Intrinsic Mode Functions Research Articles

Bearing fault diagnosis based on POA-VMD with GADF-Swin Transformer transfer learning network

To address the challenges posed by early weak fault signals hindering fault feature extraction and leading to diminished recognition accuracy in deep learning models, we present a method for diagnosing bearing faults. This method integrates the Pelican Optimization Algorithm (POA), Variational Mode Decomposition (VMD), Gramian Angular Difference Fields (GADF), and the Swin Transformer network. Initially, the VMD’s key parameter are optimized through POA to dynamically yield the optimal parameter combination. Subsequently, the refined VMD is employed to decompose the original signal. The intrinsic modal function (IMF) is subsequently filtered, and the signal is reconstructed using the weighted composite kurtosis(WCK) index. Following this step, the reconstructed 1D signal is converted into a 2D image using GADF and is then input into the constructed Swin Transformer transfer learning network model for the purpose of training. In conclusion, a fault diagnosis experiment was conducted using the measured signal, which showed the successful extraction of the signal’s characteristics and octave frequencies through the method presented in this paper. Moreover, concerning fault identification, an identification accuracy exceeding 95% was achieved.

Comparative analysis of surface roughness level for bearing coating using VMD of vibration signal

Degradation in surface finish of the material is an important concern in engineering applications. However, most of the conventional techniques available to monitor the surface roughness requires dismantling of the machine element. Though in rotary components like bearing, it is practically not feasible. Therefore, in this work, an on-board technique is proposed to compare the level of surface roughness based on vibration signature. To demonstrate, five different industrial standard coatings (Nickel, Copper, Zinc phosphate (ZnP), Silver, and Black oxide) were carried out on raw five ball bearings (NBC6205). Two sets of coated bearing were prepared, where first set was utilised for experimentation and second for the purpose of measuring surface roughness of bearing surface. These coated bearings were tested at five different RPMs ranging from 300 RPM to 1500 RPM and their vibration signals were recorded. The recorded vibration signals must be having characteristics originated from ball rolling on different level of surface roughness and hence distributed in nature. Further, to target distributed characteristics present in the signal, commonly used statistical parameters for vibration signature analysis (RMS, Crest factor, Variance, Skewness, Kurtosis, Shannon entropy and Log energy) were calculated. Then, correlation of the parameters was checked in relation to the different levels of surface roughness but no relation was found. The signals were then decomposed into six intrinsic mode functions (IMFs) using Variational Mode Decomposition (VMD) method. Again, same statistical parameters were calculated for these decomposed levels, it has been noted that Shannon entropy have shown correlation to surface roughness in at least one decomposed level between 900 to 1500 RPM with minimum value of chain index as 176.65. Moreover, in this RPM range, responsive frequency bands found to be shifted towards higher side i.e. from 567 to 4590 Hz.

Improved DOA estimation of MEMS vector hydrophone combined with CEEMDAN and wavelet transform for noise reduction

PurposeCiliated microelectromechanical system (MEMS) vector hydrophones pick up sound signals through Wheatstone bridge in cross beam-ciliated microstructures to achieve information transmission. This paper aims to overcome the complexity and variability of the marine environment and achieve accurate location of targets. In this paper, a new method for ocean noise denoising based on improved complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT) is proposed.Design/methodology/approachBased on the CEEMDAN-WT method, the signal is decomposed into different intrinsic mode functions (IMFs), and relevant parameters are selected to obtain IMF denoised signals through WT method for the noisy mode components with low sample entropy. The final pure signal is obtained by reconstructing the unprocessed mode components and the denoising component, effectively separating the signal from the wave interference.FindingsThe three methods of empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and CEEMDAN are compared and analyzed by simulation. The simulation results show that the CEEMDAN method has higher signal-to-noise ratio and smaller reconstruction error than EMD and EEMD. The feasibility and practicability of the combined denoising method are verified by indoor and outdoor experiments, and the underwater acoustic experiment data after processing are combined beams. The problem of blurry left and right sides is solved, and the high precision orientation of the target is realized.Originality/valueThis algorithm provides a theoretical basis for MEMS hydrophones to achieve accurate target positioning in the ocean, and can be applied to the hardware design of sonobuoys, which is widely used in various underwater acoustic work.

Joint sound denoising with EEMD and improved wavelet threshold for real-time drilling lithology identification

The present study proposes a joint denoising method for real-time drilling lithology identification based on drilling sound signals. The proposed method employs ensemble empirical mode decomposition (EEMD) for preliminary sound signal denoising while retaining the effective intrinsic mode function (IMF) components, which then undergo secondary denoising using the improved wavelet threshold function. Finally, the sound signal is reconstructed using the denoised IMF components. The experimental results show that the proposed method achieves a higher signal-to-noise ratio and lower root mean square error, reaching 12.94 and 0.0442, respectively. Furthermore, the proposed method demonstrated good performance in denoising rock drilling sound signals and successfully addressed issues including the susceptibility of sound signals to external interference, nonadaptive threshold selection in wavelet threshold denoising, and high reconstruction errors during EEMD in drilling environments. Simulation results show that the proposed method significantly improves the accuracy of lithology recognition by 23%, with a resulting accuracy of 72.58%.

A Comparative Analysis between FFT, EMD, and EEMD for Epilepsy Detection

Context: Epilepsy is a neurological disease that affects more than 50 million people worldwide, causing recurrent seizures, with a significant impact on patients' quality of life due to abnormally synchronized neuronal activity. Method: This article discusses three methods used for signal analysis in patients diagnosed with epilepsy. Conventional signal decomposition methods, such as the fast Fourier transform, widely used in signal analysis based on time series techniques, have some issues when analyzing nonlinear and non-stationary signals, in addition to difficulties in detecting low-order frequencies. Results: To overcome these limitations, alternatives such as empirical mode decomposition and one of its variants, called ensemble empirical mode decomposition, have been developed. These techniques allow observing different oscillation modes through intrinsic mode functions and instantaneous frequencies. Conclusions: In this study, the results obtained through the aforementioned techniques were compared, revealing the impact of nonlinear methods on the reconstruction of brain activity.

Research on a Fault Feature Extraction Method for an Electric Multiple Unit Axle-Box Bearing Based on a Resonance-Based Sparse Signal Decomposition and Variational Mode Decomposition Method Based on the Sparrow Search Algorithm.

In light of the issue that the vibration signal from an axle-box bearing collected during the operation of an electric multiple unit (EMU) is seriously polluted by background noise, which leads to difficulty in identifying fault characteristic frequency, this paper proposes a resonance-based sparse signal decomposition (RSSD) and variational mode decomposition (VMD) method based on sparrow search algorithm (SSA) optimization to extract the fault characteristic frequency of the bearing. Firstly, the RSSD method is utilized to decompose the signal based on the obtained optimal combination of quality factors, resulting in the optimal low-resonance component with periodic fault information. Then, the VMD method is performed on this low-resonance component. The parameter combinations for both methods are optimized utilizing the SSA method. Subsequently, envelope demodulation is applied to the intrinsic mode function (IMF) with maximum kurtosis, and fault diagnosis is achieved by comparing it with the theoretical fault characteristic frequency. Finally, experimental validation and comparison are conducted by utilizing simulated signals and example signals. The results demonstrate that the proposed method extracts more obvious periodic fault impact components. It effectively filters out the interference of complex noise and reduces the blindness of setting weights on parameters due to human experience, indicating excellent adaptability and robustness.

Spatial weighting EMD-LSTM based approach for short-term PM2.5 prediction research

Given the significant health and environmental risks posed by atmospheric PM2.5 pollution, accurately predicting its concentration changes is especially important. Current models fall short in researching time-series feature extraction from pollutants and spatial correlations among monitoring stations. In this study, a spatiotemporal prediction model is introduced to address these issues. The model combines spatial weighting, empirical mode decomposition (EMD), and a long short-term memory (LSTM) network. First, weights are allocated to sites using Pearson correlation analysis and distance weighting methods. Next, the pollutant time series is decomposed using the EMD method. The highly correlated intrinsic mode function (IMF) component is selected for signal reconstruction, enhancing denoising. Finally, the model uses an LSTM network to capture nonlinear and dynamic time series traits, which significantly improves the PM2.5 prediction accuracy. The model utilizes data collected from 10 monitoring stations across Hefei city during 2018-2019, employing the previous 24 h of observations to forecast PM2.5 concentrations for the subsequent hour. By comparing with RNN, HPO-RNN, GRU, LSTM, and CBAM-CNN-Bi LSTM, the results show that our model surpasses five benchmark models in terms of prediction accuracy. Relative to the best-performing CBAM-CNN-Bi LSTM model, our model reduces RMSE and MAE by 73.91% and 72.99%, respectively, and improves R2 by 8.15%. In summary, the proposed spatial weighting EMD-LSTM model offers an efficient new approach for predicting atmospheric PM2.5 pollution. It integrates spatial and time series analysis, significantly enhancing the prediction accuracy.

Deep learning approach for predicting monsoon dynamics of regional climate zones of India

The complex interplay of various complicated meteorological and oceanic processes has made it more difficult to accurately predict Indian monsoon rainfall. A future-oriented and one of the most potential methods for predictive analytics is deep learning. The proposed work exploits empirical Mode Decomposition-Detrended Fluctuation Analysis (EMD-DFA) and long short-term memory (LSTM) deep neural networks (EMD-LSTM) to build novel predictive models and analyze predictability effectively. The time series data of each homogeneous monsoon zone are decomposed into different empirical time series components known as intrinsic mode functions (IMFs). The proposed work's obtained results report that the EMD-LSTM hybrid strategy consistently outperforms other methods in terms of accuracy. Furthermore, we examined possible relationships between each homogeneous monsoon zone and multiple climate drivers, shedding light on the complicated relationships that influence monsoon patterns. This study presents a unique way of predicting complex monsoon rainfall in homogenous regions of India and marks the first application of the EMD-LSTM technique for this purpose to the best of our knowledge which is necessary for improving water conservation and distribution at different climate zones of India.

Two-dimensional Hilbert-Huang transform-based thermographic data processing for non-destructive material defect detection

ABSTRACT Previous research has established the Hilbert-Huang transform (HHT) as a powerful tool for processing and analysing one-dimensional time series signals. Its extension into multi-dimensional Euclidean spaces further demonstrates its versatility. In this study, we leverage a two-dimensional (2D) HHT to enhance non-destructive testing, utilising active infrared thermographic data to improve the detection of material defects. The proposed methodology incorporates a 2D HHT, which synergises multidimensional ensemble empirical mode decomposition with the Riesz transform (RT). This combination is adept at uncovering both global (i.e. intrinsic mode functions) and local (i.e. phase angle and spatial frequency) information. In particular, RT is integrated with a Gaussian filter, which allows for a more thorough analysis of the inherent characteristics of defects through the monogenic signal produced by RT. The experimental application of this method on a mosaic sample, intentionally embedded with defects, has yielded promising results. The approach effectively distinguishes critical defect signals from the surroundings, leading to more accurate and clearer results.

Three-dimensional reconstruction method for layered structures based on a frequency modulated continuous wave terahertz radar.

The feasibility of employing a continuous-wave terahertz detection system for non-contact and non-destructive testing (NDT) in multi-layered bonding structures is assessed in this study. The paper introduces the detection principle of terahertz frequency modulated continuous wave (FMCW) radar and outlines the two-dimensional (2D) scanning platform, which integrates optical lenses, three linear actuators, a control platform, and data acquisition units. Experimental results on two types of insulation with prefabricated defects demonstrate the capability of terahertz waves for transparent inspection imaging. These results confirm the viability of terahertz FMCW detection technology as an advanced NDT tool for multi-layered bonding structures. However, the inherent limitations of terahertz wavelength and hardware systems pose challenges in discriminating reflection peaks on upper and lower surfaces. To address this issue, a local adaptive empirical wavelet coefficient modal decomposition (LAEWCMD) method is proposed to enhance the longitudinal discrimination ability of terahertz detection. The proposed method involves segmenting the 2D terahertz detection image into regions to differentiate between defective and non-defective areas. Continuous wavelet transforms (CWT) are then applied to the range signals of each region to derive continuous wavelet coefficients (CWCs). Subsequently, empirical mode decomposition (EMD) is performed on the CWCs to decompose them into intrinsic mode functions (IMFs) and residual signals. The 1st IMF is utilized for three-dimensional (3D) reconstruction, and the regions are fused to generate the final output. The effectiveness of the proposed method is validated on aircraft thermal protection structures (TPS), achieving high-precision 3D reconstruction. This offers a novel approach for the application of terahertz computed tomography imaging and NDT.

A Wind Power Combination Forecasting Method Based on GASF Image Representation and UniFormer

In the field of wind power prediction, traditional methods typically rely on one-dimensional time-series data for feature extraction and prediction. In this study, we propose an innovative short-term wind power forecasting approach using a “visual” 2D image prediction method that effectively utilizes spatial pattern information in time-series data by combining wind power series and related environmental features into a 2D GASF image. Firstly, the wind power data are decomposed using the ICEEMDAN algorithm optimized by the BWO (Beluga Whale Optimization) algorithm, extracting the submodal IMF (Intrinsic Mode Function) components with different frequencies. Then, modal reconstruction is performed on the basis of the permutation entropy value of the IMF components, selecting meteorological features highly correlated with reconstructed components through Spearman correlation analysis for data splicing and superposition before converting them into GASF images. Finally, the GASF images are input into the UniFormer model for wind power sequence prediction. By leveraging wind power data predictions from a coastal wind farm in East China and Sotavento in Spain, this study demonstrates the significant benefits and potential applications of this methodology for precise wind power forecasting. This research combines the advantages of image feature extraction and time-series prediction to offer novel perspectives and tools for predicting renewable energy sources such as wind power.

Precise single step and multistep short-term photovoltaic parameters forecasting based on reduced deep convolutional stack autoencoder and minimum variance multikernel random vector functional network

Accurate prediction of photovoltaic (PV) power can significantly alleviate energy crises. However, the inherent randomness and intermittency of PV power pose challenges to the stability and safety of PV-penetrated grid systems. To address this, we have developed a novel hybrid model: a reduced deep convolutional stack autoencoder with a minimum variance multikernel random vector functional link network (RDCSAE-MVMRVFLN). This model enhances grid efficacy and safety. We extract the most informative band-limited intrinsic mode functions (BLIMFs) of highly nonlinear and nonstationary solar energy parameters using an entropy, kurtosis, and correlation coefficient-based information-oriented variational mode decomposition (IOVMD). These efficient BLIMFs are concatenated and input into the RDCSAE for rich, abstract, and discriminative representation computation. A less computationally complex MVMKRVFLN regression method, incorporating these refined representations, is proposed for superior prediction accuracy and reduced computational complexity. Our method shows exceptional performance in predicting solar temperature, irradiation, and power for multi-horizon forecasts with minimal error metrics (correlation coefficients of 0.999±0.001, 0.992±0.001, 0.986±0.02 and 0.978±0.02, and RMSE of 0.016±0.001, 0.024±0.001, 0.034±0.001 and 0.045±0.001 for the interval of 10 minutes, 30 minutes, 1 hour and 3 hours respectively) in both single-step and multi-step forecasting compared to conventional methods. The RDCSAE-MVMKRVFLN model is implemented on a high-speed Xilinx Virtex-5 FPGA embedded processor to validate its simplicity, robustness, and practicability. Additionally, we examine the prediction performance using real-time data from a 1 MW solar farm in Odisha, India, demonstrating the model’s effectiveness and superiority.

An EEMD‐LSTM, SVR, and BP decomposition ensemble model for steel future prices forecasting

AbstractThe forecasting of steel futures prices is important for the steel futures market, even for the steel industry. We propose a decomposition ensemble model that incorporates the Ensemble Empirical Mode Decomposition (EEMD), Long Short‐Term Memory (LSTM), Support Vector Regression (SVR), and Back Propagation (BP) neural network to forecast steel futures prices. The forecasting procedures are as follows: (1) The price data are initially decomposed into several relatively independent Intrinsic Mode Functions (IMFs) and a residue using EEMD. (2) The IMFs are then reconstructed as components representing short‐term, medium‐term, and long‐term frequencies via fine‐to‐coarse. (3) LSTM, SVR, and BP neural network are utilized to forecast the short‐term, medium‐term, and long‐term reconstructed components, respectively. (4) The prediction results for each component are simply added to the final prediction results. The accuracy of the proposed model is compared with several benchmark models by experiments and evaluated by some prediction evaluation indexes. The experimental results show that our model outperforms other models in terms of forecast accuracy, confirming its strong predictive capabilities. This study provides some suggestions for investment and decision making by participants in the steel futures market. It may promote the smooth operation of the steel futures market and shed some light on the operation of the steel industry.

Protection strategy for fault detection in AC microgrid based on MVMD & differential CUSUM

Abstract In the era of smart grids and microgrids, the transformation of the traditional grid system brings many operational, technical, and economic benefits. However, the complexity of the network due to the integration of various distributed generations (DGs), continuous change of topology, and non-linear load make fault detection a major issue that forces power engineers to focus on. In this paper, a novel fault detection scheme is suggested based on the multivariate variational mode decomposition mode (MVMD) and differential cumulative sum (DCUSUM). As a generalized extension of the original variational mode decomposition (VMD) algorithm for multivariate data residing in multidimensional spaces, the main goal of MVMD is to decompose the input signal into different band-limited intrinsic mode functions (IMFs). Due to the inherent characteristics of being insensitive to noise and very effective in decomposing the local features even with similar frequencies, it is very effective for fault detection in microgrid distribution systems. The proposed DCUSUM algorithm computes the differential cumulative energy for the remaining significant modes. A fault detection index is considered in this approach and applied for fault detection by adaptively through the threshold setting to accurately result in fault detection. To justify the proposed approach, a standard AC microgrid test system is considered and the approach is verified for fault detection under various fault conditions and resistances. The obtained results and the comparative analysis with other methods reflect the better accuracy, robustness, and reliability of the proposed approach.

Unravelling the spatiotemporal variation in the water levels of Poyang Lake with the variational mode decomposition model

AbstractPoyang Lake is a dynamic floodplain lake system that exhibits complex water level fluctuations and experiences significant regime changes over space and time, which remains to be further explored. This study used the variational mode decomposition (VMD) model to decompose the Poyang Lake's water levels from 1960 to 2022 at four key stations into six intrinsic mode functions (IMFs), namely IMF1–IMF6, representing variations on different time scales. The results present significant spatiotemporal heterogeneity. The multi‐year variation (IMF1) accounts for 5.6%–12.4% of the total variation and displays a northward decreasing trend, reflecting the lake's river‐like characteristics. The spectrum of IFM1 also reveals a significant 3.6‐year fluctuation mainly attributed to the tributary inflow, especially the Ganjiang River. The IMF1 differences between stations show abrupt decreases since the 2000s, indicating the impact of concentrated sand mining activities on the northern and central regions. The annual variation (IMF2) is the most prominent, contributing 76.1%–88.4% of the total variation, and shows a southward attenuation trend, likely due to the weakening influence of the Yangtze River flow. The intra‐annual scale (IMF3–IMF6) represents 6.0%–11.5% of the total variation and exhibits less spatial difference compared to the multi‐year and annual variations. The VMD model effectively separates the water level signals into different frequency bands, providing insights into the complex interactions between the lake, tributaries, and Yangtze River, as well as the impacts of human activities like sand mining, enhancing understanding of floodplain lake dynamics. The results also imply the importance of coping with the water level decline of Poyang Lake.

Adaptively multi-scale microstructure characterization of cancellous bone via photoacoustic signal decomposition

Osteoporosis is a systemic disease with a high incidence in the elderly and seriously affects the quality of life of patients. Photoacoustic (PA) technology, which combines the advantages of light and ultrasound, can provide information about the physiological structure and chemical information of biological tissues in a non-invasive and non-radiative way. Due to the complex structural characteristics of bone tissue, PA signals generated by bone tissue are non-stationary and nonlinear. However, conventional PA signal processing methods are not effective for non-stationary signal processing. In this study, an empirical mode decomposition (EMD)-based Hilbert-Huang transform (HHT) PA signal analysis method, called HHT PA signal analysis (HPSA), was developed to assess the microstructure information of bone tissue, which is closely related to bone health. The feasibility of the HPSA method in bone health assessment was proven by numerical simulation and experimental studies on animal samples with different bone volume/total volume (BV/TV) and bone mineral densities. First, based on adaptive EMD, the different modes correlated with multi-scale information were mined from the PA signal, the correlations between different intrinsic mode function (IMF) modes and BV/TVs were analyzed, and the optimal mode for more efficient PA time–frequency analysis was selected. Second, multi-wavelength HPSA was used to assess the changes in the chemical components of the bone tissue. The results demonstrate that the HPSA method can distinguish bones with different BV/TVs and microstructure conditions adaptively with high efficiency. They further emphasize the potential of PA techniques in characterizing biological tissues in bones for early and rapid detection of bone diseases.

A Short-Term Power Load Forecasting Approach Based on EMD-SSA-LSTM

Abstract Accurate power load forecasting is the foundation of the power system. However, economic, weather, and geographic factors can affect short-term electric load forecasting and can make it difficult to enhance load forecasting accuracy. To solve the problem, a novel load forecasting method based on empirical mode decomposition (EMD) and long short-term memory (LSTM) neural networks has been proposed. Firstly, the EMD algorithm is utilized to decompose the original signal into elementary components and a series of intrinsic mode functions (IMF). Subsequently, all components are input into the LSTM neural networks for load prediction. Moreover, to further improve the prediction accuracy, the sparrow search algorithm (SSA) is introduced to optimize the parameters of LSTM for the prediction of basic components. Integrating SSA and EMD-LSTM, the EMD-SSA-LSTM model is further proposed for short-term power load forecasting. Finally, the proposed model is validated by several experiments, and the results show that the coefficient of determination (R2) under the proposed model can reach 0.98, the mean absolute error (MAE) is 0.015, and the root mean square error (RMSE) is 0.02. It is verified that the proposed model can improve the accuracy of load forecasting.

A hybrid data-driven method based on data preprocessing to predict the remaining useful life of lithium-ion batteries

Abstract The estimation of remaining useful life (RUL) for lithium-ion batteries is an essential part for battery management system (BMS). A hybrid method is presented which is combining principal component analysis (PCA), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sparrow search algorithm (SSA), Elman neural network (Elman-NN), and gaussian process regression (GPR) to forecast battery RUL. Firstly, in the data preprocessing stage, the PCA+ICEEMDAN algorithm is creatively proposed to extract features of capacity decay and fluctuation. The PCA method is used to reduce the dimensionality of the extracted indirect health indicators (HIs), and then the ICEEMDAN algorithm is introduced to decompose the fused HI sequence and actual capacity data into residuals and multiple Intrinsic mode functions (IMFs). Secondly, in the prediction stage, feature data is corresponded one to-one with the mixed model. The prediction models of SSA-Elman algorithm and GPR algorithm are established, with the SSA-Elman algorithm predicting the capacity decay trend and the GPR algorithm quantifying the uncertainty caused by the capacity regeneration phenomenon. The final prediction results are obtained by superimposing the two sets of prediction data, and the prediction error and RUL are calculated. The effectiveness of the proposed hybrid approach is validated by RUL prediction experiments on three kinds of batteries. The comparative experimental results indicate that the mean absolute error (MAE) and root mean square error (RMSE) of the presented prediction model for lithium-ion battery capacity are less than 0.7% and 1.0%.

An ICEEMDAN-WPD based denoising method for MS signals and its engineering application

ABSTRACT The complex installation environments for microseismic (MS) monitoring systems generate significant noise in MS signals, hindering accurate event localization. This study proposes a noise reduction method combining improved complete ensemble empirical mode decomposition with adaptive noise and wavelet packet decomposition (ICEEMDAN-WPD). The signal is initially processed with ICEEMDAN to obtain intrinsic modal functions (IMFs), which are components ordered from high to low frequencies. The correlation between the original signal and each IMF is evaluated and low-correlation IMFs are eliminated for initial noise reduction. Additionally, the WPD method further decomposes and filters the highly correlated IMFs for secondary noise reduction, enhancing the noise reduction effect. Ultimately, the signal formed by reconstructing the secondary noise reduction IMF is the complete ICEEMDAN-WPD method filtered signal. The simulation results indicate that the ICEEMDAN-WPD method enhances the signal-to-noise ratio (SNR) and preserves the original signal characteristics to the greatest extent, demonstrating a significant noise reduction capability. In particular, the application of this method to the noise reduction process of MS signals from Shuangjiangkou hydropower station has led to a significant improvement in the accuracy of subsequent MS events localization. The research results can provide reference for similar signal noise reduction processing.

A new relaying approach for protecting TCSC compensated transmission line connected to DFIG based wind farm

In modern power systems, Thyristor Controlled Series Capacitor (TCSC)-compensated transmission lines are crucial in transporting bulk power from large wind farms. However, the performance of conventional distance relays is adversely impacted by the typical fault characteristics of the Doubly Fed Induction Generator (DFIG) and TCSC. To address this challenge, this paper presents a new relaying algorithm such as Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based Dominant Mode Algorithm-Hilbert Transform (CEEMDAN-DMA-HT) for fault detection and CEEMDAN assisted Enhanced Jaya Optimization-based Random Vector Functional Link Network (EJAYA-RVFL) for fault classification. In proposed fault detection algorithm, the differential current from both ends of the line is subjected to Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, leading to the extraction of distinct intrinsic mode functions (IMFs). Employing the Dominant Mode Algorithm, the IMF with the highest Pearson correlation coefficient, referred to as the dominant IMF, is identified. Subsequently, a comparison between this dominant mode IMF and the original input signal is conducted after passing the obtained signal through the Hilbert Transform (HT), enabling effective fault detection. The proposed classifier leverages a straightforward feature set that quantifies the energy of each phase and the ground. These features serve as inputs for the proposed classifier, facilitating the categorization of different fault types. To evaluate the effectiveness of the proposed relaying algorithm, several fault and non-fault scenarios are simulated on a test power system using MATLAB/Simulink. The results demonstrate that; the proposed fault detection and classification algorithm are able to detect and classify faults in less than a half-cycle. The proposed method gives 100% fault accuracy for fault detection and 99.97 % accuracy for fault classification. Furthermore, the proposed classifier achieves the performance metrics like Precision (0.998), Recall (0.997) and F1-Score (0.996), providing quantitative insights into its accuracy and dependability. Finally, a comparative analysis is carried out with existing approaches.