Perumal Nallagownden

Application of Genetic Algorithm for the Reduction of Reactive Power Losses in Radial Distribution System

Power losses in distribution system have become the most concerned issue in power losses analysis in any power system. In the effort of reducing power losses within distribution system, reactive power compensation has become increasingly important as it affects the operational, economical and quality of service for electric power systems. This paper presents the application of genetic algorithm approach for reactive power loss reduction in radial distribution system. IEEE 34-bus Standard Test System is used together with the ERACS and MATLAB as powerful tools for the analysis and simulation work. ERACS is used to perform load flow analysis while MATLAB is used for the identification of capacitor current via GAtool, and algorithm for the calculation of loss savings, its particular capacitor size and location. The result is then compared with the heuristic search strategies to evaluate the performance of genetic algorithm.

A Comparative Analysis of Neural Network Based Short Term Load Forecast Models for Anomalous Days Load Prediction

Load forecasting plays a very vital role for efficient and reliable operation of the power system. Often uncertainties significantly decrease the prediction accuracy of load forecasting which affect the operational cost dramatically. In this paper, comparison of Back Propagation (BP) and Levenberg Marquardt (LM) neural network (NN) forecast model for 24 hours ahead is presented. The impact of lagged load data, calendar events and weather variables on load demand are analyzed in order to select the best forecast model inputs. The mean absolute percentage errors (MAPE), Daily peak error and regression analysis of NN training are used to measure the NN performance. The Forecast results demonstrate that, LM based forecast model outperform than BP NN model for performance matrices. This model is used to predict the load of ISO-New England grid. Index Terms—Short Term Load Forecasting (STLF), Neural Network (NN), Back Propagation (BP), Levenberg- Marquardt (LM), Mean Absolute Percentage Error (MAPE), Regression Analysis (RA).

Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid

In this paper, a modified backpropagation neural network is combined with a chaos-search genetic algorithm and simulated annealing algorithm for very short term electrical energy demand prediction in deregulated power industry. Multiple modifications are carried out on the conventional backpropagation algorithm such as improvements in the momentum factor and adaptive learning rate. In the hybrid scheme, the initial parameters of the modified neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. The solution set provided by the optimized genetic algorithm is further improved by using the strong local search ability of simulated annealing algorithm. The real data of New South Wales, Australian grid, is used in the experimentation for 1-h-ahead forecast with an emphasis on data analysis and preprocessing framework. The correlation analysis is used for the identification and selection of the most influential input variables. The simulation results reveal that the proposed combination technique effectively enhanced the prediction accuracy as compared to the available conventional methods. The prediction of 1-h-ahead load demand is critically important for decision-making response of the modern smart grid system. The acceptable precision of the proposed model concludes that it can be applied in the smart grid to enhance its demand responsiveness and other intelligent features.

Optimal placement and sizing of DG in distribution system using accelerated PSO for power loss minimization

Electrical power distribution system is typically radial in nature, consists of huge number of branches and nodes which eventually increases the power losses and decrease voltage profile of the system. Distributed generation (DG) in distribution system not only plunges the power losses but also it improves the system voltage quality. Optimal placement and sizing of DG in distribution system is still a challenging problem for exploiting the maximum benefits from it. In this paper a new method called as accelerated particle swarm optimization (APSO) is employed to minimize the total power loss of the system. The technique satisfies the power balance, bus voltage and system capacity as constraints. The method is tested for two different types of DG on standard IEEE 33 bus radial distribution system. The proposed method is found to be very effective for power loss reduction with fast convergence.

PC Based Energy Efficient Wireless Transceiver Module with ZigBee

The aim of this research is to develop a low power, low cost and energy efficient transceiver model which is integrated with programmable microcontroller and ZigBee transponder. A combination of programmable microcontroller with ZigBee transponder is used to control the transceiver module with computer commands. It has b een simulated for transmitting and receiving the communication signals from any movable device with more efficiently and use low power consumption. ZigBee Transponder is preferable as compared to radio frequency identification (RFID) due to IEEE 802.15.4 standard that promises stable data transmission with low power consumption device and having higher network flexibility. PIC 16F877A programmable microcontroller with coding in Mikro C software is simulated and used with computer control instructions. Series of experiments has been conducted to ensure the stability and low power consumption of this model.

Optimal Chiller Loading Using Improved Particle Swarm Optimization

Reducing energy consumption is one of the most important for optimal electric-driven chiller operation. Therefore, even small reduction in power consumption will achieve significant energy savings. This paper adopts improved particle swarm optimization (IPSO), which is aiming to reduce energy consumption, and improve the performance of chillers. The method has been validated by real case study, and the results have demonstrated the effectiveness for saving energy and kept the cooling demand at satisfactory level.

Intelligent approach for optimal energy management of chiller plant using fuzzy and PSO techniques

This paper discusses the optimal energy management of chiller plant. Two intelligent approaches have been employed. Fuzzy is used to adjust the set-point and, while PSO is utilized to optimize the objective function after setting by Fuzzy. Moreover, Fuzzy is also utilized to adjust weighting factors in order to find the best values for the PSO local and global. This will improve PSO performance. The proposed method was combined two levels as Fuzzified PSO, and the model has been simulated and validated by a real case study which consists of 5 electric-driven chillers. The results have shown that the effectiveness of the proposed method compared to the conventional one, and it also has demonstrated a better power saving.

Modeling and Analysis of Linear Permanent Magnet Generator for Wave Energy Conversion Using Finite Element Method

This paper presents the design modeling and analysis of linear permanent magnet generator for wave energy conversion using finite element method. The time-stepping finite element technique is used to determine electromagnetic characteristics and analyze the efficiency. The main parts such as; copper loss is analyzed on the different variation of winding input excitation and the core loss is determined based on various specific loss curves data in order to analyze its effect on frequency. The influence of main dimension of stator is analyzed on electromagnetic characteristic and copper loss.

A New Strategy for Multiple Chillers Plant Operation Using Fuzzy Inference System

Reducing energy consumption is one of the most important things for optimal operation of the multiple-chillers. The storage system for chilled-water is used for cooling demand and shifting load consumption at peak hours. The case study in this work is based on the existing system for chillers plant in an industrial building. This paper adopts Fuzzy Inference System, in order to adjust the operating points setting for a period of 24 hours to keep the cooling demand satisfied. This is based on a new strategy for the scheduled operation of the chillers. The results have shown the effectiveness for the energy savings and also satisfying the cooling demand. This proposed control system has been simulated in Matlab and the simulation result is compared with the measured data from the existing system.

Power system studies for reliable operation of an offshore platform

The power system analysis studies serve as the basis for ensuring reliability, improving system performance and power quality, reducing operating costs, and providing a reliable supply power during system operation. Thus, this paper presents a case study for designing power supply system for an offshore platform which will include load flow study, short circuit study and transient stability study. The studies mentioned will be conducted through simulation by using Electrical Design Software Analysis, EDSA. This paper also outlines the system design requirements for designing power supply system, power system configuration and also power operating philosophy for an offshore platform. The results obtained are acceptable and confirms with the standards used by the electrical consultants.