Abstract: A method and system for monitoring an engine driven generator system (GMS) is provided herein. The system self-configures across generator types and manufacturers via a learning algorithm. Additional sensors are included in the system to provide a robust set of sensor data. Data analysis employed includes comparison to threshold levels, trending of historical data, and Wavelet analysis. A graphical touch screen is provided to users for both controlling the GMS and for viewing results. Monitoring results include operating conditions, existing faults, and warnings of undesirable conditions. Ethernet connections afford review of real time data, diagnostic feedback, and prognostic information at a central location. A sleep state of the GMS conserves generator battery life.

Abstract: A method and system for monitoring an engine driven generator system (GMS) is provided herein. The system self-configures across generator types and manufacturers via a learning algorithm. Additional sensors are included in the system to provide a robust set of sensor data. Data analysis employed includes comparison to threshold levels, trending of historical data, and Wavelet analysis. A graphical touch screen is provided to users for both controlling the GMS and for viewing results. Monitoring results include operating conditions, existing faults, and warnings of undesirable conditions. Ethernet connections afford review of real time data, diagnostic feedback, and prognostic information at a central location. A sleep state of the GMS conserves generator battery life.

Abstract: A system and method of dynamic distributed control with network topology discovery and load discovery in an isolated distribution grid is provided. Source modules connect to AC generators and output high voltage DC power on ports. Load modules receive the high voltage DC power on connected ports and convert the DC power to AC power. Loads are connected to AC outlets on load modules. A source module discovers the connected network topology through a series of states following the reading of a configuration file and creates an adjacency map. Each module has a microprocessor. Each microprocessor runs load discovery algorithms using its adjacency map as input. Load changes are detected and adjacency maps are amended to maintain consistency across all adjacency maps. Priority is afforded to desired loads when demand exceeds capacity.

Abstract: A wheeled industrial/agricultural tractor adapted for use as, for example, an aircraft tug. The tractor has an equipment support frame (3, 28) in the form of a heavy base plate (3) carried beneath an operator's station portion of a conventional tractor chassis (50, 10, 51, 52, 53) and a pair of transversely spaced struts (28) secured (25) to the front portion of the base plate (3) and arranged to extend forwardly thereof. The front ends of the struts have attachment means (19, 28a) for the support of equipment, such as a towing hitch (20), at the front of the tractor. First (1, 2, 70) and second (27, 29, 28c, 28d) attachment means are provided for respectively securing the base plate and struts to the chassis. The tractor also has an engine installation (10, 12, 13, 14) which allows downwardly sloping body panelling (16) at the front to improve vision of the front mounted equipment (20).