A genetic-based algorithm for fuzzy unit commitment model

This paper presents a fuzzy model for the unit commitment problem (UCP). The model takes the uncertainties in the forecasted load demand and the spinning reserve constraints in a fuzzy frame. The genetic algorithm (GA) approach is then used to solve the proposed fuzzy UCP model. In the implementation for the GA, coding of the UCP solutions is based on mixing binary and decimal representations. A fitness function is constructed from the total operating cost of the generating units plus a penalty term determined due to the fuzzy load and spinning reserve membership functions. Numerical results showed an improvement in the solutions costs compared to the results reported in the literature and the GA with crisp UCP mode

A genetic-based algorithm for fuzzy unit commitment model

This paper presents a fuzzy model for the unit commitment problem (UCP). The model takes the uncertainties in the forecasted load demand and the spinning reserve constraints in a fuzzy frame. The genetic algorithm (GA) approach is then used to solve the proposed fuzzy UCP model. In the implementation for the GA, coding of the UCP solutions is based on mixing binary and decimal representations. A fitness function is constructed from the total operating cost of the generating units plus a penalty term determined due to the fuzzy load and spinning reserve membership functions. Numerical results showed an improvement in the solutions costs compared to the results reported in the literature and the GA with crisp UCP mode

A simulated annealing-based optimal controller for a three phase induction motor

This paper presents a new approach for optimal controller design of a three-phase induction motor (IM), based on using the simulated annealing (SA) method to find the optimal controller gains that satisfy a specific performance criterion. Optimal control requires well-known information about the system dynamics, which will preclude its applicability with systems having partially known or unknown dynamics. Accordingly; the proposed approach is implemented to emulate the structure and hence the characteristics of the optimal controller in spite of the partially known system dynamics, inaccuracy or uncertainties of system parameter. The problem is a hard nonlinear optimization problem in continuous variables. An adaptive cooling schedule and a new method for variables discretization are implemented to enhance the speed and convergence of the original simulated annealing algorithm (SAA). The proposed algorithm comprises structure of the optimal controller, a new error system and vector control of a three phase IM. The IM is described as a three input, three output controlled object. The state equations of IM suitable for voltage control are implemented based on the vector, method. Simulation results show better system performance compared to previously obtained results

A new reactive power optimization algorithm

This paper presents an algorithm for optimizing reactive power using particle swarm algorithm. A new implementation for the particle swarm algorithm has been applied. The objective function of the proposed algorithm is to minimize the system active power loss. The control variables are generator bus voltages, transformer tap positions and switch-able shunt capacitor banks. The proposed algorithm has been applied to practical IEEE 6-bus system. The proposed algorithm shows better results as compared to previous work

A new reactive power optimization algorithm

This paper presents an algorithm for optimizing reactive power using particle swarm algorithm. A new implementation for the particle swarm algorithm has been applied. The objective function of the proposed algorithm is to minimize the system active power loss. The control variables are generator bus voltages, transformer tap positions and switch-able shunt capacitor banks. The proposed algorithm has been applied to practical IEEE 6-bus system. The proposed algorithm shows better results as compared to previous work

A new genetic algorithm approach for unit commitment

This paper presents a new genetic algorithm approach to solve the unit commitment problem in electric power systems. In the proposed algorithm, coding the solution of the unit commitment problem is based on mixing binary and decimal representations. A fitness function is constructed from the total operating cost of the generating units without penalty terms. Genetic operators are implemented to enhance the search speed and to save memory space. The problem under consideration includes two linked subproblems: a combinatorial optimization problem and a nonlinear programming problem. The former is solved using the proposed genetic algorithm while the latter problem is solved via a quadratic programming routine. Numerical results showed an improvement in the solutions costs compared to the results reported in the literatur

Solving the harmonic problems produced from the use of adjustable speed drives in industrial oil pumping field

This paper investigates the application of different ASD models, 6-pulse, 12-pulse, and 18-pulse, in offshore oil field industry. The paper studies the harmonic distortion levels produced by each type and checks which one violates the IEEE-519 limits. The study considers the worst configuration of the system, that is when only one main transformer (50 MVA 115-13.8 kV) is supplying the whole system. A frequency scan analysis is conducted to study the effect of the submarine cables shunt capacitance in introducing resonance in the system. The effect of phase shift of the transformer is investigated and used as a method to reduce the harmonic distortion

A new simulated annealing-based tabu search algorithm for unitcommitment

We propose a new hybrid algorithm for solving the unit commitment problem (UCP). The algorithm integrates the use of simulated annealing (SA) and tabu search (TS) for solving the UCP. The algorithm includes a step to find an initial control parameter, at which virtually all trial solutions are accepted. It uses a polynomial-time cooling schedule that is advocated in the SA literature. Furthermore, the short-term memory procedures of the TS method are embedded in the SA test to prevent cycling of accepted solutions. Numerical examples from the literature are solved. Results showed an improvement in the solutions costs compared to previously obtained result

Robust tuning of power system stabilizers in multimachine powersystems

Summary form only given as follows. This paper demonstrates the robust tuning of power systems stabilizers for power systems, operating at different loading conditions. A classical lead-lag power system stabilizer is used to demonstrate the technique. The problem of selecting the stabilizer parameters is converted to a simple optimization problem with an eigenvalue-based objective function, which is solved by a tabu search algorithm. The objective function allows the selection of the stabilizer parameters to optimally place the closed-loop eigenvalues in the left-hand side of a vertical line in the complex s-plane. The effectiveness of the stabilizers tuned using the suggested technique, in enhancing the stability of power systems, is confirmed through eigenvalue analysis and simulation result

A simulated annealing algorithm for unit commitment

This paper presents a simulated annealing algorithm (SAA) to solve the unit commitment problem (UCP). New rules for randomly generating feasible solutions are introduced. The problem has two subproblems: a combinatorial optimization problem; and a nonlinear programming problem. The former is solved using the SAA while the latter problem is solved via a quadratic programming routine. Numerical results showed an improvement in the solutions costs compared to previously obtained result