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]

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]

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 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]

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]

Air pollution has a major impact on human health, especially in cities, and elevated concentrations of PMx are responsible for a large number of premature deaths each year. Therefore, the amount of PM10 in the air is monitored and forecasts are made to predict the air quality. In Poland, mainly deterministic models are used to predict air pollution. Accordingly, research efforts are being made to develop other models to forecast the ambient PM10 levels. The aim of the study was to compare the machine learning models for predicting PM10 levels in the air in the city of Lublin. The following machine learning models were used: Linear regression (LR), K-Nearest Neighbors Regression (KNNR), Support Vector Machine (SVM), Regression Trees (RT), Gaussian Process Regression Models (GPR), Artificial Neural Network (ANN) and Long Short-Term Memory network (LSTM). The collected data for three consecutive years (January 2017 to December 2019) were used to develop the models. In total, 19 parameters... [more]

A load frequency controller (LFC) is a crucial part in the distribution of a power system network (PSN) to restore its frequency response when the load demand is changed rapidly. In this paper, an artificial neural network (ANN) technique is utilised to design the optimal LFC. However, the training of the optimal ANN model for a multi-area PSN is a major challenge due to its variations in the load demand. To address this challenge, a particle swarm optimization is used to distribute the nodes of a hidden layer and to optimise the initial neurons of the ANN model, resulting in obtaining the lower mean square error of the ANN model. Hence, the mean square error and the number of epochs of the ANN model are minimised to about 9.3886 × 10−8 and 25, respectively. To assess this proposal, a MATLAB/Simulink model of the PSN is developed for the single-area PSN and multi-area PSN. The results show that the LFC based on the optimal ANN is more effective for adjusting the frequency level and imp... [more]

In this present investigation, emittance and performance attributes of a diesel engine using micro-algae spirulina blended biodiesel mixtures of various concentrations (20%, 35%, 50%, 65%, 80%, and 100%) were evaluated. An optimization model was also developed using an Artificial Neural Network (ANN) to characterize the experimental parameters. Experimental findings demonstrated significant improvement in brake specific fuel consumption (BSFC) using varied blends. Furthermore, brake thermal efficiency (BTE) is decreased gradually for biodiesel blends as compared to diesel. Micro-algae spirulina blends have shown lower concentrations of NOX and HC while increasing CO2 relative to pure diesel. To develop the model, three sets of optimizers, namely, adam, nadam, and adagrad, along with activation functions, such as sigmoid, softmax, and relu, were selected. The results revealed that sigmoid activation function with adam learning optimizer by using 32 hidden layer neurons has given the lea... [more]

The spark ignition engine is a complex multi-domain system that contains many variables to be controlled and managed with the aim of attending to performance requirements. The traditional method and workflow of the engine calibration comprise measure and calibration through the design of an experimental process that demands high time and costs on bench testing. For the growing use of virtualization through artificial neural networks for physical systems at the component and system level, we came up with a likely efficiency adoption of the same approach for the case of engine calibration that could bring much better cost reduction and efficiency. Therefore, we developed a workflow integrated into the development cycle that allows us to model an engine black-box model based on an auto-generated feedfoward Artificial Neural Network without needing the human expertise required by a hand-crafted process. The model’s structure and parameters are determined and optimized by a genetic algorith... [more]

This article is devoted to the development of technology for improving the efficiency of directional well drilling by predicting and adjusting the system of static and dynamic components of the actual weight on the bit, based on the real-time data interpretation from telemetry sensors of the bottom hole assembly (BHA). Studies of the petrophysical and geomechanical properties of rock samples were carried out. Based on fourth strength theory and the Palmgren−Miner fatigue stress theory, the mathematical model for prediction of effective distribution of mechanical specific energy, using machine learning methods while drilling, was developed. An algorithm was set for evaluation and estimation of effective destruction of rock by comparing petrophysical data in the well section and predicting the shock impulse of the bit. Based on the theory provided, it is assumed that the given shock impulse is an actual representation of an excessive energy, conveyed to BHA. This excessive energy was qua... [more]

Micro turbojets are used for propelling radio-controlled aircraft, aerial targets, and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional artificial neural network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation had a similar impact in flight than at sea level. Finally, the datasets were used in the training and testing process... [more]

In cases where a dye-sensitized solar cell (DSSC) is exposed to light, thermal energy accumulates inside the device, reducing the maximum power output. Utilizing this energy via the Seebeck effect can convert thermal energy into electrical current. Similar systems have been designed and built by other researchers, but associated tests were undertaken in laboratory environments using simulated sunlight and not outdoor conditions with methods that belong to conventional data analysis and simulation methods. In this study four machine learning techniques were analyzed: decision tree regression (DTR), random forest regression (RFR), K-nearest neighbors regression (K-NNR), and artificial neural network (ANN). DTR algorithm has the least errors and the most R2, indicating it as the most accurate method. The DSSC-TEG hybrid system was extrapolated based on the results of the DTR and taking the worst-case scenario (node-6). The main question is how many thermoelectric generators (TEGs) are nee... [more]

Plant biomass is one of the most promising and easy-to-use sources of renewable energy. Direct determination of higher heating values of fuel in an adiabatic calorimeter is too expensive and time-consuming to be used as a routine analysis. Indirect calculation of higher heating values using the data from the ultimate and proximate analyses is a more rapid and less equipment-intensive method. This study assessed the fitting performance of a multilayer perceptron as an artificial neural network for estimating higher heating values of biomass. The analysis was conducted using a specially gathered large and heterogeneous dataset (720 biomass samples) that included the experimental data of ultimate and proximate analysis on grass plants, peat, husks and shells, organic residues, municipal solid wastes, sludge, straw, and untreated wood. The quantity and preprocessing of data (namely, rejection of dependent and noisy variables; dataset centralization) were shown to make a major contribution... [more]

Green energy sources are implemented for the generation of power due to their substantial advantages. Wind generation is the best among renewable options for power generation. Generally, the wind system is directly connected with the power network for supplying power. In direct connection, there is an issue of managing power quality (PQ) concerns such as voltage sag, swells, flickers, harmonics, etc. In order to enhance the PQ in a power network with a wind energy conversion system (WECS), peripheral compensation is needed. In this paper, we highlight a novel control technique to improve the PQ in WECS by adopting an Artificial Neural Network (ANN)-based Distribution Static Compensator (DSTATCOM). In our proposed approach, an online learning-based ANN Back Propagation (BP) model is used to generate the gate pulses of the DSTATCOM, which mitigate the harmonics at the grid side. It is modelled using the MATLAB platform and the total harmonic distortion (THD) of the system is compared wit... [more]

Various methods have been proposed to provide the protection necessitated by the high voltage direct current system. In this field, most of the research is confined to various types of DC and AC line faults and a maximum of two switching converter faults. The main contribution of this study is to use a new method for fault detection in HVDC systems, using the gray wolf optimization method along with artificial neural networks. Under this method, with the help of faulted and non-faulted signals, the features of the voltage and current signals are extracted in a much shorter period of the signal. Subsequently, differences are detected with the help of an artificial neural network. In the studied HVDC system, the behavior of the rectifier, along with its controllers and the required filters are completely modeled. In this study, other methods, such as artificial neural network, radial basis function, learning vector quantization, and self-organizing map, were tested and compared with the... [more]

The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer perceptron artificial neural network or long short-term memory network. Historical data related to energy consumption are used to construct a nested fuzzy cognitive map in order to better understand energy use behavior. Through the experiments, the usefulness of the nested structure in energy demand prediction is demonstrated, by calculating three popular metrics: Mean Square Error, Mean Absolute Error and the correlation coefficient. A comparative analysis is performed, applying classic multilayer perceptron artificial neural networks, long short-term memory networks and fuzzy cognitive maps. The results confirmed that the proposed approach outperforms the classic methods in terms of prediction accuracy. Moreover, the advantage of the proposed approach is the abilit... [more]

In order to improve the design of advanced wells, the performance of such wells needs to be carefully assessed by taking the reservoir uncertainties into account. This research aimed to develop data-driven proxy models for the simulation and assessment of oil recovery through advanced wells under uncertainty. An artificial neural network (ANN) was employed to create accurate and computationally efficient proxy models as an alternative to physics-based integrated well−reservoir models created by the Eclipse® reservoir simulator. The simulation speed and accuracy of the data-driven proxy models compared to physic-driven models were then evaluated. The evaluation showed that while the developed proxy models are 350 times faster, they can predict the production of oil and unwanted fluids through advanced wells with a mean error of less than 1% and 4%, respectively. As a result, the data-driven proxy models can be considered an efficient tool for uncertainty analysis where several simulatio... [more]

This paper presents the high contact resistance (HCR) and rotor bar faults by an extraction method for an induction motor using Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). The root mean square (RMS) and mean features are obtained using DWT, and ANN is used for classification using activation functions. Activation provides output by assigning the specific input with respect to the transfer function according to the nature and type of the activation function. Method: The faulty conditions are induced using MATLAB by adopting the motor current signature analysis (MCSA) method to achieve current signature signals of the healthy and faulty motors. Results: The DWT technique has been applied to obtain fault-specific features of the average continuously varying signal (RMS) and an average of the data points (mean) at levels 5, 7, 8, and 9, followed by ANN to classify the faults for condition monitoring. Utility: The utility of the results is to reduce unscheduled dow... [more]

Increasing economic and population growth has led to a rise in electricity consumption. Consequently, electrical utility firms must have a proper energy management strategy in place to improve citizens’ quality of life and ensure an organization’s seamless operation, particularly amid unanticipated circumstances such as coronavirus disease (COVID-19). There is a growing interest in the application of artificial intelligence models to electricity prediction during the COVID-19 pandemic, but the impacts of clustering methods and parameter selection have not been explored. Consequently, this study investigates the impacts of clustering techniques and different significant parameters of the adaptive neuro-fuzzy inference systems (ANFIS) model for predicting electricity consumption during the COVID-19 pandemic using districts of Lagos, Nigeria as a case study. The energy prediction of the dataset was examined in relation to three clustering techniques: grid partitioning (GP), subtractive cl... [more]

A fiber-optic gyroscope (FOG) with lower precision but higher cost advantage is typically selected according to working conditions and engineering budget. Thermal drift is the main factor affecting FOG precision. External thermal calibration methods by algorithms can effectively weaken the influence of thermal drift. This paper presents a thermal calibration method of a two-dimensional N-order polynomial (TDNP) and compares it with artificial neural network (ANN) methods to determine a software FOG thermal calibration method for landslide displacement monitoring. The TDNP thermal calibration coefficient matrix was established, and the thermal calibration capability of the TDNP method with different orders N was evaluated on the basis of error analysis. The ANN model with 1 to 18 hidden neural layers was established on the basis of LM, BR, and SCG algorithms to choose a suitable ANN. Finally, the mean absolute errors of FOG thermal calibration through the TDNP with different orders and... [more]

The reliability of capacitors is one of the most important issues in power electronics. The health status of capacitors can be evaluated through the comparison of estimated C/ESR values with their original values. In this paper, a two-input artificial neural network (ANN) is proposed for C and ESR estimation of DC-link capacitors in Maglev choppers; combined with the existing voltage and current sensors that are used for protection and control, it provides a promising solution for the health condition monitoring of the Maglev chopper in the Maglev train. Compared with prior-art research, the actual capacitor degradation progress where both C and ESR degrade is considered in training. Moreover, ANN’s advantage of fitting nonlinear and complex relationships is explored by building an aggressive mapping between the voltage ripple at 5 kHz to C/ESR at 120 Hz, which cannot be described or analyzed by linear circuit equations. Thus, cross validation must be implemented to avoid an occasional... [more]

The market share of electric vehicles (EVs) has grown exponentially in recent years to reduce air pollution and greenhouse gas emissions. The principal part of an EV is the energy storage system, which is usually the batteries. Thus, the accurate estimation of the remaining useful life (RUL) of the batteries, for an optimal health management and a decision-making policy, still remains a challenge for automakers. In this paper, the problem of battery RUL prediction is studied from a new perspective. Unlike other estimation strategies existing in the literature, the proposed technique uses an intelligent prediction of the lifespan of lithium−iron−phosphate (LFP) batteries via a modified version of neural networks. It uses a data-driven life estimation approach and optimization method and does not require any prior comprehension and initialization of any parameters of the battery model. To validate and verify the proposed technique, we use LFP battery data sets, and the experimental resul... [more]

In an electric vehicle (EV), using more than one energy source often provides a safe ride without concerns about range. EVs are powered by photovoltaic (PV), battery, and ultracapacitor (UC) systems. The overall results of this arrangement are an increase in travel distance; a reduction in battery size; improved reaction, especially under overload; and an extension of battery life. Improved results allow the energy to be used efficiently, provide a comfortable ride, and require fewer energy sources. In this research, energy management between the PV system and the hybrid energy storage system (HESS), including the battery, and UC are discussed. The energy management control algorithms called Artificial Neural Network (ANN) and Aquila Optimizer Algorithm (AOA) are proposed. The proposed combined ANN−AOA approach takes full advantage of UC while limiting the battery discharge current, since it also mitigates high-speed dynamic battery charging and discharging currents. The responses’ beh... [more]

The transformer is one of the most important electrical machines in electrical systems. Its proper operation is fundamental for the distribution and transmission of electrical energy. During its service life, it is under continuous electrical and mechanical stresses that can produce diverse types of damage. Among them, short-circuited turns (SCTs) in the windings are one of the main causes of the transformer fault; therefore, their detection in an early stage can help to increase the transformer life and reduce the maintenance costs. In this regard, this paper proposes a signal processing-based methodology to detect early SCTs (i.e., damage of low severity) through the analysis of vibroacoustic signals in steady state under different load conditions, i.e., no load, linear load, nonlinear load, and both linear and nonlinear loads, where the transformer is adapted to emulate different conditions, i.e., healthy (0 SCTs) and with damage of low severity (1 and 2 SCTs). In the signal process... [more]

In 2021, the electric vehicles (EVs) market reached a record-breaking 6.5 million vehicles, and it will continuously grow to USD 31 million in 2030. However, the risk of battery damage should be reduced using a lightweight crashworthy protection system, which can be performed through design optimization to achieve maximum Specific Energy Absorption (SEA). Maximum SEA can be gained by selecting a material with a light weight and high energy absorption properties. An auxetic-shaped cell structure was used since its negative Poisson ratio yields better energy absorption. The research was performed by varying the auxetic cell shape (Re-entrant, Double Arrow, Star-shaped, Double-U), material selection (GFRP, CFRP, aluminum, carbon steel), and geometry variables until the maximum possible SEA was reached. The Finite Element Method (FEM) was used to simulate the impact and obtain the value of the SEA of the varied auxetic cellular structure design samples. The design variation amounted to 100... [more]