A Reinforcement Learning Algorithm to Economic Dispatch Considering Transmission Losses

Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year

ECG Noise Removal using GA tuned Sign-Data Least Mean Square Algorithm

Adaptive filter is a primary method to filter Electrocardiogram (ECG), because it does not need the signal statistical characteristics. In this paper, an adaptive filtering technique for denoising the ECG based on Genetic Algorithm (GA) tuned Sign-Data Least Mean Square (SD-LMS) algorithm is proposed. This technique minimizes the mean-squared error between the primary input, which is a noisy ECG, and a reference input which can be either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Noise is used as the reference signal in this work. The algorithm was applied to the records from the MIT -BIH Arrhythmia database for removing the baseline wander and 60Hz power line interference. The proposed algorithm gave an average signal to noise ratio improvement of 10.75 dB for baseline wander and 24.26 dB for power line interference which is better than the previous reported works

A Multi-Objective Antenna Placement Genetic Algorithm for Matched Array Synthesis on Complex Platforms

A Multi-Objective Antenna Placement Genetic Algorithm (MO-APGA) has been proposed for the synthesis of matched antenna arrays on complex platforms. The total number of antennas required, their position on the platform, location of loads, loading circuit parameters, decoupling and matching network topology, matching network parameters and feed network parameters are optimized simultaneously. The optimization goal was to provide a given minimum gain, specific gain discrimination between the main and back lobes and broadband performance. This algorithm is developed based on the non-dominated sorting genetic algorithm (NSGA-II) and Minimum Spanning Tree (MST) technique for producing diverse solutions when the number of objectives is increased beyond two. The proposed method is validated through the design of a wideband airborne SAR

Genetic algorithm based indicator sequence method for exon prediction

Considerable research effort has been devoted in predicting the exon regions of genes. The binary indicator (BI), Electron ion interaction pseudo potential (EIIP), Filter method are some of the methods. All these methods make use of the period three behavior of the exon region. Even though the method suggested in this paper is similar to above mentioned methods , it introduces a set of sequences for mapping the nucleotides selected by applying genetic algorithm and found to be more promising

A Faster 2D Technique for the design of Combinational Digital Circuits Using Genetic Algorithm

Combinational digital circuits can be evolved automatically using Genetic Algorithms (GA). Until recently this technique used linear chromosomes and and one dimensional crossover and mutation operators. In this paper, a new method for representing combinational digital circuits as 2 Dimensional (2D) chromosomes and suitable 2D crossover and mutation techniques has been proposed. By using this method, the convergence speed of GA can be increased significantly compared to the conventional methods. Moreover, the 2D representation and crossover operation provides the designer with better visualization of the evolved circuits. In addition to this, a technique to display automatically the evolved circuits has been developed with the help of MATLAB

An Improved Design of Combinational Digital Circuits with Multiplexers using Genetic Algorithm

This paper presents a new approach to the design of combinational digital circuits with multiplexers using Evolutionary techniques. Genetic Algorithm (GA) is used as the optimization tool. Several circuits are synthesized with this method and compared with two design techniques such as standard implementation of logic functions using multiplexers and implementation using Shannon’s decomposition technique using GA. With the proposed method complexity of the circuit and the associated delay can be reduced significantly

Investigation on the panchromaticity of silver-doped poly(vinyl alcohol)/acrylamide photopolymer

An investigation on the panchromaticity of a silver-doped poly(vinyl alcohol)/acrylamide photopolymer system is presented in this paper. Frequency-doubled Nd:YAG (532 nm) and Arþ (488 nm) lasers were used for the characterization of the films. Previous studies using an He–Ne laser (632:8 nm) showed that plane-wave transmission grating with a high diffraction efficiency of 75% could be stored in the optimized film. From the present study, it was noted that transmission gratings with 70% diffraction efficiency could be recorded using Arþ and Nd:YAG lasers, thereby elucidating the possibility of using the developed photopolymer system as a competent panchromatic recording medium

Silver-doped photopolymer media for holographic recording

Incorporation of silver ions into a dye-sensitized poly(vinyl alcohol)/acrylamide photopolymer is observed to give better performance compared to other metal-ion-doped photopolymer holographic recording media. Plane-wave transmission gratings were recorded in the photopolymer films using a He–Ne laser, and various holographic parameters were optimized so as to explore maximum potential of the material for various holographic applications. Silver-doped films showed good energy sensitivity, and gratings recorded in optimized film exhibited a diffraction efficiency of more than 75%. The potential of the material for holographic data storage applications is also studied using peristrophic multiplexing

Studies on metal resistant bacteria in cochin estuary and its response towards antibiotics and silver nanoparticles

The Cochin estuary (CE), which is one of the largest wetland ecosystems, extends from Thanneermukkam bund in the south to Azhikode in the north. It functions as an effluent repository for more than 240 industries, the characteristics of which includes fertilizer, pesticide, radioactive mineral processing, chemical and allied industries, petroleum refining and heavy metal processing industries (Thyagarajan, 2004). Studies in the CE have been mostly on the spatial and temporal variations in the physical, chemical and biological characteristics of the estuary (Balachandran et al., 2006; Madhu et al., 2007; Menon et al., 2000; Qasim 2003;Qasim and Gopinathan 1969) . Although several monitoring programs have been initiated in the CE to understand the level of heavy metal pollution, these were restricted to trace metals distribution (Balachandran et al., 2005) or the influence of anthropogenic inputs on the benthos and phytoplankton (Madhu et al., 2007;Jayaraj, 2006). Recently, few studies were carried out on microbial ecology in the CE(Thottathil et al 2008a and b;Parvathi et al., 2009and 2011; Thomas et al., 2006;Chandran and Hatha, 2003). However, studies on metal - microbe interaction are hitherto not undertaken in this estuary. Hence, a study was undertaken at 3 sites with different level of heavy metal concentration tounderstand the abundance, diversity and mechanisms of resistance in metal resistant bacteria and its impact on the nutrient regeneration. The present work has also focused on the response of heavy metal resistant bacteria towards antibacterial agent’s antibiotics and silver nanoparticles