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Wind turbines are playing an increasingly important role in renewable power generation. Their complex and large-scale structure, however, and operation in remote locations with harsh environmental conditions and highly variable stochastic loads make fault occurrence inevitable. Early detection and location of faults are vital for maintaining a high degree of availability and reducing maintenance costs. Hence, the deployment of algorithms capable of continuously monitoring and diagnosing potential faults and mitigating their effects before they evolve into failures is crucial. Fault diagnosis and fault tolerant control designs have been the subject of intensive research in the past decades. Significant progress has been made and several methods and control algorithms have been proposed in the literature. This paper provides an overview of the most recent fault diagnosis and fault tolerant control techniques for wind turbines. Following a brief discussion of the typical faults, the most commonly used model-based, data-driven and signal-based approaches are discussed. Passive and active fault tolerant control approaches are also highlighted and relevant publications are discussed. Future development tendencies in fault diagnosis and fault tolerant control of wind turbines are also briefly stated. The paper is written in a tutorial manner to provide a comprehensive overview of this research topic.

This paper is a research article for finding the optimal control of smart power substations for improving the network parameters and reliability. The included papers are the most essential and main studies in the field, which propose a different approach to reach the best performance in electrical power systems. The parameters for improvement are the ability for tracking of the reference signal, stabilizing the system, reducing the error in steady state and controlling the behavior in transient state. The research focuses with the reaching a better transient stability considering voltage and frequency dynamic parameters. The optimal model for the control is focused on minimizing energy consumption but maintaining the controllable parameters, exploring some optimization techniques to find the optimal control, with of aim of minimizing the response time, the energy consumption, and maximizing the reliability by means of improving the controller to be more robust.

Location-based analytics will enable a plethora of new services for 5G verticals and will empower the optimized use of network resources. Such analytics build on enhanced 5G positioning obtained through new positioning signals and procedures defined within 5G standardization, together with the integration of hetero- geneous technologies for achieving sub-meter accuracy. This paper proposes an end-to-end architecture integrated within the 5G networkinfrastructure to provide location- based analytics as a service. We also present an overview of cutting-edge applications in 5G and beyond, focusing onpeople-centricandnetwork-centriclocation-based analytics.

Mobile communication at millimeter-waves (mmWaves) is affected by the rapid and random variations of the wireless environment. This requires accurate channel estimation at the receiver to compensate for the channel dynamics. The required channel training overhead has been shown to occupy a considerable fraction of the mmWave transmission bandwidth, especially when the mobile device (MD) moves at high-speed. We cope with the issue of the rapid variation of the channel by introducing a coherent transmission/detection technique, whose mechanism requires that MD has prior knowledge of its position as an alternative to the channel estimate. In particular, we consider the case of road side links with high-speed MDs and derive a closed-form expression for the optimal detector. We show that the proposed approach reduces the training overhead significantly at the expense of a signal-to-noise ratio (SNR) increase for a given bit error rate.

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., Compression algorithms; Data compression; Data processing; Power quality (PQ); Wavelet transforms

Location awareness is essential for enabling con- textual services and for improving network manage- ment in 5th generation (5G) and beyond 5G (B5G) networks. This article provides an overview of the expanding opportunities offered by location aware- ness in wireless networks, discusses soft information (SI)-based approaches for improved location aware- ness, and presents case studies in conformance with the 3rd Generation Partnership Project (3GPP) standardization by the European Telecommunica- tions Standards Institute (ETSI). Results show that SI-based approaches can provide a new level of location awareness in 5G and B5G networks.

Location awareness is essential in 5th generation (5G) ecosystem to enable location-based services and to efficiently manage the network. This paper presents a method for efficient localization based on the fusion of heterogeneous observations gathered with different technologies in the 5G ecosystem. In particular, a soft information (SI)-based approach is developed for hybrid localization fusing 5G and Wi-Fi measurements. Results obtained in an indoor environment compliant with 3rd Generation Partnership Project standards quantify the benefits of hybrid localization via SI with respect to the case of a single technology.