A Multi Agent System Design for Power Distribution Restoration Using Neural Networks

The state of the art of power distribution systems is to demand a more accurate response. It also provides more reliability for fault location and restoration respectively. A multi-agent system design for power distribution has been developed using the change of current methodology to detect and locate any type of faults. Employing the artificial intelligence for restoration process is the most important contribution to this study. Since feed-forward neural networks are weight training based back propagation concept, radial basis neural networks showed more efficiency by using the minimum error method to optimize the decision. A Probabilistic radial basis Neural Network (PNN) is designated at each feeder agent to implement the reconfiguration by analyzing the impedance and current values for each zone. The appropriate decision for the optimal reconfiguration case is a vector of activation signals associated with each switch to restore the power to the un-faulted zones of distribution feeder.;This study examines the role of Universal Asynchronous Receiver Transmitter (UART) buffer circuits in the laboratory experiment demonstration of the multi-agent system design. The main approach of a self-healing concept is the protection system. A recloser has been developed and improved for more sensitivity and faster response to detecting a fault where ever it occurs and lead the process of isolating and re-configuration. An electronic buffer circuit using digital microcontroller has been associated with the recloser and agents switches in order to offer a satisfying feedback for the proposed approach. Simulation studies, using MATLAB SimPowerSystems and, Neural Network toolboxes, for the proposed power distribution system showed improved results for fault location and restoration using Radbas neural networks. Hardware implementation with high accurate software data scoping of results has been employed to show the difference in time response using Universal Asynchronous Receiver Transmitter buffers at each switching relay in the design

Deep Space Network information system architecture study

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control

A verification technique for multiple soft fault diagnosis of linear analog circuits

The paper deals with multiple soft fault diagnosis of linear analog circuits. A fault verification method is developed that allows estimating the values of a set of the parameters considered as potentially faulty. The method exploits the transmittance of the circuit and is based on a diagnostic test leading to output signal in discrete form. Applying Z-transform a diagnostic equation is written which is next reproduced. The obtained system of equations consisting of larger number of equations than the number of the parameters is solved using appropriate numerical approach. The method is adapted to real circumstances taking into account scattering of the fault–free parameters within their tolerance ranges and some errors produced by the method. In consequence, the results provided by the method have the form of ranges including the values of the tested parameters. To illustrate the method two examples of real electronic circuits are given

Enhancing reliability in passive anti-islanding protection schemes for distribution systems with distributed generation

This thesis introduces a new approach to enhance the reliability of conventional passive anti-islanding protection scheme in distribution systems embedding distributed generation. This approach uses an Islanding-Dedicated System (IDS) per phase which will be logically combined with the conventional scheme, either in blocking or permissive modes. Each phase IDS is designed based on data mining techniques. The use of Artificial Neural Networks (ANNs) enables to reach higher accuracy and speed among other data mining techniques. The proposed scheme is trained and tested on a practical radial distribution system with six-1.67 MW Doubly-Fed Induction Generators (DFIG-DGs) wind turbines. Various scenarios of DFIG-DG operating conditions with different types of disturbances for critical breakers are simulated. Conventional passive anti-islanding relays incorrectly detected 67.3% of non-islanding scenarios. In other words, the security is as low as 32.3%. The obtained results indicate that the proposed approach can be used to theoretically increase the security to 100%. Therefore, the overall reliability of the system is substantially increased

New Aspects of Fault Diagnosis of Nonlinear Analog Circuits

The paper is focused on nonlinear analog circuits, with the special attention paid to circuits comprising bipolar and MOS transistors manufactured in micrometer and submicrometer technology. The problem of fault diagnosis of this class of circuits is discussed, including locating faulty elements and evaluating their parameters. The paper deals with multiple parametric fault diagnosis using the simulation after test approach as well as detection and location of single catastrophic faults, using the simulation before test approach. The discussed methods are based on diagnostic test, leading to a system of nonlinear algebraic type equations, which are not given in explicit analytical form. An important and new aspect of the fault diagnosis is finding multiple solutions of the test equation, i.e. several sets of the parameters values that meet the test. Another new problems in this area are global fault diagnosis of technological parameters in CMOS circuits fabricated in submicrometer technology and testing the circuits  having multiple DC operating points. To solve these problems several methods have been recently developed, which employ  different concepts and mathematical tools of nonlinear analysis. In this paper they are sketched and illustrated.  All the discussed methods are based on the homotopy (continuation) idea. It is shown that various versions of homotopy and combinations  of the homotopy with some other mathematical algorithms lead to very powerful tools for fault diagnosis of nonlinear analog circuits.  To trace the homotopy path which allows finding multiple solutions, the simplicial method, the restart method, the theory of linear complementarity problem and Lemke's algorithm are employed. For illustration four numerical examples are given

Real-time implementation of a sensor validation scheme for a heavy-duty diesel engine

With ultra-low exhaust emissions standards, heavy-duty diesel engines (HDDEs) are dependent upon a myriad of sensors to optimize power output and exhaust emissions. Apart from acquiring and processing sensor signals, engine control modules should also have capabilities to report and compensate for sensors that have failed. The global objective of this research was to develop strategies to enable HDDEs to maintain nominal in-use performance during periods of sensor failures. Specifically, the work explored the creation of a sensor validation scheme to detect, isolate, and accommodate sensor failures in HDDEs. The scheme not only offers onboard diagnostic (OBD) capabilities, but also control of engine performance in the event of sensor failures. The scheme, known as Sensor Failure Detection Isolation and Accommodation (SFDIA), depends on mathematical models for its functionality. Neural approximators served as the modeling tool featuring online adaptive capabilities. The significance of the SFDIA is that it can enhance an engine management system (EMS) capability to control performance under any operating conditions when sensors fail. The SFDIA scheme updates models during the lifetime of an engine under real world, in-use conditions. The central hypothesis for the work was that the SFDIA scheme would allow continuous normal operation of HDDEs under conditions of sensor failures. The SFDIA was tested using the boost pressure, coolant temperature, and fuel pressure sensors to evaluate its performance. The test engine was a 2004 MackRTM MP7-355E (11 L, 355 hp). Experimental work was conducted at the Engine and Emissions Research Laboratory (EERL) at West Virginia University (WVU). Failure modes modeled were abrupt, long-term drift and intermittent failures. During the accommodation phase, the SFDIA restored engine power up to 0.64% to nominal. In addition, oxides of nitrogen (NOx) emissions were maintained at up to 1.41% to nominal