The power consumption of IACs can be regulated to suppress frequency fluctuation. Nevertheless, these IACs, regulating power, can cause the deviation of indoor temperature from the temperature setting. The variation in indoor temperature should be controlled to fulfill residential comfort. This paper proposes a multi-objecti... [more]

In the literature, different authors attribute between 15% to 30% of a wind farm’s costs to logistics during the installation, e.g., for vessels or personnel. Currently, there exist only a few approaches for crew scheduling in the offshore area. However, current approaches only satisfy subsets of the offshore construction area’s specific terms and conditions. This article first presents a literature review to identify different constraints imposed on crew scheduling for offshore installations. Afterward, it presents a new Mixed-Integer Linear Model that satisfies these crew scheduling constraints and couples it with a scheduling approach using a Model Predictive Control scheme to include weather dynamics. The evaluation of this model shows reliable scheduling of persons/teams given weather-dependent operations. Compared to a conventionally assumed full staffing of vessels and the port, the model decreases the required crews by approximately 50%. Moreover, the proposed model shows good... [more]

Performance degradation is, in general, regarded as a power quality problem. One solution to recover grid performance is through the application of a unified power quality conditioner (UPQC). Although these devices are multi-input/multi-output (MIMO) systems, the most common control strategies consist of two decoupled controllers, which neglect the coupling effects and add uncertainty to the system. For this reason, this paper proposes a multivariable resonant observer-based control strategy of a UPQC system. This method includes all significant coupling effects between this system and the grid. This strategy results in a stability-based compensator, which differs from recently proposed strategies that are based on signal calculation and cannot assure closed-loop stability. In addition, this paper introduces a simplified controller tuning strategy based on optimal conventional methods without losing closed-loop performance. It implies that the controller can be easily tuned, despite th... [more]

The reduction of unwanted heat losses across the buildings’ envelope is very relevant to increase energy efficiency and achieve the decarbonization goals for the building stock. Two major heat transfer mechanisms across the building envelope are conduction and radiation, being this last one very important whenever there is an air cavity. In this work, the use of aerogel thermal break (TB) strips and aluminium reflective (AR) foils are experimentally assessed to evaluate the thermal performance improvement of double-pane lightweight steel-framed (LSF) walls. The face-to-face thermal resistances were measured under laboratory-controlled conditions for sixteen LSF wall configurations. The reliability of the measurements was double-checked making use of a homogeneous XPS single panel, as well as several non-homogeneous double-pane LSF walls. The measurements allowed us to conclude that the effectiveness of the AR foil is greater than the aerogel TB strips. In fact, using an AR foil inside... [more]

When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been established based on the Random Walk Improved Sparrow Search Algorithm to optimize auto-associative neural network (RWSSA-AANN), and is used for fault diagnosis of wind turbine group anemometers. Using the cluster analysis, six wind turbines are determined to be used as a wind turbine group. The 20,000 sets of normal historical data have been used for training and simulating of the model, and the single and multiple fault states of the anemometer are simulated. Using this model to analyze the wind speed supervisory control and data acquisition system (SCADA) data of six wind turbines in a wind farm from 2013 to 2017, can effectively diagnose the fault state and reconstruct the fault data. A... [more]

A major challenge in the development of multi-vendor HVDC networks are converter control interactions. While recent publications have reported interoperability issues such as persistent oscillations for first multi-vendor HVDC setups with AC-side coupling, multi-terminal HVDC networks are expected to face similar challenges. To investigate DC-side control interactions and mitigate possible interoperability issues, several methods based on the converters’ and DC network’s impedances have been proposed in literature. For DC network’s impedance modelling, most methods require detailed knowledge of all converters’ design and controls. However, in multi-vendor HVDC networks, converter control parameters are not expected to be shared due to proprietary reasons. Therefore, to facilitate impedance-based stability analyses in multi-vendor MTDC networks, methods that do not require the disclosure of the existing converter controls are needed. Here, detailed impedance measurements can be applied;... [more]

Real-time transient stability studies are based on voltage angle measures obtained with phasor measurement units (PMUs). A more precise calculation to address transient stability is obtained when using the rotor angles. However, these values are commonly estimated, which leads to possible errors. In this work, the kinetic energy changes in electric machines are used as a criterion for evaluating and correcting transient stability, and to determine the precise time of insertion of a special protection system (SPS). Data from the PMU of the wide-area measurement system (WAMS) are used to construct the SPS. Furthermore, it is assumed that a microcontroller can be located in each generation unit to obtain the synchronized angular velocity. Based on these measurements, the kinetic energy of the system and the respective control action are performed at the appropriate time. The results show that the proposed SPS effectively corrects the oscillations fast enough during the transient stability... [more]

In a lab-scale bioreactor system, (20 L of effective volume in our study) controlling a constant temperature inside bioreactor with a total volume 25 L is a simple process, whereas it is a complicated process in the actual full-scale system. There might exist a localized temperature difference inside the reactor, affecting bioenergy yield. In the present work, the temperature at the middle layer of bioreactor was controlled at 35 °C, while the temperature at top and bottom of bioreactor was controlled at 35 ± 0.1, ±1.5, ±3.0, and ±5.0 °C. The H2 yield of 1.50 mol H2/mol hexoseadded was achieved at ±0.1 and ±1.5 °C, while it dropped to 1.27 and 0.98 mol H2/mol hexoseadded at ±3.0 and ±5.0 °C, respectively, with an increased lactate production. Then, the reactor with automatic agitation speed control was operated. The agitation speed was 10 rpm (for 22 h) under small temperature difference (<±1.5 °C), while it increased to 100 rpm (for 2 h) when the temperature difference between top... [more]

Design change is a common but significant problem in construction projects. Issues of delay, cost overruns, claims, and disputes in projects occur as a result. However, design change studies in the power-project area are less often discussed. As a result, the primary objective of this study was to identify important cause factors of design changes according to different power-project types in Ghana. Following a thorough assessment of the literature, 36 potential causes were identified, which were narrowed down by expert reviews to 30. In this study, power projects were classified into three categories: power plant, renewable, and distribution and transmission. The results indicate owner-related financial problems as the most important cause of design change for all three project types, followed by the second and third most significant in each of the categories, respectively: errors and omission in design and problems or unforeseen site conditions in power plant projects; deficient qual... [more]

The critical infrastructure is constantly under cyber and physical threats. Applying security controls without guidance or traceability can create a false sense of security. Security standards facilitate security knowledge and control best practices in a more systematic way. However, the number of standards is continually increasing. Product providers that operate in multiple geographical regions often face the obligation to comply with multiple standards simultaneously. This introduces the problem of the convenient interpretation of different standards. Thus, a comprehensive analysis of the requirements from different security standards and guidelines applicable to the smart grid has been performed to detect similarities that can be shaped into entities of the conceptual model for requirement representation. The purpose of the model—presented in a form of a Unified Modeling Language (UML) class diagram—is to give product providers a canonical way to map requirements from arbitrary sta... [more]

The growing demand for air transportation has led to an increase in worldwide air traffic inefficiency due to capacity constraints. The impacts associated with this situation can be reduced through operational changes. To better handle the problem, the Single European Sky ATM Research (SESAR) and the Next Generation Air Transportation System (NextGen) program suggest Performance-Based Navigation (PBN) as a solution. The Area Navigation (RNAV) and Required Navigation Performance (RNP) approaches belong to the group of PBN procedures. These procedures allow for a more efficient use of airspace by reducing route distances, fuel consumption and perceived aircraft noise. This article quantifies the benefits of PBN systems for two indicator parameters—fuel burn and flight time—and compares PBN systems to conventional instrument navigation procedures. The case studies use five airports in Brazil. The results of this analysis show that the benefits of the PBN approach vary with aircraft type a... [more]

This paper is interested in implementing and controlling a modified six-phase induction motor (MSPIM) when fed from a three-phase supply either via an inverter or with a direct grid connection loaded by a centrifugal pump. The main aims of using the MSPIM are to enhance motor reliability and reduce torque pulsation. A three-to-six phase transformer has been designed, implemented, and employed to enable the SPIM to be driven from a three-phase supply. It is preferable to use the three-to-six phase transformers integrated with three-phase inverter on using the six-phase inverter to generate lower values of harmonics and lower steady-state error of speed and reduce the starting current and because also it isolates the primary circuit from the secondary, and the cost will be lower compared to the design of a special six-phase inverter. Dynamic models of SPIM, three-to-six phase transformer, and three-phase variable speed drive are derived. Then, a scalar (V/F) closed-loop control of SPIM i... [more]

An innovative dry SNCR method realized by a sorbent injection applied to a stoker furnace is presented. The process is based on urea powder admixed with halloysite, an aluminosilicate clay mineral. Field tests were performed at an industrial stoker hot water boiler of 30 MWth capacity. A unique nozzle design for injecting powdery sorbents into the combustion zone was implemented. The base NOx emission without SNCR was determined to be 365 mg/Nm3. During the reference test, the emission was reduced to avg. 175 mg/Nm3, which produces a NOx reduction of 52%. NH3 slip in the flue gas was stable and did not exceed 2 ppm. Combining urea and halloysite powders leads to a number of positive effects; not only is NOx emission reduced to values typical for wet SNCR, but also a significant, over ten-fold increase in the concentration of adsorbed mercury in fly ash was observed. When confronted with wet SNCR, dry SNCR has no adverse effect on boiler efficiency because it does not increase the stack... [more]