Alireza Moradi
@yadegar-e-imam khomeini (rah) shahre rey branch, islamic azad university, tehran, iran
Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran
RESEARCH, TEACHING, or OTHER INTERESTS
Electrical and Electronic Engineering
Scopus Publications
Scopus Publications
A. Moradi
International Digital Organization for Scientific Information (IDOSI)
The present paper proposes a novel vector control based method to connect a wind plant equipped with a Doubly-Fed Induction Generator to the grid. It also provides separate control capacity of injection power to the grid under wind shortages in addition to optimum performance in normal climatic conditions. The mathematical modeling of converters of the grid and rotor side is presented, which provides such a control tool. The idea of using an energy storage system has been presented to reduce power swings of the wind plant to grid in the dc-link of back-to-back converter of the rotor side. In order to achieve an optimal control system, the strategy of maximum wind power tracking and unit power factor of the wind farm are included in the control system. However, the power exchange strategy with the unit power factor is related to the type of operation of the grid, and reactive injection power can also be controlled when needed. Finally, a simulation of the studied system and the proposed control system in the SIMULINK environment of MATLAB software is presented, which provides a powerful tool for these types of systems. The results obtained from the conducted studies on a sample system demonstrate the efficiency and accuracy of the proposed method.
M. Faridpour and A. Moradi
Fraud refers to earn wealth including property, goods and services through immoral and non-legal channels. Fraud has always been in action and experiences an increasing trend worldwide. Fraud in financial transactions not only leads to losing huge financial resources, but also leads to reduction in trust of customers on using modern banking systems and hence, reduction in efficiency of the systems and optimal management of financial transactions. In recent years, by emerging new technologiesofbankingindustry,newmeansoffraudarediscovered. Althoughanewinformationsystem carry advantages and benefits, new opportunities are made for fraudsters. The applications of fraud detection methods encompasses detection of frauds in an organization, analysis of frauds and also user/customer behavior analytics in order to predict future behavior and reduce the fraud risks. In recentdecades, employingnewtechnologiesinmanagementofbankingtransactionshasrisen. Banks and financial institutions inevitably migrated from traditional banking to modern online banking to provide effective services. Although, the use of online banking systems improves the management of financial processes and speeds up services to customers of institutions, but some issues would also be carried. Financial frauds is one of the issues which organizations seek to prevent and reduce effects. Inthispaper, anovelmachinelearningbasedmodelispresentedtodetectfraudinelectronic banking transactions using profile data of bank customers. In the proposed method, transactional data from banks are leveraged and a multi-layer perceptron neural network with adaptive learning rate is trained to prove the validity of a transaction and hence, improve the fraud detection in electronicbanking. Theproposedmethodshowspromisingresultscomparedwithlogisticregressionand support vector machines.
Alireza Moradi, Yousef Alinejad-Beromi, and Kourosh Kiani
Hindawi Limited
A. Moradi, Y. Alinejad‐Beromi, M. Parsa and M. Mohammadi
International Digital Organization for Scientific Information (IDOSI)
In this article, a multi-objective planning is demonstrated for reactive power compensation in radial distribution networks with wind generation via unified power quality conditioner (UPQC). UPQC model, based on phase angle control (PAC), is used. In presented method, optimal locating of UPQC-PAC is done by simultaneous minimizing of objective functions such as: grid power loss, percentage of nodes with voltage drop, and capacity of UPQC. The proposed model is a complicated non-linear optimization problem. For introducing group of non-dominated solutions, a multi-objective grey wolf optimizer (MOGWO) algorithm based on probabilistic load flow is used, then a fuzzy sets theory is used to achieve the best response. In order to evaluate reliability of mentioned approach, simulation is done on 33-bus distribution network.
Alireza Moradi, Yousef Alinejad-Beromi, and Kourosh Kiani
Hindawi Limited
Summary
Integration of wind resources into the power systems has increased the technical and financial concerns in transmission expansion planning. In this paper, a stochastic structure is demonstrated for transmission expansion planning with allocation of fixed series compensation under wind and load uncertainties. Fixed series compensations have the ability to increase the transfer capacity of transmission lines. However, their significance benefit for transmission expansion planning is their higher effectiveness in power dispatching, which results in lower investment costs, compared with planning without fixed series compensations. The proposed planning model simultaneously optimizes targets such as the investment cost, the congestion cost, and the expected energy not supplied in each stage. The presented planning model is a complicated nonlinear optimizing problem. For introducing group of nondominated optimal solutions, multi-objective gray wolf optimizer algorithm based on probabilistic optimal power flow is used. Then, fuzzy approach is used to lead to the best response. For demonstrating the feasibility and susceptibilities of the proposed algorithm, the planning methodology has been illustrated on the IEEE 24-bus and Colombian test systems. The results acquired from the simulations monitor capability of the proposed structure in order to satisfy various planning indices.
A. Moradi, Y. Alinejad‐Beromi and K. Kiani
: Congestion and overloading for lines are the main problems in the exploitation of power grids. The consequences of these problems in deregulated systems can be mentioned as sudden jumps in prices in some parts of the power system, lead to an increase in market power and reduction of competition in it. FACTS devices are efficient, powerful and economical tools in controlling power flows through transmission lines that play a fundamental role in congestion management. However, after removing congestion, power systems due to targeting security restrictions may be managed with a lower voltage or transient stability rather than before removing. Thus, power system stability should be considered within the construction of congestion management. In this paper, a multi-objective structure is presented for congestion management that simultaneously optimizes goals such as total operating cost, voltage and transient security. In order to achieve the desired goals, locating and sizing of series FACTS devices are done with using components of nodal prices and the newly developed grey wolf optimizer (GWO) algorithm, respectively. In order to evaluate reliability of mentioned approaches, a simulation is done on the 39-bus New England network.
M Taghipour, A R Moradi, and M Yazdani-Asrami
IEEE
Inrush current in transformers is generated when transformer cores are driven into saturation during no-load energization. This current consists of high amplitude, large DC component and also, has much 2nd harmonic content. In the proposed paper, a new computer-aided simulation technique for teaching inrush current principles and its discrimination from normal current based on artificial intelligence has been introduced. This method can be used for educating concepts of inrush current and its identification techniques during undergraduate curriculum as an excellent approach. Evaluation of the proposed approach with undergraduate senior students is very useful in terms of their understanding of the inrush current concepts.