scholarly journals Teaching Learning Based Optimization (TLBO) for Optimal Placement of Piezo-Patches

2016 ◽  
Vol 9 (34) ◽  
Author(s):  
Santosh J. Chauhan ◽  
Vishal V. Rodrigues
Keyword(s):  
Optimal Placement ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

scholarly journals Optimal placement of power factor correction capacitors in power systems using Teaching Learning Based Optimization

2017 ◽  
Vol 2 (2) ◽  
pp. 102-109
Author(s):  
Mouloud Bouaraki ◽  
Abdelmadjid RECIOUI
Keyword(s):  
Power Systems ◽  
Power Factor ◽  
Distribution System ◽  
Optimal Placement ◽  
Placement Problem ◽  
Capacitor Placement ◽  
Tlbo Algorithm ◽  
Optimal Capacitor Placement ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

This paper presents a method to optimize the placement of capacitors in a distribution system to correct power factor and reduce losses and costs. The method uses the Teaching Learning Based Optimization (TLBO) method to solve the optimal capacitor placement problem. The combinatorial nature of the problem suggests the employment of a mixed binary and real valued TLBO algorithm. To validate the efficiency of the method, it was applied to various examples (different bus systems) and simulation results are discussed.

A Novel Method Based on Teaching-Learning-Based Optimization for Recloser Placement with Load Model Consideration in Distribution System

2016 ◽  
Vol 2 (1) ◽  
pp. 1 ◽  
Author(s):  
Sina Khajeh Ahmad Attari ◽  
Mohammad Bakhshipour ◽  
Mahmoudreza Shakarami ◽  
Farhad Namdari
Keyword(s):  
Distribution System ◽  
Network Reliability ◽  
Average Energy ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Load Model ◽  
Reliability Indices ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
System Average

<em>This paper proposed a novel technique based on teaching-learning-based optimization (TLBO) algorithm in order to find optimal placement of reclosers in the distribution networks which is applied to improve reliability. Reclosers use to eliminate transient faults, faults isolation, network management and enhance reliability to reduce customer outages. According to recloser role in network reliability, the cost for the installation and maintenance must be sustained by distribution companies. Therefore, selecting sufficient number and suitable location for reclosers are important issue. In this paper, the proposed objective function for optimal recloser number and placement has been formulated to improve three reliability indices which consists of three terms; i.e. System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI) and Average Energy Not Supplied (AENS). Besides the load model effectiveness has been considered to the simulation. To verify the efficiency of proposed method, it has been conducted to IEEE 69-bus radial distribution system. The obtained simulation results demonstrate the reliability improvement.</em>

Application of Teaching Learning-Based Optimization to the Optimal Placement of Phasor Measurement Units

2018 ◽  
pp. 407-438
Author(s):  
Abdelmadjid Recioui
Keyword(s):  
Phasor Measurement Units ◽  
Optimal Placement ◽  
Placement Problem ◽  
Electric Transmission ◽  
Phasor Measurement ◽  
Teaching Learning Based Optimization ◽  
Measurement Units ◽  
Pmu Placement ◽  
Teaching Learning ◽  
Full Network

In recent years, the placement of phasor measurement units (PMUs) in electric transmission systems has gained much attention. This chapter presents a binary teaching learning based optimization (BTLBO) algorithm for the optimal placement of phasor measurement units (PMUs). The optimal PMU placement problem is formulated to minimize the number of PMUs installation subject to full network observability at the power system buses. The efficiency of the proposed method is verified by the simulation results of IEEE14-bus, 30-bus, 57-bus-118 bus systems, respectively. The results show that the whole system can be observable with installing PMUs on less than 25% of system buses. For verification of our proposed method, the results are compared with some newly reported methods which show the method as a novel effective solution to obtain system measurements with the least number of phasor measurement units.

scholarly journals BTLBO Based SPMUs Placement Optimization for Fault Observation in Power Grid

2019 ◽  
Vol 4 (1) ◽  
pp. 18-24
Author(s):  
Abdelkader ZITOUNI ◽  
Hamid BENTARZI
Keyword(s):  
Research Work ◽  
Phasor Measurement Units ◽  
Optimal Placement ◽  
Placement Problem ◽  
Protection Scheme ◽  
Phasor Measurement ◽  
Teaching Learning Based Optimization ◽  
Measurement Units ◽  
Benchmark System ◽  
Teaching Learning

The placement of synchro-phasor measurement units in electric transmission systems has also gained much attention for enhancing the control as well as the protection scheme. In this research work, a binary teaching learning based optimization (BTLBO) algorithm for the optimal placement of synchro-phasor measurement units (SPMUs) is proposed. The optimal PMU placement problem is formulated to minimize the number of SPMUs installation subject to full network observability of the power system buses under fault conditions. The effectiveness of the proposed method is verified by the simulation of IEEE 14-bus benchmark system.

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.

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