Anomaly detection can improve the service level of the grid, effectively save human resources and reduce the operating cost of a power company. In this study, an improved arithmetic optimization-backpropagation (IAOA-BP) neural algorithm for an anomaly detection model was proposed for electricity inspection. The dynamic boundary strategy of the cosine control factor and the differential evolution operator are introduced into the arithmetic optimization algorithm (AOA) to obtain the improved arithmetic optimization algorithm (IAOA). The algorithm performance test proves that the IAOA has better solving ability and stability compared with the AOA, WOA, SCA, SOA and SSA. The IAOA was subsequently used to obtain the optimal weights and thresholds for BP. In the experimental phase, the proposed model is validated with electricity data provided by a power company. The results reveal that the overall determination accuracy using the IAOA-BP algorithm remains above 96%, and compared with other... [more]

In this study, a combined pretreatment method of sonication and alkali (KOH) liquefaction (SAL) was used to increase the production of biohydrogen from macroalgae (Chaetomorpha antennina) in an energy-efficient manner. Sonication liquefaction (SL) was accomplished by varying the sonic intensities from 10% to 70% and the pretreatment time from 5 to 60 min. The ideal liquefaction conditions in SL were determined to be 50% for sonic intensity, and 30 min of pretreatment time which produces liquefied organics (LO) release of 2650 mg/L. By adjusting the pH of the alkali (KOH) from 8 to 12, SAL was carried out under SL optimal conditions. With a liquefaction efficiency of 24.61% and LO release of 3200 mg/L, pH 11 was the best for effective macroalgal liquefaction in SAL. SAL (4500 kJ/kg TS) consumed less ultrasonic specific energy (USE) than SL (9000 kJ/kg TS). More VFA was produced in SAL (2160 mg/L) than SL (1070 mg/L). Compared to SL (120 mL H2/g COD/0.005 moles of H2/g COD), SAL produced... [more]

The vehicle market at the European level (and also elsewhere) has registered a high dynamic for the adoption of electric vehicles as the future means of transport. Government policies and decisions fully support this move, but there are still massive barriers to entry into the EV market due to consumer attitudes and perceptions. Consumer attitudes and perceptions will be decisive in the market success of future electric vehicle models, given that consumers are particularly interested in the vehicles’ technical and dynamic performance. The pressure from customers’ demands for performance leaves its mark not only on the increase of performance and technical parameters, but directly contributes to the generation of interdependence between these parameters. This article presents a comparative statistical analysis of 203 electric vehicle models (from various construction categories), present in the European vehicle market (between the years 2019 and 2022), to highlight the direct and indire... [more]

Monitoring and controlling thermoelectric power plants (TPPs) operational parameters have become essential to ensure system reliability, especially in emergencies. Due to system complexity, operating parameters control is often performed based on technical know-how and simplified analytical models that can result in limited observations. An alternative to this task is using time series forecasting methods that seek to generalize system characteristics based on past information. However, the analysis of these techniques on large diesel/HFO engines used in Brazilian power plants under the dispatch regime has not yet been well-explored. Therefore, given the complex characteristics of engine fuel consumption during power generation, this work aimed to investigate patterns generalization abilities when linear and nonlinear univariate forecasting models are used on a representative database related to an engine-driven generator used in a TPP located in Pernambuco, Brazil. Fuel consumption pr... [more]

Fast and accurate fault detection is important for the long term, stable operation of the distribution network. For the resonant grounding system, the fault signal features extraction difficulties, and the existing detection method’s accuracy is not high. A ground fault detection method based on fault data stitching and image generation of resonant grounding distribution systems is proposed. Firstly, considering the correlation between the transient zero-sequence current (TZSC) of faulty and healthy feeders under the same operating conditions, a fault data stitching method is proposed, which splices the transient zero-sequence current signals of each feeder into system fault data, and then converts the system fault data into grayscale images by combining the signal-to-image conversion method. Then, an improved convolutional neural network (CNN) is used to train the grayscale images and then implement fault detection. The simulation results show that the proposed method has high accurac... [more]

Power systems face failures, attacks and natural disasters on a daily basis, making robustness and resilience an important topic. In an electrical network, robustness is a network’s ability to withstand and fully operate under the effects of failures, while resilience is the ability to rapidly recover from such disruptive events and adapt its structure to mitigate the impact of similar events in the future. This paper presents an integrated framework for jointly assessing these concepts using two complementary algorithms. The robustness model, which is based on a cascading failure algorithm, quantifies the degradation of the power network due to a cascading event, incorporating the circuit breaker protection mechanisms of the power lines. The resilience model is posed as a mixed-integer optimisation problem and uses the previous disintegration state to determine both the optimal dispatch and topology at each restoration stage. To demonstrate the applicability of the proposed framework,... [more]

Studies on the determinants of low-carbon innovations in developed countries already exist. We test here the institutional environment in Poland (science−government−enterprise) as supporters of the technological change in industry towards a low-carbon economy. We will examine as well whether conclusions for well-developed countries are relevant for those catching up. The aim of the article is to assess the systemic nature and durability of the impact of internal and external conditions on the implementation of low-carbon technologies in Polish industry. In order to achieve the goal, two surveys were carried out for the periods 2007−2012 and 2013−2018, on sample sizes of 11,493 enterprises. To verify the hypotheses, a statistical multi−factor logit modelling was used to determine the chances of low-carbon innovations under the influence of various parallel circumstances. The results of this research point to other, often abrupt (unstable) phenomena occurring in the catching-up economy,... [more]

The intelligent use of green and renewable energies requires reliable and preferably anticipated information regarding their availability and the behavior of meteorological variables in a scenario of natural intermittency. Examples of this are the smart grids, which can incorporate, among others, a charging system for electric vehicles and modern and predictive management techniques. However, some issues associated with such procedures are data captured by sensors and transducers with noise in their signals and low information repeatability under the same reading conditions. To tackle such problems, numerous filtering and data fitting techniques and various prediction methods have been developed, but an appropriate selection can be cumbersome. Also, some filtering techniques, such as RANdom SAmple Consensus (RANSAC) appear not to have been used in prediction scenarios for smart grids, to the authors’ knowledge. In this regard, this paper aims to present a comparison in terms of average... [more]

The corrosion rate is an important indicator describing the degree of metal corrosion, and quantitative analysis of the corrosion rate is of great significance. In the present work, the support vector machine (SVM) and the artificial neural network (ANN) integrating the k-fold split method and the root-mean-square prop (RMSProp) optimizer are used to evaluate the corrosion rates of alloys, i.e., copper H65, aluminum 3003, and 20# steel, applied to the heating tower heat pump (HTHP) in various anti-freezing solutions at different corrosion times, flow velocities, and temperatures. The mean-square error (MSE) versus the epoch of the ANN model shows that the result breaks the local minimum and is at or close to the global minimum. Comparisons of the SVM-/ANN-evaluated corrosion rates and the measured ones show good agreements, demonstrating the good reliability of the obtained SVM and ANN models. Moreover, the ANN model is recommended since it performs better than the SVM model according... [more]

A method for evaluating the thermophysical characteristics of the torch is developed. Mathematically the temperature at the end of the zone of active combustion based on continuous distribution functions of particles of solid fuels, in particular coal dust. The particles have different average sizes, which are usually grouped and expressed as a fraction of the total mass of the fuel. The authors suggest taking into account the sequential nature of the entry into the chemical reactions of combustion of particles of different masses. In addition, for the application of the developed methodology, it is necessary to divide the furnace volume into zones and sections. In particular, the initial section of the torch, the zone of intense burning and the zone of afterburning. In this case, taking into account all the thermophysical characteristics of the torch, it is possible to make a thermal balance of the zone of intense burning. Then determines the rate of expiration of the fuel-air mixture... [more]

This paper summarizes the risk assessment and management workflow developed and applied to the Southwest Regional Partnership on Carbon Sequestration (SWP) Phase III Demonstration Project. The risk assessment and management workflow consists of six primary tasks, including management planning, identification, qualitative analysis, quantitative analysis, response planning, and monitoring. Within the workflow, the SWP assembled and iteratively updated a risk registry that identifies risks for all major activities of the project. Risk elements were ranked with respect to the potential impact to the project and the likelihood of occurrence. Both qualitative and quantitative risk analyses were performed. To graphically depict the interactions among risk elements and help building risk scenarios, process influence diagrams were used to represent the interactions. The SWP employed quantitative methods of risk analysis including Response Surface Method (RSM), Polynomial Chaos Expansion (PCE),... [more]

As the operation of an ocean thermal energy conversion (OTEC) plant depends on the temperature gradient between the surface and deeper water (SST), a variation in SST can significantly modify the energy produced. The aim of this paper is to present a comparative analysis of three sea-surface temperature databases (World Ocean Atlas (WOA), Satellite Oceanic Monitoring System (SATMO), and in situ sensor measurements). Simple linear regression and graphic comparisons allow correlations to be made between the distribution patterns of the SST data. The results show that there is no statistically significant difference between the three databases. To determine general regions where OTEC implementation is possible, at the macroscale, the WOA database is recommended, as a smaller amount of data must be analyzed. For meso- and microscales, such as specific areas of the Mexican exclusive economic zone. It is better to use SATMO and in situ measurements as a higher spatial resolution is required.

Cellulosic fibers from date palm are among the most promising lignocellulose feedstock for biorefinery purposes. The world production is between 1.9 and 2.4 million t/year. Initially, a pretreatment with dilute-sulphuric acid of these fibers was performed using a response surface methodology, with temperature and process time as factors. The aim is to produce bioethanol from young and old fibers from date palm, Phoenix dactylifera L. Optimal thermochemical pretreatment conditions for both fibers palms were 220 °C in hydrothermal conditions (without acid); in these conditions pretreated young fibers presented a maximum content in holocelluloses of 45.18% and old fibers 61.97%. Subsequently, during the enzymatic hydrolysis a maximum yield of total reducing sugars (TRS) was reached, 46.32 g/100 g for pretreated dry young fibers and 48.54 g/100 g for pretreated dry old fibers. After enzymatic saccharification, hydrolysates were fermented by Pachysolen tannophilus (ATCC 32691) to ethanol, r... [more]

Permanent magnet machines with segmented stator cores are affected by additional harmonic components of the cogging torque which cannot be minimized by conventional methods adopted for one-piece stator machines. In this study, a novel approach is proposed to minimize the cogging torque of such machines. This approach is based on the design of multiple independent shapes of the tooth tips through a topological optimization. Theoretical studies define a design formula that allows to choose the number of independent shapes to be designed, based on the number of stator core segments. Moreover, a computationally-efficient heuristic approach based on genetic algorithms and artificial neural network-based surrogate models solves the topological optimization and finds the optimal tooth tips shapes. Simulation studies with the finite element method validates the design formula and the effectiveness of the proposed method in suppressing the additional harmonic components. Moreover, a comparison... [more]

The aim of the study is to identify the main determinants of the capital structure of energy industry companies in the European Union. The study was based on a panel of 6122 companies from 25 EU countries, operating between 2011 and 2018. The study used multiple regression analysis. We have obtained strong evidence for a positive relationship between corporate debt and tangibility and size, and a negative relationship for profitability and liquidity. The factors that also affect the share of debt in capital have turned out to be growth (positive relationship) and non-debt tax shield (negative relationship), but the statistical significance of these relationships is ambiguous. We have shown that growth of industry business risk is accompanied by an increase in corporate debt and this is a distinguishing feature of the energy industry. For country-specific capital structure determinants, we have obtained strong evidence for the negative relationship between GDP growth, the level of stake... [more]

Nowadays, the power system is faced with some new changes from low-carbon approaches, though these approaches have proved to be effective in developing low-carbon electricity. Specifically, wind power integration and carbon trading influence the traditional economic emission dispatch (EED) mode, allowing for the disturbance of wind power uncertainties and the fluctuation of carbon trading price. Aiming at the above problems, this study firstly builds a stochastic EED model in the form of chance-constrained programming associated with wind power reliability. Next, wind power features are deduced from the statistic characteristics of wind speed, and thus the established model is converted to a deterministic form. After that, an auxiliary decision-making method based on the technique for order preference by similarity to an ideal solution (TOPSIS) is designed to draw the optimal solution based upon the specific requirements of carbon emission control. The simulation results eventually ind... [more]

In this paper we have assessed the impact of the European Union’s Emissions Trading Scheme (EU ETS) on the level of the carbon premium. The aim of the study is to determine whether there is a stable carbon premium in energy-intensive sectors. Unlike other studies, our research sample included not only companies in the energy sector, but also entities classified as energy-intensive. In the research, we used our own criterion for allocating companies to a clean and dirty portfolio, which made it possible to make the estimation of the carbon premium more resistant to changes in the rules for allocation of emission allowances. We detected a positive, statistically significant carbon premium in the years 2003−2012 and a negative one in the years 2013−2015, but we did not detect a statistically significant carbon premium in the period 2016−2019. This means that there are no grounds for concluding that there is a stable, positive carbon premium for energy-intensive companies subject to the EU... [more]

This paper undertakes liquefaction analysis with simplified procedures with standard penetration test (SPT) data and cone penetration test (CPT) data obtained from an offshore wind farm in the Changhua area. The soil liquefaction resistance calculated by the SPT-based simplified procedure suggested by the Japan Railway Association was in agreement with the laboratory results. The CPT is widely used in the site investigation of offshore wind farms. However, Taiwan’s registered professional engineers are still familiar with soil liquefaction analysis for offshore wind farms using SPT-based methods. Hence, a hybrid method that incorporates an SPT−CPT correlation into the New Japan Road Association (NJRA) method is proposed to evaluate the soil liquefaction potential for offshore wind farms in Taiwan. In the case studies of soil liquefaction with five groups of adjacent boreholes in Changhua’s offshore wind farms, the hybrid method shows that the soil liquefaction potential with CPT data i... [more]

Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20−30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some o... [more]

This paper examines the usefulness of statistical methods in forecasting the financial energy of households. The study’s objective is to create the innovative ratios that combine both financial and demographic information of households and implement them in the forecasting models. To conduct this objective, six forecasting models are developed using three different methods—discriminant analysis, logit analysis, and decision trees separately for households in Poland and Taiwan. Such a research approach will answer the question whether the implementation of constructed ratios can increase effectiveness of the forecasting model and its’ versatility between different economic regions. The research relies on four samples of households—two learning samples and two testing samples (one for each country) consisting of 2400 households from both countries. This study is one of the first attempts in the literature globally to develop forecasting models based on ratios constructed with a combinati... [more]

Among the envelope components (e.g., walls, roofs, floors, and windows, etc.) affecting the cooling and heating load of buildings, windows are the most thermally vulnerable. Shading devices can minimize the thermal load on windows due to solar radiation and decrease radiation effects. However, the load changes due to convection and conduction should be considered. Therefore, when a shading device is applied to a window, control logic for thermal blocking and heat retention is necessary to prevent the load changes. In addition, by combining the opposite features of photovoltaic (PV) that require solar radiation and the shading device to block solar radiation, energy-saving and production can be achieved simultaneously. Therefore, this study minimized the thermal effects of windows using a movable shading device integrated with PV and evaluated the PV power generation. This study evaluated the effects on window heat transfer by applying artificial intelligence techniques, which have rece... [more]

The usage of smart grids is growing steadily around the world. This technology has been proposed as a promising solution to enhance energy efficiency and improve consumption management in buildings. Such benefits are usually associated with the ability of accurately forecasting energy demand. However, the energy consumption series forecasting is a challenge for statistical linear and Machine Learning (ML) techniques due to temporal fluctuations and the presence of linear and non-linear patterns. Traditional statistical techniques are able to model linear patterns, while obtaining poor results in forecasting the non-linear component of the time series. ML techniques are data-driven and can model non-linear patterns, but their feature selection process and parameter specification are a complex task. This paper proposes an Evolutionary Hybrid System (EvoHyS) which combines statistical and ML techniques through error series modeling. EvoHyS is composed of three phases: (i) forecast of the... [more]

The objective of this paper is to develop a two-step predict and correct non-intrusive Parametric Model Order Reduction (PMOR) methodology for the problem of changing well locations in an oil field that can eventually be used for well placement optimization to gain significant computational savings. In this work, we propose a two-step PMOR procedure, where, in the first step, a Proper Orthogonal Decomposition (POD)-based strategy that is non-intrusive to the simulator source code is introduced, as opposed to the convention of using POD as a simulator intrusive procedure. The non-intrusiveness of the proposed technique stems from formulating a novel Machine Learning (ML)-based framework used with POD. The features of the ML model (Random Forest was used here) are designed such that they take into consideration the temporal evolution of the state solutions and thereby avoid simulator access for the time dependency of the solutions. The proposed PMOR method is global, since a single reduc... [more]

Developing accurate models is necessary to optimize the operation of heating systems. A large number of field measurements from monitored heat pumps have made it possible to evaluate different heat pump models and improve their accuracy. This study used measured data from a heating system consisting of three heat pumps to compare five regression and two artificial neural network (ANN) models. The models’ performance was compared to determine which model was suitable during the design and operation stage by calibrating them using data provided by the manufacturer and the measured data. A method to refine the ANN model was also presented. The results indicate that simple regression models are more suitable when only manufacturers’ data are available, while ANN models are more suited to utilize a large amount of measured data. The method to refine the ANN model is effective at increasing the accuracy of the model. The refined models have a relative root mean square error (RMSE) of less th... [more]

Hydraulic systems are widely used in the aeronautic, machinery, and energy industries. The functions that these systems perform require high reliability, which can be achieved by examining the causes of possible defects and failures and by taking appropriate preventative measures. One of the most popular methods used to achieve this goal is FMEA (Failure Modes and Effects Analysis), the foundations of which were developed and implemented in the early 1950s. It was systematized in the following years and practically implemented. It has also been standardized and implemented as one of the methods of the International Organization for Standardization (ISO) 9000 series standards on quality assurance and management. Apart from wide application, FMEA has a number of weaknesses, which undoubtedly include risk analysis based on the RPN (Risk Priority Number), which is evaluated as a product of severity, occurrence, and detection. In recent years, the risk analysis has been very often replaced... [more]