The background of power system restoration is analyzed and how this process might benefit from the use of knowledge-based systems is discussed. After describing the state of the art of knowledge-based systems for power system restoration, efforts are made to review knowledge-based techniques to handle system restoration problems. The experiences of system development are reported and evaluation of the systems is addressed. Some needed technological innovations are listed that are required before knowledge-based systems can extensively be applied to power system restoration.>

This paper proposes a systemtic method to design systolic arrays under the constraint of the number of the processors. Our basic approach is to partition the large systolic array into the smaller number of groups, whose number is coincident with the number of processors to be used. We give the mathematical method to make at most one processor execute computation in each group. Then, each group can be replaced by one processor to satisfy the constraint of the number of the processors.

Power system operators often reach a cognitive barrier when information arrives too fast during a power system emergency. At such times it becomes difficult to reach a correct diagnosis of the problem or to formulate the correct decision when actions must be taken. Artificial Intelligence gives designers of Energy Management Systems a way to solve many of the diagnosis and decision problems so as to make the EMS more useful. This paper explores reasons why AI techniques, such as knowledge-based expert systems, are being used in EMS designs and the differences between knowledge-based expert systems and traditional numeric algorithm development. The differences between expert systems and the numeric approach extend to the basic conception and design of the applications. This is illustrated using a relay fault diagnosis system, showing both the traditional and rapid prototyping approaches to its development. Finally, issues concerned with the implementation of AI in EMS computers are explored along with the authors' predictions of possible AI applications to power system operations.