Variational Mode Decomposition Technique Research Articles

New method for bearing fault diagnosis based on variational mode decomposition technique

New method for bearing fault diagnosis based on variational mode decomposition technique

Tool Wear State Identification Based on the IWOA-VMD Feature Selection Method

Complex, thin-walled components are the most important load-bearing structures in aircraft equipment. Monitoring the wear status of milling cutters is critical for enhancing the precision and efficiency of thin-walled item machining. The cutting force signals of milling cutters are non-stationary and non-linear, making it difficult to detect wear stages. In response to this issue, a system for monitoring milling cutter wear has been presented, which is based on parameterized Variational Mode Decomposition (VMD) Multiscale Permutation Entropy. Initially, an updated whale optimization technique is used, with the joint correlation coefficient serving as the fitness value for determining the VMD parameters. The improved VMD technique is then used to break down the original signal into a series of intrinsic mode functions, and the Multiscale Permutation Entropy of each effective mode is determined to generate a feature vector. Finally, a 1D Convolutional Neural Network (1D CNN) is employed as the input model for state monitoring using the feature vector. The experimental findings show that the suggested technique can efficiently extract characteristics indicating the wear condition of milling cutters, allowing for the precise monitoring of milling cutter wear states. The recognition rate is as high as 98.4375%, which is superior to those of comparable approaches.

A surface electromyography based hand gesture recognition framework leveraging variational mode decomposition technique and deep learning classifier

Deciphering and classifying surface electromyography (sEMG) signals is highly essential in rehabilitation robotics, myoelectric prosthetic control, sign languages and human–computer-interface. Researchers have generally focused on utilizing multi-channel sEMG signals for gesture classification. However, in case of amputees, the residual muscles are limited and the gesture classification needs to be reframed with minimum sEMG channels. Moreover, another key problem in the gesture classification system is the nonlinear and non-stationary nature of sEMG signals, leading to poor generalization ability. Hence, to address the aforementioned problems, this study puts forward a novel deep learning classifier framework which leverages the potentials of variational mode decomposition (VMD) technique and a hybrid convolutional neural network–long short term memory (CNN–LSTM) classifier model to recognize the hand gestures from single channel sEMG signals. Collecting the sEMG signals from forearm muscles of 25 intact subjects for ten functional and grasping actions, this work implements a VMD technique to identify the prominent frequency modes in the sEMG signals. From the decomposed modes of sEMG signals, the prominent intrinsic mode functions (IMFs) are extracted through spectral analysis to minimize the computation burden on the hybrid classifier model. Furthermore, as the muscle contractions result in substantial temporal dependencies, this work exploits the potentials of CNN and LSTM networks and extracts the spatiotemporal features of the sEMG signals for various hand gestures. The experimental results corroborate that the proposed classifier framework can achieve an average classification accuracy of 98.04% and provide 3% improvement in the classification accuracy compared to conventional CNN classifier.

A hybrid methodology using VMD and disentangled features for wind speed forecasting

Wind energy is gaining worldwide attention due to its renewable and ecological characteristics. The accurate prediction of wind speed presents a challenge due to its unpredictable and stochastic nature. In this study, a novel method for predicting wind speed is proposed. The proposed approach is called VMD-CoST-SVR and it involves the use of variational mode decomposition (VMD), contrastive learning of seasonal-trend representations (CoST), and support vector regression (SVR). The study utilizes the VMD technique to denoise the wind speed data. The CoST model, which employs contrastive learning, is then applied to the denoised wind speed data to extract the disentangled trend and seasonal features. The concatenated feature map of trend and seasonal features is subsequently fed to the SVR to predict the future wind speed. As the time interval increases, there is a corresponding decrease in the performance of predictive models. Addressing this, the proposed methodology has been subjected to training and validation for wind speed predictions at various time intervals, namely 5-min, 10-min, 15-min, 30-min, 1-hour, and 2-hour ahead using two experiments. This study utilizes wind speed data obtained from wind farms situated in Leicester and Portland. This validation has been accomplished through the use of seven individual models and seven hybrid models. The results of two experiments indicate that the proposed approach exhibited superior performance, resulting in a significant improvement across all time intervals evaluated.

A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

Reliable and precise estimation of solar energy as one of the green, clean, renewable and inexhaustible types of energies can play a vital role in energy management, especially in developing countries. Also, solar energy has less impact on the earth’s atmosphere and environment and can help to lessen the negative effects of climate change by lowering the level of emissions of greenhouse gas. This study developed thirteen different artificial intelligence models, including multivariate adaptive regression splines (MARS), extreme learning machine (ELM), Kernel extreme learning machine (KELM), online sequential extreme learning machine (OSELM), optimally pruned extreme learning machine (OPELM), outlier robust extreme learning machine (ORELM), deep extreme learning machine (DELM), and their versions combined with variational mode decomposition (VMD) as integrated models (VMD-DELM, VMD-ORELM, VMD-OPELM, VMD-OSELM, VMD-KELM, and VMD-ELM), for solar radiation estimation in Kurdistan region, Iraq. The daily meteorological data from 2017 to 2018 were used to implement suggested artificial models at Darbandikhan and Dukan stations, Iraq. The input parameters included daily data for maximum temperature (MAXTEMP), minimum temperature (MINTEMP), maximum relative humidity (MAXRH), minimum relative humidity (MINRH), sunshine duration (SUNDUR), wind speed (WINSPD), evaporation (EVAP), and cloud cover (CLOUDCOV). The results show that the proposed VMD-DELM algorithm considerably enhanced the simulation accuracy of standalone models’ daily solar radiation prediction, with average improvement in terms of RMSE of 13.3%, 20.36%, 25.1%, 27.1%, 34.17%, 38.64%, and 48.25% for Darbandikhan station and 5.22%, 10.01%, 10.26%, 21.01%, 29.7%, 35.8%, and 40.33% for Dukan station, respectively. The outcomes of this study reveal that the VMD-DELM two-stage model performed superiorly to the other approaches in predicting daily solar radiation by considering climatic predictors at both stations.

Adaptive modal identification of honeycomb thin-walled composite structures with pit defects under thermal modal testing using variational mode decomposition technique based on digital image correlation

The active imaging blue light high-speed three-dimensional digital image correlation (3D-DIC) system was combined with a transient aerodynamic heating system and random pulse excitation technology to establish a high-frequency thermal vibration optical test system capable of multi-temperature zone testing at 900 °C to accurately obtain the thermal modal parameters of thin-walled structures. The full-field temperature distribution of a novel honeycomb thin-walled composite structure (HTWCS) with pit defects under non-uniform temperature was accurately simulated through reinforcement learning. The first six modal frequencies of honeycomb thin-walled composite structure with pit defects at room temperature increased compared with the non-destructive honeycomb thin-walled composite structure, and the third and fourth modal shapes interchanged. An adaptive modal parameter identification method for full-field three-dimensional vibration measurements under high-temperature environments was developed by combining the successive multivariate variational mode decomposition (VMD) with the modal superposition method. This method can effectively identify close modes. The area ratio-based approach was used to evaluate the damping of the measured response. The finite element model (FEM) of the HTWCS with pit defects at different temperatures was updated using a multi-state step-by-step model updating method and the temperature-dependent material properties were established. The results showed that the introduction of a damping ratio improved the accuracy of the updated FEM of HTWCS with pit defects. The proposed technique is anticipated to provide an effective method for high-frequency thermal vibration optical measurement and modal identification of thin-walled structures under multi-temperature regions.

Recovering mode shapes of curved bridges by a scanning vehicle

The contribution of this paper is to recover the vertical and radial mode shapes of curved bridges from the contact responses of a single-axle scanning vehicle using the variational mode decomposition (VMD) and synchrosqueezed wavelet transform (SWT) for the first time. To catch both the vertical and radial vibrations of the curved bridge, the test vehicle is modeled as a three degree-of-freedom (DOF) system involving the vertical, rocking, and lateral (radial) displacements. Firstly, closed-form solutions for the out-of-plane and in-plane vibrations of the curved beam under the vehicle's action are derived. Then, the unified formula is derived for calculating the vertical and radial contact responses of the single-axle test vehicle. Finally, the VMD technique is employed to process the vertical and radial contact responses to obtain the component responses, followed by the SWT to generate the mode shapes. The numerical analysis demonstrated that: (1) the unified formula newly derived is good for both vertical and radial responses; (2) more bridge frequencies can be extracted from the contact than vehicle response; (3) the vertical and radial mode shapes of the curved bridge can be successfully recovered by the proposed combined VMD-SWT technique against various factors; and (4) with the aid of random traffic, the adverse effect of pavement roughness on mode shape recovery is alleviated.

Variational mode decomposition enabled temporal convolutional network model for state of charge estimation

AbstractDue to the fast growth of electric vehicles (EVs) , estimation for Battery's State‐of‐charge (SOC) received significant research interests. The reason is that an accurate SOC estimation can significantly contribute to the reliability of EVs. A Variational Mode Decomposition (VMD) technique enabled Temporal Convolutional Network (TCN) model is proposed by the authors for SOC estimation. The proposed method first adopts time‐frequency analysis techniques to decompose voltage values into different frequency domains, each of which is analysed with the VMD technique to obtain its features as the input for the TCN model. Then, the proposed method combines outputs of different frequency domains with an attention module as the final output of the TCN model. Experiments on real battery datasets indicate that the proposed method outperforms the existing methods by 7.2% in mean absolute error and 6.13% in root mean square error. In addition, the error between the estimated and actual values using the proposed method is bounded by 2%.

A hybrid VMD-LSTM/GRU model to predict non-stationary and irregular waves on the east coast of China

Accurate wave forecasting is essential for the safety of port and offshore structure operations and ship navigation. Computational fluid dynamics (CFD) and traditional time series models are ineffective in dealing with non-linearities and non-smoothness. However, long short-term memory (LSTM) and gate recurrent units (GRU) have strong non-linear handling capabilities but are deficient in non-stationary situations. Variational mode decomposition (VMD) can effectively separate the non-linearity and non-smoothness in data. In this report, a VMD-LSTM/GRU model is proposed by combining the advantages of the LSTM model, the GRU model, and the VMD technique. Based on the one-tenth maximum wave height at three locations on the east coast of China, the error of the VMD-LSTM/GRU model is shown to be lower than that of the LSTM/GRU model, and significantly lower than that of the single LSTM and single GRU models. By analyzing different forecast durations, it was found that the correlation of the VMD-LSTM/GRU model improved from 10.75% to 20.99% compared to the corresponding LSTM/GRU model. The RMSE and relative errors of the VMD-LSTM/GRU model were reduced by 96.77% and 95.52%, respectively, for the most difficult forecast result 10 h ahead. Thus, this model has proved to be superior in predicting non-linear and non-stationary waves.

Automatic modal identification of bridges based on free vibration response and variational mode decomposition technique

Free vibration tests are often performed for the dynamic characterization of structural systems and components. In the field of road bridges monitoring, such tests require minimal equipment (i.e., a sparse network of few sensors, even a single one) in order to gather – quickly and at low cost – some essential information about the dynamic behavior of the structure. The experimental free-vibration response is thus processed for estimating structural modal features which, in turn, can be useful to corroborate or update available numerical models as well as to evaluate current bridge conditions. Within this framework, an efficient computational strategy is here developed for the modal identification of bridges based on their free vibration response. The procedure combines the variational mode decomposition technique with the area ratio-based damping identification method, which ensures enhanced robustness to noise-related effects in field-test signals. A novel procedure is implemented to make the whole identification process fully automatic, in such a way that it does not depend on specific structural features (e.g., to identify even closely-spaced modes) or subjective user’s settings. After a preliminary validation with a synthetic multi-modal signal, the proposed procedure is applied to two real case-studies. The first case-study deals with the identification of natural frequencies, modal damping ratios and mode shapes of prestressed concrete girder bridge decks. Particularly, experimental natural frequencies and mode shapes are compared with numerical predictions obtained from a finite element model of the bridge. The dynamic characterization of the cables of a cable-stayed bridge is considered in the second case-study. Herein, experimental natural frequencies of all the stay-cables are initially analyzed for estimating their stress levels, which have been critically analyzed to detect the existence of relaxation losses. Moreover, modal damping ratios of all the stay-cables are identified and compared to estimates obtained by alternative ambient-vibration-based techniques.

Short-term PV power forecasting based on time series expansion and high-order fuzzy cognitive maps

Achieving short-term accurate photovoltaic power forecasting is of great significance to improve the efficiency of grid operation, especially in power stations where historical values of meteorological parameters are lagged or not recorded. Therefore, numerous studies have been presented to predict the intricate stochastic properties of photovoltaic power sequence in recent years. However, due to the limitation of algorithm performance and the effect of noise in the time series, previously proposed methods may not extract effective features from power data. Furthermore, most studies have only focused on point prediction, which ignores the uncertain information and unavoidable forecast bias. In this study, a hybrid framework based on fuzzy information granulation algorithm, improved variational mode decomposition technique, and high-order fuzzy cognitive maps is proposed to fill these gaps. Comparison experiments were set up using 5-minute photovoltaic power data from Alice Springs, Australia. The computational results not only demonstrate that the proposed framework significantly improves forecast accuracy of short-term photovoltaic power, but also achieves effective interval prediction by fuzzy information.

Enhanced grasshopper optimization algorithm with extreme learning machines for motor‐imagery classification

AbstractIn Brain Computer Interface (BCI), achieving a reliable motor‐imagery classification is a challenging task. The set of discriminative and relevant feature vectors plays a crucial role in classification. In this article, an enhanced optimization technique is implemented for selecting active feature vectors to enhance motor‐imagery classification using Electroencephalography (EEG) signals. After collecting the input EEG signals from BCI competition III‐4a and IV‐2a databases, the 6th‐order butter‐worth filter is employed for eliminating base‐line wander noise from the raw EEG signals. Further, the Variational Mode Decomposition technique is applied for separating the important signal components from the composite EEG signals, and then, the Higher Order Statistic, kurtosis, skewness, standard deviation, and entropy are utilized for feature extraction. The high‐dimensional feature values are given to the Enhanced Grasshopper Optimization Algorithm for optimum feature selection, which are given to the Extreme Learning Machines (ELM) classifier for motor‐imagery classification. Finally, in the resulting section, the optimized ELM model achieved 99.48% and 99.12% of accuracy on the BCI competition III‐4a and IV‐2a databases, where the achieved results are maximum compared to the traditional deep learning models.

Automatic incipient fault detection and health state assessment of rolling element bearings using pruned exact linear time method

Rolling element bearing is a crucial component in rotating machines. The existence of faults in the bearing causes sudden failures, resulting in catastrophic failure of machines. Incipient fault detection and health state assessment are the essential tasks in condition-based maintenance to avoid machine failures. This paper proposes a new method, the Pruned Exact Linear Time, for identifying the incipient faults and following damage states in the bearing. When a fault initiates in the bearing, there is an increment in the vibration response. This increment can be quantified by the degradation indicator computed from the vibration signal. The Variational Mode Decomposition technique is used to de-noise the vibration signals. Various statistical features are derived from the de-noised signal, and the best feature subset is chosen by the Recursive Feature Elimination method. Then, the significant bearing life degradation indicator is computed using the Reconstruction Independent Component Analysis method by fusing selected features. A novel index is formulated for computing the percentage of failure, which can identify whether the bearing is in a mild failure state or medium failure state. The efficiency of the proposed framework is demonstrated using experimental bearing datasets.

Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection

Accurate ahead forecasting of reference evapotranspiration (ETo) is crucial for effective irrigation scheduling and management of water resources on a regional scale. A variety of methods are available for ETo simulation, but the most trending is complementary artificial intelligence (AI) paradigms. In this research, a novel Multivariate Variational Mode Decomposition technique (MVMD) integrated with the Ridge Regression (RR) feature selection algorithm and Kernel Extreme Learning Machine (KELM) model (i.e., MVMD-RR-KELM) was adopted to multi-step ahead (t + 3, and t + 7) forecasting of daily ETo in different climate of India. Here, the complementary expert system hybridized with the Boosted Regression Tree (BRT) and Extreme Gradient Boosted (XGBoost) along with the standalone counterpart models (KELM, BRT, and XGBoost) were examined to validate the robustness of the primary model. The complementary (i.e., MVMD-RR-KELM, MVMD-RR-BRT, & MVMD-RR-XGBoost) and their standalone counterpart models were trained and tested using daily climatic data of Hisar (located in Haryana State), Bathinda, and Ludhiana (located in Punjab State) sites. The forecasting accuracy of standalone and hybrid AI models was assessed using six goodness-of-fit metrics, i.e., R (Correlation Coefficient), RMSE (root mean square error), MAPE (mean absolute percentage error), NSE (Nash-Sutcliffe Efficiency), IA (Index of Agreement), U95% (Uncertainty Coefficient with 95% level) along with visual interpretation. According to the testing results, the hybrid MVMD-RR-KELM models had superior performance than other AI models for forecasting three and seven days ahead ETo. The KELM model optimized using the MVMD-RR technique provides promising and robust results with higher forecasting accuracy and minimum error for multi-step ahead forecasting of ETo in semi-arid and sub-humid regions.

Multifaceted irradiance prediction by exploiting hybrid decomposition-entropy-Spatiotemporal attention based Sequence2Sequence models

Accurate prediction models enable the efficacious utilization and integration of solar energy into the power system. Therefore, this study aims to develop novel hybrid prediction models by employing correlation analysis (CA), decomposition techniques, sample entropy (SE), and spatio-temporal attention (STA) based sequence2sequence (S2S) algorithm for accurate prediction of global horizontal irradiance (GHI). The decomposition techniques include variational mode decomposition (VMD), improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), and maximum overlap discrete wavelet transform (MODWT). The VMD-STA-S2S hybrid model surmounts the associated hybrid and standalone prediction models by revealing the highest prediction efficiency and the lowest error. Compared to the SARIMAX, SVR, ANN, XGB, GRU, LSTM, and S2S models, the VMD-STA-S2S model reduced RMSE during testing by 80.927 W/m2, 75.426 W/m2, 73.487 W/m2, 62.394 W/m2, 57.811 W/m2, 52.007 W/m2, and 41.836 W/m2, respectively. Similarly, the reductions in RMSE by VMD-STA-S2S model compared to SA-S2S, TA-S2S, STA-S2S, MODWT-STA-S2S, ICEEMDAN-STA-S2S, ICEEMDAN-SE-STA-S2S, and VMD-SE-STA-S2S models are 24.054 W/m2, 20.951 W/m2, 12.702 W/m2, 15.396 W/m2, 9.921 W/m2, 6.103 W/m2 and 0.484 W/m2, respectively, during testing. Furthermore, considering NSE during testing, the VMD-STA-S2S model is 8.66%, 7.72%, 7.41%, 5.71%, 5.07%, 4.31%, 3.11%, 1.43%, 1.19%, 0.64%, 0.81%, 0.47%, 0.35% and 0.07%, more efficient than the SARIMAX, SVR, ANN, XGB, GRU, LSTM, S2S, SA-S2S, TA-S2S, STA-S2S, MODWT-STA-S2S, ICEEMDAN-STA-S2S, ICEEMDAN-SE-STA-S2S, and VMD-SE-STA-S2S models, respectively. The superior performance of VMD-STA-S2S over its counterparts corroborates the integration of the VMD technique and STA-based S2S algorithm for GHI prediction. The multivariate meteorological data of this study is decomposed by VMD into subcomponents more effectively than the ICEEMDAN and MODWT techniques. VMD decomposed subcomponents are further fed to the STA-S2S to efficiently extract and learn the spatial and temporal features, resulting in the enhanced and superior prediction outcomes of the VMD-STA-S2S model compared to all the counterpart models. Besides GHI prediction, the proposed model is also appropriate for other time-series data, including renewable energy, electrical load, and environment monitoring.

Intelligent Decision Support and Fusion Models for Fault Detection and Location in Power Grids

Computational intelligence-based diagnostic frameworks have emerged as rapidly evolving but highly efficient approaches for diagnosing faults in power grids. This work aims to build a diagnostic framework by resorting to computational intelligence techniques to improve decision-making and diagnostic accuracy. This diagnostic framework has three modules for signal processing, fault detection, and location. The signal-processing module uses the variational mode decomposition technique to extract informative time-frequency features from the voltage and frequency signals. Voltage features are then fed into the fault detection module to train a set of modular support vector machines that are used for monitoring the binary state of each node in the power grid. Once a faulty state on a node is detected, it activates the third module for identifying fault location. This module benefits from a novel zSlices-based general type-2 fuzzy fusion model for the sake of identifying the fault type as well as mitigating the false alarm rate. The exact location of the fault is then determined through a fuzzy decision support system that is equipped with a recommendation mechanism for the sake of consensus reaching. Various scenarios are simulated on the IEEE 39-bus system and on an experimental setup of a Three-Bus Two-Line transmission system, where the attained results verify the applicability, efficiency, and robustness of the proposed framework.

Near-fault earthquakes with pulse-like horizontal and vertical seismic ground motion components: Analysis and effects on elastomeric bearings

Near-fault earthquakes have been largely studied in the last years by paying special attention to the occurrence of a pulse-like horizontal seismic ground motion, and to the related effects on structural systems. Conversely, less attention has been paid on the vertical component of the ground motion in such seismic events. Within this framework, the present study is meant at investigating a fairly overlooked special case, that is the occurrence of near-fault earthquakes exhibiting a pulse-like seismic ground motion along the horizontal direction and the vertical one. Specifically, the variational mode decomposition technique is employed to prepare and characterize two subsets of near-fault earthquake records that consist of fault-normal and vertical seismic ground motion components. One subset collects earthquake records with pulse-like waveform in both velocity components, whereas a pulse-like waveform in the fault-normal velocity component only takes place in the earthquake records of the second subset. If both fault-normal and vertical components embed a dominant pulse-like waveform, then it is found that the ratio of the corresponding pulse periods well correlates with the pulse period along the fault-normal direction, while it is uncorrelated with respect to the pulse period along the vertical direction. Next, it is investigated the displacement demand of high-damping rubber bearings for base-isolated buildings under earthquake records characterized by a horizontal impulsive ground motion together with either a pulse-like or a nonpulse-like vertical shaking, provided that the pulse period in the horizontal direction is similar and the peak ground accelerations are individually the same after scaling. Final results shows that the maximum displacement of elastomeric bearings subjected to a pulse-like horizontal ground motion is moderately amplified, on average, when the vertical excitation is also pulse-like.

Machine learning analysis of features extracted from time–frequency domain of ultrasonic testing results for wood material assessment

In this paper, a systematic machine learning strategy is proposed to classify wood properties based on a contact ultrasonic testing results. As such, several aspects of the wood material including the type of wood (hardwood or softwood), the direction of an ultrasonic test with respect to the growth rings of the wood, and, whether the wood is damaged or intact are investigated. As a pre-processor, the Variational Mode Decomposition technique is applied to the nonlinearly modulated ultrasonic signals, and the centre frequencies of the decomposition results are taken as features for Machine learning Algorithms (MLAs). Then with each of the MLAs, hyperparameter settings were optimised and technical aspects of the feature engineering are discussed. Best results were achieved using Ensemble classifiers, SVM, and KNN using three features/decomposition. To explore the physics behind the nonlinear problem, the relative false discovery rate obtained from the confusion matrix associated with applying the MLAs is proposed as a metric. We show that different features are capable of exploring different aspects of the problem better. The techniques of this paper can be applied to quality assessment of wood materials. The paper also demonstrates the capability of MLAs in exploring some physics of such problems. Moreover, billets with natural imperfections harvested from the site Collie in WA, Australia are classified to demonstrate the applicability of the proposed approach in real world problems. The result of 94% 5-fold cross-validation accuracy indicates the effectiveness of the proposed approach.

Short-time wind power prediction using hybrid kernel extreme learning machine

A hybrid short-time wind power forecasting technique based on variational mode decomposition (VMD) and kernel extreme learning machine (KELM) is proposed in this paper. The non-stationary historical wind data is initially decomposed into various modes using VMD technique, which is subsequently passed through the proposed KELM (Gaussian and wavelet-based) and conventional ELM [without weight optimisation and with optimisation – chaotic firefly optimisation algorithm (CFA)], respectively, in order to predict the 30 minutes and 1 hour ahead wind power, respectively. It is observed that, rather than optimising the arbitrary input layer weights of VMD-ELM technique, the proposed Gaussian-based EMDKELM technique illustrates the most effective and accurate short-time wind power predictions for some diverse seasons. The overall results presented in the simulation (through MATLAB simulation platform) and result section are satisfactory and indicates the proposed Gaussian-based EMD-KELM technique as a highly potential prediction technique for real-time applications in power systems. The proposed model can be tested in the industry having wind generation or wind power plant where this can be applied in order to predict the future wind power to schedule the load profile in efficient manner. This can be also validated through wind test bench system.

On variational mode decomposition of wind speed for real-time thermal rating determination in power transmission system

Power transmission line capacity is restricted with the maximum temperature that can withstand without inadmissible of line sag. Regardless of the carried current, the temperature of the transmission line conductors was affected by various weather variables. However, the maximum capacity of the line current known as static thermal rating was determined based on the conservative weather conditions for safe operation, but the restriction of the line capacity may be modified which results in the additional capacity of the line. As a result, real-time thermal rating technique was applied on thermal model of the transmission line. Generally, the information about weather conditions is considered uncertainty, however, the weather variables should be dealt with and studied statistically for determining the accurate rise in the conductor temperature. The real-time thermal rating technique is evaluated using weather variables. The most important parameter is the wind speed, which greatly influence s conductor temperature and implicitly affects power transmission line capacity. Thus, in this study, the real-time thermal rating technique is developed by weather variable model based on the variational mode decomposition technique that applied only on the wind speed for adapting wind speed measurements to produce conservative evaluation of convective cooling on the conductors of power transmission system without violating the maximum operating temperature inside the core of conductors. The developed real-time thermal rating is implemented on sections of the power transmission system of western Saudi Arabia. The developed technique is compared with other techniques to investigate its applicability.