Economic Approach for Stochastic Artificial insemination by Neural Network

The most common neural network model is the multi-layer perceptron (MLP). This type of neural network is known as a supervised network because it requires a desired output in order to learn. The goal of this type of network is to create a model that correctly maps the input to the output using historical data so that the model can then be used to produce the output when the desired output is unknown. In this paper, a new MLP is proposed for insemination problem. The result of the proposed method, is shown the high performance beside a very fast respond for the problem. Moreover, the conversion of the error is analyzed by the proposed method. All the simulation and result is done in MATLAB environments

Economic Based Neural Control Switching of TCR and TSC for Optimal Reactive Power Flow and Harmonic Minimization with Fuzzy-Genetic

Optimal Reactive Power Flow (ORPF) for improving voltage profile and power loss reduction is very important in power system planning; though its method, constraints, and quality of compensation are very effective. Value of compensator, transformer tap ratio, and generator voltages are assumed as controlling variables. Usually this optimization is accompanied by harmonic production. The most important parameter of reactive power compensators is minimum production of harmonics. Nowadays by considering the improvement of power systems in power quality and the importance of harmonics in power quality, compensators by minimum harmonic distortion should be designed. In this paper, ORPF is executed in two stages. At First stage, a genetic algorithm with a fuzzy fitness model employed to solve this multi objective optimization problem. The entire discrete controlling variable is assumed discretely as their natures in all steps of this stage. Outputs of this stage are values of controlling variable that include compensations values. In Second stage, compensation considering the minimum harmonic production is applied. The issue of harmonic reduction in determining the fire angle of TCR and TSC, that are very important in FACTs, is proposed. Determination of optimum angles for minimizing the total harmonic distortion (THD) is investigated and finally for faster control and decision, Artificial Neural Network (ANN) has been used and satisfactory results have been obtained and to have minimum THD, existence of maximum possible capacitors, if bank of capacitors are employed, for both negative and positive reactive power is calculated

Economic Operation of Unit Commitment Using Multiverse Optimization Algorithm

Security Constraint Unit commitment (SCUC) is one of the challenging economic problem of the power utilities due to the ON and OFF status of the units. Indeed, in SCUC we should determine the status of the units for the day-ahead horizon. SCUC is a mixed-integer linear problem (MILP), which is hard to solve. Hence, in this paper, a new evolutionary algorithm, known as the multiverse optimization algorithm is developed to solve the problem

Economic Based Neural Control Switching of TCR and TSC for Optimal Reactive Power Flow and Harmonic Minimization with Fuzzy-Genetic

Optimal Reactive Power Flow (ORPF) for improving voltage profile and power loss reduction is very important in power system planning; though its method, constraints, and quality of compensation are very effective. Value of compensator, transformer tap ratio, and generator voltages are assumed as controlling variables. Usually this optimization is accompanied by harmonic production. The most important parameter of reactive power compensators is minimum production of harmonics. Nowadays by considering the improvement of power systems in power quality and the importance of harmonics in power quality, compensators by minimum harmonic distortion should be designed. In this paper, ORPF is executed in two stages. At First stage, a genetic algorithm with a fuzzy fitness model employed to solve this multi objective optimization problem. The entire discrete controlling variable is assumed discretely as their natures in all steps of this stage. Outputs of this stage are values of controlling variable that include compensations values. In Second stage, compensation considering the minimum harmonic production is applied. The issue of harmonic reduction in determining the fire angle of TCR and TSC, that are very important in FACTs, is proposed. Determination of optimum angles for minimizing the total harmonic distortion (THD) is investigated and finally for faster control and decision, Artificial Neural Network (ANN) has been used and satisfactory results have been obtained and to have minimum THD, existence of maximum possible capacitors, if bank of capacitors are employed, for both negative and positive reactive power is calculated

A New Optimal Operation Structure For Renewable- Based Microgrid Operation based On Teaching Learning Based Optimization Algorithm

This paper proposes a new optimization framework for the optimal power dispatch in both grid-connected and islanded microgrid modes. Solving the microgrid operation by the evolutionary algorithms can be faster than analytical models due to the complexity of the problem. To demonstrate the efficiency and high performance of the proposed technique, it is applied on the IEEE 33 bus test network. Also, the proposed technique is compared with the analytical model, and also well-known heuristic methods such as particle swarm optimization (PSO), genetic algorithm (GA)

Economic Operation of Unit Commitment Using Multiverse Optimization Algorithm

Security Constraint Unit commitment (SCUC) is one of the challenging economic problem of the power utilities due to the ON and OFF status of the units. Indeed, in SCUC we should determine the status of the units for the day-ahead horizon. SCUC is a mixed-integer linear problem (MILP), which is hard to solve. Hence, in this paper, a new evolutionary algorithm, known as the multiverse optimization algorithm is developed to solve the problem

A New Optimal Operation Structure For Renewable- Based Microgrid Operation based On Teaching Learning Based Optimization Algorithm

This paper proposes a new optimization framework for the optimal power dispatch in both grid-connected and islanded microgrid modes. Solving the microgrid operation by the evolutionary algorithms can be faster than analytical models due to the complexity of the problem. To demonstrate the efficiency and high performance of the proposed technique, it is applied on the IEEE 33 bus test network. Also, the proposed technique is compared with the analytical model, and also well-known heuristic methods such as particle swarm optimization (PSO), genetic algorithm (GA)

Economic Operation of Grid-Connected Microgrid By Multiverse Optimization Algorithm

In this paper, a new optimization algorithm known as the Multiverse Optimization Algorithm (MOA) is developed for optimal economic operation of the micrigrid (MG) in the grid-connected mode. Results show the merit of the proposed technique

Self- Supplied Microgrid Economic Scheduling Based on Modified Multiverse Evolutionary Algorithm

In this paper, a new evolutionary algorithm, known as the multiverse evolutionary algorithm (MEA) is developed for self-supplied microgrid operation. To show the effectiveness of the proposed method, it has been tested on the modified IEEE 33 bus test system. Results demonstrates the economic merit of the proposed technique