Publication

Optimization and learning of load restoration strategies

Alain Germond
1996
Conference Papers

Abstract

This paper describes an application of optimization and machine learning to load restoration in a generation-transmission system. An optimization procedure, combining a genetic algorithm and a power system dynamic simulator, generates the appropriate sequence of operations for each state of the power system. A machine learning technique (induction of decision trees) is applied to extract decision criteria that will guide the load restoration after a generalised black-out. The paper also presents the results of applying these techniques to a power system of realistic size

Official source

https://infoscience.epfl.ch/entities/publication/def874ba-c512-4784-b618-0056030637f6

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Alain Germond
1996
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/def874ba-c512-4784-b618-0056030637f6

About this result

Related concepts (32)

Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

Genetic algorithm

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.

Memetic algorithm

A memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm (GA) or more general evolutionary algorithm (EA). It may provide a sufficiently good solution to an optimization problem. It uses a suitable heuristic or local search technique to improve the quality of solutions generated by the EA and to reduce the likelihood of premature convergence. Memetic algorithms represent one of the recent growing areas of research in evolutionary computation.

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Optimization and learning of load restoration strategies

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