|
|
|
 |
Search published articles |
 |
|
Showing 5 results for haghifam
Mr. Mahdi Sedghi, Dr. Masoud Aliakbar-Golkar, Dr. Mahmoud-Reza Haghifam,
Volume 1, Issue 2 (2-2013)
Abstract
Failure rate is an important parameter in the reliability study of power systems. Failure rate of distribution feeders is usually considered as a constant parameter in power distribution systems study however in fact, it is a variable parameter which is dependent on various internal and external factors. The historical and statistic data is used to predict the variable failure rate. In this paper, the vegetation-related variable failure rate of overhead distribution feeders is considered for analysis and prediction. Whereas the collected statistic data usually contains practical errors and noises, here the Factor Analysis is used for data mining and removing the outliers. Then, a multi-layer artificial neural network is used to predict the failure rate. Moreover, the neural network is used to analyze the input data. Case studies of a typical 32-feeder distribution network show that the factor analysis and neural network methods emphasize their results. The proposed method can be implemented to reduce complexity, remove the outliers and increase reliability of the prediction.
Mohammad Esmaeil Honarmand, Mahmood Reza Haghifam, Mohammad Sadegh Ghazizadeh,
Volume 4, Issue 1 (9-2015)
Abstract
Abstract: Information about network components is of great importance for asset management decision making. This information is needed as input for the asset management decision process to come to a decision. To be able to extract the requested information, analysis and evaluation are needed. On the other hand, for efficient and economical asset management from reliability view point, analysis of failure rate for determination of origin of fault in component is an important task for asset manager. The failure rate of components is obtained by gathering data in outage management process that is available usually in detail in outage databases in electrical distribution. In this paper, failure analysis of process is obtained by assessment of process of component entry in distribution system. In this analysis, failure factors and sub-factors of each process is assigned by focus group and then relation between factors and causes of component failure is obtained by Delphi method. Finally, reasons of process-based failure are invested by modeling of failure rate for each process.
Prof. Mahmoud-Reza Haghifam, Eng. Nima Mostaghim, Dr. Mohsen Simab,
Volume 5, Issue 1 (7-2016)
Abstract
In this paper, a multi-level benchmarking method is presented for assessing efficiency of electrical distribution companies’ expenditures. Proposed method considers mutual cooperation of capital expenditure (CAPEX) and operational expenditure (OPEX) for improving quality and decreasing energy losses. Firstly, weighted fuzzy c-means clustering (WFCM) is used by proposed method to find similar companies. Then, proposed method utilizes data envelopment analysis (DEA) and slack analysis in multi levels and through creating pseudo competition between companies, estimates companies’ efficiency scores and slacks in their expenditures. Finally, proposed IBRS is applied on Iranian distribution companies and its results are discussed.
Mirsaeed Mousavizadeh, Mahmoud Reza Haghifam, Mohammad Hossein Shariatkhah,
Volume 7, Issue 1 (9-2018)
Abstract
Natural disasters and severe weather conditions can lead to extensive outages in power networks. In order to reduce the economic and social effects of blackouts, most electric utilities apply pre-determined instructions and procedures to recover the system and restore the loads. However, due to the high impacts and different nature of such incidents, traditional methods of load restoration in the distribution networks can not guarantee the desired performance of the system in these situations. Therefore, in this paper, a novel model based on mixed-integer linear programming is presented to load restoration in distribution networks after such disasters. In this model, by utilizing graph related theories, the topological features of the distribution network along with its electrical characteristics are formulated in the form of one linear optimization problem. The formation of microgrids, determination of their service areas, and the optimal management of different technologies such as distributed generation units and demand response resources have been also considered. Finally, by performing multiple simulations, the efficiency and applicability of the proposed integrated model have been verified.
Salman Sanaei, Mahmud-Reza Haghifam, Amir Safdarian,
Volume 9, Issue 3 (9-2020)
Abstract
One of the most important tasks of operators in distribution companies is to restoration after a fault in the network. In load restoration schemes, in addition to observing the load flow constraints, it is necessary to maintain the grid structure radially and, most importantly, observing the balance of consumption with the possibility of providing load due to the limitations of the backup feeder (or distributed resource constraints). This paper presents a new approach called smart load shedding in distribution networks, which aims to design a smart load shedding module in situations where it is not possible to provide load after a network failure. In this case, the network load restoration will be provided at the lowest outage cost and in the shortest possible time to maximize customer satisfaction. In order to validate the proposed method, the level of automation of the sample distribution network for smart control of loads is defined in three scenarios. The types of loads available in smart homes are also prioritized and categorized into three categories of adjustable, interruptible, and shiftable loads. The proposed method is coded in the MATLAB software environment. To demonstrate the effectiveness of the proposed model, simulation is implemented on an RBTS system. The results show the effectiveness of the proposed method in reducing costs and improving the restoration management of the distribution system.
|
|
