Designing and planning of electrical distribution systems is a task that design engineers perform during their daily activities. These designs, which are completed manually, are made according to the expertise of the designer; as a consequence, the obtained product varies depending on the person in charge of executing the layout, highlighting the fact that those designs are susceptible to involuntary human mistakes resulting in no optimal solutions and high cost consequences. The work presented below explains the implementation of an intelligent decision tool that allows the design of network distribution system planning considering the current electrical company standards, in order to have a clear and quick initial overview of the configuration that an electricity network should have in response to an increasing demand, considering not only the coverage and capacity of the transformers but also voltage drop along the conductors, which must not exceed 3% of the nominal value. The objective of this design tool is that it can be applicable in real scenarios; for this reason, the routing of the conductors and the location of the transformers are based on a georeferenced map. It is important to mention that the optimization problem is focused on minimizing the amount of transformers and at the same time ensuring a total coverge of 100% end users connected to the grid. This tool would be very useful in the educational and practical fields, since private and public electricity companies could use it to obtain a quick and efficient base product on which they could start to develop expansion and planning of distribution networks. The concept and development of such a tool is the subject of this paper.

This research focuses on restoring signals caused by power failures in transmission lines using the basis pursuit, matching pursuit, and orthogonal matching pursuit sensing techniques. The original signal corresponds to the instantaneous current and voltage values of the electrical power system. The heuristic known as brute force is used to find the quasi-optimal number of atoms k in the original signal. Next, we search for the minimum number of samples known as m; this value is necessary to reconstruct the original signal from sparse and random samples. Once the values of k and m have been identified, the signal restoration is performed by sampling sparse and random data at other bus bars of the power electrical system. Basis pursuit allows recovering the original signal from 70% of the random samples of the same signal. The higher the number of samples, the longer the restoration times, approximately 12 s for recovering the entire signal. Matching pursuit allows recovering the same percentage, but with the lowest restoration time. Finally, orthogonal matching pursuit recovers a slightly lower percentage with a higher number of samples with a significant increase in its recovery time. Therefore, for real-time electrical fault signal restoration applications, the best selection will be matching pursuit due to the fact that it presents the lowest machine time, but requires more samples compared with orthogonal matching pursuit. Basis pursuit and orthogonal matching pursuit require fewer sparse and random samples despite the fact that these require a longer processing time for signal recovery. These two techniques can be used to reduce the volume of data that is stored by phasor measurement systems.

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