Biblio

With the electric power distribution grid facing ever increasing complexity and new threats from cyber-attacks, situational awareness for system operators is quickly becoming indispensable. Identifying de-energized lines on the distribution system during a SCADA communication failure is a prime example where operators need to act quickly to deal with an emergent loss of service. Loss of cellular towers, poor signal strength, and even cyber-attacks can impact SCADA visibility of line devices on the distribution system. Neural Networks (NNs) provide a unique approach to learn the characteristics of normal system behavior, identify when abnormal conditions occur, and flag these conditions for system operators. This study applies a 24-hour load forecast for distribution line devices given the weather forecast and day of the week, then determines the current state of distribution devices based on changes in SCADA analogs from communicating line devices. A neural network-based algorithm is applied to historical events on Alabama Power's distribution system to identify de-energized sections of line when a significant amount of SCADA information is hidden.

The vehicular networks extend the internet services to road edge. They allow users to stay connected offering them a set of safety and infotainment services like weather forecasts and road conditions. The security and privacy are essential issues in computing systems and networks. They are particularly important in vehicular networks due to their direct impact on the users’ safety on road. Various researchers have concentrated their efforts on resolving these two issues in vehicular networks. A great number of researches are found in literature and with still existing open issues and security risks to be solved, the research is continuous in this area. However, the researchers may face some difficulties in choosing the correct method to prove their works or to illustrate their excellency in comparison with existing solutions. In this paper, we review a set of evaluation methodologies and metrics to measure, proof or analyze privacy and security solutions. The aim of this review is to illuminate the readers about the possible existing methods to help them choose the correct techniques to use and reduce their difficulties.

Wireless Sensor Network (WSN)is the most promising area which is widely used in the field of military, healthcare systems, flood control, and weather forecasting system. In WSN every node is connected with another node and exchanges the information from one to another. While sending data between nodes data security is an important factor. Security is a vital issue in the area of networking. This paper addresses the issue of security in terms of distinct attacks and their solutions provided by the different authors. Whenever data is transferred from source to destination then it follows some route so there is a possibility of a malicious node in the network. It is a very difficult task to identify the malicious node present in the network. Insecurity intruder attacks on data packets that are transferred from one node to another node. While transferring the data from source to destination node hacker hacks the data and changes the actual data. In this paper, we have discussed the numerous security solution provided by the different authors and they had used the Machine Learning (ML) approach to handle the attacks. Various ML techniques are used to determine the authenticity of the node. Network attacks are elaborated according to the layer used for WSN architecture. In this paper, we will categorize the security attacks according to layer-wise and type-wise and represent the solution using the ML technique for handling the security attack.

The behavior of modern power systems is becoming more stochastic and dynamic, due to the increased penetration of variable generation, demand response, new power market structure, extreme weather conditions, contingencies, and unexpected events. It is critically important to predict potential system operational issues so that grid planners and operators can take preventive actions to mitigate the impact, e.g., lack of operational reserves. In this paper, an innovative software tool is presented to assist power grid operators in a balancing authority in predicting the grid stress level over the next operating day. It periodically collects necessary information from public domain such as weather forecasts, electricity demand, and automatically estimates the stress levels on a daily basis. Advanced Neural Network and regression tree algorithms are developed as the prediction engines to achieve this goal. The tool has been tested on a few key balancing authorities and successfully predicted the growing system peak load and increased stress levels under extreme heat waves.