Seema

Bio: Seema is an academic researcher from Sant Longowal Institute of Engineering and Technology. The author has contributed to research in topics: Photovoltaic system & Harmonics. The author has an hindex of 3, co-authored 25 publications receiving 122 citations.

Comparison of different approaches for removal of baseline wander from ECG signal

Abstract: Baseline wandering can mask some important features of the Electrocardiogram (ECG) signal hence it is desirable to remove this noise for proper analysis and display of the ECG signal. This paper presents the implementation and evaluation of different methods to remove this noise. The parameters i. e. Power Spectral density (PSD), average Power & Signal to noise ratio (SNR) are calculated of signals to compare the performance of different filtering methods. IIR zero phase filtering has been proved efficient method for the removal of Baseline wander from ECG signal. The results have been concluded using Matlab software and MIT-BIH arrhythmia database.

Robust Control for Islanded and Seamless Mode Switching of Wind-PV-Grid Tied Generation System

Abstract: This paper deals with a robust control strategy for distributed generation system (DGS), which operates in both islanded and grid-connected modes. In islanded mode, the load side VSC (Voltage Source Converter) is controlled using the PR (Proportional Resonant) controller, thereby the CCP (Common Coupling Point) voltage quality is maintained within the IEEE-1547 standard. Moreover, the DGS is capable to synchronize to the grid without any transient current. In grid connected mode, the load side VSC switching is provided using the inner-loop SOGI based control algorithm. Generally during the mode change from standalone mode to grid connected mode and vice versa, large transients occur in the battery current due to the switching. This problem is resolved by proposed bidirectional DC-DC converter control strategy and robust inner-loop SOGI based PLL. The proposed control strategies in DGS, provide smooth mode transition from the grid connected mode to an islanded mode and vice versa, thereby the reliability of the DGS is enhanced. The proposed DGS is validated on the experimental prototype of DGS.

Power Quality Improvement in a PV Based EV Charging Station Interfaced with Three Phase Grid

Abstract: This paper deals with the power quality improvement in a solar photovoltaic (PV) array generation-based EV (Electrical Vehicle) charging station. This charging station is capable of operating in standalone mode and charging the EV battery with the power generated by a PV array. Moreover, it also interfaces with the utility and feeds it the remaining power. Another advantage of the charging station is the compensation of the reactive power for the improvement of the grid power quality. The charging station serves the following purposes (i) harmonics current compensation, (ii) EV battery charging/discharging control, (iii) simultaneous EV battery charging and harmonics current compensation and (iv) simultaneous discharging and harmonics current compensation. The charging station is controlled in such a manner that it operates satisfactorily even under the unbalance grid voltages and total harmonics distortion of the grid currents is reduced below 5% as suggested in the IEEE-519 standard. The control strategy is designed in such a manner that the charging station operates in grid connected mode. However, if the charging station loses the synchronism, then it operates in standalone mode and PV array charges the EV battery. A synchronization control is also developed to connect the system to the grid, when it is available.

Grid synchronization control for an autonomous PV-wind-battery based microgrid

Abstract: This paper deals with the control of PWB (Photovoltaic-Wind-Battery) based MG (Micro-Grid) in standalone and grid-connected modes. The proposed MG works in standalone mode, and during peak load condition of the utility it is able to synchronize with the grid, and it operates in the grid-connected mode. In WECS (Wind Energy Conversion System), a SCIG (Squirrel Cage Induction Generator) is used to convert the wind energy into electrical energy. An INC (Incremental Conductance) based MPPT (Maximum Power Point Tracking) approach is used to extract maximum power from solar PV (Photovoltaic) array. The battery is connected at the DC-link of load/grid side VSC (Voltage Source Converter) for balancing the power flow in dynamic conditions of the system. In grid-connected mode, PL-EPLL (Pseudo-Linear-Enhanced Phase Locked Loop) based grid control algorithm is proposed to extract fundamental components of load currents. Along with grid synchronization and desynchronization, some ancillary services are also provided by the grid/load side VSC such as harmonics mitigation, load balancing and regulation of PCI (Point of Common Interconnection) voltages. The proposed MG is modeled, and its performance is simulated in MATLAB/ SIMULINK to verify the features of PWB based MG.

PV-Hydro-Battery Based Standalone Microgrid for Rural Electrification

Abstract: This paper presents an implementation of a standalone single stage PV-hydro-battery based microgrid (MG) to meet the need of electrical energy in remote locations with renewable energy sources (RES). The SEIG (Self-Excited Induction Generator) based hydro and single stage solar PV (Photovoltaic) array with an energy storage, are used for electrification and for supplying the uninterruptible power to the critical loads. The proposed MG consists of only single VSC (Voltage Source Converter) and a bidirectional DC-DC converter (BDDC) for power management in standalone MG. The BDDC manages the storage battery charging and discharging currents and extracts the peak power from the solar PV array as well. The proposed control of VSC is based on the adaptive filter, which belongs to the family of LMS (Least Mean Square) and LMF (Least Mean of Fourth Order) algorithms. This control is able to provide load levelling, power management, harmonics mitigation, load balancing, and reactive power compensation. The efficacy of proposed single stage PV-hydro-battery based MG with an adantive control is assessed throuzh exnerimental results.

Keep the Stress Away with SoDA: Stress Detection and Alleviation System

Abstract: Long-term exposure to stress may lead to serious health problems such as those related to the immune, cardiovascular, and endocrine systems Once having arisen, these problems require a considerable investment of time and money to recover from With early detection and treatment, however, these health problems may be nipped in the bud, thus improving quality of life We present an automatic stress detection and alleviation system, called SoDA, to address this issue SoDA takes advantage of emerging wearable medical sensors (WMSs), specifically, electrocardiogram (ECG), galvanic skin response (GSR), respiration rate, blood pressure, and blood oximeter, to continuously monitor human stress levels and mitigate stress as it arises It performs stress detection and alleviation in a user-transparent manner, ie, without the need for user intervention When it detects stress, SoDA employs a stress alleviation technique in an adaptive manner based on the stress response of the user We establish the effectiveness of the proposed system through a detailed analysis of data collected from 32 participants A total of four stressors and three stress reduction techniques are employed In the stress detection stage, SoDA achieves 958 percent accuracy with a distinct combination of supervised feature selection and unsupervised dimensionality reduction In the stress alleviation stage, we compare SoDA with the ‘no alleviation’ baseline and validate its efficacy in responding to and alleviating stress

A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records

Abstract: There is a growing body of research focusing on automatic detection of ischemia and myocardial infarction (MI) using computer algorithms. In clinical settings, ischemia and MI are diagnosed using electrocardiogram (ECG) recordings as well as medical context including patient symptoms, medical history, and risk factors—information that is often stored in the electronic health records. The ECG signal is inspected to identify changes in the morphology such as ST-segment deviation and T-wave changes. Some of the proposed methods compute similar features automatically while others use nonconventional features such as wavelet coefficients. This review provides an overview of the methods that have been proposed in this area, focusing on their historical evolution, the publicly available datasets that they have used to evaluate their performance, and the details of their algorithms for ECG and EHR analysis. The validation strategies that have been used to evaluate the performance of the proposed methods are also presented. Finally, the paper provides recommendations for future research to address the shortcomings of the currently existing methods and practical considerations to make the proposed technical solutions applicable in clinical practice.

Robust and accurate anomaly detection in ECG artifacts using time series motif discovery.

Abstract: Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.