Grid interfacing of a photovoltaic system using a chain cell converter

With the rapid development of photovoltaic system installations and increased number of grid connected power systems, it has become imperative to develop an efficient grid interfacing instrumentation suitable for photovoltaic systems ensuring maximum power transfer. The losses in the power converter play an important role in the overall efficiency of a PV system. Chain cell converter is considered to be efficient as compared to PWM converters due to lower switching losses, modularized circuit layout, reduced voltage rating of the converter switches, reduced EMI. The structure of separate dc sources in chain cell converter is well suited for photovoltaic systems as there will b several separate PV modules in the PV array which can act as an individual dc source. In this work, a single phase multilevel chain cell converter is used to interface the photovoltaic array to a single phase grid at a frequency of 50Hz. Control algorithms are developed for efficient interfacing of the PV system with grid and isolating the PV system from grid under faulty conditions. Digital signal processor TMS320F 2812 is used to implement the control algorithms developed and for the generation of other control signals.

Filter Optimization for Grid Interactive Voltage Source Inverters

Higher order LCL filters are essential in meeting the interconnection standard requirement for grid-connected voltage source converters. LCL filters offer better harmonic attenuation and better efficiency at a smaller size when compared to the traditional L filters. The focus of this paper is to analyze the LCL filter design procedure from the point of view of power loss and efficiency. The IEEE 1547-2008 specifications for high-frequency current ripple are used as a major constraint early in the design to ensure that all subsequent optimizations are still compliant with the standards. Power loss in each individual filter component is calculated on a per-phase basis. The total inductance per unit of the LCL filter is varied, and LCL parameter values which give the highest efficiency while simultaneously meeting the stringent standard requirements are identified. The power loss and harmonic output spectrum of the grid-connected LCL filter is experimentally verified, and measurements confirm the predicted trends.

A Resonant Integrator Based PLL and AC Current Controller for Single Phase Grid Connected PWM-VSI

Grid connected PWM-VSIs are being increasingly used for applications such as Distributed Generation (DG), power quality, UPS etc. Appropriate control strategies for grid synchronisation and line current regulation are required to establish such a grid interconnection and power transfer. Control of three phase VSIs is widely reported in iterature. Conventionally, dq control in Synchronous Reference Frame(SRF) is employed for both PLL and line current control where PI-controllers are used to track the DC references. Single phase systems do not have defined direct (d) and quadrature (q) axis components that are required for SRF transformation. Thus, references are AC in nature and hence usage of PI controllers cannot yield zero steady state errors. Resonant controllers have the ability to track AC references accurately. In this work, a resonant controller based single phase PLL and current control technique are being employed for tracking grid frequency and the AC current reference respectively. A single phase full bridge converter is being operated as a STATCOM for performance evaluation of the control scheme.

Analysis of grid connected induction generators driven by hydro/wind turbines under realistic system constraints

Results of an investigation dealing with the behaviour of grid-connected induction generators (GCIGs) driven by typical prime movers such as mini-hydro/wind turbines are presented. Certain practical operational problems of such systems are identified. Analytical techniques are developed to study the behavior of such systems. The system consists of the induction generator (IG) feeding a 11 kV grid through a step-up transformer and a transmission line. Terminal capacitors to compensate for the lagging VAr are included in the study. Computer simulation was carried out to predict the system performance at the given input power from the turbine. Effects of variations in grid voltage, frequency, input power, and terminal capacitance on the machine and system performance are studied. An analysis of self-excitation conditions on disconnection of supply was carried out. The behavior of a 220 kW hydel system and 55/11 kW and 22 kW wind driven system corresponding to actual field conditions is discussed

Relaxation system based sub-grid scale modelling for large eddy simulation of Burgers' equation

An implicit sub-grid scale model for large eddy simulation is presented by utilising the concept of a relaxation system for one dimensional Burgers' equation in a novel way. The Burgers' equation is solved for three different unsteady flow situations by varying the ratio of relaxation parameter (epsilon) to time step. The coarse mesh results obtained with a relaxation scheme are compared with the filtered DNS solution of the same problem on a fine mesh using a fourth-order CWENO discretisation in space and third-order TVD Runge-Kutta discretisation in time. The numerical solutions obtained through the relaxation system have the same order of accuracy in space and time and they closely match with the filtered DNS solutions.

Controlled synthesis of CuO nanostructures on Cu foil, rod and grid

CuO nanowires are synthesized by heating Cu foil, rod and grid in ambient without employing a catalyst or gas flow at temperatures ranging from 400 to 800 degrees C for a duration of 1-12 h. Scanning electron microscopy (SEM) investigation reveals the formation of nanowires. The structure, morphology and phase of the as-synthesized nanowires are analyzed by various techniques such as X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR). It is found that these nanowires are composed of CuO phase and the underlying film is of Cu2O. A systematic study is carried out to find the possibilities for the transformation of one phase to another completely. A possible growth mechanism for the nanowires is also discussed. (C) 2011 Elsevier B.V. All rights reserved.

Smart applications for energy harvested WSNs

A Wireless Sensor Network (WSN) powered using harvested energies is limited in its operation by instantaneous power. Since energy availability can be different across nodes in the network, network setup and collaboration is a non trivial task. At the same time, in the event of excess energy, exciting node collaboration possibilities exist; often not feasible with battery driven sensor networks. Operations such as sensing, computation, storage and communication are required to achieve the common goal for any sensor network. In this paper, we design and implement a smart application that uses a Decision Engine, and morphs itself into an energy matched application. The results are based on measurements using IRIS motes running on solar energy. We have done away with batteries; instead used low leakage super capacitors to store harvested energy. The Decision Engine utilizes two pieces of data to provide its recommendations. Firstly, a history based energy prediction model assists the engine with information about in-coming energy. The second input is the energy cost database for operations. The energy driven Decision Engine calculates the energy budgets and recommends the best possible set of operations. Under excess energy condition, the Decision Engine, promiscuously sniffs the neighborhood looking for all possible data from neighbors. This data includes neighbor's energy level and sensor data. Equipped with this data, nodes establish detailed data correlation and thus enhance collaboration such as filling up data gaps on behalf of nodes hibernating under low energy conditions. The results are encouraging. Node and network life time of the sensor nodes running the smart application is found to be significantly higher compared to the base application.

A fixed-grid finite element based enthalpy formulation for generalized phase change problems: role of superficial mushy region

The enthalpy method is primarily developed for studying phase change in a multicomponent material, characterized by a continuous liquid volume fraction (phi(1)) vs temperature (T) relationship. Using the Galerkin finite element method we obtain solutions to the enthalpy formulation for phase change in 1D slabs of pure material, by assuming a superficial phase change region (linear (phi(1) vs T) around the discontinuity at the melting point. Errors between the computed and analytical solutions are evaluated for the fluxes at, and positions of, the freezing front, for different widths of the superficial phase change region and spatial discretizations with linear and quadratic basis functions. For Stefan number (St) varying between 0.1 and 10 the method is relatively insensitive to spatial discretization and widths of the superficial phase change region. Greater sensitivity is observed at St = 0.01, where the variation in the enthalpy is large. In general the width of the superficial phase change region should span at least 2-3 Gauss quadrature points for the enthalpy to be computed accurately. The method is applied to study conventional melting of slabs of frozen brine and ice. Regardless of the forms for the phi(1) vs T relationships, the thawing times were found to scale as the square of the slab thickness. The ability of the method to efficiently capture multiple thawing fronts which may originate at any spatial location within the sample, is illustrated with the microwave thawing of slabs and 2D cylinders. (C) 2002 Elsevier Science Ltd. All rights reserved.

A smart approach to precision attitude maneuvers of spacecrafts

A nonlinear adaptive approach is presented to achieve rest-to-rest attitude maneuvers for spacecrafts in the presence of parameter uncertainties and unknown disturbances. A nonlinear controller, designed on the principle of dynamic inversion achieves the goals for the nominal model but suffers performance degradation in the presence of off-nominal parameter values and unwanted inputs. To address this issue, a model-following neuro-adaptive control design is carried out by taking the help of neural networks. Due to the structured approach followed here, the adaptation is restricted to the momentum level equations.The adaptive technique presented is computationally nonintensive and hence can be implemented in real-time. Because of these features, this new approach is named as structured model-following adaptive real-time technique (SMART). From simulation studies, this SMART approach is found to be very effective in achieving precision attitude maneuvers in the presence of parameter uncertainties and unknown disturbances.