filter by: Publication Year
(Descending) Articles
Mehdipour birgani, A. ,
Shams, M. ,
Jannati, M. ,
Hatami aloghareh, F. Engineering Applications of Artificial Intelligence (09521976) 139
As the primary protection method for transmission lines, distance relays are prone to malfunction during power swings. In fact, the inability of distance relays to differentiate between power swings and short-circuit faults imposes a significant risk to power system stability that can result in blackouts. In recent years, there has been increasing interest in leveraging machine learning techniques to identify various types of faults and power swings in electrical systems. However, previous works mainly focus on fault classification, which is mostly done after a long period from the moment of fault initiation. This is the reason for requiring extensive post-fault data for diagnosis. To address this challenge, this study proposes a predictive protection strategy utilizing deep learning methodologies, specifically a sequence-to-sequence model, to monitor electrical power systems continuously. The objective is to effectively detect power swings from short-circuit faults with minimal reliance on post-fault data and accurately identify short-circuit faults during power swings. In the proposed approach, features are extracted from grid current signals using the Hilbert transform and empirical mode decomposition algorithms. These features are then fed into the sequence-to-sequence model, which issues block/unblock commands upon confirming the presence of a power swing or fault during the power swing. Results from various simulations conducted on an IEEE 39-bus grid in DIgSILENT and MATLAB environments demonstrate that the proposed scheme outperforms baseline methods in the detection of short-circuit faults, power swings, and short-circuit faults occurring during the power swings. The timely and correct operation of the proposed protection scheme contributes to the stability of transmission lines and power systems. © 2024 Elsevier Ltd
Results in Engineering (25901230) 23
Nowadays, clean and sustainable energy development is of essential concern, which justifies use of renewable energy sources. Wind energy is an important resource to provide such a demand. Due to the high costs of wind power generation compared to other renewable sources, the wind turbines should be designed in such a way that they usually operate at the highest point of their power. In this case, because of the random and alternating wind speed, the output power of the wind turbine generators and thus the windfarms fluctuate. When the capacity of windfarms is increased, the power injected from the windfarm into the grid can have significant negative effects on the power grid stability. In order to prevent these effects, it is necessary to use BESSs in windfarms. To this end, a waveform with acceptable fluctuations is considered for the windfarm output power. This waveform is compared with the output power waveform and then the difference between the two waveforms is compensated by a BESS. Proper tracking with the minimum delay reduces the required BESS capacity and thus initial investment cost for the windfarms is significantly reduced. In this paper, using real windfarm data, the conventional tracking methods for the windfarm output power waveform are analyzed and compared, first. Then, a novel tracking scheme, namely the MSA, is proposed, which is based on a two-fold master-slave adaptive linear neuron. Acquired results show that compared with the conventional methods, using the MSA scheme can reduce the costs of a 99 MW windfarm by 4.8 million dollars. © 2024 The Authors
IET Generation, Transmission and Distribution (17518687) 18(4)pp. 767-778
Power transformers play a critical role in the performance of power systems. This equipment is costly due to significant copper and iron prices and manufacturing costs. Therefore, maintenance and protection of such equipment is essential. Despite its robust performance, maloperation of differential protection (DP) in transformers may cause operational challenges to power system operators. The differential relay may operate incorrectly after the transformer energization leads to an inrush current (IC) and the relay identifies the event as an internal fault, and consequently issues the trip command. The other case of maloperation includes, but not limited to, a moment when the current transformer saturates due to an external fault. In this paper, a novel approach for DP is proposed, that is based on signal processing methods. In this paper, variational mode decomposition (VMD) and the deep neural network are implemented by using the convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) models. The VMD decomposes differential current signal (DCS) to intrinsic mode functions with corresponding narrow-band property frequency spectrums, which provides more detailed information about signal characteristics in different frequency bands. At the next stage, an effective feature for the BiLSTM is extracted by the CNN with the convolutional layers to classify events and proper discrimination. Extensive simulations on a 500 MVA transformer in MATLAB demonstrate the effectiveness of the proposed protection approach to differentiate ICs from internal and external faults with 99.8% accuracy in less than 1/8th of a power cycle. © 2024 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Alexandria Engineering Journal (11100168) 102pp. 327-338
Iranian Conference on Electrical Engineering, ICEE (26429527) (2024)
High Impedance Arcing Faults (HIAF) have always been considered an influential factor in the protection of electric power distribution networks (EPDNs). Characteristics such as low current levels in these faults causes the malfunction of conventional protection devices because of incorrect detection. Therefore, new methods should be provided that are able to detect the HIAF from other events in the EPDN based on these characteristics. Most of the previous fault detection techniques are dependent on a massive volume of training data to detect and classify the faults and other events, requiring a lot of time for data extraction. Furthermore, in some cases accessibility to these data is too difficult and sometimes impossible. Therefore, this paper proposes a novel protection technique based on a deep-learning algorithm to detect and classify the HIAF from other events, and also to significantly reduce the dependence on a large amount of training data. The proposed technique uses a small amount of data to extend the knowledge of pretrained SqueezeNet architecture to HIAF detection and classification problems, thereby reducing the dependence of the method on a large amount of training data. The simulation results in the presence of renewable energy sources on the modified IEEE 13-bus and 34-bus EPDNs indicate the high accuracy of the proposed technique in categorizing different network events. © 2024 IEEE.
Electric Power Systems Research (03787796) 219
Power transformer protection performs an essential role in power systems, ensuring a reliable power supply to the customers. One of the main challenges in differential protection of the transformers is to correctly discriminate inrush currents from internal faults and prevent the maloperation of the differential relay. In this regard, a novel differential protection method is proposed, which decomposes the differential current signal to multiple energy levels through the multi-resolution analysis (MRA) and selects the most useful feature to feed to the bidirectional gated recurrent unit (BIGRU) to classify the events. The use of the BIGRU results in the high accuracy and low implementation complexity of the proposed approach. Various simulations carried out on a 70 MVA transformer demonstrate that the proposed approach has an accuracy of 99.70% in discriminating inrush currents from internal faults in less than one-eighth of the power cycle. © 2023 Elsevier B.V.
IEEE Systems Journal (19379234) 17(2)pp. 3160-3171
The dependence of high-impedance faults (HIFs) detection methods on a large amount of training data has always been a fundamental problem in electrical distribution systems. This article proposes a novel protection system based on the transfer learning technique and GoogleNet architecture to reduce this dependence. The proposed protection system uses a small amount of data to extend the knowledge of pretrained GoogleNet architecture to the HIF detection problem. In this system, a small amount of third harmonic angle data of the current at the measurement point are obtained from the understudy electrical distribution system. Then, the preprocessing phase is performed, and the extracted data are converted to image data using the Wigner-Ville distribution. Afterward, these converted images are fed to the GoogleNet architecture as an input dataset to update the GoogleNet pretrained knowledge. Finally, the process of fault detection and classification is accomplished only by transferring the GoogleNet pretrained knowledge. The simulation results of the modified IEEE 13-bus and 34-bus distribution systems in EMTP-RV and MATLAB indicate the high accuracy of the proposed protection system despite the use of a small amount of input training data. © 2007-2012 IEEE.
International Journal of Electrical Power and Energy Systems (01420615) 141
The operating speed of adaptive single-phase auto-reclosure (ASPAR) is of great importance to maintain power systems stability in high-voltage power transmission lines (PTLs). This paper proposes a two-step protection scheme using the long short-term memory (LSTM) network to enhance the ASPAR performance. The discrimination between transient and permanent faults is made by an LSTM with high accuracy in the first step. If transient faults are detected in the second step, another LSTM is applied to predict the secondary arc extinction time (SAET). To this end, the second LSTM accurately foresees the voltage waveform of the faulty phase a half-power cycle earlier, and predicts the SAET in order to get a successful reclosing. The results obtained from extensive simulations using EMTP-RV and MATLAB software environments indicate that the presented protection scheme outperforms other ones in terms of fault type classification, achieving an F-measure value of 98.90%. Moreover, the results verify that the LSTM can accurately estimate the voltage waveform and the SAET, that ensures a successful reclosing of the faulty phase. © 2022 Elsevier Ltd
Electric Power Systems Research (03787796) 208
Opportune detection of transient faults and restoration of the faulty phase in the least possible time plays a key role in improving the stability of high voltage transmission lines (HVTLs). This paper presents a novel four-step strategy for the protection of HVTLs so that the stability and reliability of power systems are enhanced. Firstly, the cumulative sum (CUSUM) algorithm is used to quickly detect the transient and determine the faulty phase. In the second step, the settings of the static var compensator (SVC) are changed to reduce the reclosing dead time as much as possible. Afterward, the transient or permanent type of the fault is identified based on the proposed principal component analysis-support vector machine (PCA-SVM) algorithm. In the fourth step, if a transient fault is recognized, the successful single-phase reclosing time is identified by the PCA-SVM and the reclosing command is issued to high voltage circuit breakers (HVCBs). Simulation results obtained by using EMTP-RV and MATLAB for a sample 400 kV power system illustrate that the proposed protection strategy can increase the reliability of the power system via reduction of the dead time, correct detection of transient faults from permanent ones, and accurate detection of the reclosing time. © 2022 Elsevier B.V.
Electric Power Systems Research (03787796) 212
Low level current and similarity of High Impedance Faults (HIF) in respect of characteristics to other transient events have posed a critical challenge to the protection of distribution systems. In addition, the dependency of previous methods on large amounts of training data increases the simulation error rate, and preparing this amount of data is time-consuming. In this paper, a novel scheme based on conditional generative adversarial network (CGAN) and convolutional neural network (CNN) classifier techniques is proposed, that reduces this dependency and leads to acceptable classification accuracy. In the proposed method, a small amount of data is extracted from the under-study network as the real data. Then, the third harmonic angle of the current is extracted from the real data by an adaptive linear neuron (ADALINE) as an effective feature. The CGAN is performed to produce a large amount of pseudo data. At last, the fault data is separated from other transient network events via the CNN classifier. Five different scenarios are used to evaluate the proposed method on a 13-bus IEEE network. The simulation results show that the Precision and Recall of distinguishing HIFs from other transient events is greater than 98% in all the scenarios. These results verify that the proposed scheme is very accurate despite the low dependency on input training data. © 2022 Elsevier B.V.
IET Generation, Transmission and Distribution (17518687) 15(1)pp. 97-107
This study proposes a a novel efficient strategy for identifying symmetrical faults from power swings in order to improve the performance of distance relay. This method is based on two new indices and the use of adaptive linear neuron and moving window averaging technique, which is applied to the waveforms of the current. If the proposed algorithm detects power swings, it enables power swing blocking; and if it recognises the occurrence as a symmetrical fault, it resets the power swing blocking as quickly as possible. The efficiency of the proposed method has been tested in different conditions and compared to other methods from different points of view. Simulation results under different conditions in PSCAD and MATLAB software show that the proposed strategy is able to detect symmetrical faults from power swings with high accuracy. The high robustness of adaptive linear neuron and the moving window averaging technique has made the proposed method highly noise-resistant; and also, because of its low computational cost, the response speed of the proposed strategy is very high, and hence its practical implementation is simple. © 2020 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
Measurement: Journal of the International Measurement Confederation (02632241) 175
High impedance Arcing faults (HIAFs) are normally caused by ruptured conductors, leaning of a tree with high impedance, and/or the presence of a high impedance object between the conductor and earth. In such cases, protections available in the microgrid may not be capable of detecting the HIAFs. Hence, to increase the safety level and reliability of the microgrid, it is essential to take action for fast and reliable detection of these types of faults. Therefore, the present study introduces an appropriate strategy to detect HIAFs using a pattern recognition approach. To this end, different scenarios are implemented in the training data extraction step considering the measurement units embedded in a 25 kV microgrid in the presence of Distributed Generations (DG) and Renewable Energy Sources (RESs) in the EMTP-RV software environment. Then, after the initial processing, the scenarios are scaled-down and compared using the Pearson Correlation Coefficient (PCC) and Principal Component Analysis (PCA) methods. Next, the processed data is classified using the Support Vector Machine (SVM) method by selecting the most appropriate kernel. Simulation results in EMTP-RV and MATLAB environments demonstrate that the proposed strategy is capable of fast detection of HIAFs in microgrids with a high level of accuracy. © 2021 Elsevier Ltd
Jannati, M. ,
Foroutan, E. ,
Mousavi, S.M.S. ,
Grijalva, S. Journal of Energy Storage (2352152X) 32
Renewable energy sources (RES) provide significant environmental benefits, but are highly variable. Intelligent Parking Lots (IPL) can be utilized for smoothing renewable sources, thus reducing the need for large battery energy storage systems (BESS). However, the integration of intermittent RES with IPLs can be challenging. A proper energy management system (EMS) is need to optimize resource operations, reduce investment costs, and support higher penetrations of renewable energy. This paper presents a novel, heuristics-based EMS for an IPL coupled with wind turbines, photovoltaic systems, and expansion turbines, which avoids the need for BESS installation. Extensive simulation results for the case of the city of Isfahan show that the proposed EMS can smooth the output power of RES with a maximum variation of 5% during each 10-minute window. The method utilizes a small number of electric vehicles (EVs), and preserves the EVs battery lifetime by reducing the number of switching actions to an average of 0.2 for each EV, while satisfying the requested EV charge level. © 2020 Elsevier Ltd
Journal of Cleaner Production (09596526) 266
The wind farm output power fluctuates because of the random intermittent speed of the wind. When the capacity of wind farms increases, the effect of wind power fluctuations on the power system stability becomes more considerable. Therefore, the fluctuations are mitigated by use of Battery Energy Storage System (BESS) units. The high cost of the BESS has always been one of the major challenges in the investment for the wind farms. Therefore, it is necessary to protect the lifetime of BESS units used in wind farms by using a proper power allocation for them. In this paper, a three-layer control method is proposed to reduce the required BESS in the wind farm, while satisfying the utility constraint and increasing the lifetime of the BESS units. Moreover, 9 power allocation strategies for charging/discharging process in the BESS units are investigated and the most appropriate strategy is introduced. The simulation results based on the output power data of a real 99 MW wind farm indicate that the proposed heuristic-based method is very successful in controlling the switching actions, because the maximum and the average achieved value of switching actions are equal to 2 and 1.14, respectively. In addition, the variance of the Number Of charging/discharging Cycles (NOC) of BESS units is 0.33 which reveals that the number of BESS units with switching actions higher than the average is very small. Moreover, the low average value of life-expended of all the BESS units represents the efficient performance of the proposed control method. © 2020 Elsevier Ltd
Turkish Journal Of Electrical Engineering And Computer Sciences (13000632) 28(2)pp. 1164-1178
An appropriate method is proposed for identifying permanent faults from transient faults in double-circuit transmission lines. This method could be used in adaptive single phase auto-reclosures in order to diagnose between permanent and transient faults, determine extinguishing time of the secondary arc, and calculate issuing time of reclosing commands during the occurrence of transient single phase to ground faults. The proposed method is based on harmonic analysis of the adjacent healthy circuit and could be an effective solution for blocking permanent disconnection of the power flow, improving stability, and maintaining the power network synchronism. In this paper, transient and permanent faults are simulated on a typical power system, and then current harmonics of the healthy circuit terminal are extracted and finally the proposed index for identifying the fault type is applied. In the case of transient faults, this method determines the minimum time needed for a fast successful adaptive reclosing in the network and thus prevents instability and nonsynchronism in both sides of the transmission line. Results of the various simulations run in EMTP-RV verifies accurate performance of the proposed approach. © 2020 Turkiye Klinikleri. All rights reserved.
International Transactions on Electrical Energy Systems (20507038) 30(5)
In order to distinguish permanent single line-to-ground faults from transient faults and prevent reclosing in the case of a permanent fault occurrence, an adaptive algorithm is proposed. The proposed algorithm prevents unsuccessful reclosing if the event is transient by identifying the extinguishing time of the secondary arc, thus preventing long interruptions in the electricity production. Furthermore, an adaptive auto-reclosure is constructed based on the proposed algorithm. Extensive simulations in the EMTP environment and performing tests by the constructed reclosure and a test relay device (Vebko) using practical data verify the speed and accuracy of the proposed algorithm. © 2020 John Wiley & Sons Ltd
IET Generation, Transmission and Distribution (17518687) 14(5)pp. 873-882
As more than 80% of single line to ground faults in power transmission lines (PTLs) are transient, single pole autoreclosure (SPAR) is of considerable importance for increasing the successful SPAR rate, improving power system stability and reducing shocks to the power systems. In this study, a new two-step strategy for ASPAR is presented. In the first step, the fault type (permanent or transient) is determined using the derivative of the first harmonic of the faulty phase voltage. Then in the second step, the extinguishing moment of the secondary arc is detected in the case of transient occurrence, in which the reclosing signal is sent to the side circuit breakers of the PTL after the secondary arc is completely extinguished. The major features of the proposed scheme are: its capability to detect the permanent fault from the transient one, and determine the extinguishing moment of the secondary arc without the need to define a specific threshold level, its independence from the PTL type and its appropriate structure that facilitates its online hardware implementation and makes it more comprehensive. Extensive simulations in the ElectroMagnetic Transient Program-RV, as well as practical data testing, demonstrate the accuracy and precision of the proposed method. © The Institution of Engineering and Technology 2020
IEEE Transactions on Power Delivery (19374208) 34(4)pp. 1647-1655
Incipient faults in underground cables are often resulted from electric stress and cable aging. If such faults occur as current spikes in short periods, permanent faults may appear. Moreover, incipient faults may disturb electricity transmissions because of detection delays and thus, precise well-timed protection decisions cannot be made. Therefore, one of the most important considerations of utilities in the monitoring process is to recognize this type of faults from other conditions as soon as possible. In this paper, a precise approach based on CUmulative SUM and ADAptive LInear NEuron has been proposed. In addition to its high-speed detection ability, the practical on-line implementation of the proposed approach is simple as well. The simulation results in EMTPWorks environment demonstrate that the proposed approach has a strong capability in distinguishing the cable incipient faults from other similar conditions in distribution systems. © 1986-2012 IEEE.
Renewable Energy (09601481) 87pp. 1-14
Wind farm power fluctuations resulted from the wind random nature bring a significant challenge to the wind turbine generators operating in the maximum power point tracking. Furthermore, the smoothing process of a large wind farm in which the Battery Energy Storage System (BESS) is used, needs a considerable initial investment cost. Utilizing Smart Parking Lots (SPLs) can be considered as an applicable solution. In this paper, assuming conventional parking lots in Tehran converted to an SPL set in a near future and thus establishing an enormous charging/discharging capacity, the smoothing process is performed on a 51 MW wind farm. For this purpose, a coordinated control system based on two control algorithms is proposed. The first proposed algorithm chooses eligible SPLs for charging/discharging activity before receiving a new sample of the wind farm output power. Afterwards, the second proposed algorithm determines qualified vehicles in selected SPLs. The main aim is to minimize the number of SPLs (vehicles) taking part in the process. According to the simulation results, the required BESS capacity in the power smoothing process of a typical wind farm decreases considerably when the proposed approach is applied. Thus, the investment cost of BESS is reduced significantly. © 2015 Elsevier Ltd.
Energy (18736785) 101pp. 1-8
Most wind turbine generators installed in large wind farms are variable speed types which operate at the maximum power point tracking mode in order to increase the power generation. Due to this fact and regarding the random nature of the wind speed, the output power of the wind farm fluctuates randomly. Fluctuating power affects network operation and needs to be smoothed. In order to mitigate the output power fluctuations of a wind farm, a 4-step coordinated control technique based on ADALINE (ADAptive Linear NEuron) is proposed in this paper which uses a small BESS (Battery Energy Storage System) capacity. At first the on-line tracking of the WFOP (Wind Farm Output Power) is carried out by ADALINE. Afterwards, two constraints for maximum permissible fluctuations are imposed on the ADALINE output. Two states of charging feedback control strategies are implemented in the third and fourth steps. Reducing the battery capacity in proposed coordinated control technique is fulfilled through the accurate tracking performed by ADALINE and also by maintaining the level of BESS saved energy within the batteries safe performance region performed by state of charging feedback control strategies. Simulation results run by real data verify that the performance of the proposed approach is considerably better than the basic approach. © 2016 Elsevier Ltd.
Renewable and Sustainable Energy Reviews (13640321) 29pp. 158-172
As the wind power capacity increases, the effect of wind power fluctuations on the system stability becomes more significant. Despite its high costs, utilizing energy storage resources such as batteries is inevitable in the smoothing process of wind power fluctuations. In a wind power plant, the place where batteries are located has considerable direct effect on their required capacity and thus on the initial investment cost. Therefore, in this paper a suitable configuration which significantly reduces the batteries investment cost is proposed and then the wind power fluctuation of a large wind power plant connected to a smart distribution grid is smoothed. Additionally, existing configurations for installing batteries in large wind power plants are investigated. The proposed configuration utilizes smart parks as aggregated storage resources in load side and an aggregated battery energy storage system with limited capacity in plant side as well. Therefore, in addition to accurate smoothing of wind power fluctuations, the energy storage investment cost is reduced significantly utilizing the proposed configuration. Simulation studies in MATLAB software package are carried out to verify the performance of the proposed approach. © 2013 Published by Elsevier Ltd.
IET Renewable Power Generation (17521416) 8(6)pp. 659-669
Most wind turbine generators installed in large windfarms are of variable speed types operating at the maximum power point tracking mode to generate the maximum amount of power. Owing to this fact and regarding the random nature of the windspeed, the output power of the windfarm fluctuates. Fluctuating power is a serious problem for high capacity power plants and should be smoothed. As an effective factor on the required battery energy storage system (BESS) capacity value, tracking is the most important part performed by a coordinated control system in the power smoothing process. An ADAptive LInear NEuron (ADALINE)-based power tracking method with a flexible learning rate is proposed in this study. Furthermore, a particle swarm optimisation-based calculation of the learning rate is presented for optimising the proposed tracking method which reduces the required BESS capacity and the investment cost. Moreover, a charging/discharging algorithm for the BESS units is proposed which decreases the number of required BESS units and increases their useful life by reducing the switching activity as well. To evaluate the performance of the proposed coordinated control approach, the real output data of a 99 MW windfarm are tested. The simulation results verify the effectiveness of the proposed approach. © The Institution of Engineering and Technology 2014.
Jazebi s., ,
Hosseinian s.h., S.H. ,
Jannati, M. ,
Vahidi, B. Engineering Applications of Artificial Intelligence (09521976) 26(1)pp. 625-632
The basic principle of new adaptive reclosures are to first identify whether a fault is transient or permanent and consequently to determine the reclosing moment. In this paper a novel method to enhance self-adaptive single phase autoreclosure of transmission lines is presented. Using Gaussian Mixture Models (GMM) the redundancy of setting the threshold is omitted. The proposed algorithm could prevent closing command in permanent faults and adapt dead time in temporary events. The method is derived by processing line terminal voltage around the period of dead time. The proposed scheme uses two sampled windows from the inception of the fault and two groups of GMM. Simulations performed in EMTP/ATP environment advocate the validity of the proposed algorithm convergence speed as well as fast and accurate protection scheme for reclosing relaying. The design of GMM is easy and the relative factors of the structure elements can be regulated due to the desirable effects. Since the discrimination method is done with stochastic characteristics of signals in time domain without application of any deterministic index, more reliable and accurate classification is achieved. © 2012 Elsevier Ltd. All rights reserved.
Jannati, M. ,
Jazebi s., ,
Vahidi, B. ,
Hosseinian s.h., S.H. Journal of Electrical Engineering and Technology (19750102) 6(6)pp. 742-749
Power transmission lines are one of the most important components of electric power system. Failures in the operation of power transmission lines can result in serious power system problems. Hence, fault diagnosis (transient or permanent) in power transmission lines is very important to ensure the reliable operation of the power system. A hidden Markov model (HMM), a powerful pattern recognizer, classifies events in a probabilistic manner based on fault signal waveform and characteristics. This paper presents application of HMM to classify faults in overhead power transmission lines. The algorithm uses voltage samples of one-fourth cycle from the inception of the fault. The simulation performed in EMTPWorks and MATLAB environments validates the fast response of the classifier, which provides fast and accurate protection scheme for power transmission lines.
Energy Conversion and Management (01968904) 52(2)pp. 1354-1363
A novel differential protection approach is introduced in the present paper. The proposed scheme is a combination of Support Vector Machine (SVM) and wavelet transform theories. Two common transients such as magnetizing inrush current and internal fault are considered. A new wavelet feature is extracted which reduces the computational cost and enhances the discrimination accuracy of SVM. Particle swarm optimization technique (PSO) has been applied to tune SVM parameters. The suitable performance of this method is demonstrated by simulation of different faults and switching conditions on a power transformer in PSCAD/EMTDC software. The method has the advantages of high accuracy and low computational burden (less than a quarter of a cycle). The other advantage is that the method is not dependent on a specific threshold. Sympathetic and recovery inrush currents also have been simulated and investigated. Results show that the proposed method could remain stable even in noisy environments. © 2010 Elsevier Ltd. All rights reserved.
Jannati, M. ,
Vahidi, B. ,
Hosseinian s.h., S.H. ,
Ahadi s.m., International Journal of Electrical Power and Energy Systems (01420615) 33(3)pp. 639-646
In modern interconnected power systems, nearly 80% of faults in high voltage transmission lines are intrinsically transient. The necessity of rapid fault clearing has resulted in fast development of protection equipments. Moreover, need for reliable supply of loads has led to improvements in single phase auto-reclosing equipments. In this paper, a novel and efficient method is proposed that leading to improved performance and efficiency of single phase auto-reclosing. In the case of occurrence of permanent faults, the proposed yardstick is accurate and authentic to diagnose fault type (transient or permanent). To validate accuracy and authenticity of the proposed method, a 400 kV transmission system is simulated using EMTP software and results are presented. © 2010 Elsevier Ltd. All rights reserved.
Asia-Pacific Power and Energy Engineering Conference, APPEEC (21574847)
Various studies have showed that about 70 to 90% of single line to ground faults occurred on High voltage transmission lines have transient nature. This type of faults is cleared by temporary outage (by the single phase auto-reclosure). The interval between opening and reclosing of the faulted phase circuit breakers is named "Dead Time" that is varying about several hundred milliseconds. For adjustment of traditional single phase auto-reclosures that usually are not intelligent, it is necessary to calculate the dead time in the off-line condition precisely. If the dead time used in adjustment of single phase auto-reclosure is less than the real dead time, the reclosing of circuit breakers threats the power systems seriously. So in this paper a novel approach for precise calculation of dead time in power transmission lines based on the network equivalencing in time domain is presented. This approach has extremely higher precision in comparison with the traditional method based on Thevenin equivalent circuit. For comparison between the proposed approach in this paper and the traditional method, a comprehensive simulation by EMTP-ATP is performed on an extensive power network. ©2010 IEEE.
International Review on Modelling and Simulations (19749821) 3(6)pp. 1483-1491
Modeling of transient states is one of the important part of power system analysis. Also, the EMTP/ATP is the standard software widely used universally accepted program by the electrical engineers for digital simulation of Electromagnetic Transient phenomena, as well as electromechanically behaviors of electrical power systems. In this paper, a new method has been introduced to teach the Ms Students of electrical engineering as a part of power system transient course. Simulation of fault arc by EMTP/ATP has been presented in order to teach the principles of fault arc modeling in power systems and to analyze the simulation results of transient states. As the first part of this paper, the fault arc model in power systems and its distinction in TACS has been clarified. At the second part, the step by step simulation of arc model in TACS has been presented. The effectiveness of this method has been shown in several semesters with more than 20 students and it can improve the understanding of TACS capabilities and fault arc model. © 2010 Praise Worthy Prize S.r.l.-All rights reserved.
International Review of Electrical Engineering (25332244) 4(5)pp. 985-993
Nearly 80% of faults in extra high voltage transmission lines are intrinsically transient. The necessity of rapid fault clearing has resulted in fast development of protection equipments. Moreover, need for reliable supply of loads has led to improvements in single phase auto-reclosing equipments. The success of the single phase auto-reclosing depends on the extinction of the secondary arc. In this paper, a novel adaptive single phase auto-reclosure is introduced. This auto-reclosure is based on the faulted phase voltage fundamental harmonic to discriminate between transient and permanent faults and also detect the extinguishing time of secondary arc.Validation of the proposed algorithm is verified via various simulations in EMTP/ATP software and experimental test. © 2009 Praise Worthy Prize S.r.l.
Jannati, M. ,
Vahidi, B. ,
Hosseinian s.h., S.H. ,
Baghaee h.r., pp. 203-207
Almost 70-90% of faults in extra high voltage (EHV) transmission lines in modern interconnected power systems are intrinsically transient. The necessity of rapid fault clearing results in fast developing of protection equipments. Morever, need for reliable supplying of loads, lead to improve in single phase auto-reclosing equipments. In this paper, a new method is proposed to reduce dead time of transmission lines. Leads to improve the performance and efficiency of single phase auto-reclosing. In the occurrence of permanent faults, the proposed yardstick is accurate and authentic to diagnose fault's type (transient or permanent). To validate accuracy and authenticity of proposed method, a 765 kV transmission system is simulated in Electromagnetic Transients Program software and results are presented. © 2008 IEEE.
A novel approach for fault detection in high voltage DC transmission systems using neural networks is presented. In the presented method, at first, Harmonics of voltage waveform in rectifier side are derived rapidly by using an adaptive linear neuron. Then, different types of faults including DC line fault, AC system Fault and Converter's faults are detected property using proposed criterion. In the under study voltage DC transmission systems system, rectifier and its controllers and required filters is modeled completely. A proposed criterion is tested on a high voltage DC system by computer simulation performed in MATLAB/Simulink environment. Simulation resultes demonstrates that the proposed approach can be used for online fault detection in high voltage DC systems. © 2008 IEEE.
Jannati, M. ,
Vahidi, B. ,
Hosseinian s.h., S.H. ,
Baghaee h.r., pp. 215-220
Reducing dead time of high voltage power transmission lines is one of the most important issues in power system protection. Besides, need for reducing the dead time is a matter of grave concern to increase voltage level of power transmission lines and insulation coordination. In this paper, different methods for decreasing the capacitive coupling and consequently reducing the dead time of power transmission lines are compared. This leads to faster quenching of secondary arc and limit the transient over voltage. Moreover, a novel hybrid approach is presented for reducing dead time of power transmission lines and faster quenching of secondary arc current. Simulations performed in electromagnetic transient program are performed for different cases. Simulation results show that dead time is reduced appropriately by proposed method.
Simulation (17413133) 84(12)pp. 601-610
In modern interconnected power systems, almost 70-90% of faults in high voltage Power Transmission Lines (PTLs) are intrinsically transient. The necessity of rapid fault clearing results in fast developing of protection equipments. Moreover, need for reliable supplying of loads, lead to improvements in single-phase autoreclosure (SPAR) equipments. An ADAptive LInear NEuron (ADALINE) is suitable for important applications such as protection of power systems and digital relays. In this paper, a novel simple adaptive SPAR algorithm is introduced. This algorithm is based on learning error function of an ADALINE. It can be distinguished by fault type (transient fault or a permanent fault), and if the fault is permanent, autoreclosure should be blocked. This leads to improve the performance and efficiency of SPAR. Electromagnetic transients program-based simulation results show that the autoreclosure scheme based on learning error function of ADALINE on a typical 400 kV circuit for various system and fault conditions improves the reliability of fault discrimination.© 2008 The Society for Modeling and Simulation International.
Baghaee h.r., ,
Jannati, M. ,
Vahidi, B. ,
Hosseinian s.h., S.H. ,
Rastegar h., pp. 209-214
Modern power systems are prone to widespread failures. With the increase in power demand, operation and planning of large interconnected power system are becoming more and more complex, so power system will become less secure. Operating environment, conventional planning and operating methods can leave power system exposed to instabilities. Voltage instability is one of the phenomena which have result in a major blackout. Moreover, with the fast development of restructuring, the problem of voltage stability has become a major concern in deregulated power systems. To maintain security of such systems, it is desirable to plan suitable measures to improve power system security and increase voltage stability margins. FACTS devices can regulate the active and reactive power control as well as adaptive to voltage-magnitude control simultaneously because of their flexibility and fast control characteristics. Placement of these devices in suitable location can lead to control in line flow and maintain bus voltages in desired level and so improve voltage stability margins. This paper presents a Genetic Algorithm (GA) based allocation algorithm for FACTS devices considering Cost function of FACTS devices and power system losses. Proposed algorithm is tested on IEEE 30 bus power system for optimal allocation of multi-type FACTS devices and results are presented.
Baghaee h.r., ,
Jannati, M. ,
Vahidi, B. ,
Hosseinian s.h., S.H. ,
Jazebi s., pp. 162-166
as power transfer increases, operation of power system become gradually more complex. Short circuit level increases and so power system will become less secure. Moreover, the problem of power system, security has become a mater of grave concern in the deregulated power industry. FACTS devices can control power flow because of their flexibility and fast control characteristics. Placement of these devices in suitable location can lead to control in line flow and maintain bus voltages in desired level and so improve power system security. This paper presents a novel algorithm for allocation of FACTS devices based on Genetic Algorithm (GA). Cost function of FACTS devices and power system losses are considered in this algorithm. Proposed algorithm is tested on IEEE 30 bus power system for optimal allocation of multi-type FACTS devices and results are presented © 2008 IEEE.
