COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)
Year:2016 | Volume:7 | Issue:1|Papers Count:7

This paper proposes an optimal scheduling for charge/discharge of centralized storage units within a distribution network. The storage units are used not only for peak cutting but also for decreasing energy not supplied due to failure events. The impact of plug-in electric vehicles (PEVs) charging on the optimal scheduling is taken into account as well. PEVs load demand is modeled using a stochastic approach based on the Monte Carlo simulation. Then, a Tabu search algorithm is utilized in order to fulfill the optimal scheduling of battery energy storages considering the extracted load demand of PEVs. Numerical studies on a typical distribution network show the impacts of various penetration levels of PEVs on the optimal scheduling issue. It should be mentioned that characteristics of the distribution network as well as the location of the stationary batteries affect the optimal scheduling.

In this paper, real-coded genetic algorithm with smart mutation (RCGA-SM) is proposed to solve the economic dispatch (ED) problem. In the proposed method, the required controllingprocess is accomplished on the total amount of chromosomes and consequently there is no need to use penalty cost function for controlling sum of variables in solving economic dispatch problem. This method will begin to explore the optimal answer just within the logic and acceptable zone in addition to its capability in reducing the search range. In order to show the performance and the efficiency of the proposed method, the ED problem considering several constraints is solved in 6, 15and 40 units systems through the proposed technique. The proposed coding could effectively escape from infeasible solutions. Thereby search efficiency and solution quality are dramatically improved.The obtained results are compared with other advanced technical algorithms, which well depict the superiority of the RCGA-SM technique over the other compared methods.

In a real electricity market, complete information of rivals’ behavior is not available to market participants. Therefore, they make their bidding strategies based on the historical information of the market clearing price. In this paper, a new market simulator is introduced for a joint energy and spinning reserve market, in which market participants’ learning process is modeled using Q-learning algorithm.The main feature of this simulator is simulating a real market, in which market participants make decisions based on incomplete information of the market. Using the proposed simulator, the clearing price for each submarket is computed considering the participants’ behavior, under different load levels and/or contingency conditions. The results show that Q-learning approach can modify the agent’s strategy under different market situations.

In this paper a fuzzy logic based approach is presented for control of small-scale Wind Energy Conversion System (WECS) over wide operating range. The considered WECS is equipped with fixedpith blades and an Interior PM Synchronous Generator connected to a full-scale PWM rectifier. The proposed control strategy aims to track maximum power point at underrated wind speeds and harvest rated power of generator at overrated wind speeds. The space vector modulation-based direct torque control method is used to track the electrical torque and flux references. The reference of electrical torque is provided through a Fuzzy Logic Controller (FLC) achieving the maximum power point tracking. On the other hand, the reference flux magnitude is provided through a comprehensive strategy guarantying the machine operation over nominal rotational speed, limitation of the generated reference torque and copper-loss minimization. These are formulated asa nonlinear optimization problem which is solved numerically offline using the well-known sequential quadratic programming method. It should be noted that, the fuzzy rules are designed in such a way that not only result fast and accurate tracking performance at underrated wind speeds but also avoids irrational growth in reference electrical torque when the reference torque is limited by reference flux providing strategy. The proposed control strategy is verified by applying the strategy to a commercial 10 kW wind turbine simulated in MATLAB/Simulink.

In this paper an optimized high frequency lumped model of Induction motor is presented. Model parameters are identified and optimized using Genetic Algorithm (GA). A novel model and approach in an improved high frequency based on GA for parameter identification are used. At first, parameters are limited and then fitted using GA for best fitting. The proposed model considered accurate simulation of both differential and common mode behavior in the EMI-frequency range from 100 Hz to 30MHz. Model parameters which extracted from GA are compared with experimental data in both magnitude and phase at the same time and results show a good accordance between the experimental results and simulation results of the proposed model. A least mean square (LMS) method was used with a GA optimization method to solve the identification problem. The proposed model is suitable to obtain the simulation models to predict high frequency conducted Electromagnetic Interference (EMI), over voltage on terminated motor and common mode current in cable fed induction motor.

Due to concerns about the environmental pollution caused by the burning of fossil fuels, and its continually diminishing reserves, the use of wind power for generating electricity has been increasing over the last few decades. Nowadays, controller design for a variable speed wind turbine is one of the most challenges for engineers. In this paper, a control strategy based on multiple model predictive control techniques to control variable speed wind turbines in the below rated wind speed regime is proposed. In this region, control objectives are mainly to maximize energy capture, and to reduce dynamic loads. This has the effect of increasing the efficiency and the lifetime of the wind energy conversion system (WECS).Furthermore, in this control structure the constraints on the system variables in the controller design are considered and a multiple model structure to deal with the nonlinearities in the system is used. A 2MW wind turbine is considered to show the good performances brought by the proposed approach by presenting and discussing the simulation results.

This paper proposes an analytical dynamic model for series connected wound rotor induction generator (SCWRIG) and investigates the experimental performance of SCWRIG and compares it with an ordinary induction generator (IG) at standalone operating mode. After introducing the structure of series connected wound rotor induction generator and its different modes of performance, a suitable dynamic model is proposed for this machine. The results of experimental investigations indicate that series connected induction generator and ordinary induction generator have some similar behaviors. But, SCWRIG has higher speed range without any need to gearbox and capability of operating at higher voltage levels. So it may be proposed as a new suitable candidate for wind power generation at regions with high-speed wind.