King Fahd University of Petroleum and Minerals

M A Abido

Hanafy M Omar

Md Shafiullah

A versatile intelligent fault diagnosis (IFD) method for a distribution grid integrating renewable energy resources is described. Advanced signal processing techniques extract useful features from recorded three-phase current signals, which are input to a multilayer perceptron neural networks (MLP-NN) to diagnose i.e., to detect, classify, identify the feeder branch, and locate the faults. Once a fault is detected, classified and located, a grid operator may adjust grid parameters or dispatch a technician to correct the fault. The IFD method is independent of load demand, renewable energy generation, and fault information (resistance and inception angle) uncertainties, as well as measurement noise.

Distribution grid fault analysis under load and renewable energy uncertainties

The present invention relates to the generation of an integrated guidance law for aerodynamic missiles. Particularly, a Tabu search-based approach is utilized for generating an integrated fuzzy guidance law, which includes three separate fuzzy controllers. Each of these fuzzy controllers is activated in a unique region of missile interception. The method includes the following steps: establishing a missile guidance law &#x192;(z), where z represents a vector containing fuzzy membership functions and guidance rules associated with the missile guidance law; and optimizing the missile launch guidance law &#x192;(z) by minimizing the equation where trepresents missile interception time, r represents a distance between the missile and a target, w, wand ware weighting factors, and arepresents a missile normal acceleration, and further |r(t)|&#x3c;r, where rrepresents a pre-selected allowable miss distance, and the minimization is performed by Tabu search.

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Dhahran, Saudi Arabia

M A Abido