In this paper, we discuss the compression of waveforms obtained from measurements of power system quantities and analyze the reasons why its importance is growing with the advent of smart grid systems. While generation and transmission networks already use a considerable number of automation and measurement devices, a large number of smart monitors and meters are to be deployed in the distribution network to allow broad observability and real-time monitoring. This situation creates new requirements concerning the communication interface, computational intelligence and the ability to process data or signals and also to share information. Therefore, a considerable increase in data exchange and in storage is likely to occur. In this context, one must achieve an efficient use of channel communication bandwidth and a reduced need of storage space for power system data. Here, we review the main compression techniques devised for electric signal waveforms providing an overview of the achievements obtained in the past decades. Additionally, we envision some smart grid scenarios emphasizing open research issues regarding compression of electric signal waveforms. We expect that this paper will contribute to motivate joint research efforts between electrical power system and signal processing communities in the area of signal waveform compression.

In vector bit-plane encoding schemes, codebooks must be uniformly distributed on a hyper-sphere. Shells of regular lattices are often used, but they provide only a limited choice of number of vectors K and dimension N. The authors propose a method to generate codebooks in dimension N with arbitrary number K of vectors, almost uniformly distributed on a hyper-sphere. The uniform distribution of an arbitrary number of points on the surface of a hyper-sphere is still an open problem. Some mathematicians indeed consider it one of the mathematical challenges of the 21st century. The proposed method uses a combination of geometric and stochastic approaches to generate approximate solutions. The generated codebooks are tested in vector bit-plane encoding schemes. The results show that the proposed method is effective in generating codebooks almost uniformly distributed on a hyper-sphere.

This paper presents coherent representations of electric power systems signals. These representations are obtained by employing adaptive signal decompositions. They provide a tool to identify structures composing a signal and constitute an approach to represent a signal from its identified components. We use the matching pursuits algorithm, which is a greedy adaptive decomposition, that has the potential of decomposing a signal into coherent components. The dictionary employed is composed of damped sinusoids in order to obtain signal components closely related to power systems phenomena. In addition, we present an effective method to suppress the pre-echo and post-echo artifacts that often appear when using the matching pursuits. However, the use of a dictionary of damped sinusoids alone does not ensure that the decomposition will be meaningful in physical terms. To overcome this constraint, we develop a technique leading to efficient coherent damped-sinusoidal decompositions that are closely related to the physical phenomena being observed. The effectiveness of the proposed method for compression of synthetic and natural signals is tested, obtaining high compression ratios along with high signal-to-noise ratio.