The measurement and analysis of partial discharges (PD) are like medical examinations, such as Electrocardiogram (ECG), in which there are preestablished criteria. However, each patient will present his particularities that will not necessarily imply his condemnation. The consolidated method for PD processing has high qualifications in the statistical analysis of insulation status of electric generators. However, although the IEEE 1434 standard has well-established standards, it will not always be simple to classify signals obtained in the measurement of the hydro generator coupler due to variations in the same type of PD incidence that may occur as a result of the uniqueness of each machine subject to staff evaluation. In order to streamline the machine diagnostic process, a tool is suggested in this article that will provide this signal classification feature. These measurements will be established in groups that represent each known form of partial discharge established by the literature. It was combined with supervised and unsupervised techniques to create a hybrid method that identified the patterns and classified the measurement signals, with a high degree of precision. This paper proposes the use of data-mining techniques based on clustering to group the characteristic patterns of PD in hydro generators, defined in standards. Then, random forest decision trees were trained to classify cases from new measurements. A comparative analysis was performed among eight clustering algorithms and random forest for choosing which is the superior combination to make a better classification of the equipment diagnosis. R² was used for assessing the data trend.

In the new paradigm of urban microgrids, load-balancing control becomes essential to ensure the balance and quality of energy consumption. Thus, phase-load balance method becomes an alternative solution in the absence of distributed generation sources. Development of efficient and robust load-balancing control algorithms becomes useful for guaranteeing the load balance between phases and consumers, as well as to establish an automatic integration between the secondary grid and the supervisory center. This article presents a new phase-balancing control model based on hierarchical Petri nets (PNs) to encapsulate procedures and subroutines, and to verify the properties of a combined algorithm system, identifying the load imbalance in phases and improving the selection process of single-phase consumer units for switching, which is based on load-imbalance level and its future state of load consumption. A reliable flow of automated procedures is obtained, which effectively guarantees the load equalization in the low-voltage grid.

Computer Numerical Control (CNC) is a technology made up of several blocks. Among these, lies the Trajectory Planning block, responsible for reference profile generation that are fed to position control loops. The need for Trajectory Planning arises from the mechanical constraints inherent to every plant to which CNC technology is applied. The machine’s operational limits myst be respected, in order to avoid several issues, such as: loss of precision, early wear of machine’s parts and excessive vibration. This paper proposes a novel smooth real-time trajectory generation setup based on an embedded system platform. A real-time snap and jerk bounded control algorithm is proposed, to achieve continuous and smooth feed motion in traditional Numeric Control code file, dealing both with straight lines and arcs. A local motion blending algorithm, applicable to the proposed method, is also presented. The developed algorithm was deployed to a BeagleBone Black, an embedded System-on-Chip, single board computer and tested in a prototype router machine. A comparison between the proposed method against the seven segments and trapezoidal acceleration methods is presented, both in terms of performance and of real-time computing viability. Simulation and Experimental results demonstrate the effectiveness of the proposed method to generate velocity, acceleration, jerk and snap bounded three dimensional trajectories, reducing the RMS error in up to 8.2% and 22.38% when compared to the Seven Segments and to Trapezoidal Acceleration methods, respectively. Assessing the error area on straight angles, the proposed method produced error areas 24% and 80% smaller when compared to the Seven Segments and to Trapezoidal Acceleration methods, respectively.