One of the most critical aspects of integrating renewable energy sources into the smart grid is photovoltaic (PV) power generation forecasting. This ensemble forecasting technique combines several forecasting models to increase the forecasting accuracy of the individual models. This study proposes a regression-based ensemble method for day-ahead PV power forecasting. The general framework consists of three steps: model training, creating the optimal set of weights, and testing the model. In step 1, a Random forest (RF) with different parameters is used for a single forecasting method. Five RF models (RF1, RF2, RF3, RF4, and RF5) and a support vector machine (SVM) for classification are established. The hyperparameters for the regression-based method involve learners (linear regression (LR) or support vector regression (SVR)), regularization (least absolute shrinkage and selection operator (LASSO) or Ridge), and a penalty coefficient for regularization (λ). Bayesian optimization is perf... [more]

The grid-connected inverter is a vital power electronic equipment connecting distributed generation (DG) systems to the utility grid. The quality of the grid-connected current is directly related to the safe and stable operation of the grid-connected system. This study successfully constructed a robust control system for a grid-connected inverter through a dynamic recurrent fuzzy-neural-network imitating sliding-mode control (DRFNNISMC) framework. Firstly, the dynamic model considering system uncertainties of the grid-connected inverter is described for the global integral sliding-mode control (GISMC) design. In order to overcome the chattering phenomena and the dependence of the dynamic information in the GISMC, a model-free dynamic recurrent fuzzy-neural-network (DRFNN) is proposed as a major controller to approximate the GISMC law without the extra compensator. In the DRFNN, a Petri net with varied threshold is incorporated to fire the rules, and only the parameters of the fired rul... [more]

Many environmental parameters affect the performance of solar photovoltaics (PV), such as dust and temperature. In this paper, three PV technologies have been investigated and experimentally analyzed (mono, poly, and flexible monocrystalline) in terms of the impact of dust and thermal energy on PV behavior. Furthermore, a modular neural network is designed to test the effects of dust and temperature on the PV power production of six PV modules installed at Sohar city, Oman. These experiments employed three pairs of PV modules (one cleaned daily and one kept dusty for 30 days). The performance of the PV power production was evaluated and examined for the three PV modules (monocrystalline, polycrystalline, and flexible), which achieved 30.24%, 28.94%, and 36.21%, respectively. Moreover, the dust reduces the solar irradiance approaching the PV module and reduces the temperature, on the other hand. The neural network and practical models’ performance were compared using different indicator... [more]

We propose a method to predict performance variables according to the rotor slot shape of a three-phase squirrel cage induction motor using a convolution neural network (CNN) algorithm suitable for utilizing image data. The set of performance variables was labeled according to the images of each training dataset, and this set was generated from the efficiency, power factor, starting torque, and average torque. To verify the accuracy of the trained deep learning model, the analysis and prediction results of the CNN model were compared and verified with nine untrained double cage slot shapes and shapes optimized based on the root mean square error (RMSE). Although a large number of training data are required for high accuracy in the existing image processing deep learning model, the proposed deep learning method can predict the performance variables for various shapes with the same level of accuracy as the finite element analysis results using a small number of training data. Therefore,... [more]

Forest based power plants are alternatives in the diversification of domestic energy supply in Brazil, given the growing demand for electricity in recent decades. Evidences of space-time clusters contribute to the understanding of regional development associated with correlated activity. Therefore, this paper analyzed the space-time conglomerates for Brazilian forest-based power plants, from 2000 to 2019. The data used were from the Generation Information System of the National Electric Energy Agency (ANEEL)-SIGA. It detected the existence of clusters by means of scan statistics via space-time permutation, considering the high level of conglomeration. The results show the Center-South region of Brazil with the highest concentration of conglomerates, with black liquor and forest residues being the most used energy resources. The clusters with the highest installed power were in the early 2010s, with the black liquor plants. The regions with the formation of fast-growing forest plantatio... [more]

The authors of this paper propose a Markov-chain-based method for the synthesis of naturalistic, high-sampling-rate driving cycles based on the route segment statistics extracted from low-sampling-rate vehicle-tracking data. In the considered case of a city bus transport system, the route segments correspond to sections between two consecutive bus stations. The route segment statistics include segment lengths and maps of average velocity, station stop time, and station-stopping probability, all given along the day on an hourly basis. In the process of driving cycle synthesis, the transition probability matrix is built up based on the high-sampling-rate driving cycles purposely recorded in a separate reference city. The particular emphasis of the synthesis process is on satisfying the route segment velocity and acceleration boundary conditions, which may be equal to or greater than zero depending on whether a bus stops or passes a station. This enables concatenating the synthesized cons... [more]

Nowadays, the growing integration of renewable energy sources poses several challenges to electrical energy systems. The latter need be controlled by grid rules to ensure their stability and maintain the efficiency of renewable energy consumption. In this context, a novel HESS (hybrid energy storage system) control strategy, combining the PV (photovoltaic) generator with FLC (fuzzy logic control), SC (super-capacitor), and lithium-ion battery modules, is advanced. The proposed energy control rests on monitoring of the low-frequency and high-frequency electrical power components of the mismatch between power demand and generation, while applying the error component of the lithium-ion battery current. On accounting for the climatic condition and load variation considerations, the SC undertakes to momentarily absorb the high-frequency power component, while the low-frequency component is diverted to the lithium-ion battery. To improve the storage system’s performance, lifetime, and avoid... [more]

Jet fish pumps are efficient hydraulic machinery for fish transportation. Yet, the complex flow phenomenon in it is the major potential risk for damage to fish. The dangerous flow phenomena for fish, such as radial pressure gradient and exposure strain rate, are usually controlled by the structural parameters of jet fish pumps. Therefore, the injury rate of fish can be theoretically decreased by the structural optimization design of jet fish pumps. However, there is a complex nonlinear relation between flow phenomena and key structural parameters. To solve this problem, the present paper established a complex mapping between flow phenomena and structural parameters, based on computational fluid dynamics and a back-propagation neural network. According to this mapping, an NSGA-II multi-objective genetic algorithm was used to optimize the structure of jet fish pumps. The results showed that the optimized jet fish pumps could reduce the internal radial pressure gradient, exposure strain r... [more]

The growing population of cities means that they face many new challenges in improving their economic, social and environmental efficiency. These themes are relevant to the increasingly popular worldwide concept of smart cities. A smart city is a city that is friendly to people and the environment, in which people live better, safer, and healthier. From the point of view of management sciences, they are a result of creative development and the implementation of various solutions that should involve various stakeholders. The quality of life of city residents is largely influenced by logistics solutions, including the movement of both people and cargo in the city. This issue is directly relevant to the aim of this article, which is to try to determine the degree of cooperation between the city and city logistics stakeholders in the context of identifying gaps in the inclusion of logistics areas in the strategies of Polish cities. Both the role of stakeholders in actively shaping city str... [more]

This study presents applications of artificial neural networks and nonlinear optimization techniques for fault location in transmission lines using simulated data in an electromagnetic transient program and actual data occurring in transmission lines. The localization is performed by a modular structure of 4 neural networks and by the minimization of objective functions descriptive of the problem, defined according to the parameters of the line and the type of short circuit, submitted to the methods Quasi-Newton, Ellipsoidal, and Real Polarized Genetic Algorithm. The results obtained are compared statistically with those of a classical analytical method. The analysis of the variance of location errors presented by the methods revealed, with 5% significance, statistical evidence that allowed the conclusion that the type of method used affects fault location indication. In simulated scenarios, minor errors were obtained with the neural network and larger with the analytical method. For f... [more]

In this study, a three-phase four-wire distribution static compensator (DSTATCOM) is proposed to improve power quality, including the compensation of the three-phase unbalanced grid currents, the total harmonic distortion (THD) reduction of the grid current, and the power factor (PF) correction. Moreover, when different types of loads vary in the power system, the instantaneous power follows into or out of the DC-link capacitor in the DSTATCOM and results in poor transient responses of the grid current and DC-link voltage and performance deterioration. Hence, the DC-link voltage control plays a significant part in the DSTATCOM under load variation. For the purpose of mending the transient responses of the grid currents and DC-link voltage control and the performance of the DSTATCOM, the conventional proportional-integral (PI) controller is substituted with a novel online trained wavelet Takagi-Sugeno-Kang fuzzy neural network (WTSKFNN) controller in this study. Furthermore, the network... [more]

As telecommunications are becoming increasingly important for modern systems, ensuring secure data transmission is getting more and more critical. Specialised numerous devices that form smart grids are a potential attack vector and therefore is a challenge for cybersecurity. It requires the continuous development of methods to counteract this risk. This paper presents a heuristic approach to detecting threats in network traffic using statistical analysis of packet flows. The important advantage of this method is ability of intrusion detection also in encrypted transmissions. Flow information is processing by neural networks to detect malicious traffic. The architectures of subsequent versions of the artificial neural networks were generated based on the results obtained by previous iterations by searching the hyperparameter space, resulting in more refined models. Finally, the networks prepared in this way exhibited high performance while maintaining a small size—thereby making them an... [more]

The aim of this article is to diagnose the situation of electromobility in local government units of Polish municipalities. Besides the main features of the communes, the issue of type of strategy that is being built was raised, what are the trends in this respect, and how public transport fits into them? The empirical basis was a survey conducted in 2020 using the CAWI (computer assisted web interview) method, covering all 2477 communes in Poland. Responses were received from 2230 municipalities (90% response rate). Based on the statistical analysis, the main conclusion is that rural communes are less willing to implement the electromobility strategy, but if they decide to do so, they develop long-term strategies. Another conclusion is that the amount of budget revenue per capita does not affect decisions on building an electromobility strategy. Also, a general tendency in Poland is an increase in the propensity to invest in electromobility, mainly in infrastructure for users, compare... [more]

This study introduces Pakistan’s multidimensional energy security index (PMESI) and indices across dimensions from 1991 to 2020 through indicator optimization. Based on criteria, expert participation, and reliability testing, 27 indicators were identified and weighted based on dimension reduction utilizing the Varimax Rotation technique. As a result of robust evaluation framework, there has been a considerable change in Pakistan’s energy security when compared to other studies such as the energy security indicator of Pakistan (ESIP) and the energy security index of Pakistan (ESIOP). According to the findings, energy security decreased by 25% between 1991 and 2012, followed by a modest increase through 2020. During the study period, the “Affordability” dimension improved; however, the other four dimensions, namely “Availability,” “Technology,” “Governance,” and “Environment,” regressed. Few goals under the petroleum policy (1991), petroleum policy (2012), and power policy (2013) were pa... [more]

Load prediction with higher accuracy and less computing power has become an important problem in the smart grids domain in general and especially in demand-side management (DSM), as it can serve to minimize global warming and better integrate renewable energies. To this end, it is interesting to have a general prediction model which uses different standard machine learning models in order to be flexible enough to be used in different regions and/or countries and to give a prediction for multiple days or weeks with relatively good accuracy. Thus, we propose in this article a flexible hybrid machine learning model that can be used to make predictions of different ranges by using both standard neural networks and an automatic process of updating the weights of these models depending on their past errors. The model was tested on Mayotte Island and the mean absolute percentage error (MAPE) obtained was 1.71% for 30 min predictions, 3.5% for 24 h predictions, and 5.1% for one-week prediction... [more]

The smart grid includes wide-area applications in which inter-substation communication is required to realize innovative monitoring, protection, and control solutions. Internet-based data exchange, i.e., communication over Internet Protocol (IP), is regarded as the latest trend for inter-substation communication. Interoperability can be achieved via the use of standardized IEC 61850-90-5 messages communicating over IP. Wide-area applications can obtain benefits from IP-multicast technologies and use a one-to-many communication model among substations communicating across a communication network. Cellular Internet is being considered as a potential cost-efficient solution which can be used for the IP-multicast communication. However, it requires knowledge of communicating uncommon IP-multicast traffic over the Internet. Moreover, it presents challenges in terms of cybersecurity and real-time requirements. These challenges must be overcome to realize authentic and correct operation of th... [more]

The current work is devoted to studying combustion initiation inside the methane-oxygen torch igniter for a hybrid rocket motor. The ignition system can generate a wide range of power and oxidizer-to-fuel ratios. It has a self-cooled vortex combustion chamber with one fuel jet injector and one circumferential vortex oxidizer injector. The system adjusts the mass flow rates of the propellants through the control valves and organizes cooling of the wall and flame stabilization. Experimental analysis of the ignition limits was investigated on the laboratory test bench. The propellants’ pressure and mass-flow rates, combustion temperature, ignition delay, and spark frequency were controlled during the tests. The authors executed a series of tests with different propellants’ mass flow rates. As a result, the region of stable ignition was found as well as the regions of ignition failure or unreliable ignition. A previously validated numerical model was used to analyze the flow in the reliabl... [more]

The mining-induced water-conducting fracture zone (WCFZ) plays a critical role in roof water damage prevention and ecological protection. The measured heights of the WCFZ were collected from 52 working faces or boreholes in the Ordos Basin mining area. Four factors influencing the mining-induced height of the WCFZ, i.e., mining thickness, proportion coefficient of hard rock, working width, and mining depth, were analyzed. The optimal unitary function model of each factor and the height of the WCFZ were obtained through single-factor analysis. The grey correlation method and fuzzy ordered binary comparison method were used to determine the comprehensive weight, and the weighted improved multiple regression model was obtained by combination and iteration. The relative error of the model was basically controlled within 10%. Finally, taking the Qingshuiying Coalfield as an application case, we predicted the mining-induced height of the WCFZ by using the new prediction model. The spatial di... [more]

In this paper we present results of research on the transformation of chemical forms of two elements (Cu, Zn) that occurred at the highest concentration in sewage sludge being processed in a composting process. The factor that had impact on the direction of the observed transformation was the amount of straw added to the mix with sewage sludge at the batch preparation stage including elimination of an additional source of organic carbon (straw). The analysis of contents of Cu and Zn chemical forms was performed applying Tessiere’s methodology. It was ascertained that reduction of supplementation has positive impact on the allocation of tested elements in organic (IV) and residual (V) fractions with a simultaneous decrease of heavy metals mobile forms share in bioavailable fractions, mostly ion exchangeable (I) and carbonate (II). Using an artificial neural network (ANN), a tool was developed to classify composts based on Austrian standards taking into account only I ÷ IV fractions trea... [more]

In this paper, gamma attenuation has been utilised as a veritable tool for non-invasive estimation of the thickness of scale deposits. By simulating flow regimes at six volume percentages and seven scale thicknesses of a two phase-flow in a pipe, our study utilised a dual-energy gamma source with Ba-133 and Cs-137 radioisotopes, a steel pipe, and a 2.54 cm × 2.54 cm sodium iodide (NaI) photon detector to analyse three different flow regimes. We employed Fourier transform and frequency characteristics (specifically, the amplitudes of the first to fourth dominant frequencies) to transform the received signals to the frequency domain, and subsequently to extract the various features of the signal. These features were then used as inputs for the group method for data Hiding (GMDH) neural network framework used to predict the scale thickness inside the pipe. Due to the use of appropriate features, our proposed technique recorded an average root mean square error (RMSE) of 0.22, which is a v... [more]

In the drilling industry, it is of great significance to accurately predict the drag coefficient and settling velocity of drill cuttings falling in the non-Newtonian drilling fluid. However, the irregular shape of drill cuttings and the non-Newtonian rheological properties of drilling fluid (e.g., shear-thinning and yield stress behavior) make it challenging to predict the settling velocity. In this study, the velocity of particle settlement was studied by a visual device and high-speed camera system. Experimental data of the free settlement of 224 irregular drilling cuttings and 105 spherical particles in the Herschel−Bulkley fluid were obtained. A mechanical model dependent on the force balance of settlement particles was adopted to conduct a detailed statistical analysis of the experimental results, and a prediction model of the drag coefficient of spherical particles in the Herschel−Bulkley fluid was established. A two-dimensional shape description parameter is introduced to establ... [more]

The measurement of the growth rate, or the so-called inverse period, of a nuclear reactor is crucial for safety monitoring and control purposes. Due to the inevitable statistical fluctuation of neutron flux at low power-levels, it is difficult to precisely estimate the inverse period from the pulse counting data in the source range. Motivated by the equivalence of the measurement of inverse period and the differentiation of the logarithm of pulse count, a new differentiator is proposed, which is finite-time convergent with a bounded steady estimation error. The feasibility of this newly-built finite-time differentiator is verified by numerical simulation. Then, based on the pulse count data recorded during the startup of a test reactor, the differentiator is used to estimate the inverse period and its derivative, as well as the period and the reactivity of the reactor. The results show that the differentiator is capable of providing a satisfactory estimation of signal derivatives under... [more]

Knowledge of the number and distribution of oil palm trees during the crop cycle is vital for sustainable management and predicting yields. The accuracy of the conventional image processing method is limited for the hand-crafted feature extraction method and the overfitting problem occurs due to the insufficient dataset. We propose a modification of the Faster Region-based Convolutional Neural Network (FRCNN) for palm tree detection to reduce the overfitting problem and improve the detection accuracy. The enhanced FRCNN (EFRCNN) leads to improved performance for detecting objects (in the same image) when they are of multiple sizes by using a feature concatenation method. Transfer learning based on a ResNet50 model is used to extract the features of the input image. High-resolution images of oil palm trees from a drone are used to form the data set, containing mature, young, and mixed oil palm tree regions. We train and test the EFRCNN, the FRCNN, a CNN used recently for oil palm image... [more]

Porosity is a vital parameter in reservoir research. In the process of oil exploration, reservoir research is very important for oil and gas exploration. Because it is necessary to take cores for indoor test in order to accurately obtain the porosity value of cores, this process consumes significant manpower and material resources. Therefore, this paper introduces the method of machine learning to predict the porosity by using logging curves. This paper creatively develops a WOA (whale optimization algorithm) optimized Elman neural network model to predict porosity through logging parameters PE, DEN, M2R1, AC, GR, R25, R4 and CNL. Porosity measurement is constructed by taking cores for indoor experiments. It contains a total of 328 sample points. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity measured in the laboratory are used as the output parameter. In order to evaluate the perform... [more]

By reducing electricity consumption during peak times, peak shaving could reduce the need for carbon intensive resources and defer capacity related investments. Households, where they use electricity for space or water heating, are major contributors to the winter peak demand and promising candidates for related demand response (DR) initiatives. The impact of such initiatives is determined by comparing the actual consumption during a DR event to a baseline, i.e., the estimated consumption that would have occurred in the absence of an event. This paper explores the challenges associated with modeling a baseline in the context of residential winter DR programs with individual performance-based incentives. A sample of more than a thousand residential load profiles was used in this study to provide a statistical comparison of performance metrics for different baseline load models. Arithmetic, regression based, and matching-day models were considered. Results show that adjusted arithmetic m... [more]