This work presents a novel methodology for the optimal location of reclosers in electric distribution systems. The proposal uses a multicriteria analysis to evaluate the reliability indicators for this level 3 (distribution) by generating scenarios from pseudo-random variables. The reliability indicators considered in the study are the System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Customer Average Interruption Duration Index (CAIDI), and Average Energy Not Supplied (AENS). It is employed a deterministic random generation in a defined range associated with each analysis variable (failure rate by elements and loads, failure duration, number of customers per load point, mean power consumed at each load point, etc.) The reliability indicators (criteria) are calculated for every possible location of a recloser in the candidate primary sections, thus obtaining the decision matrix, normalized and weighted by the CRITIC method to find the optimal location according to a minimum criterion. This analysis is repeated N times through the generation of scenarios using the Montecarlo method, establishing the probability of occurrence of each winning alternative and choosing the final optimal location of the first recloser within the distribution system. The methodology also proposes the switching coordination repeating the previously described analysis for locating a second recloser considering that the first was already a winning alternative within the generation of “many” scenarios. The scope of the proposed methodology is the number of reclosers that it is desired to analyze considering cost constraints and is general for any distribution system. The analysis is carried out with comprehensive programming in the Matlab software environment. The results successfully respond to the maneuver and switching tests with the joint criterion of minimizing all reliability indicators for level 3 (distribution). With this proposal, knowledge gaps in reliability studies are solved. The absence of data is completed with generations of pseudo-random variables, and the optimal location of the reclosers responds to all reliability criteria in distribution with weighting alternatives. The novel proposed methodology is validated with an exhaustive search analysis where all possible single or multiple reconnection scenarios are analyzed. The winning location alternative found coincides with the one determined by the proposed methodology in more than 90% of the generated random scenarios.

This article presents an optimal deployment of wireless sensor network taking into account the use of spectrum electromagnetic, data link layer, physical layer and the energy consumption used to transmit information in smart homes. For the analysis a surface has been defined in which smart devices have been deployed randomly in order to ensure that all the devices perform an optimal routing, reaching the gateway through the creation of a wireless sensor network. Different technologies of wireless communication were analyzed in order to calculate the size of the information and fill fields of the data link layer to determine the real size that transport the physical layer. This information is using to optimize the time of use of channel, distances, energy consumption and requirements of bandwidth by each technology.

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.

This research shows a heuristic model for the design of scalable and reliable electrical distribution networks. The algorithms presented allow to optimize the location of transformation centers using on their database geographic information systems from which it is possible to define user locations, candidate sites, possible routes for the deployment of the electricity grid and, in general, data for the reconstruction of the scenario. The model employs clustering and triangulation methods, as well as algorithms for creating a minimally expanding tree and the consequent site assignment for transformer placement. After setting the optimal locations for the transformer site, the algorithms compute voltage drops in secondary circuits, required transformation capability, execution times, and coverage achieved. The results obtained are adjusted to the requirements of an actual distribution power grid and show a good performance on the proposed scenario.

This study presents an optimal insertion model for battery storage systems in the nodes of an electrical transmission network. The proposed model is developed through mixed integer linear programming applied to the calculation of DC power flows, considering restrictions given by the characteristics of the network and by the parameters of the generation units. The proposal’s main objective is to reduce the costs of operation and non-supplied energy produced, due to needing to meet the demand fully or partially. As a case study to evaluate the proposed methodology, the IEEE 24-bar test system is used. In this base case, electrical generators that depend on different primary energy resources are modeled: hydraulic, thermal, photovoltaic, and wind, in addition to potential electrical energy storage systems. These storage systems are assigned as possible analysis scenarios through the proposed optimization technique. The study is carried out in a time horizon of 24 h per day, according to a standard demand curve. With the incorporation of optimally selected storage systems in their capacity and location, it is possible to minimize dependence on the use of fossil fuels. In addition, considerable savings are obtained by reducing generation costs, and the stability of the energy supply is guaranteed. This novel proposal presents a methodology that covers all the variables of this problem, thus guaranteeing an authentic and precise study in terms of optimization. The results obtained highlight and demonstrate the benefits of stability, continuous attention to demand, reduction in dependence on exhaustible and polluting sources, and cost reduction.

This research proposes a high-performance algorithm for the compression rate of electrical power quality signals, using wavelet transformation. To manage the massive amount of data the telecommunications networks are constantly acquiring it is necessary to study techniques for data compression, which will save bandwidth and reduce costs extensively by avoiding having massive data storage facilities. First biorthogonal wavelet level six transform is applied, however after compression, the reconstructed signal will have a different amplitude and it will be shifted when compared to the original one. Then, normalization is used (for amplitude correction between the original signal and reconstructed one) by multiplying the reconstructed signal by the result of the division between the original signal maximum magnitude and the reconstructed signal maximum magnitude. Thirdly, the ripple in the reconstructed signal is eliminated by applying a moving average filter. Finally, the shifting is corrected by finding the difference between the maximum points in a cycle of the original signal and the reconstructed one. After the compression algorithm was performed the best rates are 99.803% for compression rate, RTE 99.9479%, NMSE 0.000434, and Cross-Correlation 0.999925. Finally, this works presents two new performance criteria, compression time and recovery time, both of them in a real scenario will determinate how fast the algorithm can perform.