The extent of fracture development is associated with the degree of enrichment of a natural gas reservoir and its productivity. Based on numerical simulation results of the paleotectonic stress field, a set of evaluation methods for determining the degree of development of reservoir tectonic fractures were established using rock rupture criteria. Taking the fourth member of the Leikoupo Formation in the Pengzhou area of western Sichuan as an example, a finite element (FE) method was employed to simulate the paleo-tectonic stress field during the period of fracture development, and the degree of tectonic fracture development was further evaluated using the above methods. The results indicated that effective fractures were created in the Himalayan period. In this time, mainly NE−NEE and nearly E−W strike tectonic fractures were developed in the target layer. The fractures were mainly low-angle and oblique fractures, while the high-angle fractures were less developed. According to the int... [more]

The synchronous generators (SGs) supplying non-linear loads have harmonically distorted terminal voltages. Hence, these distorted terminal voltages adversely affect the performance parameters of the supplied loads such as the power factor, current distortion, losses, and efficiency. To mitigate the harmonic voltages and currents, passive and active filters are generally employed. However, passive filters cause resonance problems, while active filters can cause high costs. On the other hand, in several recent studies to reduce the SG’s terminal voltage harmonic distortion, which depends on the constructional design under the no-loading condition, the conventional DC excitation current has been modulated with AC harmonic components. These field current modulation methods have high computational complexity, and require extra hardware for their implementation. In the present paper, firstly, for the reduction of the terminal voltage harmonic distortion of the SG under non-linear loading con... [more]

In grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that can be charged in an ESS, a separate energy management strategy (EMS) is required for stable microgrid operation. This paper proposes an EMS for a hybrid AC/DC microgrid based on an artificial neural network (ANN). The ANN is composed of a two-step process that operates the microgrid by outputting the operation mode and charging and discharging the ESS. The microgrid consists of an interlinking converter to link with the AC distributed system, a photovoltaic converter, a wind turbine converter, and an ESS. The control method of each converter was determined according to the mode selection of the ANN. The proposed ANN-based EMS was verified using a laboratory-scale hybrid AC/DC microgrid. Th... [more]

This paper addresses the question “Is energy that different from labor?” from the perspective of efficiency. It presents a novel statistical analysis for the auto assembly industry in North America to examine the determinants of relative energy intensity, and contrasts this with a similar analysis of the determinants of another important factor of production, labor intensity. The data used combine two non-public sources of data previously used to separately study key performance indicators (KPIs) for energy and labor intensity. The study found these two KPIs are statistically correlated (the correlation coefficient is 0.67) and the relationship is one-to-one. The paper identifies 11 factors that may influence both energy and labor intensity KPIs. The study then contrasts which of the empirical factors the two KPIs’ share and how they differ. Two novel statistical methods, Huber estimators and Multiple M-estimators, combined with regularized algorithms, are identified as the preferred m... [more]

Most wind turbines are remotely monitored 24/7 to allow for early detection of operation problems and developing damage. We present a new fault detection approach for vibration-monitored drivetrains that does not require any feature engineering. Our method relies on a simple model architecture to enable a straightforward implementation in practice. We propose to apply convolutional autoencoders for identifying and extracting the most relevant features from a broad continuous range of the spectrum in an automated manner, saving time and effort. We focus on the range of [0, 1000] Hz for demonstration purposes. A spectral model of the normal vibration response is learnt for the monitored component from past measurements. We demonstrate that the trained model can successfully distinguish damaged from healthy components and detect a damaged generator bearing and damaged gearbox parts from their vibration responses. Using measurements from commercial wind turbines and a test rig, we show tha... [more]

A hypersonic electromagnetic railgun projectile undergoes severe aero-heating with an increase in altitude. The purpose of this study was to investigate the characteristics of the shock layer flow field as well as the thermal environment of the blunt body wall of a hypersonic electromagnetic railgun projectile at different launching angles. The two-temperature model considers the thermal nonequilibrium effect and is introduced into the Navier−Stokes (N-S) equation, and it is solved using the finite volume method (FVM). The reliability of the calculation model in terms of thermal properties and composition production was verified against a blunted-cone-cylinder−flare (HB-2) test case. The surface temperature of the hypersonic blunt projectile was simulated using a radiation balance wall boundary. The thermal characteristics at the emission angles α = 60° and α = 45° were checked within an altitude range of 0−70 km, including the nonequilibrium effect, reaction heat release, aerodynamic... [more]

Synchronized phasor measurement systems are being widely used around the world and have become essential elements in the evolution of the operation of large electrical power systems (EPS). These systems, called Phasor Measurement Units (PMUs), are capable of recording and communicating dynamic data from the EPSs in a synchronized way by GPS and with a high sampling rate, generate a huge set of data that, among many applications, has the capacity to detect events. In this way, this work presents a data management system architecture applied to a real PMU system located in the state of Paraná, Brazil that detects and storages events using principal component analysis and Pearson correlation. This method can detect and store electrical events that occurred during the operation of the national interconnected system of Brazil with good results.

The planning of efficient policies based on forecasting electricity demand is essential to guarantee the continuity of energy supply for consumers. Some techniques for forecasting electricity demand have used specific procedures to define input variables, which can be particular to each case study. However, the definition of independent and casual variables is still an issue to be explored. There is a lack of models that could help the selection of independent variables, based on correlate criteria and level of importance integrated with artificial networks, which could directly impact the forecasting quality. This work presents a model that integrates a multi-criteria approach which provides the selection of relevant independent variables and artificial neural networks to forecast the electricity demand in countries. It provides to consider the particularities of each application. To demonstrate the applicability of the model a time series of electricity consumption from a southern re... [more]

The thermal performance of lightweight steel framed (LSF) facade walls depends on many factors, such as the steel studs, the batt insulation, the external thermal insulation composite systems (ETICS), and the sheathing layers. Moreover, the high thermal conductivity of steel could negatively affect their thermal performance due to the consequent thermal bridge effect. Furthermore, in LSF walls, the batt insulation is usually bridged by the steel studs. Thus, some analytical calculation procedures defined in standards (e.g., ISO 6946) are not valid, further complicating their thermal performance quantification. In this research, a parametric study to evaluate the thermal performance of facade LSF walls is presented. Seven relevant parameters are assessed, most of them related to the use of thermal break strips (TBS) and ETICS. The 2D numerical models used to predict the conductive R-values were experimentally validated, and their precision was successfully verified. As earlier found in... [more]

Wood pellets play an important role among biomass materials used as fuel. At the same time, today’s economic, environmental, political and social realities, as well as other circumstances related to fuels used for heat generation, mean that there is demand for increasingly efficient and environmentally friendly combustion sources. As is well known, each combustion source has a different efficiency due to its intended use, design, principle of operation and the type and composition of the fuel burned. The amount of pollutants emitted into the environment during combustion also largely depends on these factors. The aim of this study was to compare the flue gas emissions and efficiency of two pellet burners of different design, burning certified A1 wood pellets from different suppliers. The emission requirements were met during the combustion of wood pellets in a boiler with the two burners tested (one with a moving grate and an overfed burner). The analyses and studies carried out aim to... [more]

One of the essential factors for the selection of the drying process is energy consumption. This study intended to optimize the drying treatment of capers using convection (CD), refractive window (RWD), and vacuum drying (VD) combined with ultrasonic pretreatment by a comparative approach among artificial neural networks (ANN) and response surface methodology (RSM) focusing on the specific energy consumption (SEC). For this purpose, the effects of drying temperature (50, 60, 70 °C), ultrasonication time (0, 20, 40 min), and drying method (RWD, CD, VD) on the SEC value (MJ/g) were tested using a face-centered central composite design (FCCD). RSM (R2: 0.938) determined the optimum drying-temperature−ultrasonication-time values that minimize SEC as; 50 °C-35.5 min, 70 °C-40 min and 70 °C-24 min for RWD, CD and VD, respectively. The conduct of the ANN model is evidenced by the correlation coefficient for training (0.976), testing (0.971) and validation (0.972), which shows the high suitabi... [more]

The pressure drop for air-water two-phase flow in pipeline systems with S-shaped and vertical risers at various inclinations (−1°, −2°, −4°, −5° and −7° from horizontal) was predicted using an artificial neural network (ANN). In the designing of the ANN model, the superficial velocity of gas and liquid as well as the inclination of the downcomer were used as input variables, while pressure drop values of two-phase flows were determined as the output. An ANN network with a hidden layer containing 14 neurons was developed based on a trial-and-error method. A sigmoid function was chosen as the transfer function for the hidden layer, while a linear function was used in the output layer. The Levenberg-Marquardt algorithm was used for the training of the model. A total of 415 experimental data points reported in the literature were collected and used for the creation of the networks. The statistical results showed that the proposed network is capable of calculating the experimental pressure... [more]

This article presents a neural algorithm based on Reinforcement Learning for optimising Linear Quadratic Regulator (LQR) creation. The proposed method allows designing such a target function that automatically leads to changes in the quality and resource matrix so that the target LQR regulator achieves the desired performance. The solution’s stability and optimality are the target controller’s responsibility. However, the neural mechanism allows obtaining, without expert knowledge, the appropriate Q and R matrices, which will lead to such a gain matrix that will realise the control that will lead to the desired quality. The presented algorithm was tested for the derived quadrant model of the suspension system. Its application improved user comfort by 67% compared to the passive solution and 14% compared to non-optimised LQR.

In this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO2, noise, and relative temperature via our machine learning method and forecasting strategy. The proposed algorithms enhance the energy management system through the optimal use of the electric heating system. The Long Short-Term Memory (LSTM) neural network is a special deep learning strategy for processing time series prediction that has shown promising prediction results in recent years. To improve the performance of the LSTM algorithm, particularly for autocorrelation prediction, we will focus on optimizing weight updates using various approaches such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performances of the proposed methods are evaluated using real available datasets. Test results reveal that the GA and the PSO can forecast the parameters with higher prediction fidelity compared to the LSTM networks. Indeed, all experimental predictions rea... [more]

In modern power systems, the installation of a shunt capacitor bank is one of the cheapest and most widely used methods for improving the voltage profile. One shunt capacitor bank is composed of mass capacitor units and have ground, ungrounded, delta, wye connections that make configuration of capacitor banks is various. In the case of long-term operation, the failure of a single capacitor unit of a capacitor bank is likely to cause uneven voltage, which will lead to the breakdown and burning of the whole group, resulting in huge losses. The relay protection device can detect the simultaneous voltage and current of the capacitor. By utilizing these data from the relay, the abnormal state of the shunt capacitor banks at the initial stage of the fault can be found through monitoring the slight change in capacitance. Timely and early maintenance and repair would avoid capacitor bank faults and potentially greater economic losses. Capacitor banks have different connection modes. For ungrou... [more]

The major challenge facing renewable energy systems in Nigeria is the lack of appropriate, affordable, and available meteorological stations that can accurately provide present and future trends in weather data and solar PV performance. It is crucial to find a solution to this because information on present and future solar PV performance is important to renewable energy investors so that they can assess the potential of renewable energy systems in various locations across the country. Although Nigerian weather provides favorable weather conditions for clean power generation, there is little penetration of renewable energy systems in the region, since over 95% of the power is fossil-fuel-generated. This is because there has been no detailed report showing the potential of clean power generation systems due to the dysfunctional meteorological stations in the country. This paper sought to fill this knowledge gap by providing a machine-learning-inspired forecasting of environmental weathe... [more]

In marine storage and transportation, the sloshing of liquid oxygen disturbs the thermodynamic equilibrium and induces stress on tank walls. Numerous problems are associated with the sloshing mechanism and demand a detailed investigation. In this study, a numerical model is developed by coupling the Eulerian framework and the algebraic interface area density (AIAD) method while considering the interphase drag force to investigate the thermal behavior of sloshing liquid oxygen. The effect of the sloshing frequency on the evaporation performance of liquid oxygen is studied. Moreover, anti-sloshing is conducted by employing a T-shaped baffle. The results show that the sloshing induced a vapor explosion phenomenon due to the invalidation of the surface impedance and thermal destratification to enhance free convection, resulting in rapid depressurization and increased evaporation loss. In addition, maximum evaporation loss occurred under the vapor−liquid coupling excitation condition. The T... [more]

The photovoltaic (PV) module is a key technological advancement in renewable energy. When the PV modules fail, the overall generating efficiency will decrease, and the power system’s operation will be influenced. Hence, detecting the fault type when the PV modules are failing becomes important. This study proposed a hybrid algorithm by combining the symmetrized dot pattern (SDP) with a convolutional neural network (CNN) for PV module fault recognition. Three common faults are discussed, including poor welding, breakage, and bypass diode failure. Moreover, a fault-free module was added to the experiment for comparison. First, a high-frequency square signal was imported into the PV module, and the original signal was captured by the NI PXI-5105 high-speed data acquisition (DAQ) card for the hardware architecture. Afterward, the signal was imported into the SDP for calculation to create a snowflake image as the image feature for fault diagnosis. Finally, the PV module fault recognition wa... [more]

Since accurate load forecasting plays an important role in the improvisation of buildings and as described in EU’s “Green Deal”, financial resources saved through improvisation of the efficiency of buildings with social importance such as hospitals, will be the funds to support their mission, the social impact of load forecasting is significant. In the present paper, eight different machine learning predictors will be examined for the short-term load forecasting of a hospital’s facility building. The challenge is to qualify the most suitable predictors for the abovementioned task, which is beneficial for an in-depth study on accurate predictors’ applications in Intelligent Energy Management Systems (IEMS). Three Artificial Neural Networks using a backpropagation algorithm, three Artificial Neural Networks using metaheuristic optimization algorithms for training, an Adaptive Neuro-Fuzzy Inference System (ANFIS), and a Long-Short Term Memory (LSTM) network were tested using timeseries ge... [more]

This paper deals with a method of quantifying the harmonic contribution of each harmonic source to system voltage distortion. Assessing the harmonic contribution of individual harmonic sources is essential for mitigating and managing system harmonic levels. Harmonic contributions can be evaluated using the principle of voltage superposition with equivalent voltage models for harmonic sources. In general, the parameters of equivalent voltage models are estimated numerically because it is difficult to measure them directly. In this paper, we present an effective method for estimating equivalent model parameters based on the random sample consensus (RANSAC) and recursive least square (RLS) with a variable forgetting factor. The procedure for quantifying harmonic contributions using equivalent models is also introduced. Additionally, we propose a network diagram of harmonic contributions that makes it easy to understand the harmonic distortion contributions of all harmonic sources.

One of the most important and challenging research subjects in computer vision is visual object tracking. The information obtained from the first frame consists of limited and insufficient information to represent an object. If prior information about robust representation that can represent an object well is not sufficient, object tracking fails when not robustly responding to changes in features of the target object according to various factors, namely shape, illumination variation, and scene distortion. In this paper, a real-time single object tracking algorithm is proposed based on a Siamese network to solve this problem. For the object feature extraction, we designed a fully convolutional neural network that removes a fully connected layer and configured a convolution block consisting of a bottleneck structure that preserves the information in a previous layer. This network was designed as a Siamese network, while a regional proposal network was combined at the end of the network... [more]

Due to its significance in the operation of power systems, the optimal power flow (OPF) problem has attracted increasing interest with the introduction of smart grids. Optimal power flow developed as a crucial instrument for resource planning effectiveness as well as for enhancing the performance of electrical power networks. Transmission line losses, total generation costs, FACTS (flexible alternating current transmission system) costs, voltage deviations, total power transfer capability, voltage stability, emission of generation units, system security, etc., are just a few examples of objective functions related to the electric power system that can be optimized. Due to the nonlinear nature of optimal power flow problems, the classical approaches may become locked in local optimums, hence, metaheuristic optimization techniques are frequently used to solve these issues. The most recent optimization strategies used to solve optimal power flow problems are discussed in this paper as the... [more]

Most temperature predictions for deep geothermal applications rely on correcting bottom-hole temperatures (BHTs) to undisturbed or static formation temperatures (SFTs). The data used for BHT correction are usually of low quality due to a lack of information and poor documentation, and the uncertainty of the corrected SFT is therefore unknown. It is supposed that the error within the input data exceeds the error due to the uncertainty of the different correction schemes. To verify this, we combined a global sensitivity study with Sobol indices of six easy-to-use conventional correction schemes of the BHT data set of the Bavarian Molasse Basin with an uncertainty study and developed a workflow that aims at presenting a valid error range of the corrected SFTs depending on the quality of their input data. The results give an indication of which of the investigated correction methods should be used depending on the input data, as well as show that the unknown error in the input parameters e... [more]

Conventional fuels commonly used in cars with combustion engines and the effects of their combustion have a very negative impact on the state of the environment. The combustion of liquid fuels causes the introduction of many thousands of tons of CO2 and other harmful substances into the atmosphere every year. That is why the authorities of many countries are introducing more and more stringent emission standards for cars with internal combustion engines, and car manufacturers are trying to meet these standards. Therefore, the aim of the undertaken research was to compile and analyze the power of spark engines in individual capacity ranges, compression ratios, efficiency, CO2 emissions, dependence of combustion on engine capacity, dependence of CO2 emissions on engine capacity, and dependence of combustion on engine power. The conducted research also compared the level of average selected variables related to CO2 emission in terms of engine displacement by country of production using st... [more]

Partial shading conditions (PSC) have negative effects on the operation of photovoltaic (PV) systems. In this paper, a PV array reconfiguration method is developed to minimize power losses of PV arrays under partial shading conditions. The proposed reconfiguration method is based on equalizing the reduction of the short-circuit current of the PV modules in the PV array. Eight state-of-the-art Convolutional Neural Network models are employed to estimate the effect of shading on the short-circuit current of a PV module. These models include LeNet-5, AlexNet, VGG 11, VGG 19, Inception V3, ResNet 18, ResNet 34, and ResNet 50. Among eight models, the VGG 19 achieves the best accuracy on 1842 sample images. Therefore, this model is used to estimate the ratio of the actual short-circuit current and the estimated short-circuit current in four studied shading scenarios. This ratio decides the switching rule between PV modules throughout the PV array under PSC. A 2×2 experimental PV array shows... [more]