scholarly journals Stability Enhancement of TLBO Tuned SMIB System

2020 ◽  
Vol 8 (6) ◽  
pp. 1389-1399
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Low Frequency ◽  
Optimization Techniques ◽  
System Stability ◽  
Low Frequency Oscillation ◽  
Value Analysis ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Stability Enhancement

In this paper investigation of the application of teaching learning based optimization (TLBO) technique for the design of a modified Phillips haffron model of SMIB installed with SSSC based controller is made. The design objectives are to reduce low frequency oscillation and improve power system stability. Simulation result are demonstrated with Eigen value analysis, where various types of disturbance is applied as mechanical torque input and reference voltage settling, variation in parameter & various loading condition. The results obtained are compared with some well-known optimization techniques, such as the genetic algorithm (GA), particle swarm optimization (PSO) and the gravitational search algorithm (GSA). A comparative study of results demonstrates that the results of the proposed controller were more precise and robust

Minimization of losses and FACTS installation cost using proposed differential gravitational search algorithm optimization technique

2016 ◽  
Vol 23 (2) ◽  
pp. 235-251
Author(s):  
SN Deepa ◽  
J Rizwana
Keyword(s):  
Power System ◽  
Power Flow ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Gravitational Search Algorithm ◽  
Optimal Location ◽  
Optimization Techniques ◽  
System Stability ◽  
Gravitational Search ◽  
Installation Cost

The optimal location of Flexible AC Transmission Systems (FACTS) controllers in a multi-machine power system using proposed differential gravitational search algorithm (DGSA) optimization method is proposed in this paper. The main objective of this paper is to employ DGSA optimization technique to solve optimal power flow problem in the presence of Unified Power Flow controller for improving voltage profile by reducing losses along with the installation cost thereby enhancing the power system stability. A differential operator is incorporated into the gravitational search algorithm for effective search of the better solution. Due to this, the convergence and accuracy will be faster. The IEEE-6 bus, IEEE-14 bus and IEEE-30 bus systems are tested along with three other optimization techniques to validate the effectiveness of this proposed method. This proposed algorithm presents an optimal location of FACTS devices in transmission lines.

scholarly journals DM: Dehghani Method for Modifying Optimization Algorithms

2020 ◽  
Vol 10 (21) ◽  
pp. 7683 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Haidar Samet ◽  
Carlos Sotelo ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Objective Functions ◽  
Living Things ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In recent decades, many optimization algorithms have been proposed by researchers to solve optimization problems in various branches of science. Optimization algorithms are designed based on various phenomena in nature, the laws of physics, the rules of individual and group games, the behaviors of animals, plants and other living things. Implementation of optimization algorithms on some objective functions has been successful and in others has led to failure. Improving the optimization process and adding modification phases to the optimization algorithms can lead to more acceptable and appropriate solution. In this paper, a new method called Dehghani method (DM) is introduced to improve optimization algorithms. DM effects on the location of the best member of the population using information of population location. In fact, DM shows that all members of a population, even the worst one, can contribute to the development of the population. DM has been mathematically modeled and its effect has been investigated on several optimization algorithms including: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching-learning-based optimization (TLBO), and grey wolf optimizer (GWO). In order to evaluate the ability of the proposed method to improve the performance of optimization algorithms, the mentioned algorithms have been implemented in both version of original and improved by DM on a set of twenty-three standard objective functions. The simulation results show that the modified optimization algorithms with DM provide more acceptable and competitive performance than the original versions in solving optimization problems.

TLBO-FLN: Teaching-Learning Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 522-532 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Subhranginee Das ◽  
Mohammad Dilsad Ansari
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Fine Tuning ◽  
Functional Link ◽  
Price Prediction ◽  
Closing Price ◽  
Teaching Learning Based Optimization ◽  
Artificial Neural ◽  
Teaching Learning

Background and Objective: Stock closing price prediction is enormously complicated. Artificial Neural Networks (ANN) are excellent approximation algorithms applied to this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters either leads toward additional computational cost or local optima. Methods: Teaching Learning Based Optimization (TLBO) is a newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of Functional Link Artificial Neural Network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data made it popular and got wide applications in the stock market prediction. This article presents a hybrid model termed as Teaching Learning Based Optimization of Functional Neural Networks (TLBO-FLN) by combining the advantages of both TLBO and FLANN. Results and Conclusion: The model is evaluated by predicting the short, medium, and long-term closing prices of four emerging stock markets. The performance of the TLBO-FLN model is measured through Mean Absolute Percentage of Error (MAPE), Average Relative Variance (ARV), and coefficient of determination (R2); compared with that of few other state-of-the-art models similarly trained and found superior.

Optimal Design of Power System Stabilizer Using a Novel Evolutionary Algorithm

2018 ◽  
Vol 7 (3) ◽  
pp. 24-46
Author(s):  
Sourav Paul ◽  
Provas Roy
Keyword(s):  
Optimal Design ◽  
Power System ◽  
Search Algorithm ◽  
Low Frequency ◽  
Optimal Solution ◽  
Power System Stabilizer ◽  
Low Frequency Oscillation ◽  
Large Power ◽  
Wide Range ◽  
System Stabilizer

In this article, an Oppositional Differential search algorithm (ODSA) is comprehensively developed and successfully applied for the optimal design of power system stabilizer (PSS) parameters which are added to the excitation system to dampen low frequency oscillation as it pertains to large power system. The effectiveness of the proposed method is examined and validated on a single machine infinite bus (SMIB) using the Heffron-Phillips model. The most important advantage of the proposed method is as it reaches toward the optimal solution without the optimal tuning of input parameters of the ODSA algorithm. In order to verify the effectiveness, the simulation was made for a wide range of loading conditions. The simulation results of the proposed ODSA are compared with those obtained by other techniques available in the recent literature to demonstrate the feasibility of the proposed algorithm.

Influence Assessment of UHV Project on Jiangxi Power Grid

2014 ◽  
Vol 960-961 ◽  
pp. 1029-1033
Author(s):  
Yong Chun Su ◽  
Kai Xuan Chang
Keyword(s):  
Power System ◽  
Simulation Model ◽  
Power Transmission ◽  
Power Grid ◽  
Low Frequency ◽  
System Stability ◽  
Energy Structure ◽  
Low Frequency Oscillation ◽  
Structure Transition ◽  
Speed Up

In order to face the challenge of our economy and the environment, it is needed to speed up the energy structure transition and UItra High voltage (UHV) transmission has become an inevitable choice. Researches on the influence of UHV project to Jiangxi power grid are carried out in this paper. Using advanced digital power system simulator (ADPSS), the real-time simulation model of Jiangxi power grid is build up including the UHV project. Based on the simulation model, the problem of low frequency oscillation in Jiangxi power system is studied after the UHV power transmission project accessed. The influence of the UHV transmission line faults on system stability of Jiangxi grid is also researched.

PMU Placement Optimization for Fault Observation Using Different Techniques

2021 ◽  
pp. 196-220
Author(s):  
Hamid Bentarzi
Keyword(s):  
Cuckoo Search ◽  
Optimal Number ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Placement Optimization ◽  
Teaching Learning Based Optimization ◽  
Measurement Units ◽  
Pmu Placement ◽  
Teaching Learning ◽  
Full Network

This chapter presents different techniques for obtaining the optimal number of the phasor measurement units (PMUs) that may be installed in a smart power grid to achieve full network observability under fault conditions. These optimization techniques such as binary teaching learning based optimization (BTLBO) technique, particle swarm optimization, the grey wolf optimizer (GWO), the moth-flame optimization (MFO), the cuckoo search (CS), and the wind-driven optimization (WDO) have been developed for the objective function and constraints alike. The IEEE 14-bus benchmark power system has been used for testing these optimization techniques by simulation. A comparative study of the obtained results of previous works in the literature has been conducted taking into count the simplicity of the model and the accuracy of characteristics.

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