Power Grid Fault Research Articles

Analysis and Prospect of the Application of Wireless Sensor Networks in Ubiquitous Power Internet of Things.

With the rapid development of the economy and society, the low efficiency and high loss of the traditional power grid can no longer meet the growing social demand, and the power grid market is facing a reform. Smart grid, as a next-generation power system, it can effectively improve the performance of traditional power grids. The ubiquitous power Internet of Things (UPIOT) replaces the traditional grids with efficient, safe, reliable, and flexible new grids, improves the utilization efficiency of the grid, reduces the loss of the power grid in the transmission process, and can meet the needs of different types of markets and users. As an advanced information acquisition and processing technology, wireless sensor networks have been widely used in medical, industrial, agricultural, commercial, and public management fields. It is an important means to promote future economic development and build a harmonious society. In the power system, wireless sensor network technology can be widely used in many fields such as line fault location, real-time monitoring, smart meter reading, and relay protection. In this paper, the basic concepts and overall architecture of ubiquitous power Internet of Things are summarized. Then, we summarize the research status of the wireless sensor network in smart grid, including power equipment, line monitoring, smart grid wireless automatic meter reading, distribution network relay protection, power assets life-cycle management, power grid fault location, and power grid fault diagnosis. In view of the technical characteristics of wireless sensor networks, combined with the production links of power systems, the application framework of wireless sensor network technology in the power systems is constructed. The application of wireless sensor networks is prospected from the aspects of network development of relay protection, application research of smart substation, application research of power grid catastrophe, security protection of power system, and deep-seated ubiquitous power Internet of Things.

Intelligent Decision Support and Fusion Models for Fault Detection and Location in Power Grids

Computational intelligence-based diagnostic frameworks have emerged as rapidly evolving but highly efficient approaches for diagnosing faults in power grids. This work aims to build a diagnostic framework by resorting to computational intelligence techniques to improve decision-making and diagnostic accuracy. This diagnostic framework has three modules for signal processing, fault detection, and location. The signal-processing module uses the variational mode decomposition technique to extract informative time-frequency features from the voltage and frequency signals. Voltage features are then fed into the fault detection module to train a set of modular support vector machines that are used for monitoring the binary state of each node in the power grid. Once a faulty state on a node is detected, it activates the third module for identifying fault location. This module benefits from a novel zSlices-based general type-2 fuzzy fusion model for the sake of identifying the fault type as well as mitigating the false alarm rate. The exact location of the fault is then determined through a fuzzy decision support system that is equipped with a recommendation mechanism for the sake of consensus reaching. Various scenarios are simulated on the IEEE 39-bus system and on an experimental setup of a Three-Bus Two-Line transmission system, where the attained results verify the applicability, efficiency, and robustness of the proposed framework.

Power grid fault classification method of alarm information based on bigru-attention.

With the continuous expansion of the power grid, the number of alarm information collected by the dispatching center is also increasing. How to filter out key information from massive alarm information, delete irrelevant data, classify the importance of alarm information, and make preparations for power grid fault diagnosis based on alarm information has become an urgent problem to be solved in online fault diagnosis. Based on this, this paper proposes an importance classification method of alarm information based on bigru attention to screen the data with the strongest correlation with fault, avoid the impact of irrelevant data on fault diagnosis, and meet the needs of intelligence. Firstly, the two-way gated loop (bigru) neural network layer is used to preprocess the alarm information text and extract the features of the deep-seated information; Secondly, the attention mechanism layer is used to assign corresponding weights to the extracted text deep-seated information; Finally, the text feature information of alarm information with different weights is put into the softmax function layer for alarm information text classification. Finally, an actual fault case in an area is tested to verify the effectiveness and practicability of the proposed method.

A fault diagnosis method for power grid based on image feature extraction

In order to solve the problem that the traditional power grid fault diagnosis along the railway requires a lot of manpower and material resources, this paper proposes a fault diagnosis method on the power grid along the railway based on image feature extraction. A variable function family series method is designed to extract features of faults. For the fault image denoising, a convolution filter method is used, and the number of 2 denoising layers is selected. At the same time, extracting the decomposition coefficient features and energy features of power grid image faults. Moreover, the matching diagnosis method is given. The fault is finally identified based on the similarity. To compare with the existing fault diagnosis methods, the correct diagnosis rate of the fault diagnosis method proposed is more than 92%. From experimental comparison results, this diagnosis effect not only has higher precision, stronger anti-interference ability, but also has faster diagnosis speed.

Power‐frequency oscillation suppression algorithm for AC microgrid with multiple virtual synchronous generators based on fuzzy inference system

In the microgrid, virtual synchronous generator technology can significantly enhance the anti-interference characteristics of the system frequency and bus voltage, as well as solve the problems of insufficient damping and low inertia. However, the system frequency and active power oscillation caused by power fluctuations and grid faults threaten the stable operation of the grid seriously. Therefore, for an alternating current (AC) microgrid multi-virtual synchronous generator (VSG) parallel system, an improved virtual synchronous generator control algorithm based on a fuzzy inference system is proposed, which adjusts the values of virtual inertia and damping coefficient dynamically through fuzzy logic rules to realize the coordinated control of the two. The enhanced VSG algorithm described in this research has a substantial influence on power-frequency oscillation suppression, decreases active power and frequency overshoot, shortens the adjustment time, and improves system frequency stability active power, according to simulation and experimental findings.

Power grid fault diagnosis using polar PMU data plots

With the increasing complexity in power grid structures and fault characteristics, fast and accurate power grid fault diagnosis has become increasingly important for the safe and stable operation of power grids. Synchronous phasor measurement units (PMUs) are a promising approach to power grid fault diagnosis because of their synchronization, speed, and accuracy. In this study, PMU data are used as the source of fault information, and these data are transformed into polar plots. A power grid fault diagnosis method is proposed based on the polar PMU data plots and a convolutional neural network (CNN). The method is shown to reduce the computational cost and improve the efficiency of fault diagnosis. The method includes three steps. First, different types and quantities of electrical data were selected for identifying faulty equipment and fault types, and the corresponding PMU data were transformed into a polar plot using polar coordinates in different dimensions. Second, the faulty equipment detection model and the fault type classification model were constructed, and the faulty buses, lines and transformers were determined along with their various fault types based on the fault information features extracted by the CNN from the polar PMU data plots. Finally, a set of complete power grid fault diagnosis processes and a block diagram are proposed, and the validity and practicability of the proposed method are evaluated using actual measurement and simulation PMU data of the power grid.

A Q-learning based robust MPC method for DFIG to suppress the rotor overcurrent

During power grid faults, the rotor overcurrent in the doubly fed induction generator (DFIG)-based wind turbine may cause the activation of crowbar protection, which enlarges the voltage drop of the point of common coupling (PCC) and influences the power recovery. The existing methods, such as the demagnetization control, have the problems of overshoot and lack of robustness due to the uncertainty of the system. To solve these problems, a Q-learning based robust model predictive control (Q-RMPC) method for DFIG is proposed. A state space equation of the rotor current including the stator current differential term is listed to make the transient DC component of induced electromotive force (IEF) observable. On this basis, a RMPC-based main control to deal with the uncertainty of system is proposed. This control could realize the prediction of rotor current. To enhance the convergence of the main control, a novel correction control based on Q-learning is presented, which can correct the main control by reinforcement learning the decision strategy of perturbation. The Q-RMPC composed of the main control and the correction control is proposed to suppress the rotor overcurrent with considering the precarious of the system. The control effects of the proposed method in the scenarios of wind speed fluctuation, correction control absence, parameter disturbances and parameter offset are discussed. The simulation results verify the correctness and effectiveness of the proposed method.

PF2RM: A Power Fault Retrieval and Recommendation Model Based on Knowledge Graph

Energy saving and emission reduction have become common concerns in countries around the world. In China, with the implementation of the new strategy of “carbon peak and neutrality” and the rapid development of the new smart grid infrastructure, the amount of data of actual power grid dispatching and fault analysis show exponential growth, which has led to phenomena such as poor supervision effectiveness and difficulty in handling faults in the process of grid operation and maintenance. Existing research on retrieval recommendation methods has had a lower accuracy rate at cold-start due to a small sample of user interactions. In addition, the cumulative learning of user personalization during general retrieval results in a poor perception of potential interest. By constructing a power knowledge graph, this paper presents a power fault retrieval and recommendation model (PF2RM) based on user-polymorphic perception. This model includes two methods: the power fault retrieval method (PFR) and the user-polymorphic retrieval recommendation method (UPRR). First, we take the power grid fault dispatching business as the core and reconstruct the ontology layer of the power knowledge graph. The PFR method is used to design the graph-neighbor fault entity cluster to enhance the polymerization degree of a fault implementation scenario. This method can solve the search cold-start recommendation problem. At the same time, the UPRR method aims to form user retrieval subgraphs of the past-state and current-state and make a feature matching for the graph-neighbor fault entity cluster, and then realize the accurate prediction of the user’s general search intention. The model is compared with other current classical models through the evaluation of multiple recommendation evaluation metrics, and the experimental results show that the model has a 3–8% improvement in the cold-start recommendation effect and 2–10% improvement in regular retrieval. The model has the best average recommendation performance in multiple metrics and has good results in fault analysis and retrieval recommendation. It plays a helpful role in intelligent operation and maintenance of the power grid and auxiliary decision-making, and effectively improves the reliability of the power grid.

Analytical reliability assessment of cyber-physical distribution system with distributed feeder automation

Cyber-physical distribution system (CPDS) handles power grid faults by feeder automation. The reliability assessment of CPDS has been intensively discussed for centralized feeder automation (CFA) in existing researches. However, the inapplicability of CFA reliability models, and the low computation efficiency issue of the mostly used Monte Carlo method greatly limits their application in practical engineering cases of the large-scale CPDS with the distributed feeder automation (DFA) mode. Thus, this paper proposes a region-based analytical reliability assessment method for DFA, which has high computation efficiency in the large-scale CPDS. Firstly, the reliability impacts of cyber system anomalies in the DFA mode are analysed and the cyber reliability models are built considering both cyber device failures and cyber-attacks. Then, the cyber system status is integrated into malfunction probability modelling of switches for reliability assessment. Finally, the load reliability is assessed based on the fault region division by the proposed analytical method and the reliability indices are calculated. The accuracy, efficiency and scalability of the proposed method are verified in case studies.

Siting and sizing of synchronous compensator based on electromagnetic transient simulation

With the increasing load of power grid and the access of new energy sources and direct current lines, power grid voltage stability becomes increasingly prominent. Installing synchronous compensator is an option to supplement reactive power and improve voltage stability. Compared with other reactive power compensation equipment (SVC, SVG, etc.), synchronous compensators have advantages in service life, size, easy to maintenance and etc. Siting and sizing of synchronous compensator are critical for investment and voltage stability adjustment performance of the equipment. Considering that the power grid contains new energy and HVDC equipment, it is necessary to use electromagnetic transient (EMT) simulation to evaluate transient stability of a power system when the siting and sizing of a synchronous compensator was determined. In this paper, a method of siting and sizing the synchronous compensator based on voltage sensitivity index (VSI), severity index (SI) and EMT simulation is proposed. VSI of different bus in the system is calculated by injecting reactive power into each bus, and the suitable candidate bus location for compensator is obtained. Furthermore, the calculation of power grid fault severity index is designed, and the synchronous compensator sizing method with the SI as optimization objective is designed. An actual AC–DC hybrid power grid system with 1343 buses was used to test the proposed methods and testing results demonstrate the performance of the proposed method.

Analysis of Symmetrical and Unsymmetrical Faults Using the EEMD and Scale-Dependent Intrinsic Entropies

The rapid and accurate diagnosis of power grid faults plays a vital role in speeding up the process of accident handling and system recovery and ensuring the safe operation of the power system. This paper proposes to apply the ensemble empirical mode decomposition (EEMD) method and scale-related intrinsic entropy to diagnose the type of fault for the transmission line. First, the electrical data collected by the power system is decomposed by using the EEMD method. Then by eliminating some intrinsic mode functions, the signal is reconstructed by inspecting the correlation coefficient. Finally, the complexity of the reconstructed signal is calculated by using the scale-dependent intrinsic entropy. Since the scale-dependent intrinsic entropy reflects the complexity of one-dimensional time series, it is susceptible to signal changes. The complexity is helpful in the power system for fault signal analysis. The results show the combined method’s effectiveness and practicability through failure analysis using the IEEE 14-bus system as the simulation model.

Enhanced Anomaly-Based Fault Detection System in Electrical Power Grids

Early and accurate fault detection in electrical power grids is a very essential research area because of its positive influence on network stability and customer satisfaction. Although many electrical fault detection techniques have been introduced during the past decade, the existence of an effective and robust fault detection system is still rare in real-world applications. Moreover, one of the main challenges that delays the progress in this direction is the severe lack of reliable data for system validation. Therefore, this paper proposes a novel anomaly-based electrical fault detection system which is consistent with the concept of faults in the electrical power grids. It benefits from two phases prior to training phase, namely, data preprocessing and pretraining. While the data preprocessing phase executes all elementary operations on the raw data, the pretraining phase selects the optimal hyperparameters of the model using a particle swarm optimization (PSO)-based algorithm. Furthermore, the one-class support vector machines (OC-SVMs) and the principal component analysis (PCA) anomaly-based detection models are exploited to validate the proposed system on the VSB dataset which is a modern and realistic electrical fault detection dataset. Finally, the results are thoroughly discussed using several quantitative and statistical analyses. The experimental results confirm the effectiveness of the proposed system in improving the detection of electrical faults.

A coordinated and steadily fault ride through strategy under short-circuit fault of the wind power grid connected system based on the grid-forming control

With the continuous improvement of the penetration level of wind power in the power system, the grid-forming mode of wind turbine will gradually become one of the main modes of system synchronization. The power electronic equipment based on grid-forming control has the ability of frequency dynamic regulation, reactive power voltage regulation, weak current network operation stability and island operation similar to the conventional synchronous generator, however, the problem of strong electromagnetic stress faced by power electronic equipment under grid-forming control will be the key factor restricting the wide application and promotion of grid-forming technology. In particular, the fault ride through problem of the wind turbine under the grid-forming control will face more severe challenges. This paper is based on a switched fault ride through strategy, that is, in case of power grid fault, it is quickly converted from grid-forming control under normal working conditions to the conventional current vector control, so as to facilitate fault current limiting and realize fast dynamic reactive power support. In this paper, a fault ride through optimization control strategy of grid-forming wind turbine is proposed. During the fault, the threshold setting of entering/ exiting the fault ride through control is optimized to form hysteresis control, so as to improve the switching misoperation of control strategy caused by the large fluctuation of grid voltage in the process of fault. Finally, simulation results show that the earlier switching back to the grid-forming control is not the more conducive to the stability of the system, and there is an optimal switching time. The results verify the proposed strategy can effectively improve the transient stability of power system with grid-forming mode of the wind turbine.

Requirement Analysis of Wind Turbine High Voltage Ride-through Capability Detection and Simulation

With the number of wind farms connected to the power system increasing rapidly, the impact of wind power generation system on power grid system stability is becoming increasingly prominent. Especially when the power grid has transient fault, there will be problems such as reverse power flow, large-area power failure of wind farm and so on. This will directly lead to the power system instability and seriously affect the power grid security and stability. Especially in the stage of voltage recovery after the fault is eliminated, the low-voltage fault of the power grid might lead to a sharp rise in the voltage of power grid and overvoltage on the high-voltage line connected to the power grid with wind farm, resulting in the disconnection of the wind power grid connected system. However, China’s current standards do not specify the high-voltage crossing of wind power grid connected system, especially the high-voltage crossing detection device, detection method and detection procedure. Therefore, it is urgent to study the detection technology of wind power high voltage crossing and put forward the detection method. There are many reasons for high-voltage power grid fault, and the interaction between power grid and inverter is more complex in high-voltage fault. If the type test method is used to simulate various high-voltage fault conditions in power system and carry out the test and study on the HVRT( high voltage ride through) performance of wind turbine, the human and material cost is huge and there are many potential safety hazards. At the same time, the grid connection performance of wind turbines are largely related to the control strategy of the controller. Therefore, the semi physical simulation method can be used to research the HVRT detection technology of wind turbine. This can effectively avoid the safety problem of large current and high voltage in the primary circuit, and realize the physical connection between the real controller and the flexible high voltage fault platform model through hardware in the loop simulation. It can simulate the wind turbine capability of high-voltage ride through under various high-voltage fault conditions. It not only has low test cost, but also can deeply study the interaction between power grid and power grid.

Power grid fault diagnosis based on a deep pyramid convolutional neural network

Power grid fault diagnosis based on a deep pyramid convolutional neural network

Power Grid Fault Diagnosis Model Based on the Time Series Density Distribution of Warning Information

Power Grid Fault Diagnosis Model Based on the Time Series Density Distribution of Warning Information

Perturbation Observer Based Fractional-Order Control for SMES Systems Based on Jellyfish Search Algorithm

The electric energy storage system (EESS) is considered as an efficient and promising tool to alleviate the power imbalance of grid-connected microgrid with distributed generation (DG). This work develops a perturbation observer-based fractional-order control (POFOC) strategy for superconducting magnetic energy storage (SMES) system. Initially, a high-gain state and perturbation observer (HGSPO) is designed for reliable estimation of the combined impact of the nonlinearities, parameter uncertainties, unmodeled dynamics, and external disturbances of SMES. Then the storage function of an SMES system is designed, which takes favorable terms into serious consideration to sufficiently utilize the physical properties of the SMES system. Moreover, a fractional-order control framework is applied for complete compensation for the estimated perturbation and adopted as the attached input to boost its dynamical responses. Furthermore, a newly proposed jellyfish search algorithm (JSA) is utilized to realize optimization and tuning of control gains of the developed strategy, upon which high-quality global optimum can be obtained to ensure prominent controlling performance. Case studies, e.g., active power and reactive power supply and system restoration capability under power grid fault effectively validate the effectiveness and reliability of the POFOC strategy compared with traditional PID control and interconnection and damping assignment passivity-based controller (IDA-PBC). In particular, the overshoot of PID is 115.264% of the rated value, while POFOC has no overshoot.

Fault Location for Multi-source Distribution Network Based on Improved Chaotic Jaya Algorithm

Due to the wide access of distributed energy in the distribution network, the structure of the distribution network becomes complex and diverse, and the power flow distribution is flexible and changeable. To optimize the fault location performance of the multisource distribution network, this paper proposes a power grid fault location solution method based on the improved Jaya algorithm. By combining the chaos theory with the Jaya algorithm, the individual position iteration of the algorithm is optimized to improve the algorithm’s global optimization capability and speed up the fault location. Through the example test and comparison with the traditional algorithm, the experimental results verify the effectiveness and superiority of this method.

A Power Grid Fault Diagnosis Method in the Case of Alarm Information Loss Based on the Top-k Skyline Query Algorithm

After the failure of the power system, a large amount of alarm information will flood into the dispatching terminal instantly. At the same time, there are inevitable problems, such as the abnormal operation of the protection and the circuit breaker, the lack of alarm information, and so on. This kind of uncertainty problem brings great trouble to the fault diagnosis algorithm. As a data processing algorithm for an uncertain information set, Top-k Skyline query algorithm can eliminate the data points that do not meet the requirements in the information set, and then output the final K results in order. Based on this background, this paper proposes a power grid fault diagnosis method based on the Top-k Skyline query algorithm considering alarm information loss. Firstly, the fault area is determined by using the information of the electrical quantity and switching value. Then, backward reasoning Petri nets are established for the nodes in the fault area to form the data set of fault hypotheses. Then, the Top-k Skyline query algorithm is used to sort the hypotheses and choose the hypothesis with higher reliability. Finally, an IEEE 39-bus system example is given to verify the reliability of the proposed method.

Low voltage ride-through control strategy based on cascaded H-bridge that can avoid overmodulation and power backflow

Because of its multi-level output voltage and modular structure, the cascaded H-bridge(CHB) multi-level inverter can expand the system to higher voltage and power levels. It has good development and application prospects in the photovoltaic grid-connected market. Under the power grid fault voltage sag, the system should generate reactive power according to national codes and meet the dynamic performance requirements to maintain the connection with the grid, and achieve the minimum target requirements of low voltage ride through(LVRT). Aiming at the over-modulation and the unique power backflow problem of cascaded H-bridges, a LVRT control algorithm that can avoid over-modulation and power backflow is studied, and MATLAB simulation verification is carried out under common grid faults, which proves the correctness of the theory.