A novel intelligent detection schema of series arc fault in photovoltaic (PV) system based convolutional neural network

Series Arc Fault (SAF) can be defined as the failure that occurs between any electrical contact and any electrical circuitry. However, it considered one of the common failures that affect the operation of the PV system and causes serious problems such as fires and electrical shock hazards. Several reasons increase the possibility of this type of failures such as incorrect installation, irregular maintenance, and some environmental effects. This paper presents a new intelligent and accurate detection method of SAF in the PV system. In this method, Convolutional neural networks (CNN) which is a discriminative (supervised) deep learning algorithm used for the process of fault detection. In normal cases, the signal consists of DC component, inverter component and noise of Network. In the case of SAF, a new component will add to the signal; therefore, CNN used to discriminate against the new component to accurately detect the SAF. PSCAD is used to generate the Arc fault model; Performance evaluation and the results of the proposed method implemented using Python. The achieved accuracy of the proposed detection method is 98.9%.

Intelligent Controller for Monitoring Vehicles at the Roads

Many mobile applications use infrared (IR) and Ultrasonic sensors for distance measurements. In this paper, these two types of sensors have been used in building obstacle detection system and the attributes of each sensor has been tested, the system consists of transmitter and receiver circuit, furthermore, Arduino UNO card has been used for transmitting and receiving signal for each type of sensor based on the Arduino software. The test was performed through distributing these sensors on the road then analyze the reflected signal. Neural network trained and used for monitoring the street and producing the number of cars in each line of street and the total number of cars in the same street

Models, detection methods, and challenges in DC arc fault: A review

The power generation of solar photovoltaic (PV) technology is being implemented in every nation worldwide due to its environmentally clean characteristics. Therefore, PV technology is significantly growing in present applications and usage of PV power systems. Despite the strength of the PV arrays in power systems, the arrays remain susceptible to certain faults. An effective supply requires economic returns, the security of the equipment and humans, precise fault identification, diagnosis, and interruption tools. Meanwhile, the faults in unidentified arc lead to serious fire hazard to commercial, residential, and utility-scale PV systems. To ensure a secure and dependable distribution of electricity, the detection of such hazard is crucial in the early phases of the distribution. In this paper, a detailed review on modern approaches for the identification of DC arc faults in PV is presented. In addition, a thorough comparison is performed between various DC arc-fault models, characteristics, and approaches used for the identification of the faults

Smartphone’s off grid communication network by using Arduino microcontroller and microstrip antenna

After a major disaster, the present communication system fails in providing the services in the affected area. No means of communication proves to be more dangerous as the rescue and relief operations become more difficult. Our current research is about establishing a network in such a disaster-prone area, which would facilitate to communicate and carry out the rescue missions. This research project used Java to create a fire-chat application and used it with the smartphone android system. It used Bluetooth model HC-05 linked with Arduino UNO by the SPI interface to connect Arduino with the smartphone. The FR-model HCW69 connected with Arduino by using UART to transceiver the message. The microstrip antenna 915 MHz connected with the FR-model HCW69 to give us more distance. The maximum effective range of the transceiver was 1 kilometer, to communicate by forming a mesh network. This application is helpful in the case when the smartphone is out of service; it (smartphone) can be communicated connected to the other nearby users with a message

Smartphone’s off grid communication network by using Arduino microcontroller and microstrip antenna

After a major disaster, the present communication system fails in providing the services in the affected area. No means of communication proves to be more dangerous as the rescue and relief operations become more difficult. Our current research is about establishing a network in such a disaster-prone area, which would facilitate to communicate and carry out the rescue missions. This research project used Java to create a fire-chat application and used it with the smartphone android system. It used Bluetooth model HC-05 linked with Arduino UNO by the SPI interface to connect Arduino with the smartphone. The FR-model HCW69 connected with Arduino by using UART to transceiver the message. The microstrip antenna 915 MHz connected with the FR-model HCW69 to give us more distance. The maximum effective range of the transceiver was 1 kilometer, to communicate by forming a mesh network. This application is helpful in the case when the smartphone is out of service; it (smartphone) can be communicated connected to the other nearby users with a message

Detecting Data Poisoning Attacks in Federated Learning for Healthcare Applications Using Deep Learning

This work presents a novel method for securing federated learning in healthcare applications, focusing on skin cancer classification. The suggested solution detects and mitigates data poisoning attacks using deep learning and CNN architecture, specifically VGG16. In a federated learning architecture with ten healthcare institutions, the approach ensures collaborative model training while protecting sensitive medical data. Data is meticulously prepared and preprocessed using the Skin Cancer MNIST: HAM10000 dataset. The federated learning approach uses VGG16's powerful feature extraction to classify skin cancer. A robust strategy for spotting data poisoning threats in federated learning is presented in the study. Outlier detection techniques and strict criteria flag and evaluate problematic model modifications. Performance evaluation proves the model's accuracy, privacy, and data poisoning resilience. This research presents federated learning-based skin cancer categorization for healthcare applications that is secure and accurate. The suggested approach improves healthcare diagnostics and emphasizes data security and privacy in federated learning settings by tackling data poisoning attacks

Utilizing different types of deep learning models for classification of series arc in photovoltaics systems

In this paper, a new hybrid method of change detection and classification is proposed for precise detection and classification of series arc faults (SAFs) in photovoltaic systems. An artificial neural network (ANN) structure is applied for change detection at the first stage, which is then incorporated together with four different convolutional neural network (CNN) models with various dimensions as classifiers for the discrimination of SAFs at the second stage. The models used in the proposed method are 1D CNN, 2D CNN, 3D CNN, and 2D-based images. A comparison of the proposed approach and the state-of-the-art methods has been carried out in terms of accuracy and computational complexity. For a thorough evaluation of the proposed method's performance, studies have been conducted in both simulation and practice, considering various possible scenarios which may emerge. To such an aim, alongside the records from actual measurements in practice, nine models of SAF are also employed for simulation. The results show that the proposed method satisfies principle criteria such as reliability, fault classification error, overfitting, and vanishing solutions

Development of an Intelligent Detection Method of DC Series Arc Fault in Photovoltaic System Using Multilayer Perceptron and Bi-Directional Long Short-Term Memory

A DC series arc fault is one of the significant sources of electrical wiring fires in residential buildings. The production of extremely high temperatures may lead to the ignition of nearby combustible materials. The applications of arc fault diagnosis based machine learning are a global interest due to the immense challenge to create an accurate and efficient detection method. In this paper, a detection and classification method using a multilayer perceptron incorporated with Bi-Directional Long shortterm Memory (MLP-BiLSTM) is proposed. In order to achieve this goal, nine series arc fault models are used in conjunction with data from real-world observations for simulation purposes using Power System Computer Aided Design (PSCAD) software. The simulation and experimental results confirm that the accuracy of the proposed detection and classification method reaches 99%, which results in that the methodology is believed to be accurate for DC series arc fault detection and classification in the PV system with relatively high accuracy