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: 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: 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: 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
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: 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.
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.
Jannati, M.,
Vahidi, B.,
Hosseinian s.h., S.H.,
Ahadi s.m., Publication Date: 2011
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.
Publication Date: 2008
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.
Publication Date: 2021
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
Publication Date: 2025
International Journal of Robust and Nonlinear Control (10498923)35(18)pp. 8131-8143
This paper focuses on the challenge of integrated active fault-tolerant control (IAFTC) for linear discrete-time systems utilizing compatible linear matrix inequality (LMI) techniques. The presence of input nonlinearity, additive faults, external disturbances, and uncertainty in the system matrix makes this problem more applicable to real-world systems while expanding the complexity and functional range. In such a situation, the designer must take an integrated approach instead of synthesizing fault estimation (FE) and fault-tolerant control (FTC) as separate modules; yet, the LMI-based design conditions can be overly conservative. As a result, the reduction of the bi-directional interaction effect between FE and FTC units does not lead to satisfactory improvements in the overall behavior of the control system. To provide an integrated design approach, we propose a dynamic output feedback controller that plays the role of the FTC unit. This scheme incorporates two effective decision parameters and a descriptor observer (As FE), contributing to the IAFTC block's functionality. The design criteria are formulated using tractable LMI constraints in a convex optimization problem, and the (Formula presented.) -stability criterion is proved for the overall closed-loop system. The superiority and effectiveness of the proposed IAFTC are demonstrated through three comparative simulation examples. © 2025 John Wiley & Sons Ltd.
Publication Date: 2023
Journal of Energy Storage (2352152X)62
This paper presents a new incentive-based approach to increase the penetration of energy storage systems in distribution level. The proposed model is based on a public-private partnership which is appropriate for developing countries with budget deficit. By increasing the buying price from energy storage systems during peak-periods and providing discount on selling prices to the energy storage systems during off-peak periods, private investors are encouraged to install energy storage systems. The public section could use the installed capacity of energy storage systems for different purposes such as peak shaving. In the presented method of this paper the direct connection between the operator and the household energy storages is not required, through applying the incentive prices for energy consumption/production of energy storages. The proposed public-private partnership model of this paper is a bi-level optimization model. By implementing Karush-Kuhn-Tucker (KKT) conditions the equivalent single level optimization model is evaluated. The nonlinear model is linearized using strong duality theory and big M method. The applicability of the proposed method is analyzed using the real data of Yazd's network. In the numerical section it has been shown that a proper partnership between the public and private sectors is crated. The peak load is shaved properly, while the private household makes a reasonable profit through their investment. The penetration of the energy storage systems is increased without direct investment of the public sectors and the expansion cost is significantly reduced trough elimination of communication systems. © 2023 Elsevier Ltd
Ansari, M.R.,
Jamali j., ,
Fatehi-dindarlou m.h., ,
Rahgoshay m.a., ,
Mahdiyar o., Publication Date: 2024
International Journal of Engineering, Transactions B: Applications (1728144X)37(6)pp. 1127-1135
Implementing a proper integration scheme plays an important role in the performance of integrated navigation systems. Not only does employing a more reliable estimation method improve the accuracy of the integrated navigation system, but this can lead to a more robust solution in the presence of different types of uncertainties. Implementing an integration scheme that has a robust and simple structure is a challenging issue in the design of integrated navigation systems. By inspiring from the concept of PID control, this paper proposes a robust integration scheme for aided inertial navigation systems in the presence of aiding sensor measurement uncertainties. The proposed filter combines the concept of proportional-integral-derivative control theory and the standard Kalman filter estimator to improve the performance of the integration scheme. Thanks to the integral and derivative parts added to the proposed scheme, the integrated system attains a faster and more robust solution in the presence of observation errors and uncertainties. The simulation case studies validate the superior efficacy and capability of the proposed scheme compared to the integration method based on the standard Kalman filter. © 2024 Materials and Energy Research Center. All rights reserved.
Publication Date: 2024
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.
Publication Date: 2025
Energy Science and Engineering (20500505)
The global transition toward clean energy has intensified interest in solar power, especially in regions with favorable geographical conditions. Despite the rapid development and deployment of solar plants, operational challenges remain, particularly in optimizing energy conversion in real time. This paper proposes a practical real-time solar radiation prediction model designed to enhance the performance of solar plants by forecasting available energy, thereby improving control during the energy conversion process. To this aim, an autonomous nonlinear dynamical model with an unknown drift function is considered. A Group Method of Data Handling (GMDH)-based identification approach, supported by a comprehensive experimental dataset, is employed to estimate the drift function and confirm the feasibility of the model. Once the nonlinear model is validated, a theoretical framework is developed to enable adaptive estimation of the model's states and parameters, eliminating the need for offline identification. Experimental results across multiple scenarios demonstrate the model's effectiveness in accurately identifying unknown parameters and state variables under different environmental conditions, geographic locations, and challenging cases such as partial shading. These results highlight the practical potential of the proposed method for improving real-time control and energy efficiency in solar plant operations. © 2025 The Author(s). Energy Science & Engineering published by Society of Chemical Industry and John Wiley & Sons Ltd.
Publication Date: 2022
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.
Publication Date: 2024
Journal Of Operation And Automation In Power Engineering (24234567)12(2)pp. 175-185
With the advent of advanced measurement and supervisory devices in power systems, wide area measurement systems and real-time monitoring of power systems have become viable. Accordingly, modeling techniques should be updated as well. This paper proposes a transformer asset management model based on real-time condition monitoring in the presence of distributed generation. The model is tested under different case studies and compared with the previous models in which constant failure rate model was used for asset management of transformers. The system cost includes operation, repair, and interruption costs. The objective is to determine the hourly loading of the transformer so that the cost of system is minimized. The long-term objective is to determine the loading pattern of the transformer which guaranties the most economical pattern among various options. Results showed that the proposed model is efficiently capable of returning more accurate responses if real-time monitoring data is used. A set of sensitivity analysis studies are also performed to highlight the impact of each factor separately. The contribution of distributed generators to supply the load is also investigated. Results showed that the use of distributed generators reduces the overall cost of the system by diminishing the risk-based element of the system cost. © 2023 University of Mohaghegh Ardabili. All rights reserved.
