Identification Of Disturbances Research Articles

Identification and Classification of Power Quality Disturbances via Synchro-Reassigning Transform

Identification and Classification of Power Quality Disturbances via Synchro-Reassigning Transform

Suggestion for customized PROMIS measurements of sleep disturbance and fatigue for patients with ovarian cancer.

239 Background: Ovarian cancer (OC) patients experience a high incidence of sleep disturbance and fatigue, which impacts their health-related quality of life (HRQoL). PROMIS short forms are important tools to assess sleep disturbance and fatigue in OC patients and identify the need for additional intervention. However, these frequently used short forms (SFs) may not capture the diversity and range of issues OC patients experience. Methods: In this mixed-methods study, data were collected from stage III/IV OC patients diagnosed within 3 years with diverse treatment trajectories, race/ethnicity, and locations, including semi-structured interviews with 20 patients and surveys of 200 patients. In addition to their medical history, participants were asked to complete PROMIS Sleep Disturbance SF 8b, and Fatigue SF 8a to evaluate their current symptom burden. Participants were also asked to retrospectively assess their fatigue and sleep during each type of treatment they received with a set of custom sleep and fatigue items from the PROMIS bank. Qualitative data were used to determine the best custom survey items in the PROMIS bank to match the issues raised by patients. Construct validity was evaluated with code prevalence from interview data. Cronbach’s Alpha coefficient was used to assess the scale reliability based on survey testing. Results: Sleep disturbance SF 8b does not capture 3 prominent issues OC patients reported as disrupting their sleep: pain, anxiety, and rumination at bedtime. Related PROMIS items were tested in the survey: trouble getting in a comfortable sleeping position (Sleep 71), stress disturbing sleep (Sleep 78), and trouble stopping thoughts during bedtime (Sleep 69). Significant fatigue issues included forcing oneself to get up and do things (FATIMP47), being frustrated by being too tired to do things they want to do (AN15), feeling like fatigue made them slowed down in their thinking (FATIMP2), feeling less effective at work (FATIMP37), and having to limit social activities because of fatigue (FATIMP18 ) . For custom items, Cronbach’s α ranged from 0.898 to 0.946 for sleep and from 0.939 to 0.985 for fatigue when asked for experience during each treatment type, suggesting a possible increase in robustness of the measures over the SFs. Conclusions: Interviews with OC patients identified significant issues not captured in common PROMIS sleep and fatigue short forms. Testing use of custom PROMIS items better matching the OC patients’ profile with a survey of 200 OC patients showed excellent reliability (α ≥ 0.9). Customizing the menu of PROMIS measures used to assess symptom burden among OC patients may improve identification of sleep disturbance and fatigue issues for this patient population. Further research could help identify the best fitting set of questions for a clinical setting when screening advanced OC patients, as well as other patient groups with long term sleep and fatigue issues.

Identification of Power Quality Disturbances in Electrical Distribution System using Fast Fourier Transforms and Super Learner Ensembles

Currently, the use of non-linear loads and equipment, as well as renewable energy sources injected into the power system, tends to increase. As a result, the waveform of the electrical signal changes, and distortion occurs in the distribution system, which affects the quality and reliability of the electrical system. Importantly, sometimes this leads to malfunctions in protection equipment. This paper presents the algorithm for power quality disturbance (PQD) identification in electrical distribution systems, which involves three main steps: (1) Generating simulated waveforms using a signal processing approach; (2) extracting features using the Fast Fourier Transforms (FFT) technique; and (3) identifying the type of PQD using Super Learner Ensembles (SLE), which employs cross-validation to assess the performance of multiple machine learning models. Subsequently, the model’s efficiency is verified and tested using data from electronic energy meters installed in the distribution system of the Provincial Electricity Authority (PEA). The accuracy resulting from synthetic and experimental data sets is 99.90% and 99.69%, respectively. The results indicate that the model performs well in identifying power quality disturbances and achieves high accuracy.

The Supplementary Damping Method for CLCC HVDC Based on Projective Control Theory

This paper proposes a design method for an additional damping reduced-order controller based on the projective control theorem. Improvements are made to the projective theorem to achieve additional damping control through Controllable-Line-Commutated Converter (CLCC)-based High Voltage Direct Current (HVDC), which suppresses low-frequency oscillations. Using small disturbance identification techniques, the system’s linear model is first identified, and the state feedback control law of the system is then determined using the state-space pole assignment control method. Then, by retaining the dominant oscillation modes of the closed-loop system through the improved projective theorem, the state feedback is converted into output feedback. Ultimately, the sixth-order state feedback controller is reduced to a second-order one and applied in the CLCC HVDC additional damping strategy to suppress low-frequency oscillations. Simulation results based on the electromagnetic transient simulation software PSCAD demonstrate that the designed CLCC HVDC additional damping reduced-order projective control exhibits good suppression performance, strong robustness, and low order, which are of significant importance for engineering practice.

Comprehensive Clinical Characterization of Patients Diagnosed With Dilated Cardiomyopathy: A Cross-Sectional Study.

Background The term "cardiomyopathy" encompasses a wide range of diseases with various underlying causes. Dilated cardiomyopathy (DCM) is characterized by ventricular dilation and impaired cardiac function in the absence of congenital, valvular, hypertensive, or ischemic heart disease (IHD). This study was motivated by the high prevalence of underlying DCM and chronic heart failure, coupled with a lack of comprehensive information on DCM. The primary objective of this study was to identify the clinical characteristics and contributing factors associated with individuals diagnosed with DCM. Methods A total of 120 patients with DCM were enrolled in a two-year, hospital-based observational cross-sectional study conducted within the Medicine Department of a tertiary care center. The study assessed risk factors, including IHD, diabetes mellitus, alcohol consumption, and smoking. Results DCM was observed across all age groups, though it was notably more prevalent among middle-aged individuals (n = 50, or 41.7%) and the elderly (n = 35, or 37.5%). The condition was more commonly seen in men. IHD emerged as the predominant risk factor, affecting 75% of patients (n = 90), followed by diabetes mellitus (n = 85, or 70.8%), alcohol use (n = 75, or 62.5%), and smoking (n = 50, or 41.7%). Common symptoms included pedal edema, palpitations, easy fatigability, and exertional dyspnea. Conclusion In conclusion, DCM is a critical condition that necessitates vigilant monitoring. Consistent observation of symptoms, recognition of potential triggers, and prompt identification of adverse drug reactions, electrolyte disturbances, and echocardiographic changes are essential. This awareness and early detection were responsible for the lower mortality and early symptom improvement observed in the present study group.

An automatic recognition method for power quality disturbances based on CAB-Net

Abstract The accurate identification of power quality disturbances (PQDs) is crucial for maintaining the stability and reliability of power systems. In this paper, an automatic identification method for PQDs based on CNN-BiGRU-Attention Network (CAB-Net) is proposed. To handle the temporal characteristics and complexity of composite PQDs signals, the proposed model leverages the bidirectional information transmission of the BiGRU model to efficiently extract temporal features. Furthermore, to boost recognition accuracy, an attention mechanism is incorporated, the key information can be intelligently locked in the feature extraction stage, and the information can be efficiently screened and utilized, thereby enhancing its ability to identify power quality disturbance signals. To confirm the validity of the method, we use the simulation data for experimental proof. Experimental results demonstrate that compared to traditional power quality disturbance identification methods, the method has excellent recognition ability and stronger robustness.

A novel index for exposed coal mapping using Landsat imagery

Remotely sensing the spatial distribution of exposed coal (EC) is significant for exploring the footprints of mining activities and understanding environmental impacts. However, widely applicable methods for the identification of EC surfaces remain inadequate because the choices of recent methods confront the diverse EC spectra and backgrounds. Therefore, this study proposed a new Automated Coal Mapping Index (ACMI) which was empirically formulated by an iterative process of identifying parameters that maximize the separability of EC and non-EC surfaces. The performance of ACMI was tested in six study areas worldwide with different landscape types and coal types. Based on the qualitative evaluation, ACMI was more effective in highlighting EC surfaces and suppressing non-EC surfaces than the existing methods. Compared with the sample points obtained through direct interpretation, ACMI obtained better EC mapping results than previous methods with the F1 score and overall accuracy (OA) no less than 0.89 and 92.27 % across all the selected study areas, respectively. In addition, ACMI was demonstrated to have a stable optimal threshold and 0 can serve as its default threshold. The default threshold makes EC mapping using ACMI an automated process. The new index has the potential to support a variety of mining-activity-related studies, such as the identification of mining disturbances and illegal mining detection at multi-spatial–temporal scales.

A Review of Microgrid Power Quality Disturbance Identification Studies

A Review of Microgrid Power Quality Disturbance Identification Studies

Expectation-Maximization Based Disturbance Identification and Velocity Tracking for Gimbal Servo Systems With Dynamic Imbalance

Expectation-Maximization Based Disturbance Identification and Velocity Tracking for Gimbal Servo Systems With Dynamic Imbalance

Power Quality Transient Disturbance Diagnosis Based on Dynamic Large Convolution Kernel and Multi-Level Feature Fusion Network

Power quality is an important metric for the normal operation of a power system, and the accurate identification of transient signals is of great significance for the improvement of power quality. The diverse types of power system transient signals and strong characteristic coupling brings new challenges to the analysis and identification of power system transient signals. In order to enhance the identification accuracy of transient signals, one method of power system transient signal identification is proposed based on a dynamic large convolution kernel and multilevel feature fusion network. First, the more fine-grained and more informative features of the transient signals are extracted by the dynamic large convolution kernel feature extraction module. Then, the multi-scale local features are adaptively fused by the multilevel feature fusion module. Finally, the fused features are reduced in dimension by the fully connected layer in the classification module and fed into the SoftMax layer for transient signal type detection. The proposed method can effectively improve the small receptive field problem of convolutional neural networks and the lack of ability of Transformer network in extracting local context information. Compared with five other power quality transient disturbance identification models, the experimental results show that the proposed method has better diagnostic accuracy and anti-noise capability.

A method for disturbance identification in power quality based on cross-attention fusion of temporal and spatial features

The rapid growth of energy storage scale in power systems has posed higher demands on the monitoring and classification of power quality disturbances (PQDs). Therefore, a method based on cross-attention fusion of temporal and spatial features is proposed to improve the accuracy and robustness of fine classification of PQDs in this study.Firstly, utilizing the improved Sample Convolution and Interaction Model (SCINet) with its recursive downsampling-convolution-interaction architecture, and adding convolutional pooling layers to extract the temporal features of PQDs.Then, the improved VGG model is employed to extract the spatial features of PQDs. Finally, by introducing a cross-attention mechanism to capture the correlation and interaction between the temporal and spatial features, the representation ability of the features is enhanced to achieve disturbance recognition. This study is based on IEEE standards and mathematical models, and generates 25 types of PDQ data through Python simulation for training and testing.The proposed method achieves an average classification accuracy of 95.01 % in a high-noise environment of 20 dB, as well as compared with other mainstream deep learning models and verified through ablation study. Furthermore, the classification test of 10 types of PQDs sampled on a hardware platform demonstrates an average accuracy of 99.66 %, further validating the reliability of the proposed method.

Optimized decomposition and identification method for multiple power quality disturbances

AbstractThe complexity of power quality (PQ) concerns is intensifying in tandem with the proliferation of inverter‐based renewable energy systems. The integration of power electronic devices within the distribution network exacerbates the complexity and introduces greater temporal variability to signal components. This paper introduces an advanced, online optimization technique for the decomposition and identification of PQ disturbances (PQDs). Initially, an improved variational mode decomposition (IVMD) method is presented, leveraging an energy ratio criterion for precise decomposition of concurrent PQDs. Subsequently, utilizing the characteristic attributes derived from IVMD, an optimized support vector machine (OSVM) algorithm is developed through the synthesis of diverse kernel functions. The OSVM strategically employs distinct kernel functions to augment the discriminability of the feature set. The synergy of IVMD and OSVM enables the detection of multiple PQDs, remarkably even with a minimal amount of training data. A series of experiments have been conducted to validate the effectiveness of the proposed methodology. The results corroborate that the formulated framework exhibits robust learning capabilities and a high degree of resistance to noise interference. Moreover, the hardware platform experiments prove that the proposed method has a satisfactory real‐time performance for its practicability.

Power quality disturbance identification based on MD-EfficientNet

Power quality disturbance identification based on MD-EfficientNet

A data-driven online approach for detection and localization of forced oscillation in wind turbine integrated power system

This article introduces an innovative approach for online detection and localization of forced oscillation (FO) in power systems with significant integration of wind turbine generation (WTG). The synergistic combination of Multichannel Prony analysis with Periodogram and dissipating energy flow (DEF) approach is proposed in this paper to overcome the challenges inherent in detecting FO amidst the complexities introduced by WTG integration. An adaptive version of multichannel Prony analysis is proposed in this paper that dynamically estimates Prony’s system order via the recursive process. It estimates the frequency and damping ratio of all oscillatory modes, which is used to identify low damping modes that are potentially attributed to FO. Leveraging this information, the proposed method enhances both FO detection and source localization capabilities. The proposed method is verified on measurements taken from IEEE benchmark 4-machine, 11-bus system and 10-machine, 39-bus system with high wind energy penetration. Comprehensive testing demonstrates the proposed approach’s effectiveness across various FO scenarios, including governor valve malfunction, exciter malfunction, and cyclic loads. Furthermore, the methodology is evaluated under resonant conditions and scenarios involving the simultaneous identification of multiple disturbances.

A new deep learning method for classification of power quality disturbances using DWT-MRA in utility smart grid

Distributed generation sources (e.g. photovoltaic, wind power) are raised to fit the energy demands recently in proficient way. Further, adoption of renewable energy sources to the smart grid causes severe issues in quality of power signals. These problems can be overcome with correct identification and classification of power quality disturbances (PQDs) efficiently by the both producer and consumer. In this work, a new method for classification of PQDs based on signal processing technique and deep convolutional neural network (DCNN) is proposed. The proposed method compress the raw data of PQDs by using discrete wavelet transform (DWT) and multi-resolution analysis (MRA). Moreover, CNN has the ability to automatic feature selection and classification in a single stage instead of conventional methods i.e. artificial neural networks (ANNs). In this paper, DWT 1D-CNN is designed to extract and optimize multi-scale characteristics of disturbances. Further, 1-D convolutional, pooling and batch-normalization layers are added to facilitate normalize data, better generalization ability and, effective classification process against noisy data. A typical 11Kv distribution network is simulated in MATLAB to prove the validity of the proposed method in an essential part of smart grid.

An intelligent complex power quality disturbance recognition method based on two dimension encoding conversion and machine vison

This study proposes an intelligent methodology for accurate identification of complex power quality disturbance (PQD) based on 2D encoding conversion and machine vision. Firstly, a 2D image encoding method as improved Markov transition field (IMTF) is proposed to convert 1D PQD signals to 2D images, which can realize the enhancement of disturbance feature display. It can solve the information loss and duplication problems of traditional Markov transition field (MTF). Then, a new classification method AFF-ResNetXt50 is proposed to realize the PQD accurate recognition. It can address the problem that traditional ResNetXt50 is susceptible to the interference from irrelevant information and thus severely degrades the classification accuracy. Compared with other popular methods, this proposed method can obtain higher accuracy as 98.5 % for PQD signals recognition. Finally, PQD experimental platform is established, and IEEE PES dataset is also used to certify the proposed method has the excellent performances and robustness.

Long-term complications and outcomes of augmentation cystoplasty in children with neurogenic bladder

Augmentation cystoplasty (AC) is an effective surgical procedure for patients with neurogenic bladder whenever conservative treatments have failed. The present study aimed to determine the risks of metabolic complications, malignancy, long-term outcomes and histopathologic changes of native bladder and the augmented intestine after AC in children with neurogenic bladder. Pediatric patients < 18 years who underwent AC between 2000 and 2020 were enrolled. Early postoperative complications, long-term outcomes and histopathologic changes in mucosal biopsies of native bladder and the augmented intestine after AC were reviewed. Twenty-two patients with a mean age of 7.6 ± 4.4 years were included. The ileum was used in 19 patients and the sigmoid colon in 3 patients. The length of hospital stay was 14.8 ± 6.8 days. Post-operatively, the urinary continence rate improved from 22.7 to 81.8% (p < 0.001). Hydronephrosis resolved in 17 of 19 patients. Vesicoureteral reflux resolved in 16 (64.0%) of the refluxing ureter units and was downgraded in 7 (28.0%). Grades of hydronephrosis and reflux significantly improved following AC (p < 0.001). The estimated glomerular filtration rate also significantly increased (p = 0.012). Formation of urinary tract stones was the most frequent late complication (in 8 patients, 36.4%). Life-threatening spontaneous bladder perforation occurred in 1 patient. After a mean follow-up of 13.4 ± 5.9 years, there were no cases of mortality, new-onset symptomatic metabolic acidosis, or changes in serum electrolytes. Of the 17 patients who were followed for > 10 years, no cases of malignancy or metaplastic changes were identified in the native bladder or augmented bowel epithelium. AC is a safe and effective procedure with low surgical and metabolic complication rates. In addition, AC provides a satisfactory continence rate and long-term protection of renal function, increases functional capacity, and regresses reflux and hydronephrosis. Individualized surveillance is recommended for the early identification of urolithiasis and metabolic disturbances.

Natural Language Analysis for the Identification of Mental Disturbances in Social Networks: A Systematic Review of Studies

Las enfermedades mentales constituyen una de las principales causas de angustia en la vida de las personas a nivel individual, y repercuten en la salud y el bienestar de la sociedad. Para captar estas complejas asociaciones, las ciencias computacionales y la comunicación, a través del uso de métodos de procesamiento del lenguaje natural (NLP) en datos recolectados en redes sociales, han aportado prometedores avances para potenciar la atención sanitaria mental proactiva y ayudar al diagnóstico precoz. Por ello, se realizó una revisión sistemática de la literatura acerca de la detección de alteraciones mentales a través de redes sociales, mediante el uso de NLP en los últimos 5 años, que permitió identificar métodos, tendencias y orientaciones futuras, a través del análisis de 73 estudios, de 509 que arrojó la revisión de documentos extraídos de bases de datos científicas. El estudio reveló que, los fenómenos más comúnmente estudiados, correspondieron a Depresión e Ideación suicida, identificados a través del uso de algoritmos como el LIWC, CNN, LSTM, RF y SVM, en datos extraídos principalmente de Reddit y Twitter. Este estudio, finalmente proporciona algunas recomendaciones sobre las metodologías de NLP para la detección de enfermedades mentales, que pueden ser adoptadas en el ejercicio de profesionales interesados en la salud mental, y algunas reflexiones sobre el uso de estas tecnologías.

Multi-strategy active learning for power quality disturbance identification

As clean energy facilities are increasingly integrated into power distribution networks, the incidence of multiple power quality disturbances (MPQDs) is on the rise. Currently, most MPQDs classification models rely on supervised learning, which demands a substantial amount of labeled data. However, obtaining labeled MPQDs data from the real world is often a time-consuming and labor-intensive process, requiring annotation by experienced engineers, particularly for events involving MPQDs. To tackle this challenge, we introduce an innovative active learning (AL) approach known as Adaptive Weighting Multi-Strategy AL (AWMSAL) for MPQDs classification. In our framework, AWMSAL simultaneously considers multiple strategies using rank aggregation to select MPQDs data that is both full-information and highly representative for labeling. In addition, our method employs a unique weight calculation function to dynamically adjust weights based on the differences in data values observed with various strategies during the AL process. To enhance the classifier performance, we incorporate the Laplacian manifold regularizer into the Extreme Learning Machine in both supervised and unsupervised settings. We compared the performance of the AWMSAL method against several state-of-the-art active learning algorithms using synthetic and hardware-generated data sets. The results demonstrated that AWMSAL achieved recognition rates exceeding 99% in a shorter number of iterations. When the same termination condition was met, AWMSAL outperformed other active learning algorithms by 2.45% to 18.33% in terms of accuracy.

Disturbance Identification and Adaptive Compensation Method in Optical Target Tracking System

Disturbance Identification and Adaptive Compensation Method in Optical Target Tracking System