Optimized Maximum Power Point Tracking in Photovoltaic Systems through Hybrid Artificial Neural Network and Tree-Seed Algorithm

Photovoltaic (PV) array may receive different level of solar irradiance, such as partially shaded by clouds or nearby building. Multiple peak power points occur when PV module is under partially shaded conditions, which would significantly reduce the energy produced by PV without proper control. Therefore, a Maximum Power Point Tracking (MPPT) algorithm is used to extract maximum available PV power from the PV array. However, most of the conventional MPPT algorithms are incapable to detect global peak (GP) power point with the presence of several local peaks (LP). A hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed in this article to detect the GP power. The PV system which consists of PV array, DC–DC boost converter, and a resistive load, were simulated using MATLAB/Simulink. The performance of the proposed algorithm is compared with that of the standard PSO algorithm. The proposed algorithm is tested and verified by hardware experiment. The simulation results and the experimental results are compared and discussed. It shows that the proposed algorithm performs well to detect the GP of the PV array under partial shaded conditions. In this work, the tracking efficiency of the proposed algorithm is in the range of 92.7–99.7 %.

This paper addresses the research methodology for Maximum Power Point Tracking (MPPT). Photovoltaic (PV) Generators may receive different level of solar irradiance and temperature, such as partially shaded by clouds, tree leaves or nearby building. Under partial shaded conditions, several peak power points can occur when the PV module is shaded, which would significantly reduce the energy produced by PV Generators without proper control. Therefore, a Maximum Power Point Tracking (MPPT) Algorithm is used to extract the maximum available PV power from the PV array. However, the common used conventional MPPT algorithms are unable to detect global peak (GP) power point with the presence of several local peaks (LP). In this paper, a hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed to detect the global peak power. MATLAB/Simulink is used to simulate a PV system which consists of PV Generators, DC–DC boost converter, a hybrid PSO-ANN Algorithm, and a resistive load. The simulation results are compared and discussed. The proposed algorithm should perform well to detect the Global Peak of the PV array even under partial shaded conditions.

Photovoltaic (PV) array may receive different levels of solar irradiance and temperature under different environmental conditions, such as partially shaded by clouds or nearby building. However, all PV systems have two major drawbacks: the efficiency of PV power generation is very low and the output power of a PV system is nonlinear, which depends closely on weather conditions, such as ambient temperature and the solar irradiance. Hence, tracking the maximum power of the PV arrays at real time is very important to increase the whole system performance. Multiple peak power points occur when PV module is under partially shaded conditions, which would significantly reduce the energy produced by PV without proper control. Therefore, a Maximum Power Point Tracking (MPPT) algorithm is used to extract maximum available PV power from the PV array. However, most of the conventional MPPT algorithms are incapable to detect global peak (GP) power point with the presence of several local peaks (LP). A hybrid Artificial Neural Network and Particle Swarm Optimization (ANN-PSO) algorithm is proposed in this report to detect the global peak power. A PV system which consists of PV array, DC-DC boost converter, a hybrid ANN-PSO Algorithm, and a resistive load, is simulated using MATLAB/Simulink. The simulation results are carried out, compared and discussed. The proposed algorithm should perform well to detect the Global Peak of the PV array under different environmental conditions.

Machine learning algorithms are used to analyze medical data sets effectively in present. Today machine learning gives us several necessary tools for intelligent data analysis and research. Especially in very recent years, the digital world has provided relatively inexpensive and available means to collect and store the data. The main aim is to implement supervised machine learning concept by using datasets regarding blood cells collected from blood cells detecting and counting sensors, of a human as the input which is trained by artificial neural network algorithm and apply decision tree classification learning algorithm to perform classification which results in recognizing and also possibly predict the disease based on the nature of blood cells and classify accordingly. Artificial Neural network algorithm seems to avoid pruning problem and has higher efficiency and accuracy in training the datasets. Also the using of Decision tree is because they are easy to interpret, understand and also possess non-linear characteristics between values. This holds well the performance of the tree constructed which gives better outputs. Beside all the application developed using machine learning in day today life, the use of such learning algorithm in such medical application will enhance and benefit the medical field.

The goal of this study is to compare the forecasting performance of classical artificial neural network and the hybrid model of artificial neural network and genetic algorithm. The time series data used is the monthly gold price per troy ounce in USD from year 1987 to 2016. A conventional artificial neural network trained by back propagation algorithm and the hybrid forecasting model of artificial neural network and genetic algorithms are proposed. Genetic algorithm is used to optimize the of artificial neural network neurons. Three forecasting accuracy measures which are mean absolute error, root mean squared error and mean absolute percentage error are used to compare the accuracy of artificial neural network forecasting and hybrid of artificial neural network and genetic algorithm forecasting model. Fitness of the model is compared by using coefficient of determination. The hybrid model of artificial neural network is suggested to be used as it is outperformed the classical artificial neural network in the sense of forecasting accuracy because its coefficient of determination is higher than conventional artificial neural network by 1.14%. The hybrid model of artificial neural network and genetic algorithms has better forecasting accuracy as the mean absolute error, root mean squared error and mean absolute percentage error is lower than the artificial neural network forecasting model.

In this article, a hybrid Artificial Neural Network - Newton Raphson (ANN-NR) is introduced to mitigate the undesired lower-order harmonic content in the cascaded H-Bridge multilevel inverter for solar photovoltaic (PV). Harmonics are extracted by the excellent choice of opting switching angles by exploiting the Selective Harmonic Elimination (SHE) PWM technique accompanying a unified algorithm in order to optimize and reduce the Total Harmonic Distortion (THD). ANN is trained with optimum switching angles, and the estimates generated by the ANN are the initial guess for NR. In this study, the CHB-MLI is combined with a traditional boost converter, it boosts the PV voltage to a superior dc-link voltage Perturb and Observe (P&O) based Maximum Power Point Tracking (MPPT) algorithm is used for getting a stable output and efficient operation of solar PV. The proposed system is proved over an eleven-level H-bridge inverter, the work is carried out in MATLAB/Simulink environment, and the respective results are confirmed that the proposed technique is efficient, and offers an actual firing angles with a few iterations results in a better capability of confronting local optima values. The suggested algorithm is justified by the experimental development of eleven-level cascaded H-bridge inverter.

A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms

In this paper, a hybrid model for estimating the activity of a commercial Pt-Re/Al2O3 catalyst in an industrial scale heavy naphtha catalytic-reforming unit (CRU) is presented. This model is also capable of predicting research octane number (RON) and yield of gasoline. In the proposed model, called DANN, the decay function of heterogeneous catalysts is combined with a recurrent-layer artificial neural network. During a life cycle (919 days), fifty-eight points are selected for building and training the DANN (60%), nineteen data points for testing (20%), and the remained ones for validating steps. Results show that DANN can acceptably estimate the activity of catalyst during its life in consideration of all process variables. Moreover, it is confirmed that the proposed model is capable of predicting RON and yield of gasoline for unseen (validating) data with AAD% (average absolute deviation) of 0.272% and 0.755%, respectively. After validating the model, the octane barrel level (OCB) of the plant is maximized by manipulating the inlet temperature of reactors, and hydrogen to hydrocarbon molar ratio whilst all process limitations are taken into account. During a complete life cycle results show that the decision variables, generated by the optimization program, can increase the RON, process yield and OCB of CRU to about 1.15%, 3.21%, and 4.56%, respectively. © 2015 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0)

The freeze-drying encapsulation process was used to encapsulate dragon fruit peel extract with three distinct wall materials: maltodextrin, gum arabica, and gelatin. The process was modeled using a feed forward back propagation artificial neural network with four, eleven, and four neurons in the input, hidden, and output layers. Three of the four input neurons were concentrations of wall material (g), while the fourth was ultrasonication power. The four output neurons were encapsulation efficiency, antioxidant activity, hygroscopicity, and solubility of freeze-dried encapsulation powder. The procedure was optimized using hybrid artificial neural network (ANN) and genetic algorithm (GA) approach. The optimal wall material composition for encapsulation obtained by the integrated ANN and GA was 4.461 g maltodextrin, 3.863 g gum arabic, and 3.198 g gelatin. The optimal ultrasonication power for achieving a homogenous mixture was determined to be 123 W. At the optimal condition, the predicted values for the responses encapsulation efficiency, antioxidant activity, hygroscopicity, and solubility were found to be 88.143%, 81.702%, 6.924 g/100 g, and 32.841%, respectively. Under optimal conditions, the relative deviation between the predicted model and experimental outcomes was less than 2.077%. The thermal stability of the encapsulated powder followed the first order kinetic modeling. The results showed that the sample treated at pH of 7 was more thermally stable at 80 °C than the sample treated at pH of 3.6. The half-life time was found to be 140 min and 103 min for the sample treated at pH of 7 and 3.6, respectively.

The objective of this research is to obtain an accurate forecasting model for the amount of electricity (in kWh) that is generated from different primary energy sources in the U.S. In this research, Artificial Neural Network (ANN) and hybrid ARIMA and ANN algorithms were developed that can be used for forecasting the amount of energy production in the short, as well as, in the long run. Based on the inferences made from the available data provided by Energy Information Administration from January 2004 to December 2014, two different forecasting models for each primary energy source were constructed. These two models were validated with available data from January 2015 to November 2017, and their performance, as measured by forecasting errors computed, were compared. The results show that ANN algorithm is good for fossil fuels sources such as coal, petroleum, and natural gas. However, ARIMA - ANN hybrid works more accurately for renewable energy sources such as geothermal, hydroelectric, solar, and wind. Finally, the best predictor was selected for each primary energy source which provides valuable information regarding the future electricity generation, and future dominant energy source to generate electricity. This information will hopefully influence energy sector forecasting models and help the government to develop future regulations to shift toward dominant energy sources of the future.

According to Vision Indonesia, data on people with eye diseases in Indonesia in 2018-2019 were 3 million people or about 1.5% of the total population. So far, public information or knowledge about the recognition of eye disease disorders is still lacking. The problem in this study is how to educate the public about the introduction of eye diseases based on information on symptoms of the disease and how to apply the web-based Artificial Neural Network (ANN) algorithm for the introduction of eye diseases. The ANN algorithm in the eye disease recognition education system can conclude knowledge even though it does not have certainty and takes it into account sequentially so that the process is faster. In terms of educational content about eye disease recognition, this is a novelty to use. This research aims to create an educational system for introducing eye diseases based on information on symptoms of the disease and applying a web-based Artificial Neural Network (ANN) algorithm for the recognition of eye diseases. The method used is the Artificial Neural Network algorithm method. The work of ANN in the education system for the introduction of eye diseases is to make parameters of eye disease symptoms or indicators that will produce the type of eye disease. The research material used is data on types of eye diseases and symptoms of each type of eye disease. The research results are to create an education system that can help the public recognise eye diseases based on the symptoms of these eye diseases that can be run on a web platform. The Artificial Neural Network (ANN) algorithm can manage input analysis data from disease indicators and show the initial results of eye diseases that can be detected. suffered by someone based on Training Results Weights and Bias v11= 1.6769, v01= 0.4356, w11= -1.5233, w01= 0.3242. Based on white box testing, the test results are free from logical errors. The results of this study indicate that the use of the ANN algorithm for eye disease recognition shows accurate results based on eye disease symptom data.

Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. In order to control maximum output power, using maximum power point tracking (MPPT) system is highly recommended. This paper simulates and controls the photovoltaic source by using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum point the ANN and GA are used. Data are optimized by GA and then these optimum values are used in neural network training. The simulation results are presented by using Matlab/Simulink and show that the neural network–GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also it can increase convergence speed to achieve the maximum power point (MPP) rather than conventional method. Moreover, to control both line voltage and current, a grid side p-q controller has been applied.

Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. In order to control maximum output power, using maximum power point tracking (MPPT) system is highly recommended. This paper simulates and controls the photovoltaic source by using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum point the ANN and GA are used. Data are optimized by GA and then these optimum values are used in neural network training. The simulation results are presented by using Matlab/Simulink and show that the neural network-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also it can increase convergence speed to achieve the maximum power point (MPP) rather than conventional method. Moreover, to control both line voltage and current, a grid side p-q controller has been applied.

Scheduling issues are typically classified as constrained optimization problems that examine the allocation of machines and the sequence in which tasks are processed. Regarding the existence of one machine, identification of works processing sequence forms a complete time schedule. Therefore, following a review of previous works, the goal of the present study is designing a mathematical model for open shop scheduling (OSS) problems using different machines aiming at minimizing the maximum time required to complete the works using an artificial neural network (ANN) and genetic algorithm (GA). The research data were driven from a Shoe company carried out between the years 2019 and 2020. The GA and ANN methodologies were employed to analyze and forecast the scheduling of activities within the shoe manufacturing sector. The findings indicated that the probability associated with the third population of the GA was 0.15. Furthermore, an examination of the average values of standard error revealed that the neural network model outperformed in terms of predictive accuracy. The estimated minimum time necessary for task completion, as determined by the neural network, was calculated to be 0.96699, facilitating an optimal condition for meeting the established objectives.

Pollutants in the environment, particularly those derived from total petroleum hydrocarbons (TPH), affect soils chemically, biologically, and physically in an extremely complicated manner. Here, we examine this impact by modelling the acute phytotoxicity effects of TPH on soils. To achieve this goal, we have designed a predicted model using artificial neural network (ANN). The ANN algorithm performance depends on the hyper parameters used in it. Thus, determining the optimal hyper parameter values helps to enhance the prediction model. To achieve this goal, in this paper, we have done the hybridization of ANN and whale optimization (WO) algorithm. The whale optimization algorithm is a bio-inspired algorithm and successfully applied in different applications to determine optimal global solution. Therefore, in the proposed method, whale optimization algorithm is deployed for determine hyper parameter values of ANN. Further, MATLAB software is used for simulation purposes. The prediction model validation is done various parameters such as root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE). The result shows that the proposed method achieves lowest value of these parameters over the existing algorithms. This reflects that the proposed method is superior for predict acute phytotoxicity in petroleum contaminated soil and can be deployed for real-time applications.