Symmetrical Faults Research Articles

Fault detection during power system out-of-step oscillation using frequency difference sudden-change of voltage and current at a single terminal bus and a novel setting-free distance protection unlocking scheme

Setting blocking on the electricity transmission network distance protection is an important control measure to prevent it from malfunctioning in the out-of-step oscillation (OOSO) process. However, this operation makes it lose the ability to cut off faults. Once fault occurs, the consequences are serious, which might bring huge energy loss. How to effectively identify faults and quickly restore its protection capability is an important engineering task, but it's also filled with challenges. For the symmetrical faults occurring at δ≈180∘, the traditional technologies are susceptible to failure because the system seems to be oscillating continuously. For asymmetric faults, the sequence component method can be adopted, but limited by the sequence component extraction speed. Aiming at the above existing problems, this paper launches the targeted study and gives the corresponding solutions. To the former, it innovatively analyzes the variation law about the frequency difference of bus voltage (BV) and system current (SC) before and after fault occurs in the OOSO process, and gives its change range through strict mathematical derivation. Based on this, it proposes a novel one terminal setting-free distance protection unlocking scheme for symmetrical fault. Experiments show that the new variables defined in the criterion change abruptly and last for 3 sampling points after a fault. The new method can effectively identify faults occurring at δ≈180∘. This is an important advantage over the traditional methods. To the latter, it constructs a phasor imaginary part estimator, and proposes a sequence component transient calculation method. It greatly improves the asymmetric fault identification speed of the related method.

Integration of Wind Systems with SVC and STATCOM during Various Events to Achieve FRT Capability and Voltage Stability: Towards the Reliability of Modern Power Systems

FACTS tools in modern power systems provide a vital solution to the problems of voltage deviation and fault ride-through (FRT) capability in electrical power systems especially during the integration of wind power. Many distinct journals highlight that the wind-driven squirrel cage generator (SCIG) still accounts for around 15% of operating wind generators so far. To enhance voltage stability and FRT capacity, this paper recommends a cost-effective static VAR compensator (SVC) which has a size rating of six MVAR, and this improves the efficiency of the electrical power system. Different events are considered in this study such as high turbulent wind speed, low turbulent wind speed, unsymmetrical faults, and symmetrical faults to validate the suggested option. Moreover, the suggested solution is compared with the static synchronous compensator (STATCOM) and a fixed capacitor to ensure that during the studied wind speed profiles and faults, voltage stability, reactive power consumption, and FRT capability are realized. An overall comparison among them is performed under all studied scenarios to summarize their benefits and impacts. The simulated results show the effectiveness and superiority of SVC in improving the operation of an integrated wind system based on a grid-linked SCIG and the performance of the power system. The modeling of SCIG, SVC, and STATCOM is designed by MATLAB/Simulink toolbox.

A novel cyber‐attack modelling and detection in overcurrent protection relays based on wavelet signature analysis

AbstractCyber attacks can disrupt the digital protection relays and change their output command to the circuit breaker. The aim of this research is to provide new condition monitoring for overcurrent relays during cyber treatments. This research is modeled three common types of attacks with randomize concept and its detection method via MATLAB/SIMULINK software into IEEE 3‐machine 9‐bus model that is applicable in real time situation. Finally a new index has been defined for detection that employs wavelet analysis of the substation currents and its energy which has been called cyber attack detection index (CADI). Proposed index is robust to variation of sampling signal, sampling rate, cyber attack type and current transformer saturation. The results show that the index has an acceptable performance with accuracy of 100% without any false trigger. This method is not dependent on operating point, relay protection scheme and attack type and also could distinguish the abnormal actions of breakers due to unexpected cyber contingencies caused by natural disasters or software malfunctions rather than cyber attacks in a certain operating points. The level of CADI curve in cyber attacks is always higher than symmetrical fault and current signal detects them more quickly and clearly than other signals.

Battery Power Control Strategy for Intermittent Renewable Energy Integrated Modular Multilevel Converter-Based High-Voltage Direct Current Network

Modular multilevel converters (MMC) play a dominant role in integrating remotely located renewable energy resources (RER) over the high-voltage direct current (HVDC) transmission network. The fault ride-through capabilities of the MMC-HVDC network during low-voltage faults and the power fluctuation due to RER intermittency are the major obstacles to the effective integration of renewable energy. In response, this article proposes a local voltage-based combined battery energy control scheme for a PV-wind-battery connected MMC-HVDC system to regulate the HVDC-link voltage during low-voltage faults at the point of common coupling of alternating current grids and to reduce the intermittent RER power fluctuation. The proposed technique removes the dynamic braking resistor from the HVDC-link and smoothly integrates the RER without active power reduction of renewable energy under low-voltage faults. Symmetrical and unsymmetrical low-voltage faults have been conducted to validate the effectiveness of the proposed control scheme for the battery in mitigating surplus energy in the HVDC-link. Additionally, wind speed, solar radiation, and temperature have been changed to confirm the improved performance of the battery energy management system. The complete systems have been simulated and tested in a real-time digital simulator (RTDS) and using dSPACE-based controller hardware in a loop setup.

A Novel Protection Method to Enhance the Grid-Connected Capability of DFIG based on Wind Turbines

This paper proposes a robust protection technology to enhance the grid-connected capability of the doubly fed induction generator (DFIG) based on wind turbines (WTs) under low voltage ride through (LVRT) conditions. The proposed method to control the rotor side converter (RSC) and grid side converter (GSC) uses proportional–integral (PI) controllers. The protection devices are the static compensator (STATCOM), crowbar, series dynamic resistor (SDR), and DC-chopper, with the proposed method to control STATCOM using proportional–integral (PI) controllers and the proposed method to control the rest of the protection devices based on the allowable threshold values of rotor current and DC-link voltage. The protection coordination strategy between them is proposed to avoid the disconnection between the rotor circuit and grid, maintain the DC-link at constant voltage, enhance the dynamic response of WT, and improve the voltage response at the point of common coupling (PCC) of WT. The DFIG-WT of 2 MW-575 V connecting to the 34-bus IEEE network is carried out to verify the effectiveness of the proposed method under the conditions of symmetrical and asymmetrical faults, and the LVRT requirements of the Spanish grid code are considered. The obtained simulation results using the PSCAD/EMTDC software show that the rotor current, DC-link voltage, voltage PCC, and power oscillation damping are significantly improved compared to not using it or applying the conventional method based on passivity theory. Therefore, the LVRT capability of the proposed DFIG-WT system is significantly enhanced, and it has enough ability to continue to provide electrical energy to the power network. Abbreviation DFIG, Doubly Fed Induction Generator; DVR, Dynamic Voltage Restorer; FACTS, Flexible AC Transmission System; FRT, Fault Ride Through; FS-WT, Fixed-Speed Wind Turbine; GSC, Grid Side Converter; LVRT, Low Voltage Ride Through; PCC, Point of Common Coupling; PI, Proportional–Integral; PMSG, Permanent Magnet Synchronous Generator; PWM, Pulse Width Modulated; RSC, Rotor Side Converter; SDR, Series Dynamic Resistor; STATCOM, Static Compensator; SVC, Static Var Compensator; UPFC, Unified Power Flow Controller; VS-WT, Variable-Speed Wind Turbine; SLG, Single Line-to-Ground; LL, Line-to-Line; LLG, Double Line-to-Ground; WT, Wind Turbine

Contrasting faulting styles of salt domes and volcanoes: Can unsupervised learning techniques differentiate fault styles?

An attractive feature seen on seismic data, also known as funny-looking thing (FLT), has a wide range of interpretations, from noise patterns to amplitude anomalies. An example of an FLT is the similar faulting patterns between a volcanic intrusion and a salt intrusion from the point of view of a machine learning (ML) algorithm. Oftentimes, seismic interpreters do not have a complete data set or geologic background to determine the genesis of the observed features. This can be particularly perplexing when trying to determine if an intrusion is volcanic or halokinetic in origin because they exhibit similar geomorphologies. Examining the differences in these features in the Gulf of Mexico, a well-documented salt basin, and the Taranaki Basin in New Zealand, which is igneous prone, can help to understand the differences. The analysis aims to discern geologic features based on the geometries and attributes shared by seismic data and remote sensing tools. Seismic attributes and ML techniques highlight differences and similarities between the intrusions, including the discussion of using ML techniques, such as self-organized maps (SOMs), an unsupervised ML technique, and cluster fault systems, without regard to the geologic context. The attributes used in the SOM are fault probability, fault dip azimuth, fault dip magnitude, and thin-bed detector. Fault probability is performed through a combination of convolutional neural network fault prediction and a skeletonization process. Once the faults are clustered using SOM, the visualization of fault architecture due to the existing mount (either volcano or salt dome) is done considering high fault probabilities (>75%). The methodology consists of selecting the neurons from the SOM grid corresponding to the presence of faults and combining them with fault probability and a fault dip azimuth using a crossplot. The crossplot product assists in the automatic extraction of the fault planes using: (1) a voxel representation of the fault planes and (2) fault patches representing the fault planes. Moreover, the visualization technique defined demonstrates that the crossplot product yields better-defined fault planes. With the fault system characterized, compared horizon slices using coherence, fault dip magnitude, and azimuth against remote sensing images with similar attributes. In conclusion, our methodology combines technologies to differentiate the genesis of intrusion — salt or igneous — using the fault presence and could be helpful in frontier exploration or planetary exploration. Geologic feature: Comparison of extensional faults due to salt and igneous intrusions Seismic appearance: In the symmetric salt dome, crown faulting style with a set of synthetic and antithetic faults around the salt dome; for the igneous systems, symmetric radial faults Formation: Upper and Lower Wilcox (Gulf of Mexico); Ariki Formation — Mo hakatino Formation (Taranaki) Age: Upper Miocene and Lower Pliocene (Gulf of Mexico); Mid Pliocene (Taranaki) Location: Offshore Gulf of Mexico and Offshore Northeast New Zealand Seismic data: From the Gulf of Mexico: B-78-89-LA-3D, B-69-94-LA-3D, B-54d-94-LA-3D, and B-69a-94-LA-3D. From New Zealand: Nimitz and Kora 3D Analysis tools: Structural-oriented filter, Sobel similarity, fault dip magnitude and azimuth, CNN automatic fault prediction, and SOM classification

Impact of large-scale photovoltaic-energy storage power generation system access on differential protection of main transformer under symmetrical faults

The low voltage ride-through (LVRT) requirements demand large-scale photovoltaic (PV) power generation system remain connected to the grid during faults. It results in considerable impact on the characteristics of fault current. This paper combines charge-discharge characteristics of the energy storage (ES) with PV generation system to enhance the LVRT capability. Based on the inverter control strategy and specific LVRT requirements, fault current characteristics of the PV-ES power generation system is discussed in this paper. In order to analyze the fault characteristic, the fault current expression as three-phase short-circuit faults occurs on both sides of the main transformer is calculated. Furthermore, taking the winding connection of the transformer into account, the ratio of differential current to braking current is further derived to find out the factors influencing the performance of the transformer differential protection. It is found that factors influencing the transformer differential protection include the rated capacity of the PV-ES generation system, fault severity, the length of transmission line and so on. And as the rated capacity of the PV-ES power generation system increase, the transformer differential protection would experience reduced sensitivity or even do not trip. The findings of this paper can lay a foundation for further improvement of the transformer differential protection. The elaborate performance evaluation of transformer differential protection, including the operation condition of the second harmonic braking element, is presented and verified by simulation using MATLAB/Simulink.

Improving the LVRT Capability of the DFIG- Based Wind Turbines during Fault

According to the coordination control of a dynamic voltage restorer (DVR) and an inductive fault current limiter (FCL), this paper proposes an efficient low-voltage ride-through (LVRT) scheme for a doubly fed induction generator (DFIG) based wind turbine. The DVR is located to the DFIG’s stator circuit for stabilizing the terminal voltage and decreasing the generator current. The inductive FCL is connected to the DFIG’s rotor circuit for suppressing the rotor overcurrent and protecting the converter. Theoretical discussions on structure, principle and scale criterion of the combined DVR-FCL are conducted, and simulation analyses of the proposed approach to handle symmetrical and asymmetrical faults are done in MATLAB/Simulink. In this study, the dynamic characteristics of the DFIG during the faults are analyzed from multiple aspects, and a detailed comparison of the proposed approach and the single action of DVR or FCL is carried out. From the simulation results, the effectiveness and superiority of the proposed approach are well demonstrated.

Power flow management and LVRT enhancement by using multi-functional capacitive bridge-type fault current limiter in DFIG system

This paper proposes a new concept of the fault current limiter (FCL) named multi-functional capacitive bridge-type fault current limiter (MCBFCL). It acts as a variable capacitor to manage active power flow under normal operating condition and enhances the low-voltage ride-through (LVRT) performance of wind farms (WFs) under fault operating conditions. The MCBFCL have been presented based on the capacitive bridge-type fault current limiter (BFCL) and is modified to compensate the transmission line impedance in order to manage power flow, while it is not bypassed under normal operating conditions unlike the conventional FCLs. In this paper, first, the power circuit and principle performance of the MCBFCL are discussed. Then time domain simulations are conducted in PSCAD/EMTDC environment to check the MCBFCL performance under both normal and fault operating conditions. Also, the MCBFCL performance is compared to the resistive and inductive BFCLs in transmission lines with series capacitive compensation. In this work doubly fed induction generator (DFIG)-based wind turbines have been utilized. Simulation results showed that the MCBFCL can manage the power flow under normal operating condition and enhances the LVRT performance of the WF under both symmetrical and asymmetrical fault operating conditions. Also, it has a superior performance in terms of LVRT indices values compared to the resistive and inductive BFCLs.

An Intelligent Distance Relay Sensing Methodology Immune to Power Swing and Load Encroachment

A distance relay (DR) is a complex sensor that operates based on apparent impedance computed using inputs from the current and voltage sensors. Power swing and load encroachment are two stressed conditions that a DR finds challenging to differentiate from a fault condition. Conventional DRs use additional techniques to detect such conditions and block the DR. However, a symmetrical fault goes undetected during such a block period and can lead to a blackout in the worst case scenario. Also, a highly reactive load encroachment cannot be detected by the conventional relay and, hence, may maloperate. This article critically analyzes the apparent impedance behavior during a symmetrical fault, power swing, and load encroachment. Using the insights from the analysis, an intelligent DR sensing methodology that does not use additional techniques to sense the power swing and load encroachment conditions is proposed. The results from numerous case studies of faults, power swing, faults during power swing, load encroachment, and faults during load encroachment in the WSCC nine bus system in MATLAB/Simulink prove the efficacy of the proposed methodology. The DR employing the proposed sensing methodology can refrain from issuing false trip signals during power swing and load encroachment conditions. It can detect symmetrical and unsymmetrical faults within one cycle in any given condition. The proposed methodology is superior to other methods in detecting symmetrical and unsymmetrical faults during any given condition within one cycle.

Support Vector Machine Parameters Optimization for 500 kV Long OHTL Fault Diagnosis

Faults can seriously damage high-voltage (HV) power systems, particularly if they occur on the long overhead transmission line (OHTL) that connects the nuclear power plant (NPP) to the electrical grid. Finding OHTL problems quickly and accurately is essential for the economy, safety, and dependability of the HV power systems. It is essential to pinpoint the problematic phase to avoid unneeded power outages. Thus, one of the most crucial research challenges is now how to identify, classify, and locate OHTL faults. In this study, transient current with high frequency oscillations that arise immediately after a defect at the sending end is investigated in a single-circuit, single-side fed Egyptian 500-kV HV long OHTL. Asymmetric and symmetric faults and locations are also represented in the Alternative Transients Program-Electro Magnetic Transients Program (ATP/EMTP) simulation model under varying fault resistance and inception angles. The proposed solution in this paper is an Optimized Support Vector Machine (OSVM), whose characteristics are optimized via a mutant particle swarm optimization (MPSO) method to detect 500 kV long OHTL faults. The localizer model is also built for practical applications, including power system noise contaminating fault signals. The findings prove that the suggested approach locates the fault in 0.012 seconds from the start of the event, with a 0.0098 percent average percentage error, and without being impacted by differences in fault distance, fault resistance, noise, or fault inception angle. Additionally, the optimised classifier reaches a 99.85% accuracy rate, enhancing line system dependability and advancing nuclear system development.

Performance analysis of protection technique for AC microgrid from symmetrical faults

The research paper is presenting an implementation of Microgrid which assumes lots of loads & sources operated under a one controllable unit system providing power to the localized area. A microgrid model is built integrating wind energy, solar energy, diesel generator and residential load model and analyzing the impact of Renewable Energy Sources with microgrid on various quantities like active power, reactive power, current & voltage as shown and presented in the research paper during normal and fault condition. The various factors on which production of energy depends are PV farm’s size & it’s efficiency and speed of wind for Wind farm. The load is residential which has a consumption profile at given power factor. Highest solar intensity is during mid-day. The development of model is aimed to provide a sustainable energy supply to load without affecting the nature and compromising the needs of the future generation. In our research work we have used high-speed tripping protection technique for symmetrical faults.

CNN-Based Transformer Model for Fault Detection in Power System Networks

Fault detection and localization in electrical power lines has long been a crucial challenge for electrical engineers as it allows the detected fault to be isolated and recovered promptly. These faults, if neglected, can rupture the normal operation of the network and drastically damage the power lines and the equipment attached to it. The wastage of power and money due to these faults can be harmful to the economy of an industry or even a country. Therefore, efficient fault detection mechanisms have become crucial for the well-being of this power-hungry world. This research presents an end-to-end deep learning strategy to detect and localize symmetrical and unsymmetrical faults as well as high-impedance faults (HIFs) in a distribution system. This research proposes a novel deep convolutional neural network (CNN) transformer model to automatically detect the type and phase of the fault as well as the location of the fault. The proposed model utilizes 1-D deep CNNs for feature extraction and transformer encoder for sequence learning. The transformer encoder utilizes an attention mechanism to integrate the sequence embeddings and focus on significant time steps to learn long-term dependence to extract the context of the temporal current data. The different faults were simulated in MATLAB Simulink using IEEE 14-bus distribution system. The proposed models were found to produce better performance on the test database when evaluated using F1-Score, Matthews correlation coefficient (MCC), and accuracy. The models also produced better predictions on HIFs compared to conventional fault-detection techniques.

Improving the performance of grid connected wind generator with a PI control scheme based on the metaheuristic golden eagle optimization algorithm

• A novel Golden Eagle optimization algorithm (GEOA) is proposed. • GEOA is used to improve the Transient and Dynamics stability of wind farm. • Transient and Dynamics stability of grid-tied PMSG-VSWT is investigated during symmetrical, unsymmetrical faults and Stochastics wind variation at that site. • The results of GEOA are compared with the results of NR, PSO and GOA. • The results shown that the GEOA is robust and competitive. Wind power installations are globally established thoroughly, resulting in significant electrical power penetration into power grids. Consequently, tremendous efforts are expended to improvise the performance of the grid-connected Permanent Magnet Synchronous Generator driven by a Variable-Speed Wind Turbine (PMSG-VSWT). In this work, eight cascaded Proportional Integral (PI) controllers in the machine side and grid side converters are optimally tuned by the golden eagle optimization algorithm with the purpose of enhancing the dynamics and transient stability performances of the grid connected PMSG-VSWT.The integration of the square error criterion was utilised as a fitness function in the optimization process, which was processed using the simulation-based optimization technique. The proposed control technique's efficacy was compared to various optimal PI controllers under symmetrical and unsymmetrical fault conditions. This study also employed the measured real-time events collected at the Coimbatore wind farm in Tamil Nadu (India) to produce realistic results and aid in the pursuit of higher performance. The wind farm modelling and simulation were processed using the DIgSILENT power factory/MATLAB Simulink environment and was utilized to test the proposed control approach's achievability. Finally, the study demonstrates that the golden eagle optimization technique improves the wind farm's performance when compared to observed events and other techniques.

Fault Ride Through and Intermittency Improvement of Renewable Energy Integrated MMC-HVDC System Employing Flywheel Energy Storage

Modular multilevel converter (MMC)-based high voltage direct current (HVDC) transmission networks integrate remotely located distributed renewable energy resources (RER). The intermittent nature of RER and symmetrical and asymmetrical AC-side low-voltage faults produce operational difficulties. It reduces the MMC’s ability to transfer the rated power, which raises the voltage of the HVDC link. Therefore, the MMC-HVDC network’s fault ride-through (FRT) capabilities and the power fluctuation brought on by RER intermittency are potential challenges to the efficient integration of renewable energy resources. In response, this article suggests an energy control scheme for the flywheel energy storage of the PV-wind-MMC-HVDC system in order to regulate the HVDC-link voltage during low voltage faults at the point of common coupling (PCC) of the AC grids and to address the problem of power fluctuation caused by intermittent RER and sudden load changes. The suggested method eliminates the dynamic braking resistor (DBR) from the HVDC link and seamlessly integrates the RER without actively reducing renewable energy power during low voltage faults. To test the effectiveness of the proposed control method for the flywheel energy storage in reducing excess energy in the HVDC link, symmetrical and asymmetrical low voltage faults have been conducted. In addition, changes in wind speed, solar radiation, and temperature have been made to validate the flywheel energy management system’s performance. A real-time digital simulator (RTDS) and dSPACE-based controller hardware in loop (CHIL) configuration with a complete system have been used to simulate and evaluate the entire system.

Coordinated Rotor-Side Control Strategy for Doubly-Fed Wind Turbine under Symmetrical and Asymmetrical Grid Faults

In order to solve the problems of rotor overvoltage, overcurrent and DC side voltage rise caused by grid voltage drops, a coordinated control strategy based on symmetrical and asymmetrical low voltage ride through of rotor side converter of the doubly-fed generator is proposed. When the power grid voltage drops symmetrically, the generator approximate equation under steady-state conditions is no longer applicable. Considering the dynamic process of stator current excitation, according to the change of stator flux and the depth of voltage drop, the system can dynamically provide reactive power support for parallel nodes and suppress the rise of DC side voltage and rotor over-current. When the grid voltage drops asymmetrically, the positive and negative sequence components are separated in the rotating coordinate system. The doubly fed generator model is established to suppress the rotor positive sequence current and negative sequence current respectively. At the same time, the output voltage limit of the converter is discussed, and the reference value is adjusted within the allowable output voltage range. In order to adapt to the occurrence of different types of power grid faults and complex operating conditions, a fast switching module of fault type detection and rotor control mode is designed to detect the type of power grid faults and voltage drop depth in real time and switch the rotor side control mode dynamically. Finally, the simulation model of the doubly fed wind turbine is constructed in Matlab/Simulink. The simulation results verify that the proposed control strategy can improve the low-voltage ride through performance of the system when dealing with the symmetrical and asymmetric voltage drop of the power grid and identify the power grid fault type and provide the correct control strategy.

Investigation of Voltage Dip Problems during Faults on a Grid-Tied Doubly Fed Induction Generator in a Wind Energy System

A voltage dip is a sudden drop of voltages - generally between 10 and 90 % of the rated RMS value - during a period lasting from half a cycle to a few seconds on the phases of the power lines. It is one of the most important power quality problems affecting the stability of the Doubly Fed Induction Generator (DFIG) in Wind Energy Conversion System (WECS) and hence needs to be analyzed for a given machine for its performance analysis under grid disturbances. When a voltage dip problem happens in the given power as of faults, the magnitude of the rotor and stator currents of DFIG get increased, and hence disturbs steady state operation of the system. Therefore, in this paper, the worst voltage dip of 90 % is tested for grid faults on a 5 kW DFIG to validate its performance under this phenomenon. Based on the results of the simulation - along with its experimental validation - the machine is found to be robust for faults staying a shorter period without disconnecting it from the grid. On the other hand, operating a machine for a longer period while keeping it connected to a grid during a heavy dip, may result in its degradation as the rated limits are violated. To rectify and mitigate the aforesaid problems, a control system - with and without a crowbar - is developed for symmetrical faults to tackle such a problematic situation, and to discuss its fulfillment of the current grid code requirements. Finally, a complete model of the DFIG coupled with the grid is developed, modeled, analyzed and simulated using MATLAB/Simulink user-defined function toolbox block.

A Case Study on the LVRT Capability of an Egyptian Electrical Grid Linked to the Al-Zafarana Wind Park using Series Resistor

In wind farms, a doubly fed induction generator (DFIG) has proven to be the most successful wind power generator in recent years. DFIG features various speeds of operation and autonomous management of reactive and active powers. Nevertheless, the extreme DFIG sensibility to voltage dips brings a lot of defiance in terms of complying with grid code regulations set by the electrical utility operators. Now, these grid regulations impose tougher constraints, particularly for low-voltage ride-through (LVRT). Indeed, this disruption brought on by the stator circuit affects the generating system. Originally, when a failure occurred, generators had to be disconnected. In time, a disconnect could cause the entire system to fail. Recent grid codes require that in addition to meeting operating irregularities like the dc-link voltage and stator and rotor currents, the generators also remain connected. To increase the LVRT capacity for the Egyptian Electrical Grid linked to the Al-Zafarana Wind Park. This article introduces the design and modelling of a protection scheme employing a series resistor. The protection scheme ensures the safety of energy converters, rotor circuits, and dc-link capacitors while limiting rotor currents, dc-link voltages, and torque changes. The effectiveness of the protective scheme was also assessed for asymmetrical and symmetrical fault situations. Matlab/Simulink is used to produce the simulation findings. According to the simulation findings, the protection scheme demonstrated an effective solution for improving the LVRT feature, of the Egyptian Electrical Grid linked to the Al-Zafarana Wind Park.

A Practical Estimation Method of Short-Circuit Current Contribution of Inverter-Based Resources for Symmetrical Faults

A Practical Estimation Method of Short-Circuit Current Contribution of Inverter-Based Resources for Symmetrical Faults

Machine Learning Based Protection Scheme for Low Voltage AC Microgrids

The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification and isolation are major concerns with IIDGs-based active MGs where IIDGs reveal arbitrary impedance and thus different fault characteristics. Moreover, bidirectional complex power flow creates extra difficulties for fault analysis. This makes the conventional methods inefficient, and a new paradigm in protection schemes is needed for IIDGs-dominated MGs. In this paper, a machine-learning (ML)-based protection technique is developed for IIDG-based AC MGs by extracting unique and novel features for detecting and classifying symmetrical and unsymmetrical faults. Different signals, namely, 400 samples, for wide variations in operating conditions of an MG are obtained through electromagnetic transient simulations in DIgSILENT PowerFactory. After retrieving and pre-processing the signals, 10 different feature extraction techniques, including new peaks metric and max factor, are applied to obtain 100 features. They are ranked using the Kruskal–Wallis H-Test to identify the best performing features, apart from estimating predictor importance for ensemble ML classification. The top 18 features are used as input to train 35 classification learners. Random Forest (RF) outperformed all other ML classifiers for fault detection and fault type classification with faulted phase identification. Compared to previous methods, the results show better performance of the proposed method.