Communication Faults Research Articles

Failures statistics of on-line monitoring devices for gases dissolved in oil

The on-line monitoring device of dissolved gas in oil is an important starting point to understand the internal operation condition of large oil filled equipment such as transformer. However, the device has no unified maintenance strategy. At present, it basically adopts the strategy of “repair only when it is broken”, which seriously affects the real-time perception ability of the device to the health state of primary equipment. According to the components of the device, the device faults were divided into four categories: Status information access controller (CAC) fault, carrier gas fault, communication fault and monitoring device body fault. In addition, they were subdivided into eight secondary faults according to the detection principle and process. Through the big data analysis of the operation and maintenance records of a province in recent three years, it was found that the main causes of the device faults were the under voltage of the carrier gas and the fault of the monitoring device body. It was suggested to popularize the device using carrier gas maintenance free type or photoacoustic spectrum principle to reduce the frequency of device failure. At the same time, it was suggested that the device design should be transformed to modularization to facilitate the fault operation and maintenance of the device body. The degassing module and intelligent monitoring module of the unit should be focused when formulating the maintenance strategy of the unit in the future.

Mobile robot communication fault diagnosis method based on swarm intelligence algorithm

The current communication fault diagnosis methods mainly focus on the classifier with fault probability, which often leads to the low diagnosis efficiency. In order to overcome the above problems, mobile robot communication fault diagnosis method based on swarm intelligence algorithm is proposed in the paper. Firstly, the abnormal data is extracted after the analysis of the communication data with Kalman filter. Secondly, the supporting decision model was designed to standardize the communication exchange process and locate the fault range. Futher more, the ant colony optimization algorithm combined with particle swarm optimization was used to locate the fault area, and the multi-model hybrid method was adopted to comprehensively judge the communication fault. The different interference ratio was used in the experiment to test the performance of the proposed algorithm compared with the SVM and Bayesian model. Finally, the experiment results show the validity of mobile robort communication fault diagnosis based on swarm intelligence algorithm.

Improved constrained min-max optimization for MPC tracking control in industrial networked process systems

Based on a modified min-max optimization strategy, an improved design of model predictive tracking control (MPC) is proposed to guarantee the performance of industrial process control systems when the networked system suffers from communication faults. Packet losses and uncertainties exist in the networked control system, which may deteriorate the performance of the systems and even cause a safety accident. To handle with this problem, an extended state space model is utilized in the proposed MPC design for enhancing control performance firstly. Then an improved min-max optimization approach is adopted for obtaining the optimal solution of the corresponding MPC method, where two separate optimization steps are proposed to enhance the control system operation. Different from MPC strategies that employ traditional state space models, the state variables can be tuned additionally for the modified MPC design because the tracking error information and the state variables are united in the improved model. As a consequence, extra degree of freedom is acquired for the relevant MPC scheme, and then better control performance is expected. Simulations on the temperature regulation process under model/plant mismatches and packet losses are simulated to evaluate the validity of the proposed MPC method finally.

Prioritized sharing of charging energy in a multiple charging unit based batteries swapping station at communication fault

This paper presents a multiple-unit charging system for a battery swapping station. The main focus of the paper is communication system post-fault operation which can be used equivalently to the healthy communication based system. The main goal of the control system is to keep the charging priority for batteries with a higher state of charge and low priority for the batteries with a lower state of charge, when the available energy is limited. This is to obtain, at all times, as much as possible fully charged batteries ready for swapping at limited availability of primary energy. The goal with communication between charging units can be easily obtained through interchange of the information about each battery state of charge. In the case of communication channel fault, the proposed method implemented individually in each separate charging unit is based on two-dimensional nonlinear characteristic depending on each respective battery state of charge and the DC bus voltage common for all charging units. The method is verified in different conditions using a model of five charging units serving separately an 83.2 Ah battery each, fed from a common feeding power source.

Wireless ultraviolet cooperative unmanned aerial vehicle formation communication link maintenance method.

A stable formation communication network can improve the task efficiency of a unmanned aerial vehicle (UAV) cluster. Aiming at the topology construction of the UAV formation communication network, combined with the optimal rigid graph theory, we design a three-dimensional UAV formation communication network generation algorithm based on the optimal rigid graph. We construct a new link weight function by introducing node residual energy and communication path loss to reduce the overall energy consumption of formation. Aiming at the problem that the communication link of the UAV is interrupted when the formation network is moving, a UAV communication beam tracking and holding method based on a multiple-input multiple-output (MIMO) structure and position prediction is designed. Simulation results show that the network topology constructed by the UAV formation communication network generation algorithm has a good average node degree, and effectively improves the network connectivity and communication fault tolerance. Compared with the tracking and holding algorithm based on the received signal strength, the beam tracking and holding algorithm significantly reduces the number of link interruptions, and the communication success holding rate can be basically maintained at about 90%.

Observer‐based adaptive sliding‐mode control under dynamic event‐triggered scheme with actuator and communication faults

AbstractThe adaptive technique and sliding‐mode algorithm (SMA) for observer‐based fault tolerant control is discussed with dynamic event‐triggered scheme (DETS) in this article. Both the actuator and communication faults are considered, which implies that the inputs of system and controller are unreliable. Moreover, the switched DETS for controller‐actuator channel is proposed to reduce bandwidth usage and actuator action frequency, which is different from the traditional event‐triggered strategies. To deal with communication faults, which can be described as packet losses, the closed‐loop system is modeled as a stochastic system. The SMA‐based observer is designed to estimate system states, which lays the foundation for the design of control algorithm to mitigate the effects of faults and DETS. Then, a novel observer‐based adaptive sliding‐mode control (SMC) law based on hyperbolic tangent function is proposed to guarantee the stochastic globally uniformly ultimately boundedness and reduce chattering. At last, the proposed DETS and observer‐based adaptive SMC law are applied into a robotic arm to illustrate the validity.

Reduced-order observer-based fuzzy adaptive dynamic event-triggered consensus control for multi-agent systems with communication faults

Reduced-order observer-based fuzzy adaptive dynamic event-triggered consensus control for multi-agent systems with communication faults

Distributed event-triggered control for multiagent systems with non-continuous communication faults and dynamic uncertainties

Distributed event-triggered control for multiagent systems with non-continuous communication faults and dynamic uncertainties

Adaptive Resilient Secondary Control for Microgrids With Communication Faults.

In this article, we consider the resilience problem in the presence of communication faults encountered in distributed secondary voltage and frequency control of an islanded alternating current microgrid. Such faults include the partial failure of communication links and some classes of data manipulation attacks. This practical and important yet challenging issue has been taken into limited consideration by existing approaches, which commonly assume that the measurement or communication between the distributed generations (DGs) is ideal or satisfies some restrictive assumptions. To achieve communication resilience, a novel adaptive observer is first proposed for each individual DG to estimate the desired reference voltage and frequency under unknown communication faults. Then, to guarantee the stability of the closed-loop system, voltage and frequency restoration, and accurate power sharing regardless of unknown communication faults, sufficient conditions are derived. Some simulation results are presented to verify the effectiveness of the proposed secondary control approach.

Graph Spectral Regularized Tensor Completion for Traffic Data Imputation

In intelligent transportation systems (ITS), incomplete traffic data due to sensor malfunctions and communication faults, seriously restricts the related applications of ITS. Recovering missing data from incomplete traffic data becomes an important issue for ITS. Existing works on traffic data imputation cannot achieve satisfactory accuracy due to inefficiently exploiting the underlying topological structure of the traffic data. In this paper, we model the topology of the road network as a graph and introduce graph Fourier transform (GFT) to process the traffic data. Then we utilize an algebraic framework termed as graph-tensor singular value decompositions (GT-SVD) to extract the hidden spatial information of traffic data. Furthermore, we propose a novel graph spectral regularized tensor completion algorithm based on GT-SVD and construct temporal regularized constraints to improve the recovery accuracy. The extensive experimental results on real traffic datasets demonstrate that the proposed algorithm outperforms the state-of-the-art methods under different missing patterns.

Adaptive-observer-based consensus tracking with fault-tolerant network connectivity of uncertain time-delay nonlinear multiagent systems with actuator and communication faults

In this study, a distributed output-feedback design approach for ensuring fault-tolerant initial network connectivity and preselected-time consensus tracking performance is proposed for a class of uncertain time-delay nonlinear multiagent systems (TDNMSs) with unexpected actuator and communication faults. It is assumed that time-varying state delays and system nonlinearities in TDNMSs are unknown. The main contribution of this study is to provide a delay-independent output-feedback control strategy to address a fault-tolerant initial connectivity preservation problem in the consensus tracking field. A local delay-independent adaptive state observer using neural networks is designed for each follower, and the boundedness of local observation errors is proved by constructing a Lyapunov–Krasovskii functional and adaptive tuning laws. Then, the local nonlinear relative output errors using a time-varying function with a preselected convergence time are derived to design simple local delay-independent trackers. The stability of the proposed consensus tracking system is analyzed, and simulation comparison results demonstrate the validity of the proposed strategy.

Electronic Communication Fault Signal Recognition Based on Data Mining Algorithm

In order to solve the technical problem of fault signal recognition in the field of communication, this paper proposes an electronic communication fault signal recognition method based on data mining algorithm. Firstly, the K-means clustering algorithm is used to determine the cluster number k according to some attributes or class characteristics of the communication class samples, and the communication sample types are classified into a certain class so that the communication sample data in the cluster can be closely distributed and the data within a certain class range can be calculated by Euclidean distance formula. Then, this paper clusters the data. In the clustering data, BP neural network model is used to train and calculate the obtained clustering data again, which can map and deal with the complex nonlinear relationship between the fault information data of different clustering categories. The results show that the final error accuracy can be raised to about 20% by using the method in this paper. Conclusion. The algorithm designed in this paper can quickly predict the factors affecting the communication and find the communication fault information.

Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model

Multirotors play an important role in electric power inspection, border control, modern agriculture, forest fire fighting, flood control, disaster prevention, etc. Multirotor failures, such as a communication fault, a sensor failure, or a power system anomaly, may well lead to mission interruption, multirotor crashes, and even casualties. To ensure flight safety, a multirotor decision module should be established to prevent or reduce the adverse effects caused by failure. Therefore, this paper proposes a real-time safety decision-making method for multirotor flight strategies based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Firstly, the flight of the multirotor was simulated based on the Rflysim UAV flight simulation platform, and a fault-injection module was constructed to simulate different types of faults, so as to realize real-time monitoring of the flight status of the multirotor, and to collect flight data under various faults to establish condition assessment information sources. Then, based on the random forest algorithm, a failure level classification model of the multirotor was constructed, the model was trained and verified by inputting flight data of three types of safety level failures, and the model effectively classified the failure levels of the multirotor. Under this framework, a real-time safety decision-making model for the multirotor based on the TOPSIS model was constructed to realize the flight safety decision-making of the multirotor under different faults. This method can effectively realize the real-time decision-making for the flight strategy of a multirotor. By comparison with other models, the classification accuracy of the failure level classification model is higher, and the consideration of flight decision-making is more comprehensive and accurate, thus effectively ensuring the flight safety of the multirotor.

Internet of Things Detection and Location Based on Remote Communication Fault of Power Consumption Acquisition Module

The access of distributed generation makes the single-source radial distribution network become a complex multi-source network. The power flow, network loss, voltage, and so on are also changing. At the same time, it aggravates the complexity of remote communication fault location of remote communication fault power acquisition module based on the Internet of Things. In this paper, a new switching function and evaluation function are used to adapt to the situation of distributed generation access. Considering that the fault section location is a discontinuous problem, the binary particle swarm algorithm is used to make full use of its fast convergence and combine with the operation of diversity preservation to realize the fault section location. The experimental results show that the hybrid algorithm in this paper has a good effect on fault recovery, and has certain advantages and theoretical research value in optimization ability.

The Diagnosability of Interconnection Networks with Missing Edges and Broken-Down Nodes Under the PMC and MM* Models

Abstract Diagnosability is often considered as an important factor for measuring the self-diagnostic ability of network systems. However, classic system-level diagnosis focuses only on processor faults and ignores the objective reality of communication faults. Under real circumstances, missing edges and node failures usually occur simultaneously in multiprocessor systems (called hybrid fault circumstances). Therefore, it is important to study the diagnosability of multiprocessor systems under hybrid fault circumstances. In this paper, we propose several diagnosabilities of interconnection networks with missing edges and faulty nodes. By exploring some important relationships between diagnosability and the minimum degree of a network under hybrid fault circumstances, we present and prove the diagnosability of several classic interconnection networks, including BC (bijective connection) networks, star graphs, folded hypercubes, exchanged hypercubes, exchanged crossed cubes, k-ary n-cubes, bubble-sort star graphs and balanced hypercubes, with missing edges and broken-down nodes under the PMC (Preparata, Metze and Chien) and MM* (Maeng and Malek) models.

Priority-Driven Self-Optimizing Power Control Scheme for Interlinking Converters of Hybrid AC/DC Microgrid Clusters in Decentralized Manner

Hybrid ac/dc microgrid clusters are key building blocks of smart grid to support sustainable and resilient urban power systems. Practically, in networked microgrid clusters, the subgrid load-priorities and power quality requirements for different areas vary significantly. In order to realize optimal power exchanges among microgrid clusters, this article proposes a decentralized self-optimizing power control scheme for interlinking converters (ILCs). First, a priority-driven optimal power exchange model of ILCs is built that fully considers the priorities and capacities in subgrids. The whole optimization objective is to minimize the total dc-voltage/ac-frequency state deviations of subgrids. Second, to realize the decentralized power flow control, an optimal-oriented quasi-droop control strategy of ILCs is introduced. Consequently, as each of ILCs only monitors the local ac-side frequency and dc-side voltage signals, the whole optimal power control of the wide-area microgrid clusters is achieved in a decentralized manner without any communication link. Thus, the proposed control algorithm has the features of decreased cost, increased scalability, reduced geographic restrictions and high resilience and robustness in terms of communication faults. Finally, the proposed method is validated by three cases in hardware-in-loop environment.

Energy efficient secured cluster based distributed fault diagnosis protocol for IoT

The rapid growth of internet and internet services provision offers wide scope to the industries to couple the various network models to design a flexible and simplified communication infrastructure. A significant attention paid towards Internet of things (IoT), from both academics and industries. Connecting and organizing of communication over wireless IoT network models are vulnerable to various security threats, due to the lack of inappropriate security deployment models. In addition to this, these models have not only security issues; they also have many performance issues. This research work deals with an IoT security over WSN model to overcome the security and performance issues by designing a Energy efficient secured cluster based distributed fault diagnosis protocol (EESCFD) Model which combines the self-fault diagnosis routing model using cluster based approach and block cipher to organize a secured data communication and to identify security fault and communication faults to improve communication efficiency. In addition we achieve an energy efficiency by employing concise block cipher which identifies the ideal size of block, size of key, number of rounds to perform the key operations in the cipher.

Research on Distribution Network Fault Location based on Improved Genetic Algorithm

Compared with the traditional recloser fault location without communication channel, feeder automation based on FTU is in line with the development of modern distribution system. FTU plays a key role in the whole system, but the location is inaccurate due to the lack of information caused by FTU itself or communication fault. When using genetic alogrithms for distribution network fault location, the result often converges prematurely or converges to an infeasible solution due to algorithm reasons. Aiming at the above problems, an improved genetic algorithm is proposed for distribution network fault location. Through simulation analysis, it can be found that the improved genetic algorithm has high convergence and good optimal fitness, which can better solve the convergence problems of the unmodified algorithm, which is of great significance for distribution network fault location.

Research on DC voltage droop strategy based on optimal DC power flow

A coordinated control based on the optimal DC power flow for MTDC is proposed to improve the transmission efficiency of multi-terminal HVDC, which adopts hierarchical structure including processing layer and implementation layer. The dcOPF model of optimal DC power flow is established in the solving layer. The minimum of the generation cost is set as the objective function, and the constraints include the line capacity, generator capacity, converter station capacity and power balance. The optimal solution is applied to the executing layer. When disturbs occur in some converter stations, new optimal solution will be re–calculated and updated to the executing layer and reduce the generation cost. The stability of the system will not be affected by communication fault. A four-terminal VSC-MTDC simulation model in the MATLAB/Simulink platform is built in the paper. The simulation results show that the proposed control strategy can minimize the generation cost and the control strategy has the advantages of simple structure and easy implementation.

Data Interpolation of Traffic Flow Algorithm Using Wavelet Transform for Traffic Generative Modeling

The real-time data acquisition is an important foundation for Parallel Transportation Systems (PTSs). In practical application, due to the sensor or communication faults, it leads to traffic flow data missing, which will affect the computational experiments and decision-making of the parallel transportation system. Inspired by the idea of parallel intelligence, in this work, we use discrete wavelet transform (DWT) to decompose the complete traffic flow data into low-frequency data and high-frequency data. These decomposed sequences are used as training data for Generative Adversarial Network (GAN) to generate two sequences with different frequencies. A Denoising Autoencoder (DAE) is introduced to interpolate the missing traffic flow data. And it is trained by the dataset through combining the real-time traffic flow data and the generated data. The computational experiments are carried out by using the PeMS dataset. The experimental results show that our algorithm can generate more realistic traffic flow data and improve the performance of data interpolation for missing traffic flow data.