Showing papers in "IEEE Transactions on Power Delivery in 2021"
TL;DR: Three-phase, electromechanical models for both grid-forming and grid-following inverters are developed and integrated into an open source, three-phase distribution network solver, thereby enabling dynamic simulation of large-scale, two-phase unbalanced distribution systems with high penetration of inverter-based DERs.
Abstract: Historically, distribution system planning studies mainly focused on steady state and quasi-steady state analysis, with limited attention paid to dynamic analysis. This paper develops three-phase, electromechanical models for both grid-forming and grid-following inverters, and integrates them into an open source, three-phase distribution network solver, thereby enabling dynamic simulation of large-scale, three-phase unbalanced distribution systems with high penetration of inverter-based DERs. The proposed inverter models are validated against electromagnetic simulation and field test data from the CERTS/AEP microgrid testbed, and simulated in an islanded 5252 node distribution system in the GridLAB-D simulation environment. Simulation verifies the effectiveness of the proposed inverter models for large-scale distribution system analysis. Study results show that compared to traditional grid-following inverters, the high penetration of grid-forming inverters can improve the voltage and frequency stability of islanded distribution systems.
93 citations
TL;DR: A fragile model is developed to evaluate the nodal SCF probability considering the insulation aging of equipment and extreme weather condition, and a response framework for extreme weather events is developed for a transmission system to defend the cascading impacts of expected SCFs.
Abstract: Due to global climate change, the effect of extreme weather on power systems has attracted extensive attention. In the prior-art grid resilience studies, the hurricanes or wildfires are mainly defended in terms of expected line damages, while they are prone to trigger short-circuit fault (SCF) evolved with dynamic influence in reality. In this paper, a fragile model is developed to evaluate the nodal SCF probability considering the insulation aging of equipment and extreme weather condition. Then, a response framework for extreme weather events is developed for a transmission system to defend the cascading impacts of expected SCFs. Specifically, switches are shifted to restrain the out-of-range short-circuit currents (SCCs) so that to ensure the SCFs can be removed by circuit breakers, generation rescheduling and load shedding are arranged to maintain the post-fault system transient stability. The above measures are optimized simultaneously by an integrated Mixed-Integer Nonlinear Programming (MINLP). Considering the error or uncertainty of weather event forecasts, a multi-state model is established to provide the most cost-effective grid resilience enhancement scheme, in which the expected urgent adaptions of the initial scheme subject to weather state transition is included in the overall cost. The proposed model and techniques are validated using the IEEE 39-bus New-England test system and realistic meteorological data.
87 citations
TL;DR: A novel 5G-based centralized switch FCL (CSF) framework as well as a method to allocate such flexible FCLs optimally is proposed in this paper, and numerical results are provided to verify the proposed allocation model, including its defending effect against SCFs in terms of fault current limiting, voltage sags relieving, and its cost-effectiveness.
Abstract: The allocation of fault current limiters (FCLs) is increasingly challenging in transmission systems these days. Specifically, the utilized deterministic expected short-circuit fault (SCF) scenarios are prone to cause over-configuration of FCLs. Moreover, the well-established local switching framework (LSF) renders inappropriate FCL switching and may further harm the system safe operation. Aiming at the above deficiencies, a novel 5G-based centralized switch FCL (CSF) framework as well as a method to allocate such flexible FCLs optimally is proposed in this paper. In the proposed CSF, the FCLs are switched by a FCL dispatching (FD) model considering system security constraints of both fault current and voltage sags. By exploiting the fast communication capability of 5G network as well as an off-line fault scanning strategy, the FD model is enabled to give online FCL switching schemes to meet the fast requirement of power system protection. Moreover, considering the probabilistic characteristic of SCFs, a bi-level FCL allocation model is established, in which the upper-level model sites and sizes FCLs considering the installation and expected switching costs while the lower-level model determines the optimal switched FCLs under each specific SCF scenario. Finally, numerical results are provided to verify the proposed allocation model, including its defending effect against SCFs in terms of fault current limiting, voltage sags relieving, as well as its cost-effectiveness.
68 citations
TL;DR: The objective is to show potential protection misoperation issues, identify the cause, and propose potential solutions, to determine the key features that need to be considered in practical protection studies.
Abstract: Inverter-Based Resources (IBRs), including Wind turbine generators (WTGs), exhibit substantially different negative-sequence fault current characteristics compared to synchronous generators (SGs). These differences may cause misoperation of customary negative-sequence-based protective elements set under the assumption of a conventional SG dominated power system. The amplitude of the negative-sequence fault current of a WTG is smaller than that of an SG. This may lead to misoperation of the negative-sequence overcurrent elements 50Q/51Q. Moreover, the angular relation of the negative-sequence current and voltage is different under WTGs, which may result in the misoperation of directional negative-sequence overcurrent element 67Q. This paper first studies the key differences between the WTGs and SG by comparing their equivalent negative-sequence impedances with SG's. Then, simulation case studies are presented showing the misoperation of 50Q and 67Q due to wind generation and the corresponding impact on communication-assisted protection and fault identification scheme (FID). The impact on directional element is also experimentally validated in a hardware-in-the-loop real-time simulation set up using a physical relay. Finally, the paper studies the impact of various factors such as WTG type (Type-III/Type-IV) and Type-IV WTG control scheme (coupled/decoupled sequence) to determine the key features that need to be considered in practical protection studies. The objective is to show potential protection misoperation issues, identify the cause, and propose potential solutions.
58 citations
TL;DR: In this article, the authors presented a method based on UV-Visible video processing, deep learning, and experimental information to detect power equipment in each frame using Faster R-CNN, and then, it is tracked through the whole video frames to compensate the camera movement.
Abstract: As one of the non-contact methods for incipient fault diagnosis of power distribution lines, UV-Visible imaging has become more popular due to its good performance and robustness against environmental parameters. This paper presents a method based on UV-Visible video processing, deep learning, and experimental information. First, some videos are acquired from power distribution lines using CoroCam 6D2 considering observation distance, the UV gain of the camera, air pressure, and humidity as effective parameters on the discharge area in images. Frames are extracted from acquired video with the rate according to the nominal voltage of the line. Power equipment is detected in each frame using Faster R-CNN, and then, it is tracked through the whole video frames to compensate the camera movement. Then, color thresholding is used to identify corona discharges in the image for each device separately. Finally, based on the ratio of the detected discharge area to the area of the equipment, the incipient fault severity level is determined. The proposed method not only performs better than state-of-the-art but also it is a practical method without the need for user skill, and it can automatically identify defects in distribution lines, even in videos containing several possible defective devices.
53 citations
TL;DR: This work first considers the system from the attacker's point of view with a limited attack budget to study the smart grid vulnerability, referred to as Maximum-Impact through Critical-Line with Limited Budget (MICLLB) problem, and proposes an efficient algorithm by considering the interdependency property of the system, called Greedy Based Partition Algorithm (GBPA) to solve the MICLLB problem.
Abstract: Most of today's smart grids are highly vulnerable to cascading failure attacks in which the failure of one or more critical components may trigger the sequential failure of other components, resulting in the eventual breakdown of the whole system. Existing works design different ranking methods for critical node or link identifications that fail to identify potential cascading failure attacks. In this work, we first consider the system from the attacker's point of view with a limited attack budget to study the smart grid vulnerability, referred to as Maximum-Impact through Critical-Line with Limited Budget (MICLLB) problem. We propose an efficient algorithm by considering the interdependency property of the system, called Greedy Based Partition Algorithm (GBPA) to solve the MICLLB problem. In addition, we design an algorithm, namely Homogeneous-Equality Based Defense Algorithm (HEBDA) to help reduce damages in case the system is suffering from the cascading failure attacks. Through rigorous theoretical analysis and experimentation, we demonstrate that the investigated problem is NP-complete problem and our proposed methods perform well within reasonable bounds of computational complexity.
50 citations
TL;DR: The digital twin method enables monitoring of distribution T/F that avoids MV instrumentation, does not suffer in accuracy and may be readily deployable.
Abstract: Real-time monitoring of distribution systems has become necessary, due to the deregulation of electricity markets and the wide deployment of distributed energy resources. To monitor voltage and current at sub-cycle detail, requires, typically, major investment undertaking and disruptions to the operation of the grid. In this work, measurements of the low voltage (LV) side of distribution transformers (T/F) are used to calculate in real time the waveforms of their medium voltage (MV) sides, based on a mathematical model of said T/F. This model is, essentially, the digital twin of the MV side of the T/F. The method calculates T/F MV waveforms of voltage and current, and active and reactive power as accurately as an instrument T/F, captures all harmonics content, is unaffected by asymmetrical loading and identifies most system faults on the MV side of the T/F. The digital twin method enables monitoring of distribution T/F that avoids MV instrumentation, does not suffer in accuracy and may be readily deployable. Field data from an actual MV-LV T/F, agree with simulation results showcasing the efficacy of the digital twin method.
50 citations
TL;DR: A novel feature extraction method based on vibration analysis is proposed, which converts the vibration monitoring data with load information into a vibration image, which is then used to classify the images belong to different classes.
Abstract: Winding condition assessment is an essential task for operating transformers, and the vibration method provides a low-cost and non-intrusive approach. In this paper, a novel feature extraction method based on vibration analysis is proposed, which converts the vibration monitoring data with load information into a vibration image. Then, a deep learning approach based on convolutional neural network (CNN) is used to classify the images belong to different classes. In the laboratory experiment, free vibration tests are performed on an on-load winding model, which are used to verify the relationship between the natural frequency and the electromagnetic force under different clamping forces. During the field experiment, transformers are divided into three categories, including normal, degraded and anomalous, and the proposed scheme is trained and tested by using the vibration samples acquired from more than 100 operating transformers. The performance of the CNN classifier under different input sizes is investigated, which achieves 98.3% overall accuracy. Besides, the confusion matrices obtained by other methods are compared, such as artificial neural network (ANN), support vector machine (SVM) and naive Bayes classifier (NBC). The results show that the proposed scheme including the vibration image extraction method and the CNN classifier offers superior performance in winding fault diagnosis.
49 citations
TL;DR: The proposed model contemplates the traveling time of crew teams to the MS sites in the transportation system in order to achieve the best switching sequence and pre-positions the crews and mobile emergency generators in staging locations to hasten the likely post-disturbance operations.
Abstract: Natural calamities always have been a serious threat to energy systems. In this regard, this paper constitutes a stochastic mixed integer linear programming (SMILP) model to enhance the resilience of power distribution systems to deal with disastrous events. In particular, the proposed model is developed to enhance both survivability and restoration capability of distribution systems. In this regard, to increase the preparedness of the power distribution system, the system operator reconfigures the network by utilizing remote-control switches (RCSs), manual switches (MSs), and distributed generations (DGs) before the natural calamity hits. The proposed model contemplates the traveling time of crew teams to the MS sites in the transportation system in order to achieve the best switching sequence. In addition, it pre-positions the crews and mobile emergency generators (MEGs) in staging locations to hasten the likely post-disturbance operations. To do so, likely post-event operations are included in the model using the scenario generation technique. To validate the performance of the developed model a distribution system is employed. The results of simulations confirm the effectiveness of the proposed approach in declining the interruption of electric energy for customers.
48 citations
TL;DR: A coupled simulation model both considering the DC metro system and the AC power system is proposed and the dc current distribution is evaluated and analyzed, indicating that the dcCurrent distribution is affected by the trains operating conditions and the relative position.
Abstract: With the rapid development of the DC metro system, the stray current may bring new challenges to the AC power system. Especially, the stray current of the DC metro system flowing in the AC power system may cause many serious consequences such as the corrosion of the grounding grids, plenty of harmonics, and the dc bias of transformers. Therefore, it is significant to evaluate the dc current distribution in the AC power system caused by the stray current. In order to evaluate the dc current distribution, a coupled simulation model both considering the DC metro system and the AC power system is proposed in this paper. Moreover, the earth and the grounding system are both used as the coupling elements in the proposed model. The correctness of the proposed model is verified by the comparison of the calculation results and the field test data. Furthermore, based the proposed model, the dc current distribution in the AC power system is evaluated and analyzed. The results indicate that the dc current distribution is affected by the trains operating conditions and the relative position between the DC metro system and the AC power system.
47 citations
TL;DR: In this paper, an energy storage system (ESS) for recycling the regenerative braking energy in the high-speed railway was proposed, in which a supercapacitor-based storage system was integrated at the DC bus of the back to back converter that is connected to the two power phases of the traction power system (TPS).
Abstract: This paper proposes an energy storage system (ESS) for recycling the regenerative braking energy in the high-speed railway. In this case, a supercapacitor-based storage system is integrated at the DC bus of the back to back converter that is connected to the two power phases of the traction power system (TPS). In order to ensure the suitability of the ESS in the TPS, the operation modes are classified by considering the load conditions of the TPS and the state-of-charge limit of the supercapacitor. Then, a master-slave control strategy with a central controller is proposed. In which, the central controller realizes the flexible management for all operation modes, by means of the state machine logic. Meanwhile, the master-slave control serves for coordinating the operation of the multiple converters according to the commands from the central controller. Finally, the capabilities of the proposed ESS are validated by sufficient experiments under different operation modes and the simulation based on the field data.
TL;DR: A protection algorithm based on travelling wave simulation and analysis is proposed to detect abrupt transient signals that shows high efficiency, reliability, selectivity and has low sampling frequency requirements.
Abstract: HVDC technologies are widely acknowledged as one of solutions for the interconnection of renewable energy resources with the main electric power grid. The application of the latest modular multi-level converter (MMC) makes power conversion much more efficient. Due to the relatively low impedance in a DC system, DC fault currents may rise to an extremely high level in a short period of time, which can be very dangerous for HVDC converters. To improve the sustainability and security of electricity transmission, protection solutions for HVDC systems are being developed. Nevertheless, they have various drawbacks on fault signal detection and timely clearance. This paper proposes a protection method that provides a fast and reliable solution addressing those drawbacks. A protection algorithm based on travelling wave simulation and analysis is proposed to detect abrupt transient signals. The algorithm shows high efficiency, reliability, selectivity and has low sampling frequency requirements. The proposed protection method has been validated through a cyber-physical simulation platform, developed using a real-time digital simulator (RTDS) and IEC 61850 communication links. The obtained results show that the proposed method has good potential for practical applications.
TL;DR: A practical rotor-side subsynchronous damping controller (RSDC) is considered and its performance is validated through controller hardware-in-the-loop (CHIL) simulations, and then implemented in a real-world DFIG connected to a series compensated line, enhancing the practical confidence of the SSCI mitigation strategy based on RSC's converter control modification.
Abstract: In the past decade, several subsynchronous control interaction (SSCI) incidents have been reported in various large-scale grid-interfaced doubly-fed induction generator (DFIG) based wind farms. The subsynchronous oscillation caused by the SSCI endangers the safe and reliable operation of wind power systems. The DFIG's converter controls play a crucial role in such interactions; therefore, several SSCI mitigation schemes have been reported, which modify the DFIG's converter controls to stabilize the subsynchronous oscillation caused by the SSCI. However, the effectiveness of such converter control modifications has not been verified through laboratory or field experiments. This paper considers a practical rotor-side subsynchronous damping controller (RSDC) and fills the gap by first validating the RSDC's performance through controller hardware-in-the-loop (CHIL) simulations, and then implementing it in a real-world DFIG connected to a series compensated line. The RSDC fundamentally reshapes the effective impedance of the DFIG to stabilize the subsynchronous oscillation. The CHIL simulations, as well as the field tests, successfully validated the RSDC's ability to suppress the SSCI. The results enhanced the practical confidence of the SSCI mitigation strategy based on RSC's converter control modification.
TL;DR: A novel islanding detection method (IDM) for grid-connected photovoltaic systems (GCPVSs) through a disturbance injection in the maximum power point tracking (MPPT) algorithm that endorse timely and accurately detection with negligible non-detection zone (NDZ) as well as no false tripping in non-islanding disturbances.
Abstract: This paper proposes a novel islanding detection method (IDM) for grid-connected photovoltaic systems (GCPVSs) through a disturbance injection in the maximum power point tracking (MPPT) algorithm. When an absolute deviation of the output voltage exceeds a threshold, the applied disturbance shifts system operating point from its maximum power point (MPP) condition. This leads to a sharp active power output reduction and consequently, a significant voltage drop in islanded mode beyond the standard voltage limit. The proposed algorithm is defined in a way that the distributed generator (DG) can be restored to MPP after islanding classification. It is thereby effective in microgrid in where the power injection at maximum level to cater the critical loads and maintain the stability of the isolated area are pursued. An intentional time delay has also been considered to avoid nuisance tripping in short-circuit faults which do not require tripping. The assessment of the proposed technique has been conducted for a sample network containing two GCPVSs in a real-time platform including actual relays in hardware-in-the-loop (HiL). The provided results under extensive islanding scenarios defined in islanding standards endorse timely and accurately detection with negligible non-detection zone (NDZ) as well as no false tripping in non-islanding disturbances. The comparative analysis of the presented scheme with a few recent IDMs for GCPVS highlights its overall superiorities, including very small NDZ, fast detection, thresholds self-standing determination, no adverse effect on power quality, and simple and inexpensive integration.
TL;DR: In this article, a fault detection and location method for mesh-type DC microgrid is proposed, which is based on similarity analysis on the sampled lines current and the lines current reference by employing an improved Pearson correlation coefficient.
Abstract: Fault detection and location method are two significant techniques to recognize and clear the fault timely. This paper proposes a novel fault detection and location method for mesh-type DC microgrid. The key of this method is to implement similarity analysis on the sampled lines current and the lines current reference by employing an improved Pearson correlation coefficient. Firstly, the fault detection can be implemented by comparing the sampled lines current with steady-state lines current in a movable time window. And the fault line and fault type can be recognized based on the analysis on the correlation coefficient. Secondly, the fault location can be obtained by comparing the sampled lines current with calculation results of transient lines current, which corresponds to the estimated fault location scheme, within a fixed time window. The estimated location scheme, including the fault position and fault resistance, is updated by genetic algorithm, and will be determined when the correlation coefficient is higher than the threshold. Moreover, both numerical simulation and experiment results validate that the proposed method can recognize the fault line and fault type timely, while obtain the fault position and fault resistance accurately.
TL;DR: This paper has attempted to make a bridge from past to future research trends in the failure diagnosis of HVCBs, and presents challenges dealing with real-time assessment of the diagnostic signals relating to measurements, and analyses.
Abstract: High voltage circuit breakers (HVCBs) play a critical role on providing the desired reliability, and resiliency in power systems. In order to extend their lifetime and predict the failures, various maintenance policies could be applied on these critical components. Amongst these strategies, condition-based maintenance (CBM) provides a satisfactory agreement with future smart environment. This paper aims to provide an insight into the relevant developments in this subject and to explore the viable visions compatible with future research stream. Accordingly, three directions, i.e., diagnostic signals, intelligent modelling and using monitoring data in asset management have been addressed in this paper. It presents challenges dealing with real-time assessment of the diagnostic signals relating to measurements, and analyses. Subsequently, the issues associated with using artificial intelligent (AI) and Machine learning for providing intelligent algorithms have been discussed. Finally, the connection between the monitoring data and the asset management approach is investigated. The latter is looking for the subjects including remaining lifetime estimation, prioritization, and health index definitions. This paper has attempted to make a bridge from past to future research trends in the failure diagnosis of HVCBs.
TL;DR: Several physical variables as seen from the point of common coupling (PCC) are found to contribute to the detrimental behavior of the VSC under weak connections and eliminating their impacts through feedforward eliminates their influence and significantly improves the active power capability.
Abstract: Despite attempts to increase the active power capability of vector-controlled voltage source converters (VSCs) connected to very weak grids, the interaction between the control dynamics and physical system is not completely understood. The result is often complex strategies that are difficult to implement. This paper proposes an intuitive modification of the VSC control based on physical considerations and dynamics of existing control. Several physical variables as seen from the point of common coupling (PCC) are found to contribute to the detrimental behavior of the VSC under weak connections. Hence, eliminating the impacts of these variables through feedforward eliminates their influence and significantly improves the active power capability. Notably, the basic structure of the vector controlled VSC is kept and its output-impedance is effectively reshaped. The proposed modification is validated through nonlinear time-domain simulations in MATLAB/Simulink Simscape Power System and results demonstrate the simplicity and intuitiveness of the modified structure.
TL;DR: In this article, the authors developed a DLR forecasting model with respect to historical meteorological data using ensemble learning algorithms, which yielded an approximate capacity increase of 30% for 400kV lines between Ghadamgah and Binalood wind farms, which is enough to relieve the congestion issue.
Abstract: The transmission congestion issue from the high penetration of renewable energies places a premium on accurate dynamic line rating (DLR) as a short-term solution for the more efficient exploitation of the existing transmission infrastructure and the efficient and reliable operation of the power grids. Even though the DLR methods produce a worthy estimation of ampacity, they need the placement of measurement devices and communication networks along with the precise calibration of the estimators and the installation of sensors on the conductor surface. Herein, as a viable alternative, the DLR forecasting models with respect to historical meteorological data were developed using ensemble learning algorithms. Several cases were designed to explore the resiliency and accuracy of the proposed method for different forecasting horizons. The result of simulations proved that ensemble learning algorithms can be fruitfully used for the DLR forecasting, even in the presence of severe cyberattacks. The proposed method yielded an approximate capacity increase of 30\% for 400kV lines between Ghadamgah and Binalood wind farms, which is enough to relieve the congestion issue. Experiments revealed the generalizability and reliability of the forecasting models for the DLR at various points of the line without the deployment of measurement devices and communication infrastructures.
TL;DR: An innovative method for fault location in distribution networks based on classical circuit analyses and a new impedance matrix manipulation procedure is proposed so that the distribution system is investigated in partitions across multiple subsystems.
Abstract: This paper presents an innovative method for fault location in distribution networks based on classical circuit analyses. Two synchronized and few nonsynchronized pre- and during-fault voltages are required at few buses along with the impedancematrix. A new impedance matrix manipulation procedure is proposed so that the distribution system is investigated in partitions across multiple subsystems. This procedure allows the fault location process to be performed by solving systems of determined equations, making the method more technically accessible. The fault is located by analyzing the voltage sag at the terminal-bus of each subsystem separately. The proposed method is validated on the IEEE 33-bus, 12.66 kV distribution system with/without distributed generation (DG). Simulation results show the robustness and accuracy of the method under several pre- and during-fault scenarios and measurements errors.
TL;DR: A novel nested restoration decision system (NRDS) is proposed, which aims to minimize unused capacity of distributed generation for service restoration due to contingency during islanding stage, based on the concept of nested microgrid formation.
Abstract: This paper proposes a novel nested restoration decision system (NRDS), which aims to minimize unused capacity of distributed generation (DG) for service restoration due to contingency during islanding stage. The proposed algorithm is based on the concept of nested microgrid formation, where the network control schemes are framed on a layered structure. The power-sharing strategy between neighboring microgrids in the network is from outer to inner microgrid loads, where the outermost microgrid exchanges power with utility grid only. The first stage refers to pre-fault scenario, which finds a solution for networked microgrid distributed generation as the initial setting. Furthermore, the second stage calculates additional redistribution requirements based on respective microgrid's deficiency using a solution index matrix. The proposed control has been evaluated on a networked microgrid system, while performing islanding.
TL;DR: In this article, a hierarchical harmonic contribution (HC) determination method is proposed for systems with multiple harmonic sources, which can be applied to a radial distribution system with any number of D-PMU placements.
Abstract: With the rapid development of renewable energy resources, harmonics in modern distribution systems have become more serious owing to the wide adoption of power electronic devices. Today, distribution-level phasor measurement units (D-PMUs), which can provide synchronized harmonic data with high-time resolution, are being rapidly developed. To utilize the D-PMUs, a hierarchical harmonic contribution (HC) determination method is proposed in this paper for systems with multiple harmonic sources. The HCs were determined using a widely linear complex partial least squares (WL-CPLS) method, which is both accurate and efficient. In an actual power system, the D-PMUs are not fully installed at all buses and the harmonics monitored by each D-PMU are actually the aggregate effects of multiple harmonic sources in the subarea. Based on the above consideration, a hierarchical HC strategy is proposed to locate the main harmonic contributors, layer by layer. The proposed method can be applied to a radial distribution system with any number of D-PMU placements. The methodologies are explained and verified using the IEEE 37-bus system. The real case study results based on the demonstration project are also analyzed in this paper.
TL;DR: In this paper, a multi-task graph convolutional neural network (MT-GCN) was proposed for parameter identification in electric power transmission systems, where GCN can extract the structure information to enhance local feature extraction and FCN is a decoding module following GCN module.
Abstract: Parameter Identification plays an important role in electric power transmission systems. Existing approaches for parameter identification tasks typically have two limitations: (1) They generally ignored development trend of historical data, and did not mine characteristics of corresponding power grid branches. (2) They did not consider the constraints of power grid topology, and treated different branches independently. Therefore, they could not characterize correlations between the center node and its neighborhoods. To overcome these limitations, this work proposes a multi-task graph convolutional neural network (MT-GCN) which utilizes the graph convolutional network (GCN) and the fully convolutional network (FCN) as building blocks for parameter identification. Specially, GCN can extract the structure information to enhance local feature extraction. FCN is a decoding module following GCN module, and it is used to identify the parameters of each branch according to its characteristics. Compared with previous methods, the proposed method is significantly improved in accuracy. Besides, this method is robust to measurement noise and errors, and can cope with multiple conditions in real power transmission systems.
TL;DR: In this paper, a single-ended fault location for hybrid distribution lines is proposed based on the morphological spectrum-based fault-section identification technique, where a location function is built by the forward and backward traveling-wave components to describe the characteristic distribution of the traveling wave along the line.
Abstract: In terms of single-ended fault location for hybrid distribution lines, the most significant challenge is in the identification of the fault section and reflected wave from the fault point. Based on this, in this paper we introduce a new fault- section-location identification scheme for hybrid distribution feeders with only single-ended measurement. With the prior morphological spectrum-based fault-section identification technique, a location function is then built by the forward and backward traveling-wave components to describe the characteristic distribution of the traveling wave along the line. With the application of two-dimensional information (which is about time and space) of the traveling wave, it is simpler to locate the actual point of failure. The proposed scheme is investigated under different fault locations, fault resistances, sampling rates, fault inception angles, fault types, etc. The influences of immeasurable voltage traveling wave, adjacent sound lines, and measurable joint point are also investigated. Simulation results confirm the feasibility of the fault-location scheme for all test cases.
TL;DR: In this article, a novel adaptive reclosing scheme based on pulse injection from parallel energy absorption module is proposed to overcome the drawbacks of the traditional reclosing schemes, which can overcome the secondary strike of fault in a short time, and seriously endanger the service life of DCCB.
Abstract: Modular multilevel converter (MMC) based high voltage direct current (HVDC) transmission system is one of the important development trends of DC power grid. After fault isolation, the traditional reclosing method of MMC-HVDC system is directly reclosing DC circuit breaker (DCCB). Once the fault point still exists, DCCB will be tripped again. This method will make the DC system suffer from the secondary strike of fault in a short time, and seriously endanger the service life of DCCB. In order to overcome the drawbacks of the traditional reclosing schemes, this paper proposes a novel adaptive reclosing scheme based on pulse injection from parallel energy absorption module. When dc fault current is interrupted by DCCB, the fault energy stored in current-limiting reactors (CLR) and dc lines is transferred to energy absorption module automatically. By switching on IGBTs embedded in absorption module for a short time, a voltage pulse is injected directly into the faulty line without interference from CLR. Then the nature of fault is identified and the fault distance can also be calculated in the case of permanent faults. The simulation results verify the effectiveness of the proposed reclosing scheme.
TL;DR: Wang et al. as mentioned in this paper proposed a many-objective distribution network reconfiguration (MDNR) model, with the consideration of renewable energy curtailment, voltage deviation, power loss, statistic voltage stability, and generation cost.
Abstract: With the increasing penetration of renewable energy (RE), the operation of distribution network is threatened and some issues may appear, i.e., large voltage deviation, deterioration of statistic voltage stability, high power loss, etc. In turn, RE accommodation would be significantly impacted. Therefore, we propose a many-objective distribution network reconfiguration (MDNR) model, with the consideration of RE curtailment, voltage deviation, power loss, statistic voltage stability, and generation cost. This aims to assess the trade-off among these objectives for better operations of distribution networks. As this proposed model is a non-convex, non-linear, many-objective optimization problem, it is difficult to be solved. We further propose a deep reinforcement learning (DRL) assisted multi-objective bacterial foraging optimization (DRLMBFO) algorithm. This algorithm combines the advantages of DRL and MBFO, and is targeted to find the Pareto front of proposed MDNR model with better searching efficiency. Finally, case study based on a modified IEEE 33-bus distribution system verifies the effectiveness of MDNR model and outperformance of DRL-MBFO.
TL;DR: In this paper, an improved ICC and a FEA capable of describing phenomena occurring during PD are developed, both developed models are coded and implemented either in MATLAB or in COMSOL Multiphysics linked with MATLAB, and their simulation results are compared and analyzed, and the influence of different parameters including void shape, void size, and void air pressure on PD parameters are studied.
Abstract: Although much work has been done and significant progress has been made on partial discharges (PD) measurement and detection techniques, this is not the case for PD modeling. Four types of internal PD modeling, in sequential order from first to last developed, are three-capacitance (abc), induced charge concept (ICC), finite element analysis (FEA), and Multiphysics models. The abc model is too simple to provide an understanding of the physical phenomena affecting PD. In contrast, Multiphysics models are immature. Multiphysics models require a high number of physical parameters that need to be experimentally determined, which is not a trivial task. Moreover, adjusting the physical parameters to achieve a good agreement between simulation results and measurement results for a specific geometry and dimension may not work for other geometries and dimensions. The FEA model, a relatively recent model, and the ICC model, which fall between the abc and Multiphysics models in terms of modeling complexity, can partly explain mechanisms and physical phenomena associated with internal PD. However, to the best of our knowledge, there is only one paper comparing ICC and FEA models for a case study; thus, further research is needed to elucidate various aspects of these two models. In this paper, 1) an improved ICC and a FEA capable of describing phenomena occurring during PD are developed, 2) both developed models are coded and implemented either in MATLAB or in COMSOL Multiphysics linked with MATLAB, and their simulation results are compared and analyzed, and 3) the influence of different parameters including void shape, void size, and void air pressure on PD parameters are studied.
TL;DR: Compared to the existing DSE based fault location methods, the proposed method only needs to solve a series of linear DSE problems, which overcomes the issues such as large numerical error and high computational burden especially for transmission lines in MMC-HVDC grids.
Abstract: This paper proposes a new fault location method for transmission lines in MMC-HVDC grids based on dynamic state estimation (DSE) and gradient descent. The method only requires a short data window of 5 ms after the occurrence of the fault and therefore is applicable for MMC-HVDC grids with high-speed tripping techniques. The method first builds a high-fidelity linear dynamic model of the DC transmission line, which accurately describes physical laws of the transmission line during the fault. Afterwards, the consistency between the measurements and the linear dynamic model is evaluated through the DSE algorithm. Finally, the actual fault location which corresponds to the best consistency is determined via the gradient descent algorithm. Compared to the existing DSE based fault location methods which solve highly nonlinear DSE problems, the proposed method only needs to solve a series of linear DSE problems, which overcomes the issues such as large numerical error and high computational burden especially for transmission lines in MMC-HVDC grids. Numerical experiments validates the effectiveness of the proposed method, with different fault types, resistances and locations. In addition, the method only requires a relatively low sampling rate of 20k samples per second.
TL;DR: An adaptive mitigation scheme to damp the SSO for full range of subsynchronous frequency variations due to different operating conditions, verified through extensive EMT simulations on a real-world wind power system facing SSO using PSCAD.
Abstract: The frequency of subsynchronous oscillation (SSO) in doubly-fed induction generator (DFIG)-based wind farms connected to a series-compensated network is determined by system-wide operating conditions, including the wind speed, number of in-service wind turbines, and degree of series-compensation. This paper proposes an adaptive mitigation scheme to damp the SSO for full range of subsynchronous frequency variations due to different operating conditions. The proposed scheme comprises of 1) a subsynchronous frequency estimator (SSFE) and 2) an adaptive subsynchronous damping controller (ASDC). The SSFE detects the SSO mode and tracks its subsynchronous frequency accurately. The ASDC utilizes voltage signals to extract the SSO at the estimated subsynchronous frequency, generates appropriate current signals, and injects them into the grid through a shunt-voltage sourced converter (SVSC). The ASDC behaves like a variable impedance, which essentially reshapes the systems impedance response by adding a positive resistance at the subsynchronous frequency. The parameters of the phase-shifters are computed using Brents root-finding method such that the desired phase-shift at different subsynchronous frequencies is maintained. The controls adaptiveness and damping performance under different operating scenarios is verified through extensive EMT simulations on a real-world wind power system facing SSO using PSCAD.
TL;DR: A general high-speed equivalent EMT modelling method of PET and the developed PET models on PSCAD/EMTDC are shown to be two orders of magnitude faster than the currently available fully-detailed models with negligible loss of accuracy.
Abstract: The high-speed accurate electromagnetic transient (EMT) simulation of the power electronic transformers (PET) has become a challenge, due to the microsecond-range time steps and the large number of high-frequency semiconductor switches and isolating transformers. Taking the input-series-output-parallel (ISOP) connected cascaded H-bridge (CHB) type dual active bridge (DAB) based PET as an example, this paper proposes a general high-speed equivalent EMT modelling method of PET. First, the isolating transformer within each DAB is discretized into two-port Norton circuits. Second, each CHB-DAB is equivalent to two single-port circuits by eliminating the internal nodes. And the input-side is represented by a Thevenin circuit and the output-side is represented by a Norton circuit. Third, all the CHB-DABs are equivalent to a two-port circuit with all the internal node information preserved. Fourth, the implementation of PET blocking is also considered for startup and fault protective actions. Fifth, the stability of the proposed discrete decoupling method of the PET is verified that it does not introduce specific limitation on the simulation step size. Finally, the developed PET models on PSCAD/EMTDC are shown to be two orders of magnitude faster than the currently available fully-detailed models with negligible loss of accuracy.
TL;DR: In this paper, a distortion-based algorithm is proposed to improve the reliability of HIF detection under various conditions, including the distortion offset caused by the lag of heat dissipation, and interference of background noise.
Abstract: Detection of the high impedance fault (HIF) in distribution systems is significant for power utilization safety. In addition to the low fault currents, traditional approaches are invalid to detect HIFs also due to the diverse characteristics, including the slight HIF nonlinearity during the weak arcing process, the distortion offset caused by the lag of heat dissipation, and the interference of background noise. This paper proposes a distortion-based algorithm to improve the reliability of HIF detection under various conditions. Firstly, the challenges brought by the diversity of HIF distortions are explained according to the field experiments in a 10 kV real-world distribution system. HIFs are classified into five types according to the distortions of their current waveforms. Secondly, a definition of interval slope is introduced to describe waveform distortions. The interval slope is extracted by combining methods of linear least square filtering (LLSF) and Grubbs-criterion-based robust local regression smoothing (Grubbs-RLRS), so that the distortions under different fault conditions can be uniformly described. Thirdly, an algorithm is proposed to judge the features presented by the interval slope, and distinguish from non-fault conditions. Finally, the reliability and security of the proposed algorithm are thoroughly analyzed with real-world HIFs and the simulated HIFs obtained in IEEE 34-bus and IEEE 123-bus systems. Results show the improvements of the proposed algorithm by the comparisons with other advanced algorithms.