Robustness analysis for power systems based on the structured singular value tools and the [nu] gap metric

Modern power systems are operated more stressed than ever because of the advent of deregulation and competition. One of the important issues in the design of controllers for a stressed system is to evaluate the stability of the controlled system over a range of operating conditions.;The conventional controllers are designed to make the system stable under certain conditions of operation. The time consuming time domain simulation is then used to evaluate the controllers for a few selected operating conditions around which the controllers are designed. Such a design and evaluation procedure cannot guarantee robustness of the controller over the whole range of operating conditions.;In this dissertation, practical algorithms to perform robustness analysis based on two tools, structured singular value and the nu gap metric, are investigated. The power system stabilizer is used as the controller and small signal stability is of interest.;The robustness problem in the SSV framework is set up for the multimachine power system. In this formulation, an improved uncertainty characterization has been used to capture the effect of parameter variations in terms of the varying elements of the linearized system matries, which are derived from the component differential equations and the network algebraic equations separately. SVD decomposition is used to reduce the size of the problem. Based on the resulting framework, a branch and bound scheme is proposed to intelligently select frequency intervals on which the frequency sweep test can be performed further to find the peak of mu. Instead of blindly choosing frequency intervals to sweep, which could ignore important frequency points on the mu plots, this scheme provides searching under guidance. The analysis procedure accurately predicts the range of stable operating conditions which are verified by repeated eigenvalue analysis.;Fir the robustness in terms of nu gap metric, we set up the feedback configuration for multimachine power system. The frequency response of the nu gap metric is plotted and its relationship with that of the stability margin is used to determine the stability of the perturbed systems. A weighted nu gap metric is defined and its frequency domain interpretation is explored to further reduce the conservatism of the results.;Finally, a feedback configuration is carefully developed to carry out the McFarlane and Glover Hinfinity loop shaping design procedure. The effect of the damping controller on improving system dynamic performance is also examined.;Comparisons are made between the two major analysis tools via the results on the same test systems with the same scenarios

State filtering and parameter estimation for two input two output systems with time delay

This paper focuses on presenting a new identification algorithm to estimate the parameters and state variables for two-input two-output dynamic systems with time delay based on canonical state space models. First, the related input-output equation is determined and transformed into an identification oriented model, which does not involve in the unmeasurable states, and then a residual based least squares identification algorithm is presented for the estimations. After the parameters being estimated, the system states are subsequently estimated by using the estimated parameters. Through theoretical analysis, the convergence of the algorithm is derived to provide assurance for applicability. Finally, a selected simulation example is given for a meaningful case study to show the effectiveness of the proposed algorithm

PCA and deep learning based myoelectric grasping control of a prosthetic hand

Background For the functional control of prosthetic hand, it is insufficient to obtain only the motion pattern information. As far as practicality is concerned, the control of the prosthetic hand force is indispensable. The application value of prosthetic hand will be greatly improved if the stable grip of prosthetic hand can be achieved. To address this problem, in this study, a bio-signal control method for grasping control of a prosthetic hand is proposed to improve patient’s sense of using prosthetic hand and the thus improving the quality of life. Methods A MYO gesture control armband is used to collect the surface electromyographic (sEMG) signals from the upper limb. The overlapping sliding window scheme are applied for data segmentation and the correlated features are extracted from each segmented data. Principal component analysis (PCA) methods are then deployed for dimension reduction. Deep neural network is used to generate sEMG-force regression model for force prediction at different levels. The predicted force values are input to a fuzzy controller for the grasping control of a prosthetic hand. A vibration feedback device is used to feed grasping force value back to patient’s arm to improve patient’s sense of using prosthetic hand and realize accurate grasping. To test the effectiveness of the scheme, 15 able-bodied subjects participated in the experiments. Results The classification results indicated that 8-channel sEMG applying all four time-domain features, with PCA reduction from 32 to 8 dimensions results in the highest classification accuracy. Based on the experimental results from 15 participants, the average recognition rate is over 95%. On the other hand, from the statistical results of standard deviation, the between-subject variations ranges from 3.58 to 1.25%, proving that the robustness and stability of the proposed approach. Conclusions The method proposed hereto control grasping power through the patient’s own sEMG signal, which achieves a high recognition rate to improve the success rate of grip and increases the sense of operation and also brings the gospel for upper extremity amputation patients

Robustness analysis for power systems based on the structured singular value tools and the [nu] gap metric

Modern power systems are operated more stressed than ever because of the advent of deregulation and competition. One of the important issues in the design of controllers for a stressed system is to evaluate the stability of the controlled system over a range of operating conditions.;The conventional controllers are designed to make the system stable under certain conditions of operation. The time consuming time domain simulation is then used to evaluate the controllers for a few selected operating conditions around which the controllers are designed. Such a design and evaluation procedure cannot guarantee robustness of the controller over the whole range of operating conditions.;In this dissertation, practical algorithms to perform robustness analysis based on two tools, structured singular value and the nu gap metric, are investigated. The power system stabilizer is used as the controller and small signal stability is of interest.;The robustness problem in the SSV framework is set up for the multimachine power system. In this formulation, an improved uncertainty characterization has been used to capture the effect of parameter variations in terms of the varying elements of the linearized system matries, which are derived from the component differential equations and the network algebraic equations separately. SVD decomposition is used to reduce the size of the problem. Based on the resulting framework, a branch and bound scheme is proposed to intelligently select frequency intervals on which the frequency sweep test can be performed further to find the peak of mu. Instead of blindly choosing frequency intervals to sweep, which could ignore important frequency points on the mu plots, this scheme provides searching under guidance. The analysis procedure accurately predicts the range of stable operating conditions which are verified by repeated eigenvalue analysis.;Fir the robustness in terms of nu gap metric, we set up the feedback configuration for multimachine power system. The frequency response of the nu gap metric is plotted and its relationship with that of the stability margin is used to determine the stability of the perturbed systems. A weighted nu gap metric is defined and its frequency domain interpretation is explored to further reduce the conservatism of the results.;Finally, a feedback configuration is carefully developed to carry out the McFarlane and Glover Hinfinity loop shaping design procedure. The effect of the damping controller on improving system dynamic performance is also examined.;Comparisons are made between the two major analysis tools via the results on the same test systems with the same scenarios.</p

The safety and efficacy of tolvaptan in the treatment of patients with autosomal dominant polycystic kidney disease: A systematic review and meta-analysis

Background: The irreversible progression of autosomal dominant polycystic kidney disease (ADPKD) to end-stage renal disease (ESRD) is delayed by tolvaptan. Therefore, we aim to systematically estimate and evaluate the efficacy and safety of tolvaptan in the treatment of ADPKD. Methods: Two reviewers independently searched all published randomized controlled trials studies in PubMed, EMBASE, Web of Science and Cochrane databases, extracted data, assessed bias risk and rated the quality of evidence. Data were analyzed by the RevMan software. Results: We identified 8 trials including 2135 patients. Both of the decline of estimated glomerular filtration rate (eGFR) [MD = 1.89, 95% CI (0.74, 3.04), P = 0.001] and total kidney volume (TKV) [MD = −3.32, 95% CI (−4.57, −2.07), P < 0.001] were delayed in tolvaptan group compared with placebo group in ADPKD patients. The use of tolvaptan delayed TKV progression in the different-month subgroups [MD = −69.99, 95% CI (−91.05, −48.94), P < 0.001]. Tolvaptan reduced renal pain [RR = 0.66, 95% CI (0.54, 0.81), P < 0.001] and hematuria events [RR = 0.55, 95% CI (0.41, 0.74), P < 0.001] in ADPKD patients. However, the prevalence of thirst [RR = 2.75, 95% CI (2.34, 3.24), P < 0.001] and nocturia events [RR = 3.01, 95% CI (1.27, 7.11), P = 0.01] were increased in tolvaptan group. There is no significant difference of hypertension events [RR = 0.92, 95% CI (0.82, 1.03), P = 0.13] in tolvaptan group compared placebo group. Conclusions: This meta-analysis suggests that tolvaptan may improve clinical progression in patients with ADPKD without significantly increasing the risk of adverse reactions. Resumen: Antecedentes: La progresión irreversible de la enfermedad renal poliquística autosómica dominante (ERPAD) a enfermedad renal en etapa final (ESRD) es demorada por tolvaptan. Por tanto, nuestro objetivo fue estimar y calcular sistemáticamente la eficacia y seguridad de tolvaptan en el tratamiento de ERPAD. Métodos: Dos revisores buscaron de manera independiente todos los estudios publicados sobre ensayos controlados aleatorizados en las bases de datos de PubMed, Embase, Web of Science y Cochrane, extrayendo datos, evaluando el riesgo de sesgo y calificando la calidad de la evidencia. Los datos fueron analizados utilizando el software RevMan. Resultados: Identificamos ocho ensayos, que incluyeron 2.135 pacientes. Tanto la reducción de la tasa de filtración glomerular estimada (eGFR) [MD = 1,89, IC 95% (0,74, 3,04), p = 0,001] como el volumen renal total (VRT) [MD = −3,32, IC 95% (−4,57, −2,07), p < 0,001] se demoraron en el grupo tolvaptan, en comparación con el grupo placebo en los pacientes con ERPAD. El uso de tolvaptan demoró la progresión del VRT en los subgrupos de diferentes meses [MD = −69,99, IC 95% (−91,05, −48,94), p < 0,001]. Tolvaptan redujo el dolor renal [RR = 0,66, IC 95% (0,54, 0,81), p < 0,001] y los episodios de hematuria [RR = 0,55, IC 95% (0,41, 0,74), p < 0,001] en los pacientes con ERPAD. Sin embargo, la prevalencia de episodios de sed [RR = 2,75, IC 95% (2,34, 3,24), p < 0,001] y nocturia [RR = 3,01, IC 95% (1,27, 7,11), p = 0,01] se incrementó en el grupo tolvaptan. No existe diferencia significativa en cuanto a episodios de hipertensión [RR = 0,92, IC 95% (0,82, 1,03), p = 0,13] en el grupo tolvaptan, en comparación con el grupo placebo. Conclusiones: Este metaanálisis sugiere que tolvaptan puede mejorar la progresión clínica en los pacientes con ERPAD, sin incrementar significativamente el riesgo de reacciones adversas

sEMG signal filtering study using synchrosqueezing wavelet transform with differential evolution optimized threshold

Most gesture recognition studies based on surface electromyography (sEMG) signals focus on filtering, in which the lack of diversity for considered noises can still be the problem. In this work, a denoising method based on Synchrosqueezing Wavelet Transform with Differential Evolution optimized threshold (DEOT-SWT) is proposed. The sEMG signals of ten gestures with three mixed noises, including power line interference (PLI), baseline drift (BW), and white Gaussian noise (WGN), are firstly investigated and filtered by DEOT-SWT, which are collected from seven subjects by utilizing two wearable sEMG signal sensors. Then, the most commonly used Hudgins time-domain feature set is extracted for recognizing ten gestures. Three metrics are adopted to evaluate filtering performance: signal-to-noise ratio (SNR), root mean square error and R-squared value. The gesture recognition accuracy is utilized to verify the practical effect of DEOT-SWT in sEMG-based gesture recognition applications. The results of the experiments demonstrate that the DEOT-SWT algorithm accomplishes desirable denoising performance with an average recognition accuracy of 95.95% (±3.88) in comparison to the classic Infinite Impulse Response (IIR) algorithm and the empirical mode decomposition (EMD) algorithm

Fault detection of swarm robots based on WHD-CRM

Fault detection using cross regulation model(CRM) was an important method for swarm robots fault detection based on artificial immune model. Aiming at the limited generality of CRM in fault detection of swarm robots under various swarm behaviors, cross regulation model enhanced with weighted hamming distance (WHD-CRM) was proposed by studying the influence levels of behavioral feature under different swarm behaviors. Compared with CRM, different weights to the robot behavior characteristics of different swarm behaviors were assigned by WHD-CRM, which obtained more accurate intermediate result(affinity), and improved the detection rate of faulty robots under different swarm behaviors. Experimental results showed that compared with method using CRM, the fault detection rate of WHD-CRM based method was improved by 13%