Optimal Parameters of Static Synchronous Series Compensator (SSSC) Connected to a Power System

The objective of this project is to develop a Static Synchronous Series Compensator (SSSC) controller with the purpose to control the power flow in the transmission lines. The optimal parameters of this controller are sized using an optimization technique so that the transmission line losses can be minimized. SSSC is a part of Flexible AC Transmission System (FACTS) technology that has the ability to control the interrelated parameters that govern the operation of transmission systems. The optimization of the parameters of SSSC is formed as an optimization problem with the objective of minimizing the transmission loss in the power system network. Particle Swarm Optimization (PSO) technique is used to solve the problem and the Newton-Raphson method for power flow is modified to consider the insertion of SSSC in the network. The proposed method is applied using MATLAB and tested on IEEE 14-bus system to observe the voltage profile and the transmission loss of the power system network. This report also covers basic principles and operation of SSSC, the power flow model and PSO technique. The result and outcome of the project are included as well as the recommendation for future work

Nonlinear finite element analysis of reinforced concrete beams strengthened with textile fine grained mortar

Nowadays, there was an increasing need of repairing and upgrading the reinforced concrete (RC) structure due to the deterioration of the structure. The fibre reinforced polymer (FRP) was commonly used for structural retrofitting purposes. However, owing to the debonding of the FRP from the concrete substrate and high cost of epoxy, it was gradually replaced with textile fine grained mortar (TFGM) nowadays. The TFGM system has been widely used in the construction field nowadays to repair the structure. Our study focus on the strain performances of the concrete surface, steel reinforcement and the textile itself. There were many proven experimental results showing that the TFGM was more effective than the other strengthening method such as FRP plate method. The experimental work done by previous researcher on investigation of strain performances of the concrete surface, steel reinforcement and the textile itself which consists of eleven (11) RC beams with dimension 150 x 200 x 2500 mm. The RC beams were strengthened with FGM and TFGM with 4 layers. The investigation continued with the finite element (FE) strain performance analysis with using Advanced Tool for Engineering Nonlinear Analysis (ATENA) software. The strain of the concrete surface, steel reinforcement and the textile were measured at a mid-point of RC beam. Then, the results of the finite element analysis software ATENA compared against the experimental results. The strain performances of the concrete and steel reinforcement improved noticeably when the number of layers of textile reinforcement used increased

The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems

The artificial neural network (ANN) has recently been applied in many areas, such as medical, biology, financial, economy, engineering and so on. It is known as an excellent classifier of nonlinear input and output numerical data. Improving training efficiency of ANN based algorithm is an active area of research and numerous papers have been reviewed in the literature. The performance of Multi-layer Perceptron (MLP) trained with back-propagation artificial neural network (BP-ANN) method is highly influenced by the size of the data-sets and the data-preprocessing techniques used. This work analyzes the advantages of using pre-processing datasets using different techniques in order to improve the ANN convergence. Specifically Min-Max, Z-Score and Decimal Scaling Normalization preprocessing techniques were evaluated. The simulation results showed that the computational efficiency of ANN training process is highly enhanced when coupled with different preprocessing techniques

Pipeline leakage detection and characterisation with adaptive surrogate modelling using particle swarm optimisation.

Pipelines are often subject to leakage due to ageing, corrosion, and weld defects, and it is difficult to avoid as the sources of leakages are diverse. Several studies have demonstrated the applicability of the machine learning model for the timely prediction of pipeline leakage. However, most of these studies rely on a large training data set for training accurate models. The cost of collecting experimental data for model training is huge, while simulation data is computationally expensive and time-consuming. To tackle this problem, the present study proposes a novel data sampling optimisation method, named adaptive particle swarm optimisation (PSO) assisted surrogate model, which was used to train the machine learning models with a limited dataset and achieved good accuracy. The proposed model incorporates the population density of training data samples and model prediction fitness to determine new data samples for improved model fitting accuracy. The proposed method is applied to 3-D pipeline leakage detection and characterisation. The result shows that the predicted leak sizes and location match the actual leakage. The significance of this study is two-fold: the practical application allows for pipeline leak prediction with limited training samples and provides a general framework for computational efficiency improvement using adaptive surrogate modelling in various real-life applications

Optimal parameters selection of particle swarm optimization based global maximum power point tracking of partially shaded PV

This paper presents optimal parameters selection of particle swarm optimization (PSO) algorithm for determining the global maximum power point tracking of photovoltaic array under partially shaded conditions. Under partial shading, the power-voltage characteristics have a more complex shape with several local peaks and one global peak. The two proposed controllers include dynamic Particle Swarm Optimization, and constant particle swarm optimization. The developed algorithms are implemented in MATLAB/Simulink platform, and their performances are evaluated. The results indicate that the dynamic particle swarm optimization algorithm can very fast track the GMPP within 128 ms for different shading conditions. In addition, the average tracking efficiency of the proposed algorithm is higher than 99.89%, which provides good prospects to apply this algorithm in the control search unit for the global maximum power point in stations

Hybridizing PSO With SA for Optimizing SVR Applied to Software Effort Estimation

This study investigates Particle Swarm Optimization (PSO) hybridization with Simulated Annealing (SA) to optimize Support Vector Machine (SVR). The optimized SVR is used for software effort estimation. The optimization of SVR consists of two sub-problems that must be solved simultaneously; the first is input feature selection that influences method accuracy and computing time. The next sub-problem is finding optimal SVR parameter that each parameter gives significant impact to method performance. To deal with a huge number of candidate solutions of the problems, a powerful approach is required. The proposed approach takes advantages of good solution quality from PSO and SA. We introduce SA based acceptance rule to accept new position in PSO. The SA parameter selection is introduced to improve the quality as stochastic algorithm is sensitive to its parameter. The comparative works have been between PSO in quality of solution and computing time. According to the results, the proposed model outperforms PSO SVR in quality of solutio

Investigation of service selection algorithms for grid services

Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters

Investigation of service selection algorithms for grid services

Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters

Investigation of service selection algorithms for grid services

Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters