International Journal of Smart Electrical Engineering
Volume:4 Issue: 2, Spring 2015

The use of renewable energy in power generation and sudden changes in load and fault in power transmission lines  may cause a voltage drop in the system and challenge the reliability of the system. One way to compensate the changing nature of renewable energies in the short term without the need to disconnect loads or turn on other plants, is the use of renewable energy storage. The use of energy storage improved electrical stability, power quality and improve the peak power load. In this paper, we have used the reinforcement learning to present an optimal method for charge and discharge the consumer battery.  In this way the uncertainty of production due to the random nature of wind energy is improved. Simulation results indicate not only the use of renewable energy and battery is successfully enhanced but also the cost of annual payments and peak consumption times is reduced.

In this paper, the electrical consumption of a Research Center Building is derived of the bills, Then with defining of fuzzy Bus Thermal Coefficient objective function for heating, the amount of available heat selling to Research Center Building consumer that is produced by Biomass Solar Hybrid Source is calculated. With the capacity determination of Biomass Solar Hybrid Source to provide Thermal and electrical energy of Research Center Building and calculation of photovoltaic systems to supplying electrical energy, each technical calculation is presented. Then, Economic Calculation of each project will be addressed separately. The Economic optimization in designing and operation of electrical systems is usually done through a review of investment criteria, that in this study, are include: Net present value (NPV), Internal Rate of Return (IRR) and Return period (PP) that with regard to the economic evaluation methods, the economic evaluation of project is doing. The economic calculation shows that the use of Biomass Solar Hybrid Source is adequate but using of photovoltaic system is not adequate.

Reliability evaluation of a large-scale composite power system faces to numerous events/outage and consequently imposes an extensive burden of calculations. In order to simplify the problem, determination of an equivalent system for large-scale power system is inevitable. This paper proposes a framework as reduction technique to separate a composite power system to three areas: external area, optimization area and equipment outage area. This separation enables policy makers of power systems to evaluate reliability of large-scale power systems with less time of calculation and extraordinary precision. The reduction technique is applied to composite power system of Iran with more than 4600 buses to determine an equivalent network for reliability evaluation of Semnan Province network. Comparative discussions and simulations for case study are presented at the end.

Electric and hybrid electric vehicles are attractive candidates for sustainable transportation due to its higher efficiency and low emission. The critical choice on the electric motors is its capability of motoring and regenerative braking characteristics. Switched reluctance machines are viable candidate as with proper control and extended constant power range operation replacing the multi-gear transmission. Recently developed dual magnetic circuit reluctance machine configuration make it added advantage with its feasibility on the optimizing the magnetic flux flow. The dual magnetic circuit reluctance machines through double rotor is presented and compared with conventional machine for its power density. Further to evaluate the dynamic performance the proposed machine is compared with that of the conventional machine. It is found that the torque density of the proposed machine is 65% higher than that of the conventional machine and show promising candidate for the EV and HEV applications in the near future.

In this paper a new algorithm based on phasor measurements for estimating the Thévenin Equivalent (TE) and system inertia constant is proposed. The method estimates the network TE seen from a generator terminal and equivalent system inertia constant by means of phasor data that is available at Phasor Data Concentrator (PDC) center. It is shown that the parameters of the TE could be estimated at any system condition, but the value of system inertia constant is available only after some disturbances. The algorithm is tested on a standard Nine-Bus test system in different conditions. Results show that this method can efficiently estimate the TE both in steady-state and transient conditions as well as system inertia constant.

In most industrial zones, electric energy is one of the most important energy sources. Since electrical motors are the main energy consumers of industrial factories, consumption optimization in these motors can be considered as a main option related to energy saving. One very effective way to reduce the consumption of these equipment is to use a motor speed controllers or drives. Since the loss of inductive motor has a direct relationship with motor flux, in this paper, the rotor flux vector control has been used. Due to the strength of fuzzy controllers in load failure and noise generation states, this controller has been used to adjust the drive speed. Two fuzzy logic inputs including speed error and speed variation derivative, and a fuzzy output, motor reference torque (Te*) are estimated. The genetic optimization algorithm has been used in order to improve the Efficiency and reduce the losses. As such, the drive performance in GA and Fuzzy-Genetic (FG) states is reviewed and the simulation results are presented. Finally, the obtained results in this paper have been compared to the results of FOC inductive motor with PI controller and without optimization. It can be seen that when FG method is employed, the results show a higher performance and losses are reduced up to almost 40 to 50% in different loads, and the amount of input power is also reduced up to approximately 30%.