Fast non-recursive extraction of individual harmonics using artificial neural networks

A collaborative work between Northumbria University and University of Peradeniya (Sri Lanka). It presents a novel technique based on Artificial Neural Networks for fast extraction of individual harmonic components. The technique was tested on a real-time hardware platform and results obtained showed that it is significantly faster and less computationally complex than other techniques. The paper complements other publications by the author (see paper 1) on the important area of “Power Quality” of electric power networks. It involves the application of advanced techniques in artificial intelligence to solve power systems problems

Design of an Elastic Actuation System for a Gait-Assistive Active Orthosis for Incomplete Spinal Cord Injured Subjects

A spinal cord injury severely reduces the quality of life of affected people. Following the injury, limitations of the ability to move may occur due to the disruption of the motor and sensory functions of the nervous system depending on the severity of the lesion. An active stance-control knee-ankle-foot orthosis was developed and tested in earlier works to aid incomplete SCI subjects by increasing their mobility and independence. This thesis aims at the incorporation of elastic actuation into the active orthosis to utilise advantages of the compliant system regarding efficiency and human-robot interaction as well as the reproduction of the phyisological compliance of the human joints. Therefore, a model-based procedure is adapted to the design of an elastic actuation system for a gait-assisitve active orthosis. A determination of the optimal structure and parameters is undertaken via optimisation of models representing compliant actuators with increasing level of detail. The minimisation of the energy calculated from the positive amount of power or from the absolute power of the actuator generating one human-like gait cycle yields an optimal series stiffness, which is similar to the physiological stiffness of the human knee during the stance phase. Including efficiency factors for components, especially the consideration of the electric model of an electric motor yields additional information. A human-like gait cycle contains high torque and low velocities in the stance phase and lower torque combined with high velocities during the swing. Hence, the efficiency of an electric motor with a gear unit is only high in one of the phases. This yields a conceptual design of a series elastic actuator with locking of the actuator position during the stance phase. The locked position combined with the series compliance allows a reproduction of the characteristics of the human gait cycle during the stance phase. Unlocking the actuator position for the swing phase enables the selection of an optimal gear ratio to maximise the recuperable energy. To evaluate the developed concept, a laboratory specimen based on an electric motor, a harmonic drive gearbox, a torsional series spring and an electromagnetic brake is designed and appropriate components are selected. A control strategy, based on impedance control, is investigated and extended with a finite state machine to activate the locking mechanism. The control scheme and the laboratory specimen are implemented at a test bench, modelling the foot and shank as a pendulum articulated at the knee. An identification of parameters yields high and nonlinear friction as a problem of the system, which reduces the energy efficiency of the system and requires appropriate compensation. A comparison between direct and elastic actuation shows similar results for both systems at the test bench, showing that the increased complexity due to the second degree of freedom and the elastic behaviour of the actuator is treated properly. The final proof of concept requires the implementation at the active orthosis to emulate uncertainties and variations occurring during the human gait

Compressive Sensing-Based Harmonic Sources Identification in Smart Grids

Identifying the prevailing polluting sources would help the distribution system operators in acting directly on the cause of the problem, thus reducing the corresponding negative effects. Due to the limited availability of specific measurement devices, ad hoc methodologies must be considered. In this regard, compressive sensing (CS)-based solutions are perfect candidates. This mathematical technique allows recovering sparse signals when a limited number of measurements are available, thus overcoming the lack of power quality meters. In this article, a new formulation of the ell _{1} -minimization algorithm for CS problems, with quadratic constraint, has been designed and investigated in the framework of the identification of the main polluting sources in smart grids. A novel whitening transformation is proposed for this context. This specific transformation allows the energy of the measurement errors to be appropriately estimated, and thus, better identification results are obtained. The validity of the proposal is proven by means of several simulations and tests performed on two distribution networks for which suitable measurement systems are considered along with a realistic quantification of the uncertainty sources

The estimate of amplitude and phase of harmonics in power system using the extended kalman filter

Nowadays, the amplitude of the harmonics in the power grid has increased unwittingly due to the increasing use of the nonlinear elements and power electronics. It has led to a significant reduction in power quality indicators. As a first step, the estimate of the amplitude, and the phase of the harmonics in the power grid are essential to resolve this problem. We use the Kalman filter to estimate the phase, and we use the minimal squared linear estimator to assess the amplitude. To test the aforementioned method, we use terminal test signals of the industrial charge consisting of the power converters and ignition coils. The results show that this algorithm has a high accuracy and estimation speed, and they confirm the proper performance in instantaneous tracking of the parameters