This paper presents a new robust control strategy which is based on Adaline approach. The purpose of the proposed technique is to eliminate load and filter current sensors, normally present in conventional Adaline based control of Shunt Active Power filter(SAPF) thus reducing its hardware cost. The proposed method also enables SAPF to work under highly dynamic load conditions where other similar approaches with reduced sensors ceases to perform. A comparative analysis with other similar classical approach under same load conditions is done to show the effectiveness of proposed method. MATLAB/SIMULINK results are shown under fast varying load conditions to validate the proposed work.
SPV based LED Streetlight has advantages over other conventional lighting systems as no power conversion is needed. LED work on DC and energy optimization is possible by controlling the duty cycle of the LED driver. The components of Solar Fed LED Street lighting system are SPV array, MPPT, dc-dc converter and battery unit. In this paper, the intensity of solar fed Street light is controlled from traffic hours to non-traffic hours which results in saving the electricity consumption. A hybrid street light model is also designed and developed. The simulation studies are performed in Matlab-Simulink environment.
Energy scarcity and environmental concerns are driving the World energy sector towards renewable energy sources. But, practical feasibility,reliability and high costs are the major hurdles. So, a judicious combination of all these factors and the various Renewable energy sources is the need of the hour. In this paper, a hybrid optimization technique for a residential building in Faridabad, Haryana, India has been proposed. A new scheme of utilizing battery costs as fixed deposits based on present market rates of installation makes the system financially viable. The proposed algorithm evaluates the system for desired LLP values & then economically optimizes it to provide a financially viable system with least total life cycle costs.
Traction system in the electric powertrain (PT) plays the crucial role of transmitting energy from battery to the wheels of electric vehicle (EV). Various motors have been employed for such transmission in the system but the most efficient one is permanent magnet synchronous motor (PMSM) because of its elevated torque and power density. The PMSM has comparatively high efficiency due to the absence of windings on the rotor. In this paper a MATLAB/Simulink model of the complete EVPT is developed and analyzed under different operating condition. A Voltage Source Inverter (VSI) is used to feed PMSM for variable speed operation. The speed of the system is regulated via field control of the electric motor. The simulation results are presented and verified under different operating conditions to the system. This analysis is helpful for understanding and developing advanced controls in electric PT system.
The three-term PID controllers are significantly used in most of the benchmarked systems as controllers. However, the benchmarked Single Input Multiple Output (SIMO) systems may require more than one PID controllers in the control loops, the way to design PID controllers is not an easy task. Although, the linear PID (L-PID) controllers are not yielded a satisfactory control results for a benchmarked system due to the presence of inherent nonlinearities. In order to improve performance, the linear PID controllers are modified by nonlinear characteristics. In this paper, the linear and nonlinear PID (NL-PID) controllers are implemented for the stabilization and control of Gantry Crane System as a test problem. The experimental results prove that the multi-loop NL-PID controllers exhibit more robustness to outer large with fast external disturbances. The L-PID and NL-PID controllers are compared to find out the best controller, which gives optimum responses and can also handle external disturbances.
This paper focuses on the analysis of two different Maximum Power Point Tracking (MPPT) techniques for the solar photovoltaic (PV) system under partial shaded and un-shaded condition. Perturb & Observe (P&O) and Particle Swarm optimization (PSO) method of MPPT tracking have been incorporated in the solar PV system and the results have been compared among each other. The main aim of this paper to apply the MPPT technique which can track global maxima and not the local maxima under the partial shaded condition of the PV module. The above two MPPT techniques have been tested under partial shaded condition and uniformly shaded condition of PV module. The test system is modeled and simulated using MATLAB/Simulink.
A novel method for controlling the current source converter (CSC) based D-STATCOM is presented in this paper using with Fuzzy-PI controller for mitigation of voltage sag, improvement of THD of source current, load current, load voltage and also maintains DC link Voltage profile. The model has been tested and simulated in MATLAB/SIMULINK environment. The simulation result under normal and change in load conditions has been studied without and with D-STATCOM.
This paper proposes an autonomous off-grid generation system fed through clustered cage-rotor induction generators (IGs) acting in parallel and forming a micro-grid. The soul of the control for proposed generation system is rested in the selection of the capacity of participating generators in binary weighted IG unit. The arrangement provides optimal capacity utilization of each source, power matching during load perturbations acting together with very small capacity storage system and easy management of the source leading to regulated voltage and frequency at point of common coupling (PCC). The aforesaid system is supported with current-controlled voltage source converter (VSC) with smaller capacity battery energy storage (BES) to substantiate the deficit real power matching during disturbances. The VSC also supplies reactive power both for generator excitation and voltage regulation at PCC. The current control of VSC is actuated in tandem with source scheduling routine during perturbations of the load. The proposed scheme offers optimal head conversion opportunities for the micro-hydro turbines with improved efficiency and reduced cost. The performance of the system is evaluated in Matlab/ Simulink and its effectiveness is gauged through simulation results.
Automatic generation control (AGC) of four-area interlinked power system with generation rate constraints (GRC) is presented in this paper. The four-area interconnected power system having two areas with steam turbines and other areas are nuclear and hydro turbine tied together with power lines is considered in this paper. The AGC of multi-area power system using PID, Fuzzy and ANFIS controllers, and their comparative performance analysis are presented in this paper. ANFIS is a hybrid intelligent controller formed by parallel combination of fuzzy logic and neural network. This controller is able to control the frequency deviation efficiently and at minimum time, so ANFIS controller gives improved result over fuzzy and PID controllers.
This paper presents sediment microbial fuel cell (SMFC) and a Dc-Dc boost converter. In which, an SMFC generates a maximum voltage of 1.16V, which is not sufficient to drive the low power electronic device. Therefore, a self-sustainable Dc-Dc boost converter is designed to boost up the SMFC voltage from 1.161V to 3.289V. This is simulated in the LTspice software by using an LTC3108 integrated circuit (IC). The performance of this proposed boost converter is analyzed at different load resistance. During the analysis, we found that output voltage and current of the boost converter are almost constant when the load resistance made equal to the internal resistance of the SMFC. The energy harvester Dc-Dc boost converter can deliver upto 3.289V, which is sufficient to power sensor devices, remote water sensing and other remote broadcasts etc. The proposed Dc-Dc boost converter is self-sustainable because it is powered entirely from harvested energy without requiring extra external power sources.
In the present era, need of compact devices with low power consumption & portably has created the need of Integrated circuits and when these devices are used for signal processing, multipliers plays a vital role & has become dominant functional blocks in processors and microcontrollers. Thus it has become crucial to explore a algorithm for fast multiplier that enhance structural design to increase speed & minimize area. This paper introduces the Array, Vedic, Wallace & Dadda Algorithms & a performance comparison between them. In this manuscript simulation has been done for the mentioned algorithms for different bit lengths i.e. two, four, eight & sixteen bit on Model-Sim using VHDL language and then their implementation them on Xilinx for comparative analysis.
Organic solar PV (OPV) cells are gaining attention due to their light weight, flexibility and low cost. Charging of smart phones using OPV modules is a prime application area. However, the chargers developed using organic PV modules are bulky as they require carrying long rolls of PV arrays. This paper proposes an integrated charger for smart phones, which includes an OPV module and a charging circuit with an op-amp based precision diode. The proposed charger uses an OPV module of size equal to that of the smart phone display. The proposed design reduces the size and weight of charger significantly. The experimental results of OPV module characterization, battery characteristics, blocking diode performance, and increase in state of charge (SOC) of the smart phone using proposed charger are presented.
In this paper the parameters of a static voltage model of Nexa-1.2 KW proton exchange membrane fuel cell are identified using nonlinear optimization technique. The model is based on semi-empirical formulae defined by parametrical equations characterizing the voltage current relationship of the fuel cell operation. Trust region optimization algorithm is used successively to minimize the objective functions based on two different performance criterion. The results of the optimized model obtained from simulation conform with the experimental data with very small uniform error. The derived model shows significant reduction in the mean square error as compared to the existing optimized PSO model.
In this paper the application of Probabilistic Neural Network for power quality disturbance prediction is given. Wavelet transform is used for the extraction of input patterns. The accuracy of the PNN is evaluated using Pattern Recognition Neural Network. The PNN has shown to be the faster predictor than PRNN.
In this paper, an adaptive sliding mode controller (ASMC) based direct torque control of position sensorless permanent magnet synchronous motor (PMSM) driven single stage solar photovoltaic (PV) water pumping system is proposed. An ASMC based DTC is used as it reduces the influence of external disturbances and parameter variations on the proposed system since the conventional SMC is robust only in the course of sliding mode. To operate solar PV array at its maximum point, a modified perturb and observe (PO) maximum power point tracking (MPPT) algorithm is used. The MPPT algorithm is based on fractional short-circuit current (FSCC) calculation. It is used due to its better tracking performance under rapidly changing solar irradiation and low-power oscillations around MPP. This increases the efficiency of FSCC-PO algorithm in comparison to conventional PO-MPPT algorithm. The angular rotor position is estimated using stator flux linkage based method, thus eliminating the mechanical rotor position sensors. This improves the reliability and decreases overall cost of the system. The 3-level and 2-level hysteresis controllers are used for processing torque error and flux error respectively. The appropriate switching states based on torque and flux error are, applied to the inverter. A laboratory prototype of the proposed system is made. The experimental results are obtained for steady-state, starting and dynamic conditions under variable solar irradiation.
STATCOMs is used widely in power systems these days. Traditionally, this converter was controlled using a double-loop control or Direct Output Voltage (DOV) controller. But DOV controller do not function properly during a three-phase fault and has a lot of overshoot. Also, the number of PI controllers used in double-loop control is high, which led to complexities when adjusting the coefficients. Therefore, in this paper, an improved DOV method is proposed which, in addition to a reduced number of PI controllers, has a higher speed, lower overshoots and a higher stability in a wider range. By validating the proposed DOV method for controlling the STATCOMs, it has been attempted to improve the dynamical behaviors of induction motor using Matlab/Simulink, and the results indicate a better performance of the proposed method as compared to the other methods.
This paper deals with a system with an improved designed induction motor for photovoltaic (PV) array fed water pumping system. The induction motor drive (IMD) is made mechanical sensorless using a proposed adaptive observer based neuro-Luenberger technique to reduce both cost and complexity with the single stage PV array based maximum power point tracking (MPPT) providing cost-effective solution along with simultaneous assurance of optimum power utilization of a PV array. The proposed system is controlled by field-oriented control (FOC). The MPPT as well as DC link voltage, is regulated by three-phase voltage source inverter (VSI). The estimation of motor speed eliminates the use of mechanical sensor and makes the system cheaper and robust. A new robust speed adaptive algorithm is presented, which is less dependent on parameters. A detailed study of various factors affecting the efficiency of the motor, is given to improve the behavior of the IMD for water pumping. The designed motor is tested on the developed prototype in the laboratory and its suitability is judged through various test results under steady state and dynamic conditions of insolation variations.
This paper applies bird swarm algorithm (BSA) to finding the solution of optimal power flow (OPF) problem. BSA is a recently developed bio-inspired evolutionary algorithm. It uses swarm intelligence derived from the social interactions and social behaviors in bird swarms for searching global optimal solution. The purpose of solving an OPF problem is to find the steady state operating point of a given network that optimizes a certain objective function. The BSA has been applied to carry out OPF for minimization of fuel cost, improvement of voltage profile, total emission cost minimization and power losses minimization. In order to show the efficacy of the proposed BSA, the standard IEEE 30-bus test systems has been selected to solve OPF problem with above mentioned objectives. The comparison of results obtained using BSA and other evolutionary computing based methods reported in the literature. It is clearly show that the proposed BSA based technique gives better result compare to other EC based techniques when solving the optimal power flow problem.
This paper deals with the autonomous operation of solar photovoltaic (PV) and battery energy storage (BES) based microgrid. The single stage PV system is incorporated to the DC link of voltage source converter (VSC). The BES is incorporated across the DC link through a bidirectional converter for load management. VSC operates on a voltage control algorithm in standalone mode. A perturb and observe approach is implemented to achieve the peak power from a solar PV array. The charging and discharging currents of battery, are controlled by the bidirectional converter. The solar PV-BES microgrid in standalone mode, controls the power requirement by the loads and maintains the frequency and voltage within stipulated limits. The implementation of bidirectional converter has reduced the battery rating as compared to the battery incorporation across the DC link of VSC directly. The operation of proposed microgrid is observed satisfactory in steady state and dynamic conditions and validated through test results on a developed prototype.
This paper proposes an isolated multi-input multi-output power electronics converter for interfacing with multiple renewable energy sources. The converter provides the capability of multi-level output based upon the secondary side series-parallel configuration. The modular architecture of the converter enable plug and play operation. In Control algorithm proposed for the converter ensures equal load sharing between the inputs during continuous and discontinuous operating modes while maintaining the output voltage during load perturbations. The system is modeled and simulated under MATLAB Simulink environment with the presented results verifying the performance of the proposed converter.
