A Review of Short Term Load Forecasting using Artificial Neural Network Models

AbstractThe electrical short term load forecasting has been emerged as one of the most essential field of research for efficient and reliable operation of power system in last few decades. It plays very significant role in the field of scheduling, contingency analysis, load flow analysis, planning and maintenance of power system. This paper addresses a review on recently published research work on different variants of artificial neural network in the field of short term load forecasting. In particular, the hybrid networks which is a combination of neural network with stochastic learning techniques such as genetic algorithm(GA), particle swarm optimization (PSO) etc. which has been successfully applied for short term load forecasting (STLF) is discussed thoroughly

Robust and Constrained Portfolio Optimization using Multiobjective Evolutionary Algorithms

Optimization plays an important role in many areas of science, management,economics and engineering. Many techniques in mathematics and operation research are available to solve such problems. However these techniques have many shortcomings to provide fast and accurate solution particularly when the optimization problem involves many variables and constraints. Investment portfolio optimization is one such important but complex problem in computational finance which needs effective and efficient solutions. In this problem each available asset is judiciously selected in such a way that the total profit is maximized while simultaneously minimizing the total risk. The literature survey reveals that due to non availability of suitable multi objective optimization tools, this problem is mostly being solved by viewing it as a single objective optimization problem

A Comparative performance evaluation of a set of swarm intelligence based optimization algorithms for economic operation with FACTS devices in Power system

In this article an effective Reactive Power Management (RPM) method by using Flexible AC Transmission System (FACTS) has been proposed which minimizes the loss of energy, improve the power transfer capacity and reduce the overall cost of transmission network lines. The position of FACTS is optimized by two recently proposed heuristic optimization techniques, i.e., Gravitational Search Algorithm (GSA) and Teaching Learning Based Optimization (TLBO). The optimal positions of these FACTS are found by utilizing these GSA and TLBO algorithms. It is observed that by installing the FACTS in this optimal position improves the voltage profile with minimum installation cost. The overall performance of the proposed approaches is investigated by implementing it on IEEE 30-bus network

A Comparative Performance Evaluation of a Set of Swarm Intelligence based Optimization Algorithms for Economic Operation with FACTS Devices in Power Systems

499-507In this article an effective Reactive Power Management (RPM) method using a Flexible AC Transmission System (FACTS) has been proposed, which minimizes the loss of energy, improves the power transfer capacity and reduces the overall cost of transmission network lines. The position of FACTS was optimized by two recently proposed heuristic optimization techniques, i.e., Gravitational Search Algorithm (GSA) and Teaching Learning Based Optimization (TLBO). It is observed that installing the FACTS in this optimal position improves the voltage profile at minimum installation cost. The overall effectiveness of the proposed approaches were examined by implementing it on a IEEE 30-bus network

A Comparative Performance Assessment of Evolutionary Fractional Order PID Controllers for Magnetic Levitation Plant with Time Delay

Fractional Order controllers have been extensively applied to various fields of science and engineering, since several decades, because of the ability to control more parameters and consequent better control. However, to achieve this advantage, proper tuning of the associated parameters plays an important role. To achieve this objective, this paper employs a multi-agent symbiotic organisms search (MASOS) algorithm for appropriately tuning the parameters of fractional order proportional-integral-derivative (FOPID) controller for stabilizing a magnetic levitation plant (MLP) with time delay. Three different FOPID controllers have been precisely tuned and their performance has been evaluated and compared in this paper. The results demonstrate that the I-PD configuration produces the best performance in terms of time domain as well as frequency domain specifications, when compared with the other configurations

A Comparative Performance Assessment of Evolutionary Fractional Order PID Controllers for Magnetic Levitation Plant with Time Delay

322-327Fractional Order controllers have been extensively applied to various fields of science and engineering, since several decades, because of the ability to control more parameters and consequent better control. However, to achieve this advantage, proper tuning of the associated parameters plays an important role. To achieve this objective, this paper employs a multi-agent symbiotic organisms search (MASOS) algorithm for appropriately tuning the parameters of fractional order proportional-integral-derivative (FOPID) controller for stabilizing a magnetic levitation plant (MLP) with time delay. Three different FOPID controllers have been precisely tuned and their performance has been evaluated and compared in this paper. The results demonstrate that the I-PD configuration produces the best performance in terms of time domain as well as frequency domain specifications, when compared with the other configurations

Multilingual Neural Machine Translation System for Indic to Indic Languages

This paper gives an Indic-to-Indic (IL-IL) MNMT baseline model for 11 ILs implemented on the Samanantar corpus and analyzed on the Flores-200 corpus. All the models are evaluated using the BLEU score. In addition, the languages are classified under three groups namely East Indo- Aryan (EI), Dravidian (DR), and West Indo-Aryan (WI). The effect of language relatedness on MNMT model efficiency is studied. Owing to the presence of large corpora from English (EN) to ILs, MNMT IL-IL models using EN as a pivot are also built and examined. To achieve this, English- Indic (EN-IL) models are also developed, with and without the usage of related languages. Results reveal that using related languages is beneficial for the WI group only, while it is detrimental for the EI group and shows an inconclusive effect on the DR group, but it is useful for EN-IL models. Thus, related language groups are used to develop pivot MNMT models. Furthermore, the IL corpora are transliterated from the corresponding scripts to a modified ITRANS script, and the best MNMT models from the previous approaches are built on the transliterated corpus. It is observed that the usage of pivot models greatly improves MNMT baselines with AS-TA achieving the minimum BLEU score and PA-HI achieving the maximum score. Among languages, AS, ML, and TA achieve the lowest BLEU score, whereas HI, PA, and GU perform the best. Transliteration also helps the models with few exceptions. The best increment of scores is observed in ML, TA, and BN and the worst average increment is observed in KN, HI, and PA, across all languages. The best model obtained is the PA-HI language pair trained on PAWI transliterated corpus which gives 24.29 BLEU.Comment: 38 pages, 2 figure

Identification of Real-Time Maglev Plant using Long-Short Term Memory Network based Deep Learning Technique

1101-1105Deep neural network has emerged as one of the most effective networks for modeling of highly non-linear complex real-time systems. The long-short term memory network (LSTM) which is a one of the variants of recurrent neural network (RNN) has been proposed for the identification of a highly nonlinear Maglev plant. The comparative analysis of its performance is carried out with the functional link artificial neural network- least mean square (FLANN-LMS), FLANN-particle swarm optimization (FLANN-PSO), FLANN-teaching learning based optimization (FLANN-TLBO) and FLANN-black widow optimization (FLANN-BWO) algorithm. The proposed LSTM model is a feed forward neural network trained by a simple iterative method called the ADAM algorithm. The obtained results indicate that the proposed network has better performance than the other competitive networks in terms of the MSE, CPU time and convergence rate. To validate the dominance of the proposed network, a statistical tests, i.e. the Friedman test, is also applied

Identification of Real-Time Maglev Plant using Long-Short Term Memory network based Deep learning Technique

Deep neural network has emerged as one of the most effective networks for modeling of highly non-linear complex real-time systems. The Long-Short Term Memory network (LSTM) which is a one of the variants of Recurrent Neural Network (RNN) has been proposed for the identification of a highly nonlinear Maglev plant. The comparative analysis of its performance is carried out with the Functional Link Artificial Neural Network- Least Mean Square (FLANN-LMS), FLANN-Particle Swarm Optimization (FLANN-PSO), FLANN-Teaching Learning Based Optimization (FLANN-TLBO) and FLANN-Black Widow Optimization (FLANN-BWO) algorithm. The proposed LSTM model is a feed forward neural network trained by a simple iterative method called the ADAM algorithm. The obtained results indicate that the proposed network has better performance than the other competitive networks in terms of the MSE, CPU time and convergence rate. To validate the dominance of the proposed network, a statistical tests, i.e. the Friedman test, is also applied.

Inverted Meckel’s diverticulum: a rare cause of intussusception in children

Invagination of proximal segment of intestine to distal one results in intussusceptions and is a common cause of intestinal obstruction in children. In most of the cases of intussusceptions, the cause is idiopathic in nature; the other causes may be infection, polyp or anatomical abnormalities. Occasionally, Meckel’s diverticulum may cause intussusception and inverted Meckel’s diverticulum leading to intussusceptions is very rare in children. It is difficult to diagnose inversion of Meckel’s diverticulum preoperatively. Here in we report a case of 6 yrs old male child, who was operated for intussusception and found to have inverted Meckel’s diverticulum as lead point.