A new approach for protection of power distribution systems in intelligent buildings has been presented in this paper. Directly adopting the wavelet transform coefficients (WTCs) requires longer computation time and larger memory requirements for the non-intrusive fault monitoring (NIFM) identification process. However, the WTCs contain plenty of information needed for the symmetric and asymmetric transient signals of fault events. To effectively reduce the number of WTCs representing fault transient signals without degrading performance, a power spectrum of the WTCs in different scales calculated by Parseval's Theorem is proposed in this paper. In this paper, artificial neural networks (ANNs), in combination with power-spectrum-based wavelet transform, are used to identify fault types and locations in power distribution systems of industrial buildings by using NIFM. The high success rates of fault event recognition for load-bus faults and transmission-line faults from simulations have proved that the proposed algorithm is applicable to fault identifications of non-intrusive monitoring applications. (C) 2016 Elsevier B.V. All rights reserved.