This paper describes a method for detecting, localizing, and classifying fault events in high voltage transmission lines with Unified Power Flow Controller compensation. The proposed approach employs a single-end method that uses the discrete wavelet transform in combination with a probabilistic neural network. The analysis of the interaction of the UPFC with the signals of the traveling waves is presented. The results of the error for the fault location are obtained in three different fault inception angles, three different values of fault resistance and three different compensation levels. The obtained results indicate that the algorithm accuracy for estimating the fault distance is less than 0.7 percent of the total line length, and the classification efficiency is 95.45%. The test system is simulated in Simulink/Matlab platform and the algorithm is implemented in Python environment.

The introduction of renewable energy sources, distributed energy systems, and power electronics equipment has led to the emergence of the Smart Grid. However, these developments have also caused the worsening of power quality. Selecting the correct sampling frequency and feature extraction techniques are essential for appropriately analyzing power quality disturbances. This work compares the performance of an algorithm based on a Support Vector Machine and Discrete Wavelet Transform for the classification of power quality disturbances using eight sampling rates and five different mother wavelets. The algorithm was tested in noisy and noiseless scenarios to show the methodology. The results indicate that a success rate of 99.9% is obtained for the noiseless signals using a sampling rate of 9.6 kHz and 95.2% for signals with a signal-to-noise ratio of 30 dB with a sampling rate of 30 kHz.

This paper presents a comprehensive analysis of the effects of noise on the detection and localization of faults in transmission lines compensated with a unified power flow controller using traveling wave-based methods. This study focuses on the impact of harmonic and transient noises, which are inherent to power generation, transmission, and UPFC operation. A novel algorithm is proposed combining the Discrete Wavelet Transform and Clarke Transform to detect and localize faults under various noise conditions. The algorithm is tested on a simulated transmission line model in MATLAB/Simulink (Version R2022b) with noise levels of 20 dB, 30 dB, and 40 dB and transient frequencies of 1 kHz, 5 kHz, and 10 kHz. The results demonstrate that the algorithm achieves an average fault localization error of 0.523% under harmonic noise and 0.777% under transient noise, with fault detection rates of 96.3% and 90.75%, respectively. This study highlights the robustness of the traveling wave method in noisy environments and provides insights into the challenges posed by UPFC-compensated lines.

This paper presents a Hardware in the Loop simulation for the detection, classification, and location of faults in transmission lines with Unified Power Flow Controller compensation. Hardware devices are proposed to demonstrate that the traveling wave concept for fault location in a Hardware in the Loop simulation is possible and replicable under laboratory conditions. The devices used are a MacBook Air M1 2020 computer to get the SIMULINK values from the local end of the transmission line, two FeelElec FY8300S generators to import the simulation data, then they send the signals to an AT91SAM3X8E microcontroller to perform the fault detection, when the cycle of the signals where the fault is obtained, they are sent to the UDOO BOLT v8 device to get the classification and location of the fault. The results obtained indicate that the error for estimating the fault distance is less than 0.71% of the total line length, and the classification efficiency is 94.18%.

Power quality improvement and Power quality disturbance (PQD) detection are two significant concerns that must be addressed to ensure an efficient power distribution within the utility grid. When the process to analyze PQD is migrated to real-time platforms, the possible occurrence of a phase mismatch can affect the algorithm’s accuracy; this paper evaluates phase shifting as an additional stage in signal acquisition for detecting and classifying eight types of single power quality disturbances. According to their mathematical models, a set of disturbances was generated using an arbitrary waveform generator BK Precision 4064. The acquisition, detection, and classification stages were embedded into a BeagleBone Black. The detection stage was performed using multiresolution analysis. The feature vectors of the acquired signals were obtained from the combination of Shannon entropy and log-energy entropy. For classification purposes, four types of classifiers were trained: multilayer perceptron, K-nearest neighbors, probabilistic neural network, and decision tree. The results show that incorporating a phase-shifting stage as a preprocessing stage significantly improves the classification accuracy in all cases.

Three-channel sEMG recordings may contain different signatures of lower-limb activity, but separating movements is less straightforward when they recruit some of the same muscles. We examined this problem in seven right-leg activities using the conditioned envelopes recorded over rectus femoris, vastus lateralis, and semitendinosus. The experiment comprised five repeated movements and two sustained postures, each recorded for 40 s during one session with a 25-year-old participant without amputation. We divided the recordings into 300-ms windows with 60% overlap, extracted seven time-domain descriptors, and used them in a support vector machine (SVM). The stored test matrix contains 464 windows and 401 correct assignments, corresponding to 86.42% accuracy. The findings describe the discrimination achieved with the selected channels and indicate aspects of electrode placement and cycle labeling that warrant further testing.