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In concern with the slow-convergence disadvantage of GA, PSO is combined with GA in this paper for a load distribution optimization among turbine-generators. By comparison with GA method, it is shown that PSO-GA is better than the GA method in the aspect of calculation speed, bearing excellent stability and fast convergence.
State space pruning is a methodology that has been successfully applied to improve the computational efficiency and convergence of Monte Carlo Simulation (MCS) when computing the reliability indices of power systems. This methodology increases performance of MCS by pruning state spaces in such a manner that a conditional state space with a higher density of failure states than the original state space...
In this paper, particle swarm optimization method is proposed to determine the optimal bidding strategy in competitive electricity market. The market includes Generating companies (Genco's), large consumers who participate in demand side bidding, and small consumers whose demand is present in aggregate form. The effectiveness of the proposed method is tested with IEEE-30 bus system in which six generators...
Excitation system plays a key role in realistic simulation and analysis of the dynamic performance of electrical power systems. However, simulation results with parameters provided by manufacture can usually not match the real operation. Therefore, parameter identification based on field data is focused on. In this paper, parameter identification methods based on particle swarm optimization (PSO)...
State space pruning is a methodology that has been used to improve the computational efficiency and convergence of Monte Carlo Simulation (MCS) when computing the reliability indices of power systems. This methodology improves performance of MCS by pruning state spaces in such a manner that a new state space with a higher density of failure states than the original state space is created. We have...
Optimal Power Flow (OPF) is one of the most vital tools for power system operation analysis, which requires a complex mathematical formulation to find the best solution. Conventional methods such as Linear Programming, Newton-Raphson and Non-linear Programming were previously offered to tackle the complexity of the OPF. However, with the emergence of artificial intelligence, many novel techniques...
This paper is involved in reactive power optimization. The combining strategy of genetic algorithm and particle swarm algorithm is proposed for the optimization problem of reactive power in this paper. It is necessary that the initial individuals are feasible ones, and good individuals are chosen as the initial particles in the combining strategy. The numerical examples of IEEE-6 and IEEE-30 power...
Economic dispatch (ED) is a power system optimization problem and its objective is to reduce the total generation cost of units while satisfying constraints. The presence of nonlinearities in practical generator operation makes solving the ED problem more challenging. These generator nonlinearities are modeled as constraints to be met in the form of ramp-rate limits and prohibited operating zones...
This paper proposes a new application of Honey Bee Colony Optimization (BCO) to solve the Economic Dispatch (ED) problem with generator constraints. The fundamental constraints of ED problem are the load demand and spinning reserve capacity. Additionally, some practical operation constraints of generators, i.e. ramp rate limits and prohibited operating zone are taken into consideration. To demonstrate...
A comparative analysis using different intelligent techniques has been carried out for the economic load dispatch (ELD) problem considering line flow constraints for the regulated power system to ensure a practical, economical and secure generation schedule. The objective of this paper is to minimize the total production cost of the thermal power generation. Economic load dispatch (ELD) has been applied...
This paper explores a comparative performance study of two new classes of particle swarm optimization (PSO) techniques and binary coded genetic algorithm (GA) applied to the optimization of proportional-integral-derivative (PID) gains of PID-controlled automatic voltage regulator (AVR). The two novel swarm optimization techniques are velocity update relaxation particle swarm optimization (VURPSO)...
This paper describes the application of various search techniques to the problem of automatic empirical code optimization. The search process is a critical aspect of auto-tuning systems because the large size of the search space and the cost of evaluating the candidate implementations makes it infeasible to find the true optimum point by brute force. We evaluate the effectiveness of Nelder-Mead Simplex,...
Optimal load-shedding during contingency situations is one of the most important issues in planning, security, and operation of power systems. Load-shedding is necessary to prevent phenomena such as voltage collapse and line over load which may lead to cascade outages and then black-out. In this paper, a new optimal load-shedding approach is presented. One of the important specifications of this method...
Optimal load-shedding during contingency situations is one of the most important issues in planning, security, and operation of power systems. Load-shedding is necessary to prevent phenomena such as voltage collapse and line over load which may lead to cascade outages and then black-out In this paper, a new optimal load-shedding approach is presented. One of the important specifications of this method...
This paper proposes a particle swarm optimization (PSO) method for solving the economic dispatch (ED) problem in power systems. Many nonlinear characteristics of the generator are considered for practical generator operation, such as ramp rates, prohibited operating zones, and non-smooth cost functions. The feasibility of the proposed method is demonstrated for two different systems, and is Compared...
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