Grid Fault Detection Research Articles

Assessment of PMU‐based wide‐area angle criterion for fault detection in microgrid

The penetration of distributed generation in the power distribution system has increased significantly in recent years, bringing serious protection challenges. Hence, it is mandated to develop effective and reliable fault detection scheme for present-day microgrid environment. This study presents a phasor measurement unit-based (PMU) wide-area phase angle criterion for building a protection system for the microgrid. The absolute value of the rate of change of voltage phase angle difference between the point of common coupling and the bus closest to the fault point is utilised for fault detection in grid connected as well as in the islanded modes of operation. C37.118.1 complied PMUs are employed at the appropriate locations for accurate and faithful measurement of voltage phase angle and the rate of angle difference (RAD) for detecting faulted conditions. PMUs are connected to phasor data concentrator, which receives the time synchronised phasor data and acts as a centralised processing unit. The proposed protection index has been extensively validated on MATLAB/Simulink environment as well as the real-time digital simulator with external PMUs. The test results show that the proposed RAD is a potential candidate for providing effective fault detection measure for microgrid including large variations in operating conditions.

Power Flow Control Strategy and Reliable DC-Link Voltage Restoration for DC Microgrid under Grid Fault Conditions

In this paper, an effective power flow control strategy (PFCS) based on the centralized control method and a reliable DC-link voltage (DCV) restoration algorithm for a DC microgrid (DCMG) under grid fault conditions are proposed. Considering the relationship of supply-demand power and the statuses of system units, thirteen operating modes are presented to ensure the power balance in DCMG under various conditions. In the PFCS, the battery charging/discharging procedure is implemented considering the battery power limit to avoid overheating and damage. Moreover, load shedding and load reconnection algorithms are presented to maintain the system power balance, even in critical cases. To prevent the system power imbalance in DCMG caused by the delay of grid fault detection, a reliable DCV restoration algorithm is also proposed in this paper. In the proposed scheme, as soon as abnormal behavior of the DCV is detected, the battery or wind power generation system instantly enters a local emergency control mode to restore the DCV rapidly to the nominal value, regardless of the control mode assigned from the central controller. Comprehensive simulations and experiments based on the DCMG testbed are carried out to prove the effectiveness of the PFCS and the proposed DCV restoration algorithm.

Support Vector Machine-Based Islanding and Grid Fault Detection in Active Distribution Networks

Many techniques used and still in usage for solving the problem of islanding detection are intrinsically passive, active, or hybrid of both. Each one of them has its own benefits and drawbacks. In this paper, we propose a method, which takes the advantage of a machine learning (ML)-based algorithm, namely, support vector machine (SVM), in order to produce the results more efficiently. The results of the simulations based on the model and experimentally measured parameters of a real-life practical photovoltaic (PV) plant give much better output than the traditional reported methods. During the tests and simulations, an additional problem, namely, grid fault, emerged, posing new challenges for the proposed method. Occurrences of islanding and grid fault are grouped together with the same kernel dimension and no custom hyperplane bordering. Discrimination between islanding and grid fault events is an essential dilemma, which is handled by the proposed SVM-based algorithm to achieve more precision in islanding detection and simultaneously detect the grid faults authentically. Nondetection zones (NDZs) and detection time (DT) are tested using two dimensions, namely, the generated active energy from PV plant (0%–110% of $P_{n}$ ) and distribution network voltage levels (±10% of $U_{n}$ ). Simulations based on the model and parameters of a real-life practical PV power plant are performed in MATLAB/Simulink environment, and several tests are executed for several scenarios. Finally, comparisons with previously reported techniques prove the effectiveness, authenticity, selectivity, accuracy, and precision of the proposed islanding and grid fault detection strategy with allowable impact on power quality according to UL1741 and its superiority over other methods.

Modeling and Estimation for Advanced Battery Management

The commercialization of lithium-ion batteries enabled the widespread use of portable consumer electronics and serious efforts to electrify trans-portation. Managing the potent brew of lithium-ion batteries in the large quantities necessary for vehicle propulsion is still challenging. From space applications a billion miles from Earth to the daily commute of a hybrid electric automobile, these batteries require sophisticated battery management systems based on accurate estimation of battery internal states. This system is the brain of the battery and is responsible for estimating the state of charge, state of health, state of power, and temperature. The state estimation relies on accurate prediction of complex electrochemical, thermal, and mechanical phenomena, which increases the importance of model and parameter accuracy. Moreover, as the batteries age, how should the parameters of the model change to accurately represent the performance, and how can we leverage the limited sensor information from the measured terminal voltage and sparse surface temperatures available in a battery system? With a frugal sensor set, what is the optimal sensor placement? This article reviews estimation techniques and error bounds regarding sensor noise and modeling errors, and concludes with an outlook on the research that will be necessary to enable fast charging, repurposing of batteries for grid energy storage, degradation prediction, and fault detection.

Grid topology and technology influences on selective protection concepts for multi‐terminal medium voltage DC grids

The integration of distributed renewable energy resources in combination with advancements in power electronics make the application of multi-terminal medium voltage DC grids a promising and flexible solution. Fast and selective protection concepts in combination with DC (disconnecting) switches for fault handling are required to ensure high reliability and safe grid operation of these systems. Medium voltage DC grids may be realised based on several converter technologies with different fault clearing capability. Depending on the converter technology, switches for isolating faulted branches or DC circuit breakers (CBs) for fault clearance become necessary. DC medium voltage CB concepts have already been proposed. However, the switching times of these CBs vary significantly depending on the presence or absence of mechanical components resulting in different requirements for fault detection. This contribution evaluates the relations between different converter and DC CB technologies, DC grid topologies and fault detection methods according to selective fault clearing. Transient simulation studies are carried out for the development of protection concepts. Based on the results, reasonable technological combinations of the above-mentioned technologies are identified. Special consideration is given to the dimensioning of series reactors as fault current rise limiting devices.

S-Transform Based FFNN Approach for Distribution Grids Fault Detection and Classification

Detection and classification of any anomaly at its commencement are very crucial for optimal management of assets in power system grids. This paper presents a novel hybrid approach that combines S-transform (ST) and feedforward neural network (FFNN) for the detection and classification of distribution grid faults. In this proposed strategy, the measured three-phase current signals are processed through ST with a view to extracting useful statistical features. The extracted features are then fetched to FFNN in order to detect and classify different types of faults. The proposed approach is implemented in two different test distribution grids modeled and simulated in real-time digital simulator and MATLAB/SIMULINK. The obtained results justify the efficacy of the presented technique for both noise-free and noisy data. In addition, the developed technique is independent of fault resistance, inception angle, distance, and prefault loading condition. Besides, the comparative results confirm the superiority and competitiveness of the developed technique over the available techniques reported in the literature.

A compositive control method of low‐voltage ride through for PMSG‐based wind turbine generator system

Converting the surplus power into rotor kinetic energy by changing the control functions of machine side converter (MSC) and grid side converter (GSC) is considered to be an efficient way for permanent magnet synchronous generator (PMSG)-based wind turbine generator system (WTGS) to accomplish low-voltage ride through (LVRT) without additional hardware components. However, because of the time delay of grid fault detection and the control system inertia, the dc-link capacitor may still be subjected to overvoltage in the early tens of milliseconds after a grid fault happens. This study proposes a compositive control method of LVRT for PMSG-based WTGS, in which the surplus power during LVRT is converted into rotor kinetic energy by controlling the electrical power of the PMSG according to the grid-connected power of the GSC, and a crowbar circuit is reserved to consume the surplus power before MSC reacts to the grid fault. The proposed method does not change the control functions of MSC and GSC, and the related control parameters resetting can be avoid. Besides, it can also provide reactive power support to assist the grid voltage recovery. Simulations and experiments proved the proposed method to be feasible and effective.

Smart grid fault detection and classification with multi‐distributed generation based on current signals approach

Smart grid (SG) containing clean distributed generation (DG) has gained intensive research for reliable digital protection against fault conditions. The main objective of this study is to develop and validate a fast and efficient fault detection and classification algorithm taking into account the dynamic behaviour of DG units. The proposed algorithm does not require additional measurements as it based only on the synchronised measured three-phase currents through the communication facilities available in SG. The advanced communication technologies in SG are currently utilised for efficient and real-time data transfer from the protective relays to the monitoring and protection centre. The transferred data are processed for fault diagnosing before taking the necessary trip actions. In the proposed algorithm, the statistical cross-alienation coefficients are calculated for the measured current signals at sending and receiving ends of each feeder. Fault detection and classification process will be achieved taking into account the changes in the synchronised and discretised waveform of current signals within a movable window of (1/4 cycle). The proposed algorithm is tested on real system to detect and classify the different fault condition. The results indicated that the proposed technique is fast and reliable for SG digital protection schemes.

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.

Design of Current Signal Source on DDS for Rural Power Grid Fault Detection

Design of Current Signal Source on DDS for Rural Power Grid Fault Detection

New procedure for fault detection in grid connected PV systems based on the evaluation of current and voltage indicators

In this work we present a new procedure for automatic fault detection in grid connected photovoltaic (PV) systems. This method is based on the evaluation of new current and voltage indicators. Thresholds for these indicators are defined taking into account the PV system configuration: number of PV modules included and series and parallel interconnection to form the array. The procedure to calculate the thresholds that allow the identification of the faults is described. A simulation study was carried out to verify the evaluation of current and voltage indicators and their corresponding thresholds for a set of PV systems with different sizes and different configurations of interconnection of PV modules. The developed method was experimentally validated and has demonstrated its effectiveness in the detection of main faults present in grid connected applications. The computational analysis has been reduced and the number of monitoring sensors minimized. The fault detection procedure can be integrated into the inverter without using simulation software or additional external hardware.

The Research of an Automatic Phase Displacement Island Protection Method of Photovoltaic Power Generation System

This paper proposes a lonely island detection method based on automatic type phase displacement. Different form the traditional island detection methods, this method solve the problem of its some kind of detection under the condition of failure , improving active island detection technology of slip type automatic frequency quadratic displacement method. This method only need to change the output current of the initial angle without changing its frequency to detect whether the grid failure, compared with the traditional island detection methods, improve the accuracy of the grid fault detection. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.4173

Automatic fault detection in grid connected PV systems

This paper presents a detailed procedure for automatic supervision, fault detection, and diagnosis of possible failure sources leading to total or partial loss of productivity in grid connected PV systems. The diagnostic procedure is part of the monitoring system allowing, at the same time, modeling and simulation of the whole system and variables measurements in real time. The fault detection algorithm is based on the comparison of simulated and measured yields by analyzing the losses present in the system while the identification of the kind of fault is carried out by analyzing and comparing the amount of errors deviation of both DC current and voltage with respect to a set of errors thresholds evaluated on the basis of free fault system. The proposed method has been validated in with experimental data in a grid connected PV system in the Centre de Developpement des Energies Renouvelables (CDER) in Algeria.

Fault detection in metallic grid scattering

The canonical problem of detecting and localizing missing scatterers (faults) inside a known grid of small cross section perfect electric conducting cylinders is dealt with. The case of a TM scalar two-dimensional geometry is considered. The scattering by a fault is modeled as the radiation of a proper magnetic current, by exploiting the Green's function of the complete grid. An approximated linear model of the scattering is proposed and discussed in terms of the achievable probability of detection, also in the case of two faults, and checked against model error and noisy synthetic data.

Online estimation and application of power grid impedance matrices based on synchronised phasor measurements

Grid impedance matrices estimation is important in monitoring and protection of wide-area power systems. In this study, a recursive least-squares estimation algorithm with a forgetting factor is presented to identify impedance parameters of power grids. This approach is based on synchronised phasor measurements and makes it possible to track changing parameters. The algorithm is tested on two power systems using MATPOWER-generated data, and case studies are conducted for estimation of grid impedances followed by voltage stability margin analysis and fault detection. The results show that the proposed method is effective for online estimation of grid impedances, fault detection and voltage stability monitoring.

Characterization of Double Structure Tabular Microcrystals of Silver Halide Emulsions by Means of Electron Energy-Loss Spectroscopy, Zero-Loss Electron Spectroscopic Imaging and Energy-dispersive X-ray Microanalysis

Electron energy-loss spectroscopy (EELS) and zero-loss electron spectroscopic imaging (ZLESI) in conjunction with spot energy-dispersive X-ray microanalysis (EDX) were applied to the structural and chemical characterization of practical emulsion core (AgBr)-shell (AgBr1-x Ix) tabular microcrystals. The samples were cryocooled to minimize damage under the electron beam. Recorded energy-loss spectra of the crystals in the initial state and after a 2-minute development contained several low intensity bands, which were tentatively assigned to silver and halides, as well as bulk, surface and defect plasmons and interband transitions. Spatially resolved energy-loss spectra of silver specks on extractive carbon replicas of silver halide grains showed the presence of the delayed Ag M4, 5edge at 400 eV Results of EELS-microanalysis have been confirmed by EDX spot analysis of the same emulsion samples. The improved contrast in the ZLESI mode enabled the detection of dislocation grid and stacking faults on the tabular grains as well as the visualization of the morphology and defect structure of silver filaments formed during development of microcrystals. Zero-loss filtering was also used to obtain high-resolution point diffraction patterns of the crystals with low background, containing extra reflections at commensurate positions in between the Bragg reflections probably due to a number of defects in the shell region.