Publication Date: 2010
Discrete Applied Mathematics (0166218X)158(3)pp. 219-221
A block graph is a graph whose blocks are cliques. For each edge e = u v of a graph G, let Ne (u) denote the set of all vertices in G which are closer to u than v. In this paper we prove that a graph G is a block graph if and only if it satisfies two conditions: (a) The shortest path between any two vertices of G is unique; and (b) For each edge e = u v ∈ E (G), if x ∈ Ne (u) and y ∈ Ne (v), then, and only then, the shortest path between x and y contains the edge e. This confirms a conjecture of Dobrynin and Gutman [A.A. Dobrynin, I. Gutman, On a graph invariant related to the sum of all distances in a graph, Publ. Inst. Math., Beograd. 56 (1994) 18-22]. © 2009 Elsevier B.V. All rights reserved.
Publication Date: 2017
Discrete Mathematics, Algorithms and Applications (17938317)9(2)
A set W ∪ V (G) is called a resolving set, if for each pair of distinct vertices u,v ϵ V (G) there exists t ϵ W such that d(u,t)=d(v,t), where d(x,y) is the distance between vertices x and y. The cardinality of a minimum resolving set for G is called the metric dimension of G and is denoted by dimM(G). A k-tree is a chordal graph all of whose maximal cliques are the same size k + 1 and all of whose minimal clique separators are also all the same size k. A k-path is a k-tree with maximum degree 2k, where for each integer j, k ≤ j < 2k, there exists a unique pair of vertices, u and v, such that deg(u) =deg(v) = j. In this paper, we prove that if G is a k-path, then dimM(G) = k. Moreover, we provide a characterization of all 2-trees with metric dimension two. © 2017 World Scientific Publishing Company.
Publication Date: 2017
Journal of Information Science (01655515)43(2)pp. 204-220
One of the important issues concerning the spreading process in social networks is the influence maximization. This is the problem of identifying the set of the most influential nodes in order to begin the spreading process based on an information diffusion model in the social networks. In this study, two new methods considering the community structure of the social networks and influence-based closeness centrality measure of the nodes are presented to maximize the spread of influence on the multiplication threshold, minimum threshold and linear threshold information diffusion models. The main objective of this study is to improve the efficiency with respect to the run time while maintaining the accuracy of the final influence spread. Efficiency improvement is obtained by reducing the number of candidate nodes subject to evaluation in order to find the most influential. Experiments consist of two parts: first, the effectiveness of the proposed influence-based closeness centrality measure is established by comparing it with available centrality measures; second, the evaluations are conducted to compare the two proposed community-based methods with well-known benchmarks in the literature on the real datasets, leading to the results demonstrate the efficiency and effectiveness of these methods in maximizing the influence spread in social networks. © Chartered Institute of Library and Information Professionals.
Publication Date: 2020
Scientific Reports (20452322)10(1)
Rare or orphan diseases affect only small populations, thereby limiting the economic incentive for the drug development process, often resulting in a lack of progress towards treatment. Drug repositioning is a promising approach in these cases, due to its low cost. In this approach, one attempts to identify new purposes for existing drugs that have already been developed and approved for use. By applying the process of drug repositioning to identify novel treatments for rare diseases, we can overcome the lack of economic incentives and make concrete progress towards new therapies. Adrenocortical Carcinoma (ACC) is a rare disease with no practical and definitive therapeutic approach. We apply Heter-LP, a new method of drug repositioning, to suggest novel therapeutic avenues for ACC. Our analysis identifies innovative putative drug-disease, drug-target, and disease-target relationships for ACC, which include Cosyntropin (drug) and DHCR7, IGF1R, MC1R, MAP3K3, TOP2A (protein targets). When results are analyzed using all available information, a number of novel predicted associations related to ACC appear to be valid according to current knowledge. We expect the predicted relations will be useful for drug repositioning in ACC since the resulting ranked lists of drugs and protein targets can be used to expedite the necessary clinical processes. © 2020, The Author(s).
Publication Date: 2023
ISA Transactions (00190578)138pp. 212-225
This paper proposes an active fault-tolerant control (FTC) approach based on the controller management and virtual actuator idea for linear discrete-time systems subject to unknown L2-bounded disturbances, input constraint, and time-varying additive actuator faults. The closed-loop faulty system, which includes the modified nominal controller, the fault and state estimator, and the virtual actuator, suppresses the effects of disturbances and faults, while ensuring input-constraint satisfaction. The management of the nominal controller is performed through an online optimization method – in the form of a standard quadratic programming problem – by manipulating the reference input and intervening in the nominal controller evolution. The proposed method proves the input-to-state stability (ISS) criterion of the overall closed-loop faulty system. The problem of minimizing the ultimate bound of the ISS criterion is formulated in terms of tractable linear matrix inequality (LMI) conditions that allow the fault and state estimation errors to converge to a small neighborhood of the origin. To illustrate the capabilities and advantages of the proposed control strategy, comparative simulation results are presented for a flexible joint robotic system, tracking control of a DC motor's angular velocity, and the multivariable VTOL aircraft. © 2023
Publication Date: 2022
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
Mehdipour birgani, A.,
Shams, M.,
Jannati, M.,
Hatami aloghareh, F. Publication Date: 2025
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
Publication Date: 2026
Engineering Applications of Artificial Intelligence (09521976)168
As the global transition toward intelligent, resilient energy infrastructures accelerates, integrating artificial intelligence (AI) into power system protection has become a critical enabler of operational efficiency and reliability. A major challenge is the accurate identification and classification of high impedance faults (HIFs) in energy distribution grids (EDGs), where low current levels often cause conventional protection devices to malfunction. Traditional schemes require extensive training data, which is often difficult or impossible to obtain. This study presents a novel protection scheme that combines a pre-trained compact convolutional neural network (SqueezeNet) with Wigner-Ville distribution (WVD) and S-transform-based feature fusion to detect and classify HIFs using minimal data. Leveraging transfer learning, the approach reduces model retraining needs and accelerates deployment. Simulation results on modified IEEE 13-bus and 34-bus EDGs show F 1-scores exceeding 97 %, successful cross-network knowledge transfer without retraining, and rapid detection within 20 msec using only 250 training samples, highlighting its suitability for lightweight, scalable, and real-time smart-grid protection. © 2026 Elsevier Ltd.
Publication Date: 2018
Mathematical Methods of Operations Research (14325217)88(1)pp. 81-98
In this paper, we consider the rectilinear distance location problem with box constraints (RDLPBC) and we show that RDLPBC can be reduced to the rectilinear distance location problem (RDLP). A necessary and sufficient condition of optimality is provided for RDLP. A fast algorithm is presented for finding the optimal solution set of RDLP. Global convergence of the method is proved by a necessary and sufficient condition. The new proposed method is provably more efficient in finding the optimal solution set. Computational experiments highlight the magnitude of the theoretical efficiency. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
Publication Date: 2022
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.
Publication Date: 2021
International Transactions on Electrical Energy Systems (20507038)31(12)
In each regulatory period, the parameters of the reward and penalty scheme (RPS) vary based on the investment made by electricity distribution companies (EDCs) and imposed costs. In this paper, a novel dynamic model for determining the RPS parameters for each EDC is presented. This new model has three stages: (i) determination of the RPS parameters for the first regulatory period, (ii) the RPS decision-making model, and (iii) determination of the RPS parameters for the subsequent regulatory period. The proposed model was implemented for Iranian EDCs. Results verified the effectiveness of the proposed model in enhancing distribution system reliability. © 2021 John Wiley & Sons Ltd.
Publication Date: 2022
Physica A: Statistical Mechanics and its Applications (03784371)604
Community detection is one of the most essential issues in social networks analysis field. Among the available categories of algorithms, the label propagation algorithm-based (LPA-based) methods, due to their proper time complexity, are of high concern. As all the social networks explicitly or implicitly include signed relationships, the attempt here is to suggest an LPA-based approach for community detection in the directed signed social networks. The direction of edges is not addressed in available LPA-based community detection methods for signed social networks. In this respect, 1) a weighting method is suggested in order to utilize the direction information that converts the network into an undirected weighted signed social network, 2) this weight is combined with a second weight obtained from the sign information of the edges, and 3) the LPA is extended, where the combined weights are applied in label propagation. Moreover, the directed signed modularity and the directed signed flow-based capacity measures are proposed. The findings of the run experiments indicate that the proposed method as to the directed signed modularity, directed signed flow-based capacity, and frustration measures on real-world and synthetic data sets, outperforms its counterparts. © 2022 Elsevier B.V.
Publication Date: 2026
Computers and Electrical Engineering (00457906)130
Electricity distribution systems are vulnerable to damage from extreme weather events like hurricanes and floods. Although predicting outage locations in these systems is a significant challenge, it provides operators with critical data for implementing proactive measures. This paper presents a decision tree-based learning method to predict potential outages in distribution branches during hurricanes. The challenges of input data, component diversity, and numerous affecting factors are highlighted and effectively addressed. Our model considers all potentially effective static and dynamic features to estimate the damage risk for each branch. The data for training and testing the classifier were acquired from historical records and synthesized samples based on expert knowledge, with a separate set of real data used for validation. Beyond outage prediction, the classifier also serves as a feature selection tool by identifying the most discriminative features. Numerical simulations confirmed a high level of accuracy with a negligible error rate. The method was successfully implemented on a modified IEEE 33-bus distribution system. Copyright © 2025. Published by Elsevier Ltd.
Mohseni, M.,
Eajal, A.A.,
Amirioun, M.H.,
Al-durra, A.,
El-saadany, E. Publication Date: 2023
International Journal of Electrical Power and Energy Systems (01420615)147
This paper presents a proactive operation scheme for improving distribution system resiliency against natural hazards, specifically windstorms. In this context, important attributes associated with the windstorm consisting the distance from the windstorm route, the wind speed, the distance from tall trees and buildings, and cable type are used in a deep neural network (DNN) engine to identify the vulnerable branches and predict their failure during the windstorm. The DNN predictive model is integrated in the proposed scheme. Afterwards, a power flow-based optimization engine is employed to proactively enhance the grid resiliency. Grid resiliency is measured by the inevitable action of load shedding. For minimum load shedding, the optimization engine reconfigures the network topology, optimizes the droop parameter settings, and allocates mobile energy storage systems (ESSs) before the arrival of the windstorm. This optimization engine is integrated in the proposed scheme. To validate its performance, the proposed proactive scheme is tested on a 33-bus test system with a mix of diesel units (DUs), wind turbines (WTs), and photovoltaic units (PVs). The simulation results demonstrate that without the proposed learning mechanism, the load shedding can reach up to 36% for the system under study, while the learning-based scheme can reduce the load shedding to 13%. The proposed learning-based proactive operation scheme would substantially improve the distribution system resiliency during windstorms. © 2022
Publication Date: 2018
International Journal of Engineering, Transactions A: Basics (17281431)(1)
In the present paper, a supersonic wind-tunnel is designed to maintain a flow with Mach number of 3 in a 30cm×30cm test section. An in-house CFD code is developed using the Roe scheme to simulate flow-field and detect location of normal shock in the supersonic wind-tunnel. In the Roe scheme, flow conditions at inner and outer sides of cell faces are determined using an upwind biased algorithm. The in-house CFD code has been parallelized using OpenMp to reduce the computational time. Also, an appropriate equation is derived to predict the optimum number of cores for running the program with different grid sizes. In the design process of the wind-tunnel, firstly geometry of the nozzle is specified by the method of characteristics. The flow in the nozzle and test section is simulated in the next step. Then, design parameters of the diffuser (convergence and divergence angles, area of the throat, and ratio of the exit area to the throat area) are determined by a trial and error method. Finally, an appropriate geometry is selected for the diffuser which satisfies all necessary criteria. © 2018 Materials and Energy Research Center. All rights reserved.
Publication Date: 2024
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
Gilani, M.A.,
Dashti, R.,
Ghasemi, M.,
Amirioun, M.H.,
Shafie-khah, M. Publication Date: 2022
Sustainable Cities and Society (2210-6707)83
In recent years, resilience enhancement of electricity distribution systems has attracted much attention due to the significant rise in high-impact rare (HR) natural event outages. The performance of the post-event restoration after an HR event is an effective measure for a resilient distribution network. In this paper, a multi-objective restoration model is presented for improving the resilience of an electricity distribution network. In the first objective function, the load shedding in the restoration process is minimized. As the second objective function, the restoration cost is minimized which contradicts the first objective function. Microgrid (MG) formation, distributed energy resources (DERs), and demand response (DR) programs are employed to create the necessary flexibility in distribution network restoration. In the proposed model, DERs include fossil-fueled generators, renewable wind-based and PV units, and energy storage system while demand response programs include transferable, curtailable, and shiftable loads. The proposed multi-objective model is solved using ɛ-constraint method and the optimal solution is selected using the fuzzy satisfying method. Finally, the proposed model was successfully examined on 37-bus and 118-bus distribution networks. Numerical results verified the efficacy of the proposed method as well. © 2022
Publication Date: 2021
International Journal of Engineering, Transactions B: Applications (1728144X)34(8)pp. 1974-1981
Neural networks are powerful tools for evaluating the thermophysical characteristics of nanofluids to reduce the cost and time of experiments. Dynamic viscosity is an important property in nanofluids that usually needs to be accurately computed in heat transfer and nanofluid flow problems. In this paper, the rheological properties of nanofluid phase change material containing mesoporous silica nanoparticles are predicted by the artificial neural networks (ANNs) method based on the experimental database reported in literature. Experimental inputs include nanoparticle mass fractions (0-5 wt.%), temperatures (35-55℃), shear rates (10-200 s-1), targets include dynamic viscosities and shear stresses. A multilayer perceptron feedforward neural network with Levenberg-Marquardt back-propagation training algorithm is utilized to predict rheological properties. The optimal network architecture consists of 22 neurons in the hidden layer based on the minimum mean square error (MSE). The results showed that the developed ANN has an MSE of 6.67×10-4 and 6.55×10-3 for the training and test dataset, respectively. The predicted dynamic viscosity and shear stress also have the maximum relative error of 6.26 and 0.418%, respectively. © 2021 Materials and Energy Research Center. All rights reserved.
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.
Publication Date: 2023
Optimization (10294945)72(12)pp. 2893-2923
This paper focuses on the constrained minimax location problem with the closest distance. Some properties concerning the existence and uniqueness of the optimal solution are provided. To achieve these results, we apply non-smooth approach that allow us to give the explicit solution structure of the constrained problem. Moreover, we develop an effective algorithm for solving this class of problems and we provide its convergence under some mild assumptions. At the end, some computational test problems are provided to illustrate the effectiveness of the method and certify the theoretical results. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2018
Journal of Aerospace Technology and Management (21759146)10
In this study, an efficient methodology is proposed for robust design optimization by using preference function and fuzzy logic concepts. In this method, the experience of experts is used as an important source of information during the design optimization process. The case study in this research is wing design optimization of Boeing 747. Optimization problem has two objective functions (wing weight and wing drag) so that they are transformed into new forms of objective functions based on fuzzy preference functions. Design constraints include transformation of fuel tank volume and lift coefficient into new constraints based on fuzzy preference function. The considered uncertainties are cruise velocity and altitude, which Monte Carlo simulation method is used for modeling them. The non-dominated sorting genetic algorithm is used as the optimization algorithm that can generate set of solutions as Pareto frontier. Ultimate distance concept is used for selecting the best solution among Pareto frontier. The results of the probabilistic analysis show that the obtained configuration is less sensitive to uncertainties. © 2018, Journal of Aerospace Technology and Management. All rights reserved.
Publication Date: 2023
International Journal of Advanced Manufacturing Technology (02683768)128(9-10)pp. 4003-4022
In this paper, a virtualized ball-end milling model is presented in Unity game engine environment, in which the machining process is simulated by proposing a new geometric approach. The model calculates and illustrates cutter-workpiece engagement area and cutting forces in a real-time manner. To calculate cutter-workpiece engagement, the workpiece surface is considered a set of nodes. Then, using a new geometric method, the engagement area is calculated at any node on the engaging surface. Utilizing the calculated engagement area and adopting mechanistic force model, the cutting forces applied from each node on the workpiece surface to the tool’s cutting edge are calculated. Adopting the proposed new geometric method simplifies the mechanistic force model to such an extent that the cutting forces are calculated in a real-time manner. The extracted cutter-workpiece engagement areas have been compared with solid model results, and the cutting forces have been compared with the available experimental data. Good agreement between the results proved that the model can calculate the engagement area and cutting forces accurately. By changing the geometrical parameters of the model, it was shown that the speed of analyses can be increased to such an extent that the machining process can be simulated faster than 30 frames per second. The presented model is compatible with any game engine and can be used for augmented reality applications and machining process monitoring. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Publication Date: 2014
Energy (0360-5442)69pp. 186-198
As electric vehicles offer a promising choice to deal with the growing air pollution and the global consumption of fossil fuels in the future smart grids, integrating their full benefit in the power system should be of a high priority. Numerous studies surveyed the possibility of charging/discharging modes of vehicles such as vehicle-to-grid, grid-to vehicle and vehicle-to-building and one introduced a new mode as vehicle-to-vehicle. However, none of them considered all available modes in a study. In the future smart grids, electric vehicles will be integrated with other generation or consumption parts such as distributed energy resources, smart homes and the external grid. As a result, a comprehensive perspective toward the simultaneous scheduling of combined energy exchange modes should be established. In this paper, advantages of 18 energy exchange modes are integrated. The presented model facilitates the participation of sub-aggregators in the aggregation of electric vehicles in a residential complex. The complex consists of a smart building and a smart parking lot. The proposed model promises higher income for sub-aggregators and less energy not charged for vehicles while ensuring the convenience for residents. This will result in more incentive for both sub-aggregators and residents to cooperate. © 2014 Elsevier Ltd.
Jannati, M.,
Jazebi s., ,
Vahidi, B.,
Hosseinian s.h., S.H. Publication Date: 2011
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.
Publication Date: 2009
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.
Publication Date: 2011
Energy Conversion and Management (0196-8904)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.
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.
