This work presents techniques for obtaining a reliable electrical load-curve based on comparative analysis between the different compressed sensing algorithms. Therefore, the goal is implementing compressed sensing (CS) when a wireless heterogeneous network, that exchanges information between electrical enterprise and smart meters, has a fault. Then, the data cannot be sent totally, and we would have the data only of some smart meters; thus, using the adequate technique of compressed sensing is possible to the reconstruction of load-curve required for generating demand response (DR) with the minimum error. In the advanced metering infrastructure (AMI) there may be communication faults; then, it is necessary to have other forms for estimating the demand response using few measurements. In addition, using a dictionary based on the DCT transform does not mean that the sea is the best option for the representation of a signal. For example, among other results, in this work we obtain an average of percent root mean square difference nearest to the 5% in relation with a Gaussian function or Wavelet basis with values between 1.4 and 1.7% average PRD.
|Título de la publicación alojada||2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2017|
|Editorial||Institute of Electrical and Electronics Engineers Inc.|
|Número de páginas||6|
|ISBN (versión digital)||9781538633120|
|Estado||Publicada - 1 dic. 2017|
|Publicado de forma externa||Sí|
|Evento||2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2017 - Quito, Ecuador|
Duración: 20 sep. 2017 → 22 sep. 2017
Serie de la publicación
|Nombre||2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2017|
|Conferencia||2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2017|
|Período||20/09/17 → 22/09/17|
Nota bibliográficaPublisher Copyright:
© 2017 IEEE.