Search Results (9)

The rapid integration of renewable energy sources into modern power systems has exacerbated power quality challenges, particularly broadband oscillation phenomena that threaten grid symmetry and stability. The proposed symplectic geometric mode decomposition (SGMD) method advances the field; however, issues like mode aliasing and over-decomposition are unresolved within the symplectic geometric paradigm. To resolve these limitations in existing methods, this paper proposes a novel time-frequency-coupled symmetry mode decomposition technique. The approach first applies symplectic symmetry geometric mode in the time domain, then iteratively refines the modes using frequency-domain Local Outlier Factor (LOF) detection to suppress aliasing. Final mode integration employs Dynamic Time Warping (DTW) for optimal alignment, enabling accurate extraction of oscillation characteristics. Comparative evaluations demonstrate that the average error of the amplitude and frequency identification of the proposed method are 1.39% and 0.029%, which are lower than the results of SVD at 5.09% and 0.043%. Full article

Electrical networks face operational challenges from power quality-affecting disturbances. Since disturbance signatures directly affect classifier performance, optimized feature selection becomes critical for accurate power quality assessment. The pursuit of robust feature extraction inevitably constrains the dimensionality of the discriminative feature set, but the complexity of the recognition model will be increased and the recognition speed will be reduced if the feature vector dimension is too high. Building upon the aforementioned requirements, in this paper, we propose a feature extraction framework that combines improved symplectic geometric mode decomposition, refined generalized multiscale quantum entropy, and refined generalized multiscale reverse dispersion entropy. Firstly, based on the intrinsic properties of power quality disturbance (PQD) signals, the embedding dimension of symplectic geometric mode decomposition and the adaptive mode component screening method are improved, and the PQD signal undergoes tri-band decomposition via improved symplectic geometric mode decomposition (ISGMD), yielding distinct high-frequency, medium-frequency, and low-frequency components. Secondly, utilizing the enhanced symplectic geometric mode decomposition as a foundation, the perturbation features are extracted by the combination of refined generalized multiscale quantum entropy and refined generalized multiscale reverse dispersion entropy to construct high-precision and low-dimensional feature vectors. Finally, a double-layer composite power quality disturbance model is constructed by a deep extreme learning machine algorithm to identify power quality disturbance signals. After analysis and comparison, the proposed method is found to be effective even in a strong noise environment with a single interference, and the average recognition accuracy across different noise environments is 97.3%. Under the complex conditions involving multiple types of mixed perturbations, the average recognition accuracy is maintained above 96%. Compared with the existing CNN + LSTM method, the recognition accuracy of the proposed method is improved by 3.7%. In addition, its recognition accuracy in scenarios with small data samples is significantly better than that of traditional methods, such as single CNN models and LSTM models. The experimental results show that the proposed strategy can accurately classify and identify various power quality interferences and that it is better than traditional methods in terms of classification accuracy and robustness. The experimental results of the simulation and measured data show that the combined feature extraction methodology reliably extracts discriminative feature vectors from PQD. The double-layer combined classification model can further enhance the model’s recognition capabilities. This method has high accuracy and certain noise resistance. In the 30 dB white noise environment, the average classification accuracy of the model is 99.10% for the simulation database containing 63 PQD types. Meanwhile, for the test data based on a hardware platform, the average accuracy is 99.03%, and the approach’s dependability is further evidenced by rigorous validation experiments. Full article

This study addresses the issue of resistance plate deterioration in ultra-high voltage energy devices by proposing an improved symplectic geometric mode decomposition-wavelet packet (ISGMD-WP) algorithm that effectively extracts the component characteristics of leakage currents. The extracted features are subsequently input into the I-Informer network, allowing for the prediction of future trends and the provision of early short-term warnings. First, we enhance the symplectic geometric mode decomposition (SGMD) algorithm and introduce wavelet packet decomposition reconstruction before recombination, successfully isolating the prominent harmonics of leakage current. Second, we develop an advanced I-Informer prediction network featuring improvements in both the embedding and distillation layers to accurately forecast future changes in DC characteristics. Finally, leveraging the prediction results from multiple adjacent columns mitigates the impact of power grid fluctuations. By integrating these data with the deterioration interval, we can issue timely warnings regarding the condition of lightning arresters across each column. Experimental results demonstrate that the proposed ISGMD-WP effectively decomposes leakage current, achieving a decomposition ability evaluation index (EIDC) 1.95 under intense noise. Furthermore, in long-term prediction, the I-Informer network yields mean absolute error (MAE) and root mean square error (RMSE) indices of 0.02538 and 0.03175, respectively, enabling the accurate prediction of the energy device’s fault. Full article

Under the dual carbon objectives, wind power penetration has accelerated markedly. However, the inherent volatility and insufficient peak regulation capability in energy storage allocation hamper efficient grid integration. To address these challenges, this paper presents a hybrid storage capacity configuration method that combines Symplectic Geometry Mode Decomposition (SGMD) with Particle Swarm Optimization (PSO). SGMD provides fine-grained, multi-scale decomposition of load–power curves to reduce modal aliasing, while PSO determines globally optimal ESS capacities under peak-shaving constraints. Case-study simulations showed a 25.86% reduction in the storage investment cost compared to EMD-based baselines, maintenance of the state of charge (SOC) within 0.3–0.6, and significantly enhanced overall energy management efficiency. The proposed framework thus offers a cost-effective and robust solution for energy storage at renewable energy plants. Full article

Due to the broad frequency domain and nonlinear characteristics of ferroresonance signals, traditional time–frequency analysis methods often face challenges such as misjudgment, difficulty in threshold setting, and noise interference when extracting features from ferroresonance overvoltage signals. A fault identification method for ferroresonance based on the Gramian Angular Summation Field (GASF) and an improved cloud model is proposed to address the identified problems. Firstly, this paper employs Symplectic Geometric Mode Decomposition (SGMD) to denoise the ferroresonance overvoltage signal, extract its characteristic modal components, and reconstruct the signal. Secondly, the reconstructed one-dimensional signal is transformed into a two-dimensional image using GASF. Subsequently, we extract texture features of GASF images with different resonance types by grey-level co-occurrence matrix (GLCM) and establish the corresponding cloud distribution model to characterize these textures. Finally, we calculate the membership degree between the standard cloud for the signal to be identified and the index cloud in the cloud distribution model, enabling accurate identification of the type of ferroresonance based on this membership degree. Simulation and actual measurement data analyses validate the feasibility and effectiveness of the proposed method. Full article

The non-stationary characteristics of the vibration signals of rolling bearings will be aggravated under variable speed conditions. Meanwhile, multichannel signals can provide a more comprehensive characterization of state information, providing multiple sources of information that facilitate information fusion and enhancement. However, traditional adaptive signal decomposition methods generally assume that the frequency information is constant and stationary, and it is difficult to achieve a unified decomposition when dealing with multichannel time-varying signals. Therefore, the intention of this paper is to propose a multichannel signal adaptive decomposition method applicable to variable speed conditions. Specifically, this paper takes advantage of the strong adaptability and robustness of symplectic geometric mode decomposition (SGMD). To improve its applicability to multichannel time-varying signals at variable rotational speeds, a generalized multivariate symplectic sparsest united decomposition (GMSSUD) method is proposed. In GMSSUD, firstly, the completely adaptive projection (CAP) method is employed to achieve a unified representation of the multichannel signals. Then, the generalized demodulation method is introduced to stabilize the signal and subsequently reduce the noise through component screening and reconstruction. Finally, with the new proposed operator as the optimization objective, the constructed sparse filter parameters are optimized to achieve the frequency band segmentation. The analysis results demonstrate that the GMSSUD method possesses higher decomposition precision for multichannel signals with variable speeds and also has a stronger diagnosis ability for variable-speed bearing faults. Full article

When the rotating machinery fails, the signal generated by the faulty component often no longer maintains the original symmetry, which makes the vibration signal with nonlinear and non-stationary characteristics, and is easily affected by background noise and other equipment excitation sources. In the early stage of fault occurrence, the fault signal is weak and difficult to extract. Traditional fault diagnosis methods are not able to easily diagnose fault information. To address this issue, this paper proposes an early fault diagnosis method for symplectic geometry mode decomposition (SGMD) based on the optimal weight spectrum index (OWSI). Firstly, using normal and fault signals, the optimal weight spectrum is derived through convex optimization. Secondly, SGMD is used to decompose the fault signal, obtaining a series of symplectic geometric modal components (SGCs) and calculating the optimal weight index of each component signal. Finally, using the principle of maximizing the OWSI, sensitive components reflecting fault characteristics are selected, and the signal is reconstructed based on this index. Then, envelope analysis is performed on the sensitive components to extract early fault characteristics of rolling bearings. OWSI can effectively distinguish the interference components in fault signals using normal signals, while SGMD has the characteristic of unchanged phase space structure, which can effectively ensure the integrity of internal features in data. Using actual fault data of rolling bearings for verification, the results show that the proposed method can effectively extract sensitive components that reflect fault characteristics. Compared with existing methods such as Variational Mode Decomposition (VMD), Feature Mode Decomposition (FMD), and Spectral Kurtosis (SK), this method has better performance. Full article

Extracting the fault characteristic information of rolling bearings from intense noise disturbance has been a heated research issue. Symplectic geometry mode decomposition (SGMD) has already been adopted for bearing fault diagnosis due to its advantages of no subjective customization of parameters and the ability to reconstruct existing modes. However, SGMD suffers from rapidly decreasing calculation efficiency as the amount of data increases, in addition to invalid symplectic geometry components affecting decomposition accuracy. The regularized composite multiscale fuzzy entropy (RCMFE) operator is constructed to evaluate the complexity of each initial single component and minimize the residual energy. Combined with the partial reconstruction threshold indicator to filter out specific significant initial single components, the raw signal can be decomposed into multiple physically meaningful symplectic geometric mode components. Therefore, the decomposition efficiency and accuracy can be enhanced. Thus, a rolling bearing fault diagnosis method is proposed based on partial reconstruction symplectic geometry mode decomposition (PRSGMD). Both simulated and experimental analysis results show that PRSGMD can improve the speed of SGMD analysis while increasing the decomposition accuracy, thereby augmenting the robustness and effectiveness of the algorithm. Full article

Symplectic geometric mode decomposition (SGMD) is a newly proposed signal processing method. Because of its superiority, it has gained more and more attention in the field of fault diagnosis. However, the similar component reorganization problem involved in this method has not been clearly stated. Aiming at this problem, this paper proposes the SGMD-CS method based on the SGMD method and the cosine similarity (CS) and has been compared and verified on the simulation signal and the actual rolling bearing signal. In addition, in order to realize the intelligent diagnosis of the wind turbine bearing fault, the symplectic geometric entropy (SymEn) is extracted as the fault feature and input it into the AdaBoost classification model. In summary, this paper proposes a new wind turbine fault feature extraction method based on the SGMD-CS and AdaBoost framework, and the validity of the method is verified by the rolling bearing vibration data of the Electrical Engineering Laboratory of Case Western Reserve University. Full article