The global optimization of sensor locations for structural health monitoring systems is studied in this paper. First, the performance function based on damage detection is presented. Then, genetic algorithms (GAs) are adopted to search for the optimal locations of sensors. However, the simple GAs can result in infeasible solutions to the problem. Some improved strategies are presented in this paper, such as crossover based on identification code, mutation based on two gene bits, and improved convergence. The analytical results from the improved genetic algorithm are compared with the penalty function method and the forced mutation method. It is concluded that the convergence speed with the proposed improved genetic algorithm is faster than that with the penalty function method and the forced mutation method, and the result of placement optimization is better.

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This paper presents a new approach for single phase-earth fault protection in distribution systems. Traditional protection schemes are analyzed and compared with one another for effective fault feature extraction. Maximum likelihood method is also carried out on several copula functions to find the optimal copula to fit the fault feature data. Then, copula rank correlation is calculated by the optimal copula, and the principal component analysis technique is improved to preprocess and reduce the dimension of the fault data by decomposing the copula rank correlation matrix instead of computing the eigenvalues and their eigenvectors of the covariance matrix. Finally, distance discriminant function is defined and operation criterion is proposed, and distance discriminant analysis is used to discriminate the faulty feeder. Simulation results of a practical 10kV distribution system show that the proposed approach of fault protection is able to achieve better identification accuracy than hierarchical clustering algorithm and FCM algorithms based approaches. Index Terms-protection; distribution systems; single phase-earth fault; principal component analysis; discriminant analysis 0885-8977 (c)

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