Dr. Hadi Zayandehroodi received his B.S.c from Islamic Azad University, Tehran, Iran, in 2003, M.S.c from Tarbiat Modares University (TMU), Tehran, Iran, in 2005, and PhD from National University of Malaya (UKM) in 2011 in Power Systems Engineering. Since 2004, he has been a faculty member in Department of Electrical Engineering at Islamic Azad University, Kerman Branch, Iran. He also has more than 10 years of consultancy experience with power system companies in Iran. His main research interests are designing the protection system for power systems, especially distribution networks in the presence of distributed generations (DG), as well as distribution systems restructuring and software development for electrical distribution systems. He is currently a Member of IEEE, Power System Society (PES) and Sazman Nezam Mohandesi of Iran.
Abstract: The research in this paper seeks ways for manufacturing companies to accomplish projects in real time with less cost. This paper describes a new model using critical success factors of implementing projects in small- and medium-sized enterprises (SMEs). The factors in the conceptual model were identified in past studies and are validated from interviews with 20 project managers in the electronics industry in Iran. Several prior researches have suggested critical success factors for reducing time and cost in the development of a new product. These factors are related to design, product, planning and procurement. However, none of these studies has proposed a model that specifies practices that should be undertaken to facilitate the progression to reduce time and cost in developing new products in SMEs. Following this, a development of a new model will likely prove important to help SMEs to understand what is required in order to be an effective global organization in terms of reducing time and cost through electronic collaboration (E-collaboration) and project management. The concept and value of E-collaboration and project management, and its strengths and weakness on SMEs, is also explained.
Abstract: This paper presents an overview of solutions proposed in the literature for the overcurrent relay coordination problem in the presence of distributed generators (DGs). Furthermore, several protection issues are identified to study the requirements for protection in the presence of DGs, and possible innovative solutions for resolving the operational conflicts between distribution networks and DGs are also discussed.
Abstract: Though distributed generators (DGs) have significant economic and environmental benefits, increased penetration of DGs will impose significant technical barriers for the efficient and effective operation of bulk power systems. Increased fault current contribution and load flow changes are the major two impacts on utility systems, and these will affect existing protective relaying, especially overcurrent relays. To ensure safe and selective protection relay coordination, the impact of DGs should be taken into account when planning DG interconnection. This paper presents an introduction of DGs and an overview of the impacts of DGs on system protection relay coordination. Several protection issues are identified to study the requirements for protection in the presence of DGs are also discussed.
Abstract: Distributed generations (DGs) have been increasingly connected on the distribution networks that will have the unfavorable impact on the traditional protection methods because the distribution system is no longer radial in nature and is not supplied by a single main power source. This paper presents a new automated protection method using radial basis function neural network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is determined first by normalizing the fault currents of the main source. For implementing fault location considering various types of faults, two staged RBFNNs have been developed. The first RBFNN is used for determining the fault distance from each power source and the second RBFNN is used for identifying the faulty line. To isolate the fault, another RBFNN has been developed for determining which circuit breakers (CBs) that must be open or close. Several case studies have been made to verify the accuracy of the method to specify the fault location and protection of the system in distribution networks with DGs. The predicted results showed that the proposed RBFNN based protection method can accurately determine the location of faults and isolate the faulted line in the test power system.
Abstract: High penetration of Distributed Generation (DG) units will have unfavorable impacts on the traditional fault location methods because the distribution system is no longer radial in nature and is not supplied by a single main power source. This study presents an automated fault location method using Radial Basis Function Neural Network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is determined first by normalizing the fault currents of the main source. Then to determine the fault location, two RBFNNs have been developed for various fault types. The first RBFNN is used for detraining fault distance from each source and the second RBFNN is used for identifying the exact faulty line. Several case studies have been used to verify the accuracy of the method. Furthermore, the results of RBFNN and the conventional Multi Layer Perception Neural Network (MLPNN) are also compared. The results showed that the proposed method can accurately determine the location of faults in a distribution system with several DG units.
Abstract: The increase in interconnection of distributed generators (DGs) to distribution network will greatly affect the configuration and operation mode of the power system, especially with respect to the protection scheme. However, when DG units are connected to a distribution network, the system is no longer radial, which causes a loss of coordination among network protection devices and will have unfavorable impacts on the traditional fault location methods. In this paper a new automated fault location method by using radial basis function neural network (RBFNN) for a distribution network with DGs has presented. The suggested approach is able to determine the accurate type and location of faults using RBF neural network. Several case studies have been made to verify the accuracy of the proposed method for fault diagnosis in a distribution system with DGs using a MATLAB based developed software and DIgSILENT Power Factory 14.0.523. Results showed that the proposed method can accurately determine the location of faults in a distribution system with several DG units.
Abstract: When distributed generation (DG) penetrates into a distribution system, it will have unfavorable impact on the traditional protection methods because the distribution system is no longer radial in nature and is not supplied by a single main power source. This paper presents a new automated protection method using radial basis function neural network (RBFNN) for a distribution system with high penetration DG units. In the proposed method, for implementing fault location considering various types of faults, three staged RBFNNs have been developed. The first RBFNN is used for determining the fault distance from each power source and the second RBFNN is used for identifying the faulty line. To isolate the fault, the third RBFNN has been developed for determining which circuit breakers (CBs) that must open or close. The proposed protection scheme is implemented on a practical test distribution network.
Abstract: With high penetration of distributed generations (DGs), power distribution system is regarded as a multisource system in which fault location scheme must be direction sensitive. This paper presents an automated fault location method using radial basis function neural network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is first determined by normalizing the fault currents of the main source and then fault location is predicted by using RBFNN. Several case studies have been considered to verify the accuracy of the RBFNN. A comparison is also made between the RBFNN and the conventional multilayer perceptron neural network for locating faults in a power distribution system with DGs. The test results showed that the RBFNN can accurately determine the location of faults in a distribution system with several DG units.
Abstract: The recent changes in utility structures, development in renewable technologies and increased attention on environmental concerns have led to increasing installation of renewable energy based distributed generations (DGs) in distribution networks. Presence of such DG units in a distribution network increases the complexity of protection relay coordination, control and maintenance of power distribution systems. High penetration of DGs will have unfavorable impact on the traditional protection methods because the distribution system is no longer radial in nature and is not supplied by a single main power source. With DGs in a distribution network, it becomes more active and the power flows and fault currents may have new directions. To ensure safe and selective protection relay coordination, the impact of DGs should be taken into account when planning DG interconnection. This paper presents an overview of the impacts of DGs on system protection relay coordination particularly in cases where DGs are added to a LV distribution feeder. The main objectives of this paper are to address the protection issues that have been identified in the presence of DGs, to study the protection requirements in the presence of DGs and to discuss the possible innovative solutions in resolving the operation conflicts between distribution network and DGs.
