Hybrid Assistive Limb (HAL) is an assistive technology device for supporting physically disabled persons by understanding the percentage of their disability. This work aims to design and develop a HAL based on Electromyogram (EMG) signals. The EMG signal is a biomedical signal that measures electrical currents generated by muscles. These signals can be used for clinical/biomedical applications if advanced methods for detection, decomposition, processing, and classifi cation are available. The pattern of the EMG signal produced may differ depending on the activity of the muscle movement. Four types of biceps muscle activities are identifi ed using the signal pattern generated from raw surface EMG data. Threshold detection method and pattern recognition method were carried out and it is found that pattern recognition method is more generalized method for classifi cation as threshold method is user dependent. The overall classifi cation rate of about (80-83)% obtained using LDA and a classifi cation rate of more than 90% obtained using ANN. Control commands for a stepper motor used for driving artifi cial limb are developed from the classifi ed EMG signal and stepper motor control is achieved through computer parallel port.

An analytical method is demonstrated which allows the level of negative phase sequence (NPS) voltage at a busbar to be expressed as a sum of phasors representing independent sources. The method is extended to enable the balancing capability of Static Var Compensators (SVCs) with individual phase voltage control to be assessed. The capability of such SVCs and the allowable levels of NPS voltage on the system, including any short term limits, can be combined in a capability chart showing the unbalanced loads which can be supplied from a substation.The approach facilitates the treatment of fixed unbalances due to filters or intentional offsets designed to maximise the SVC balancing range for specific loads. Field test results are presented which validate the analytical methods used.

No abstract

In this work, a new control methodology is proposed for Type-IV wind energy conversion system (WECS) using a self-recurrent wavelet neural network (SRWNN) control with a Vienna rectifier as the machine side converter (MSC). A SRWNN combines excellent dynamic properties of recurrent neural networks and the fast convergence speed of wavelet neural network. Hidden neurons of SRWNN contains local self-feedback loops, which provide the memory feature and the necessary information of past values of the signals, allowing it to track maximum power from WECS under varying wind speeds. The Vienna rectifier allows unity power factor operation to increase electrical efficiency. Frame angle-controlled wavelet modulation is proposed for the grid side converter (GSC). Wavelet modulated inverter produces output voltage fundamental components with higher magnitudes than those obtained from the pulse width modulated inverters. The non-linear load compensation and power quality enhancement are achieved by executing frame angle control for WM inverter. The overall system is modeled, and performance is verified in MATLAB Simulink. The hardware prototype is developed, and the switching pulses for the rectifier and inverter are generated using dSPACE1104 controller. The results prove that the system provides low harmonic content and high magnitude of the fundamental current component at the machine and grid sides and ensures maximum power operation at various wind speeds. INDEX TERMS Wind Energy Conversion System (WECS), Wavelet modulation (WM), Maximum power point tracking (MPPT), Self recurrent wavelet neural network (SRWNN)

No abstract