Model-based, real-time control of electrical power systems

Automated control of a large electrical power distribution network through a single controller can provide advantages in efficiency and reliability as well as reduction in maintenance costs. For control to be most effective, it is necessary that a global view of the entire network be had by the controller, so that it can reason as to the cause of the readings of the various sensing devices located throughout the network. Traditional approaches to power system control have involved a set of local devices (i. e., protective relays) that base their decision on the instantaneous reading of a single sensor. These single-parameter decisions can sometimes be incorrect due to sensor failures. Furthermore, a special type of fault called a soft fault, where a fault impedance limits the current to a value below the relay operating point, are nearly impossible to detect with decisions based on a single local parameter. By reasoning over an entire suit of sensing devices spread throughout the entire network, protection decisions based on a global view can become more reliable as well as comprehensive. Some recent approaches have implemented global control with varying degrees of success through the use of rule-based knowledge-based systems. This paper describes an alter native knowledge-based approach that makes use of so-called models of structure-and-behavior, to which model-based diagnosis is applied. The objective of this approach is to develop a system that can reliably diagnose faults in power distribution networks (especially soft faults), identify sensor failures, and carry out appropriate corrective action automatically. An Intelligent Forcer Controller (IPC) which has these capabilities is described. This IPC was rigorously tested in an DC electrical power distribution system testbed and found to successfully carry out the required functions. This paper also describes in detail the tests and the conclusions drawn from their results.