Signal Power Research Articles

Sequential Deactivation Across the Hippocampus-Thalamus-mPFC Pathway During Loss of Consciousness.

How consciousness is lost in states such as sleep or anesthesia remains a mystery. To gain insight into this phenomenon, concurrent recordings of electrophysiology signals in the anterior cingulate cortex and whole-brain functional magnetic resonance imaging (fMRI) are conducted in rats exposed to graded propofol, undergoing the transition from consciousness to unconsciousness. The results reveal that upon the loss of consciousness (LOC), there is a sharp increase in low-frequency power of the electrophysiological signal. Additionally, fMRI signals exhibit a cascade of deactivation across a pathway including the hippocampus, thalamus, and medial prefrontal cortex (mPFC) surrounding the moment of LOC, followed by a broader increase in brain activity across the cortex during sustained unconsciousness. Furthermore, sliding window analysis demonstrates a temporary increase in synchrony of fMRI signals across the hippocampus-thalamus-mPFC pathway preceding LOC. These data suggest that LOC may be triggered by sequential activities in the hippocampus, thalamus, and mPFC, while wide-spread activity increases in other cortical regions commonly observed during anesthesia-induced unconsciousness may be a consequence, rather than a cause of LOC. Taken together, the study identifies a cascade of neural events unfolding as the brain transitions into unconsciousness, offering insight into the systems-level neural mechanisms underpinning LOC.

Analysis of high-quality ultrashort pulse generation based on a dual-drive Mach-Zehnder modulator and chirp compensation

This study reports on high-quality picosecond pulse generation using a single-stage dual-drive Mach-Zehnder modulator (DDMZM) and chirp compensation. Sinusoidal microwave signals with different amplitudes are sent to two RF ports of the DDMZM to form a pulse train and pulse compression is achieved by compensating for the linear frequency chirp with a single-mode fiber (SMF). Three parameters encompassing the power difference between two RF signals, the power of RF signals, and the bias point of the DDMZM that affect the pulse formation have been numerically studied. The optimum length of SMF used for chirp compensation is obtained by simulating the temporal propagation and evolution of the pulse in SMF and 3.56-ps chirp-compensated ultrashort pulses are realized at 1.55 µm with a 25-GHz repetition rate experimentally. Modulator-based flexible ultrashort pulse generation can be achieved easily by tuning the RF generator and light source, and customized high-quality pulses according to practical applications can be expected.

Investigation of block interleavers in OFDM radio communication systems with convolutional encoding in multipath channels with fast fading

Noise immunity codecs are good at correcting single errors. Group errors often occur in multipath radio channels with fading. Interleaving is used to convert group errors into single errors. Matching the interleaver structure with the signal and code parameters allows the interleaver to work efficiently. Optimization of the interleaver parameters depending on the characteristics of the communication channel increases the efficiency of the interleaver. The purpose of the work is to determine the parameters of block interleavers for an OFDM radio communication system with convolutional coding in conditions of signal scattering in time and frequency. A model of a channel with two beams and Rayleigh fades is considered. The influence of interleaver parameters on the noise immunity of the OFDM radio communication system depending on the channel parameters (fading rate, multipath interval) has been analyzed. Block deterministic and pseudorandom interleavers are considered. With magnification lengths the interleaver increases noise immunity of the communication system. But at the same time, the delay in transmitting the message increases. The optimal length has been determined, providing a compromise between delay and noise immunity. This length is determined depending on the coherence time of the channel. The number of rows of the matrix interleaver is depending on the decoding depth. This is the number of rows provides better noise immunity at optimal length. But sometimes the optimal length is too long. Therefore a function has been introduced that characterizes the minimum distance between bits in the “time-frequency-code” space at the input of the interleaver. The distance between the bits in the time domain is normalized for the coherence time of the channel. The distance between the bits in the frequency domain is calculated as a normalized deviation from the oscillation period in the signal power spectrum at the output of a channel with two beams. The distance between the bits in the “code space” is calculated as the distance normalized the effective decoding depth of the code used. Given the function describes optimal and critical values well parameters of deterministic interleavers of any length. Deterministic interleavers have a higher probability of error per bit than random interleavers. The definition of parameters using the proposed function allows the deterministic interleaver to work with the same low the probability of error per bit as well as random interleavers.

A gear fault diagnosis method based on reactive power and semi-supervised learning

Abstract In gearbox gear fault diagnosis based on motor current signals, the gear fault characteristic frequency component is often overshadowed by the fundamental frequency component of the current. In addition, the complex working conditions during actual production and use make it difficult to collect gear operation monitoring data containing labeled feature information. To address the above problems, a semi-supervised learning method based on reactive power signals is proposed for gear fault diagnosis of gearboxes. First, the method utilizes the Hilbert transform to process the current signal of the drive motor in the mechanical system, from which the reactive power is constructed. Then, the reactive power signal is analyzed by spectral analysis as a basis for gear fault diagnosis. Subsequently, the GAF-CNN-MTDL(Gramian angular field—convolutional neural network-mean teacher deep learning) fault diagnosis model is proposed to convert the reactive power signal into a two-dimensional image by using the GAF, and the semi-supervised training method of the average teacher is applied to input the fault dataset into the gear fault diagnosis model which is based on the CNN as the main backbone after the fault dataset has been divided into the labeled and the unlabeled dataset in accordance with a certain ratio. Finally, the gear fault dataset is used for method validation. The experimental outcomes demonstrate the method’s proficiency in effectively emphasizing the fault feature information pertaining to the gear part, and the introduced GAF-CNN-MTDL fault diagnosis model enables the utilization of a minimal number of labeled samples to achieve highly accurate gear fault diagnosis.

The Influence of Switching Frequency on Control in Voltage Source Inverters

This paper aims to show how the switching frequency influences the properties of the digitally controlled voltage source inverter (VSI). The measurements of the Bode plots of the inverter are shown and discussed to present the existing signal delays and power conversion efficiency, depending on the switching/sampling frequency. Two types of controllers are presented, Single-Input–Single-Output (SISO) and Multi-Input–Single-Output (MISO), and adequate prediction units (the Smith Predictor for SISO—Coefficient Diagram Method and the full-state Luenberger Observer for MISO—Passivity Based Control) were used to compensate for the delays. It will be shown by comparing the THD of the VSI output voltage that prediction is useful with low VSI switching frequency (about 10 kHz) but is not important for the middle switching frequencies (about 25 kHz) or the high switching frequency (>50 kHz). This paper shows that increasing the switching frequency simplifies digital control without reasonably decreasing efficiency. The theoretical considerations, the Matlab/Simulink 2021b simulations, the final experimental laboratory verification are presented.

Enhanced ABLO-OFDM performance by using composite multi-mode index modulation in IM/DD systems

In intensity modulation direct detection (IM/DD) systems, an adaptively biased layered optical orthogonal frequency division multiplexing (ABLO-OFDM) scheme is proposed based on composite multi-mode index modulation (CMM-IM). The ABLO-OFDM has a simple structure, easy demodulation, low complexity, and effectively reduces the peak-to-average power ratio (PAPR). Since CMM-IM extends the index to the constellation domain, it increases the number of index bits to further improve the spectral efficiency (SE). By dynamically adjusting the number of activated subcarriers and the number of layers, various signal power can be achieved to enhance system flexibility and reliability. The system performance of four schemes is compared and analyzed in IM/DD systems. The result shows that, for the same SE, CMM-ABLO (4,3) achieves a 3 dB receiver sensitivity gain compared with CMM-ABLO (8,2). When L is 3, four schemes can transmit over about 50 km single-mode fiber under the soft decision forward error correction (SD-FEC) threshold. When L is 1 and the sample rate is less than 20 GSa/s, both CMM-ABLO (4,2) and CMM-ABLO (4,3) can achieve error-free transmission. Moreover, as the number of layers increases, PAPR decreases and it reaches a maximum reduction of 9.6 dB.

Ultrasensitive Electrochemical Biosensor with Powerful Triple Cascade Signal Amplification for Detection of MicroRNA.

In this work, by ingeniously integrating catalytic hairpin assembly (CHA), double-end Mg2+-dependent DNAzyme, and hybridization chain reaction (HCR) as a triple cascade signal amplifier, an efficient concatenated CHA-DNAzyme-HCR (CDH) system was constructed to develop an ultrasensitive electrochemical biosensor with a low-background signal for the detection of microRNA-221 (miRNA-221). In the presence of the target miRNA-221, the CHA cycle was initiated by reacting with hairpins H1 and H2 to form DNAzyme structure H1-H2, which catalyzed the cleavage of the substrate hairpin H0 to release two output DNAs (output 1 and output 2). Subsequently, the double-loop hairpin H fixed on the electrode plate was opened by the output DNAs, to trigger the HCR with the assistance of hairpins Ha and Hb. Finally, methylene blue was intercalated into the long dsDNA polymer of the HCR product, resulting in a significant electrochemical signal. Surprisingly, the double-loop structure of the hairpin H could prominently reduce the background signal for enhancing the signal-to-noise ratio (S/N). As a proof of concept, an ultrasensitive electrochemical biosensor was developed using the CDH system with a detection limit as low as 9.25 aM, achieving favorable application for the detection of miRNA-221 in various cancer cell lysates. Benefiting from its enzyme-free, label-free, low-background, and highly sensitive characteristics, the CDH system showed widespread application potential for analyzing trace amounts of biomarkers in various clinical research studies.

Analysis of 4G/LTE Network Performance in North-Central Nigeria: A Comprehensive Drive Test Approach

The proliferation of mobile devices and escalating demand for data services have resulted in a substantial increase in data traffic across Nigeria, especially in the North Central region due to heavy migration of individuals and organizations towards Abuja, the federal capital. However, despite the growing adoption of 4G/LTE networks in North Central Nigeria, users encounter persistent challenges in accessing high-speed internet and poor service network. This study investigates the performance of 4G/LTE networks in North-Central Nigeria using a comprehensive drive test methodology carried out in Abuja, Lafia and Makurdi. It covered a duration of 3hrs (12.00noon to 15.00pm) for 3days (3rd - 6th January, 2024). For each test day, drive tests covered a period of data measurements divided into 3,600 time-steps, with each time-step sized at 1sec; and scaled to 1:150 units. Key metrics including network speed, latency, uptime, coverage, and signal power were evaluated across major Mobile Network Operators. Result shows that D-NGN has the highest mean speed (9.543Mbps), packet loss (2.007%), uplink percentage (97.714%), and Network coverage (87.514%). It has the least latency (25.921) but high packet loss (2.007%). However, C-NGN shows lowest mean speed (6.638Mbps), uplink percentage (38.706%) and Signal power (-78.057dBm) but lowest mean packet loss (0.121%). A-NGN has the highest mean signal power (-61.867dBm) while B-NGN has highest latency (44.070ms) and lowest Network coverage (60.731%). This reveals that D-NGN has dominance in network performance, outperforming A-NGN, B-NGN, and C-NGN. It is recommended that MNOs in North-Central Nigeria should strategically enhance their network infrastructure to improve service delivery, particularly in suburban and rural areas where performance lags. D-NGN, the frontrunner in network speed and coverage, could further solidify its market leadership by addressing identified latency and packet loss issues especially in Makurdi. Moreover, MNOs with lower performance metrics could invest in expanding coverage and optimizing network configurations to meet user demands, thereby increasing competitiveness and customer satisfaction across the region.

In silico modelling of neuron signal impact of cytokine storm-induced demyelination.

In this study, we develop an in silico model of a neuron's behaviour under demyelination caused by a cytokine storm to investigate the effects of viral infections in the brain. We use a comprehensive model to measure how cytokine-induced demyelination affects the propagation of action potential (AP) signals within a neuron. We analysed the effects of neuron-neuron communications by applying information and communication theory at different levels of demyelination. Our simulations demonstrate that virus-induced degeneration can play a role in the signal power and spiking rate, which compromise the propagation and processing of information between neurons. We propose a transfer function to model the weakening effects on the AP. Our results show that demyelination induced by a cytokine storm not only degrades the signal but also impairs its propagation within the axon. Our proposed in silico model can analyse virus-induced neurodegeneration and enhance our understanding of virus-induced demyelination.

Homophily-Enhanced Self-Supervision for Graph Structure Learning: Insights and Directions.

Graph neural networks (GNNs) have recently achieved remarkable success on a variety of graph-related tasks, while such success relies heavily on a given graph structure that may not always be available in real-world applications. To address this problem, graph structure learning (GSL) is emerging as a promising research topic where task-specific graph structure and GNN parameters are jointly learned in an end-to-end unified framework. Despite their great progress, existing approaches mostly focus on the design of similarity metrics or graph construction, but directly default to adopting downstream objectives as supervision, which lacks deep insight into the power of supervision signals. More importantly, these approaches struggle to explain how GSL helps GNNs, and when and why this help fails. In this article, we conduct a systematic experimental evaluation to reveal that GSL and GNNs enjoy consistent optimization goals in terms of improving the graph homophily. Furthermore, we demonstrate theoretically and experimentally that task-specific downstream supervision may be insufficient to support the learning of both graph structure and GNN parameters, especially when the labeled data are extremely limited. Therefore, as a complement to downstream supervision, we propose homophily-enhanced self-supervision for GSL (HES-GSL), a method that provides more supervision for learning an underlying graph structure. A comprehensive experimental study demonstrates that HES-GSL scales well to various datasets and outperforms other leading methods. Our code will be available in https://github.com/LirongWu/Homophily-Enhanced-Self-supervision.

Design of seismoelectric experiment and logging instrument in full-scale borehole models

Abstract In this work, a full-scale horizontally placed sandstone semi-wellbore borehole model is built for seismoelectric signals measurement experiments. A set of seismoelectric signal-measuring instruments is designed, including excitation and receiving systems. The excitation system is composed of a signal generator, power amplifier, and acoustic-electric transducer, which can generate high-power, frequency-adjustable sound waves to excite seismic signals. The receiving system consists of an antenna array, receiving circuits, and a shield, which can receive, amplify and filter seismoelectric signals at different reception distances. The SNR of the receiving system is improved by using pre-amplification and electromagnetic shielding, it has a sensitivity of 1 μV, a dynamic range of 100dB, and a noise level of 0.75 μV. The instrument has been verified and measured seismoelectric signals in semi-wellbore borehole models.

Optimizing High-Speed Serial Links for Multicore Processors and Network Interfaces

In modern computing environments, high-speed serial links have become a critical component for ensuring efficient data transfer between multicore processors and network interfaces. These links, characterized by their ability to transmit data at very high rates over single or multiple lanes, are essential for meeting the increasing bandwidth demands of contemporary applications. The optimization of these serial links is crucial for maintaining performance, reliability, and energy efficiency in systems that leverage multicore processors and advanced network interfaces. This paper explores the key strategies for optimizing high-speed serial links in the context of multicore processors and network interfaces. We begin by examining the architectural considerations and design principles that influence the performance of serial links, including signal integrity, power consumption, and thermal management. We also discuss the impact of link speed and data encoding techniques on overall system efficiency. One of the central challenges in optimizing high-speed serial links is managing signal integrity across different operating conditions. Techniques such as equalization and adaptive filtering are essential for mitigating the effects of signal degradation due to channel impairments and crosstalk. The paper provides a detailed analysis of these techniques, including their implementation in both hardware and software.

Optimizing High-Speed Serial Links for Multicore Processors and Network Interfaces

In modern computing environments, high-speed serial links have become a critical component for ensuring efficient data transfer between multicore processors and network interfaces. These links, characterized by their ability to transmit data at very high rates over single or multiple lanes, are essential for meeting the increasing bandwidth demands of contemporary applications. The optimization of these serial links is crucial for maintaining performance, reliability, and energy efficiency in systems that leverage multicore processors and advanced network interfaces. This paper explores the key strategies for optimizing high-speed serial links in the context of multicore processors and network interfaces. We begin by examining the architectural considerations and design principles that influence the performance of serial links, including signal integrity, power consumption, and thermal management. We also discuss the impact of link speed and data encoding techniques on overall system efficiency.

An Erbium‐Doped Waveguide Amplifier on Thin Film Lithium Niobate with an Output Power Exceeding 100 mW

AbstractA high‐power thin film lithium niobate (TFLN) erbium‐doped waveguide amplifier (EDWA) is demonstrated with a maximum on‐chip output power of 113 mW and a gain of 16 dB. The on‐chip integrated EDWA is composed of large mode area (LMA) waveguide structures with a total length of 7 cm and a footprint of 1 × 1 cm2. Segmented LMA waveguides are connected with waveguide tapers to achieve on‐chip mode conversion, which maintains single‐mode propagation all over the EDWA even at the waveguide bends. The design leads to a significant increase in the amplified signal power by orders of magnitude and will open an avenue for applications such as on‐chip high‐power lasers and amplifier systems.

High-performance optical dialogue for power electronic system

High-accuracy signal and high-quality power transmissions play an essential role in power electronics systems. Nowadays, the severe electromagnetic interference (EMI) by the switching converter threatens the stable operation of conventional signal and power transmissions. To mitigate the effects of EMI, optical fiber is a better solution due to its unmatched isolation properties. Here, we perform a transmission configuration, named optical dialogue, based on a single fiber for simultaneous bidirectional analog optical signal (AOS) and analog optical power (AOP) transmission, to ensure EMI immunity. This optical dialogue transmission not only utilizes AOS to achieve high-accuracy signal transmission, but also utilizes AOP to achieve high-quality power transmission. We validate the proposed optical dialogue transmission using discrete off-the-shelf components, demonstrating the high frequency signal transmission and high efficiency power transmission. Our proposed optical dialogue transmission offers a high-performance optical solution for signal and power transmission, which is significant step forward in the development of more accurate, reliable, and safer power electronic systems.

Non-Intrusive Load Monitoring Based on Dimensionality Reduction and Adapted Spatial Clustering

Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%.

Cooperative edge-caching based transmission with minimum effective delay in heterogeneous cellular networks

In heterogeneous cellular networks (HCNs), neighboring users often request similar contents asynchronously. Based on the content popularity, base stations (BSs) can download and cache contents when the network is idle, and transmit them locally when the network is busy, which can effectively reduce the backhaul burden and the transmission delay. We consider a two-tier HCN, where macro base stations (MBSs) and small base stations (SBSs) can cooperatively and probabilistically cache contents. Each user is associated to the BS with the maximum average received signal power in any tier. With the cooperative content transfer between MBS tier and SBS tier, users can adaptively obtain contents from BSs or remote content servers. We properly model both wired and wireless delays when a user requests an arbitrary content, and propose the concept of effective delay. Content caching probabilities are optimized using the Marine Predators Algorithm via minimizing the average effective delay. Numerical results show that our proposed cooperative caching scheme achieves much shorter delays than the benchmark caching schemes.

Ultrasonic Assessment of Liver Fibrosis Using One-Dimensional Convolutional Neural Networks Based on Frequency Spectra of Radiofrequency Signals with Deep Learning Segmentation of Liver Regions in B-Mode Images: A Feasibility Study.

The early detection of liver fibrosis is of significant importance. Deep learning analysis of ultrasound backscattered radiofrequency (RF) signals is emerging for tissue characterization as the RF signals carry abundant information related to tissue microstructures. However, the existing methods only used the time-domain information of the RF signals for liver fibrosis assessment, and the liver region of interest (ROI) is outlined manually. In this study, we proposed an approach for liver fibrosis assessment using deep learning models on ultrasound RF signals. The proposed method consisted of two-dimensional (2D) convolutional neural networks (CNNs) for automatic liver ROI segmentation from reconstructed B-mode ultrasound images and one-dimensional (1D) CNNs for liver fibrosis stage classification based on the frequency spectra (amplitude, phase, and power) of the segmented ROI signals. The Fourier transform was used to obtain the three kinds of frequency spectra. Two classical 2D CNNs were employed for liver ROI segmentation: U-Net and Attention U-Net. ROI spectrum signals were normalized and augmented using a sliding window technique. Ultrasound RF signals collected (with a 3-MHz transducer) from 613 participants (Group A) were included for liver ROI segmentation and those from 237 participants (Group B) for liver fibrosis stage classification, with a liver biopsy as the reference standard (Fibrosis stage: F0 = 27, F1 = 49, F2 = 51, F3 = 49, F4 = 61). In the test set of Group A, U-Net and Attention U-Net yielded Dice similarity coefficients of 95.05% and 94.68%, respectively. In the test set of Group B, the 1D CNN performed the best when using ROI phase spectrum signals to evaluate liver fibrosis stages ≥F1 (area under the receive operating characteristic curve, AUC: 0.957; accuracy: 89.19%; sensitivity: 85.17%; specificity: 93.75%), ≥F2 (AUC: 0.808; accuracy: 83.34%; sensitivity: 87.50%; specificity: 78.57%), and ≥F4 (AUC: 0.876; accuracy: 85.71%; sensitivity: 77.78%; specificity: 94.12%), and when using the power spectrum signals to evaluate ≥F3 (AUC: 0.729; accuracy: 77.14%; sensitivity: 77.27%; specificity: 76.92%). The experimental results demonstrated the feasibility of both the 2D and 1D CNNs in liver parenchyma detection and liver fibrosis characterization. The proposed methods have provided a new strategy for liver fibrosis assessment based on ultrasound RF signals, especially for early fibrosis detection. The findings of this study shed light on deep learning analysis of ultrasound RF signals in the frequency domain with automatic ROI segmentation.

Fixed Automated stations location and UAVs routing problems in urban road Networks: A tailored Branch-&-price algorithm

Unmanned aerial vehicles (UAVs) serve as vital tools for collecting real-time traffic data, enabling rapid responses to events, and optimizing traffic management based on their cost-effectiveness, high flexibility, and wide coverage range advantages. The urban UAV cruising problem primarily focuses on selecting the locations of fixed automated station (FAS) and optimizing UAVs’ cruising routes. This paper introduces a strategy where UAVs can take off and land on different FASs to enhance urban UAVs’ cruising efficiency. Firstly, the problem is formulated as an integrated linear integer model (FU-ILP) which simultaneously determines the locations of FASs and the routes of UAVs. The objective is to minimize redundant cruising distance and cruising cycle time. The FU-ILP model takes into account UAV‘s flight capabilities, FAS’s signal coverage range, power requirements, and battery charging. Moreover, we design a tailored branch-and-price (TB&P) algorithm to decompose the FU-ILP model, which can reduce the variable scale for solving larger-scale cases. Finally, we test the model and the algorithm on the Sioux Falls Test Network. The TB&P algorithm improves computational efficiency by nearly 80%. Moreover, our strategy yields superior results compared to the strategy where UAVs must return to their take-off FASs for recharging. We also demonstrate that increasing the number of UAVs does not reduce redundant cruising distance in the whole network, but it can shorten the cruising cycle. These policy conclusions also apply to the case of Xiongan. This provides a basis for managers to calculate the minimum UAV’s configuration based on the cities’ actual cruising frequency requirements.

A novel wind power forecast diffusion model based on prior knowledge

AbstractTo improve the forecast accuracy of wind power, diffusion model based on prior knowledge (DMPK) is proposed. Different from the traditional diffusion model (DM), where the noise perturbation in the diffusion or generation process is random, the noise added in DMPK is modified aiming to the characteristics of wind power signals. The distribution of wind power forecast errors is not a standard Gaussian. Wind power forecast errors are related to forecast methods, weather conditions, and other factors, containing both random signals and certain regularity. This paper adapts the Gaussian distribution to fit the historical forecast error to represent the prior knowledge of wind power. Then, the sampling distribution is derived from its relationship with the fitted prior distribution to replace the standard Gaussian in DM. Taking the prior knowledge into account during the process of noise sampling, the data in the forward process of DMPK can be guided by the distribution of historical errors for diffusion, while the generated result by the reverse process is more consistent with the actual wind power signal. Finally, the superiority of the proposed method is verified by using the wind power data from two real‐world wind farms.