• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2177-2193
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• 2018

This paper studies resource allocation schemes for the relay-aided cooperative system consisting of multiple source-destination pairs and decode-forward (DF) relays. Specially, relaying selection, multisubcarrier pairing and assignment, and power allocation are investigated jointly. We consider a combinatorial optimization problem on quality of experience (QoE) and energy consumption based on relay-aided cooperative system. For providing better QoE and lower energy consumption we formulate a multi-objective optimization problem to maximize the total mean opinion score (MOS) value and minimize the total power consumption. To this end, we employ the nondominated sorting genetic algorithm version II (NSGA-II) and obtain sets of Pareto optimal solutions. Specially, two formulas are devised for the optimal solutions of the multi-objective optimization problems with and without a service priority constraint. Moreover, simulation results show that the proposed schemes are superior to the existing ones.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.31-44
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• 2024

People have been using more energy in the last years. Several research studies were conducted to develop sustainable energy sources that can produce clean energy to fulfill our energy requirements. Using renewable energy sources helps to decrease the harm to the environment caused by conventional power plants. Choosing the right location and capacity for DG-RESs can greatly impact the performance of Radial Distribution Systems. It is beneficial to have a good and stable electrical power supply with low energy waste and high effectiveness because it improves the performance and reliability of the system. This research investigates the ideal location and size for solar and wind power systems, which are popular methods for producing clean electricity. A new artificial intelligent algorithm called Nutcracker Optimization Algorithm (NOA) is used to find the best solution in two common electrical systems named IEEE 33 and 69 bus systems to examine the improvement in the efficiency & reliability of power system network by reducing power losses, making voltage deviation smaller, and improving voltage stability. Finally, the NOA method is compared with another method called PSO and developed Hybrid Algorithm (NOA+PSO) to validate the proposed algorithm effectiveness and enhancement of both efficiency and reliability aspects.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1027-1037
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• 2017

Penetration of renewable energy sources makes the modern interconnected power systems to have more intelligence and flexibility in the control. Hence, it is essential to maintain the system frequency and tie-line power exchange at nominal values using Load Frequency Control (LFC) for efficient, economic and reliable operation of power systems. In this paper, intelligent tuning of the Proportional Integral Derivative (PID) controller for LFC in an interconnected power system is considered as a main objective. The chosen problem is formulated as an optimization problem and the optimal gain parameters of PID controllers are computed with three innovative swarm intelligent algorithms named Particle Swarm Optimization (PSO), Bacterial Foraging Optimization Algorithm (BFOA) and hybrid Bacterial Foraging Particle Swarm Optimization (BFPSO) and a comparative study is made between them. A new objective function designed with necessary time domain specifications using weighted sum approach is also offered in this report and compared with conventional objective functions. All the simulation results clearly reveal that, the hybrid BFPSO tuned PID controller with proposed objective function has better control performances over other optimization methodologies.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.337-345
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• 2009

Deregulation in power industry has made the reactive power ancillary service management a critical task to power system operators from both technical and economic perspectives. Reactive power management in power systems is a complex combinatorial optimization problem involving nonlinear functions with multiple local minima and nonlinear constraints. This paper proposes a practical market-based reactive power ancillary service management scheme to tackle the challenge. In this paper a new model for voltage security and reactive power management is presented. The proposed model minimizes reactive support cost as an economic aspect and insures the voltage security as a technical constraint. For modeling validation study, two optimization algorithm, a genetic algorithm (GA) and particle swarm optimization (PSO) method are used to solve the problem of optimum allocation of reactive power in power systems under open market environment and the results are compared. As a case study, the IEEE-30 bus power system is used. Results show that the algorithm is well competent for optimal allocation of reactive power under practical constraints and price based conditions.

• Journal of Power Electronics
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• v.3 no.2
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• pp.69-80
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• 2003

Computer aided engineering (CAE) is a systematic approach to develop a better product/application with maximum possible options and minimum transition time. This paper presents a comprehensive feasibility analysis of various CAE techniques for evaluation and optimization of power electronic converters and systems. Different CAE methods for analysis, design, and performance improvement are classified. In addition, their advantages compared to the conventional workbench experimental methods are explained in detail and through examples.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.714-716
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• 1996

This paper presents an optimization method for optimal reactive power dispatch which minimizes real power loss and improves voltage profile of power systems using evolutionary computation such as genetic algorithms(GAs), evolutionary programming(EP). and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most these approaches have the common defect of being caught to a local minimum solution. Recently, global search methods such as GAs, EP, and ES are introduced. The proposed methods were applied to the IEEE 30-bus system. Each simulation result, compared with that obtained by using a conventional gradient-based optimization method, Sequential Quadratic Programming (SQP), shows the possibility of applications of evolutionary computation to large scale power systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.247-253
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• 2011

A sustainability of electricity supply has emerged as a critical issue for low carbon green growth in South Korea. Wind power is the fastest growing source of renewable energy. However, due to its own intermittency and volatility, the power supply generated from wind energy has variability in nature. Hence, accurate forecasting of wind speed and power plays a key role in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. This paper presents a short-term wind speed prediction method based on support vector regression. Moreover, particle swarm optimization is adopted to find an optimum setting of hyper-parameters in support vector regression. An illustration is given by real-world data and the effect of model regularization by particle swarm optimization is discussed as well.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2145-2157
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• 2018

Congestion Management (CM) is an attractive research area in the electrical power transmission with the power compensation abilities. Reconfiguration and the Flexible Alternating Current Transmission Systems (FACTS) devices utilization relieve the congestion in transmission lines. The lack of optimal power (real and reactive) usage with the better transfer capability and minimum cost is still challenging issue in the CM. The prediction of suitable place for the energy resources to control the power flow is the major requirement for power handling scenario. This paper proposes the novel optimization principle to select the best location for the energy resources to achieve the real-reactive power compensation. The parameters estimation and the selection of values with the best fitness through the Symmetrical Distance Travelling Optimization (SDTO) algorithm establishes the proper controlling of optimal power flow in the transmission lines. The modified fitness function formulation based on the bus parameters, index estimation correspond to the optimal reactive power usage enhances the power transfer capability with the minimum cost. The comparative analysis between the proposed method with the existing power management techniques regarding the parameters of power loss, cost value, load power and energy loss confirms the effectiveness of proposed work in the distributed renewable energy systems.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.291-293
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• 2006

In power systems, the state estimation takes an important role in security control. At present, the weighted least squares(WLS) method has been widely used to the state estimation computation. This paper presents an application of Particle Swarm Optimization(PSO) to state estimation in power systems. PSO is a modern heuristic optimization method to overcome the problems of classical optimization. PSO is employed to solve state estimation on the IEEE-30 bus system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4865-4886
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• 2017

The user experience of practical indoor power line communication (PLC) applications is greatly affected by the system quality-of-service (QoS) criteria. With a general broadcast-and-multi-access (BMA) relay scheme, in this work we investigate the joint source and relay power optimization of the amplify-and-forward (AF) relay systems used under indoor broad-band PLC environments. To achieve both time diversity and spatial diversity from the relay-involved PLC channel, which is time-varying in nature, the source node has been configured to transmit an identical message twice in the first and second signalling phase, respectively. The QoS constrained power allocation problem is not convex, which makes the global optimal solution is computationally intractable. To solve this problem, an alternating optimization (AO) method has been adopted and decomposes this problem into three convex/quasi-convex sub-problems. Simulation results show the fast convergence and short delay of the proposed algorithm under realistic relay-involved PLC channels. Compared with the two-hop and broadcast-and-forward (BF) relay systems, the proposed general relay system meets the same QoS requirement with less network power assumption.