The techniques of Dynamic Line Rating (DLR) for Overhead Transmission Line (OTL)’s are currently dynamically developed. DLR systems typically rely on weather, temperature, inclination, and current measurements to calculate tension and sag, where sensors need to be installed directly on wires. Such systems are very reliable and ensure high accuracy in determining maximum allowable current. However, their installation may require switching off the transmission line from the operation. In order to receive precise values regarding the actual operating conditions of the whole transmission line, DLR sensors measuring wire temperature or tension should be installed at many points of OTL. The minimum number of installation points should cover at least each tension section and critical spans, thereby increasing installation costs. The alternative method that allows for the monitoring of OTL is the use of the vision system based on cameras. Installed on the OTLs’ poles, cameras can take photos which, appropriately processed, can provide data about the sag and temperature of wires, without the necessity of switching OTL from the operation for installation or further maintenance. Such a vision system facilitates also data transmission, because it does not require measurement data to be transmitted from the sensor station installed on the wire to the base station located on the pole (for instance, via radio). This article aims to present the concept of a vision system that monitors sag and temperature of Overhead Transmission Lines (OTLs)’ using Long Range (LoRa) wireless communication and data transmission. The developed system consists of a camera and a microcomputer equipped with LoRa communication module. The whole system monitors OTLs’ spans by taking photos, processing images for wire sag-temperature estimation, and sending results to the operator’s Supervisory Control And Data Acquisition (SCADA). The system communication architecture is also proposed and investigated for data transmission time when monitoring the whole OTL.

Existing transmission power grids suffer from high maintenance costs and scalability issues along with a lack of effective and secure system monitoring. To address these problems, we propose to use Wireless Sensor Networks (WSNs) as a technology to achieve energy efficient, reliable, and low-cost remote monitoring of transmission grids. With WSNs, smart grid enables both utilities and customers to monitor, predict and manage energy usage effectively and react to possible power grid disturbances in a timely manner. However, the increased application of WSNs also introduces new security challenges, especially related to privacy, connectivity, and security management, repeatedly causing unpredicted expenditures. Monitoring the status of the power system, a large amount of sensors generates massive amount of sensitive data. In order to build an effective Wireless Sensor Network (WSN) for a smart grid, we focus on designing a methodology of efficient and secure delivery of the data measured on transmission lines. We perform a set of simulations, in which we examine different routing algorithms, security mechanisms and WSN deployments in order to select the parameters that will not affect the delivery time but fulfill their role and ensure security at the same time. Furthermore, we analyze the optimal placement of direct wireless links, aiming at minimizing time delays, balancing network performance and decreasing deployment costs.

The number of fraud occurrences in electronic banking is rising each year. Experts in the field of cybercrime are continuously monitoring and verifying network infrastructure and transaction systems. Dedicated threat response teams (CSIRTs) are used by organizations to ensure security and stop cyber attacks. Financial institutions are well aware of this and have increased funding for CSIRTs and antifraud software. If the company has a rule-based antifraud system, the CSIRT can examine fraud cases and create rules to counter the threat. If not, they can attempt to analyze Internet traffic down to the packet level and look for anomalies before adding network rules to proxy or firewall servers to mitigate the threat. However, this does not always solve the issues, because transactions occasionally receive a “gray” rating. Nevertheless, the bank is unable to approve every gray transaction because the number of call center employees is insufficient to make this possible. In this study, we designed a machine-learning-based rating system that provides early warnings against financial fraud. We present the system architecture together with the new ML-based scoring extension, which examines customer logins from the banking transaction system. The suggested method enhances the organization’s rule-based fraud prevention system. Because they occur immediately after the client identification and authorization process, the system can quickly identify gray operations. The suggested method reduces the amount of successful fraud and improves call center queue administration.