• Journal of Electrical Engineering and Technology
• /
• v.6 no.4
• /
• pp.493-504
• /
• 2011

The present paper presents a genetic algorithm (GA) to maximize social welfare and perform congestion management by optimally placing and sizing one Thyristor-Controlled Series Capacitor (TCSC) device in a double-sided auction market. Simulation results, with line flow constraints before and after the compensation, are compared through the Sequential Quadratic Programming SQP method, and are used to analyze the effect of TCSC on the congestion levels of modified IEEE 14-bus and 30-bus test systems. Quadratic, smooth and nonsmooth (with sine components due to valve point loading effect) generator cost curves, and quadratic smooth consumer benefit functions are considered. The main aims of the present study are the inclusion of customer benefit in the social welfare maximization and congestion management objective function, the consideration of nonsmooth generator characteristics, and the optimal locating and sizing of the TCSC using real code-based GA to guarantee fast convergence to the best solution.

• ETRI Journal
• /
• v.41 no.5
• /
• pp.626-636
• /
• 2019

A novel algorithm for joint user selection and optimal power allocation for Stackelberg game-based revenue maximization in a downlink non-orthogonal multiple access (NOMA) network is proposed in this study. The condition for the existence of optimal solution is derived by assuming perfect channel state information (CSI) at the transmitter. The Lagrange multiplier method is used to convert the revenue maximization problem into a set of quadratic equations that are reduced to a regular chain of expressions. The optimal solution is obtained via a univariate iterative procedure. A simple algorithm for joint optimal user selection and power calculation is presented and exhibits extremely low complexity. Furthermore, an outage analysis is presented to evaluate the performance degradation when perfect CSI is not available. The simulation results indicate that at 5-dB signal-to-noise ratio (SNR), revenue of the base station improves by at least 15.2% for the proposed algorithm when compared to suboptimal schemes. Other performance metrics of NOMA, such as individual user-rates, fairness index, and outage probability, approach near-optimal values at moderate to high SNRs.

• Journal of Electrical Engineering and Technology
• /
• v.7 no.4
• /
• pp.501-512
• /
• 2012

Social welfare maximization in a double-sided auction market is performed by implementing an aggregation-based particle swarm optimization (CAPSO) algorithm for optimal placement and sizing of one Static Synchronous Series Compensator (SSSC) device. Dallied simulation results (without/with line flow constraints and without/with SSSC) are generated to demonstrate the impact of SSSC on the congestion levels of the modified IEEE 14-bus test system. The proposed CAPSO algorithm employs conventional quadratic smooth and augmented quadratic nonsmooth generator cost curves with sine components to improve the accurate of the model by incorporating the valve loading effects. CAPSO also employs quadratic smooth consumer benefit functions. The proposed approach relies on particle swarm optimization to capture the near-optimal GenCos and DisCos, as well as the location and rating of SSSC while the Newton based load flow solution minimizes the mismatch equations. Simulation results of the proposed CAPSO algorithm are compared to solutions obtained by sequential quadratic programming (SQP) and a recently implemented Fuzzy based genetic algorithm (Fuzzy-GA). The main contributions are inclusion of customer benefit in the congestion management objective function, consideration of nonsmooth generator characteristics and the utilization of a coordinated aggregation-based PSO for locating/sizing of SSSC.

• Advances in Energy Research
• /
• v.1 no.2
• /
• pp.97-106
• /
• 2013

This study designs and tests a photovoltaic system with distributed maximum power point tracking (DMPPT) methodology using a field programmable gate array (FPGA) controller. Each solar panel in the distributed PV system is equipped with a newly designed DC/DC converter and the panel's voltage output is regulated by a FPGA controller using PI control. Power from each solar panel on the system is optimized by another controller where the quadratic maximization MPPT algorithm is used to ensure the panel's output power is always maximized. Experiments are carried out at atmospheric insolation with partial shading conditions using 4 amorphous silicon thin film solar panels of 2 different grades fabricated by Chi-Mei Energy. It is found that distributed MPPT requires only 100ms to find the maximum power point of the system. Compared with the traditional centralized PV (CPV) system, the distributed PV (DPV) system harvests more than 4% of solar energy in atmospheric weather condition, and 22% in average under 19% partial shading of one solar panel in the system. Test results for a 1.84 kW rated system composed by 8 poly-Si PV panels using another DC/DC converter design also confirm that the proposed system can be easily implemented into a larger PV power system. Additionally, the use of NI sbRIO-9642 FPGA-based controller is capable of controlling over 16 sets of PV modules, and a number of controllers can cooperate via the network if needed.

• Smart Structures and Systems
• /
• v.6 no.7
• /
• pp.767-792
• /
• 2010

In this paper, the optimal design of vibration control system for smart structures has been investigated semi-analytically via the optimization of geometric parameters like the placements and sizes of piezoelectric sensors and actuators (S/As) bonded on the structures. The criterion based on the maximization of energy dissipation was adopted for the optimization of the control system. Based on the sensing and actuating equations, the total energy stored in the system which is used as the objective function was analytically derived with design variables explicitly presented. Two cases of single and combined vibration modes were addressed for a simply supported beam and a simply supported cylindrical shell. For single vibration mode, the optimal distributions of the piezoelectric S/As could be obtained analytically. However, the Sequential Quadratic Programming (SQP) method has to be employed to solve those which violated the prescribed constraints and to solve the case of combined vibration modes. The results of three examples, which include a simply supported beam, a simply supported cylindrical shell and a simply supported plate, showed good agreement with those obtained by the Genetic Algorithm (GA) method. Moreover, in comparison with the GA method, the proposed method is more effective in obtaining better optimization results and is much more efficient in terms of computation time.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.3
• /
• pp.351-357
• /
• 2003

A method of design optimization for minimization of force ripple and maximization of thrust force in a linear brushless permanent magnet motor without finite element analysis is represented. The design optimization method calculated the driving force in the function of electric and geometric parameters of a linear brushless PM motor using the sequential quadratic programming method. Using electric and geometric parameters obtained by this method, the normalized force ripple is reduced 7.7% (9.7% to 2.0%) and the thrust force is increased 12.88N (111.55N to 124.43N) compared to those not using design optimization.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
• /
• v.4B no.4
• /
• pp.184-189
• /
• 2004

In this paper, multi-object optimization based on a progressive quadratic response surface method (PQRSM) and a time stepping finite element method (FEM) is proposed. The new PQRSM and FEM are able to decide optimal geometric and electric variables of the switched reluctance motor (SRM) with two objective functions: torque ripple minimization and average torque maximization. The result of the optimum design for SRM demonstrates improved performance of the motor and enhanced relationship between torque ripple and average torque.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.570-571
• /
• 2008

This paper presents a method for the determination of total transfer capability in interconnected power systems, which is based on sequential quadratic programming (SQP). The objective function is the maximization of the interconnected line flow. To calculate TTC the control variables are the active power of the generating units, the voltage magnitude of the generator, transformer tap settings and SVC setting. The state variables are the bus voltage magnitude, the reactive power of the generating unit, line flows and the tie line flow. The method proposed is applied to the modified IEEE 14 buses model system.

• Proceedings of the KIEE Conference
• /
• 2002.11d
• /
• pp.118-120
• /
• 2002

In this paper, a multi-object optimization based on a progressive quadratic response surface method (PQRSM) and a time stepping finite element method (FEM) is proposed. The new PQRSM and FEM are able to decide optimal geometric and electric variables of the switched reluctance motor (SRM) with two objective functions: torque ripple minimization and average torque maximization. The result of the optimum design for SRM show an improved performance of motor and a relationship between torque ripple and average torque.

• Journal of Electrical Engineering and Technology
• /
• v.7 no.4
• /
• pp.636-645
• /
• 2012

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.