This work proposes the use of fuzzy-logic-based voltage frequency control (VFC) and adaptive inertia to improve the frequency response of a virtual synchronous generator (VSG)-based isolated microgrid system. The joint VFC and inertial control scheme is proposed to limit frequency deviations in these isolated microgrid systems, mainly caused by the increasing penetration of intermittent distributed energy resources, which lack rotational inertia. The proposed controller uses artificial neural networks (ANN) to estimate the exponent of voltage-dependent loads and modulate the system frequency by adjusting the output voltage of the VSGs, which increases the system’s active power reserves while providing inertial control by adjusting the inertia of VSGs to minimize frequency and VSG DC-link voltage excursions. A genetic algorithm (GA)-based optimization strategy is developed to optimally adjust the parameters of the fuzzy logic controller to diminish the impact of disturbances on the syst... [more]

Inadequate drill cuttings removal can cause costly problems such as excessive drag, lower rate of penetration, and even mechanical pipe sticking. Cuttings bed height is usually used to evaluate hole-cleaning efficiency in horizontal wells. In this study, artificial intelligence models, including artificial neural network (ANN), support vector regression (SVR), recurrent neural network (RNN), and long short-term memory (LSTM), were employed to predict cuttings bed height in the well-bore. A total of 136 different tests were conducted, and cuttings bed height under different conditions were measured in our previous study. By training four different artificial intelligence models with the experiment data, it was found that the ANN model performed best among other artificial intelligence models. The ANN model outperformed the dimensionless cuttings bed height model proposed in our previous study. Due to the amount of data points, the memory ability of RNN and LSTM models has not been entir... [more]

Data on transport usage is important in a wide range of areas. These data are often obtained manually through costly and inaccurate interviews. In the last decade, several researchers explored the use of smartphone sensors for the automatic detection of transport modes. However, such works have focused on developing centralized machine learning mechanisms. This centralized approach requires user data to be transferred to a central server and, therefore, does not satisfy a transport mode detection mechanism’s practical response time and privacy needs. This research presents the Federated System for Transport Mode Detection (FedTM). The main contribution of FedTM is exploring Federated Learning on transport mode detection using smartphone sensors. In FedTM, both the training and inference process is moved to the client side (smartphones), reducing response time and increasing privacy. The FedTM was designed using a Neural Network for the classification task and obtained an average accura... [more]

Transient stability (TS) and short-term voltage stability (STVS) assessment are of fundamental importance for the operation security of power systems. Both phenomena can be mutually influenced in weak power systems due to the proliferation of power electronic interface devices and the phase-out of conventional heavy machines (e.g., thermal power plants). There is little research on the assessment of both types of stability together, despite the fact that they develop over the same short-term period, and that they can have a major influence on the overall transient performance driven by large electrical disturbances (e.g., short circuits). This work addresses this open research challenge by proposing a methodology for the joint assessment of TS and STVS. The methodology aims at estimating the resulting short-term stability state (STSS) in stable, or unstable conditions, following critical events, such as the synchronism loss of synchronous generators (SG) or the stalling of induction mo... [more]

Three-dimensional Direct Numerical Simulations (DNS) data has been utilised to analyse statistical behaviours of the scalar dissipation rate (SDR) and its transport for homogeneous methane-air mixture turbulent Moderate or Intense Low oxygen Dilution (MILD) combustion for different O2 dilution levels and turbulence intensities for different reaction progress variable definitions. Additional DNS has been conducted for turbulent premixed flames and passive scalar mixing for the purpose of comparison with the SDR statistics of the homogeneous mixture MILD combustion with that in conventional premixed combustion and passive scalar mixing. It has been found that the peak mean value of the scalar dissipation rate decreases with decreasing O2 concentration for MILD combustion cases. Moreover, SDR magnitudes increase with increasing turbulence intensity for both MILD and conventional premixed combustion cases. The profiles and mean values of the scalar dissipation rate conditioned upon the rea... [more]

In this work, different analytical methods for calculating the mechanical stresses in the rotors of permanent magnet machines are presented. The focus is on interior permanent magnet machines. First, an overview of eight different methods from the literature is given. Specific differences are pointed out, and a brief summary of the analytical approach for each method is provided. For reference purposes, a finite element model is created and simulated for each rotor geometry studied. A total of seven rotors rom representative automotive powertrains are considered in their specific speed range. The analytical methods are used to determine the maximum mechanical stress concentration factors for the seven rotor geometries, in which we are determined to find maximum mechanical stress as a final step of the analytical process. For each geometry and each respective operating speed range, the deviations from the finite element reference are determined. In addition, the error in the selected ge... [more]

An anaerobic digestion process performed in two stages has the advantages of the production of hydrogen in addition to methane, and of further degradation of the substrate over the conventional process. The effectiveness of the implementation of this system for the treatment of lignocellulosic waste has been demonstrated. In 2020, more than 180 million tons of organic waste were generated worldwide from tomato crop production, posing a serious environmental risk. In the present investigation, methane production was compared in a two-stage system versus one-stage system from non-pretreated tomato plant residues. For this, different temperature (37 and 55 °C) and initial pH (5.5 and 6.5) conditions were evaluated during hydrogenesis and a constant temperature (37 °C, without pH adjustment) during methanogenesis. At the same time, a one-stage treatment (37 °C, without pH adjustment) was run for comparison purposes. The two-stage treatment in which the highest production of hydrogen, 12.4... [more]

The aim of this study is to develop a model for a proprietary SO2 removal technology by using machine learning techniques and, more specifically, by exploiting the potentialities of artificial neural networks (ANNs). This technology is employed at the Eni oil and gas treatment plant in southern Italy. The amine circulating in this unit, that allows for a reduction in the SO2 concentration in the flue gases and to be compliant with the required specifications, is a proprietary solvent; thus, its composition is not publicly available. This has led to the idea of developing a machine learning (ML) algorithm for the unit description, with the objective of becoming independent from the licensor and more flexible in unit modelling. The model was developed in MatLab® by implementing ANNs and the aim was to predict three targets, namely the flow rate of SO2 that goes to the Claus unit, the emissions of SO2, and the flow rate of steam sent to the regenerator reboiler. These represent, respectiv... [more]

The rise in power demand today necessitates its generation and transmission at high voltages. The efficient transmission of electric power requires transformers with an insulation system that exhibits excellent dielectric properties. In this paper ZnO and CuO nanomaterials are utilized to investigate the dielectric characteristics of pure synthetic ester oil and its related nanofluids (NFs) from room temperature up to 60 °C at increments of 20 °C, including AC breakdown voltage, Dielectric Dissipation factor, and DC resistivity. The breakdown testing is carried out in accordance with experimental IEC-60156 requirements. The DC resistivity and dissipation factor of oils are measured using the Dissipation Factor meter, resistivity meter, and a heating chamber with an oil cell that follows IEC 60247 standard. The statistical analysis is performed on the breakdown voltages test values using the Weibull probability distribution model for better accuracy. From the results, it has been found... [more]

The podded propulsor has gradually become an important propulsion device for high technology ships in recent years because of its characteristics of high maneuverability, high efficiency, low noise, and vibration. The performance of podded propulsor is closely related to its flow field. To study the unsteady flow field characteristics of podded propulsor, the DDES (delayed detached eddy simulation) method was used to carry out high-precision transient numerical simulations. Results showed that the pod has a significant influence on the unsteady flow field. The rotor−stator interaction between the propeller and pod can be observed, leading to the periodic fluctuation of thrust on the propeller. On the surface of pod, pressure distribution changes with time, leading to the difference of local lateral force. In the spatial region affected by the propeller wake flow, pressure distribution presents a spiral characteristic, both in the region far away from the pod, and in the region of the w... [more]

Real-time monitoring of the battery thermal status is important to ensure the effectiveness of battery thermal management system (BTMS), which can effectively avoid thermal runaway. In the study of BTMS, driver behavior is one of the factors affecting the performance of the battery thermal status, and it is often neglected in battery temperature studies. Therefore, it is necessary to predict the dynamic heat generation of the battery in actual driving cycles. In this work, a thermal equivalent circuit model (TECM) and an artificial neural network (ANN) thermal model based on the driving data, which can predict the thermal behavior of the battery in real-world driving cycles, are proposed and established by MATLAB/Simulink tool. Driving behaviors analysis of different drivers are simulated by PI control as input, and battery temperature is used as output response. The results show that aggressive driving behavior leads to an increase in battery temperature of nearly 1.2 K per second, an... [more]

The extensive use of renewable energy sources (RESs) in energy sectors plays a vital role in meeting the present energy demand. The widespread utilization of allocated resources leads to multiple usages of converters for synchronization with the power grid, introducing poor power quality. The integration of distributed energy resources produces uncertainties which are reflected in the distribution system. The major power quality problems such as voltage sag/swell, voltage unbalancing, poor power factor, harmonics distortion (THD), and power transients appear during the transition of micro-grids (MGs). In this research, a single micro-grid is designed with PVs, wind generators, and fuel cells as distributed energy resources (DERs). A nonlinear auto regressive exogenous input neural network (NARX-NN) controller has been investigated in this micro-grid in order to maintain the above power quality issues within the specific standard range (IEEE/IEC standards). The performance of the NARX-N... [more]

Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine... [more]

The energy balance constitutes a powerful management instrument for government agencies, as it offers an overview of the energy situation of the country (or region) and serves as a guide for energy policies and monitoring of these policies. Although Brazil has published the national energy balance for more than half a century, the national publication does not adequately address energy statistics at the level of the states. This occurs either due to the lack of specific data or the absence of total disaggregation. Accordingly, the elaboration and implementation of public policies for the energy sector in the Brazilian states lack consistent energy statistics. Therefore, this paper aims to present a model for the Brazilian states to elaborate the energy balance. The proposed model consists of applying internationally referenced methodologies to develop a user-friendly software, which includes automatic energy unit conversions, different chart styles, high-level data organization, and Sa... [more]

Lack of electricity in rural communities implies inequality of access to information and opportunities among the world’s population. Hybrid renewable energy systems (HRESs) represent a promising solution to address this situation given their portability and their potential contribution to avoiding carbon emissions. However, the sizing methodologies for these systems deal with some issues, such as the uncertainty of renewable resources. In this work, we propose a sizing methodology that includes long short-term memory (LSTM) cells to predict weather conditions in the long term, multivariate clustering to generate different weather scenarios, and a nonlinear mathematical formulation to find the optimal sizing of an HRES. Numerical experiments are performed using open-source data from a rural community in the Pacific Coast of Mexico as well as open-source programming frameworks to allow their reproducibility. We achieved an improvement of 0.1% in loss of load probability in comparison to... [more]

Due to their numerous advantages, Wavelet transform processor-based acoustic wave devices constitute an interesting approach for various engineering disciplines, such as signal analysis, speech synthesis, image recognition and atmospheric and ocean wave analysis. The major aim of this paper is to review the most recent methods for implementing wavelet transform processor-based surface acoustic wave devices. Accordingly, the goal of this paper is to compare different models, and it will provide a generalized model with small insertion loss values and side lobe attenuation, making it suitable for designing multiplexer filter banks and also to ease the way for the continued evolution of device design. In this paper, a generalized framework on surface acoustic wave devices is presented in terms of mathematical equations, types of materials, crystals types, and interdigital transducer design in addition to addressing some relevant problems.

This paper presents the development of a neural inverse optimal control (NIOC) for a regenerative braking system installed in electric vehicles (EVs), which is composed of a main energy system (MES) including a storage system and an auxiliary energy system (AES). This last one is composed of a supercapacitor and a buck−boost converter. The AES aims to recover the energy generated during braking that the MES is incapable of saving and using later during the speed increase. To build up the NIOC, a neural identifier has been trained with an extended Kalman filter (EKF) to estimate the real dynamics of the buck−boost converter. The NIOC is implemented to regulate the voltage and current dynamics in the AES. For testing the drive system of the EV, a DC motor is considered where the speed is controlled using a PID controller to regulate the tracking source in the regenerative braking. Simulation results illustrate the efficiency of the proposed control scheme to track time-varying references... [more]

Coiled tubing (CT) is widely used in drilling, workover, completion, fracturing and stimulation in the field of oil and gas exploration and development. During CT operation, the tubing will present a gas−liquid two-phase flow state. The prediction of frictional pressure drop for fluid in the tube is an important part of hydraulic design, and its accuracy directly affects the success of the CT technique. In this study, we analyzed the effects of the gas void fraction, curvature ratio and fluid inlet velocity on frictional pressure drop in CT, numerically. Experimental data verified simulated results. Flow friction sensitivity analysis shows the frictional pressure drop reaches its peak at a gas void fraction of 0.8. The frictional pressure gradient increases with the increase in curvature ratio. As the strength of secondary flow increases with the increase in inlet velocity, the increased trend of gas−liquid two-phase flow friction is aggravated. The correlation of friction factor for g... [more]

Currently, solar energy is one of the leading renewable energy sources that help support energy transition into decarbonized energy systems for a safer future. This work provides a comprehensive review of mathematical modeling used to simulate the performance of photovoltaic (PV) modules. The meteorological parameters that influence the performance of PV modules are also presented. Various deterministic and probabilistic mathematical modeling methodologies have been investigated. Moreover, the metaheuristic methods used in the parameter extraction of diode models of the PV equivalent circuits are addressed in this article to encourage the adoption of algorithms that can predict the parameters with the highest precision possible. With the significant increase in the computational power of workstations and personal computers, soft computing algorithms are expected to attract more attention and dominate other algorithms. The different error expressions used in formulating objective functi... [more]

Compared with traditional injection tubular strings, the stresses on CO2 injection tubular strings are more complex. The results from field applications show that the phase transition of CO2 fluid in CO2 injection strings is an important factor in the calculation of temperature distribution and analysis of string mechanics. Therefore, we propose a strength analysis method for CO2 injection tubular strings that considers the CO2 phase transition. We selected four CO2 injection strings in an oil field in China as examples to evaluate their strength and safety. First, we established coupled differential equations for the temperature, pressure, and physical parameters of CO2 injection strings according to the theory of fluid flow and heat transfer. Then, we used an adaptive fuzzy neural network to construct the model for calculating the CO2 convection heat transfer coefficient and used this to obtain the high-precision convection heat transfer coefficients of tubular strings under conditio... [more]

Thirty oil samples collected from the eastern Fukang Sag were analyzed geochemically for their biomarkers and carbon isotopic compositions. The chemometric methods of principal component analysis and hierarchical cluster analysis, employed to thirteen parameters indicating source and depositional environment, classified the oil samples into three genetically distinct oil families: Family A oils were mainly derived from lower aquatic organisms deposited in a weakly reducing condition of fresh−brackish water, Family B oils came from a source containing predominantly terrigenous higher-plant organic matter laid down in an oxidizing environment of fresh water, and Family C oils received sources from both terrigenous and marine organic matter deposited in a weakly oxidizing to oxidizing environment of brackish water. Indirect oil−source correlations implied that Family A oils were probably derived from Permian source rocks, Family B oils originated mainly from Jurassic source rocks, and Fam... [more]

The peer-to-peer (P2P) strategy as a new trading scheme has recently gained attention in local electricity markets. This is a practical framework to enhance the flexibility and reliability of energy hubs, specifically for industrial prosumers dealing with high energy costs. In this paper, a Norwegian industrial site with multi-energy hubs (MEHs) is considered, in which they are equipped with various energy sources, namely wind turbines (WT), photovoltaic (PV) systems, combined heat and power (CHP) units (convex and non-convex types), plug-in electric vehicles (EVs), and load-shifting flexibility. The objective is to evaluate the importance of P2P energy transaction with on-site flexibility resources for the industrial site. Regarding the substantial peak power charge in the case of grid power usage, this study analyzes the effects of P2P energy transaction under uncertain parameters. The uncertainties of electricity price, heat and power demands, and renewable generations (WT and PV) a... [more]

Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on the means and methods of planning electricity production. Forecasting is one of the planning tools since the availability of an accurate forecast is a mechanism for increasing the validity of management decisions. This study provides an overview of the methods used to predict electricity supply requirements to different objects. The methods have been reviewed analytically, taking into account the forecast classification according to the anticipation period. In this way, the methods used in operative, short-term, medium-term, and long-term forecasting have been considered. Both classical and modern forecasting methods have been identified when forecasting electric energy consumption. Classical forecasting methods are based on the theory of regression and statistical analysis (regression, autoregressive models); probabilistic forecasting methods and modern forecasting methods... [more]

The remaining useful life (RUL) of a lithium-ion battery is directly related to the safety and reliability of the electric system powered by a lithium-ion battery. Accurate prediction of RUL can ensure timely replacement and maintenance of the batteries of the power supply system, and avoid potential safety hazards in the lithium-ion battery power supply system. In order to solve the problem that the prediction accuracy of the RUL of lithium-ion batteries is reduced due to the local capacity recovery phenomenon in the process of the capacity degradation of lithium-ion batteries, a prediction model based on the combination of the whale optimization algorithm (WOA)-variational mode decomposition (VMD) and short-term memory neural network (LSTM) was proposed. First, WOA was used to optimize the VMD parameters, so that the WOA-VMD could fully decompose the capacity signal of the lithium-ion battery and separate the dual component with global attenuation trend and a series of fluctuating co... [more]

Monitoring centers in the smart grid exchange the collected data by sensors and smart meters to monitor the current conditions and performance of electric power components. Distribution Power Transformers (DPTs) have a key role in maintaining the integrity of power flow in the smart grid. Online monitoring of DPTs to detect possible faults can potentially increase the reliability of modern power systems. Mechanical defects of DPTs are the major issues in their proper operation that must be detected in their early stage of occurrence. One of the most effective solutions for diagnosing mechanical defects in DPTs is Frequency Response Analysis (FRA). In this study, an appropriate condition monitoring scheme for DPTs is developed to identify even minor winding defects. Disk-Space Variation (DSV), a common DPT windings fault, is applied to the 20 kV-winding of a 1.6 MVA DPT in various locations and with different severity. Their corresponding frequency responses are then computed, and all f... [more]