It is very important to accurately detect wind direction and speed for wind energy that is one of the essential sustainable energy sources. Studies on the wind speed forecasting are generally carried out for long-term predictions. One of the main reasons for the long-term forecasts is the correct planning of the area where the wind turbine will be built due to the high investment costs and long-term returns. Besides that, short-term forecasting is another important point for the efficient use of wind turbines. In addition to estimating only average values, making instant and dynamic short-term forecasts are necessary to control wind turbines. In this study, short-term forecasting of the changes in wind speed between 1-20 minutes using deep learning was performed. Wind speed data was obtained instantaneously from the feedback of the emulated wind turbine's generator. These dynamically changing data was used as an input of the deep learning algorithm. Each new data from the generator was used as both test and training input in the proposed approach. In this way, the model accuracy and enhancement were provided simultaneously. The proposed approach was turned into a modular independent integrated system to work in various wind turbine applications. It was observed that the system can predict wind speed dynamically with around 3% error in the applications in the test setup applications.
Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy f...IJECEIAES
The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other wellknown method in the literature.
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.
The Renewable energy sources, especially wind turbine generators, are considered as
important generation alternatives in electric power systems due to their non-exhausted nature and
benign environmental effects [1]. The fact that wind power penetration continues to increase has
motivated a need to develop more widely applicable methodologies for evaluating the actual benefits
of adding wind turbines to conventional generating systems. In this paper reliability evaluation of
wind power generation system is carried. Reliability evaluation of generating systems with wind
energy sources is a complex process. It requires an accurate wind speed forecasting technique for the
wind farm site. The method requires historical wind speed data collected over many years for the
wind farm location to determine the necessary parameters of the wind speed models for the
particular site [3]. The evaluation process should also accurately model the intermittent nature of
power output from the wind farm. For the data analysis excel data analysis tool is used and
probability distribution of wind speeds are calculated [10]. This study shows the system availability
for the generation of power from wind turbine generators installed at the Hanamasagar, a village
near Gajendragada of Karnataka State.
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.
Design of spark ignition engine speed control using bat algorithm IJECEIAES
The most common problem in spark ignition engine is how to increase the speed performance. Commonly researchers used traditional mathematical approaches for designing speed controller of spark ignition engine. However, this solution may not be sufficient. Hence, it is important to design the speed controller using smart methods. This paper proposes a method for designing speed controller of a spark ignition engine using the bat algorithm (BA). The simulation is carried out using the MATLAB/SIMULINK environment. Time domain simulation is carried out to investigate the efficacy of the proposed method. From the simulation results, it is found that by designing speed controller of spark ignition engine using PI based bat algorithm, the speed performance of spark ignition engine can be enhanced both in no load condition and load condition compared to conventional PI controler.
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.
An Application of Genetic Programming for Power System Planning and OperationIDES Editor
This work incorporates the identification of model
in functional form using curve fitting and genetic programming
technique which can forecast present and future load
requirement. Approximating an unknown function with
sample data is an important practical problem. In order to
forecast an unknown function using a finite set of sample
data, a function is constructed to fit sample data points. This
process is called curve fitting. There are several methods of
curve fitting. Interpolation is a special case of curve fitting
where an exact fit of the existing data points is expected.
Once a model is generated, acceptability of the model must be
tested. There are several measures to test the goodness of a
model. Sum of absolute difference, mean absolute error, mean
absolute percentage error, sum of squares due to error (SSE),
mean squared error and root mean squared errors can be used
to evaluate models. Minimizing the squares of vertical distance
of the points in a curve (SSE) is one of the most widely used
method .Two of the methods has been presented namely Curve
fitting technique & Genetic Programming and they have been
compared based on (SSE)sum of squares due to error.
Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy f...IJECEIAES
The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other wellknown method in the literature.
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.
The Renewable energy sources, especially wind turbine generators, are considered as
important generation alternatives in electric power systems due to their non-exhausted nature and
benign environmental effects [1]. The fact that wind power penetration continues to increase has
motivated a need to develop more widely applicable methodologies for evaluating the actual benefits
of adding wind turbines to conventional generating systems. In this paper reliability evaluation of
wind power generation system is carried. Reliability evaluation of generating systems with wind
energy sources is a complex process. It requires an accurate wind speed forecasting technique for the
wind farm site. The method requires historical wind speed data collected over many years for the
wind farm location to determine the necessary parameters of the wind speed models for the
particular site [3]. The evaluation process should also accurately model the intermittent nature of
power output from the wind farm. For the data analysis excel data analysis tool is used and
probability distribution of wind speeds are calculated [10]. This study shows the system availability
for the generation of power from wind turbine generators installed at the Hanamasagar, a village
near Gajendragada of Karnataka State.
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.
Design of spark ignition engine speed control using bat algorithm IJECEIAES
The most common problem in spark ignition engine is how to increase the speed performance. Commonly researchers used traditional mathematical approaches for designing speed controller of spark ignition engine. However, this solution may not be sufficient. Hence, it is important to design the speed controller using smart methods. This paper proposes a method for designing speed controller of a spark ignition engine using the bat algorithm (BA). The simulation is carried out using the MATLAB/SIMULINK environment. Time domain simulation is carried out to investigate the efficacy of the proposed method. From the simulation results, it is found that by designing speed controller of spark ignition engine using PI based bat algorithm, the speed performance of spark ignition engine can be enhanced both in no load condition and load condition compared to conventional PI controler.
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.
An Application of Genetic Programming for Power System Planning and OperationIDES Editor
This work incorporates the identification of model
in functional form using curve fitting and genetic programming
technique which can forecast present and future load
requirement. Approximating an unknown function with
sample data is an important practical problem. In order to
forecast an unknown function using a finite set of sample
data, a function is constructed to fit sample data points. This
process is called curve fitting. There are several methods of
curve fitting. Interpolation is a special case of curve fitting
where an exact fit of the existing data points is expected.
Once a model is generated, acceptability of the model must be
tested. There are several measures to test the goodness of a
model. Sum of absolute difference, mean absolute error, mean
absolute percentage error, sum of squares due to error (SSE),
mean squared error and root mean squared errors can be used
to evaluate models. Minimizing the squares of vertical distance
of the points in a curve (SSE) is one of the most widely used
method .Two of the methods has been presented namely Curve
fitting technique & Genetic Programming and they have been
compared based on (SSE)sum of squares due to error.
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.
23 9754 assessment paper id 0023 (ed l)2IAESIJEECS
This paper presents a risk assessment method for assessing the cyber security of power systems in view of the role of protection systems. This paper examines the collision of transmission and bus line protection systems positioned in substations on the cyber-physical performance of the power systems. The projected method simulates the physical feedback of power systems to hateful attacks on protection system settings and parameters. The relationship between protection device settings, protection logic, and circuit breaker logic is analyzed. The expected load reduction (ELC) indicator is used in this paper to determine potential losses in the system due to cyber attacks. The Monte Carlo simulation is used to calculate ELC’s account to assess the capabilities of the attackers and bus arrangements are changed. The influence of the projected risk assessment method is illustrated by the use of the 9-bus system and the IEEE-68 bus system.
On-line Power System Static Security Assessment in a Distributed Computing Fr...idescitation
The computation overhead is of major concern when
going for increased accuracy in online power system security
assessment (OPSSA). This paper proposes a scalable solution
technique based on distributed computing architecture to
mitigate the problem. A variant of the master/slave pattern is
used for deploying the cluster of workstations (COW), which
act as the computational engine for the OPSSA. Owing to the
inherent parallel structure in security analysis algorithm, to
exploit the potential of distributed computing, domain
decomposition is adopted instead of functional decomposition.
The security assessment is performed utilizing the developed
composite security index that can accurately differentiate the
secure and non-secure cases and has been defined as a function
of bus voltage and line flow limit violations. Validity of
proposed architecture is demonstrated by the results obtained
from an intensive experimentation using the benchmark IEEE
57 bus test system. The proposed framework, which is scalable,
can be further extended to intelligent monitoring and control
of power system
Predict the Average Temperatures of Baghdad City by Used Artificial Neural Ne...IJERA Editor
This paper utilizes artificial neural networks (ANN) technique to improve temperature forecast performance of
Baghdad city. Our study based on Feed Forward Backpropagation Artificial Neural Networks (BPANN)
algorithm of which trained and tested by used a real world daily average temperatures of Bagdad city for ten
years past for months of January and July. Aimed at providing forecasts in a schedule, for all Days of the month
to help the meteorologist to foresee future weather temperature accurately and easily. Forecasts by ANN model
has been compared with the actual results and the realistic output (with IMOS). The results has been Compared
to the practical temperature prediction results, and shows that the BPANN forecasts have accuracy that gave
reasonably very good result and can be considered as a good method for temperature predicting..
IMPROVED NEURAL NETWORK PREDICTION PERFORMANCES OF ELECTRICITY DEMAND: MODIFY...csandit
Accurate prediction of electricity demand can bring extensive benefits to any country as the
forecast values help the relevant authorities to take decisions regarding electricity generation,
transmission and distribution much appropriately. The literature reveals that, when compared
to conventional time series techniques, the improved artificial intelligent approaches provide
better prediction accuracies. However, the accuracy of predictions using intelligent approaches
like neural networks are strongly influenced by the correct selection of inputs and the number of
neuro-forecasters used for prediction. This research shows how a cluster analysis performed to
group similar day types, could contribute towards selecting a better set of neuro-forecasters in
neural networks. Daily total electricity demands for five years were considered for the analysis
and each date was assigned to one of the thirteen day-types, in a Sri Lankan context. As a
stochastic trend could be seen over the years, prior to performing the k-means clustering, the
trend was removed by taking the first difference of the series. Three different clusters were
found using Silhouette plots, and thus three neuro-forecasters were used for predictions. This
paper illustrates the proposed modified neural network procedure using electricity demand
data.
Novel framework of retaining maximum data quality and energy efficiency in re...IJECEIAES
There are various unseen and unpredictable networking states in Wireless Sensor Network (WSN) that adversely affect the aggregated data quality. After reviewing the existing approaches of data quality in WSN, it was found that the solutions are quite symptomatic and they are applicable only in a static environment; however their successful applicability on dynamic and upcoming reconfigurable network is still a big question. Moreover, data quality directly affects energy conservation among the nodes. Therefore, the proposed system introduces a simple and novel framework that jointly addresses the data quality and energy efficiency using probability-based design approach. Using a simplified analytical methodology, the proposed system offers solution in the form of selection transmission of an aggergated data on the basis of message priority in order to offer higher data utilization factor. The study outcome shows proposed system offers a good balance between data quality and energy efficiency in contrast to existing system.
A Joint QRS Detection and Data Compression Scheme for Wearable Sensorsecgpapers
Abstract—This paper presents a novel electrocardiogram (ECG)
processing technique for joint data compression and QRS detection
in awireless wearable sensor. The proposed algorithm is aimed
at lowering the average complexity per task by sharing the computational
load among multiple essential signal-processing tasks
needed for wearable devices. The compression algorithm, which is
based on an adaptive linear data prediction scheme, achieves a lossless
bit compression ratio of 2.286x. The QRS detection algorithm
achieves a sensitivity (Se) of 99.64% and positive prediction (+P)
of 99.81% when tested with the MIT/BIH Arrhythmia database.
Lower overall complexity and good performance renders the proposed
technique suitable for wearable/ambulatory ECG devices.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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.
Real-time monitoring system for weather and air pollutant measurement with HT...journalBEEI
This article discusses devising an IoT system to monitor weather parameters and gas pollutants in the air along with anHTML web-based application. Weather parameters measured include; speed and direction of the wind, rainfall, air temperature and humidity, barometric pressure, and UV index. On the other side, the gases measured are; ammonia, hydrogen, methane, ozone, carbon monoxide, and carbon dioxide. This article is introducing a technique to send all parameter data. All parameters read by each sensor are converted into a string then joined into a string dataset, where this dataset is sent to the server periodically. On the UI side, the dataset that has been downloaded from the server-parsed for processing and then displayed. This system uses Google Firebase as a real-time database server for sensor data. Also, using the GitHub platform as a web hosting. The web application uses the HTML programming platform. The results of this study indicate that the device operates successfully to provide information about the weather and gases condition as real-time data.
Short term residential load forecasting using long short-term memory recurre...IJECEIAES
Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions.
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.
23 9754 assessment paper id 0023 (ed l)2IAESIJEECS
This paper presents a risk assessment method for assessing the cyber security of power systems in view of the role of protection systems. This paper examines the collision of transmission and bus line protection systems positioned in substations on the cyber-physical performance of the power systems. The projected method simulates the physical feedback of power systems to hateful attacks on protection system settings and parameters. The relationship between protection device settings, protection logic, and circuit breaker logic is analyzed. The expected load reduction (ELC) indicator is used in this paper to determine potential losses in the system due to cyber attacks. The Monte Carlo simulation is used to calculate ELC’s account to assess the capabilities of the attackers and bus arrangements are changed. The influence of the projected risk assessment method is illustrated by the use of the 9-bus system and the IEEE-68 bus system.
On-line Power System Static Security Assessment in a Distributed Computing Fr...idescitation
The computation overhead is of major concern when
going for increased accuracy in online power system security
assessment (OPSSA). This paper proposes a scalable solution
technique based on distributed computing architecture to
mitigate the problem. A variant of the master/slave pattern is
used for deploying the cluster of workstations (COW), which
act as the computational engine for the OPSSA. Owing to the
inherent parallel structure in security analysis algorithm, to
exploit the potential of distributed computing, domain
decomposition is adopted instead of functional decomposition.
The security assessment is performed utilizing the developed
composite security index that can accurately differentiate the
secure and non-secure cases and has been defined as a function
of bus voltage and line flow limit violations. Validity of
proposed architecture is demonstrated by the results obtained
from an intensive experimentation using the benchmark IEEE
57 bus test system. The proposed framework, which is scalable,
can be further extended to intelligent monitoring and control
of power system
Predict the Average Temperatures of Baghdad City by Used Artificial Neural Ne...IJERA Editor
This paper utilizes artificial neural networks (ANN) technique to improve temperature forecast performance of
Baghdad city. Our study based on Feed Forward Backpropagation Artificial Neural Networks (BPANN)
algorithm of which trained and tested by used a real world daily average temperatures of Bagdad city for ten
years past for months of January and July. Aimed at providing forecasts in a schedule, for all Days of the month
to help the meteorologist to foresee future weather temperature accurately and easily. Forecasts by ANN model
has been compared with the actual results and the realistic output (with IMOS). The results has been Compared
to the practical temperature prediction results, and shows that the BPANN forecasts have accuracy that gave
reasonably very good result and can be considered as a good method for temperature predicting..
IMPROVED NEURAL NETWORK PREDICTION PERFORMANCES OF ELECTRICITY DEMAND: MODIFY...csandit
Accurate prediction of electricity demand can bring extensive benefits to any country as the
forecast values help the relevant authorities to take decisions regarding electricity generation,
transmission and distribution much appropriately. The literature reveals that, when compared
to conventional time series techniques, the improved artificial intelligent approaches provide
better prediction accuracies. However, the accuracy of predictions using intelligent approaches
like neural networks are strongly influenced by the correct selection of inputs and the number of
neuro-forecasters used for prediction. This research shows how a cluster analysis performed to
group similar day types, could contribute towards selecting a better set of neuro-forecasters in
neural networks. Daily total electricity demands for five years were considered for the analysis
and each date was assigned to one of the thirteen day-types, in a Sri Lankan context. As a
stochastic trend could be seen over the years, prior to performing the k-means clustering, the
trend was removed by taking the first difference of the series. Three different clusters were
found using Silhouette plots, and thus three neuro-forecasters were used for predictions. This
paper illustrates the proposed modified neural network procedure using electricity demand
data.
Novel framework of retaining maximum data quality and energy efficiency in re...IJECEIAES
There are various unseen and unpredictable networking states in Wireless Sensor Network (WSN) that adversely affect the aggregated data quality. After reviewing the existing approaches of data quality in WSN, it was found that the solutions are quite symptomatic and they are applicable only in a static environment; however their successful applicability on dynamic and upcoming reconfigurable network is still a big question. Moreover, data quality directly affects energy conservation among the nodes. Therefore, the proposed system introduces a simple and novel framework that jointly addresses the data quality and energy efficiency using probability-based design approach. Using a simplified analytical methodology, the proposed system offers solution in the form of selection transmission of an aggergated data on the basis of message priority in order to offer higher data utilization factor. The study outcome shows proposed system offers a good balance between data quality and energy efficiency in contrast to existing system.
A Joint QRS Detection and Data Compression Scheme for Wearable Sensorsecgpapers
Abstract—This paper presents a novel electrocardiogram (ECG)
processing technique for joint data compression and QRS detection
in awireless wearable sensor. The proposed algorithm is aimed
at lowering the average complexity per task by sharing the computational
load among multiple essential signal-processing tasks
needed for wearable devices. The compression algorithm, which is
based on an adaptive linear data prediction scheme, achieves a lossless
bit compression ratio of 2.286x. The QRS detection algorithm
achieves a sensitivity (Se) of 99.64% and positive prediction (+P)
of 99.81% when tested with the MIT/BIH Arrhythmia database.
Lower overall complexity and good performance renders the proposed
technique suitable for wearable/ambulatory ECG devices.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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.
Real-time monitoring system for weather and air pollutant measurement with HT...journalBEEI
This article discusses devising an IoT system to monitor weather parameters and gas pollutants in the air along with anHTML web-based application. Weather parameters measured include; speed and direction of the wind, rainfall, air temperature and humidity, barometric pressure, and UV index. On the other side, the gases measured are; ammonia, hydrogen, methane, ozone, carbon monoxide, and carbon dioxide. This article is introducing a technique to send all parameter data. All parameters read by each sensor are converted into a string then joined into a string dataset, where this dataset is sent to the server periodically. On the UI side, the dataset that has been downloaded from the server-parsed for processing and then displayed. This system uses Google Firebase as a real-time database server for sensor data. Also, using the GitHub platform as a web hosting. The web application uses the HTML programming platform. The results of this study indicate that the device operates successfully to provide information about the weather and gases condition as real-time data.
Short term residential load forecasting using long short-term memory recurre...IJECEIAES
Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions.
Wind power prediction using a nonlinear autoregressive exogenous model netwo...IJECEIAES
The monitoring of wind installations is key for predicting their future behavior, due to the strong dependence on weather conditions and the stochastic nature of the wind. However, in some places, in situ measurements are not always available. In this paper, active power predictions for the city of Santa Marta-Colombia using a nonlinear autoregressive exogenous model (NARX) network were performed. The network was trained with a reliable dataset from a wind farm located in Turkey, because the meteorological data from the city of Santa Marta are unavailable or unreliable on certain dates. Three training and testing cases were designed, with different input variables and varying the network target between active power and wind speed. The dataset was obtained from the Kaggle platform, and is made up of five variables: date, active power, wind speed, theoretical power, and wind direction; each with 50,530 samples, which were preprocessed, and in some cases, normalized, to facilitate the neural network learning. For the training, testing and validation processes, a correlation coefficient of 0.9589 was obtained for the best scenario with the data from Turkey, while the best correlation coefficient for the data from Santa Marta was 0.8537.
Data acquisition of wind speed, wind direction and environmental temperature are needed to get the data potential of wind power. The aim of this research is to generate a device of wind speed, wind direction and temperature with the real time condition. With this device, we will obtain an analysis about the potential of wind power electrical generation around the Puger beach, Jember, Indonesia. In this study, parameters investigated were made into three types of measurement variables that measure of wind speed, wind direction, temperature and a data to show real time data..The device which is used to measure wind speed using hall effect sensor as a transducer. With using of the active magnet that spins will be created pwm that will be read by sensor to get the wind speed. As for the shows wind direction, we use a compass sensor CMPS 03 is a digital sensor that outputs in the form of digital bits so that be able to show wind direction from 0° to 360°. The magnitude of angle will be used in analyzing the direction of the wind, the real time clock (RTC) will be used to directly to determine the time and date of recording data. Then the temperature DS1621 sensor to show environmental temperature.
This paper presents artificial intelligence approach of artificial neural network (ANN) and random forest (RF) that used to perform short-term photovoltaic (PV) output current forecasting (STPCF) for the next 24-hours. The input data for ANN and RF is consists of multiple time lags of hourly solar irradiance, temperature, hour, power and current to determine the movement pattern of data that have been denoised by using wavelet decomposition. The Levenberg-Marquardt optimization technique is used as a back-propagation algorithm for ANN and the bagging based bootstrapping technique is used in the RF to improve the results of forecasting. The information of PV output current is obtained from Green Energy Research (GERC) University Technology Mara Shah Alam, Malaysia and is used as the case study in estimation of PV output current for the next 24-hours. The results have shown that both proposed techniques are able to perform forecasting of future hourly PV output current with less error.
Cuckoo search algorithm based for tunning both PI and FOPID controllers for ...IJECEIAES
Wind Energy has received great attention in this century. It influences the new power systems, adding new challenges to the power system expansion problem. Nowadays, double feed induction generator (DFIG) wind turbines are used majorly in wind farms, due to their advantages over other types. Therefore, the analysis of the system using this type has become very important. In this paper, a wind turbine modelling was introduced with suggested controllers, in order to enhance the system response, with respect to both pitch control and maximum output power. Cuckoo search algorithm (CSA), a meta-heuristic optimization technique, was implemented to determine the gains of a proportional-integral (PI) controller and fractional order proportional-integral-derivative (FOPID) controller to optimize the system, which considered three control loops: pitch, rotor-side converter, and grid-side converter control loop. Simulation results were determined using MATLAB/Simulink. The comparative analysis of the results showed that the PI Controller gave the simplest and the best response in case of the pitch and rotor-side control loops while the FOPID was the best when applied to the grid-side control loop. Based on the results and discussion, a suggestion of using a compination of each controller was introduced.
This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compared to Wavelet Neural Network (WNN) and the results of the proposed model are shown superior to compared methods.
PVPF tool: an automated web application for real-time photovoltaic power fore...IJECEIAES
In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records’ time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.
Weather monitoring and forecasting are very important in agricultural sectors. There are several data need to be collected in real-time to support weather monitoring and forecasting systems, such as temperature, humidity, air pressure, wind speed, wind direction, and rainfall. The purpose of this research to develop a real-time weather monitoring system using a parallel computation approach and analyze the computational performance (i.e., speed up and efficiency) using the ARIMA model. The developed system wireless has been implemented on sensor networks (WSN) platform using Arduino and Raspberry Pi devices and web-based platform for weather visualization and monitoring. The experimental data used in our research work is a set of weather data acquired and collected from January until March 2017 in Bogor area. The result of this research is that the speed up of the using eight processors computation three times faster than using a single processor, with the efficiency of 50%.
A novel wind power prediction model using graph attention networks and bi-dir...IJECEIAES
Today, integrating wind energy forecasting is an important area of research due to the erratic nature of wind. To achieve this goal, we propose a new model of wind speed prediction based on graph attention networks (GAT), we added a new attention mechanism and a learnable adjacency matrix to the GAT structure to obtain attention scores for each weather variable. The results of the GAT-based model are merged with the bi-directional deep learning long and short-term memory (BiLSTM) layer to take advantage of the geographic and temporal properties of historical weather data. The experiments and analyzes are carried out using precise meteorological data collected from wind farms in the Moroccan city of Tetouan. We show that the proposed model can learn complex input-output correlations of meteorological data more efficiently than previous wind speed prediction algorithms. Due to the resulting attention weights, the model also provides more information about the main weather factors for the evaluated forecast work.
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.
Insights into the Efficiencies of On-Shore Wind Turbines: A Data-Centric Anal...Gurdal Ertek
Literature on renewable energy alternative of wind turbines does not include a multidimensional benchmarking study that can help investment decisions as well as design processes. This paper presents a data-centric analysis of commercial on-shore wind turbines and provides actionable insights through analytical benchmarking through Data Envelopment Analysis (DEA), visual data analysis, and statistical hypothesis testing. The paper also introduces a novel visualization approach for the understanding and the interpretation of reference sets, the set of efficient wind turbines that should be taken as benchmark by inefficient ones.
http://research.sabanciuniv.edu.
A Novel Technique to Enhance the Lifetime of Wireless Sensor Networks through...IJECEIAES
In the most of the real world scenarios, wireless sensor networks are used. Some of the major tasks of these types of networks is to sense some information and sending it to monitoring system or tracking some activity etc. In such applications, the sensor nodes are deployed in large area and in considerably large numbers [1]-[3]. Each of these node will be having constrained resources whether it might be energy, memory or processing capability. Energy is the major resource constraint in these types of networks. Hence enough care to be taken in all aspects such that energy can be used very efficiently. Different Activities which will be taking place in a sensor node are sensing, radio operations and receiving and computing. Among all these operations, radio consumes maximum power. Hence there is a need of reducing the power consumption in such radio operations. In the proposed work a software module is developed which will reduce the number of transmissions done to the base station. The work compares the consecutively sensed data and if these data are same then the old data then the old data will be retained. In other case the newly sensed data will be sent to the sink node. This technique reduces the number of data transmissions in a significant way. With the reduced number of transmissions, the energy saved in each node will be more, which will increase the lifetime of the entire network.
Survey on deep learning applied to predictive maintenance IJECEIAES
Prognosis health monitoring (PHM) plays an increasingly important role in the management of machines and manufactured products in today’s industry, and deep learning plays an important part by establishing the optimal predictive maintenance policy. However, traditional learning methods such as unsupervised and supervised learning with standard architectures face numerous problems when exploiting existing data. Therefore, in this essay, we review the significant improvements in deep learning made by researchers over the last 3 years in solving these difficulties. We note that researchers are striving to achieve optimal performance in estimating the remaining useful life (RUL) of machine health by optimizing each step from data to predictive diagnostics. Specifically, we outline the challenges at each level with the type of improvement that has been made, and we feel that this is an opportunity to try to select a state-of-the-art architecture that incorporates these changes so each researcher can compare with his or her model. In addition, post-RUL reasoning and the use of distributed computing with cloud technology is presented, which will potentially improve the classification accuracy in maintenance activities. Deep learning will undoubtedly prove to have a major impact in upgrading companies at the lowest cost in the new industrial revolution, Industry 4.0.
Real Time and Wireless Smart Faults Detection Device for Wind Turbineschokrio
In new energy development, wind power has boomed. It is due to the proliferation of wind parks and their operation in supplying the national electric grid with low cost and clean resources. Hence, there is an increased need to establish a proactive maintenance for wind turbine machines based on remote control and monitoring. That is necessary with a real-time wireless connection in offshore or inaccessible locations while the wired method has many flaws. The objective of this strategy is to prolong wind turbine lifetime and to increase productivity. The hardware of a remote control and monitoring system for wind turbine parks is designed. It takes advantage of GPRS or Wi-Max wireless module to collect data measurements from different wind machine sensors through IP based multi-hop communication. Computer simulations with Proteus ISIS and OPNET software tools have been conducted to evaluate the performance of the studied system. Study findings show that the designed device is suitable for application in a wind park.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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many methods have been used to make short- and long-term forecasts from wind speed data. Most of
the methods use statistical analysis techniques. Time series modeling techniques such as ARIMA or neural
networks have been widely used in most studies for the prediction of wind speed [12-16].
Statistical learning method; estimates wind speed by deducting control system data and similar
historical data [11]. The model uses for prediction is usually a time series model and estimates parameters
using some iterations on the dataset. In some studies, data mining algorithms have also been used for
prediction. Similar learning algorithms are used in data mining, just as they are used in artificial neural
networks [11, 16, 17]. Statistical learning methods are preferred for two main reasons, which are prediction
models that are easy to create and have high prediction accuracy for the short-term. Models based on physical
systems can also be used in wind forecasting. These methods consist of a series of differential equations and
are used for numerical weather forecasts [11]. These physical methods can be used in long-term systems that
require more detailed information. Also, when these physical systems are compared with the estimations
made using statistical learning methods, success rates are lower. However, there are approaches that use both
the physical model and statistical models together. These are called hybrid model approach, where numerical
weather forecast data is solved using statistical models. In general, multi-input like wind speed, direction, and
other meteorological data are used for inputs of the prediction algorithm [18-21].
The Artificial Intelligence (AI) method offers very successful results thanks to its ability to map
nonlinear features. The main advantage of AI methods is to estimate possible wind speed series without
a predefined mathematical tool. As the representative of artificial intelligence methods, artificial neural
networks [22, 23], feed-forward neural network [24] and adaptive linear element neural network for wind
speed prediction are currently used by researchers [24-26].
Deep learning algorithms are preferred over traditional neural networks to reveal more detailed
features of wind speed series. Deep learning algorithms provide very successful results in many computer-
based sensing, vision, speech and signal processing [27]. A deep learning method has been applied to reveal
the complex features of wind speed data [28-30]. Experimental results show that deep learning algorithms
have the best performance among comparison models. While many analyses in wind speed prediction studies
produced the predicted wind speed values directly from the raw wind speed data, the non-stationary effects of
the wind speed series were also ignored. By comparison, deep learning is a subset of machine learning.
However, although deep learning, and machine learning function similarly, their capabilities are different.
deep-learning automatically finds the features to be used for classification, while machine learning provides
these features manually. In addition, compared to machine learning, deep learning requires stronger
processors and larger training data for its results. Compared to ANN, deep learning offers more layers
working [31-37].
In this study, a short-term wind speed forecasting system is proposed using deep learning for wind
turbines applications. In section 2, the system architecture and synthetic data generation are explained.
Then the short-term wind speed forecasting system is described in the same section. Short-term wind speed
forecasting results are presented in Section 3. Finally, conclusions are presented in section 4.
2. THE PROPOSED SYSTEM ARCHITECTURE
Global wind data at a 2.5-degree resolution have been published by the National Centres for
Environmental Information, national oceanic and atmospheric administration (NOAA) [38]. Daily wind-
velocity-averages since 1948 have been given in the dataset provided by NOAA. In order to simulate
a realistic situation, a data generation model was designed using the daily average wind-velocity values from
the dataset and generated random values having the final average value was still consistent with the dataset.
We hereby interpolated the daily average values to minute-by-minute values.
The data generation model was designed in Simulink and is shown in Figure 1. In this model, data
generated by the method explained above were applied to a PID control system which controls a DC motor
emulating the wind-turbine. A quadrature encoder was connected to the rotor measuring the rotational speed
of the motor. Interpolated wind-data were simulated by this actual system and 4 hours of real-time data were
collected for each day of each year. Therefore, we could be able to generate more detailed data that is
consistent with the dataset. In the Simulink model given below, Encoder block measured the angular velocity
of the rotor. Angular velocity than was fed to the PID Controller block as the feedback value. Rotor speed
was generated using the actual data received from the data block and summed with a random value generated
by the Random Number block. Time Pulses block generated timing pulses for each minute and hour in order
to proceed to the next value in the dataset. Selector block used the hour pulse to pick the next value from
the dataset.
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Figure 1. Interpolation/generation model
3. THE SHORT-TERM WIND SPEED FORECASTING SYSTEM
In this section, a short-term wind speed prediction was performed with two datasets, separately.
KERAS library was used for the training model. 15 iterations were performed for network training with
15 minutes of data from both datasets. The input layer of the deep learning model consisted of a single
neuron with linear activation. The hidden layer was built as the hidden LSTM layer by using 128 neurons
with ReLU (Rectified Linear Unit) activation function for each neuron. The output layer was built as
the hidden dense layer by using a single neuron with a linear activation function. Nvidia Jetson Nano
microcomputer which directly connected to the motor output was used for both learning and forecasting.
Thus, it will be easier to transfer the forecast results to any unit in the wind turbine and to process
accordingly. Test results are presented in Section 4.
𝑒 𝑣 = |𝑣 𝑎𝑣𝑔 + 𝛼|
𝑖
− 𝑣 𝑎𝑐𝑡 (1)
Wind speed error ev is given in (1) where vavg was the daily average wind speed, α was a random
number representing the daily fluctuation of wind speed, vact was the actual wind speed measured by
the quadrature encoder, and i was the index marking every minute. ev was fed to the PI controller running
the DC motor emulating the turbine and synthetic data based on actual wind speed data were collected by
the system minute by minute. Using the model given above two synthetic datasets were generated for every
15th
January with 1 minute and 6 minutes intervals, as shown in Figures 2 and 3, respectively.
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Figure 2. Generated data with a 1-minute time interval
Figure 3. Generated data with 6 minutes time interval
4. RESULTS AND DISCUSSION
In this section, the test results of the system are presented. The test result after data training for
the first dataset is shown in Figure 4. For a 1-minute-interval dataset, 30% (approximately 5100 singular
data) of the data were used for the test. According to test results, the proposed algorithm has a 3.315%
prediction error rate. Using a 6-minute-interval dataset, again 30% (approximately 850 singular data) of
the data were used for the test, as well. The test results are shown in Figure 5. According to test results,
the proposed algorithm has a 10.93% prediction error rate. According to the test results, the system was
observed to produce better forecasting results with approximately 3.315% forecasting error by using the first
dataset which has data with a 1-minute time interval. However, time and space complexity are higher than in
the second dataset. The forecasting result had an acceptable error rate (approximately 10.93%) using
the second dataset. Therefore, the second set can be preferred for low time and space complexity due to
the dynamic structure of the system. Computational costs were not a decisive factor because data would be
gathered and calculated in a separate unit for both training and forecasting. The proposed approach had
acceptable error rates with both datasets for wind turbine applications. In cases where data storage is limited,
a 6-minute time interval dataset may be preferred. If there is no such limitation, it will be appropriate to use
a 1-minute time interval data.
Figure 4. Test results with a 1-minute time interval
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Figure 5. Test results with 6 minutes time interval
5. CONCLUSION
In this study, the short-term wind speed forecasting system using deep learning for wind turbines
applications is presented. The proposed system produces short-term wind speed forecasts to be used in
different kinds of wind turbine applications. The system can be easily integrated into wind turbines. Thus,
it can be used as a brake system, emergency warning, etc. According to test results which were presented in
section 3, it could be seen that the proposed approach produced better results using 1-minute time interval
data for training. Moreover, the error rate achieved when using the 6-minute time interval data is also
acceptable. The most important differences between the two datasets which were shown in Figures 4 and 5,
were error rates and space complexity. These criteria should be kept in mind when integrating the proposed
system into actual applications.
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BIOGRAPHIES OF AUTHORS
Gokhan Erdemir received his B.Sc., M.Sc. and Ph.D. degrees from Marmara University, Turkey,
respectively. During his Ph.D., we worked as a research scholar at Michigan State University,
Department of Electrical and Computer in East Lansing MI, USA. Now, he is an assistant
professor at Istanbul Sabahattin Zaim University, Department of Electrical and Electronics
Engineering. His research topics include robotics, control systems, and intelligent algorithms.
Aydın Tarık Zengin received his Electrical and Electronics Engineering B.S. degree from Ege
University, Turkey, in 2007. Later on, received M.E. and Ph.D. degrees from Department of
Computer Science and Electrical Engineering, Kumamoto University, Japan, in 2010 and 2013,
respectively. Currently, he is an Asst. Prof. at Istanbul S. Zaim University. His research interests
include autonomous systems and control theory.
T. Cetin Akinci received B.S degrees in Electrical Engineering. M.Sc. and Ph.D. degrees from
Marmara University, Istanbul-Turkey. His research interests include artificial neural networks,
deep learning, machine learning, image processing, signal processing and, data analysis. He has
been working as an Associate Professor in Electrical Engineering Department of Istanbul
Technical University (ITU) in Istanbul, Turkey.