Electric power is a basic need in today’s life. Due to the extensive usage of power, there is a need to look
for an alternate clean energy source. Recently many researchers have focused on the solar energy as a
reliable alternative power source. Photovoltaic panels are used to collect sun radiation and convert it into
electrical energy. Most of the photovoltaic panels are deployed in a fixed position, they are inefficient as
they are fixed only at a specific angle. The efficiency of photovoltaic systems can be considerably increased
with an ability to change the panels angel according to the sun position. The main goal of such systems is
to make the sun radiation perpendicular to the photovoltaic panels as much as possible all the day times.
This paper presents a dual axis design for a fuzzy inference approach-based solar tracking system. The
system is modeled using Mamdani fuzzy logic model and the different combinations of ANFIS modeling.
Models are compared in terms of the correlation between the actual testing data output and their
corresponding forecasted output. The Mean Absolute Percent Error and Mean Percentage Error are used
to measure the models error size. In order to measure the effectiveness of the proposed models, we
compare the output power produced by a fixed photovoltaic panels with the output which would be
produced if the dual-axis panels are used. Results show that dual-axis solar tracker system will produce
22% more power than a fixed panels system.
KEYWORDS
Fuzzy, Membership function, Universe of discourse, PV, ANFIS, DC motor, FLC.
1. INTRODUCTION
Fuzzy logic can be viewed as an extension of classical logical
The accurate prediction of solar irradiation has been
a leading problem for better energy scheduling approach.
Hence in this paper, an Artificial neural network based solar
irradiance is proposed for five days duration the data is
obtained from National Renewable Energy Laboratory, USA
and the simulation were performed using MATLAB 2013. It
was found that the neural model was able to predict the solar
irradiance with a mean square error of 0.0355.
In this paper, the artificial neural network (ANN) has been utilized for rotating machinery faults detection and classification. First, experiments were performed to measure the lateral vibration signals of laboratory test rigs for rotor-disk-blade when the blades are defective. A rotor-disk-blade system with 6 regular blades and 5 blades with various defects was constructed. Second, the ANN was applied to classify the different x- and y-axis lateral vibrations due to different blade faults. The results based on training and testing with different data samples of the fault types indicate that the ANN is robust and can effectively identify and distinguish different blade faults caused by lateral vibrations in a rotor. As compared to the literature, the present paper presents a novel work of identifying and classifying various rotating blade faults commonly encountered in rotating machines using ANN. Experimental data of lateral vibrations of the rotor-disk-blade system in both x- and y-directions are used for the training and testing of the network.
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
Automated Solar Tracking System for Efficient Energy Utilizationvivatechijri
This paper proposes a project that involves an automated solar tracking system which will make use
of LDR’s to track the position of sun. The output of LDR’s will be compared and analyzed to provide correct
alignment of the solar panel. Also another tracking technique is being implemented along, which uses the relation
of sun earth position at a given location. This telemetric data is given to microcontroller which will drive the
motors to align the solar panel. This is useful during cloudy weather and rainy days when it is difficult to check
the position of sun. Solar panels give output efficiency of around 15% to 20% based on the type of panel. The use
of solar tracking system increases it to a range of about 30% to 35%. This project further involves use of reflective
sheets on the sides of solar panel which will concentrate the reflected rays on the panel. Due to this the efficiency
is further increased around 40%. This project is a cost effective solution for stationary solar systems to increase
efficiency.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
The accurate prediction of solar irradiation has been
a leading problem for better energy scheduling approach.
Hence in this paper, an Artificial neural network based solar
irradiance is proposed for five days duration the data is
obtained from National Renewable Energy Laboratory, USA
and the simulation were performed using MATLAB 2013. It
was found that the neural model was able to predict the solar
irradiance with a mean square error of 0.0355.
In this paper, the artificial neural network (ANN) has been utilized for rotating machinery faults detection and classification. First, experiments were performed to measure the lateral vibration signals of laboratory test rigs for rotor-disk-blade when the blades are defective. A rotor-disk-blade system with 6 regular blades and 5 blades with various defects was constructed. Second, the ANN was applied to classify the different x- and y-axis lateral vibrations due to different blade faults. The results based on training and testing with different data samples of the fault types indicate that the ANN is robust and can effectively identify and distinguish different blade faults caused by lateral vibrations in a rotor. As compared to the literature, the present paper presents a novel work of identifying and classifying various rotating blade faults commonly encountered in rotating machines using ANN. Experimental data of lateral vibrations of the rotor-disk-blade system in both x- and y-directions are used for the training and testing of the network.
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
Automated Solar Tracking System for Efficient Energy Utilizationvivatechijri
This paper proposes a project that involves an automated solar tracking system which will make use
of LDR’s to track the position of sun. The output of LDR’s will be compared and analyzed to provide correct
alignment of the solar panel. Also another tracking technique is being implemented along, which uses the relation
of sun earth position at a given location. This telemetric data is given to microcontroller which will drive the
motors to align the solar panel. This is useful during cloudy weather and rainy days when it is difficult to check
the position of sun. Solar panels give output efficiency of around 15% to 20% based on the type of panel. The use
of solar tracking system increases it to a range of about 30% to 35%. This project further involves use of reflective
sheets on the sides of solar panel which will concentrate the reflected rays on the panel. Due to this the efficiency
is further increased around 40%. This project is a cost effective solution for stationary solar systems to increase
efficiency.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
A Comparative Analysis of Intelligent Techniques to obtain MPPT by Metaheuris...IJMTST Journal
Main objective of this paper is to develop an intelligent and efficient Maximum Power Point Tracking (MPPT) technique. Most recently introduced of intelligent based algorithm Cuckoo search algorithm has been used in this study to develop a novel technique to track the Maximum Power Point (MPP) of a solar cell module. The performances of this algorithm has been compared with other evolutionary soft computing techniques like ABC, FA and PSO. Simulations were done in MATLAB/SIMULINK environment and simulation results show that proposed approach can obtain MPP to a good precision under different solar irradiance and environmental temperatures.
A cost effective computational design of maximum power point tracking for pho...IJECEIAES
Maximum Power Point Tracking (MPPT) is one of the essential controller operations of any Photo-Voltaic (PV) cell design. Developing an efficient MPPT system includes a significant challenge as there are various forms of uncertainty factors that results in higher degree of fluctuation in current and voltage in PV cell. After reviewing existing system, it has been found that there is no presence of any benchmarked model to ensure a better form of computational model. Hence, this paper presents a novel and very simple design of MPPT without using any form of complex design mechanism nor including any form of frequently used iterative approach. The proposed model is completely focused on developing an algorithm that takes the input of voltage (open circuit), current (short circuit), and max power in order to obtain the peak power to be extracted from the PV cells. The study outcome shows faster response time and better form of analysis of current-voltage-power for given state of PV cells.
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
Solar Panel Control System using an Intelligent Control: T2FSMC and Firefly A...TELKOMNIKA JOURNAL
Solar panel is a solar energy converter to electrical energy. On solar tracker, there is a controller which sets the movement of solar panel such that it is perpendicular with solar rays. Previous research had designed Type 2 Fuzzy Sliding Mode Control (T2FSMC) controller to control the position of solar panel. However, there was trial and error process to determine gain scale factor so the development of optimization method is needed. This paper aims to modify gain scale factor using Firefly algorithm to increase performance of system. The simulation shows that T2FSMC Firefly has better performance than T2FSMC. T2FSMC Firefly shows the increase of performance on rise time, settling time, and integral time absolute error.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
Power output from a small solar panel can be affected by its power consumption when it consumes power from the solar panel. There has been a lack of proper research and experiment in the use of small solar panel with tracking systems. Its significance was detailed in this paper where the voltage output are compared with those which were externally powered. The solar trackers and a microcontroller have been designed and fabricated for this research. Due to the use of the tracking system (single axis and dual axis), the power consumption varies from one to another and its effect on the voltage output. Several experiments have been conducted and it was concluded that small solar panels are not efficient enough to utilize with tracking capabilities due to an increase in power consumption. The externally powered system was found to generate 18% more output compared to a selfsustaining system and that the increase in average power consumptions compared to a fixed panel were 31.7% and 82.5% for single-axis and dualaxis tracker respectively. A concrete evidence was made that utilizing solar tracking capabilities for low power rated solar panel is unfeasible.
Nano-satellites are key features for sharing the space data and scientific researches. They embed subsystems that are fed from solar panels and batteries. Power generated from these panels is subject to environmental conditions, most important of them are irradiance and temperature. Optimizing the usage of this power versus environmental variations is a primary task. Synchronous DC-DC buck converter is used to control the power transferred from PV panels to the subsystems while maintaining operation at maximal power. In this paper, artificial intelligence techniques: neural networks and adaptive neural fuzzy inference systems (ANFIS) are used to accomplish the tracking task. Simulation and experimental results demonstrate their efficiency, robustness and tracking quality.
Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple ...journalBEEI
Solar radiation forecasting is important in solar energy power plants (SEPPs) development. The electrical energy generated from the sunlight depends on the weather and climate conditions in the area where the SEPPs are installed. The condition of solar irradiation will indirectly affect the electrical grid system into which the SEPPs are injected, i.e. the amount and direction of the power flow, voltage, frequency, and also the dynamic state of the system. Therefore, the prediction of solar radiation condition is very crucial to identify its impact into the system. There are many methods in determining the prediction of solar radiation, either by mathematical approach or by heuristic approach such as artificial intelligent method. This paper analyzes the comparison of two methods, Adaptive Neuro Fuzzy Inference (ANFIS) method, which belongs into the heuristic methods, and Multiple Linear Regression (MLP) method, which uses a mathematical approach. The performance of both methods is measured using the root mean square error (RMSE) and the mean absolute error (MAE) values. The data of the Swiss Basel city from Meteoblue are used to test the performance of the two methods being compared. The data are divided into four cases, being classified as the training data and the data used as predictions. The solar radiation prediction using the ANFIS method indicates the results which are closer to the real measurement results, being compared to the the use MLP method. The average values of RMSE and MAE achieved are 123.27 W/m2 and 90.91 W/m2 using the ANFIS method, being compared to 138.70 W/m2 and 101.56 W/m2 respectively using the MLP method. The ANFIS method gives better prediction performance of 12.51% for RMSE and 11.71% for MAE with respect to the use of the MLP method.
A Comparative study on Different ANN Techniques in Wind Speed Forecasting for...IOSRJEEE
There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
Navigation of Mobile Inverted Pendulum via Wireless control using LQR TechniqueIJMTST Journal
Mobile Inverted Pendulum (MIP) is a non-linear robotic system. Basically it is a Self-balancing robot
working on the principle of Inverted pendulum, which is a two wheel vehicle, balances itself up in the vertical
position with reference to the ground. It has four configuration variables (Cart position, Cart Velocity,
Pendulum angle, Pendulum angular velocity) to be controlled using only two control inputs. Hence it is an
Under-actuated system. This paper focuses on control of translational acceleration and deceleration of the
MIP in a dynamically reasonable manner using LQR technique. The body angle and MIP displacement are
controlled to maintain reference states where the MIP is statically unstable but dynamically stable which
leads to a constant translational acceleration due to instability of the vehicle. In this proposal, the
implementation of self balancing robot with LQR control strategy and the implementation of navigation
control of the bot using a wireless module is done. The simulation results were compared between PID control
and LQR control strategies.
Energy harvesting earliest deadline first scheduling algorithm for increasing...IJECEIAES
In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life. Energy is a parameter by which the lifetime of system can be enhanced. To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches.
An interactive approach for solar energy system:design and manufacturing IJECEIAES
The energy production in the word is a very complex problem with decreasing the pollution. Therefore, the aim is to find an optimal solution, this research focuses on the development and the optimization of parabolic concentrator using an interactivity approach and virtual design tools. Recently, several works have been developed in this area. In this study, a new conception, design Optimization approach has been involved in system energy design including new concept. The design strategy has been successfully applied to design problems. The optimizer tool developed for based on Heuristic: Gravitational Search Algorithm. The results of the presented in this paper are significant in the system energy design, which presents an effective approach of development by reducing the cost of installation, the time of analysis by increasing the radiation and solar flux concentrated within the parabola generating an increase in accumulated energy.
A Comparative Analysis of Intelligent Techniques to obtain MPPT by Metaheuris...IJMTST Journal
Main objective of this paper is to develop an intelligent and efficient Maximum Power Point Tracking (MPPT) technique. Most recently introduced of intelligent based algorithm Cuckoo search algorithm has been used in this study to develop a novel technique to track the Maximum Power Point (MPP) of a solar cell module. The performances of this algorithm has been compared with other evolutionary soft computing techniques like ABC, FA and PSO. Simulations were done in MATLAB/SIMULINK environment and simulation results show that proposed approach can obtain MPP to a good precision under different solar irradiance and environmental temperatures.
A cost effective computational design of maximum power point tracking for pho...IJECEIAES
Maximum Power Point Tracking (MPPT) is one of the essential controller operations of any Photo-Voltaic (PV) cell design. Developing an efficient MPPT system includes a significant challenge as there are various forms of uncertainty factors that results in higher degree of fluctuation in current and voltage in PV cell. After reviewing existing system, it has been found that there is no presence of any benchmarked model to ensure a better form of computational model. Hence, this paper presents a novel and very simple design of MPPT without using any form of complex design mechanism nor including any form of frequently used iterative approach. The proposed model is completely focused on developing an algorithm that takes the input of voltage (open circuit), current (short circuit), and max power in order to obtain the peak power to be extracted from the PV cells. The study outcome shows faster response time and better form of analysis of current-voltage-power for given state of PV cells.
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
Solar Panel Control System using an Intelligent Control: T2FSMC and Firefly A...TELKOMNIKA JOURNAL
Solar panel is a solar energy converter to electrical energy. On solar tracker, there is a controller which sets the movement of solar panel such that it is perpendicular with solar rays. Previous research had designed Type 2 Fuzzy Sliding Mode Control (T2FSMC) controller to control the position of solar panel. However, there was trial and error process to determine gain scale factor so the development of optimization method is needed. This paper aims to modify gain scale factor using Firefly algorithm to increase performance of system. The simulation shows that T2FSMC Firefly has better performance than T2FSMC. T2FSMC Firefly shows the increase of performance on rise time, settling time, and integral time absolute error.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
Power output from a small solar panel can be affected by its power consumption when it consumes power from the solar panel. There has been a lack of proper research and experiment in the use of small solar panel with tracking systems. Its significance was detailed in this paper where the voltage output are compared with those which were externally powered. The solar trackers and a microcontroller have been designed and fabricated for this research. Due to the use of the tracking system (single axis and dual axis), the power consumption varies from one to another and its effect on the voltage output. Several experiments have been conducted and it was concluded that small solar panels are not efficient enough to utilize with tracking capabilities due to an increase in power consumption. The externally powered system was found to generate 18% more output compared to a selfsustaining system and that the increase in average power consumptions compared to a fixed panel were 31.7% and 82.5% for single-axis and dualaxis tracker respectively. A concrete evidence was made that utilizing solar tracking capabilities for low power rated solar panel is unfeasible.
Nano-satellites are key features for sharing the space data and scientific researches. They embed subsystems that are fed from solar panels and batteries. Power generated from these panels is subject to environmental conditions, most important of them are irradiance and temperature. Optimizing the usage of this power versus environmental variations is a primary task. Synchronous DC-DC buck converter is used to control the power transferred from PV panels to the subsystems while maintaining operation at maximal power. In this paper, artificial intelligence techniques: neural networks and adaptive neural fuzzy inference systems (ANFIS) are used to accomplish the tracking task. Simulation and experimental results demonstrate their efficiency, robustness and tracking quality.
Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple ...journalBEEI
Solar radiation forecasting is important in solar energy power plants (SEPPs) development. The electrical energy generated from the sunlight depends on the weather and climate conditions in the area where the SEPPs are installed. The condition of solar irradiation will indirectly affect the electrical grid system into which the SEPPs are injected, i.e. the amount and direction of the power flow, voltage, frequency, and also the dynamic state of the system. Therefore, the prediction of solar radiation condition is very crucial to identify its impact into the system. There are many methods in determining the prediction of solar radiation, either by mathematical approach or by heuristic approach such as artificial intelligent method. This paper analyzes the comparison of two methods, Adaptive Neuro Fuzzy Inference (ANFIS) method, which belongs into the heuristic methods, and Multiple Linear Regression (MLP) method, which uses a mathematical approach. The performance of both methods is measured using the root mean square error (RMSE) and the mean absolute error (MAE) values. The data of the Swiss Basel city from Meteoblue are used to test the performance of the two methods being compared. The data are divided into four cases, being classified as the training data and the data used as predictions. The solar radiation prediction using the ANFIS method indicates the results which are closer to the real measurement results, being compared to the the use MLP method. The average values of RMSE and MAE achieved are 123.27 W/m2 and 90.91 W/m2 using the ANFIS method, being compared to 138.70 W/m2 and 101.56 W/m2 respectively using the MLP method. The ANFIS method gives better prediction performance of 12.51% for RMSE and 11.71% for MAE with respect to the use of the MLP method.
A Comparative study on Different ANN Techniques in Wind Speed Forecasting for...IOSRJEEE
There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
Navigation of Mobile Inverted Pendulum via Wireless control using LQR TechniqueIJMTST Journal
Mobile Inverted Pendulum (MIP) is a non-linear robotic system. Basically it is a Self-balancing robot
working on the principle of Inverted pendulum, which is a two wheel vehicle, balances itself up in the vertical
position with reference to the ground. It has four configuration variables (Cart position, Cart Velocity,
Pendulum angle, Pendulum angular velocity) to be controlled using only two control inputs. Hence it is an
Under-actuated system. This paper focuses on control of translational acceleration and deceleration of the
MIP in a dynamically reasonable manner using LQR technique. The body angle and MIP displacement are
controlled to maintain reference states where the MIP is statically unstable but dynamically stable which
leads to a constant translational acceleration due to instability of the vehicle. In this proposal, the
implementation of self balancing robot with LQR control strategy and the implementation of navigation
control of the bot using a wireless module is done. The simulation results were compared between PID control
and LQR control strategies.
Energy harvesting earliest deadline first scheduling algorithm for increasing...IJECEIAES
In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life. Energy is a parameter by which the lifetime of system can be enhanced. To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches.
An interactive approach for solar energy system:design and manufacturing IJECEIAES
The energy production in the word is a very complex problem with decreasing the pollution. Therefore, the aim is to find an optimal solution, this research focuses on the development and the optimization of parabolic concentrator using an interactivity approach and virtual design tools. Recently, several works have been developed in this area. In this study, a new conception, design Optimization approach has been involved in system energy design including new concept. The design strategy has been successfully applied to design problems. The optimizer tool developed for based on Heuristic: Gravitational Search Algorithm. The results of the presented in this paper are significant in the system energy design, which presents an effective approach of development by reducing the cost of installation, the time of analysis by increasing the radiation and solar flux concentrated within the parabola generating an increase in accumulated energy.
A Binary Bat Inspired Algorithm for the Classification of Breast Cancer Data ijscai
Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing
Development of a Smart Mechatronic Tracking System to Enhance Solar Cell Pan...IJMER
Two degree of freedom Mechatronic solar tracking system was developed in the present study
to improve the performance of photovoltaic cell panels. The present tracking control algorithm was
applied on a small prototype, simulating a solar cells panel tracking system, designed and constructed in
this work. The Mechatronic tracking hardware section consists mainly of a commercial arduino micro-controller with built in, two servo motor drivers, data input/output, and micro processor modules. Other
components of the tracking hardware are, servo motors actuators and four LDR light intensity sensors. A
feedback control soft ware program, designed and constructed in the present work, enables the solar
tracker to automatically compensate for the sun location’s change to enhance the PV cells efficiency. The
LDR sensors are employed to continuously detect the sun rays intensity at four, light exposed isolated
positions, representing up-right, up-left, down-right, and down-left sides of the solar panel. LDRs data is
hence sent to the control software. The data is used to decide proper actuation actions and send them to
the servomotors to redirect the PV cells panel perpendicular to incident sun rays. Sensors and actuation
signals are exchanged via the in/out data module of the Arduino package. Results of the present
experimental work show that using the present tracking system increases the PV cell out power by about
38% compared with that of a fixed collector
Optimal artificial neural network configurations for hourly solar irradiation...IJECEIAES
Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.
An IntelligentMPPT Method For PV Systems Operating Under Real Environmental C...theijes
The sun irradiance (G) and temperature (T) are the two main factors that affect the output power gained from the photovoltaic (PV) DC–DC converter. Therefore, to enhance the performance of the overall system; a mechanism to track the maximum power point (MPP) is required. Conventional maximum power point tracking approaches, such as observation and perturbation technique, experience difficulty in identifying the true MPP. Therefore, intelligent systems including fuzzy logic controllers (FLC) are introduced for the maximum power point tracking system (MPPT). The selection of the membership functions (MFs) and the fuzzy sets (FSs) numbers are crucial in the performance of the FLC based MPPT. Accordingly, this work presents numerous adaptive neuro-fuzzy systems to automatically adjustthe fuzzy logic controller membership functions as an alternative to the trial and error approach, which waste time and effort in MPPT design. For this purpose an adaptive neuro-fuzzy system is developed in MATLAB/Simulink to determine suitable MFs and the FSs for the fuzzy logic controller. The effects of different types of MFs and the FSs are deeply investigated using real data collected from the rooftop PV system. The investigations show that the fuzzy logic controller with a triangular membership function and seven fuzzy setsprovides the best results
Development of a Smart Mechatronic Tracking System to Enhance Solar Cell Pane...IJMER
Two degree of freedom Mechatronic solar tracking system was developed in the present study
to improve the performance of photovoltaic cell panels. The present tracking control algorithm was
applied on a small prototype, simulating a solar cells panel tracking system, designed and constructed in
this work. The Mechatronic tracking hardware section consists mainly of a commercial arduino microcontroller
with built in, two servo motor drivers, data input/output, and micro processor modules. Other
components of the tracking hardware are, servo motors actuators and four LDR light intensity sensors. A
feedback control soft ware program, designed and constructed in the present work, enables the solar
tracker to automatically compensate for the sun location’s change to enhance the PV cells efficiency. The
LDR sensors are employed to continuously detect the sun rays intensity at four, light exposed isolated
positions, representing up-right, up-left, down-right, and down-left sides of the solar panel. LDRs data is
hence sent to the control software. The data is used to decide proper actuation actions and send them to
the servomotors to redirect the PV cells panel perpendicular to incident sun rays. Sensors and actuation
signals are exchanged via the in/out data module of the Arduino package. Results of the present
experimental work show that using the present tracking system increases the PV cell out power by about
38% compared with that of a fixed collector
This project aims to develop dual axis solar tracker with IOT monitoring system using Arduino. Generally, solar energy is the technology to get useful energy from sunlight. Solar energy has been used in many traditional technologies over the centuries and has been widely used in the absence of other energy supplies. Its usefulness is widespread when awareness of the cost of the environment and the supply is limited by other energy sources such as fuel. The solar tracking system is the most effective technology to improve the efficiency of solar panels by tracking and following the sun's movement. With the help of this system, solar panels can improve the way of sunlight detection so that more electricity can be collected as solar panels can maintain a sunny position. Thus the project discusses the development of two-axis solar-tracking developers using Arduino Uno as main controller the system. For develops this project, four light-dependent resistors (LDRs) have been used for sunlight detection and a maximum light intensity. Two servo motors have been used to rotate the solar panel according to the sun's light source detected by the LDR. Next a WIFI ESP8266 device is used as an intermediary between device and IOT monitoring system. The IOT monitoring system is a website that functions to store data. The efficiency of this system has been tested and compared with a single axial solar tracker. As a result, the two-axis solar tracking system generates more power, voltage and current.
Optimization of Solar Energy Production using PLC and SCADAijtsrd
This paper focuses on the maximizing the solar energy produced by Solar cells through the development of such a Sun-Tracking system that can be implemented using PLC and SCADA. The developed tracking system is innovative in relation to the usual sun tracking systems available in the market. In fact, the developed solution has many advantages in relation to similar existing devices, as this system can automatically work in order to optimize the energy production of photovoltaic cells as we know that in case of fixed Solar cells, the efficiency is very poor. This efficiency of power generation by Solar cells can be increased using this system, so that as the position of sun changes, the position of Solar cell is automatically adjusted by using stepper motors. An experimental prototype was built and field results have proven the good performance of the developed tracking system. Abhishek Kumar Chambel | Er. Bharti Sood "Optimization of Solar Energy Production using PLC & SCADA" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18912.pdf
http://www.ijtsrd.com/engineering/electrical-engineering/18912/optimization-of-solar-energy-production-using-plc-and-scada/abhishek-kumar-chambel
EXPERIMENTAL INVESTIGATION ON EFFICIENCY ENHANCEMENT OF THE SOLAR PANELS WITH...IAEME Publication
Nowadays solar energy is getting much attention in all the available renewable
sources. Most of the solar devices consist of a solar receptor arranged to face the sun to
get the maximum amount of sunlight on a photovoltaic panel using mirrors and auto
tracking technology. However the main defect occurs with the day and seasonal
variations. The amount of electricity produced from PV panel depends on the amount of
radiation that is focused on the PV panel. More the radiation on panel results in more
amount of electricity. In this present work an attempt is made to increase the amount of
radiation by introducing the mirror and auto tracking arrangement to allow more rays to
be concentrated on a small area of photovoltaic panel. A new platform made in shape in
parabolic which is adjusted to follow the sun. Mirrors and PV panels are attached on a
Flat shaped frame at an angle of 120° between them. A fuzzy logic controller is proposed
to estimate the exact time for sun tracking. The closet location while getting the sunlight
is considered as input taken from the database. This method minimizes the number of
motors for initial start and helps for the less quantity of energy loss in partial or full cloud
conditions. The comparison has studied for fixed PV panel and this new PV panel with
mirror and results are tabulate. The results show an increase of 33% in average of
efficiency by using mirror and tracking system
Optimization of photovoltaic energy by a microcontroller saad motahhir
One of the major challenges of all nations today is to find new energy sources to meet the needs for continued growth in Energy Term. The conversion of sunlight into electricity via photovoltaic solar cells is becoming a necessity in particular through the observation of a global evolution in clean energy that respects the environment. The main challenge is to optimize as much as possible the cost / energy ($/watt) ratio thus boosting both energy performance and at the same time take full advantage of the sun's rays throughout the day.In this context the sun trackers are such devices for efficiency improvement.
Adaptive maximum power point tracking using neural networks for a photovoltai...Mellah Hacene
Adaptive Maximum Power Point Tracking Using Neural Networks for a Photovoltaic Systems According Grid
Electrical Engineering & Electromechanics, (5), 57–66, 2021. https://doi.org/10.20998/2074-272X.2021.5.08
Arduino based Dual Axis Smart Solar TrackerIJAEMSJORNAL
Solar energy is rapidly advancing as an important means of renewable energy resource. It is radiant light and heat from the Sun that is harnessed using a range of ever-evolving technologies such as solar heating, photovoltaic, solar thermal energy, solar architecture, molten salt power plants and artificial photosynthesis. Trackers direct solar panels or modules toward the sun. These devices change their orientation throughout the day to follow the sun’s path to maximize energy capture. The use of solar trackers can increase electricity production by around a third, and some claim by as much as 40% in some regions, compared with modules at a fixed angle. In any solar application, the conversion efficiency is improved when the modules are continually adjusted to the optimum angle as the sun traverses the sky. This paper presents the designing of a solar tracking system which is based on Arduino UNO and which provides movement of solar panel in the direction of maximum sun light incident. As a result of which we get more efficient system which is compact, low cost as well as easy to use.
The significance of the solar energy is to intensify the effectiveness of the Solar Panel with the use of a primordial solar tracking system. Here we propounded a solar positioning system with the use of the global positioning system (GPS) , artificial neural network (ANN) and image processing (IP) . The azimuth angle of the sun is evaluated using GPS which provide latitude, date, longitude and time. The image processing used to find sun image through which centroid of sun is calculated and finally by comparing the centroid of sun with GPS quadrate to achieve optimum tracking point. Weather conditions and situation observed through AI decision making with the help of IP algorithms. The presented advance adaptation is analyzed and established via experimental effects which might be made available on the memory of the cloud carrier for systematization. The proposed system improve power gain by 59.21% and 10.32% compare to stable system (SS) and two-axis solar following system (TASF) respectively. The reduced tracking error of IoT based Two-axis solar following system (IoT-TASF) reduces their azimuth angle error by 0.20 degree.
Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.
Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System IJECEIAES
The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The nonlinear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.
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State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
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Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
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Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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Design of Dual Axis Solar Tracker System Based on Fuzzy Inference Systems
1. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
DOI :10.5121/ijscai.2016.5302 23
DESIGN OF DUAL AXIS SOLAR TRACKER SYSTEM
BASED ON FUZZY INFERENCE SYSTEMS
Hamzah Hijawi and Labib Arafeh
Department of Computer Science, Arab American University, Jenin, Palestine
ABSTRACT
Electric power is a basic need in today’s life. Due to the extensive usage of power, there is a need to look
for an alternate clean energy source. Recently many researchers have focused on the solar energy as a
reliable alternative power source. Photovoltaic panels are used to collect sun radiation and convert it into
electrical energy. Most of the photovoltaic panels are deployed in a fixed position, they are inefficient as
they are fixed only at a specific angle. The efficiency of photovoltaic systems can be considerably increased
with an ability to change the panels angel according to the sun position. The main goal of such systems is
to make the sun radiation perpendicular to the photovoltaic panels as much as possible all the day times.
This paper presents a dual axis design for a fuzzy inference approach-based solar tracking system. The
system is modeled using Mamdani fuzzy logic model and the different combinations of ANFIS modeling.
Models are compared in terms of the correlation between the actual testing data output and their
corresponding forecasted output. The Mean Absolute Percent Error and Mean Percentage Error are used
to measure the models error size. In order to measure the effectiveness of the proposed models, we
compare the output power produced by a fixed photovoltaic panels with the output which would be
produced if the dual-axis panels are used. Results show that dual-axis solar tracker system will produce
22% more power than a fixed panels system.
KEYWORDS
Fuzzy, Membership function, Universe of discourse, PV, ANFIS, DC motor, FLC.
1. INTRODUCTION
Fuzzy logic can be viewed as an extension of classical logical systems; the basic concept of the
fuzzy set theory was first introduced by Zadeh in 1965 [1]. It provides an effective framework to
deal with the problem of knowledge representation in an environment of uncertainty and
imprecision. The concept of fuzzy logic is based on the degree of truth rather than the usual crisp
Boolean values [2], it includes 0 and 1 as extreme cases of truth in addition to the various states
between them. The importance of fuzzy logic was basically derived from the fact of human
reasoning which are approximate values in nature. One of the main characteristics of the fuzzy
models is the development of rules which relates the fuzzy input and the required output
according to predefined membership functions [3]. A number of membership functions MF have
been proposed in the past few years, namely the triangular, trapezoidal, Gaussian and bell shape
functions. MF is defined as a graph that defines how each point in the input space is mapped to a
value between 0 and 1. The inputs are often referred as a universe of discourse which contains all
the possible elements of concern [4].
2. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
24
The Adaptive Neuro-Fuzzy Inference System (ANFIS), developed in the early 90s by Jang in
1993 [5], that combines the concepts of fuzzy logic and neural networks to form a hybrid
intelligent system to enhance the ability to automatically learn and adapt. Hybrid systems have
been used by researchers for modeling and predictions in various engineering systems. The basic
idea behind these neuro-adaptive learning techniques is to provide a method for the fuzzy
modeling procedure to learn information [6].
In this paper, we present different types of fuzzy models for the dual-axis solar tracking system.
The system is used to move photovoltaic panel according to the sun position in order to maximize
the power produced. The system is modeled with different combinations of Mamdani fuzzy logic
and adaptive neural fuzzy inference system ANFIS modeling, the testing data is compared with
forecasted results using several metrics including: their Correlation Coefficients, The Mean
Absolute Percent Error, MAPE, and the Mean Percentage Error, MPE errors.
The organization of the paper is as follow: we present different related works by other researchers
in section two. Systems’s deisgn is covered in section three. While several types of fuzzy models
for the dual-axis solar tracking system are shown in sections four and five, section six discusses
the results and section seven concludes the paper.
2. RELATED WORK
Fuzzy logic modeling has been widely used in many applications [4, 5]; fuzzy control is an
example. However, fuzzy logic can be used to increase the output power of photovoltaic panels,
sun tracking can increase the output power production by keeping the panel parallel to the sun,
which makes the sun radiation perpendicular with the panel. Such system requires dual-axis
tracking system. However, it was found that over 25 years’ period PV dual-axis tracker will
produce about 10 years’ worth of additional solar energy [6, 7]. In spite of the development of
power electronics resources, the direct current machine became more and more useful. For
example, the speed of DC motor can be adjusted to a great extent as to provide controllability
easy and high performance [11]. Controllers can be in different types: PID Controller, Fuzzy
Logic Controller; or the combination between them: Fuzzy-Neural Networks, Fuzzy-Genetic
Algorithm. In [12], a MATLAB Simulink model for speed control of DC model using Mamdani
fuzzy logic is provided, the simulation shows that the proposed fuzzy logic controller gives a
smooth speed control with less overshoot and without oscillation. In [10], an application of
ANFIS control for DC motor speed is presented. First, the system is modeled according to fuzzy
rules, then an adaptive Neuro-Fuzzy controller of the DC motor speed is design and simulated.
ANIFS has the advantage of the expert knowledge of the fuzzy inference system and the learning
capability of the neural network. Results show that ANFIS models give better performance and
high robustness than fuzzy rules alone.
In [13], a comparison is made for tuning methods of PID controllers to improve the performance
of control system of DC motor. The system is first tuned using the conventional Zeigler method
then the supervisory feature of fuzzy logic was used. The performance of the two approaches was
compared, results show that the fuzzy logic approach has minimum overshoot, minimum
transient, and steady-state parameters. [13] Provided a fuzzy logic control model in order to
maximize the power point tracking algorithm for photovoltaic, the power variation and output
voltage variation are used as inputs to the model. In order to improve the performance of the
proposed method, the asymmetric membership function is used. Results show that FLC method
can simultaneously shorten the tracking time and increase the tracking accuracy.
3. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
25
3. SYSTEM DESIGN
The proposed system consists of a PV panel, four light sensors, two stepper motors. Sensors are
placed in positions to read the sun illumination from the four directions; East, West, South, and
North. One stepper motor is used to rotate the panel horizontally according to East and West light
sensors readings, the other one is used to rotate the panel vertically according to South and North
sensors readings as shown in figure 1.
Figure1. PV dual axis panel
4. FUZZY LOGIC MODELING
The proposed model is based on the four light sensors readings inputs. Preprocessing phase takes
the sensor readings as resistance values in ohm and converts them to a luminance scale between 0
to 1000 lux. Mamdani model is used to design the system. It is based on fuzzy inference system
which employs the fuzzy if-then rules and can model the human knowledge and reasoning
process without employing precise quantitative analysis [8]. A two-stage system is proposed as
shown in figure 2, and the system model is described in figure 3.
Figure 2. Proposed system block diagram
4. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
26
Figure 3. System model
East and West luminance values control the rotation speed and direction of the first motor in the
horizontal axis while South and North values control the rotation speed and direction of the
second motor in the vertical axis. The goal is to rotate the PV panel to a place which makes the
sun radiation perpendicular to the PV panel, which in terms increases the total produced power.
Trapezoidal member functions are used to describe the inputs and the outputs. Figure 4 shows the
universe of discourse for each input, it has four membership functions; dim, dark, overcast and
bright.
Figure 4. Inputs Membership functions
The output represents the rotation speed and direction of horizontal and vertical motors. Figure 5
shows the universe of discourse for each output, it has six trapezoidal membership functions in
the range of -180 to 180; clockwise slow, clockwise mid, clockwise fast, counter clockwise slow,
counter-clockwise mid and counter clockwise fast.
5. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
27
Figure 5. Output membership functions
Table 1 shows the fuzzy rules used to map east and west luminance values to a horizontal motor
rotation speed and direction.
Table 1. Horizontal rotation rules
West Luminance
EastLuminance
Dim Dark Overcast Bright
Dim - Clockwise slow Clockwise mid
Clockwise
fast
Dark
Counter clockwise
slow
- Clockwise slow
Clockwise
mid
Overcast
Counter clockwise
mid
Counter clockwise
slow
-
Clockwise
slow
Bright
Counter clockwise
fast
Counter clockwise
mid
Counter clockwise
slow
-
Rules in table 1 can be explained as follow:
1. If East luminance is dim and west luminance is dim then don’t rotate.
2. If East luminance is dim and west luminance is dark then rotate clockwise with slow speed.
3. If East luminance is dim and west luminance is overcast then rotate clockwise with mid
speed.
4. If East luminance is dim and west luminance is bright then rotate clockwise with fast speed.
5. If East luminance is dark and west luminance is dim then rotate counter clockwise with slow
speed.
6. If East luminance is dark and west luminance is dark then don’t rotate
7. If East luminance is dark and west luminance is overcast then rotate clockwise with slow
speed.
6. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.5, No.2/3, August 2016
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8. If East luminance is dark and west luminance is bright then rotate clockwise with mid speed.
9. If East luminance is overcast and west luminance is dim then rotate counter clockwise with
mid speed.
10. If East luminance is overcast and west luminance is dark then rotate counter clockwise with
slow speed.
11. If East luminance is overcast and west luminance is overcast then don’t rotate.
12. If East luminance is overcast and west luminance is bright then rotate clockwise with slow
speed.
13. If East luminance is bright and west luminance is dim then rotate counter clockwise with
slow fast.
14. If East luminance is bright and west luminance is dark then rotate counter clockwise with
mid speed.
15. If East luminance is bright and west luminance is overcast then rotate counter clockwise with
slow speed.
16. If East luminance is bright and west luminance is bright then don’t rotate.
The second motor which is responsible for vertical rotation is controlled by the other two inputs,
south and north sensors. The rotation speed and direction of the motor is based on rules described
in table 2.
Table 2. Vertical rotation rules
South Luminance
NorthLuminance
Dim Dark Overcast Bright
Dim -
Counter clockwise
slow
Counter clockwise
mid
Counter
clockwise fast
Dark Clockwise slow -
Counter clockwise
slow
Counter
clockwise mid
Overcast Clockwise mid Clockwise slow -
Counter
clockwise slow
Bright Clockwise fast Clockwise mid Clockwise slow -
Rules in table 2 can be explained as follow:
1. If north luminance is dim and south luminance is dim then don’t rotate.
2. If north luminance is dim and south luminance is dark then rotate counter clockwise with
slow speed.
3. If north luminance is dim and south luminance is overcast then rotate counter clockwise with
mid speed.
4. If north luminance is dim and south luminance is bright then rotate counter clockwise with
fast speed.
5. If north luminance is dark and south luminance is dim then rotate clockwise slow.
6. If north luminance is dark and south luminance is dark then don’t rotate.
7. If north luminance is dark and south luminance is overcast then rotate counter clockwise
with slow speed.
8. If north luminance is dark and south luminance is bright then rotate counter clockwise with
mid speed.
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9. If north luminance is overcast and south luminance is dim then rotate clockwise mid.
10. If north luminance is overcast and south luminance is dark then rotate clockwise slow.
11. If north luminance is overcast and south luminance is overcast then don’t rotate.
12. If north luminance is overcast and south luminance is bright then rotate counter clockwise
with slow speed.
13. If north luminance is bright and south luminance is dim then rotate clockwise fast.
14. If north luminance is bright and south luminance is dark then rotate clockwise mid.
15. If north luminance is bright and south luminance is overcast then rotate clockwise slow.
16. If north luminance is bright and south luminance is bright then don’t rotate.
5. ANFIS MODELING TECHNIQUES
Adaptive neuro-fuzzy inference system ANFIS is a kind of artificial neural network that it is
based on Sugeno fuzzy inference system [9]. ANFIS is a multilayer feedforward network which
uses neural network learning algorithms and fuzzy reasoning to map an input space to an output
space. With the ability to combine the verbal power of a fuzzy system with the numeric power of
a neural system adaptive network, ANFIS has been shown to be powerful in modeling numerous
processes.
The proposed system described in section 2 is also modeled with ANFIS modeling techniques.
These are; grid partition without training, grid partition with hybrid training, grid partition with
backpropagation training, subtractive clustering without training, subtractive clustering with
hybrid training and subtractive clustering with backpropagation training. Grid partition divides
the data space into rectangular subspaces using axis-paralleled partition based on a pre-defined
number of membership functions and their types in each dimension [14]. The wider applications
of grid partition in fuzzy logic and fuzzy inference systems are blocked by the curse of
dimensions, which means that the number of fuzzy rules increases exponentially when the
number of input variables increases. The subtractive clustering method clusters data points in an
unsupervised way by measuring the potential of data points in the feature space. When there is
not a clear idea how many clusters there should be used for a given data set, it can be used for
estimating the number of clusters and the cluster centers [18]. Subtractive clustering assumes that
each data point is a potential cluster center and calculates the potential for each data point based
on the density of surrounding data points.
As discussed in section 2, the system has four inputs and two outputs, first and second inputs
affect the results of the first output while the third and fourth inputs affect the results of the
second output. Since ANFIS doesn’t support multiple outputs, the system is divided into two
single output models with two inputs for each one as shown in figure 6.
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Figure 6. ANFIS system model
The number and the types of membership functions are the same as what is used in Mamdani
model. For each input, the number of the membership functions is set to four and the type is set
to trapmf. After generating FIS, the models structure will be as shown in figure 7. The figure
shows the number of inputs, the number of membership functions for each input, the number of
rules and the number of output membership functions.
Figure 7. ANFIS model structure
To make the system operational, the models is trained with selected train datasets. Mainly,
there are two optimum methods available for FIS training; hybrid and back propagation. Error
tolerance is related to the error size and is used to decrease the average testing error and
increase the system accuracy. Each model is trained with a dataset of 7k points.
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6. RESULTS AND DISCUSSION
In order to compare the accuracy of each model, a cross-validation testing with a dataset of 1.7K
points is used. Three comparison measures are used; correlation coefficient, MAPE, and MPE. A
correlation coefficient is a statistical measure of the degree to which changes to the value of one
variable predict change to the value of another. The Mean Absolute Percent Error MAPE is the
most common measure of forecast error while Mean Percentage Error MPE is the computed
average of percentage errors by which forecasts of a model differ from actual values of the
quantity being forecast. Formulas 1, 2 and 3 show how each of these measures can be calculated.
Results are summarized in table3.
(1)
(2)
(3)
Where,
X: Actual results vector.
Y: Forecast results vector.
: Mean of X.
: Mean of Y.
Table 3. Testing measures comparison
Model Output
Correlation
%
MAPE
%
MPE
%
Mamdani
x_rotation 98.28 6.84 5.21
y_rotation 98.33 8.26 4.37
Grid Partition without training
x_rotation 0 48.74 100
y_rotation 0 50.45 100
Grid Partition with hybrid
training
x_rotation 98.77 1.53 -7.78
y_rotation 98.77 3.49 -9.378
Grid Partition with backpropa
training
x_rotation 98.73 2.03 -7.88
y_rotation 98.72 4.16 -12.81
Clustering without training
x_rotation 98.60 1.0 -4.70
y_rotation 98.45 1.81 -7.66
Clustering with hybrid training
x_rotation 98.60 1.0 -4.70
y_rotation 98.45 1.81 -7.66
Clustering with backpropa
training
x_rotation 98.60 1.67 -2.27
y_rotation 98.67 2.15 -5.80
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As shown in table 3, the testing measures for all models are so close. However, the maximum
correlation obtained is for grid partitioning with hybrid training while the minimum MAPE error
is for clustering with and without training and the minimum MPE is for clustering with
backpropagation model. Each model has its training method and this explains the small differ in
values between all models. For the case of grid partitioning without training, the correlation
between actual results and forecasted results is zero and the errors are at the maximum values,
this is because the default training option for the grid modeling is without training. In such case,
the system has not any previous knowledge about the testing data and is unable to recognize
them. This is not the same for the case of cluster modeling, in which the default training option is
hybrid. This explains the exact values obtained in clustering without training and with hybrid
training.
Figures 8 a-g show plots comparison between the actual 1.7k data test points and their
corresponding forecasted results. Plots show how the forecasted results are so close to the actual
results.
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In order to show the effectiveness of the proposed models, we used the second stage of the
proposed system model shown in figure 2 and applied ANFIS grid partitioning modeling with
hybrid training option. The stage has three inputs, two of them are the outputs from the first stage.
These are, horizontal_rotation_speed and vertical_rotation_speed, the third input is the real power
produced by fixed axis PV system. The output of this stage represents the power which will be
produced if the dual-axis solar tracker is used. In order to compare the actual power produced by
a fixed photovoltaic system with the power produced by the dual-axis system proposed in this
paper. The hourly average solar radiation data from the year of 2013 in Al-Najah solar panel
system is used, results are summarized in table 4 and figure 9.
Table 1. Fixed and dual-axis power comparison
Actual power in the fixed panels system W/m2 848595.7
Expected power in the dual-axis system W/m2 1039045.2
Gain 22.44 %
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Figure 9. Fixed panels vs. Dual-axis output power % for the year of 2013
CONCLUSIONS
In this paper many fuzzy logic models of dual-axis solar tracking systems were proposed and a
fuzzy logic based DC motor speed control system was designed. First, the system was modeled
using Mamdani fuzzy logic modeling then various cases of ANIFS models were applied. Same
testing dataset is applied to each model and results were compared in terms of actual data
correlation with forecasted data, MAPE and MPE error. In order to show the effectiveness of the
proposed dual-axis solar tracking models, a second stage of fuzzy inference system was used to
forecast the output power. Results from this model showed that the proposed dual-axis solar
tracking system provided 22% more power than the fixed PV system.
ACKNOWLEDGEMENTS
We acknowledge that the data that is used in this comparison has been obtained from Al-Najah
solar system in the year of 2013 [15].
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AUTHORS
Hamzah Hijawi received his BS in computer system engineering from Birzeit
University, Ramallah, Palestine, in 2011. He is currently working with Exalt
technologies as a software engineer since 2010, and currently pursuing his Master of
Computer Science from Arab American University, Jenin, Palestine. His researches
interest include Computer Networks, Information Security, Wireless Sensor Networks
and Data Mining.
Labib Arafeh has over Thirty years of professional experience including Information
Technology applications, teaching, training, administration, development and planning,
tied with hands-on exposure monitoring, evaluation & supervisory responsibilities. Dr.
Arafeh has obtained his expertise from working experience at three universities, study
visits and as the director of the National Accreditation & Quality Assurance Commission,
as well leading and participating in developing and implementing several local and global
related projects. He has also been involved in managing, supervising and implementing
several international & local projects such as developing e-Learning and quality assurance
policies for EMUNI university. Dr. Arafeh has been involved in leading and participating in related several
international & local projects including the UNESCO funded IT & Electrical Engineering Benchmarks,
RAND (US)-Al-Quds University funded effective teaching project, EU-supported FINSI, ICT-LEAP and
RUFO Tempus projects. In addition, Dr. Arafeh has participated in evaluating & studying positive and
negative impacts of several World Bank & EU proposed projects. Also, he evaluated the technology 5-10
school curricula and developed the textbook for the 8th grade. Furthermore, Dr. Arafeh’s main research
topics including Applying Neurofuzzy modeling techniques to applications like: Water and Power Load
Demand Predictions, Management Key Knowledge Areas, Entrepreneurship Key Competencies, etc.,
quality eLearning systems, Multimedia, Automatic Essay Grading and Scoring, mobile Text-To-Speech,
and Software Engineering.