Harmonics worsen the quality of electrical signals, hence, there is a need to eliminate them. The test objects under discussion are single-phase versions of cascaded H-bridge (CHB) multilevel inverters (MLIs) whose switching angles are optimized to eliminate specific harmonics. The Dragonfly Algorithm (DA) is used to eradicate low-order harmonics, and its statistical performance is compared to that of many other optimization techniques, including Particle Swarm Optimization (PSO), Accelerated Particle Swarm Optimization (APSO), Differential Evolution (DE), and Grey Wolf Optimization (GWO). Various scenarios of the algorithms’ search agent population for inverters with seven, nine, and eleven levels of output voltages are comprehensively addressed in this research. No algorithm shows total dominance in every scenario. The DA is least impacted by the change in dimensions of the narrated problem.

The Unified Power Quality Conditioner (UPQC) is a technology that has successfully addressed power quality issues. In this paper, a photovoltaic system with battery storage powered Unified Power Quality Conditioner is presented. Total harmonic distortion of the grid current during extreme voltage sag and swell conditions is more than 5% when UPQC is controlled with synchronous reference frame theory (SRF) and instantaneous reactive power theory (PQ) control. The shunt active filter of the UPQC is controlled by the artificial neural network to overcome the above problem. The proposed artificial neural network controller helps to simplify the control complexity and mitigate power quality issues effectively. This study aims to use a neural network to control a shunt active filter of the UPQC to maximise the supply of active power loads and grid and also used to mitigate the harmonic problem due to non-linear loads in the grid. The performance of the model is tested under various case scen... [more]

Physics-informed neural network (PINN) models are developed in this work for solving highly anisotropic diffusion equations. Compared to traditional numerical discretization schemes such as the finite volume method and finite element method, PINN models are meshless and, therefore, have the advantage of imposing no constraint on the orientations of the diffusion tensors or the grid orthogonality conditions. To impose solution positivity, we tested PINN models with positivity-preserving activation functions for the last layer and found that the accuracy of the corresponding PINN solutions is quite poor compared to the vanilla PINN model. Therefore, to improve the monotonicity properties of PINN models, we propose a new loss function that incorporates additional terms which penalize negative solutions, in addition to the usual partial differential equation (PDE) residuals and boundary mismatch. Various numerical experiments show that the PINN models can accurately capture the tensorial e... [more]

Herein, hydrodynamic analysis from a large-eddy simulation in Couette−Taylor−Poiseuille (CTP) geometry is numerically investigated. The present geometry is inspired by a previous experimental work in which heat transport phenomena were investigated in a heat recovery system devoted to a rotary kiln facility. The streamwise and spanwise components of the velocity and the Reynolds stress tensor are firstly validated using an experimental benchmark. The effect of the axial flow rates is studied at a fixed rotational velocity. It is shown that the streamwise velocity component damps the vortex flow organization known in Couette−Taylor (CT) flow. The bulk region and its wall footprint are therefore characterized by various methods (spectral and statistical analysis, Q-criterion). It is shown that the turbulent kinetic energy of the streamwise component in the near-wall region is augmented leading to a multi-scale nature of turbulence.

Offshore wind energy has become a hot spot in new-energy development due to its abundant reserves, long power generation time, high unit capacity and low land occupation. In response to the current situation whereby wind energy, and natural and human factors have not been taken into account in the selection of sites for offshore wind-energy-resource development in the traditional “21st Century Maritime Silk Road” region, this paper intends to establish a new risk assessment framework that comprehensively considers the influence of wind resources, the natural environment, and the geopolitical and humanistic environment. The rationality of the new index system and weight determination methods are separately investigated. Some interesting results are obtained by comparing the new framework with traditional frameworks. The results show that the Persian Gulf, the Timor Sea in northern Australia, and the northern part of Sri Lanka in southern India are rich in wind-energy resources and have... [more]

Knowledge of the distinctive frequencies and amplitudes of broken rotor bar (BRB) faults in the induction motor (IM) is essential for most fault diagnosis methods. Fast Fourier transform (FFT) is widely applied to diagnose the faults within BRBs. However, this method does not provide satisfactory results if it is applied directly to the stator current signal at low slip because a high-resolution spectrum is required to separate the different components of the frequency. To address this problem, this paper proposes an efficient method based on a Hilbert fast Fourier transform (HFFT) approach, which is used to extract the envelope from the stator current using the Hilbert transform (HT) at low slip. Then, the stator current envelope is analyzed using the fast Fourier transform (FFT) to obtain the amplitude and frequency of the particular harmonic. These data were recently collected and selected as BRB fault features and were employed as adaptive neuro-fuzzy inference system (ANFIS) input... [more]

A hybrid short-term wind power prediction model based on data decomposition and combined deep neural network is proposed with the inclusion of the characteristics of fluctuation and randomness of nonlinear signals, such as wind speed and wind power. Firstly, the variational mode decomposition (VMD) is used to decompose the wind speed and wind power sequences in the input data to reduce the noise in the original signal. Secondly, the decomposed wind speed and wind power sub-sequences are reconstructed into new data sets with other related features as the input of the combined deep neural network, and the input data are further studied for the implied features by convolutional neural network (CNN), which should be passed into the long and short-term memory neural network (LSTM) as input for prediction. At the same time, the improved particle swarm optimization algorithm (IPSO) is adopted to optimize the parameters of each prediction model. By superimposing each predicted sub-sequence, th... [more]

Increased focus on sustainability and energy decentralization has positively impacted the adoption of nanogrids. With the tremendous growth, load forecasting has become crucial for their daily operation. Since the loads of nanogrids have large variations with sudden usage of large household electrical appliances, existing forecasting models, majorly focused on lower volatile loads, may not work well. Moreover, abrupt operation of electrical appliances in a nanogrid, even for shorter durations, especially in “Peak Hours”, raises the energy cost substantially. In this paper, an ANN model with dynamic feature selection is developed to predict the hour-ahead load of nanogrids based on meteorological data and a load lag of 1 h (t-1). In addition, by thresholding the predicted load against the average load of previous hours, peak loads, and their time indices are accurately identified. Numerical testing results show that the developed model can predict loads of nanogrids with the Mean Square... [more]

The safety of power transmission systems in wind turbines is crucial to the wind turbine’s stable operation and has attracted a great deal of attention in condition monitoring of wind farms. Many different intelligent condition monitoring schemes have been developed to detect the occurrence of defects via supervisory control and data acquisition (SCADA) data, which is the most commonly applied condition monitoring system in wind turbines. Normally, artificial neural networks are applied to establish prediction models of the wind turbine condition monitoring. In this paper, an alternative and cost-effective methodology has been proposed, based on the group method of data handling (GMDH) neural network. GMDH is a kind of computer-based mathematical modelling and structural identification algorithm. GMDH neural networks can automatically organize neural network architecture by heuristic self-organization methods and determine structural parameters, such as the number of layers, the number... [more]

Over the past few years, there has been a significant increase in the interest in and adoption of solar energy all over the world. However, despite ongoing efforts to protect photovoltaic (PV) plants, they are continuously exposed to numerous anomalies. If not detected accurately and in a timely manner, anomalies in PV plants may degrade the desired performance and result in severe consequences. Hence, developing effective and flexible methods capable of early detection of anomalies in PV plants is essential for enhancing their management. This paper proposes flexible data-driven techniques to accurately detect anomalies in the DC side of the PV plants. Essentially, this approach amalgamates the desirable characteristics of ensemble learning approaches (i.e., the boosting (BS) and bagging (BG)) and the sensitivity of the Double Exponentially Weighted Moving Average (DEWMA) chart. Here, we employ ensemble learning techniques to exploit their capability to enhance the modeling accuracy a... [more]

Researchers have made great progress in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracy, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weather due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for sustainable energy), a novel deep learning-based auto-selective approach and tool that, instead of generalizing a specific model for all climates, predicts the best performing deep learning model for global horizontal irradiance (GHI) forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets created through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyze the tool in great detail through a variety of metrics and means for performance... [more]

The exploration of superhydrophobic drag reduction has been and continues to be of significant interest to various industries. In the present work, direct numerical simulation (DNS) is utilized to investigate the effect of the parameters on the drag-reducing performance of superhydrophobic surfaces (SHS). Simulations with a friction Reynolds number of 180 were carried out at solid fraction values of ϕs=116,111, and 14, and three distribution shapes: aligned, staggered, and random. The top wall is the smooth one, and the bottom wall is a superhydrophobic surface (SHS). Drag reduction and Reynolds stress profiles are compared for all cases. The turbulent kinetic energy budget, including production, dissipation, and diffusion, is presented with respect to the solid fraction and type of distribution to investigate the drag reduction mechanism. The sizes of the longitudinal vortices and formation of hairpin vortices are investigated through the observation of coherent structures. The simula... [more]

Power quality (PQ) problems, including voltage sag, flicker, and harmonics, are the main concerns for the grid operator. Among these disturbances, voltage sag, which affects the sensitive loads in the interconnected system, is a crucial problem in the transmission and distribution systems. The determination of the voltage sag relative location as a downstream (DS) and upstream (US) is an important issue that should be considered when mitigating the sag problem. Therefore, this paper proposes a novel approach to determine the voltage sag relative location based on voltage sag event records of the power quality monitoring system (PQMS) in the real distribution system. By this method, the relative location of voltage sag is defined by Gaussian naive Bayes (Gaussian NB) and K-nearest neighbors (K-NN) algorithms. The proposed methods are compared with support vector machine (SVM) and artificial neural network (ANN). The results indicate that K-NN and Gaussian NB algorithms define the relati... [more]

With the increasing awareness of environmental protection and the support of national policy, as well as the maturing of solar power generation technology, solar power generation has become one of the most promising renewable energies. However, due to changes in external factors such as season, time, weather, cloud cover, etc., solar radiation is uncertain, and it is difficult to predict energy output, even for the next hour. This inherent instability is a particularly difficult issue for the prediction of energy output in the effective operation of solar power systems. This paper proposes a grey wolf optimization (GWO)-based general regression neural network (GRNN), which is expected to provide more accurate predictions with shorter computational times. Therefore, a self-organizing map (SOM) is utilized to realize the weather clustering and the training of the GRNN with a GWO model. The performance of the proposed model is investigated using short-term and ultra-short-term forecasting... [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]

Generation Z is gaining more and more importance in the market—not only is it attaining purchasing power, but it is also setting trends. This is the generation that spends a lot of time on various social media channels, and the content posted there is a source of information, inspiration and motivation for them. Its representatives are very skeptical about traditional marketing messages, so the best way to reach them is to use influencer marketing. They are also sensitive to environmental problems and ecology. For this reason, the purpose of this paper was to identify the possibility of using influencer marketing to promote green energy in the perspective of Generation Z in Poland. The CAWI method of research was carried out April−June 2022 on a sample of 533 people aged 18 to 26, selected using a quota method. The analysis used statistically significant structure indices (percentages) and measures of correlations between the variables. The results presented confirmed the enormous popu... [more]

Partial discharge (PD) has caused considerable challenges to the safety and stability of high voltage equipment. Therefore, highly accurate and effective PD detection has become the focus of research. Hilbert−Huang Transform (HHT) features have been proven to have great potential in the PD analysis of transformer, gas insulated switchgear and power cable. However, due to the insufficient research available on the PD features of power lines, its application in the PD recognition of power lines has not yet been systematically studied. In the present study, an enhanced light gradient boosting machine methodology for PD recognition is proposed; the HHT features are extracted from the signal and added to the feature pool to improve the performance of the classifier. A public power-line PD recognition contest dataset is introduced to evaluate the effectiveness of the proposed feature. Numerical studies along with comparison results demonstrate that the proposed method can achieve promising p... [more]

Underground mining is one of the riskiest industries. It is well established that the investigation of geomechanical parameters at the design stage of an underground mine provides the approximate rock mass characteristics, which are associated with some risks in the design. From a realistic risk assessment point of view, it is essential to classify risky design parameters as relevant to risk groups and determine a suitable weighting strategy for risk-prone elements aiming at risk assessment. Therefore, a realistic weighting procedure is an essential step in making realistic design decisions to increase the safety of mining operations and economic vitality. This study aimed to develop a realistic weighting procedure to assess and compare various geomechanical parameters that pose a risk to opening stability. In this research, sub-level stoping mining methods, which are commonly used in the Kazakhstan mining industry, were selected to test the developed weighting algorithm. In this study... [more]

The present study numerically investigated a cylinder under oscillating motions at a low Reynolds number. The effects of two oscillation frequencies and amplitudes on the lift drag coefficient, near-field surface pressure fluctuation, and far-field noise were studied. The models were examined at a Mach number of 0.05, corresponding to a Reynolds number of 1.0 × 105. In this paper, the incompressible Navier−Stokes equations (INSE) and linearized perturbed compressible equations (LPCE) were coupled to form a hybrid noise prediction method, which was used to solve the flow field and acoustic radiation field. Based on the simulation results of the acoustic radiation field, the frequency characteristics of the acoustic waves were analyzed by the dynamic modal decomposition (DMD) method. It was observed that when the oscillation amplitude was the same, the variation amplitude and mean value of the lift-drag coefficient increased with the increase in the oscillation frequency. Under the same... [more]

By conserving natural resources and reducing the consumption of fossil fuels, sustainable energy development plays a crucial role in energy planning. Specifically, demand-side planning must be researched and anticipated based on electricity consumption at the grounded level. Due to the global warming crisis, atmospheric conditions are among the most influential components that have altered electricity consumption patterns. In this study, 66 climate variables from the ERA5 reanalysis and the observed power demand at four grid substations (GSs) in Cambodia were examined using recurrent neural networks (RNNs). Using the cross-correlation function between power demand and each climate variable, statistically significant climate variables were sorted out. In addition, a wide range of feedback delays (FDs) was generated from the data on power demand and defined using 95% confidence intervals. The combination of the improved complete ensemble empirical mode decomposition with adaptive noise (... [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]

Sustainable public transport is an important factor to boost urban economic development, and it is also an important part of building a low-carbon environmental society. The application of driverless technology in public transport injects new impetus into its sustainable development. Road traffic sign recognition is the key technology of driverless public transport. It is particularly important to adopt innovative algorithms to optimize the accuracy of traffic sign recognition and build sustainable public transport. Therefore, this paper proposes a convolutional neural network (CNN) based on k-means to optimize the accuracy of traffic sign recognition, and it proposes a sparse maximum CNN to identify difficult traffic signs through hierarchical classification. In the rough classification stage, k-means CNN is used to extract features, and improved support vector machine (SVM) is used for classification. Then, in the fine classification stage, sparse maximum CNN is used for classificati... [more]

Different types of partial discharge (PD) in gas-insulated switchgear (GIS) cause different damage to GIS insulation, correctly identifying the PD type is very important for evaluating the insulation status of GIS. This paper proposes a PD pattern recognition method based on an improved feature fusion convolutional neural network (IFCNN) to fully use the time-frequency features of PD pulses to realize PD pattern recognition. Firstly, the one-dimensional time-domain feature sequence of the PD pulse and the corresponding wavelet time-frequency diagram are applied as inputs. Secondly, the convolutional neural network (CNN) with two parallel channels is used for feature extraction, the extracted fault information is fused, and the shallow features of the wavelet time-frequency diagram are fused to prevent feature loss caused by pooling operation. Finally, the extracted features are sent to the classifier to recognize different types of PD. The discharge data of different types of PD are ob... [more]

Pressure-retarded osmosis (PRO) is a promising technology that harvests salinity gradient energy. Even though PRO has great power-generating potential, its commercialization is currently facing many challenges. In this regard, this review highlights the discrepancies between the reported power density obtained by lab-scale PRO systems, as well as numerical investigations, and the significantly low power density values obtained by PRO pilot plants. This difference in performance is mainly due to the effect of a pressure drop and the draw pressure effect on the feed channel hydrodynamics, which have significant impacts on large-scale modules; however, it has a minor or no effect on small-scale ones. Therefore, this review outlines the underlying causes of the high power density values obtained by lab-scale PRO systems and numerical studies. Moreover, other challenges impeding PRO commercialization are discussed, including the effect of concentration polarization, the solution temperature... [more]

This study proposes a method for improving the capability of a data-driven multi-agent system (MAS) to perform condition monitoring and fault detection in industrial processes. To mitigate the false fault-detection alarms, a co-operation strategy among software agents is proposed because it performs better than the individual agents. Few steps transform this method into a valuable procedure for improving diagnostic certainty. First, a failure mode and effects analysis are performed to select physical monitoring signals of the industrial process that allow agents to collaborate via shared signals. Next, several artificial neural network (ANN) models are generated based on the normal behavior operation conditions of various industrial subsystems equipped with monitoring sensors. Thereafter, the agents use the ANN-based expected behavior models to prevent false alarms by continuously monitoring the measurement samples of physical signals that deviate from normal behavior. Finally, this me... [more]