New Model Reference Adaptive System Speed Observer for Field-Oriented Control Induction Motor Drives Using Neural Networks

One of the primary advantages of field-oriented controlled induction motor for high performance application is the capability for easy field weakening and the full utilization of voltage and current rating of the inverter to obtain a wide dynamic speed rangeThis paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Matlab/Simulink. Simulation result shows a good performance of speed estimator. The simulation results show good performance in various operating conditions. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to parameter variations especially resistances of stator

A Simple Approach to Detect High Impedance Fault Using Morphological Gradient Edge Detector

Detection of high impedance faults (HIF) is one of the biggest challenges in power distribution networks. HIF usually occurs when conductors in the distribution network are broken and accidently come into contact the ground or a tree branch. The current of this fault is close to the load current level and cannot be detected by overcurrent relays. Also, some regular system phenomena such as capacitor switching, load switching, and inrush current and saturation phenomena in current transformer (CT) represent some features which may overlap the components of HIF; making HIF detection schemes more complex. In this paper, a new method for HIF detection is presented which is able to distinguish any type of HIF from regular system phenomena. To achieve this, the scheme of morphological gradient edge detection (MGED) is used to process voltage signals. The MGED extracts two main features from the processed signals: first, the edges or changes in the signal are elicited and then, these features are extracted after two cycles from the onset of the fault. Then, based on these features, a high impedance fault detection index (HIFDI) is introduced for distinguishing and classifying HIF from other regular system phenomena. The simulation results for different types of HIF fault in a sample 20 kV distribution feeder and IEEE 34-bus distribution test system using EMTP confirm the fast and accurate performance of the proposed method

An inventory model with random discount offerings

We consider an inventory model in which a supplier makes deal offers with random discount prices at random points in time. Assuming that discount offerings follow a Poisson process and discount price is a discrete random variable with a known distribution, we propose a continuous-review control policy for the model and derive optimality conditions for the policy parameters. The model is then extended to the case of multiple suppliers that offer discount deals with supplier-specific Poisson processes and discount prices. Numerical examples are presented to demonstrate cost savings due to discount offers.Inventory management Price discount Stochastic price Multiple suppliers