Architecture for Enhancing Communication Security with RBAC IoT Protocol-Based Microgrids.

In traditional power grids, the unidirectional flow of energy and information has led to a decrease in efficiency. To address this issue, the concept of microgrids with bidirectional flow and independent power sources has been introduced. The components of a microgrid utilize various IoT protocols such as OPC-UA, MQTT, and DDS to implement bidirectional communication, enabling seamless network communication among different elements within the microgrid.

Performance Analysis of Routable GOOSE Security Algorithm for Substation Communication through Public Internet Network.

Traditional unidirectional power systems that produce large-scale electricity and supply it using an ultra-high voltage power grid are changing globally to increase efficiency. Current substations' protection relays rely only on internal substation data, where they are located, to detect changes. However, to more accurately detect changes in the system, various data from several external substations, including micro-grids, are required.

An Interoperable Communication Framework for Grid Frequency Regulation Support from Microgrids.

Renewable energy sources, which are controllable under the management of the microgrids with the contribution of energy storage systems and smart inverters, can support power system frequency regulation along with traditionally frequency control providers. This issue will not be viable without a robust communication architecture that meets all communication specification requirements of frequency regulation, including latency, reliability, and security. Therefore, this paper focuses on providing a communication framework of interacting between the power grid management system and microgrid central controller.

An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis.

This research work put forward an intelligent method for diagnosis and classification of power transformers faults based on the instructive Dissolved Gas Analysis Method (DGAM) attributes and machine learning algorithms. In the proposed method, 14 attributes obtained through DGAM are utilized as the initial and unprocessed inputs of Adaptive Neuro-Fuzzy Inference System (ANFIS). In this method, attribute selection and improved learning algorithm are utilized to enhance fault detection and recognition precision.