Biblio

In order to avoid the situation that the diagnosis model based on single sensor data is easily disturbed by environmental noise and the diagnosis accuracy is low, an intelligent fault fusion diagnosis method for marine diesel engine is proposed. Firstly, the support vector machine which is optimized by genetic algorithm is used to learn the fault sample data from different sensors, then multiple fault diagnosis models and results can be got. After that, multiple groups of diagnosis results are taken as evidence bodies and fused by evidence theory to obtain more accurate diagnosis results. By analyzing the sample data obtained from the fault simulation experiment of marine diesel engine based on AVL BOOST software, the proposed method can improve the fault diagnosis accuracy of marine diesel engine and reduce the uncertainty value of diagnosis results.

The United States and European Union have an increasing number of projects that are engaging end-use devices for improved grid capabilities. Areas such as building-to-grid and vehicle-to-grid are simple examples of these advanced capabilities. In this paper, we present an innovative concept study for a ship-to-grid integration. The goal of this study is to simulate a two-way power flow between ship(s) and the grid with GridLAB-D for the port of Kyllini in Greece, where a ship-to-shore interconnection was recently implemented. Extending this further, we explore: (a) the ability of ships to meet their load demand needs, while at berth, by being supplied with energy from the electric grid and thus powering off their diesel engines; and (b) the ability of ships to provide power to critical loads onshore. As a result, the ship-to-grid integration helps (a) mitigate environmental pollutants from the ships' diesel engines and (b) provide resilience to nearby communities during a power disruption due to natural disasters or man-made threats.