Accurate and effective wind power prediction plays an important role in wind power generation, distribution, and management. Inthis paper, a hybrid algorithm based on gradient descent and meta-heuristic optimization is designed to improve the accuracy of prediction and reduce the computational burden. The hybrid algorithm includes three steps: in the first step, we use the gradient descent algorithm to get the initial parameters. Secondly, we input the initial parameters into the meta-heuristic optimization algorithm to search for the “best parameters” (high-quality inferior solutions). Finally, we input optimized parameters into the RMSProp optimization algorithm and conduct gradient descent again to find a better solution. We used 2021 wind power data from Guangxi, China for the experiment. The results show that the hybrid prediction algorithm has better performance than the traditional Back Propagation (BP) in accuracy, stability, and efficiency.

The reliability of a coal mill's operation is strongly connected with optimizing the combustion process. Monitoring the temperature of a dust−air mixture significantly increases the coal mill's operational efficiency and safety. Reliable and accurate information about disturbances can help with optimization actions. The article describes the application of an additive regression model and data mining techniques for the identification of the temperature model of a dust−air mixture at the outlet of a coal mill. This is a new approach to the problem of power unit modeling, which extends the possibilities of multivariate and nonlinear estimation by using the backfitting algorithm with flexible nonparametric smoothing techniques. The designed model was used to construct a disturbance detection system in the position of hot and cold air dampers. In order to achieve the robust properties of the detection systems, statistical measures of the differences between the real and modeled temperature... [more]

Severe slugging is an unstable multiphase flow pattern occurs in a pipeline riser with low gas and liquid flowrates. It is highly undesired in practical operation because of the pressure and mass flow oscillations induced. Riser outlet choking has shown effectiveness in eliminating or reducing the severity of the slugging. This work presents an experimental and numerical study on the elimination of severe riser-induced slug by means of riser outlet choking. The test loop consists of a horizontal pipeline with 50 mm i.d. and 15 m in length, followed by a downward inclined section and a vertical riser of 2 m. It was found that by choking the flow at riser outlet, flow pattern in the riser changes from severe slugging first into slug flow and then into bubbly flow. The recognition of the flow regimes was basically according to the trends of the riser base pressure. The flow patterns were characterized in terms of pressure at riser base, as well as liquid holdup at riser top. A numerical m... [more]

Many authors have reported the use of deep learning techniques to model wind power forecasts. For shorter-term prediction horizons, the training and deployment of such models is hindered by their computational cost. Neuromorphic computing provides a new paradigm to overcome this barrier through the development of devices suited for applications where latency and low-energy consumption play a key role, as is the case in real-time short-term wind power forecasting. The use of biologically inspired algorithms adapted to the architecture of neuromorphic devices, such as spiking neural networks, is essential to maximize their potential. In this paper, we propose a short-term wind power forecasting model based on spiking neural networks adapted to the computational abilities of Loihi, a neuromorphic device developed by Intel. A case study is presented with real wind power generation data from Ireland to evaluate the ability of the proposed approach, reaching a normalised mean absolute error... [more]

High-performance thermal insulators allow a dramatic reduction in the thickness of coatings, thanks to their low thermal conductivity. This study provides an overview about thermal insulation materials, with regards to heat reflective insulators in particular. Then, the numerical investigation method adopted to compute the thermal resistance associated with reflective insulators is introduced. This method has been used in turn to check the accuracy of the declared, measured performance of different, heat-reflective materials on the market. Many manufacturers of reflective insulators were available to provide information and a good agreement between the declared and expected thermal resistance has been found. The choice of a non-experimental approach is meant to check the validity of an already performed test on a reflective insulator using a predictive approach instead of standard, additional testing. Then, the insulation of five typical walls at three different sites in Italy has been... [more]

Electrical power transformers are the most exorbitant and tactically prominent components of the South African electrical power grid. In contrast, they are burdened by internal winding faults predominantly on account of insulation system failure. It is essential that these faults must be swiftly and precisely uncovered and suitable measures should be adopted to separate the faulty unit from the entire system. The frequency response analysis (FRA) is a technique for tracking a transformer’s mechanical integrity. Nevertheless, classifying the category of the fault and its gravity by benchmarking measured FRA responses is still backbreaking and for the most part, anchored in personnel proficiency. This work presents a quantum leap to normalize the FRA interpretation procedure by suggesting an interpretation code criteria based on an empirical survey of transformers ranging from 315 kVA to 40 MVA. The study then proposes an analysis of variance (ANOVA) based interpretation tool for diagnos... [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]

Rapid transients are particularly dangerous in industrial hydro-transport systems, where solid-liquid mixtures are transported via long pressure pipelines. A mathematical description of such flow is difficult due to the complexity of phenomena and difficulties in determining parameters. The main aim of the study was to examine the influence of the simplified mixture density and wave celerity description on satisfactory reproduction of pressure characteristics during the transient flow of slurry at low concentrations. The paper reports and discusses the selected aspects of experimental and numerical analyses of transient slurry flow in a polyethylene pipe. The experiments were conducted by using the physical model of a slurry’s transportation pressure. The aim of the experiments was to determine the wave celerity during a transient flow in slurries. A low concertation of slurries, which was used during experiments, is typical for one of the biggest slurry networks in Poland. A compariso... [more]

Short-term load forecasting (STLF) has a significant role in reliable operation and efficient scheduling of power systems. However, it is still a major challenge to accurately predict power load due to social and natural factors, such as temperature, humidity, holidays and weekends, etc. Therefore, it is very important for the efficient feature selection and extraction of input data to improve the accuracy of STLF. In this paper, a novel hybrid model based on empirical mode decomposition (EMD), a one-dimensional convolutional neural network (1D-CNN), a temporal convolutional network (TCN), a self-attention mechanism (SAM), and a long short-term memory network (LSTM) is proposed to fully decompose the input data and mine the in-depth features to improve the accuracy of load forecasting. Firstly, the original load sequence was decomposed into a number of sub-series by the EMD, and the Pearson correlation coefficient method (PCC) was applied for analyzing the correlation between the sub-s... [more]

The rubber ring is an essential component of high-pressure hydrogen storage systems. However, the fretting damage can lead to the seal failure of the rubber ring, which may cause hydrogen leakage. Rubber X-ring has been proven to own excellent static sealing performance, while its fretting characteristics under high-pressure hydrogen remain unclear. In this study, a numerical model is developed to explore the fretting characteristics of the X-ring combined seal, in which the effect of hydrogen swelling is well considered. The stress distribution of the fretting seal and the effects of fretting amplitude, friction coefficient, hydrogen pressure, and pre-compression ratio on the fretting behavior of the X-ring are investigated. Moreover, the similarities and differences in the fretting performance of X-ring and O-ring under high-pressure hydrogen are discussed. It is shown that the evolution of the stress concentration zone inside the X-ring is closely linked to the cover’s drag directio... [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]

Aiming at the issues of unreasonable cooperation schemes and inappropriate setting of parameters of the refrigeration system with multi-chiller plants, this paper presents a cooperative optimization method to improve the energy performance of the system composed of water-cooled chillers and air-cooled heat pumps. The cooperative optimization process includes scheme optimization and parameter optimization. To content the dynamic cooling load, the working sequence of air-cooled heat pumps and water-cooled chillers with variable frequency chilled water pumps is first optimized. Based on the optimal scheme, a back-propagation neural network (BPNN) coupled with particle swarm optimization (PSO) is implemented to explore the preferred operating parameters of multiple chiller plants corresponding to the best coefficient of performance (COP). Compared with the performance of the initial operation module, the energy consumption of the water pump and fan decreases by over 50%, and the COP of the... [more]

There are many factors affecting the assembly quality of rotate vector reducer, and the assembly quality is unstable. Matching is an assembly method that can obtain high-precision products or avoid a large number of secondary rejects. Selecting suitable parts to assemble together can improve the transmission accuracy of the reducer. In the actual assembly of the reducer, the success rate of one-time selection of parts is low, and “trial and error assembly” will lead to a waste of labor, time cost, and errors accumulation. In view of this situation, a dendritic neural network prediction model based on mass production and practical engineering applications has been established. The size parameters of the parts that affected transmission error of the reducer were selected as influencing factors for input. The key performance index of reducer was transmission error as output index. After data standardization preprocessing, a quality prediction model was established to predict the transmiss... [more]

Currently, AC motors are a key element of industrial and commercial drive systems. During normal operation, the machines may become damaged, which may pose a threat to the users. Therefore, it is important to develop a fault detection method that allows for the detection of a fault at an early stage. Among the currently used diagnostic systems, applications based on deep neural structures are dynamically developed. Despite many examples of applications of deep learning methods, there are no formal rules for selecting the network structure and parameters of the training process. Such methods would make it possible to shorten the implementation process of deep networks in diagnostic systems of AC machines. The article presents a detailed analysis of the influence of deep convolutional network hyperparameters and training procedures on the precision of the interturn short-circuits detection system. The studies take into account the direct analysis of phase currents through the convolution... [more]

Increasingly people, especially those residing in urban areas with the urban heat island effect, are getting exposed to extreme heat due to ongoing global warming. A number of methods have been developed, so far, to assess urban heat vulnerability in different locations across the world concentrating on diverse aspects of these methods. While there is growing literature, thorough review studies that compare, contrast, and help understand the prospects and constraints of urban heat vulnerability assessment methods are scarce. This paper aims to bridge this gap in the literature. A systematic literature review with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach is utilized as the methodological approach. PRISMA is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses. The results are analyzed in three aspects—i.e., indicators and data, modelling approaches, and validation approaches. The main findings disclo... [more]

Both the compressor performance and the 3D flows inside the stator passage are significantly impacted by the stator hub gap. The interplay between leakage flow and corner separation within a cantilevered stator of a highly loaded, low-speed axial compressor with a succession of stator hub gaps was examined numerically in this paper. Firstly, the simulated results were compared with the measured results, including the compressor characteristics, the 3D flow structures, and the flow fields at the stator outlet. The results revealed that the used CFD solver, as well as the corresponding setup, can reproduce the flow not only in terms of the trend along with the stator hub gap, but also in terms of the specific scale of the 3D flow structure. Hence, it is feasible enough to be applied in the present investigation. Secondly, the flow mechanisms of the interplay between the corner separation and the leakage flow with different stator hub gaps were analyzed. It was found that the velocity of... [more]

Employment and the competencies of employees in the energy sector are coming into particular prominence in economies around the world. It is one of the few sectors positively affected by the COVID-19 pandemic. As a result, a significant global change in the awareness of society occurred in favor of increasing pro-health and pro-environmental activities, which can be seen in the green transformation. Poland can also boast such changes in recent years, as evidenced by the dynamic development of renewable energy sources (boom for photovoltaics) and the increase in prosumption. Correlated with this is the increase in demand for employees with specific competencies, the so-called multi-competencies that are a compilation of technical, business, and soft and hard competencies, as well as interdisciplinary ones. The paper emphasizes the need to better adjust the education system to the real needs of the labor market in a turbulent environment with the use of the Sectoral Qualifications Framew... [more]

The rapid adoption of the smart grid’s nascent load-management capabilities, such as demand-side management and smart home systems, and the emergence of new classes of controllable high-wattage loads, such as energy storage systems and electric vehicles, magnify the smart grid’s exposure to load-altering cyberattacks. These attacks aim at disrupting power grid services by staging a synchronized activation/deactivation of numerous customers’ high-wattage appliances. A proper defense plan is needed to respond to such attacks and maintain the stability of the grid, and would include prevention, detection, mitigation, incident response, and/or recovery strategies. In this paper, we propose a solution to detect load-altering cyberattacks using a time-delay neural network that monitors the grid’s load profile. As a case study, we consider a cyberattack scenario against demand-side management programs that control the loads of residential electrical water heaters in order to perform peak shav... [more]

To maintain reservoir pressure, water is injected into oil reservoirs. In carbonate rock, water quickly breaks through fractures and highly permeable formations to production wells. This study analyzes the effect of the permeability, oil viscosity, pressure drop, and distance on the water velocity from an injection well to a production well. In the Tempest MORE hydrodynamic simulator (Roxar), a three-layer model of an oil reservoir was created, and water flow from an injection well to a production well was simulated with various values of the permeability, oil viscosity, and bottom hole pressure. The water velocity in the reservoir was estimated based on the mobility factor (k/µo). The results showed that at a mobility factor of less than 2 μm2/Pa s at a distance of 100 m in the reservoirs, the time of water migration from the injection well to the production well increased sharply, and at a mobility factor of more than 2 μm2/Pa s, it became shorter. An analysis of the time of water mi... [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]

Although various lightning protection methods have been used in the industry, many outdoor high-voltage (HV) substations are still experiencing high failure rates due to lightning strikes. The applications of these rule-of-thumb-based methods generally lack coherence among the practitioners. IEC 62305-2 provides a systematic way for practitioners to assess the lightning risk for buildings or structures in a probabilistic way. However, this standard has not explicitly covered the application of HV substations. Moreover, IEC 62305-2 involves a tedious set of risk factors which may hinder many practitioners from applying the aforementioned standards while other preferred rule-of-thumb methods are available. As IEC 62305-2 does not specify the applicability to lightning risk assessment in HV substations, this paper proposes a novel approach to complement the standard-based risk assessment process. During this integrated risk assessment process, significant risks are identified, followed by... [more]

This paper presents the results of an experiment on the effect of the cold plasma (He+O2 or He+Air) pre-sowing stimulation of seeds of the Wolska cultivar of onion on the process of their germination. Four groups of seeds characterized by different exposure times (60, 120, 240 and 480 s) were used. Untreated seeds were used as a control. The distance between the electrode and the tested material was 50 mm. Pre-sowing plasma stimulation improved germination parameters such as germination capacity and germination energy for all the tested groups relative to the control. The highest fractions of germinated seeds were observed for an exposure time of 120 s. Analysis of the data showed a statistically significant impact of RF plasma on the seed germination parameters of the onion. SEM analysis showed that the interaction with plasma produced tension in the cells, leading to a change in their shape. No visible damage to the onion seed cells was observed, apart from the effect of depletion of... [more]

This article analyzes and compares the integration of two different maximum power point tracking (MPPT) control methods, which are tested under partial shading and fast ramp conditions. These MPPT methods are designed by Improved Particle Swarm Optimization (IPSO) and a combination technique between a Neural Network and the Perturb and Observe method (NN-P&O). These two methods are implemented and simulated for photovoltaic systems (PV), where various system responses, such as voltage and power, are obtained. The MPPT techniques were simulated using the MATLAB/Simulink environment. A comparison of the performance of the IPSO and NN-P&O algorithms is carried out to confirm the best accomplishment of the two methods in terms of speed, accuracy, and simplicity.

Energy determines the social, economic, and environmental aspects that enable the advancement of communities. For this reason, this paper aims to analyze the quality of the energy service in the Non-Interconnected Zones (NIZ) of Colombia. For this purpose, clustering techniques (K-means, K-medoids, divisive analysis clustering, and heatmaps) are applied for data analysis in the context of the NIZ to identify patterns or hidden information in the Colombian government data related to the state of the electricity service in these localities during the years 2019−2020. A descriptive statistical analysis and validation of the results of the clustering techniques is also carried out using R software. Through the implementation of clustering algorithms such as K-means, K-medoids, and divisive analysis clustering, potential areas for the development of renewable and alternative energy projects are identified, considering places with deficiencies in their current electricity service, higher con... [more]

Prior to the implementation of digitisation processes, the handwritten signature in an attendance sheet was the preferred way to prove the presence of each student in a classroom. The method is still preferred, for example, for short courses or places where other methods are not implemented. However, human verification of handwritten signatures is a tedious process. The present work describes two methods for classifying signatures in an attendance sheet as valid or not. One method based on Optical Mark Recognition is general but determines only the presence or absence of a signature. The other method uses a multiclass convolutional neural network inspired by the AlexNet architecture and, after training with a few pieces of genuine training data, shows over 85% of precision and recall recognizing the author of the signatures. The use of data augmentation and a larger number of genuine signatures ensures higher accuracy in validating the signatures.