Biblio

The parameters of transmission line vary with environmental and operating conditions, thus the paper proposes a digital twin-based transmission line model. Based on synchrophasor measurements from phasor measurement units, the proposed model can use the maximum likelihood estimation (MLE) to reduce uncertainty between the digital twin and its physical counterpart. A case study has been conducted in the paper to present the influence of the uncertainty in the measurements on the digital twin for the transmission line and analyze the effectiveness of the MLE method. The results show that the proposed digital twin-based model is effective in reducing the influence of the uncertainty in the measurements and improving the fault location accuracy.

Transformer is the key equipment of power system, and its stable operation is very important to the security of power system In practical application, with the progress of technology, the performance of transformer becomes more and more important, but faults also occur from time to time in practical application, and the traditional manual fault diagnosis needs to consume a lot of time and energy. At present, the rapid development of artificial intelligence technology provides a new research direction for timely and accurate detection and treatment of transformer faults. In this paper, a method of transformer fault diagnosis using artificial neural network is proposed. The neural network algorithm is used for off-line learning and training of the operation state data of normal and fault states. By adjusting the relationship between neuron nodes, the mapping relationship between fault characteristics and fault location is established by using network layer learning, Finally, the reasoning process from fault feature to fault location is realized to realize intelligent fault diagnosis.

In this work, a novel approach to detect and diagnose single and combined faults in the Induction Motor (IM) is proposed. In Condition Monitoring Systems (CMS) based on the Motor Current Signature Analysis (MCSA), the simultaneous occurrence of multiple faults is a major challenge. An innovative technique called Multiple Windowed Harmonic Wavelet Packet Transform (MWHWPT) is used in order to discriminate between the faulty components of the IM, even during compound faults. Thus, each motor component is monitored by a specific Fault Index (FI) which allows the fault diagnosis without the need for a classifier. The tests carried on Rotor and Bearing faults show high fault diagnosis rate even during compound faults and proves the competitive performance of the proposed approach with literature works.

Industrial robots are playing an important role in now a day industrial productions. However, due to the increasing in robot hardware modules and the rapid expansion of software modules, the reliability of operating systems for industrial robots is facing severe challenges, especially for the light-weight edge computing platforms. Based on current technologies on resource security isolation protection and access control, a novel resource management model for real-time edge system of multiple robot arms is proposed on light-weight edge devices. This novel resource management model can achieve the following functions: mission-critical resource classification, resource security access control, and multi-level security data isolation transmission. We also propose a fault location and isolation model on each lightweight edge device, which ensures the reliability of the entire system. Experimental results show that the robot operating system can meet the requirements of hierarchical management and resource access control. Compared with the existing methods, the fault location and isolation model can effectively locate and deal with the faults generated by the system.

At present, the AE method has the advantages of live measurement, online monitoring and easy fault location, so it is very suitable for insulation defect detection of power equipments such as GIS, etc. In this paper, development of a data processing software for PD acoustic detection based on a general fast analysis algorithm model is introduced. With considering the signal flow chart of current acoustic detection system widely used in operation and maintenance of power system equipments, the main function of the developed PD AE signals analysis software was designed, including the detailed analysis of individual data file, identification with phase compensation based on 2D PRPD histograms, batch processing analysis of data files, management of discharge fingerprint library and display of typical defect discharge data. And all of the corresponding developed software pages are displayed.

At present, the acoustic emission (AE) method has the advantages of live measurement and easy fault location, so it is very suitable for insulation defect detection of power equipments such as GIS, etc. While the conventional AE detection system or instruments always can't give a right discrimination result, because them always work based on the reference voltage or phase information from an auxiliary 220V voltage signal source rather than the operation high voltage (HV) with the real phase information corresponding to the detected AE pulsed signals. So there is a random phase difference between the reference phase and operation phase. The discharge fingerprint formed by the detected AE pulsed signals with reference phase using the same processing process is compared to the discharge fingerprint database formed in the HV laboratory with the real phase information, therefore, the system may not be able to discriminate the discharge mode of the field measured data from GIS in substation operation. In this paper, in order to design and develop a general fast analysis algorithm model for PD acoustic detection system to make an assistant diagnosis, the pattern identification with phase compensation was designed and applied. The results show that the method is effective and useful to deatl with AE signals meased in operation situation.

The autonomic logistic system (ALS), first used by the U.S. military JSF, is a new conceptional system which supports prognostic and health management system of aircrafts, including such as real-time failure monitoring, remaining useful life prediction and maintenance decisions-making. However, the development of ALS faces some challenges. Firstly, current ALS is mainly based on client/server architecture, which is very complex in a large-scale aircraft control center and software is required to be reconfigured for every accessed node, which will increase the cost and decrease the expandability of deployment for large scale aircraft control centers. Secondly, interpretation of telemetry parameters from the aircraft is a tough task considering various real-time flight conditions, including instructions from controllers, work statements of single machines or machine groups, and intrinsic physical meaning of telemetry parameters. It is troublesome to meet the expectation of full representing the relationship between faults and tests without a standard model. Finally, typical diagnostic algorithms based on dependency matrix are inefficient, especially the temporal waste when dealing with thousands of test points and fault modes, for the reason that the time complexity will increase exponentially as dependency matrix expansion. Under this situation, this paper proposed a distributed ALS under complex operating conditions, which has the following contributions 1) introducing a distributed system based on browser/server architecture, which is divided overall system into primary control system and diagnostic and health assessment platform; 2) designing a novel interface for modelling the interpretation rules of telemetry parameters and the relationship between faults and tests in consideration of multiple elements of aircraft conditions; 3) proposing a promoted diagnostic algorithm under parallel computing in order to decrease the computing time complexity. what's more, this paper develops a construction with 3D viewer of aircraft for user to locate fault points and presents repairment instructions for maintenance personnels based on Interactive Electronic Technical Manual, which supports both online and offline. A practice in a certain aircraft demonstrated the efficiency of improved diagnostic algorithm and proposed ALS.

Implementation of self-healing control system in smart grid is a persisting challenge. Self-Healing control strategy is the important guarantee to implement the smart grid. In addition, it is the support of achieving the secure operation, improving the reliability and security of distribution grid, and realizing the smart distribution grid. Although self-healing control system concept is presented in smart grid context, but the complexity of distribution network structure recommended to choose advanced control and protection system using a self-healing, this system must be able to heal any disturbance in the distribution system of smart grid to improve efficiency, resiliency, continuity, and reliability of the smart grid. This review focuses mostly on the key technology of self-healing control, gives an insight into the role of self-healing in distribution system advantages, study challenges and opportunities in the prospect of utilities. The main contribution of this paper is demonstrating proposed architecture, control strategy for self-healing control system includes fault detection, fault localization, faulted area isolation, and power restoration in the electrical distribution system.

Fault Location, Isolation and System Restoration (FLISR) mechanisms allow for rapid restoration of power to customers that are not directly implicated by distribution network failures. However, depending on where the logic for the FLISR system is located, deployment may have security implications for the distribution network. This paper discusses alternative FLISR placements in terms of cyber security considerations, concluding that there is a case for both local and centralized FLISR solutions.

Traditionally, utility crews have used faulted circuit indicators (FCIs) to locate faulted line sections. FCIs monitor current and provide a local visual indication of recent fault activity. When a fault occurs, the FCIs operate, triggering a visual indication that is either a mechanical target (flag) or LED. There are also enhanced FCIs with communications capability, providing fault status to the outage management system (OMS) or supervisory control and data acquisition (SCADA) system. Such quickly communicated information results in faster service restoration and reduced outage times. For distribution system protection, protection devices (such as recloser controls) must coordinate with downstream devices (such as fuses or other recloser controls) to clear faults. Furthermore, if there are laterals on a feeder that are protected by a recloser control, it is desirable to communicate to the recloser control which lateral had the fault in order to enhance tripping schemes. Because line sensors are typically placed along distribution feeders, they are capable of sensing fault status and characteristics closer to the fault. If such information can be communicated quickly to upstream protection devices, at protection speeds, the protection devices can use this information to securely speed up distribution protection scheme operation. With recent advances in low-power electronics, wireless communications, and small-footprint sensor transducers, wireless line sensors can now provide fault information to the protection devices with low latencies that support protection speeds. This paper describes the components of a wireless protection sensor (WPS) system, its integration with protection devices, and how the fault information can be transmitted to such devices. Additionally, this paper discusses how the protection devices use this received fault information to securely speed up the operation speed of and improve the selectivity of distribution protection schemes, in add- tion to locating faulted line sections.

Multiple Inductive Loop Detectors are advanced Inductive Loop Sensors that can measure traffic flow parameters in even conditions where the traffic is heterogeneous and does not conform to lanes. This sensor consists of many inductive loops in series, with each loop having a parallel capacitor across it. These inductive and capacitive elements of the sensor may undergo open or short circuit faults during operation. Such faults lead to erroneous interpretation of data acquired from the loops. Conventional methods used for fault diagnosis in inductive loop detectors consume time and effort as they require experienced technicians and involve extraction of loops from the saw-cut slots on the road. This also means that the traffic flow parameters cannot be measured until the sensor system becomes functional again. The repair activities would also disturb traffic flow. This paper presents a method for automating fault diagnosis for series-connected Multiple Inductive Loop Detectors, based on an impulse test. The system helps in the diagnosis of open/short faults associated with the inductive and capacitive elements of the sensor structure by displaying the fault status conveniently. Since the fault location as well as the fault type can be precisely identified using this method, the repair actions are also localised. The proposed system thereby results in significant savings in both repair time and repair costs. An embedded system was developed to realize this scheme and the same was tested on a loop prototype.
 

The reliability theory used in the design of complex systems including electric grids assumes random component failures and is thus unsuited to analyzing security risks due to attackers that intentionally damage several components of the system. In this paper, a security risk analysis methodology is proposed consisting of vulnerability analysis and impact analysis. Vulnerability analysis is a method developed by security engineers to identify the attacks that are relevant for the system under study, and in this paper, the analysis is applied on the communications network topology of the electric grid automation system. Impact analysis is then performed through co-simulation of automation and the electric grid to assess the potential damage from the attacks. This paper makes an extensive review of vulnerability and impact analysis methods and relevant system modeling techniques from the fields of security and industrial automation engineering, with a focus on smart grid automation, and then applies and combines approaches to obtain a security risk analysis methodology. The methodology is demonstrated with a case study of fault location, isolation and supply restoration smart grid automation.