Power Grid Fault Research Articles

New Energy Power Prediction and Warning Based on Multi-source Prediction and Scene Classification Recognition

This article proposes a new method for predicting and warning of new energy electricity based on a multi-source prediction data and scene classification recognition algorithm. The study uses ECMWF, GFS, and DERF2.0 multi-source prediction data to improve the accuracy of offshore wind power prediction and disaster warning. In order to overcome the limitations of traditional prediction methods, this study adopts a scene classification recognition method. Based on meteorological, power grid, and environmental data, the study uses a scene recognition algorithm to establish three types of factor feature extraction models and combines them with fault information to establish a wind power prediction and warning model. This model can achieve the prediction of wind power and warn of possible power grid faults under meteorological disasters. Case analysis shows that this method significantly improves the accuracy of new energy electricity prediction and meteorological disaster warning. This study emphasizes the importance of multi-source prediction data and scene classification recognition methods in improving offshore wind power prediction and other new energy fields. Additionally, this method has the potential to make valuable contributions to the renewable energy industry.

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 When the power grid voltage drops symmetrically, the generator approximate equation under steady-state conditions is no longer applicable.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.

GridOnto: Knowledge Representation and Extraction for Fault Events in Power Grid

An accurate and comprehensive fault knowledge representation is indispensable for an automated and intelligent processing of power grid failures. Current knowledge graphs are incapable of capturing the complex relations among power grid failures. This paper extends the current knowledge graph representation mechanism through temporal, spatial, and causal representations to facilitate the knowledge representation of power grid faults, allowing for ontology modeling of power event elements and event relationships. During the modeling process, this paper proposes an extraction method which includes Bi-directional Encoder Representation from Transformers (BERT), Bi-directional Long Short-Term Memory (BiLSTM), and Conditional Random Field (CRF) for entities and relationships in power grid faults. The innovation of the method lies in the clever combination of the three, BERT learning semantic representation, BiLSTM further learning semantic features, and CRF joint modeling of labels to improve accuracy, and the results verify the effectiveness and practicality of the method presented in this paper.

Anomaly Detection and Classification in Streaming PMU Data in Smart Grids

The invention of Phasor Measurement Units (PMUs) produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make possible. PMUs are used in transmitting data to Phasor Data Concentrators (PDC) placed in control centers for monitoring purpose. A primary concern of system operators in control centers is maintaining safe and efficient operation of the power grid. This can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal data. The normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or abnormal conditions in power grid. As a result, detecting anomalies/abnormal conditions in the fast flowing PMU data that reflects the status of the power system is critical. A novel methodology for detecting and categorizing abnormalities in streaming PMU data is presented in this paper. The proposed method consists of three modules namely, offline Gaussian Mixture Model (GMM), online GMM for identifying anomalies and clustering ensemble model for classifying the anomalies. The significant features of the proposed method are detecting anomalies while taking into account of multivariate nature of the PMU dataset, adapting to concept drift in the flowing PMU data without retraining the existing model unnecessarily and classifying the anomalies. The proposed model is implemented in Python and the testing results prove that the proposed model is well suited for detection and classification of anomalies on the fly.

An active fault management for microgrids resilience safety-assurance

Active fault management (AFM) based on federated learning is established to realize ultra-integration of hundreds of microgrids, enabling them to output reference values fast enough during fault ride through (see the Figure, which is reprinted with permission from ref. iEnergy, 4: 453–462, 2022 © 2022 The Author(s)). AFM is first formulated as a distributed optimization problem. Then, federated is used to learning to train each microgrid's neural network. One concern for integrating optimization into power grid fault management and dynamic control is real-time performance because optimization usually takes more time to get reference values than widely used PID feedback control. To address this concern, controller hardware-in-the-loop (HIP) simulation with RTDS simulators is used to demonstrate the real-time performance of distributed-optimization-based fault management algorithms. In the hardware setup, one individual computer exclusively runs one microgrid or PV farm's control algorithm. Real-time simulation results demonstrate that the algorithms can output reference values within 100 ms, which can be considered well enough for fault management and dynamic control.

Application of knowledge graph in smart grid fault diagnosis

Abstract The stability and high quality of electricity are the basic factors which ensure that the residents and enterprises lead a happy and productive life. Therefore, in order to meet the requirements of residents’ life and enterprise production, it is necessary to improve the efficiency and accuracy of power grid fault diagnosis. In this paper, the knowledge graph is integrated into the power grid fault diagnosis, and the fault diagnosis system of the knowledge graph is constructed to realise the fault diagnosis of the power grid. The article first completes the knowledge graph construction through knowledge extraction, knowledge fusion and knowledge processing; then, it completes the construction of the fault scheduling knowledge graph through power grid equipment fault records, entity attribute extraction, coreference resolution, relation extraction, relation screening and data integration; finally, combined with the fault information knowledge analysis technology, it builds a power grid fault diagnosis system using a knowledge graph. Experiments show that using the system to diagnose the fault quantity, fault location and fault analysis information of the pilot power grid not only has ideal efficiency but also has high accuracy.

Control Strategy of Doubly-Fed Induction Generator under Zero Voltage Fault of Power Grid

For improving the zero-voltage ride through the capability of a doubly fed induction generator in high proportion new energy grid in extreme faults, a coordinated control scheme of hardware and optimal control strategy is proposed. A high-temperature superconductive-fault current limiter suppresses stator fault current, adaptive virtual impedance control and active dynamic reactive power support control act on the back-to-back converter of wind turbines as optimal control strategies. Optimizing the control strategy without changing the controller structure is beneficial to engineering implementation. After mathematical derivation and simulation verification, the coordinated control strategy adopted in this paper can effectively avoid the rotor current and voltage exceeding the limit when the wind turbine is facing extreme faults, actively provide reactive power support for the busbar, realize zero voltage ride through and reduce the risk of high voltage failure at the point of failure. The control effect is obviously better than the traditional virtual impedance control.

Power grid fault diagnosis model based on the time series density distribution of warning information

Fast and intelligent power grid fault diagnosis is a key means of improving the intelligence level of power grid dispatching. Currently, data-driven power grid fault diagnosis lacks a method to quantify the time series characteristics of alarm information, and it is difficult to build a deep learning model. This paper proposes a power grid fault diagnosis method based on the time series density distribution of alarm information. First, the alarm information is classified; quantized time sequence feature extraction is performed for the device, and the obtained quantized time sequence features are superimposed. After graphical coding, a time series density distribution map of the alarm information of each device and a mosaic map reflecting the overall fault event are generated, providing a unified data representation for the deep learning model. Then, a faulty equipment type classification model and equipment state judgment model are constructed based on a residual network. The faulty equipment and fault type are determined using a sequential evaluation strategy that first determines the type of faulty equipment and then determines the state of the equipment. The proposed method can meet the requirements of on-line fault diagnosis of a power grid and provide emergency decision support for dispatchers.

Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning

Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning

Power System Fault Diagnosis Method Based on Deep Reinforcement Learning

Intelligent power grid fault diagnosis is of great significance for speeding up fault processing and improving fault diagnosis efficiency. However, most of the current fault diagnosis methods focus on rule diagnosis, relying on expert experience and logical rules to build a diagnosis model, and lack the ability to automatically extract fault knowledge. For switch refusal events, it is difficult to determine a refusal switch without network topology. In order to realize the non-operating switch identification without network topology, this paper proposes a power grid fault diagnosis method based on deep reinforcement learning for alarm information text. Taking the single alarm information of the non-switch refusal sample as the research object, through the self-learning ability of deep reinforcement learning, it learns the topology connection relationship and action logic relationship between equipment, protection and circuit breakers contained in the alarm information, and realizes the detection of fault events. The correct prediction of the fault removal process after the occurrence, based on this, determines the refusal switch when the switch refuses to operate during the fault removal process. The calculation example results show that the proposed method can effectively diagnose the refusal switch of the switch refusal event, which is feasible and effective.

Design of an Underground Transmission Line Condition Fault Monitoring System for Power Grids Based on Data Analysis Algorithms

With the continuous expansion of the scale, capacity, and coverage of the modern transmission network, the role of the power system in the national economy is increasingly prominent, and the disconnection of the power system will have a huge impact on society and people’s lives. Due to the long transport distance and wide coverage of transmission lines, natural conditions and human factors have caused great difficulties in line operation and maintenance. How to effectively improve the operation and maintenance of transmission lines to ensure the stability and safety of the power grid has become a common problem for the power industry and scientific researchers to discuss. In recent years, the information society has stepped into the era of big data, and big data has developed rapidly, becoming a hot area favored by academia and industry, and is widely used. Through big data analysis, potential operation rules can be discovered from a large amount of grid information, providing maintenance personnel with corresponding maintenance decision support. Using big data technology for transmission line fault analysis can effectively reduce accident processing time and avoid accident expansion. Therefore, this paper combines the underground transmission line fault of power grid with the fuzzy KNN algorithm model to apply the underground transmission line state intelligent monitoring system and conducts the study of real-time data collection and fault diagnosis analysis of the underground transmission line fault of power grid, and this paper conducts the transmission line fault analysis experiment, which fully confirms the feasibility and effectiveness of the algorithm model proposed in the paper and concludes that the data analysis model proposed in this paper. The proposed data analysis model has good innovation and practical feasibility.

Experimental results and technique evaluation based on correlation function for a backup protection of synchronous generator stator windings

Unbalanced loads and faults cause power quality problems in power grids, which may lead to system elements failure, particularly to synchronous generators. Faults detection and identification of the disturbance severity level are essential in the grids. Important equipment are often protected with dual protection systems. A backup protection operates and trips appropriate circuit breaker(s) only if the main protection fails to detect the disturbance presence or miss opening the breakers. This paper proposes a backup protection scheme for detecting and evaluating the disruption severity of voltage and current signals for protecting the synchronous generators. This scheme is based on a numerical technique of correlation applied for the electrical signals to detect and assess the fault situation. Different fault types are experimentally conducted on a motor-generator set to test the proposed algorithm effectiveness using Data Acquisition Card (DAC) and LABVIEW software. The scheme has proven to be successful for fault detection and assessment. The algorithm has shown high reliability at various faults and operating conditions. Moreover, the practical results reveal that the scheme is able to evaluate the severity degree of disturbances/unbalances, and it is effective and secure considering the acceptable overloading, harmonics and temporary faults in power grids.

DC fault current-limiting method based on dual-loop active control for modular multilevel converters

PurposeIn the event of a DC short-circuit fault in a flexible DC power grid, the high peak value of the fault current puts forward more stringent requirements on the DC circuit breaker. The existing fault current cutoff mainly focuses on changing the topology structure. To suppress the development of fault current and reduce the investment cost of the DC grid, this paper aims to propose a dual-loop active current-limiting control based on energy difference.Design/methodology/approachFirstly, the equivalent circuit at fault is established, and the parameters related to the fault current are analyzed. Then, the relationship between the output voltage change of the bridge arm and the difference between the AC and DC energy is deduced. Finally, the experimental results are discussed on the real-time simulation platform Opal-RT.FindingsThe proposed current-limiting measures can greatly reduce the fault current, reduce the breaking current of the circuit breaker and increase the capacitor voltage during the fault period, which is beneficial to the stability of the AC system. It is verified that the proposed method is also applicable to a certain high-resistance fault.Originality/valueThis paper applies the method of AC fault to DC fault and deduces the relationship between energy difference and voltage variation corresponding to different step lengths based on digital simulation. In addition, two variables are used as control structure parameters to reduce the probability of system misoperation.

Robust Achromatic All-Dielectric Metalens for Infrared Detection in Intelligent Inspection.

Metalens has the advantages of high design freedom, light weight and easy integration, thus provides a powerful platform for infrared detection. Here, we numerically demonstrated a broadband achromatic infrared all-dielectric metalens over a continuous 800 nm bandwidth, with strong environmental adaptability in air, water and oil. By building a database with multiple 2π phase coverage and anomalous dispersions, optimizing the corrected required phase profiles and designing the sizes and spatial distributions of silicon nanopillars, we numerically realized the design of broadband achromatic metalens. The simulation results of the designed metalens show nearly constant focal lengths and diffraction-limited focal spots over the continuous range of wavelengths from 4.0 to 4.8 μm, indicating the ability of the designed metalens to detect thermal signals over a temperature range from various fault points. Further simulation results show that the metalens maintains good focusing performance under the environment of water or oil. This work may facilitate the application of metalens in ultra-compact infrared detectors for power grid faults detection.

Modeling and fault diagnosis of distribution networks cyber physical system based on IEC61850

In order to improve the fault tolerance and solution efficiency of power grid fault diagnosis model, a distribution network cyber physical system (CPS) fault diagnosis model based on advanced digital power system simulator (ADPSS) and IEC61850 is proposed. Firstly, the action rules and action status of CPS fault mode of distribution network from the whole correlation of fault element, protection action and circuit breaker trip, and the CPS grid topology of distribution network is constructed in ADPSS based on the topology and component characteristics of system. Secondly, according to the IEC61850 unified information model and modeling rules for the protection function of system equipment and operation function of circuit breaker, an information model with alarm, measurement, protection and fault recording logic is established. Then, the action status is obtained and used for the establishment of the distribution network fault diagnosis model by parsing the GOOSE message. Finally, the proposed modeling method and fault diagnosis strategy is validated through the test case.

Application of solar cells and wireless system for detecting faults in phasor measurement units using non-linear optimization

This research study examines the integration of wireless system and solar cells for detection and location of faults in power grids by implementing minimum number of Phasor Measurement Units (PMUs) after considering zero injection buses. A fault in the grid networks indicates a failure in the entire network that interfaces the normal flow of current which can be measured using wireless systems. The fault location in power grid is problematic because of high existence in several lateral tap-offs. In addition, the identification of exact location with presence of faults in the power system and severity of the faults are challenging tasks due to bulk and complex nature of the electrical power system state estimation. The major requirement of PMU placement approach is to find the minimum number of PMUs and their location with maximum wireless coverage under fault conditions with a prerequisite for the projected algorithm. The integrated algorithm is tested and validated for different fault conditions where the results are compared with different conventional methods using different optimization models such as heuristics, decision rules and mathematical programming where the outcomes proves to be much effective in terms of number of PMU installation for proper placements and detection, location in number of fault systems thus achieving 82% reliability using Ant Lion Optimizer (ALO).

Detection of Faults in Electrical Power Grids Using an Enhanced Anomaly-Based Method

Detection of Faults in Electrical Power Grids Using an Enhanced Anomaly-Based Method

Simulation and risk assessment of power system with cascading faults caused by strong wind weather

Global warming has led to the frequent occurrence of extreme weather events and threatened the stable operation of the power system. The faults caused by extreme weather events are of long time and massive scale characteristics, which pose new challenges to assessing power system operation risk. The model of the relationship between strong wind information and power grid faults was constructed considering the uncertain of line galloping and complexity of line trajectory. A meteorological-electric hybrid simulation approach was constructed with alternative and adaptive simulation strategies to simulate the cascading faults scenarios under the extreme weather of strong wind. The risk assessment method and index combining risk and fragility were proposed based on the proposed hybrid simulation approach. Case studies were performed to verify the feasibility of the proposed risk assessment method.

Transient stability enhancement in multiple‐microgrid networks by cloud energy storage system alongside considering protection system limitations

AbstractThe gas turbine synchronous generators (GTSGs) are widely deployed as distributed generations (DGs) in countries with massive natural gas production owing to their low prices. However, due to the low inertia time constants of these synchronous‐based DGs, they are more susceptible to power grid faults which stands as a transient stability issue in networks with multiple microgrids (MGs). On the other hand, the cloud energy storage system (CESS) is a new concept that centralizes the individual distributed energy storage of one or more MGs. Here, the employment of CESS with synchronverter grid connections creates a suitable opportunity for improving the transient stability of the network by providing higher inertia. This is while the fault current contribution of the synchronverter‐based CESS imperils protection constraints in these networks. Therefore, a proper protection coordination index (PCI) is considered in the conducted study to identify the optimal size of the synchronverter‐based CESS through a two‐stage optimization algorithm that preserves the protection constraints among protective devices. Finally, the transient stability of the network with synchronverter‐based CESS is assayed by calculation of the critical clearing time (CCT) for faults. Numerical studies are carried out on the IEEE 33‐bus test system. Results are discussed in depth.

Power Leakage Monitoring and Warning Device for Power Grid Fault Diagnosis: Design and Field Test

Electrical equipment insulation damage, metal shell conduction on rainy days, and wire short circuits are easy to produce leakage, which form step voltage on the ground and endangers the life and safety of nearby personnel. In addition to the existing leakage protector, a new method is proposed to use leakage energy to obtain energy and early warning. The step voltage generated by the leakage current is collected through the electrode, which is rectified and reduced by the nano power LTC3588, while energy is stored in the supercapacitor for the superheterodyne transmitting module to send the leakage signal to the nearby receiving device. Based on the possibility of the superheterodyne transmitting module sending the leakage signal, it is judged whether leakage occurs here. The experimental results illustrate that when 10 mA leakage occurs in a 220 V wire, the electrode distance is 1.0 m, while the energy extraction device can obtain 360 MW of power for the superheterodyne transmitting module to send the leakage signal.