Journal of Electrical Engineering & Technology
The distribution network is reconfigured by modifying the topology arrangement of the network fee... more The distribution network is reconfigured by modifying the topology arrangement of the network feeders. Because the voltage stability of the distribution networks can differ within a range following network reconfiguration, the calculation of steady-state voltage stability plays a significant role in real-time feeder reconfiguration. Examining the state of security and estimating it for the next operational configuration is crucial for making real-time decisions. Online security evaluation needs minimal complexity and computing time. Standard methods of assessing the distribution network’s steady-state voltage stability can be insufficient for online and real-time environments due to their high complexity and long computing period. This study proposes a machine learning (ML) approach for classifying configuration states and adopts the decision tree technique to interpret the online applications in the feeder reconfiguration. For the classification, the single line equivalent L-index voltage stability and switching configurations of the feeders are employed as training information for the ML models. A modified IEEE 14-bus and 30-bus test systems verifies the feasibility of the suggested solution. Once trained, the proposed system provides a quick and accurate classification for unknown configurations of the specific security state in 0.3 seconds.
Advanced metering infrastructure allows the two-way sharing of information between smart meters a... more Advanced metering infrastructure allows the two-way sharing of information between smart meters and utilities. However, it makes smart grids more vulnerable to cyber-security threats such as energy theft. This study suggests ensemble machine learning (ML) models for the detection of energy theft in smart grids using customers' consumption patterns. Ensemble ML models are meta-algorithms that create a pool of several ML approaches and combine them smartly into one predictive model to reduce variance and bias. A number of algorithms, including adaptive boosting, categorical boosting, extreme-boosting, light boosting, random forest, and extra trees, were tested to find their false positive and detection rates. A data pre-processing method was employed to improve detection performance. The statistical approach of minority over-sampling was also employed to tackle overfitting. An extensive analysis based on a practical dataset of 5000 customers reveals that bagging models outperform other algorithms. The random forest and extra trees models achieve the highest area under the curve score of 0.90. The precision analysis shows that the proposed bagging methods perform better.
Journal of Ambient Intelligence and Humanized Computing, 2020
Surveillance drones are remarkable devices for monitoring, as they have strong spatial and remote... more Surveillance drones are remarkable devices for monitoring, as they have strong spatial and remote sensing capabilities. The prompt detection of peripheral damage to the blades of wind turbines is necessary to reduce downtime and prevent the potential failure of wind farms. Computer vision breakthroughs with deep learning have developed and been refined over time, mainly using convolution neural networks. From this perspective, we suggest a deep learning model for monitoring and diagnosing the blade health of wind turbines based on images captured by surveillance drones. The main limitations of standard monitoring devices are their poor detection accuracy and lack of real-time performance, making it complex to obtain the attributes of blades from aerial images. Based on the foregoing, this study introduces a method for increasing detection accuracy when carrying out operations in real time using You Only Look at Once version 3 (YOLOv3). We train and evaluate three deep learning models on the wind turbine image dataset. We find that many aerial images are unclear because of blurred motion. As avoiding such low-resolution images for training can affect accuracy, we use a super-resolution convolution neural network to reconstruct a blurred picture as a high-resolution one. The computational results demonstrate that YOLOv3 outperforms traditional models in terms of both accuracy and handling time.
The inspection of electrical components has long been an important issue in the power distributio... more The inspection of electrical components has long been an important issue in the power distribution system. Unmanned drones are impressive surveillance systems with a powerful spatial and remote sensing capability. This paper proposes a system to monitor the health of the ceramic insulators that uses aerial images as a source of information and deep structured learning model for the data interpretation. The key drawbacks of existing monitoring systems are poor detection accuracy and lack of real-time execution, making it more complicated to obtain attributes from aerial photographs. The focus of this paper is to increase accuracy of detection while operating in real-time using You Only Look Once version 3 (YOLOv3). The novelty of the proposed system is that it combines deep learning and the Internet of Things using a single embedded device called Raspberry Pi. For the scientific investigation, we equipped Raspberry Pi with a test image as an input to detect an insulator’s health status using YOLOv3. Many aerial images are not clear due to motion blur. Excluding such low-resolution training images will affect accuracy. So we used a super-resolution CNN to reconstruct a blurred image as high-resolution image. The efficiency of the proposed system has been tested using a private data set consisting of a variety of scenes containing high-voltage power line insulators. The results show that the suggested system is quick and accurate in the identification and classification of insulators.
Electricity is an essential commodity that must be generated in response to demand. Hydroelectric... more Electricity is an essential commodity that must be generated in response to demand. Hydroelectric power plants, fossil fuels, nuclear energy, and wind energy are just a few examples of energy sources that significantly impact production costs. Accurate load forecasting for a specific region would allow for more efficient management, planning, and scheduling of low-cost generation units and ensuring on-time energy delivery for full monetary benefit. Machine learning methods are becoming more effective on power grids as data availability increases. Ensemble learning models are hybrid algorithms that combine various machine learning methods and intelligently incorporate them into a single predictive model to reduce uncertainty and bias. In this study, several ensemble methods were implemented and tested for short-term electric load forecasting. The suggested method is trained using the influential meteorological variables obtained through correlation analysis and the past load. We used...
2016 5th International Conference on Wireless Networks and Embedded Systems (WECON), 2016
This paper presents an IOT device which help to manage and plan the usage of water. This system c... more This paper presents an IOT device which help to manage and plan the usage of water. This system can be easily installed in residential societies. Sensors placed in the tank which continuously informs the water level at the current time. This information will be updated on the cloud and using an android application, user can visualize the water level on a Smartphone anywhere that is connected to Internet. According to the level of water in the tank the motor functioning will be automatically controlled, at low level of water motor will automatically turn on and when tank is about to fill up it will cut off.
This document has been prepared to serve as a laboratory manual for EE 200 Digital Design course ... more This document has been prepared to serve as a laboratory manual for EE 200 Digital Design course for electrical engineering students. The manual consists of a set of experiments designed to allow students to build, and verify digital circuits and systems. This set of experiments cover relevant topics prescribed in the syllabus and are designed to reinforce the theoretical concepts taught in the classroom with practical experience in the lab. By the end of the course, students are expected to have a good understanding of digital logic design and implementation with SSI and MSI devices.
Journal of Electrical Engineering & Technology
The distribution network is reconfigured by modifying the topology arrangement of the network fee... more The distribution network is reconfigured by modifying the topology arrangement of the network feeders. Because the voltage stability of the distribution networks can differ within a range following network reconfiguration, the calculation of steady-state voltage stability plays a significant role in real-time feeder reconfiguration. Examining the state of security and estimating it for the next operational configuration is crucial for making real-time decisions. Online security evaluation needs minimal complexity and computing time. Standard methods of assessing the distribution network’s steady-state voltage stability can be insufficient for online and real-time environments due to their high complexity and long computing period. This study proposes a machine learning (ML) approach for classifying configuration states and adopts the decision tree technique to interpret the online applications in the feeder reconfiguration. For the classification, the single line equivalent L-index voltage stability and switching configurations of the feeders are employed as training information for the ML models. A modified IEEE 14-bus and 30-bus test systems verifies the feasibility of the suggested solution. Once trained, the proposed system provides a quick and accurate classification for unknown configurations of the specific security state in 0.3 seconds.
Advanced metering infrastructure allows the two-way sharing of information between smart meters a... more Advanced metering infrastructure allows the two-way sharing of information between smart meters and utilities. However, it makes smart grids more vulnerable to cyber-security threats such as energy theft. This study suggests ensemble machine learning (ML) models for the detection of energy theft in smart grids using customers' consumption patterns. Ensemble ML models are meta-algorithms that create a pool of several ML approaches and combine them smartly into one predictive model to reduce variance and bias. A number of algorithms, including adaptive boosting, categorical boosting, extreme-boosting, light boosting, random forest, and extra trees, were tested to find their false positive and detection rates. A data pre-processing method was employed to improve detection performance. The statistical approach of minority over-sampling was also employed to tackle overfitting. An extensive analysis based on a practical dataset of 5000 customers reveals that bagging models outperform other algorithms. The random forest and extra trees models achieve the highest area under the curve score of 0.90. The precision analysis shows that the proposed bagging methods perform better.
Journal of Ambient Intelligence and Humanized Computing, 2020
Surveillance drones are remarkable devices for monitoring, as they have strong spatial and remote... more Surveillance drones are remarkable devices for monitoring, as they have strong spatial and remote sensing capabilities. The prompt detection of peripheral damage to the blades of wind turbines is necessary to reduce downtime and prevent the potential failure of wind farms. Computer vision breakthroughs with deep learning have developed and been refined over time, mainly using convolution neural networks. From this perspective, we suggest a deep learning model for monitoring and diagnosing the blade health of wind turbines based on images captured by surveillance drones. The main limitations of standard monitoring devices are their poor detection accuracy and lack of real-time performance, making it complex to obtain the attributes of blades from aerial images. Based on the foregoing, this study introduces a method for increasing detection accuracy when carrying out operations in real time using You Only Look at Once version 3 (YOLOv3). We train and evaluate three deep learning models on the wind turbine image dataset. We find that many aerial images are unclear because of blurred motion. As avoiding such low-resolution images for training can affect accuracy, we use a super-resolution convolution neural network to reconstruct a blurred picture as a high-resolution one. The computational results demonstrate that YOLOv3 outperforms traditional models in terms of both accuracy and handling time.
The inspection of electrical components has long been an important issue in the power distributio... more The inspection of electrical components has long been an important issue in the power distribution system. Unmanned drones are impressive surveillance systems with a powerful spatial and remote sensing capability. This paper proposes a system to monitor the health of the ceramic insulators that uses aerial images as a source of information and deep structured learning model for the data interpretation. The key drawbacks of existing monitoring systems are poor detection accuracy and lack of real-time execution, making it more complicated to obtain attributes from aerial photographs. The focus of this paper is to increase accuracy of detection while operating in real-time using You Only Look Once version 3 (YOLOv3). The novelty of the proposed system is that it combines deep learning and the Internet of Things using a single embedded device called Raspberry Pi. For the scientific investigation, we equipped Raspberry Pi with a test image as an input to detect an insulator’s health status using YOLOv3. Many aerial images are not clear due to motion blur. Excluding such low-resolution training images will affect accuracy. So we used a super-resolution CNN to reconstruct a blurred image as high-resolution image. The efficiency of the proposed system has been tested using a private data set consisting of a variety of scenes containing high-voltage power line insulators. The results show that the suggested system is quick and accurate in the identification and classification of insulators.
Electricity is an essential commodity that must be generated in response to demand. Hydroelectric... more Electricity is an essential commodity that must be generated in response to demand. Hydroelectric power plants, fossil fuels, nuclear energy, and wind energy are just a few examples of energy sources that significantly impact production costs. Accurate load forecasting for a specific region would allow for more efficient management, planning, and scheduling of low-cost generation units and ensuring on-time energy delivery for full monetary benefit. Machine learning methods are becoming more effective on power grids as data availability increases. Ensemble learning models are hybrid algorithms that combine various machine learning methods and intelligently incorporate them into a single predictive model to reduce uncertainty and bias. In this study, several ensemble methods were implemented and tested for short-term electric load forecasting. The suggested method is trained using the influential meteorological variables obtained through correlation analysis and the past load. We used...
2016 5th International Conference on Wireless Networks and Embedded Systems (WECON), 2016
This paper presents an IOT device which help to manage and plan the usage of water. This system c... more This paper presents an IOT device which help to manage and plan the usage of water. This system can be easily installed in residential societies. Sensors placed in the tank which continuously informs the water level at the current time. This information will be updated on the cloud and using an android application, user can visualize the water level on a Smartphone anywhere that is connected to Internet. According to the level of water in the tank the motor functioning will be automatically controlled, at low level of water motor will automatically turn on and when tank is about to fill up it will cut off.
This document has been prepared to serve as a laboratory manual for EE 200 Digital Design course ... more This document has been prepared to serve as a laboratory manual for EE 200 Digital Design course for electrical engineering students. The manual consists of a set of experiments designed to allow students to build, and verify digital circuits and systems. This set of experiments cover relevant topics prescribed in the syllabus and are designed to reinforce the theoretical concepts taught in the classroom with practical experience in the lab. By the end of the course, students are expected to have a good understanding of digital logic design and implementation with SSI and MSI devices.
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