Identification of multiple partial discharge sources in high voltage transformer windings

Partial discharge (PD) analysis is an important tool for assessing the lifespan of power equipment especially in cables and high voltage (HV) transformers. Different PD sources have different effects on the condition and performance of power equipment insulation system. Therefore, the ability to identify different PD sources is a great interest for both system utilities and equipment manufacturers. Hence, an experiment has been designed at the Tony Davies High Voltage Laboratory, University of Southampton to access the methodologies for the identification of multiple PD sources within a HV transformer windings. Previous work at Southampton developed a non-linear based technique that facilitates identification of the location of a PD source within an interleaved winding. This project is concerned with the feasibility of locating several sources simultaneously based only on measurement data from wideband RFCTs placed at the neutral to earth point and the bushing tap-point to earth. Initial results from a simple experiment are presented and development of an analytical approach described

A new real-time maximum power point tracking scheme for PV-BASED microgrid STABILITY using online DEEP ridge extreme learning machine algorithm

A new deep representation-based Maximum Power Point Tracking (MPPT) controller is proposed in this paper for accurate Control references calculation in Photovoltaic (PV) based Micro Grid (MG) operation. The deep representation is obtained by two-step estimation: Data dimension reduction and MPPT Tracker towards optimal computation. The considered deep learning architecture is targeted for N number (large scale) of PV-based DGs, connected locally in the distribution system (DC link extended to AC utility). The collected data of solar irradiation (in W/m2) and PV panel temperature (in oC) profiles of local DGs are subjected to data dimensions using Extreme Learning Machine (ELM) based on Moore-Penrose inverse technique. The compressed represented PV-DG data is further communicated to the Tertiary Control side MPPT Tracker, where Ridge Regression-based ELM is presented for estimating Maximum Power Point Power (PMPP) and Voltage (VMPP) values for kth instant. The initial randomness present in the proposed Deep Representation based Ridge Regression Extreme Learning Machine (DR-RRELM) is further minimized by adopting Huber's characteristic distribution-based likelihood estimator. The proposed MPPT scheme is effectively implemented for accurate control reference in DC-DC and DC-AC converters in the MG. The proposed controller is also suitable for stability improvement at point of common coupling (PCC). Three different case studies such as past data verification, stability analysis under various operating conditions, irradiant change and source power variation. The efficacy of the proposed deep representation-based MPPT scheme is evidenced in MATLAB-based simulation. The proposed technique provides better tracking ability, faster learning and effective reference generation. The case study with irradiation change is validated in TMS320C6713 (32-bit) based Hardware-in-Loop (HIL) validation

Classification and localisation of multiple partial discharge sources within a high voltage transformer winding

Partial discharge (PD) analysis is widely adopted for assessing the condition of the insulation systems within high voltage (HV) transformers. Different PD sources have different effects on the insulation condition of HV transformers. In a typical field environment, multiple PD sources may occur in HV transformer simultaneously. Therefore, source classification is very important to identify the types of defects causing discharges in a HV transformer. In recent years, several classification techniques have been proposed for application in PD analysis. This paper proposes automatic techniques to classify and localize multiple PD sources within a HV transformer winding. The proposed processing technique relies on the assumption that the PD pulses generated from different defects exhibit unique waveform characteristics. Surface and void discharges which are the common types of defect events that may occur within HV transformer windings have been experimentally generated. Each pair combination was injected simultaneously into different locations along the HV transformer winding with analysis of two wideband radio frequency current transformers (RFCTs) data captured from each end of the winding. After PD pulses extraction and wavelet analysis, this paper presents two approaches using two different methods to accurately locate multiple PD sources within an HV transformer winding. The performances of the two approaches for this type of application are presented

Wavelet and mathematical morphology as the de-noising methods for PD analysis of high voltage transformer windings

Partial discharge (PD) analysis is one of the most important techniques to evaluate the condition of the insulation systems within high voltage (HV) transformers. However, in typical field environments, measurements of PD signals can be distorted by noise sources. This greatly reduces the ability to identify PD sources in HV transformer windings. Therefore, denoising methods in PD analysis are very important. In recent years, several noise reduction techniques have been proposed for application in PD analysis. The common types of discharge events that may occur within high voltage transformer windings namely void, surface, corona and floating discharge have been experimentally generated. Each type of discharge was injected into different locations along a HV transformer winding and then measured using two wideband radio frequency current transformers (RFCTs) positioned at each end of the winding. Then, either the Discrete Wavelet Transform (DWT) and or Mathematical Stationary Wavelet Transform (SWT) or Mathematical Morphology (MM) were applied to reduce the noise in the raw captured PD signals. This paper presents the comparison of performance of the techniques in terms of noise reduction for this type of application

Lung disease recognition methods using audio-based analysis with machine learning

The use of computer-based automated approaches and improvements in lung sound recording techniques have made lung sound-based diagnostics even better and devoid of subjectivity errors. Using a computer to evaluate lung sound features more thoroughly with the use of analyzing changes in lung sound behavior, recording measurements, suppressing the presence of noise contaminations, and graphical representations are all made possible by computer-based lung sound analysis. This paper starts with a discussion of the need for this research area, providing an overview of the field and the motivations behind it. Following that, it details the survey methodology used in this work. It presents a discussion on the elements of sound-based lung disease classification using machine learning algorithms. This includes commonly prior considered datasets, feature extraction techniques, pre-processing methods, artifact removal methods, lung-heart sound separation, deep learning algorithms, and wavelet transform of lung audio signals. The study introduces studies that review lung screening including a summary table of these references and discusses the literature gaps in the existing studies. It is concluded that the use of sound-based machine learning in the classification of respiratory diseases has promising results. While we believe this material will prove valuable to physicians and researchers exploring sound-signal-based machine learning, large-scale investigations remain essential to solidify the findings and foster wider adoption within the medical community

Case report: Unusual cause of difficulty in intubation and ventilation with asthmatic-like presentation of Endobronchial Tuberculosis

Endobronchial Tuberculosis is hazardous in causing circumferential narrowing of tracheobronchial tree despite the eradication of tubercle bacilli in the initial insult from Pulmonary Tuberculosis. They may present as treatment resistant bronchial asthma and pose challenge to airway management in the acute setting. We present a 25 year-old lady who was newly diagnosed bronchial asthma with a past history of Pulmonary Tuberculosis that had completed treatment. She presented with sudden onset of difficulty breathing associated with noisy breathing for 3 days and hoarseness of voice for 6 months. Due to resistant bronchospasm, attempts were made to secure the airway which led to unanticipated difficult intubation and ventilation. Subsequent investigations confirmed the diagnosis of Endobronchial Tuberculosis and patient was managed successfully with anti TB medication, corticosteroids and multiple sessions of tracheal dilatation for tracheal stenosis. This case highlights the unusual cause of difficulty in intubation and ventilation due to Endobronchial Tuberculosis, which required medical and surgical intervention to improve the condition