COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)
Year:2017 | Volume:8 | Issue:3 |Papers Count:6

In the present paper, the shape optimization is done for the switched reluctance machine to decrease the noise and vibration of this motor. The main reason for noise and vibration in the switched reluctance machine is the instantaneous radial force which is applied on the stator poles of the machine. Using the ANSYS finite element package, an electromagnetic simulation model has been developed for the switched reluctance motor by which the instantaneous radial force applied to stator poles and the natural frequencies are obtained precisely from the done electromagnetic and modal analyses. Applying the developed simulation model to a 6.4 switched reluctance motor, the simulation results are presented and they are validated using the given experimental results. To reduce the noise of the considered switched reluctance motor, a different structure is suggested for the stator and the optimal design parameters are determined using the developed simulation model and different algorithms are proposed in the design of experiment method.

Electric vehicles (EVs) are new and growing loads in distribution networks. Increasing number of electric vehicles in a distribution network causes increase of electricity energy demand. Therefore, in the absence of any energy consumption management, some distribution system operation constraints (e.g. bus voltage magnitude) may be violated. Power electronic devices used for charging and discharging the batteries, are usually called chargers.The charger could be unidirectional (transfer the energy from network to the battery) or bidirectional. Bidirectional chargers work in four areas of PQ power plane. In this paper, firstly, the active and reactive power management of smart distribution network using electric vehicles as non-linear problem is presented. Then, the problem is converted to mixed integer linear programming (MILP) problem using specific linearization method and is solved by GAMS package. The proposed scheme has been tested on the 33-bus distribution network and its performance and capability have been evaluated by simulation results.

Constructing new transmission lines requires various mechanical, protection and control facilities, therefore, it is expensive. Significant cost and shortage of government investment are two factors that prevented proper expansion of transmission lines. Due to recent increase in electricity consumption as well as power production in power plants, the need to invest for constructing new transmission lines has been increased. In this aspect, presence of private parties in transmission expansion planning is one of the main approaches to overcome difficulties associated with constructing new transmission lines. However, power system restructuring and deregulation has increased uncertainties in transmission expansion planning and made investment in electrical transmission lines more complicated and less appealing for private parties. In this paper, a comprehensive model for transmission expansion planning and congestion management is proposed such that despite various uncertainties, economical and technical issues make the transmission expansion planning more appealing for private parties. To do that, a multi objective programming problem which is based on Bat Inspired Algorithm is proposed.Three objective functions including minimizing investment cost, minimizing lines congestion cost and maximizing the investment from private parties for constructing transmission lines are considered.The proposed optimization problem is a nonlinear and non-convex multi-objective optimization and accordingly, a bat inspired based algorithm is proposed for solving it. For accelerating the optimization process and preventing local optimum trapping, new heuristic approaches are included to the original algorithm. Solving the multi-objective optimization problem using the proposed algorithm, results in several optimal plans showing compromise between objective functions. The final plan, among the generated plans, is selected using a max-min fuzzy decision making. The proposed method is applied on the IEEE 24 bus test system and effectiveness of the proposed method is verified. Simulation results show that in the presence of various uncertainties, the proposed algorithm in addition to minimizing the investment and reducing the congestion costs identifies low risk and profitable transmission lines to be invested by private parties.

This paper investigates frequency stability in a three area power system including steam and gas turbines by taking into physical nonlinear constraints due to reheat steam turbine, generation rate constraint (GRC), governor dead band (GDB) and boiler dynamics (BD). A new load frequency controller based on nonlinear PID controller with derivative filter (NL-PIDF) is proposed and optimized. In order to improve dynamic stability, superconducting magnetic energy storage (SMES) is considered in first area and parallel HVDC links are used between some interconnected areas. Krill Herds optimization algorithm with hybrid cost function is used to optimally design integral control (AGC), proportional control and inductor current feedback gain of SMES and NL-PIDF controlling parameters. Simulation results in time domain and Eigen values studies demonstrate fast, stable, robust and also desirable performance of proposed control strategies in damping frequency and active power oscillations in the face with random step and sinusoidal load perturbations, variation in nonlinear GRC and GDB constraints and wide uncertainty in dynamic parameters.

The presence of DG units in existing networks compared with conventional generators has created challenges. Hence, network standards make limitations. For this purpose, network standards do not allow the small DG, function properly in Low-Voltage Ride Through (LVRT). and in the presence of a fault, they will be disconnected from the network. Due to the fact that, lack of sufficient reactive power in the network will lead to slow voltage recovery, fault fixation and consequently disconnection of DGs from the network happens. This paper represents a method for optimal placement of capacitors and DSTATCOM in industrial distributing networks in the presence of DGs to provide reactive power required by the network in the steady state and providing dynamic reactive power (transient), and eventually helps combining DGs with network by considering authorized network restrictions. While, considering the high price of Custom Power such as DSTATCOM, it is required to implement efficient methods to use them in the network. As a result, the present paper proposes a method in order to determine DSTATCOM control parameters with the aim of fast voltage recovering, in order to obtain thorough economic benefits.

Electric Vehicle (EVs) usage causes to decrease the consumption of fossil fuel resources and pollution. If such EVs include vehicle to grid (V2G) capability, then a smart scheduling can be used in order to obtain more profit. The goal of this paper is to present a charge and discharge scheduling to maximize the profit of EV owner, using genetic algorithm (GA). The suggested method can be applicable without deteriorating the normal usage of EV. Besides that, in this method the hourly traveled distance and state of charge (SOC) of EV are considered in each hour of day and night. To evaluate the accuracy of this algorithm, stochastic data are generated and the algorithm is repeated 1000 times. Finally, the expected profit of EV owner is calculated for 3 modes named without V2G ability, with V2G and 72 ampere charger and with V2G and supercharger. The results show that the 3rd mode is more profitable than the other ones.