Mahdi Mirzaei

Bio: Mahdi Mirzaei is an academic researcher. The author has contributed to research in topics: Fault (power engineering) & Transmission system. The author has an hindex of 3, co-authored 3 publications receiving 51 citations.

Fault location on a series-compensated three-terminal transmission line using deep neural networks

Abstract: In this study, discrete wavelet transform (DWT) and deep neural network (DNN) are utilised for fault location in a series-compensated three-terminal transmission line. The series compensation causes challenges in fault location schemes of the three-terminal transmission lines. The presented fault location method has been extensively tested using the SIMULINK model of a three-terminal transmission line. Features extracted from synchronous measurements of fault currents at the three terminals using DWT are fed to the DNN. Faulted section determination and fault distance calculation are carried out using a single intelligent network simultaneously. Faulted section is determined with 100% accuracy, and the efficiency of algorithm is validated for symmetrical and unsymmetrical faults, and different values of fault resistance, inception angle, and location. The accuracy of the algorithm is acceptable for large fault resistances (above 100 Ω) and fault inception angles near zero. Total mean error for test data is 0.0458% which is much improved with respect to other similar works.

Accurate fault location and faulted section determination based on deep learning for a parallel-compensated three-terminal transmission line

Abstract: Parallel flexible AC transmission systems (FACTS) devices affect the performance of protection relays and conventional phasor-based fault location schemes in transmission lines. This study focuses on both multi-terminal and parallel-compensated lines, not investigated simultaneously in previous works. An algorithm based on deep neural networks is proposed for fault location in a three-terminal transmission line with the presence of parallel FACTS device. The line model and fault occurrence are simulated in SIMULINK and features are extracted from voltages at the three terminals by wavelet transform. The generated features are used to train a deep neural network which determines faulted line section and fault distance simultaneously. The adopted intelligence-based approach has the advantage of not requiring pre-knowledge of line specifications, FACTS devices modelling and the uncertainty in compensator parameters. A large number of fault scenarios are investigated. The faulted section is recognised correctly in 100% of test cases. The algorithm performance is acceptable for both symmetrical and unsymmetrical fault types, small fault inception angles and high fault resistance. The accuracy of fault location is improved compared to previous schemes (total mean error of 0.0993%). The proposed algorithm provides an accurate, fast and robust tool for fault location in parallel-compensated three-terminal transmission lines.

Feasibility analysis and optimal planning of renewable energy systems for industrial loads of a dairy factory in Tehran, Iran

Abstract: This paper presents the feasibility study and optimal design of different stand-alone and grid-connected renewable energy systems (RES) to supply power for a dairy factory in Tehran, Iran. To achieve an optimal system, different technical, economical, and environmental factors including net present cost (NPC), cost of energy (COE), renewable fraction (RF), and greenhouse gases emission (GHGE) are considered in the design process. An optimal system is a system that supplies the load with specified constraints, while having the least NPC, COE, and GHGE. Modeling, simulation, and analysis of power supply systems are implemented in Hybrid Optimization Model for Electric Renewables (HOMER). Due to the current low price of energy and subsidies in Iran, the best choice for the energy supply is the utilization of the external grid. Despite the laws related to the exchange of RES-produced electricity in Iran, no laws are legislated to deal with the environmental issues especially the GHGE. However, as the environmental protection laws are to be passed in the near future, we assumed typical values for the sellback rate and emission penalties and investigated the effect of these two parameters on NPC, COE, RF, and GHGE. In this paper, a systematic approach to select an optimal power supply system by effective utilization of dedicated optimization software like HOMER is presented. The simulations showed that when considering both the increase in the energy cost and suitable penalties for GHGE, the grid-connected RES was the optimum configuration for the energy supply. In general, if environmental protection laws take effect in developing countries, the grid-connected RES shows its great potential as an economic and pollution-free supply of energy.

Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier

Abstract: This paper presents the methodology to detect and identify the type of fault that occurs in the shunt compensated static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes (NB) classifiers. To study this, the network model is designed using Matlab/Simulink. Different types of faults, such as Line to Ground (LG), Line to Line (LL), Double Line to Ground (LLG) and the three-phase (LLLG) fault, are applied at disparate zones of the system, with and without STATCOM, considering the effect of varying fault resistance. The three-phase fault current waveforms obtained are decomposed into several levels using Daubechies (db) mother wavelet of db4 to extract the features, such as the standard deviation (SD) and energy values. Then, the extracted features are used to train the classifiers, such as Multi-Layer Perceptron Neural Network (MLP), Bayes and the Naive Bayes (NB) classifier to classify the type of fault that occurs in the system. The results obtained reveal that the proposed NB classifier outperforms in terms of accuracy rate, misclassification rate, kappa statistics, mean absolute error (MAE), root mean square error (RMSE), percentage relative absolute error (% RAE) and percentage root relative square error (% RRSE) than both MLP and the Bayes classifier.

Transmission Line Faults in Power System and the Different Algorithms for Identification, Classification and Localization: A Brief Review of Methods

Abstract: Transmission lines are one of the most widely distributed engineering systems meant for transmitting bulk amount of power from one corner of a country to the farthest most in the other directions. The expansion of the lines over different terrains and geographic locations makes these most vulnerable to different kinds of atmospheric calamities which more often develops faults in line. It is imperative to remove the faulty line at the earliest to restrict undue outflow of bulk power through the faulted point as well as restore system stability earliest to resume normal power flow operation. Here lays the importance of having a robust fault identification, classification and localization algorithm which would be successfully able to drive as well as actuate the digital relaying system. Researchers have worked out several methodologies in developing improved power system protection algorithms which would be able to serve to eliminate faults immediately on occurrence of the same. A brief yet exhaustive review has been presented in this article including the several methodologies adopted by numerous researchers for developing effective fault diagnosis schemes, mentioning about the highlights as well as the shortcoming of each of the methods. This compact and effective survey of literature works would help researchers to take up appropriate techniques for different purposes of transmission line fault analysis.

Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review

Abstract: The protection of AC microgrids (MGs) is an issue of paramount importance to ensure their reliable and safe operation. Designing reliable protection mechanism, however, is not a trivial task, as many practical issues need to be considered. The operation mode of MGs, which can be grid-connected or islanded, employed control strategy and practical limitations of the power electronic converters that are utilized to interface renewable energy sources and the grid, are some of the practical constraints that make fault detection, classification, and coordination in MGs different from legacy grid protection. This article aims to present the state-of-the-art of the latest research and developments, including the challenges and issues in the field of AC MG protection. A broad overview of the available fault detection, fault classification, and fault location techniques for AC MG protection and coordination are presented. Moreover, the available methods are classified, and their advantages and disadvantages are discussed.

Accurate fault location and faulted section determination based on deep learning for a parallel-compensated three-terminal transmission line

Abstract: Parallel flexible AC transmission systems (FACTS) devices affect the performance of protection relays and conventional phasor-based fault location schemes in transmission lines. This study focuses on both multi-terminal and parallel-compensated lines, not investigated simultaneously in previous works. An algorithm based on deep neural networks is proposed for fault location in a three-terminal transmission line with the presence of parallel FACTS device. The line model and fault occurrence are simulated in SIMULINK and features are extracted from voltages at the three terminals by wavelet transform. The generated features are used to train a deep neural network which determines faulted line section and fault distance simultaneously. The adopted intelligence-based approach has the advantage of not requiring pre-knowledge of line specifications, FACTS devices modelling and the uncertainty in compensator parameters. A large number of fault scenarios are investigated. The faulted section is recognised correctly in 100% of test cases. The algorithm performance is acceptable for both symmetrical and unsymmetrical fault types, small fault inception angles and high fault resistance. The accuracy of fault location is improved compared to previous schemes (total mean error of 0.0993%). The proposed algorithm provides an accurate, fast and robust tool for fault location in parallel-compensated three-terminal transmission lines.

Optimal Operation of Networked Microgrids for Enhancing Resilience Using Mobile Electric Vehicles

Abstract: The increased intensity and frequency of natural disasters have attracted the attention of researchers in the power sector to enhance the resilience of power systems. Microgrids are considered as a potential solution to enhance the resilience of power systems using local resources, such as renewable energy sources, electric vehicles (EV), and energy storage systems. However, the deployment of an additional storage system for resilience can increase the investment cost. Therefore, in this study, the usage of existing EVs in microgrids is proposed as a solution to increase the resilience of microgrids with outages without the need for additional investment. In the case of contingencies, the proposed algorithm supplies energy to islanded microgrids from grid-connected microgrids by using mobile EVs. The process for the selection of EVs for supplying energy to islanded microgrids is carried out in three steps. Firstly, islanded and networked microgrids inform the central energy management system (CEMS) about the required and available energy stored in EVs, respectively. Secondly, CEMS determines the microgrids among networked microgrids to supply energy to the islanded microgrid. Finally, the selected microgrids determine the EVs for supplying energy to the islanded microgrid. Simulations have shown the effectiveness of the proposed algorithm in enhancing the resilience of microgrids even in the absence of power connection among microgrids.