The optimization of photovoltaic (PV) systems relies on the development of an accurate model of the parameter values for the solar/PV generating units. This work proposes a modified artificial jellyfish search optimizer (MJSO) with a novel premature convergence strategy (PCS) to define effectively the unknown parameters of PV systems. The PCS works on preserving the diversity among the members of the population while accelerating the convergence toward the best solution based on two motions: (i) moving the current solution between two particles selected randomly from the population, and (ii) searching for better solutions between the best-so-far one and a random one from the population. To confirm its efficacy, the proposed method is validated on three different PV technologies and is being compared with some of the latest competitive computational frameworks. The numerical simulations and results confirm the dominance of the proposed algorithm in terms of the accuracy of the final res... [more]

Worst-case execution time (WCET) is an important metric in real-time systems that helps in energy usage modeling and predefined execution time requirement evaluation. While basic timing analysis relies on execution path identification and its length evaluation, multi-thread code with critical section usage brings additional complications and requires analysis of resource-waiting time estimation. In this paper, we solve a problem of worst-case execution time overestimation reduction in situations when multiple threads are executing loops with the same critical section usage in each iteration. The experiment showed the worst-case execution time does not take into account the proportion between computational and critical sections; therefore, we proposed a new worst-case execution time calculation model to reduce the overestimation. The proposed model results prove to reduce the overestimation on average by half in comparison to the theoretical model. Therefore, this leads to more accurate... [more]

This paper reviews the fundamentals of the Exergy Cost Theory, an energy cost accounting methodology to evaluate the physical costs of products of energy systems and their associated waste. Besides, a mathematical and computationally approach is presented, which will allow the practitioner to carry out studies on production systems regardless of their structural complexity. The exergy cost theory was proposed in 1986 by Valero et al. in their “General theory of exergy savings”. It has been recognized as a powerful tool in the analysis of energy systems and has been applied to the evaluation of energy saving alternatives, local optimisation, thermoeconomic diagnosis, or industrial symbiosis. The waste cost formation process is presented from a thermodynamic perspective rather than the economist’s approach. It is proposed to consider waste as external irreversibilities occurring in plant processes. A new concept, called irreversibility carrier, is introduced, which will allow the identif... [more]

The article deals with issues related to the processes occurring in the wear result of steel surfaces of machine components in the presence of mineral grains. This type of destruction of cooperating surfaces usually takes place during the development of roadways or during mining of coal with use of longwall methods. Wear tests were carried out using the author’s ring-on-ring test stand, on which the conditions of real wear of machine components in the presence of rocks were simulated. An abrasive material based on clayey rocks with an admixture of carbonaceous substance was used in the tests. Based on the analyses, it was found that the obtained results related to the damages are typical for wear mechanisms: microcracking and low-cycle fatigue. On the surface of the steel samples, numerous effects of micro-cutting and chipping could be observed, which were the result of the clayey impact of wear products and grains of the mineral substance. Under friction, a part of the abrasive and th... [more]

In this paper, a brief survey of measurable factors affecting the adoption of cybersecurity enhancement methods in the smart grid is provided. From a practical point of view, it is a key point to determine to what degree the cyber resilience of power systems can be improved using cost-effective resilience enhancement methods. Numerous attempts have been made to the vital resilience of the smart grid against cyber-attacks. The recently proposed cybersecurity methods are considered in this paper, and their accuracies, computational time, and robustness against external factors in detecting and identifying False Data Injection (FDI) attacks are evaluated. There is no all-inclusive solution to fit all power systems requirements. Therefore, the recently proposed cyber-attack detection and identification methods are quantitatively compared and discussed.

The article presents the results obtained during the inspection of the load-bearing structure of a power unit that suffered from fire. The inspection, consisting in the assessment of both the structure’s technical condition and durability of welded joints, was performed on seven height levels of the power unit. The vibration spectrum of the unit’s steel structure was analyzed, and frequency characteristics were, thus, obtained for individual measurement levels. Thermal vision measurements were also performed in the unit’s all connection points to check for possible unsealing of some elements in the boiler island of the inspected power unit. The next stage consisted of performing strength calculations of the steel structure with a goal to estimate the structure’s stress state. The conclusions contain suggestions for modernization of welded joints in order to maintain the power unit’s design strength.

The authors seek to investigate the characteristics of outage-causing faults that can be observed in a short time frame after their occurrence: waveform of the voltages and currents. The aim is to identify which characteristics can be used to estimate the failure type immediately after its occurrence. This paper lays the groundwork to determine which features display a stronger relation to four failure types with the aim of using this information in a later work, not presented in this paper, aimed at designing a reliable failure type estimator from readily available data. This paper focuses on the most common failures of the underground cable MV networks in Portugal: cable insulation; cable joint; secondary substation busbar; and excavation-motivated failures. A set of 206 waveform records of real underground MV network failures was available for analysis. After investigating the waveforms, the authors identified seven waveform characteristics which can be used for failure type estimat... [more]

The degradation of lithium-ion cells with respect to increases of internal resistance (IR) has negative implications for rapid charging protocols, thermal management and power output of cells. Despite this, IR receives much less attention than capacity degradation in Li-ion cell research. Building on recent developments on ‘knee’ identification for capacity degradation curves, we propose the new concepts of ‘elbow-point’ and ‘elbow-onset’ for IR rise curves, and a robust identification algorithm for those variables. We report on the relations between capacity’s knees, IR’s elbows and end of life for the large dataset of the study. We enhance our discussion with two applications. We use neural network techniques to build independent state of health capacity and IR predictor models achieving a mean absolute percentage error (MAPE) of 0.4% and 1.6%, respectively, and an overall root mean squared error below 0.0061. A relevance vector machine, using the first 50 cycles of life data, is emp... [more]

In order to locate the short-circuit fault in power cable systems accurately and in a timely manner, a novel fault location method based on traveling waves is proposed, which has been improved by unsupervised learning algorithms. There are three main steps of the method: (1) build a matrix of the traveling waves associated with the sheath currents of the cables; (2) cluster the data in the matrix according to its density level and the stability, using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN); (3) search for the characteristic cluster point(s) of the two branch clusters with the smallest density level to identify the arrival time of the traveling wave. The main improvement is that high-dimensional data can be directly used for the clustering, making the method more effective and accurate. A Power System Computer Aided Design (PSCAD) simulation has been carried out for typical power cable circuits. The results indicate that the hierarchical struc... [more]

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the... [more]

The study of the rates of innovative development of various sectors of the modern economy makes it possible to determine the existence of a scientific and practical problem, eliciting the need for urgent identification of the reasons for non-innovative development of Oil and Gas Companies and development of the directions for innovation development. Based on a number of methods, including methods of graphical analysis, time series forecasting, construction of linear trends, correlation analysis and scenario forecasting, the authors stated the fact of the serious depth of the problem of innovative insufficiency in the oil sector in comparison with other sectors and they built six scenarios for the development of these companies. The applied methods made it possible to not only come to the conclusion that with the current level of investment in R&D in the oil and gas sector, Oil Companies may find themselves in difficult conditions, especially if breakthrough technologies show themselves... [more]

In Rwanda, most small-scale hydropower systems are connected to the national grid to supply additional generation capacity. The Rwandan rivers are characterized by low flow-rates and a majority of plants are below 5 MW generation capacity. The purpose of this study is to provide a scientific overview of positive and negative factors affecting the sustainability of small-scale hydropower plants in Rwanda. Based on interviews, field observation, and secondary data for 17 plants, we found that the factors contributing to small-scale hydropower plant sustainability are; favorable regulations and policies supporting sale of electricity to the national grid, sufficient annual rainfall, and suitable topography for run-of-river hydropower plants construction. However, a decrease in river discharge during the dry season affects electricity production while the rainy season is characterized by high levels of sediment and soil erosion. This shortens turbine lifetime, causes unplanned outages, and... [more]

Non-intrusive load monitoring (NILM) is an important research direction and development goal on the distribution side of smart grid, which can significantly improve the timeliness of demand side response and users’ awareness of load. Due to rapid development, deep learning becomes an effective way to optimize NILM. In this paper, we propose a novel load identification method based on long short term memory (LSTM) on deep learning. Sequence-to-point (seq2point) learning is introduced into LSTM. The innovative combination of the LSTM and the seq2point brings their respective advantages together, so that the proposed model can accurately identify the load in process of time series data. In this paper, we proved the feature of reducing identification error in the experimental data, from three datasets, UK-DALE dataset, REDD dataset, and REFIT dataset. In terms of mean absolute error (MAE), the three datasets have increased by 15%, 14%, and 18% respectively; in terms of normalized signal ag... [more]

The paper presents the novel HF-GEN method for determining the characteristics of Electrical Appliance (EA) operating in the end-user environment. The method includes a measurement system that uses a pulse signal generator to improve the quality of EA identification. Its structure and the principles of operation are presented. A method for determining the characteristics of the current signals’ transients using the cross-correlation is described. Its result is the appliance signature with a set of features characterizing its state of operation. The quality of the obtained signature is evaluated in the standard classification task with the aim of identifying the particular appliance’s state based on the analysis of features by three independent algorithms. Experimental results for 15 EAs categories show the usefulness of the proposed approach.

Data-driven models, either simplified or detailed, have been extensively used in the literature for energy assessment in buildings and districts. However, the uncertainty of the estimated parameters, especially of thermal masses in resistance−capacitance (RC) models, still remains a significant challenge, given the wide variety of buildings functionalities, typologies, structures and geometries. Therefore, the sensitivity analysis of the estimated parameters in RC models with respect to different geometric characteristics is necessary to examine the accuracy of identified models. In this work, heavy- and light-structured buildings are simulated in Transient System Simulation Tool (TRNSYS) to analyze the effects of four main geometric characteristics on the total heat demand, maximum heat power and the estimated parameters of an RC model (4R3C), namely net-floor area, windows-to-floor ratio, aspect ratio, and orientation angle. Executing more than 700 simulations in TRNSYS and comparing... [more]

Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas−liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O2 battery. The extended Kalman Filter (EKF) algorithm is elaborated to couple the offline model and online model to achieve the goal of quickly eliminating initial errors in the online SOC estimation. An implementation of the hybrid pulse power characterization test is performed to identify the offline parameters and determine the open-circuit voltage vs. SOC curve. Apart from the standard cycles including Constant Current cycle, Federal Urban Driving Schedule cycle, Urban Dynamometer Driving Schedule cycle and Dynamic Stress Test cycle, a combined cycle is constructed for experimental validation. Furthermore, the study of the effect of sampling time on estimation accuracy and the robustness analysis of the initial value are carried out... [more]

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. T... [more]

In this paper the D-decomposition technique is investigated as a source of non-linear boundaries used with the Genetic Algorithm (GA) search of a PI voltage compensator gains of the boost converter operating in Continuous Conduction Mode (CCM). The well known and appreciated boost converter has been chosen as a test object due to its right-half plane zero in the control-to-output (c2o) voltage transfer function. The D-decomposition, as a technique relying on the frequency sweeping, clearly indicates not only the global stability but, in its extended version, regions satisfying the required gain (GM) and phase (PM) margins. Such results are in form of easy to interpret functions KI=f(KP). The functions are easy to convert to the GA constraints. The GA search, with three different performance indexes as the fitness functions, is applied to a control structure with time delays basing on identified c2o voltage transfer functions. The identification took place in an experiment and in simula... [more]

This research focuses on problem identification due to faults in power transformers during operation by using dissolved gas analysis such as key gas, IEC ratio, Duval triangle techniques, and fuzzy logic approaches. Then, the condition of the power transformer is evaluated in terms of the percentage of failure index and internal fault determination. Fuzzy logic with the key gas approach was used to calculate the failure index and identify problems inside the power transformer. At the same time, the IEC three-gas ratio and Duval triangle are subsequently applied to confirm the problems in different failure types covering all possibilities inside the power transformer. After that, the fuzzy logic system was applied and validated with DGA results of 244 transformers as reference cases with satisfactory accuracy. Two transformers were evaluated and practically confirmed by the investigation results of an un-tanked power transformer. Finally, the DGA results of a total of 224 transformers w... [more]

The paper presents the results of a study concerned with the hydrodynamics of an annular downward multiphase flow of gas and two mutually non-mixing liquids through a vertical pipe with a diameter of 12.5 mm. The air, oil and water were used as working media in this study with changes in superficial velocities in the ranges of jg = 0.34−52.5 m/s for air, jo = 0.000165−0.75 m/s for oil, and jw = 0.02−2.5 m/s for water, respectively. The oil density and viscosity were varied within the ranges of ρo = 859−881 kg/m3 and ηo = 29−2190 mPas, respectively. The research involved the identification of multiphase flow patterns and determination of the void fraction of the individual phases. New flow patterns have been identified and described for the gravitational flow conditions of a two-phase water−oil liquid and a three-phase air−water−oil flow. New flow regime maps and equations for the calculation of air, oil and water void fractions have been developed. A good conformity between the calcula... [more]

Distributed photovoltaic power stations are an effective way to develop and utilize solar energy resources. Using high-resolution remote sensing images to obtain the locations, distribution, and areas of distributed photovoltaic power stations over a large region is important to energy companies, government departments, and investors. In this paper, a deep convolutional neural network was used to extract distributed photovoltaic power stations from high-resolution remote sensing images automatically, accurately, and efficiently. Based on a semantic segmentation model with an encoder-decoder structure, a gated fusion module was introduced to address the problem that small photovoltaic panels are difficult to identify. Further, to solve the problems of blurred edges in the segmentation results and that adjacent photovoltaic panels can easily be adhered, this work combines an edge detection network and a semantic segmentation network for multi-task learning to extract the boundaries of ph... [more]

The paper undertakes the problem of proper structuring of multi-criteria decision support models. To achieve that, a methodological framework is proposed. The authors’ framework is the basis for the relevance analysis of individual criteria in any considered decision model. The formal foundations of the authors’ approach provide a reference set of Multi-Criteria Decision Analysis (MCDA) methods (TOPSIS, VIKOR, COMET) along with their similarity coefficients (Spearman correlation coefficients and WS coefficient). In the empirical research, a practical MCDA-based wind farm location problem was studied. Reference rankings of the decision variants were obtained, followed by a set of rankings in which particular criteria were excluded. This was the basis for testing the similarity of the obtained solutions sets, as well as for recommendations in terms of both indicating the high significance and the possible elimination of individual criteria in the original model. When carrying out the ana... [more]

The paper presents the characteristics of the original optoelectronic system for measuring the values of hydrodynamics of two-phase downward gas-very viscous liquid flow. The measurement methods and results of the research on selected values describing gas−oil two-phase flow are presented. The study was conducted in vertical pipes with diameters of 12.5, 16, 22, and 54 mm. The research was conducted with the superficial velocities of air jg = 0−29.9 m/s and oil jl = 0−0.254 m/s, which corresponded to the values of gas stream density gg = (0−37.31) kg/(m2s) and of liquid gl = (0.61−226.87) kg/(m2s), in order to determine the influence of air and oil streams on the character of liquid films. The variations in oil viscosity were applied in the range ηl = (0.055−1.517) Pas. The study results that were obtained with optical probes along with computer image analysis system revealed vast research opportunities in terms of the identification of gas−liquid two-phase downward flow structures tha... [more]

Poor indoor air quality (IAQ) in schools is associated with impacts on pupils’ health and learning performance. We aimed to identify the factors that affect IAQ in primary schools. The following objectives were set: (a) to develop a questionnaire to assess the prevalence of factors in primary schools, (b) to conduct content validity of the questionnaire, and (c) to assess the prevalence of factors that affect the IAQ in Slovenian primary schools. Based on the systematic literature review, we developed a new questionnaire to identify factors that affect the IAQ in primary schools and conducted its validation. The questionnaires were sent to all 454 Slovenian primary schools; the response rate was 78.19%. The results show that the most important outdoor factors were the school’s micro location and the distance from potential sources of pollution, particularly traffic. Among the indoor factors, we did not detect a pronounced dominating factor. Our study shows that the spatial location of... [more]

The prevalence of batch and batch-like operations, in conjunction with the continued resurgence of artificial intelligence techniques for clustering and classification applications, has increasingly motivated the exploration of the applicability of deep learning for modeling and feedback control of batch and batch-like processes. To this end, the present study seeks to evaluate the viability of artificial intelligence in general, and neural networks in particular, toward process modeling and control via a case study. Nonlinear autoregressive with exogeneous input (NARX) networks are evaluated in comparison with subspace models within the framework of model-based control. A batch polymethyl methacrylate (PMMA) polymerization process is chosen as a simulation test-bed. Subspace-based state-space models and NARX networks identified for the process are first compared for their predictive power. The identified models are then implemented in model predictive control (MPC) to compare the cont... [more]