Comparison of SVC and TCSC Installation in Transmission Line with Loss Minimization and Cost of Installation via Particle Swarm Optimization

2015 ◽  
Vol 785 ◽  
pp. 495-499
Author(s):  
Siti Amely Jumaat ◽  
Ismail Musirin
Keyword(s):  
Particle Swarm Optimization ◽  
Transmission Line ◽  
Objective Function ◽  
Transmission Loss ◽  
Particle Swarm ◽  
Static Var Compensator ◽  
Loss Minimization ◽  
Swarm Optimization ◽  
The Cost ◽  
Thyristor Controlled Series Compensator

The paper presents a comparison of performance Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) with objective function to minimize the transmission loss, improve the voltage and monitoring the cost of installation. Simulation performed on standard IEEE 30-Bus RTS and indicated that EPSO a feasible to achieve the objective function.

Comparison of EPSO, PSO and EP Approaches in Transmission Loss Minimization in Power System Network

2015 ◽  
Vol 785 ◽  
pp. 3-8
Author(s):  
Siti Amely Jumaat ◽  
Ismail Musirin ◽  
Nur Azzamudin Rahmat
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Intelligence ◽  
Transmission Loss ◽  
System Simulation ◽  
Evolutionary Programming ◽  
Optimal Placement ◽  
Static Var Compensator ◽  
Voltage Profile ◽  
Loss Minimization ◽  
Swarm Optimization

The paper presents a comparison of Computational Intelligence techniques are Evolutionary Programming Swarm Optimization (EPSO), Particle Swarm Optimization (PSO), Evolutionary Programming (EP) to optimal placement and sizing of Static Var Compensator. The technique has been implemented to minimize the transmission loss and improve the voltage profile of the system. Simulation performed on standard IEEE 118-Bus RTS and indicated that EPSO a feasible to achieve the objective function.

Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

2021 ◽  
pp. 1-17
Author(s):  
Mohammed Al-Andoli ◽  
Wooi Ping Cheah ◽  
Shing Chiang Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Complex Networks ◽  
Learning Community ◽  
Community Detection ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Detection Algorithms ◽  
Real World Datasets ◽  
The Cost

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

Inverse estimation of time-varying heat transfer coefficients for a hollow cylinder by using self-learning particle swarm optimization

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Kun-Yung Chen ◽  
Te-Wen Tu
Keyword(s):  
Heat Transfer ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Hollow Cylinder ◽  
General Type ◽  
Particle Swarm ◽  
Time History ◽  
Time Varying ◽  
Swarm Optimization ◽  
Self Learning

Abstract An inverse methodology is proposed to estimate a time-varying heat transfer coefficient (HTC) for a hollow cylinder with time-dependent boundary conditions of different kinds on inner and outer surfaces. The temperatures at both the inner surface and the interior domain are measured for the hollow cylinder, while the time history of HTC of the outer surface will be inversely determined. This work first expressed the unknown function of HTC in a general form with unknown coefficients, and then regarded these unknown coefficients as the estimated parameters which can be randomly searched and found by the self-learning particle swarm optimization (SLPSO) method. The objective function which wants to be minimized was found with the absolute errors between the measured and estimated temperatures at several measurement times. If the objective function converges toward the null, the inverse solution of the estimated HTC will be found eventually. From numerical experiments, when the function of HTC with exponential type is performed, the unknown coefficients of the HTC function can be accurately estimated. On the contrary, when the function of HTC with a general type is conducted, the unknown coefficients of HTC are poorly estimated. However, the estimated coefficients of an HTC function with the general type can be regarded as the equivalent coefficients for the real function of HTC.

scholarly journals Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2000
Author(s):  
Jin-Hwan Lee ◽  
Woo-Jung Kim ◽  
Sang-Yong Jung
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Design ◽  
Cost Function ◽  
Particle Swarm ◽  
Torque Ripple ◽  
Rate Of Change ◽  
Swarm Optimization ◽  
Traction Motor ◽  
Target Performance ◽  
The Cost

This paper proposes a robust optimization algorithm customized for the optimal design of electric machines. The proposed algorithm, termed “robust explorative particle swarm optimization” (RePSO), is a hybrid algorithm that affords high accuracy and a high search speed when determining robust optimal solutions. To ensure the robustness of the determined optimal solution, RePSO employs the rate of change of the cost function. When this rate is high, the cost function appears as a steep curve, indicating low robustness; in contrast, when the rate is low, the cost function takes the form of a gradual curve, indicating high robustness. For verification, the performance of the proposed algorithm was compared with those of the conventional methods of robust particle swarm optimization and explorative particle swarm optimization with a Gaussian basis test function. The target performance of the traction motor for the optimal design was derived using a simulation of vehicle driving performance. Based on the simulation results, the target performance of the traction motor requires a maximum torque and power of 294 Nm and 88 kW, respectively. The base model, an 8-pole 72-slot permanent magnet synchronous machine, was designed considering the target performance. Accordingly, an optimal design was realized using the proposed algorithm. The cost function for this optimal design was selected such that the torque ripple, total harmonic distortion of back-electromotive force, and cogging torque were minimized. Finally, experiments were performed on the manufactured optimal model. The robustness and effectiveness of the proposed algorithm were validated by comparing the analytical and experimental results.

scholarly journals Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves

2020 ◽  
Vol 14 (4) ◽  
pp. 285-311
Author(s):  
Bernd Bassimir ◽  
Manuel Schmitt ◽  
Rolf Wanka
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Upper Bounds ◽  
Local Optimum ◽  
Adaptive Potential ◽  
Stopping Criteria ◽  
Swarm Optimization ◽  
Self Adaptive

Abstract We study the variant of Particle Swarm Optimization that applies random velocities in a dimension instead of the regular velocity update equations as soon as the so-called potential of the swarm falls below a certain small bound in this dimension, arbitrarily set by the user. In this case, the swarm performs a forced move. In this paper, we are interested in how, by counting the forced moves, the swarm can decide for itself to stop its movement because it is improbable to find better candidate solutions than the already-found best solution. We formally prove that when the swarm is close to a (local) optimum, it behaves like a blind-searching cloud and that the frequency of forced moves exceeds a certain, objective function-independent value. Based on this observation, we define stopping criteria and evaluate them experimentally showing that good candidate solutions can be found much faster than setting upper bounds on the iterations and better solutions compared to applying other solutions from the literature.

The Influence of Inertia Weight on Particle Swarm Optimization in Boundary Element Inverse Analysis for Rebar Corrosion Detection

2013 ◽  
Vol 686 ◽  
pp. 266-272 ◽  
Author(s):  
Syarizal Fonna ◽  
M. Ridha ◽  
Syifaul Huzni ◽  
Ahmad Kamal Ariffin
Keyword(s):  
Particle Swarm Optimization ◽  
Cost Function ◽  
Boundary Element ◽  
Inverse Analysis ◽  
Particle Swarm ◽  
Concrete Surface ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Rebar Corrosion ◽  
The Cost

Particle Swarm Optimization (PSO) has been applied as optimization tool in various engineering problems. Inverse analysis is one of the potential application fields for PSO. In this research, the behavior of PSO, related to its inertia weight, in boundary element inverse analysis for detecting corrosion of rebar in concrete is studied. Boundary element inverse analysis was developed by combining BEM and PSO. The inverse analysis is carried out by means of minimizing a cost function. The cost function is a residual between the calculated and measured potentials on the concrete surface. The calculated potentials are obtained by solving the Laplace’s equation using BEM. PSO is used to minimize the cost function. Thus, the corrosion profile of concrete steel, such as location and size, can be detected. Variation in its inertia weight was applied to analyze the behavior of PSO for inverse analysis. The numerical simulation results show that PSO can be used for the inverse analysis for detecting rebar corrosion by combining with BEM. Also, it shows different behavior in minimizing cost function depending on inertia weight.

scholarly journals Adaptive Phase-Shifted PWM based on Particle Swarm Optimization for Cascaded H-Bridge Inverter with Unequal Dc-link Voltages

2018 ◽  
Vol 7 (4.15) ◽  
pp. 469
Author(s):  
Pakedam Lare ◽  
Byamakesh Nayak ◽  
Srikanta Dash ◽  
Jiban Ballav Sahu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Industrial Applications ◽  
High Order ◽  
Control Technique ◽  
Voltage Source ◽  
Switching Frequency ◽  
Swarm Optimization ◽  
The Cost

The cascaded H-Bridge Multilevel Inverter has been found a promising technology in industrial applications because of its higher voltage with less distortion production. Various PWMs techniques have been proposed to push the harmonics frequencies higher than the switching frequency and thus reduces the THD as compared to non-carrier control technique based upon grid frequency. The Phase-Shifted PWM technique has an advantage over others PWM techniques because its harmonics orders are multiples of switching frequency and also depend on the number of levels of the inverter. The phase shifting angle is uniform when the equal voltage sources are adopted. However, in applications where sets of different voltage source levels feed the H-Bridge cells, the Phase Shifted PWM suffers its high order harmonics elimination capability. As a solution to alleviate this problem, an adaptive variable angle approach is proposed in this paper using Particle Swarm Optimization (PSO) algorithm to eliminate desired higher order harmonics. The algorithm is used to minimize the cost function based on high order sideband harmonics elimination equations. The results through MATLAB/Simulink environment shown in this paper confirm the reduction of sideband harmonics of higher orders, and the overall THD.  

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