This paper presents the use of a fuzzy-based statistical feature extraction from the air gap disturbances for diagnosing broken rotor bars in large induction motors fed by line or an inverter. The method is based on the analysis of the magnetic flux density variation in a Hall Effect Sensor, installed between two stator slots of the motor. The proposed method combines a fuzzy inference system and a support vector machine technique for time-domain assessment of the magnetic flux density, in order to detect a single fault or multiple broken bars in the rotor. In this approach, it is possible to detect not only the existence of failures, but also its severity. Moreover, it is not necessary to estimate the slip of the motor, usually required by other methods and the damaged rotor detection was also evaluated for oscillating load conditions. Thus, the present approach can overcome some drawbacks of the traditional MCSA method, particularly in operational cases where false positive and false... [more]

This article studies the combustion of refined sunflower, virgin sunflower and virgin rapeseed oils in a low-pressure auxiliary air fluid pulverization burner in order to establish the optimal operating conditions. The influence of varying the type of vegetable oil, fuel flow rate and secondary airflow rate in the combustion process was analyzed. These three factors are independent in the combustion process, which means having to carry out numerous assays, combining the various factors with one another. Given the amount of variables to be optimized and the existence of three factors, a statistical approach is adopted to help interpret the results obtained and to evaluate how each factor influences the combustion results. Optimal combustion is determined based on three criteria, minimum pollutant emissions (CO, NOx and CxHy), maximum combustion performance, and minimum excess air. The result of this study showed that airflow was the principal factor affecting emissions, whereas for comb... [more]

The aim of this study was to develop a mathematical regression model for predicting end-use energy consumption in the residential sector. To this end, housing characteristics were collected through a field survey and in-depth interviews with residents of 71 households (15 apartment complexes) in Seoul, South Korea, and annual data on end-use energy consumption were collected from measurement systems installed within each apartment unit. Based on the data collected, correlativity between the field-survey data and end-use energy consumption was analyzed, and effective independent variables from the field-survey data were selected. Regression models were developed and validated for estimating six end uses of energy consumption: heating, cooling, domestic hot water (DHW), lighting, electric appliances, and cooking. Regression analysis for ventilation was not applied, and instead a calculation formula was derived, because the energy-consumption proportion was too low. The adj-R2 of the esti... [more]

Wind power, one of renewable energy resources, is a fluctuating source of energy that prevents its further participation in the power market. To improve the stability of the wind power injected into the power grid, a short-term wind speed predicting model is proposed in this work, named VMD-P-(ARIMA, BP)-PSOLSSVM. In this model, variational mode decomposition (VMD) is combined with phase space reconstruction (P) as data processing method to determine intrinsic mode function (IMF) and its input−output matrix in the prediction model. Then, the linear model autoregressive integrated moving average model (ARIMA) and typical nonlinear model back propagation neural network (BP) are adopted to forecast each IMF separately and get the prediction of short-term wind speed by adding up the IMFs. In the final stage, particle swarm optimization least squares support vector machine (PSOLSSVM) uses the prediction results of the two separate models from previous step for the secondary prediction. For... [more]

This paper focuses on the influence of detailed engineering maturities on offshore engineering, procurement, and construction (EPC) project procurement and construction cost performance. The authors propose a detailed engineering completion rating index system (DECRIS) to estimate the engineering maturities, from contract award to beginning of construction or steel cutting. The DECRIS is supplemented in this study with an artificial neural network methodology (ANN) to forecast procurement and construction cost performances. The study shows that R2 and mean error values using ANN functions are 20.2% higher and 19.7% lower, respectively, than cost performance estimations using linear regressions. The DECRIS cutoff score at each gate and DECRIS forecasting performance of total cost impact were validated through the results of fifteen historical offshore EPC South Korean mega-projects, which contain over 300 procurement cost performance data points in total. Finally, based on the DECRIS an... [more]

Driven by the recent advances and applications of smart-grid technologies, our electric power grid is undergoing radical modernization. Microgrid (MG) plays an important role in the course of modernization by providing a flexible way to integrate distributed renewable energy resources (RES) into the power grid. However, distributed RES, such as solar and wind, can be highly intermittent and stochastic. These uncertain resources combined with load demand result in random variations in both the supply and the demand sides, which make it difficult to effectively operate a MG. Focusing on this problem, this paper proposed a novel energy management approach for real-time scheduling of an MG considering the uncertainty of the load demand, renewable energy, and electricity price. Unlike the conventional model-based approaches requiring a predictor to estimate the uncertainty, the proposed solution is learning-based and does not require an explicit model of the uncertainty. Specifically, the M... [more]

To examine the effects of solar heat utilization, it is necessary to classify the solar-use climate in East Asia based on climatic factors that affect load reduction by solar heating systems. In this study, annual load simulations are performed on weather data in 842 sites in Japan to analyze the climatic factors that impact the load reduction effect of the solar heating system. The analysis results were based on three climatic factors that strongly affect the heating load of a building: heating degree day (HDD18-18), daily solar global horizontal irradiance, and solar-heat available days. Furthermore, to distinguish the climate for the use of solar heat in East Asia, climate classification was done according to three climatic factors using the weather data of 1176 sites in East Asia. Consequently, the climate of solar thermal utilization in East Asia can be classified into 88 areas. Based on the regional classification for solar heat utilization proposed in this study, it will be poss... [more]

Wind power has grown popular in past recent years due to environmental issues and the search for alternative energy sources. Thus, the viability for wind power generation projects must be studied in order to attend to the environmental concerns and still be attractive and profitable. Therefore, this article aims to perform a sensitive analysis in order to identify the variables that influence most in the viability of a wind power investment for small size companies in the Brazilian northeast. For this, a stochastic analysis of viability through Monte Carlo Simulation (MCS) will be made and afterwards, Artificial Neural Networks (ANN) models will be applied for the most relevant variables identification. Through the sensitivity, it appears that the most relevant factors in the analysis are the speed of wind, energy tariff and the investment amount. Thus, the viability of the investment is straightly tied to the region where the wind turbine is installed, and the government incentives ma... [more]

To generate performance predictions of borehole thermal energy storage (BTES) systems for both seasonal and short-term storage of industrial excess heat, e.g., from high to low production hours, models are needed that can handle the short-term effects. In this study, the first and largest industrial BTES in Sweden, applying intermittent heat injection and extraction down to half-day intervals, was modelled in the IDA ICE 4.8 environment and compared to three years of measured storage performance. The model was then used in a parametric study to investigate the change in performance of the storage from e.g., borehole spacing and storage supply flow characteristics at heat injection. For the three-year comparison, predicted and measured values for total injected and extracted energy differed by less than 1% and 3%, respectively and the mean relative difference for the storage temperatures was 4%, showing that the performance of large-scale BTES with intermittent heat injection and extrac... [more]

Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium-ion battery is proposed. Firstly, the multi-scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regenerat... [more]

Wind power forecasting plays a vital role in renewable energy production. Accurately forecasting wind energy is a significant challenge due to the uncertain and complex behavior of wind signals. For this purpose, accurate prediction methods are required. This paper presents a new hybrid approach of principal component analysis (PCA) and deep learning to uncover the hidden patterns from wind data and to forecast accurate wind power. PCA is applied to wind data to extract the hidden features from wind data and to identify meaningful information. It is also used to remove high correlation among the values. Further, an optimized deep learning algorithm with a TensorFlow framework is used to accurately forecast wind power from significant features. Finally, the deep learning algorithm is fine-tuned with learning error rate, optimizer function, dropout layer, activation and loss function. The algorithm uses a neural network and intelligent algorithm to predict the wind signals. The proposed... [more]

Large-scale grids have gradually become the dominant trend in power systems, which has increased the importance of solving the challenges associated with large-scale economic dispatch (LED). An increase in the number of decision variables enlarges the search-space scale in LED. In addition to increasing the difficulty of solving algorithms, huge amounts of computing resources are consumed. To overcome this problem, we proposed a surrogate-assisted adaptive bat algorithm (GARCBA). On the one hand, to reduce the execution time of LED problems, we proposed a generalized regression neural network surrogate model based on a self-adaptive “minimizing the predictor” sampling strategy, which replaces the original fuel cost functions with a shorter computing time. On the other hand, we also proposed an improved hybrid bat algorithm (RCBA) named GARCBA to execute LED optimization problems. Specifically, we developed an evolutionary state evaluation (ESE) method to increase the performance of the... [more]

This article describes the results of laboratory tests on an oil-wedge-type electrode system, which were supplemented by FEM (finite element method) simulations. The studies were focused on the comparison of the partial-discharge inception voltage (PDIV) in the abovementioned system when immersed in different liquid dielectrics, namely inhibited mineral oil, uninhibited mineral oil, synthetic ester, and natural ester. In addition, the electric field stress obtained from the simulations was used in each case to determine the safe level for the actual transformer insulation. The studies were performed under AC voltage. Both electrical and optical detection methods were applied in order to properly determine the discharge inception. The statistical analysis of the results obtained from the laboratory measurements was carried out using Weibull distribution. We found that both mineral oils demonstrated better properties than the ester liquids in terms of resistance against partial-discharge... [more]

Solar energy is considered the most abundant form of energy available on earth. However, the efficiency of photovoltaic (PV) panels is greatly reduced due to the accumulation of dust particles on the surface of PV panels. The optimization of the cleaning cycles of a PV power plant through condition monitoring of PV panels is crucial for its optimal performance. Specialized equipment and weather stations are deployed for large-scale PV plants to monitor the amount of soil accumulated on panel surface. However, not much focus is given to small- and medium-scale PV plants, where the costs associated with specialized weather stations cannot be justified. To overcome this hurdle, a cost-effective and scalable solution is required. Therefore, a new centralized cloud-based solar conversion recovery system (SCRS) is proposed in this research work. The proposed system utilizes the Internet of Things (IoT) and cloud-based centralized architecture, which allows users to remotely monitor the amoun... [more]

Non-intrusive load monitoring (NILM) techniques are central techniques to achieve the energy sustainability goals through the identification of operating appliances in the residential and industrial sectors, potentially leading to increased rates of energy savings. NILM received significant attention in the last decade, reflected by the number of contributions and systematic reviews published yearly. In this regard, the current paper provides a meta-analysis summarising existing NILM reviews to identify widely acknowledged findings concerning NILM scholarship in general and neural NILM algorithms in particular. In addition, this paper emphasizes federated neural NILM, receiving increasing attention due to its ability to preserve end-users’ privacy. Typically, by combining several locally trained models, federated learning has excellent potential to train NILM models locally without communicating sensitive data with cloud servers. Thus, the second part of the current paper provides a su... [more]

To be able to predict the DC flashover characteristics of composite insulators, a four-layer BP neural network model is established with composite insulator shed structure parameters as the input. Three algorithms (gradient descent with momentum, RMSProp gradient descent, and Adam gradient descent) are applied, and the DC pollution flashover experimental data of composite insulators are used as training data. The results show that all three algorithms have good prediction capabilities. Among them, the Adam gradient descent model has the best prediction result, which can make the average prediction with an error of less than 4% and a maximum error of less than 8%, so these results can provide a reference for the design of composite insulators in DC voltage and product performance verifications.

Estimating the amount of solar radiation is very important in evaluating the amount of energy that can be received from the sun for the construction of solar power plants. Using machine learning tools to estimate solar energy can be a helpful method. With a high number of sunny days, Iraq has a high potential for using solar energy. This study used the Wavelet Artificial Neural Network (WANN), Wavelet Support Vector Machine (WSVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques to estimate solar energy at Wasit and Dhi Qar stations in Iraq. RMSE, EMA, R2, and IA criteria were used to evaluate the performance of the techniques and compare the results with the actual measured value. The results showed that the WANN and WSVM methods had similar results in solar energy modeling. However, the results of the WANN technique were slightly better than the WSVM technique. In Wasit and Dhi Qar stations, the value of R2 for the WANN and WSVM methods was 0.89 and 0.86, respectively. T... [more]

A businesses with a green label is associated with resources that are sustainable. This business is linked to the green economy, which can be described as a form of economy that is responsible in relation to the environment and economic growth, and thus complementary. In this type of economy, viable products are created, but also solutions and practices that take the environment into account. It is well known that eco-innovation activities are closely linked to the development of an eco-business. The research sample consisted of 10 countries, namely the Slovak Republic, the Czech Republic, Poland, Hungary, Austria, Germany, France, Italy, Sweden, and Finland, which were selected by purposive sampling. In this article, we look at eco innovations in selected countries, specifically ranking them, where we have divided countries into different levels, from countries that are at the super eco-innovation level, to countries that are in the middle zone, to countries that are significantly lag... [more]

Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because this machine is the core that provides the power and generates the motion of the industrial process. However, also, it is possible to diagnose other types of faults that occur in other elements in the kinematic chain, which are reflected as problems in the motor operation. With this purpose, the coupling between the motor and the final load in the chain requires, in many situations, the use of a gearbox that balances the torque−velocity relationship. Thus, the gear wear in this component is addressed in many works, but the study of gradual wear has not been completely covered yet at different operating frequencies. Therefore, in this work, a methodology is proposed based on statistical fea... [more]

One of the issues associated with the supply of electricity is its generation capacity, and this has led to prevalent power cuts and high costs of usage experienced in many developing nations, including South Africa. Historical research has shown that the annual rate of increase for electricity has grown at an alarming rate since 2008 and, in some years, has grown as much as 16%. The objectives of this study are to estimate the cost analysis of electricity usage at the twenty-nine residences of the University of Johannesburg (UJ-Res) and propose a model for our university, as well as other South African universities, to become more energy-efficient. This was achieved by analyzing the tariffs between 2015 and 2021. A forecast was made for a period of five years (2021 to 2026) using a non-linear autoregressive exogenous neural network (NARX-NN) time-series model. From the results obtained, the better NARX-NN model studied has a root mean squared error (RMSE) of 2.47 × 105 and a determina... [more]

The uniqueness of this investigation lies in empirically testing and proving the contagion spillover of Bitcoin attention to carbon futures. Specifically, several models are adopted to investigate the explanatory and predictive abilities of Bitcoin attention to carbon futures. The results can be generalized as follows. First, Bitcoin attention Granger causes the variation of carbon futures. Second, Bitcoin attention shows a negative impact on carbon futures and an addition, an invert U-shaped connection exists. Third, the Bitcoin attention-based models can beat the commonly used historical average benchmark during out-of-sample forecasting both in statistical and economic levels. Fourth, we complete robustness checks to certify that the contagion spillover from Bitcoin attention to the pricing of carbon futures does exist. Finally, we prove the linear and non-linear impacts from Bitcoin attention to realized volatility of carbon futures. All the results prove that Bitcoin attention is... [more]

Hydrogen refueling stations are one of the key infrastructure components for the hydrogen-fueled economy. Skid-mounted hydrogen refueling stations (SHRSs) can be more easily commercialized due to their smaller footprints and lower costs compared to stationary hydrogen refueling stations. The present work modeled hydrogen explosions in a skid-mounted hydrogen refueling station to predict the overpressures for hydrogen-air mixtures and investigate the protective effects for different explosion vent layouts and protective wall distances. The results show that the explosive vents with the same vent area have similar overpressure reduction effects. The layout of the explosion vent affects the flame shape. Explosion venting can effectively reduce the inside maximum overpressure by 61.8%. The protective walls can reduce the overpressures, but the protective walls should not be too close to the SHRS because high overpressures are generated inside the walls due to the confined shock waves. The... [more]

The tightening of diesel pollutant emission regulations has made Internal Combustion Engine (ICE) management through steady-state maps obsolete. To overcome the map’s scarce performance and efficiently manage the engine, control systems must cope with ICE transient operations, the coupling between its subsystem dynamics, and the tradeoff between different requirements. The work demonstrates the effectiveness of a reference generator that coordinates the air path and combustion control systems of a turbocharged heavy-duty diesel engine. The control system coordinator is based on neural networks and allows for following different engine-out Nitrogen Oxide (NOx) targets while satisfying the load request. The air path control system provides the global conditions for the correct functioning of the engine, targeting O2 concentration and pressure in the intake manifold. Through cooperation, the combustion control targets Brake Mean Effective Pressure (BMEP) and NOx to react to rapid changes... [more]

Electronic component classification often constitutes the uncomplicated task of classifying a single object on a simple background. It is because, in many applications, a technological process employs constant lighting conditions, a fixed camera position, and a designated set of classified components. To date, there has not been an adequate attempt to develop a method for object classification under the above conditions in industrial applications. Therefore, this work focuses on the classification problem of a particular technological process. The process classifies electronic components on an assembly line using a fixed-mounted camera. The research investigated all the essential steps required to build a classification system, such as image acquisition, database creation, and neural network development. The first part of the experiment was devoted to creating an image dataset utilising the proposed image acquisition system. Then, custom and pre-trained networks were developed and test... [more]

The energy crisis caused first by the COVID-19 pandemic and continued by the Russo-Ukrainian War has demonstrated that energy is a determining factor in the conduct of activities of any state. Several studies have examined the relationship between energy consumption and economic growth, developing various theories, but there is no consensus. This study investigates relationships by analyzing several regression models and choosing the fittest. Then, the verification of its validity is performed through the neural network, which gives greater credibility to the results obtained. Furthermore, using a structural analysis, the investigation is expanded to ranking the impact of sector-specific energy consumption on economic growth. The research includes data from 1995 to 2020 for the European Union (EU) and Romania. The results indicate that short-term energy consumption can have a positive or negative impact on economic growth, both in the EU and Romania. The structural analysis highlights... [more]