A Novel ANN Based UPFC for Voltage Stability and Reactive Power Management in a Remote Hybrid System

AbstractThis paper gives a novel idea of application of ANN based UPFC controller for Reactive Power compensation in a standalone hybrid power system and thereby enhances the stability of the system .For the Isolated WECS a linear small signal transfer function model of the hybrid wind Diesel model is considered with different loading conditions. The reactive power compensation and stability analysis have been carried out with UPFC Controller with an IEEE exciter I. A feed forward neural network with back propagation technique is designed to tune the parameters of UPFC controller. From the matlab Simulation it is clear that the system parameters attend steady state value with lesser time and complexities

Fuzzy Logic and ANFIS based Short Term Solar Energy Forecasting

Accurate forecasting of solar energy is a key issue for a meaningful integration of the solar power plants into the grid. Solar photovoltaic technology is most preferable and vital all other sources of renewable energy. We know that the solar Energy is very irregular so the result output of solar voltaic systems (SPV) diverted by the atmospheric nature like temperatures, humidity, wind velocity, solar irradiance and other climatologically facts. It�s necessary to prediction of solar energy is most important to minimise uncertainty in power harness from solar photovoltaic system. In this work fuzzy logic model and ANFIS model have been developed for manipulating solar irradiation (w/m2) data to forecasting short term solar energy. In the month of September 2017 has been monitoring for an hourly data of solar irradiance used as input and actual desired output. In the present paper sets the Normalization of input and desired output in between 0.1 to 0.9 for reducing confluence problems. Acquired results are match up to the manipulated data and get valid result. The implementation of the model is estimated on the basis of mean absolute percentage error

Reactive Power Compensation in a Stand-alone Wind-diesel-tidal Hybrid System by a Fuzzy Logic Based UPFC

AbstractThis paper gives a novel idea of application of fuzzy based UPFC controller for Reactive Power compensation in an isolated hybrid system and also discusses the improvement of stability in the hybrid system. For detailed analysis a small signal linear model of the hybrid wind- Diesel- tidal model is considered with different loading conditions. The reactive power compensation and stability analysis have been thoroughly analysed by a UPFC Controller. A fuzzy logic controller is designed to tune the parameters of UPFC controller. Simulation result shows that the system parameters attend steady state value with lesser time and complexities

Robust fuzzy-sliding mode based UPFC controller for transient stability analysis in autonomous wind-diesel-PV hybrid system

This study presents a comparative study of transient stability and reactive power compensation issues in an autonomous wind–diesel-photovoltaic based hybrid system (HS) using robust fuzzy-sliding mode based unified power flow controller (UPFC). A linearised small-signal model of the different elements of the HS is considered for the transient stability analysis in the HS under varying loading conditions. An IEEE type 1 excitation system is considered for the synchronous generator in the HS, with negligible saturation characteristic, for detailed voltage stability analysis. It is noted from the simulation results that the performance of UPFC is superior to static VAR compensator and static synchronous compensator in improving the voltage profile of the HS. Further, fuzzy and fuzzy-sliding mode based UPFC controller is designed in order to improve the transient performance. Simulation results reflect the robustness of the proposed fuzzy-sliding mode controller for better reactive power management to improve the voltage stability in comparison with the conventional PI and fuzzy-PI controllers. In addition to this, system stability analysis is performed based on eigenvalue, bode and popov for supporting the robustness of the proposed controller

VLSI Design and Implementation for Adaptive Filter using LMS Algorithm

Adaptive filters, as part of digital signal systems, have been widely used, as well as in applications such as adaptive noise cancellation, adaptive beam forming, channel equalization, and system identification. However, its implementation takes a great deal and becomes a very important field in digital system world. When FPGA (Field Programmable Logic Array) grows in area and provides a lot of facilities to the designers, it becomes an important competitor in the signal processing market. In general FIR structure has been used more successfully than IIR structure in adaptive filters. However, when the adaptive FIR filter was made this required appropriate algorithm to update the filter’s coefficients. The algorithm used to update the filter coefficient is the Least Mean Square (LMS) algorithm which is known for its simplification, low computational complexity, and better performance in different running environments. When compared to other algorithms used for implementing adaptive filters the LMS algorithm is seen to perform very well in terms of the number of iterations required for convergence. This phenomenon can be achieved by a sufficient choice of bit length to represent the filter’s coefficients. This paper presents a lowcost and high performance programmable digital finite impulse response (FIR) filter. It follows the adaptive algorithm used for the development of the system. The architecture employs the computation sharing algorithm to reduce the computation complexity

A Bayesian analysis of total factor productivity persistence

'This paper studies persistence properties of total factor productivity (TFP) from Bayesian perspective. Emphasizing that classical unit root test for TFP cannot determine the probability whether a stochastic shock to the series is permanent, we design a Bayesian unit root test for TFP. Examination for a set of African economies' TFP data show that the probability of having a unit root is very high for majority of countries. The evidence of high-persistence has implications for perpetual growth and business cycles.' (author's abstract

Solitary bone metastasis to the tibia from colorectal cancer- A case report

The onset of osseous metastases during the course of colorectal cancer is not common. Although rare, they usually appear in the axial skeleton. In our report, we refer to the case of a 48-year-old patient who presented with colon cancer and eventually developed a solitary bone metastasis in the upper end of left tibia. At the time of diagnosis and staging investigations, the patient had only a primary disease.------------------------------------------------Cite this article as:Alnajjar A, Mohanty AK. Solitary bone metastasis to the tibia from colorectal cancer- A case report. Int J Cancer Ther Oncol 2014; 2(4):02045. DOI: 10.14319/ijcto.0204.

Bacterial Foraging Optimized STATCOM for Stability Assessment in Power System

This paper presents a study of improvement in stability in a single machine connected to infinite bus (SMIB) power system by using static compensator (STATCOM). The gains of Proportional-Integral-Derivative (PID) controller in STATCOM are being optimized by heuristic technique based on Particle swarm optimization (PSO). Further, Bacterial Foraging Optimization (BFO) as an alternative heuristic method is also applied to select optimal gains of PID controller. The performance of STATCOM with the above soft-computing techniques are studied and compared with the conventional PID controller under various scenarios. The simulation results are accompanied with performance indices based quantitative analysis. The analysis clearly signifies the robustness of the new scheme in terms of stability and voltage regulation when compared with conventional PID

Bacterial Foraging Optimized STATCOM for Stability Assessment in Power System

This paper presents a study of improvement in stability in a single machine connected to infinite bus (SMIB) power system by using static compensator (STATCOM). The gains of Proportional-Integral-Derivative (PID) controller in STATCOM are being optimized by heuristic technique based on Particle swarm optimization (PSO). Further, Bacterial Foraging Optimization (BFO) as an alternative heuristic method is also applied to select optimal gains of PID controller. The performance of STATCOM with the above soft-computing techniques are studied and compared with the conventional PID controller under various scenarios. The simulation results are accompanied with performance indices based quantitative analysis. The analysis clearly signifies the robustness of the new scheme in terms of stability and voltage regulation when compared with conventional PID

Load Balancing in Wireless Sensor Network using Divisible Load Theory

In this thesis, optimal load allocation strategies are proposed for a wireless sensor network which is connected in a star topology. The load considered here is of arbitrarily divisible kind, such that each fraction of the job can be distributed and assigned to any processor for computation purpose. Divisible Load Theory emphasizes on how to partition the load among a number of processors and links, such that the load is distributed optimally. Its objective is to partition the load in such a way so that the load can be distributed and processed in the shortest possible time. The existing strategies for both star and bus topologies are investigated. The performance of the suggested strategy is compared with the existing ones and it is found that it reduces the overall communication and processing time if allocation time is considered in the previous strategies