International Journal of Smart Electrical Engineering
Volume:8 Issue: 3, Summer 2019

Reducing electricity losses is the main objective in distribution feeder reconfiguration (DFR) problem. Distribution feeder reconfiguration is an optimization problem in power system which is performed through changing switching state. In this study, distribution feeder reconfiguration is optimized in the presence of distributed generators (DGs). In common DFR problems, reliability constraint is not satisfied and power losses or voltage deviation of buses is selected as the objective function. In this study, multi-objective problem is considered as a combination of reliability along with power losses. By adding reliability, the problem becomes more complex and requires an accurate method for solving multi-objective optimization problem. For this purpose, in this paper proposed a new hybrid evolutionary algorithm for solving the DFR problem. The proposed hybrid evolutionary algorithm is the combination of PSO (particle swarm optimization) and SFLA (shuffled frog leaping algorithm), called Improved particle swarm optimization (IPSO). In order to investigate efficiency of the proposed method, two 33-bus and 70-bus test systems are tested and the results are compared with GA and PSO algorithms.

Due to the ever-growing load, especially peak load, the increase in the capacity of plants is inevitable for the response to this growth. Peak load causes increases in customer costs and vast investments in generating and transmission parts. Therefore, restructuring in the electrical industry, competition in the electrical market and Demand Response Programs (DRPs) are of special importance in power systems. In DRPs, customers in certain periods, such as peak or times when the price is high, decrease self-consumption. It means profit for costumers and prevention of expensive production in peak time for a genera-tion source. Moreover, to decrease the operation cost of network and ever-growing technology significantly, the power sys-tems operators have employed new sources of energy production as well as thermal units, and it has led to the emergence of Electric Vehicles (EVs) technology as a new source of energy production. This paper studies the simultaneous presence of DRPs and EVs to minimize the total operation cost of a network from one hand and from the other to improve the level of system reserve in Unit Commitment (UC) problem with considering the security constraint. Here, the proposed framework is structured as a Mixed Integer Programming (MIP) and solved using CPLEX solver.

In this study, a new method for designing the damping controller was proposed to improve the transient power system stability in a single machine network connected to an infinite bus. The STATCOM controller problem in a wide area of the system function was considered as an optimization problem with multi-purpose objective function. Also, the Honey Bee Mating Optimization Algorithm was used to determine its parameters. In this paper, a new method for designing a damping controller was presented to improve the transient power system stability in a single machine network connected to an infinite bus. The STATCOM controller design problem in a wide area of system function was considered as an optimization problem with a multi-purpose objective function and the honey bee mating optimization algorithm was used to determine its parameters. As shown, STATCOM improved system stability. For a more comprehensive analysis, the results were tested in three different scenarios. Simulation of the STATCOM controller design for small signal stability was conducted in the MATLAB / SIMULINK environment. The tuning variables in designing the controller were variables c and φ that were used to adjust the modulation index and firing angle of switches. In order to achieve the best possible state, the artificial honey bee colony algorithm was used.

Fault diagnosis has always been an essential aspect of control system design. This is necessary due to the growing demand for increased performance and safety of industrial systems is discussed. Support vector machine classifier is a new technique based on statistical learning theory and is designed to reduce structural bias. Support vector machine classification in many applications in various fields of machine learning has been successful and appears to be effective for fault diagnosis in industrial systems. This project is to design a support vector machine fault diagnosis system for a distillation tower as a key component of the process. The study included 41 stage distillation condenser and boiler theory is that a combination of two partial products of 99% purity breaks Based on the calculations, modeling and simulation is a tray to tray. Considering the variety of different origins faults in the system under study, a multi-class classification problem can be achieved two techniques commonly used to solve multi-class classification for support vector machine as "one to one" and "one against all" is used. The classifier models designed to detect faults in the systems studied were evaluated as successful results were obtained for all types of faults. The model was designed based on the speed in detecting various faults were compared on the basis of support vector machine model based on a technique called "One on One" have delivered a better performance.

How to reconfigure a logic gate for a variety of functions is an interesting topic. In this paper, a different method of designing logic gates are proposed. Initially, due to the training ability of the multilayer perceptron neural network, it was used to create a new type of logic and full adder gates. In this method, the perceptron network was trained and then tested. This network was 100% accurate to determine outputs based on inputs. The results of comparison showed that the multilayer perceptron network had higher velocity and less delay in most cases, and used a smaller number of neurons, which will reduce the loss of power. Meanwhile, implementation of these gates will require less space through the multi-layer perceptron network. This method is prioritized in terms of the number of neurons and the level of implementation, and the speed of the detection of output compared to the other design. It also occupies less hardware space and is less complicated.

With the development of critical loads, power quality problem has become particularly important and poor power quality will be cause many adverse effects and the cost of many deaths will follow. To improve power quality and protection of sensitive loads against grid disturbances have been used new equipment based on power electronics similar FACTs devices, they are Custom Power. One of the most important Custom Power Device is DVR that it is used for compensation Swell and Sag voltage in the distribution network. The DVR injected voltage to compensate for the Swell and Sag voltage in distribution network. Compensation voltage accuracy depends on by its ability to design PWM, the controller and choice of filter parameters. For control of DVR can be used traditional controller, such as PI, PID and intelligent controller, such as fuzzy controller. In This paper, Fuzzy controller is used for controlling DVR in the Distribution Network.