To avoid power supply hazards caused by cable failures, this paper presents an approach of incipient cable failure recognition and classification based on variational mode decomposition (VMD) and a convolutional neural network (CNN). By using VMD, the original current signal is decomposed into seven modes with different center frequencies. Then, 42 features are extracted for the seven modes and used to construct a feature vector as input of the CNN to classify incipient cable failure through deep learning. Compared with using the original signals directly as the CNN input, the proposed approach is more efficient and robust. Experiments on different classifiers, namely, the decision tree (DT), K-nearest neighbor (KNN), BP neural network (BP) and support vector machine (SVM), and show that the CNN outperforms the other classifiers in terms of accuracy.

This paper describes a hierarchy of increasingly complex statistical models for wind power generation in Alberta applied to wind power production data that are publicly available. The models are based on combining spatial and temporal correlations. We apply the method of Gaussian random fields to analyze the wind power time series of the 19 existing wind farms in Alberta. Following the work of Gneiting et al., three space-time models are used: Stationary, Separability, and Full Symmetry. We build several spatio-temporal covariance function estimates with increasing complexity: separable, non-separable and symmetric, and non-separable and non-symmetric. We compare the performance of the models using kriging predictions and prediction intervals for both the existing wind farms and a new farm in Alberta. It is shown that the spatial correlation in the models captures the predominantly westerly prevailing wind direction. We use the selected model to forecast the mean and the standard devia... [more]

An abundant volume of literature has been devoted to the analysis of the drivers of renewable electricity capacity additions in general and wind energy in particular. Nevertheless, whereas the direct influence of several explanatory variables has been considered, indirect effects, which refer to impacts of explanatory variables on another explanatory variable which, in turn, influence capacity additions, have been neglected. However, those effects need to be taken into account in order to properly grasp the full influence of the explanatory variables in general, and the policy variable in particular, on capacity additions (whether in wind energy generation or other energy systems). The aim of this paper is to identify the drivers of wind energy capacity additions. Based on data over the 1998−2015 period for Spain, a country with a substantial deployed wind capacity, we estimate a three-stage least squares multiecuational econometric model, which allows the analysis of direct and indire... [more]

Data-driven models are essential tools for the development of surrogate models that can be used for the design, operation, and optimization of industrial processes. One approach of developing surrogate models is through the use of input−output data obtained from a process simulator. To enhance the model robustness, proper sampling techniques are required to cover the entire domain of the process variables uniformly. In the present work, Monte Carlo with pseudo-random samples as well as Latin hypercube samples and quasi-Monte Carlo samples with Hammersley Sequence Sampling (HSS) are generated. The sampled data obtained from the process simulator are fitted to neural networks for generating a surrogate model. An illustrative case study is solved to predict the gas stabilization unit performance. From the developed surrogate models to predict process data, it can be concluded that of the different sampling methods, Latin hypercube sampling and HSS have better performance than the pseudo-r... [more]

Load forecasting is of crucial importance for smart grids and the electricity market in terms of the meeting the demand for and distribution of electrical energy. This research proposes a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic algorithm II (NSGA II) is employed for selecting the input vector, where its fitness function is a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the output of the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fed to the Adaptive Neuro-Fuzzy Inference System (ANFIS) as its input and the results demonstrate an improved forecasting accuracy of the MLPNN-ANFIS compared to the MLPNN and ANFIS models. In addition, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), and imperialistic competitive algorithm (ICA) are used for optimized design of the ANFIS. Electricity demand da... [more]

The authors wish to make the following corrections to this paper [...]

In microgrids, forecasting solar power output is crucial for optimizing operation and reducing the impact of uncertainty. To forecast solar power output, it is essential to forecast solar irradiance, which typically requires historical solar irradiance data. These data are often unavailable for residential and commercial microgrids that incorporate solar photovoltaic. In this study, we propose an hourly day-ahead solar irradiance forecasting model that does not depend on the historical solar irradiance data; it uses only widely available weather data, namely, dry-bulb temperature, dew-point temperature, and relative humidity. The model was developed using a deep, long short-term memory recurrent neural network (LSTM-RNN). We compare this approach with a feedforward neural network (FFNN), which is a method with a proven record of accomplishment in solar irradiance forecasting. To provide a comprehensive evaluation of this approach, we performed six experiments using measurement data fro... [more]

It is of great significance for wind power plant to construct an accurate multi-step wind speed prediction model, especially considering its operations and grid integration. By integrating with a data pre-processing measure, a parameter optimization algorithm and error correction strategy, a novel forecasting method for multi-step wind speed in short period is put forward in this article. In the suggested measure, the EEMD (Ensemble Empirical Mode Decomposition) is applied to extract a series of IMFs (intrinsic mode functions) from the initial wind data sequence; the LSTM (Long Short Term Memory) measure is executed as the major forecasting method for each IMF; the GRNN (general regression neural network) is executed as the secondary forecasting method to forecast error sequences for each IMF; and the BSO (Brain Storm Optimization) is employed to optimize the parameter for GRNN during the training process. To verify the validity of the suggested EEMD-LSTM-GRNN-BSO model, eight models w... [more]

Frequency Response Analysis (FRA) is an important tool used for diagnostic measurements of power transformers. Standard test configuration applied in the industry is the end-to-end open test setup; however, an interwinding capacitive configuration is also used. This paper presents a method—Cross Test Comparison (CTC)—for simultaneous analysis of results coming from both the mentioned test setups. Such an approach could offer a more sensitive tool for detecting some faults; moreover, it takes into consideration the influence of both voltage sides of a transformer in a one test result. The authors have used several indices to quantitatively assess the test results and proposed new approach to data interpretation. CTC method was tested using data from measurements performed in three cases: a unit tested in laboratory with introduced controlled deformations; transformers measured under industrial conditions; and a transformer with FRA changes resulting from tap-changer operations. The resu... [more]

Economic load dispatch (ELD) is an important optimization problem for operating and controlling modern power systems, and if ELD is effectively executed, power systems work stably and economically. The main objective of this paper is to develop a novel method to solve the ELD with the purpose of minimizing the total fuel cost of all available generating units while requirements are to satisfy all constraints regarding thermal units, generators, and transmission power networks. The proposed high performance cuckoo search algorithm (HPCSA) is developed from the efficient technique for the second new solution generation of conventional cuckoo search algorithm (CCSA), called adaptive mutation technique. This proposed technique diversifies the local search ability based on a new comparison criterion. The HPCSA is verified on difference systems under special conditions, namely the 10-unit system with multi fuels, 15-unit system considering prohibited operating zones, and three IEEE systems w... [more]

Conventional photoplesthymograph (PPG) measurements for heart rate (HR) determination require direct contact between the patient and the PPG device sensor. When using the conventional method, it is possible for users to suffer undesirable skin irritation, discomfort and soreness. Thus, the development of non-contact PPG has been investigated with various technologies and methods. One of the technologies that able to measure PPG in a non-contact way and at low cost is using digital cameras such as webcams. Various filters have been implemented to do non-contact PPG using digital cameras. This paper proposes a non-contact PPG filter system utilizing singular value decomposition (SVD) and Burg’s algorithm. The main role of SVD is for noise removal and as PPG signal extractor. As for the Burg algorithm, it was utilized for estimating the heart rate value from the filtered PPG signal. In this paper, we show and analyze an experiment for HR measurement using our method and a previous method... [more]

Flow maldistribution represents a problem of particular interest in the engineering field for several thermal systems. In flat plate solar collectors, the thermal efficiency strongly depends on the flow distribution through the riser tubes, where a uniform distribution causes a uniform temperature distribution and therefore a higher efficiency. In this work, a Computational Fluid Dynamics (CFD) numerical analysis has been carried out using the commercial software FLUENT®, in order to determine the flow distribution, pressure drop and hence the thermal efficiency of a solar collector with distribution flow plates inside the manifolds. The obtained numerical solution for this type of thermal systems has been validated with experimental results available in literature for laminar and turbulent flow. Four distribution plate configurations were analyzed. Results show that using two distribution plates in each of both manifolds improves the flow uniformity up to 40% under the same operating... [more]

In this research paper, a hybrid Artificial Neural Network (ANN)-Fuzzy Logic Control (FLC) tuned Flower Pollination Algorithm (FPA) as a Maximum Power Point Tracker (MPPT) is employed to amend root mean square error (RMSE) of photovoltaic (PV) modeling. Moreover, Gaussian membership functions have been considered for fuzzy controller design. This paper interprets the Luo converter occupied brushless DC motor (BLDC)-directed PV water pump application. Experimental responses certify the effectiveness of the suggested motor-pump system supporting diverse operating states. The Luo converter, a newly developed DC-DC converter, has high power density, better voltage gain transfer and superior output waveform and can track optimal power from PV modules. For BLDC speed control there is no extra circuitry, and phase current sensors are enforced for this scheme. The most recent attempt using adaptive neuro-fuzzy inference system (ANFIS)-FPA-operated BLDC directed PV pump with advanced Luo conver... [more]

Recently, the well-industry-production-scheme (WIPS) has attracted more and more attention to improve tight oil recovery. However, multi-well pressure interference (MWPI) induced by well-industry-production-scheme (WIPS) strongly challenges the traditional transient pressure analysis methods, which focus on single multi-fractured horizontal wells (SMFHWs) without MWPI. Therefore, a semi-analytical methodology for multiwell productivity index (MPI) was proposed to study well performance of WIPS scheme in tight reservoir. To facilitate methodology development, the conceptual models of tight formation and WIPS scheme were firstly described. Secondly, seepage models of tight reservoir and hydraulic fractures (HFs) were sequentially established and then dynamically coupled. Numerical simulation was utilized to validate our model. Finally, identification of flow regimes and sensitivity analysis were conducted. Our results showed that there was good agreement between our proposed model and nu... [more]

As grid-connected wind farms become more common in the modern power system, the question of how to maximize wind power generation while limiting downtime has been a common issue for researchers around the world. Due to the complexity of wind turbine systems and the difficulty to predict varying wind speeds, artificial intelligence (AI) and machine learning (ML) algorithms have become key components when developing controllers and control schemes. Although, in recent years, several review papers on these topics have been published, there are no comprehensive review papers that pertain to both AI and ML in wind turbine control systems available in the literature, especially with respect to the most recently published control techniques. To overcome the drawbacks of the existing literature, an in-depth overview of ML and AI in wind turbine systems is presented in this paper. This paper analyzes the following reviews: (i) why optimizing wind farm power generation is important; (ii) the cha... [more]

Malfunctions in relay protection devices are predominantly caused by current transformer (CT) saturation which produces distortion in current measurements and disturbances in power system protection. The development of deep learning in power system protection is on the rise recently because of its robustness. This study presents a CT saturation detection where the secondary current becomes distorted. The proposed scheme offers a wide range of saturation detection and consists of a moving-window technique and stacked denoising autoencoders. Moreover, Bayesian optimization was used to minimize the difficulty of determining neural network structure for the proposed approach. The performance of the algorithm was evaluated for a-g faults on 154 kV and 345 kV overhead transmission line in South Korea. The waveform variation has been generated by PSCAD for different scenarios that heavily influence CT saturation. Moreover, a comparative analysis with other methods demonstrated the superiority... [more]

Since existing energy system models often represent storage behavior in a simplified way, in this work, a tool chain for deriving consistent storage model parameters for optimization models is developed. The aim of our research work is to identify what are non-negligible influences on the the technical characteristics and dynamic behavior of the storage, to quantify the effect of these influences, and represent these effects in the model. This paper describes the developed tool chain and presents its application using an example. The tool chain consists of the steps “parameter screening”, “dynamic simulation”, “regression analysis” and “refining optimization model”. It is investigated which parameters have an influence on the storage system (here pumped hydroelectric energy storage (PHES)), how the storage behavior is modeled, which influencing factors have a measurable effect on the system, and how these findings can be integrated into optimization models. The main finding is that in... [more]

The phase selection and fault location methods of generator stator winding single-phase grounding fault are greatly affected by the transition resistance. A new phase selection and generator stator ground fault location approach based on the BP neural network is proposed in this research from a data-driven angle. This method uses a neural network to calculate the probability of three-phase fault occurrence to identify the fault phase and directly calculate the fault location that takes the amplitude and phase angle characteristics of zero-sequence voltage as input. The simulation results show that the stator ground fault phase selection and location algorithm based on the neural network can achieve correct phase selection and small positioning error, which has verified the effectiveness of the method.

Gas−liquid two-phase countercurrent flow limitation (CCFL) phenomena widely exist in nuclear power plants. In particular, the gas−liquid countercurrent flow limitation phenomena in a pressurized water reactor (PWR) during a loss-of-coolant accident (LOCA) or a small-break loss-of-coolant accident (SBLOCA) play an important role in nuclear reactor safety research. Over several decades, a series of experimental investigations and numerical studies have been carried out to study the CCFL phenomena in a PWR. For the experimental investigations, numerous experiments have been conducted, and different CCFL mechanisms and CCFL characteristics have been obtained in various test facilities simulating different scenarios in a PWR. The CCFL phenomena are affected by many factors, such as geometrical characteristics, liquid flow rates, and fluid properties. For the numerical studies, more and more numerical models were presented and applied to the calculations of two-phase countercurrent flow over... [more]

Torrefaction is an effective technology to overcome the defects of biomass which are adverse to its utilization as solid fuels. For assessing the torrefaction process, it is essential to characterize the properties of torrefied biomass. However, the preparation and characterization of torrefied biomass often consume a lot of time, costs, and manpower. Developing a reliable method to predict the fuel properties of torrefied biomass while avoiding various experiments and tests is of great value. In this study, a machine learning (ML) model of back propagation neural network (BPNN) hybridized with genetic algorithm (GA) optimization was developed to predict the important properties of torrefied biomass for the fuel purpose involving fuel ratio (FR), H/C and O/C ratios, high heating value (HHV) and the mass and energy yields (MY and EY) based on the proximate analysis results of raw biomass and torrefaction conditions. R2 and RMSE were examined to evaluate the prediction precision of the m... [more]

The present research carries out an in-detail study of the VAPEX process as one of the most recent solvent-based heavy oil recovery techniques in fractured reservoirs to evaluate the effect of fracture parameters on process performance. To achieve this purpose, several fractured patterns with distinct features were designed and engraved on glass pieces to manufacture state-of-the-art microfluidic models mimicking a typical Canadian heavy oil reservoir. A heavy oil sample of viscosity 1514 cP was utilized during the conducted experiments with pure propane and pure carbon dioxide as the injection solvents. A thorough image analysis operation was carried out over the experimental models to determine heavy oil produced, residual oil saturation, ultimate recovery factors, and monitor solvent chamber expansion. Numerical simulations of the same experiments were carried out for history matching and predicting other designed scenarios. Error analysis revealed average absolute errors of below 8... [more]

Smart grid-training systems enable trainers to achieve the high safety standards required for power operation. Effective methods for the rational segmentation of continuous fine actions can improve smart grid-training systems, which is of great significance to sustainable power-grid operation and the personal safety of operators. In this paper, a joint algorithm of a spatio-temporal convolutional neural network and multidimensional cloud model (STCNN-MCM) is proposed to complete the segmentation of fine actions during power operation. Firstly, the spatio-temporal convolutional neural network (STCNN) is used to extract action features from the multi-sensor dataset of hand actions during power operation and to predict the next moment’s action to form a multi-outcome dataset; then, a multidimensional cloud model (MCM) is designed based on the motion features of the real power operation; finally, the corresponding probabilities are obtained from the distribution of the predicted data in th... [more]

A numerical simulation of convective heat transfer coefficient (hconv) was studied with Cu-Water and TiO2-Water nanofluids flowing through a circular tube subjected to uniform wall heat flux boundary conditions under laminar and turbulent regimes. Four different concentrations of nanofluids (ɸ = 0.5, 1, 1.5 and 2%) were used for the analysis and the Reynolds number (Re) was varied from laminar (500 to 2000) to turbulent flow regime (5000 to 20,000). The dependence of hconv on Re and ɸ was investigated using a single-phase Newtonian approach. In comparison to base fluid, average hconv enhancements of 10.4% and 7.3% were noted, respectively, for the maximum concentration (ɸ = 2%) and Re = 2000 for Cu-Water and TiO2—water nanofluids in the laminar regime. For the same ɸ under the turbulent regime (Re = 20,000), the enhancements were noted to be 14.6% and 13.2% for both the nanofluids, respectively. The random motion (Brownian motion) and heat diffusion (thermophoresis) by nanosized partic... [more]

The energy security of each country is one of the main factors of its proper functioning. Currently, in the era of problems related to energy security resulting from, among other things, the war in Ukraine, this topic is particularly important. This article presents issues related to Poland’s energy security, understood as the financial and economic stability of enterprises operating in the energy industry. This stability is considered in two aspects: macroeconomic, where the focus is mainly on the aspect of state intervention in market processes; and microeconomic, where factors determining the financial security of energy enterprises were identified, including internal and external factors affecting the functioning of these entities. In order to achieve the assumed research goals, the analysis of the indicated problems was based on non-reactive research, consisting in the assessment of the available information. It included studies of normative acts, official statistical data, indust... [more]

The number of data centers worldwide is increasing year by year, mostly because of the development of cloud services and applications. In the near future, the rate of construction of data centers will grow, with a corresponding increase in their electrical energy consumption. The requirements of the reliability of the electrical power supply of data centers are one of the highest among industrial power consumers, since uninterrupted power supply is critically important for the continuous functioning of server hardware. The assessment of electrical power supply reliability is one of the most important parts of the design process of data centers. However, the speed of the development of new power equipment does not always make it possible to use classical probabilistic and statistical methods for reliability assessment. Therefore, the development of new methods for reliability assessment based on alternative approaches, which can eliminate the disadvantages of probabilistic and statistic... [more]