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The cognitive radio network (CRN), an instrumental part of the next-generation wireless communication systems, is mainly dependent on spectrum sensing to function properly. The radio spectrum can help in clean energy transition and load capacity factors by providing a more efficient and accurate spectrum utilization. By using it, the number of spectrums that is used for communication can be optimized, which can reduce the amount of energy consumed by the network. Additionally, 5G radio spectrum sensing can be used to identify and classify different types of signals, which can help reduce the amount of interference in the network and improve the efficiency of energy utilization. It can also allow for the digitization of clean energy infrastructure and facilitate better decision-making processes that take into account environmental impacts while optimizing energy use because of its efficient characteristics like non-linearity, detection, scalability, robustness, generalization, non-stationarity in wireless environments, dynamic entity, weighted sum of Gaussian functions centered at specific frequencies, and robustness against noise and interference. It can adapt to noise and interference by adjusting its parameters, and this allows accurate distinguishing between primary and secondary users in the wireless spectrum, which is why a radial basis function is a popular choice for spectrum sensing in 5G networks. Radial basis function networks (RBFNs) can work better in 5G spectrum sensing for better signal classification, dynamic adaptation, fast detection, faster decision-making, and improved noise and interference reduction. One of the most sought-after goals in the field of wireless research is the creation of spectrum-sensing technology that is dependable and intelligent because multilayer learning approaches are inappropriate for dealing with time-series data due to the higher misclassification rate and inherent computational complexity. To address this, the study proposed the radial basis function network that learns the temporal aspects from spectral data and makes use of additional environmental statistics such as frequency, efficiency, energy, spectrum allocation, distance, and duty-cycle time, which are considered environmental data that may be used to fine-tune sensor performance. The scheme is simulated with real-time parameters, and the results are quite promising in terms of evaluation parameters.

Summary: Most of the existing adaptive filter algorithms pay more attention to improving performance while ignoring the computational complexity and the impact of the impulsive environment. When encountering the impulsive noise environments, the performance of traditional nonlinear adaptive filter may be significantly reduced and usually needs high computational cost. Therefore, this article proposes a hyperbolic tangent Leclerc robust nonlinear adaptive filter based on the low complexity decomposable Volterra model (HTLNAF‐DVM). The filter is implemented by imposing a rank‐one structure on the full Volterra model to get a product of linear filters, and employs a hyperbolic tangent Leclerc function as a robust norm to effectively improve the robustness against the impulsive noise. In addition, we give the theoretical analyses of the steady‐state mean‐square performance of the proposed HTLNAF‐DVM. Finally, the simulation results prove that the proposed HTLNAF‐DVM algorithm has better performance than the existing algorithms and fit well with the theory. [ABSTRACT FROM AUTHOR]

Summary: The time delay unit‐based PLLs (TD‐PLLs) are widely used in grid synchronization in single‐phase power systems. However, the TD‐PLL is vulnerable to frequency variations and harmonic disturbances, leading to grid synchronization failures. Analysis shows that the performance degradation is due to the inability to obtain the variation of the input signal frequency. This paper proposes a new time delay‐based phase‐locked loop called immune to double frequency component TD‐PLL (IDFC‐TD‐PLL) based on a feedback structure to accurately capture the variation of input signal frequency, which can improve the dynamic performance and the performance against harmonic interference. Simulations and experiments have evaluated the performance of the proposed phase‐locked loop, demonstrating its superior accuracy and dynamic performance compared to the phase‐locked loops referred in the paper. [ABSTRACT FROM AUTHOR]

With the development of smart meters and other intelligent electronic devices, more and more data‐driven fault location methods based on wide area measurement are emerging. However, these diagnostic methods for dealing with the whole tested system often appear complex. This paper presents an innovative subsystems‐based fault location strategy in distribution grid by the sparsity promoted Bayesian learning algorithm. To avoid taking measurement for the whole distribution system, the fault‐included subsystem is selected according to the distribution characteristics of negative sequence voltage. Then the data for fault location is measured by allocating meters in subsystem, which can reduce the number of required meters. For accurately estimating the fault location, a sparse prior is proposed for the Bayesian learning, which could improve the accuracy of the fault location algorithm by about 4%. The performance is tested on a 12.66‐kV, 69‐bus distribution system in response to various fault scenarios. The results show that the accuracy of the proposed method for the fault section location can reach 90%. It also verifies the robustness and accuracy for fault line location, faced different fault types, fault resistance, noise, etc. [ABSTRACT FROM AUTHOR]