The invention discloses a self-adaptive reconstruction and an uncompressing method for
power quality data based on a compressive sensing theory. A
power quality data compression process with concurrent sampling and compression is achieved through a random measurement matrix, compressive sensing thoughts are used to perform sparse
decomposition on the
power quality data, sparse signals are subjected to
Gaussian measurement encoding, and a self-
adaptive matching pursuit
algorithm is applied to reconstruct signals. According to the self-adaptive reconstruction and the uncompressing method, the random measurement matrix is simple in structure and quick in operation, in no need of
intermediate variable storage space and independent of power disturbance
signal characteristics, and has universality; compared with greedy algorithms of an orthogonal
matching pursuit and the like, known sparseness is not needed, self adaption and regularization processes are provided, the
operation time is short, and accurate reconstruction can be achieved; and constraints of compression after sampling of traditional
data compression methods are broken through, little sampling can recover original power quality signals well, and accordingly, requirements for hardware can be reduced, and the compression efficiency is improved.