The hybrid power system is a combination of renewable energy power plants and conventional energy power plants. This integration causes power quality issues including poor settling times and higher transient contents. The main issue of such interconnection is the frequency variations caused in the hybrid power system. Load Frequency Controller (LFC) design ensures the reliable and efficient operation of the power system. The main function of LFC is to maintain the system frequency within safe limits, hence keeping power at a specific range. An LFC should be supported with modern and intelligent control structures for providing the adequate power to the system. This paper presents a comprehensive review of several LFC structures in a diverse configuration of a power system. First of all, an overview of a renewable energy-based power system is provided with a need for the development of LFC. The basic operation was studied in single-area, multi-area and multi-stage power system configura... [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]

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]

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]

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]

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]

Well planning for every drilling project includes cost estimation. Maximizing the rate of penetration (ROP) reduces the time required for drilling, resulting in reducing the expenses required for the drilling budget. The empirical formulas developed to predict ROP have limited field applications. Since real-time drilling data acquisition and computing technologies have improved over the years, we implemented the data-driven approach for this purpose. We investigated the potential of machine learning and deep learning algorithms to predict the nonlinear behavior of the ROP. The well was drilled to confirm the geothermal reservoir characteristics for the FORGE site. After cleaning and preprocessing the data, we selected two models and optimized their hyperparameters. According to our findings, the random forest regressor and the artificial neural network predicted the behavior of our field ROP with a maximum absolute mean error of 3.98, corresponding to 19% of the ROP’s standard deviatio... [more]

The life expectancy of power transformers is primarily determined by the integrity of the insulating oil and cellulose paper between the conductor turns, phases and phase to earth. During the course of their in-service lifetime, the solid insulating system of windings is contingent on long-standing ageing and decomposition. The decomposition of the cellulose paper insulation is strikingly grievous, as it reduces the tensile strength of the cellulose paper and can trigger premature failure. The latter can trigger premature failure, and to realize at which point during the operational life this may occur is a daunting task. Various methods of estimating the DP have been proposed in the literature; however, these methods yield different results, making it difficult to accurately estimate a reliable DP. In this work, a novel approach based on the Feedforward Backpropagation Artificial Neural Network has been proposed to predict the amount of DP in transformer cellulose insulation. Presentl... [more]

In view of the flow instability problem caused by the strong shock wave and secondary flow in the channel of the high-load counter-rotating compressor, this paper adopts the design method of coupling aerodynamic optimization technology and CFD and establishes a three-dimensional aerodynamic optimization design platform for the blade channel based on an artificial neural network and genetic algorithm. The aerodynamic optimization design and internal flow-field diagnosis of a high-load counter-rotating compressor with a 1/2 + 1 aerodynamic configuration are carried out. The research indicates that the optimized blade channel can drive and adjust the flow better, and the expected supercharging purpose and efficient energy conversion process are achieved by controlling the intensity of the shock wave and secondary flow in the channel. The total pressure ratio at the design point of the compressor exceeds 2.9, the adiabatic efficiency reaches 87%, and the aerodynamic performance is excellen... [more]

This paper presents an advanced methodology for restoration of the electric power transmission system after its partial or complete failure. This load-optimized restoration is dependent on sectioning of the transmission system based on artificial neural networks. The proposed methodology and the underlying algorithm consider the transmission system operation state just before the fallout and, based on this state, calculate the power grid parameters and suggest the methodology for system restoration for each individual interconnection area. The novel methodology proposes an optimization objective function as a maximum load recovery under a set of constraints. The grid is analyzed using a large amount of data, which results in an adequate number of training data for artificial neural networks. Once the artificial neural network is trained, it provides an almost instantaneous network recovery plan scheme by defining the direct switching order.

Conservation through voltage reduction (CVR) aims to reduce the peak load and energy savings in electric power systems and is being deployed at various utilities. The effectiveness of the CVR program depends on the load characteristics, i.e., the sensitivity of the load to voltage variation, and voltage regulation device settings. In the current literature, there is a lack of discussion on the CVR factor calculation using different measurements, and there is a lack of method for active power loss estimation using substation measurements. This paper provides insights into CVR factor calculation based on the measurements captured at the substation and those at the load location. This paper also proposes a new method based on curve fitting and artificial neural network to estimate the active power loss using input active power, input reactive power and input voltage at the substation. The CVR comparison study conducted in this paper helps in understanding the factors affecting CVR factor... [more]

The quality of the electrical energy distribution service has a significant impact on consumer satisfaction and the guarantee of the right of concession for the distribution companies. For the utility that is the object of the case study, the main continuity of service indicators was at levels below the regulatory limits. Still, due to budget constraints, the forecast of the benefit that improvement or expansion projects bring to continuity indicators must be assertive for a proper direction of investments and decision making. In this work, a methodology for evaluating projects to improve the quality of service was proposed, with the realization of the estimated benefit associated with the reduction in continuity indicators (DEC and FEC), using concepts of artificial neural networks and evolutionary algorithms. The results were obtained from a three-year history of execution of the utility’s projects. Based on the correlation analysis, a variable selection procedure was developed, wher... [more]

Using the mixture of carbonized rice husk and shungite from the Kazakhstan Koksu deposit and the experimentally determined oil sorption capacity from contaminated soil with oil originating in the Karazhanbas oil field, a set of Artificial Neural Network (ANN) models were built for sorption predictions. The ANN architecture design, training, validation and testing methodology were performed, and the sorption capacity prediction was evaluated. The ANN models were successfully trained for capturing the sorption capacity dependence on time and on a carbonized rice husk and shungite mixture ratio for the 10% and 15% oil-contaminated soil. The best trained ANNs revealed a very good prediction capability for the testing data subset, demonstrated by the high coefficient of the determination values of R2 = 0.998 and R2 = 0.981 and the mean absolute percentage errors ranging from 1.60% to 3.16%. Furthermore, the ANN sorption models proved their interpolation ability and utility for predicting th... [more]

The exactitude of petroleum fluid molecular weight correlations affects significantly the precision of petroleum engineering calculations and can make process design and trouble-shooting inaccurate. Some of the methods in the literature to predict petroleum fluid molecular weight are used in commercial software process simulators. According to statements made in the literature, the correlations of Lee−Kesler and Twu are the most used in petroleum engineering, and the other methods do not exhibit any significant advantages over the Lee−Kesler and Twu correlations. In order to verify which of the proposed in the literature correlations are the most appropriate for petroleum fluids with molecular weight variation between 70 and 1685 g/mol, 430 data points for boiling point, specific gravity, and molecular weight of petroleum fluids and individual hydrocarbons were extracted from 17 literature sources. Besides the existing correlations in the literature, two different techniques, nonlinear... [more]

In this work, the emulsified liquid membrane (ELM) extraction process was studied as a technique for separating different pollutants from an aqueous solution. The emulsified liquid membrane used consisted of Sorbitan mono-oleate (Span 80) as a surfactant with n-hexane (C6H14) as a diluent; the internal phase used was nitric acid (HNO3). The major constraint in the implementation of the extraction process by an emulsified liquid membrane (ELM) is the stability of the emulsion. However, this study focused first on controlling the stability of the emulsion by optimizing many operational factors, which have a direct impact on the stability of the membrane. Among the important parameters that cause membrane breakage, the surfactant concentration, the emulsification time, and the stirring speed were demonstrated. The optimization results obtained showed that the rupture rate (Tr) decreased until reaching a minimum value of 0.07% at 2% of weight/weight of Span 80 concentration with an emulsif... [more]

Smart grids are the latest technology to generate and dispatch an optimal amount of power. Thus, there is a need for stability analysis in smart grid systems. If the smart grid is incorporated into the power system, then the phasor measurement unit (PMU) is used to measure the voltage, current, and frequency. Additionally, the central control unit monitors and controls the power. However, there is a possibility of inserting wrong data into the smart grid as the PMUs are transmitting the data through the Internet and other wireless protocols. There is a need to find solutions to this threat to make the power flow safe and secure in the future. In this paper, two-area load frequency control (LFC) is used for testing the game-theory based security treatment and improving the system’s stability by using an artificial neural network. The two-area system and five-area system are used to test the stability of the power system.

In 2021, the residential sector had an electricity consumption of around 39% in México. Householders influence the quantity of energy they manage in a home due to their preferences, culture, and economy. Hence, profiling the householders’ behavior in communities allows designers or engineers to build strategies that promote energy reductions. The household socially connected products ease routine tasks and help profile the householder. Furthermore, gamification strategies model householders’ habits by enhancing services through ludic experiences. Therefore, a gamified smart community concept emerged during this research as an understanding that this type of community does not need a physical location but has similar characteristics. Thus, this paper proposes a three-step framework to tailor interfaces. During the first step, the householder type and consumption level were analyzed using available online databases for Mexico. Then, two artificial neural networks were built, trained, and... [more]

The article is a continuation of the authors’ ongoing research related to power flow and voltage control in LV grids. It outlines how the Distribution System Operator (DSO) can use Machine Learning (ML) technology in a future grid. Based on supervised learning, a Selectively Coherent Model of Converter System Control for an LV grid (SCM_CSC) is proposed. This represents a fresh, new approach to combining off and on-line computing for DSOs, in line with the decarbonisation process. The main kernel of the model is a neural network developed from the initial prediction results generated by regression analysis. For selected PV system operation scenarios, the LV grid of the future dynamically controls the power flow using AC/DC converter circuits for Battery Energy Storage Systems (BESS). The objective function is to maintain the required voltage conditions for high PV generation in an LV grid line area and to minimise power flows to the MV grid. Based on the training and validation data pr... [more]

Daily electricity peak load forecasting is important for electricity generation capacity planning. Accurate forecasting leads to saving on excessive electricity generating capacity, while maintaining the stability of the power system. The main challenging tasks in this research field include improving forecasting accuracy and reducing computational time. This paper proposes a hybrid model involving variational mode decomposition (VMD), empirical mode decomposition (EMD), fast Fourier transform (FFT), stepwise regression, similar days selection (SD) method, and artificial neural network (ANN) for daily electricity peak load forecasting. Stepwise regression and similar days selection method are used for input variable selection. VMD and FFT are applied for data decomposition and seasonality capturing, while EMD is employed for determining an appropriate decomposition level for VMD. The hybrid model is constructed to effectively forecast special holidays, which have different patterns fro... [more]

The synchronous generators (SGs) supplying non-linear loads have harmonically distorted terminal voltages. Hence, these distorted terminal voltages adversely affect the performance parameters of the supplied loads such as the power factor, current distortion, losses, and efficiency. To mitigate the harmonic voltages and currents, passive and active filters are generally employed. However, passive filters cause resonance problems, while active filters can cause high costs. On the other hand, in several recent studies to reduce the SG’s terminal voltage harmonic distortion, which depends on the constructional design under the no-loading condition, the conventional DC excitation current has been modulated with AC harmonic components. These field current modulation methods have high computational complexity, and require extra hardware for their implementation. In the present paper, firstly, for the reduction of the terminal voltage harmonic distortion of the SG under non-linear loading con... [more]

In grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that can be charged in an ESS, a separate energy management strategy (EMS) is required for stable microgrid operation. This paper proposes an EMS for a hybrid AC/DC microgrid based on an artificial neural network (ANN). The ANN is composed of a two-step process that operates the microgrid by outputting the operation mode and charging and discharging the ESS. The microgrid consists of an interlinking converter to link with the AC distributed system, a photovoltaic converter, a wind turbine converter, and an ESS. The control method of each converter was determined according to the mode selection of the ANN. The proposed ANN-based EMS was verified using a laboratory-scale hybrid AC/DC microgrid. Th... [more]

The planning of efficient policies based on forecasting electricity demand is essential to guarantee the continuity of energy supply for consumers. Some techniques for forecasting electricity demand have used specific procedures to define input variables, which can be particular to each case study. However, the definition of independent and casual variables is still an issue to be explored. There is a lack of models that could help the selection of independent variables, based on correlate criteria and level of importance integrated with artificial networks, which could directly impact the forecasting quality. This work presents a model that integrates a multi-criteria approach which provides the selection of relevant independent variables and artificial neural networks to forecast the electricity demand in countries. It provides to consider the particularities of each application. To demonstrate the applicability of the model a time series of electricity consumption from a southern re... [more]

One of the essential factors for the selection of the drying process is energy consumption. This study intended to optimize the drying treatment of capers using convection (CD), refractive window (RWD), and vacuum drying (VD) combined with ultrasonic pretreatment by a comparative approach among artificial neural networks (ANN) and response surface methodology (RSM) focusing on the specific energy consumption (SEC). For this purpose, the effects of drying temperature (50, 60, 70 °C), ultrasonication time (0, 20, 40 min), and drying method (RWD, CD, VD) on the SEC value (MJ/g) were tested using a face-centered central composite design (FCCD). RSM (R2: 0.938) determined the optimum drying-temperature−ultrasonication-time values that minimize SEC as; 50 °C-35.5 min, 70 °C-40 min and 70 °C-24 min for RWD, CD and VD, respectively. The conduct of the ANN model is evidenced by the correlation coefficient for training (0.976), testing (0.971) and validation (0.972), which shows the high suitabi... [more]

The major challenge facing renewable energy systems in Nigeria is the lack of appropriate, affordable, and available meteorological stations that can accurately provide present and future trends in weather data and solar PV performance. It is crucial to find a solution to this because information on present and future solar PV performance is important to renewable energy investors so that they can assess the potential of renewable energy systems in various locations across the country. Although Nigerian weather provides favorable weather conditions for clean power generation, there is little penetration of renewable energy systems in the region, since over 95% of the power is fossil-fuel-generated. This is because there has been no detailed report showing the potential of clean power generation systems due to the dysfunctional meteorological stations in the country. This paper sought to fill this knowledge gap by providing a machine-learning-inspired forecasting of environmental weathe... [more]

Since accurate load forecasting plays an important role in the improvisation of buildings and as described in EU’s “Green Deal”, financial resources saved through improvisation of the efficiency of buildings with social importance such as hospitals, will be the funds to support their mission, the social impact of load forecasting is significant. In the present paper, eight different machine learning predictors will be examined for the short-term load forecasting of a hospital’s facility building. The challenge is to qualify the most suitable predictors for the abovementioned task, which is beneficial for an in-depth study on accurate predictors’ applications in Intelligent Energy Management Systems (IEMS). Three Artificial Neural Networks using a backpropagation algorithm, three Artificial Neural Networks using metaheuristic optimization algorithms for training, an Adaptive Neuro-Fuzzy Inference System (ANFIS), and a Long-Short Term Memory (LSTM) network were tested using timeseries ge... [more]