Recognition Of Disturbances Research Articles

Recognition of Complex Power Quality Disturbances Using S-Transform Based Ruled Decision Tree

Deteriorated quality of power leads to problems, such as equipment failure, automatic device resets, data errors, failure of circuit boards, loss of memory, power supply issues, uninterrupted power supply (UPS) systems generate alarm, corruption of software, and heating of wires in distribution network. These problems become more severe when complex (multiple) power quality (PQ) disturbances appear. Hence, this manuscript introduces an algorithm for identification of the complex nature PQ events in which it is supported by Stockwell’s transform (ST) and decision tree (DT) using rules. PQ events with complex nature are generated in view of IEEE-1159 standard. Eighteen different types of complex PQ issues are considered and studied which include second, third, and fourth order disturbances. These are obtained by combining the single stage PQ events such as sag & swell in voltage, momentary interruption (MI), spike, flicker, harmonics, notch, impulsive transient (IT), and oscillatory transient (OT). The ST supported frequency contour and proposed plots such as amplitude, summing absolute values, phase and frequency-amplitude obtained by multi-resolution analysis (MRA) of signals are used to identify the complex PQ events. The statistical features such as sum factor, Skewness, amplitude factor, and Kurtosis extracted from these plots are utilized to classify the complex PQ events using rule-based DT. This is established that proposed approach effectively identifies a number of complex nature PQ events with accuracy above 98%. Performance of the proposed method is tested successfully even with noise level of 20 dB signal to noise ratio (SNR). Effectiveness of the proposed algorithm is established by comparing it with the methods reported in literature such as fuzzy c-means clustering (FCM) & adaptive particle swarm optimization (APSO), Wavelet transform (WT) & neural network (NN), spline WT & ST, ST & NN, and ST & fuzzy expert system (FES). Results of simulations are validated by comparing them with real time results computed by Real Time Digital Simulator (RTDS). Different stages for design of complex PQ monitoring device using the proposed approach are also described. It is verified that the proposed approach can effectively be employed for design of the online complex PQ monitoring devices.

Respiratory Involvement in Patients with Neuromuscular Diseases: A Narrative Review.

Respiratory muscle weakness is a major cause of morbidity and mortality in patients with neuromuscular diseases (NMDs). Respiratory involvement in NMDs can manifest broadly, ranging from milder insufficiency that may affect only sleep initially to severe insufficiency that can be life threatening. Patients with neuromuscular diseases exhibit very often sleep-disordered breathing, which is frequently overlooked until symptoms become more severe leading to irreversible respiratory failure necessitating noninvasive ventilation (NIV) or even tracheostomy. Close monitoring of respiratory function and sleep evaluation is currently the standard of care. Early recognition of sleep disturbances and initiation of NIV can improve the quality of life and prolong survival. This review discusses the respiratory impairment during sleep in patients with NMDs, the diagnostic tools available for early recognition of sleep-disordered breathing and the therapeutic options available for overall respiratory management of patients with NMDs.

Recognition method of voltage sag causes based on Bi‐LSTM

In recent years, the power quality problem has become more complicated in power grids because of the extensive usage of power electronics and multisource multitransformation features. The method, based on physical characteristics such as time domain, frequency domain and transform domain, is facing challenges in terms of adaptability, algorithm efficiency and accuracy for the recognition of complex disturbance recognition. The bidirectional long short‐term memory network is an algorithm in deep learning. It is based on data for characterization learning, which can effectively overcome the problem of information loss and generalization ability of physical methods. Moreover, it has the characteristics of memory, which can simultaneously consider historical information and future information and can better learn data features with time series characteristics. Aiming at the transient voltage sag time series data, this paper proposes a recognition method of the voltage sag causes based on the bidirectional long short‐term memory network's extraction eigenvalue, the full‐connection layer's high‐dimensional feature extraction and the Softmax network layer's classification. The experiment uses simulation data and measured data to prove that the model has good recognition ability and good antinoise performance in the recognition of voltage sag causes and can be reliably applied in practical engineering. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Power Quality Disturbance Recognition Base on Maximum Variance Unfolding

Power Quality Disturbance Recognition Base on Maximum Variance Unfolding

Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main steps: computer simulation of PQD signals, signal decomposition, feature extraction, heuristic selection of feature selection, and classification. First, different types of PQD signals are generated by computer simulation. Second, variational mode decomposition (VMD) is used to decompose the signals into several instinct mode functions (IMFs). Third, the statistical features are calculated in the time series for each IMF. Next, a two-stage feature selection method is imported to eliminate the redundant features by utilizing permutation entropy and the Fisher score algorithm. Finally, the selected feature vectors are fed into a multiclass support vector machine (SVM) model to classify the PQDs. Several experimental investigations are performed to verify the performance and effectiveness of the proposed method in a noisy environment. Moreover, the results demonstrate that the start and end points of the PQD can be efficiently detected.

Power quality disturbances classification using rotation forest and multi‐resolution fast S‐transform with data compression in time domain

Considering the high spatial complexity of signal processing methods and the low diversity between the base classifiers in the ensemble classifier, a new method with optimal multi-resolution fast S-transform (OMFST) with low spatial complexity and rotation forest (ROF) was proposed. Firstly, Gini importance of features is evaluated by random forest (RF), and the sequence forward search method was adopted for feature selection. Then, the intermediate matrix was constructed on the optimal feature set. The results of inverse fast Fourier transform in the main frequency points of signals based on OMFST are compressed in time domain. The intermediate matrix was used to extract features for power quality (PQ) disturbances recognition. Finally, the ROF was adopted to encourage simultaneously individual accuracy and diversity within the ensemble. The optimal ROF was applied to identify 17 kinds of PQ disturbance signals. The simulation results show that the new method can effectively compress the time–frequency matrix of the existing S-transform (ST) method; the space complexity of the ST modular matrix is reduced significantly and has higher accuracy. Besides, the results of the experiment with real PQ data prove that the new method was effective for practical industrial applications.

Disturbance pattern recognition based on an ALSTM in a long‐distance φ‐OTDR sensing system

AbstractIn this article, a new pattern recognition method for disturbance signals detected by phase‐sensitive optical time domain reflectometry (φ‐OTDR) distributed optical fiber sensing systems is proposed. Currently, most of the disturbance signal recognition methods for φ‐OTDR exploit the global features of disturbance signals as the basis of classification, neglect the local details of disturbance signals, and thus have poor performances on long‐distance monitoring tasks. In the method proposed in this article, an adaptive denoising method based on spectral subtraction is utilized to enhance signal features. For each frame of disturbance signals, Mel‐frequency cepstral coefficients are extracted as frequency‐domain features, while short‐time energy ratio and short‐time level crossing rate are extracted as time‐domain features. An attention‐based long short‐term memory network is exploited as a classifier to recognize different types of disturbance signals. Experiments show the proposed disturbance recognition method can achieve a classification accuracy of 94.3% with five typical disturbances, namely, walking, digging, vehicle‐passing, climbing, and heavy rain, at ranges of up to 50 km.

A Sequence-to-Sequence Deep Learning Architecture Based on Bidirectional GRU for Type Recognition and Time Location of Combined Power Quality Disturbance

In this paper, a sequence-to-sequence deep learning architecture based on the bidirectional gated recurrent unit (Bi-GRU) for type recognition and time location of combined power quality disturbance is proposed. Especially, the proposed methodology can determine the type of each element in input sequence, which is different from existing sequence-to-sequence model employing encoder–decoder network. First, the input sequence is normalized and batched. Second, deep features are extracted from input sequence by constructing Bi-GRU recurrent neural network, where multiple Bi-GRU layers are stacked together in both forward direction and backward direction. Third, according to aforementioned extracted features, fully connected layer and Softmax are employed to calculate the corresponding probability indicating the category that each element in input sequence is classified to. Fourth, Argmax or Top_K operation is further integrated to determine the type of each element in input sequence by selecting the maximal probability. Finally, the type is recognized, and meanwhile, starting–ending times of disturbances are also located just at the moment when the type is changed. The proposed model is further validated and tested by synthetic signals and practical field signals, respectively. Experimental results demonstrate that the accuracy of type recognition is over 98% for 96 kinds of disturbances including single and combined disturbances with signal-to-noise ration being 20 dB. Besides, the starting–ending times are also located with the absolute error less than six sampling points when sampling frequency is 256 points per cycle with noisy environment.

Objective measures of sleep disturbances in children with Potocki–Lupski syndrome

Potocki-Lupski syndrome (PTLS; MIM 610883) is a neurodevelopmental disorder caused by a microduplication, a 3.7 Mb copy number variant, mapping within chromosome 17p11.2, encompassing the dosage-sensitive RAI1 gene. Whereas RAI1 triplosensitivity causes PTLS, haploinsufficiency of RAI1 due to 17p11.2 microdeletion causes the clinically distinct Smith-Magenis syndrome (SMS; MIM 182290). Most individuals with SMS have an inversion of the melatonin cycle. Subjects with PTLS have mild sleep disturbances such as sleep apnea with no melatonin abnormalities described. Sleep patterns and potential disturbances in subjects with PTLS have not been objectively characterized. We delineated sleep characteristics in 23 subjects with PTLS who underwent a polysomnogram at Texas Children's Hospital. Eleven of these subjects (58%) completed the Child's Sleep Habits Questionnaire (CSHQ). Urinary melatonin was measured in one patient and published previously. While the circadian rhythm of melatonin in PTLS appears not to be disrupted, we identified significant differences in sleep efficiency, percentage of rapid eye movement sleep, oxygen nadir, obstructive apnea hypopnea index, and periodic limb movements between prepubertal subjects with PTLS and previously published normative data. Data from the CSHQ indicate that 64% (7/11) of parents do not identify a sleep disturbance in their children. Our data indicate that younger individuals, <10 years, with PTLS have statistically significant abnormalities in five components of sleep despite lack of recognition of substantial sleep disturbances by parents. Our data support the contention that patients with PTLS should undergo clinical evaluations for sleep disordered breathing and periodic limb movement disorder, both of which are treatable conditions.

Fat talk: Influences on body image in childcare.

This research explored the use of "fat talk" by early childhood educators and their awareness of their potential influence on the developing body image of young children. Mixed methods comprising focus groups, telephone interviews and demographic surveys with 44 early childhood educators Australia-wide. Findings showed many educators in this research recognised that they had a role to play in the development of children's body image, though some were unsure whenbody image began to develop. Educators engaged widely in "fat talk," in the vicinity of children or other educators, and accepted such talk as normal. "Fat talk" features in the workplace and is commonly used by early childhood educators. Its influence on the development of body image on young children did not appear to be well understood by early years' educators or of a concern to them. Since body image develops in children from around threeyears of age, the role of early childhood educators in its development should not be overlooked. SO WHAT? Body image is a misunderstood public health concern, with long-term impacts on weight, mental health and physical health. Promotion of positive body image and the early recognition of body image disturbance are crucial, as children who exhibit body dissatisfaction in their formative years have a greater risk of severe body image disturbance as they progress through childhood and adolescence. Since early childhood educators spend significant time with children, encouraging them to avoid engaging in "fat talk" and instead to promote positive body image is warranted.

Power Quality Disturbances Recognition Using Modified S Transform and Parallel Stack Sparse Auto-encoder

The effective automatic recognition and classification of power quality (PQ) disturbance is of significance to the control of power grid pollution before any reasonable solution is taken. In this paper, a novel method to PQ disturbances recognition is proposed based on the modified S transform (MST) and parallel stacked sparse auto-encoder (PSSAE). A Kaiser window is used in MST for a better energy concentration in time-frequency matrix. Thereafter, not only the time-frequency matrix but also the Fourier transform spectrum is utilized to automatically extract features, as input of the two sub-model in PSSAE. Furthermore, the dimensionality reduction and visual analysis of features are achieved as an example. The recognition of PQ disturbances is then identified with the softmax classifier. The effectiveness and robustness of the proposed algorithm is validated by conducting a series of experiments with different types of single and combined signals.

Novel Method Based on Variational Mode Decomposition and a Random Discriminative Projection Extreme Learning Machine for Multiple Power Quality Disturbance Recognition

Power quality events are usually associated with more than one disturbance and their recognition is typically based on multilabel learning. In this study, we propose a new method for recognizing multiple power quality disturbances (MPQDs) based on variational mode decomposition (VMD) and a random discriminative projection extreme learning machine for multilabel learning (RDPEML). First, VMD is employed to decompose the MPQDs into several intrinsic mode functions and the standard energy differences of each mode are extracted as features that form the input vectors of the classifier. Second, a novel multilabel classifier called RDPEML is constructed by combining a random discriminative projection multiclass extreme learning machine (ELM) and a thresholding learning method-based kernel ELM. In order to obtain better classification performance, a tenfold cross-validation embedded particle swarm optimization approach is utilized to search for the optimal values of the structural parameters. Finally, a test study was conducted using MATLAB synthetic signals and real signals sampled from a three-phase standard source under different noise conditions. Compared with the several recent state-of-the-art multilabel learning algorithms, RDPEML achieved better classification performance with superior computational speed.

The Study of Power Quality Analysis by S-transform

Power quality disturbance recognition is the basis of the power quality control. The application of S-transform (ST) and its different improved forms in power quality signal processing and feature extraction are summarized in this paper. After that, the existing disturbance recognition methods based on ST feature extraction are analysed. Finally, On the basis of summarizing the existing research, the prospect of future research is prospected.

Vision-related Quality of Life in Glaucoma Patients and its Correlations With Psychological Disturbances and Visual Function Indices.

The purpose of this study was to assess the vision-related quality of life (VR-QoL) in glaucoma patients and its correlations with psychological disturbances and visual function components. The 25-item National Eye Institute Visual Functioning Questionnaire (NEI VFQ-25) and Hospital Anxiety and Depression Scale (HADS) questionnaires were administered to 428 Chinese glaucoma patients to evaluate their VR-QoL and anxiety and depression disorders, respectively. Sociodemographical and clinical factors were collected at the same time. Univariate analyses were used to investigate the associations between the variables and the VR-QoL. Multivariate linear regression analyses were used to identify the independent psychological and visual functional predictors of the VR-QoL. Standardized partial regression analyses were used to reveal the variables that mostly relevant to the VR-QoL. The composite score (mean±SD) was 71.88±14.44 for NEI VFQ-25 and 13.17±6.56 for HADS. Visual function indices, including best-corrected visual acuity and mean deviation of both eyes in addition to psychological symptoms including anxiety and depression were both correlated with VR-QoL significantly, even after adjusting for sociodemographical and clinical factors. Standardized partial regression analyses further suggested that psychological disorders, especially anxiety rather than visual function components, were mostly relevant to VR-QoL. Deterioration of vision impairment and visual field defects in addition to increased recognition of psychological disturbances reduce the VR-QoL of glaucoma patients significantly. Alleviating psychological symptoms, especially anxiety, perhaps have a greater influence on the improvement of VR-QoL.

Targets for improving dispatcher identification of acute stroke

Accurate identification of acute stroke by Emergency Medical Dispatchers (EMD) is essential for timely and purposeful deployment of Emergency Medical Services (EMS), and a prerequisite for operating mobile stroke units. However, precision of EMD stroke recognition is currently modest. We sought to identify targets for improving dispatcher stroke identification. Dispatch codes and EMS patient records were cross-linked to investigate factors associated with an incorrect dispatch code in a prospective observational cohort of 625 patients with a final diagnosis of acute stroke or transient ischemic attack (TIA), transported to our stroke center as candidates for recanalization therapies. Call recordings were analyzed in a subgroup that received an incorrect low-priority dispatch code indicating a fall or unknown acute illness (n = 46). Out of 625 acute stroke/TIA patients, 450 received a high-priority stroke dispatch code (sensitivity 72.0%; 95% CI, 68.5-75.5). Independent predictors of dispatcher missed acute stroke included a bystander caller (aOR, 3.72; 1.48-9.34), confusion (aOR, 2.62; 1.59-4.31), fall at onset (aOR, 1.86; 1.24-2.78), and older age (aOR [per year], 1.02; 1.01-1.04). Of the analyzed call recordings, 71.7% revealed targets for improvement, including failure to recognize a Face Arm Speech Time (FAST) test symptom (21/46 cases, 18 with speech disturbance), or failure to thoroughly evaluate symptoms (12/46 cases). Based on our findings, efforts to improve dispatcher stroke identification should primarily focus on improving recognition of acute speech disturbance, and implementing screening of FAST-symptoms in emergency phone calls revealing a fall or confusion. URL: http://www.clinicaltrials.gov . Unique identifier: NCT02145663.

Identification of Power Quality Transient Disturbance Based on S Transform and Wavelet Transform

With the rapid advancement of the country's industrialization, intelligence, and information technology, the nonlinearity, asymmetry, and impact of power system load are becoming increasingly prominent. On the other hand, users' demands for power quality are increasing. The national grid inevitably faces severe challenges. The power quality problem in circuit systems is particularly important. The type identification of power quality transient disturbances is of great significance for analyzing power quality problems and solving power quality problems. In this paper, a method based on S transform and wavelet transform for transient power quality disturbance identification is proposed. Firstly, the wavelet signal is used to denoise the power signal. Then, after obtaining the feature vector of the power signal by the S transform and the wavelet transform, The Relieff algorithm is used to obtain the feature weights of the feature vectors generated by S transform and wavelet transform and to select features. Finally, training and classification are performed using the Support Vector Machine (SVM) classifier. The experimental results show that the power quality transient disturbance classification method proposed in this paper can accurately identify the type of disturbance and have strong robustness, and fully meet the requirements of the relevant industry for the accuracy of power quality transient disturbance recognition.

Recognition and Classification of Power Quality Disturbances by DWT-MRA and SVM Classifier

Electrical power system is a large and complex network, where power quality disturbances (PQDs) must be monitored, analyzed and mitigated continuously in order to preserve and to re-establish the normal power supply without even slight interruption. Practically huge disturbance data is difficult to manage and requires the higher level of accuracy and time for the analysis and monitoring. Thus automatic and intelligent algorithm based methodologies are in practice for the detection, recognition and classification of power quality events. This approach may help to take preventive measures against abnormal operations and moreover, sudden fluctuations in supply can be handled accordingly. Disturbance types, causes, proper and appropriate extraction of features in single and multiple disturbances, classification model type and classifier performance, are still the main concerns and challenges. In this paper, an attempt has been made to present a different approach for recognition of PQDs with the synthetic model based generated disturbances, which are frequent in power system operations, and the proposed unique feature vector. Disturbances are generated in Matlab workspace environment whereas distinctive features of events are extracted through discrete wavelet transform (DWT) technique. Machine learning based Support vector machine classifier tool is implemented for the classification and recognition of disturbances. In relation to the results, the proposed methodology recognizes the PQDs with high accuracy, sensitivity and specificity. This study illustrates that the proposed approach is valid, efficient and applicable.

Recognition of disturbances in hybrid power system interfaced with battery energy storage system using combined features of Stockwell transform and Hilbert transform

This paper presents an algorithm using combined features of Stockwell transform and Hilbert transform for analysis of disturbances in the hybrid power system interfaced with battery energy storage system (BESS). Hybrid power system is realized using five nodes test network to which BESS supported by distribution static compensator (DSTATCOM), wind and solar photovoltaic (PV) generators are integrated. A disturbance detection index (DDI) based on combined features of Stockwell transform and Hilbert transform is proposed for detection of various types of disturbances. Results are obtained in the absence and presence of the proposed BESS supported by DSTATCOM to investigate the effect of BESS on performance of the hybrid power system. Investigated events include the switching ON/OFF the resistive load, outage of wind generator and simultaneous outage of both wind and solar PV generators. It is anticipated that performance of proposed method will be high in all investigated cases of study. This could be established in MATLAB/Simulink environment. Proposed BESS will be effective to reduce the disturbance level up to 91%.

Power Quality Disturbance Recognition Based on Multiresolution S-Transform and Decision Tree

It is important to find an effective method for power quality (PQ) disturbance recognition under the challenges of increasing power system pollution. This paper proposes a PQ disturbance signal recognition method based on Multiresolution S transform (MST) and decision tree (DT). For improving recognition accuracy, adjustment factors are introduced to obtain a controllable time-frequency resolution. On this basis, five feature statistics are obtained to quantitatively reflect the characteristics of the analyzed power quality disturbance signals, which is less than the traditional S-transform-based method. As the proposed methodology can effectively identify the PQ disturbances, the efficiency of the DT classifier could be guaranteed. In addition, the noise impacts are also taken into consideration, and 16 types of noisy PQ signals with a signal-to-noise ratio (SNR) scoping from 30 to 50 dB are used as the analyzed dataset. Finally, a comparison between the proposed method and other popular recognition algorithms is conducted. The experimental results demonstrate that the proposed method is effective in terms of detection accuracy, especially for combined PQ disturbances.

Biochemical abnormalities in neonatal seizures in term and preterm neonates

Background: Neonatal seizures may arise as a result of diverse etiologies and can have varied presentations. Biochemical abnormalities are commonly observed in neonates which can be either primary or secondary. Early recognition and treatment of biochemical disturbances is essential for optimal management and satisfactory long-term outcome.Methods: A total of 100 neonates presenting with seizures admitted to NICU of JJM Medical College, Davanagere, from November 2015 to April 2017 were enrolled in the study. Detailed antenatal, natal, postnatal history along with detailed examination was done along with baseline characteristics of convulsing were recorded at admission along with relevant biochemical investigations before instituting any specific treatment.Results: In the present study, out of 100 neonates studied, 64 were full term of which 49(76.5%) were AGA and 15(23.5%) were SGA, whereas 36 cases were preterm. Most neonatal seizures occurred in first 3 days of life, i.e. 59% of which majority occurred on first day of life (34%). Birth asphyxia and septicemia are common cause of neonatal seizures in present study (38 cases each), followed by pure metabolic disturbances 19%. In pure metabolic seizures, hypoglycemia (47.8%) is most common more in preterm babies (55%) followed by hypocalcemia.Conclusions: Biochemical abnormalities are common in neonatal seizures and often go unrecognized and may significantly contribute to seizure activity. Hence, a biochemical work up is necessary for all cases of neonatal seizures.