Abstract: This paper presents a new optimization technique based on quantum computing principles to solve a security constrained power system economic dispatch problem (SCED). The proposed technique is a population-based algorithm, which uses some quantum computing elements in coding and evolving groups of potential solutions to reach the optimum following a partially directed random approach. The SCED problem is formulated as a constrained optimization problem in a way that insures a secure-economic system operation. Real Coded Quantum-Inspired Evolution Algorithm (RQIEA) is then applied to solve the constrained optimization formulation. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that RQIEA is very applicable for solving security constrained power system economic dispatch problem (SCED).
Abstract: Direct search methods are evolutionary algorithms used to solve optimization problems. (DS) methods do not require any information about the gradient of the objective function at hand while searching for an optimum solution. One of such methods is Pattern Search (PS) algorithm. This paper presents a new approach based on a constrained pattern search algorithm to solve a security constrained power system economic dispatch problem (SCED). Operation of power systems demands a high degree of security to keep the system satisfactorily operating when subjected to disturbances, while and at the same time it is required to pay attention to the economic aspects. Pattern recognition technique is used first to assess dynamic security. Linear classifiers that determine the stability of electric power system are presented and added to other system stability and operational constraints. The problem is formulated as a constrained optimization problem in a way that insures a secure-economic system operation. Pattern search method is then applied to solve the constrained optimization formulation. In particular, the method is tested using one system. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that pattern search (PS) is very applicable for solving security constrained power system economic dispatch problem (SCED).
Abstract: Multilevel inverters supplied from equal and constant
dc sources almost don-t exist in practical applications. The variation
of the dc sources affects the values of the switching angles required
for each specific harmonic profile, as well as increases the difficulty
of the harmonic elimination-s equations. This paper presents an
extremely fast optimal solution of harmonic elimination of multilevel
inverters with non-equal dc sources using Tanaka's fuzzy linear
regression formulation. A set of mathematical equations describing
the general output waveform of the multilevel inverter with nonequal
dc sources is formulated. Fuzzy linear regression is then
employed to compute the optimal solution set of switching angles.
Abstract: This study presents a new approach based on Tanaka's
fuzzy linear regression (FLP) algorithm to solve well-known power
system economic load dispatch problem (ELD). Tanaka's fuzzy linear
regression (FLP) formulation will be employed to compute the
optimal solution of optimization problem after linearization. The
unknowns are expressed as fuzzy numbers with a triangular
membership function that has middle and spread value reflected on
the unknowns. The proposed fuzzy model is formulated as a linear
optimization problem, where the objective is to minimize the sum of
the spread of the unknowns, subject to double inequality constraints.
Linear programming technique is employed to obtain the middle and
the symmetric spread for every unknown (power generation level).
Simulation results of the proposed approach will be compared with
those reported in literature.
Abstract: Selective harmonic elimination-pulse width modulation techniques offer a tight control of the harmonic spectrum of a given voltage waveform generated by a power electronic converter along with a low number of switching transitions. Traditional optimization methods suffer from various drawbacks, such as prolonged and tedious computational steps and convergence to local optima; thus, the more the number of harmonics to be eliminated, the larger the computational complexity and time. This paper presents a novel method for output voltage harmonic elimination and voltage control of PWM AC/AC voltage converters using the principle of hybrid Real-Coded Genetic Algorithm-Pattern Search (RGA-PS) method. RGA is the primary optimizer exploiting its global search capabilities, PS is then employed to fine tune the best solution provided by RGA in each evolution. The proposed method enables linear control of the fundamental component of the output voltage and complete elimination of its harmonic contents up to a specified order. Theoretical studies have been carried out to show the effectiveness and robustness of the proposed method of selective harmonic elimination. Theoretical results are validated through simulation studies using PSIM software package.