Identifying prognostic indicators for electrical treeing in solid insulation through pulse sequence analysis

Predictive maintenance attempts to evaluate the condition of equipment and predict the future trend of the equipment's aging, in order to reduce costs when compared to the two traditional approaches: corrective and preventive maintenance. This prediction requires an accurate prognostic model of aging. In solid insulation, the ultimate goal of prognostics is to predict the advent of failure, i.e., insulation breakdown, in terms of remaining useful life (RUL). One fault is electrical treeing, which is progressive thus leading to potentially catastrophic failure. Research has shown that diagnosis of faults can be achieved based on partial discharge (PD) monitoring [1], i.e., phase-resolved and pulse sequence analysis (PSA). This work will explore the extension of this concept towards predicting evolution of the defect: moving beyond diagnostics towards prognostics. To do this, there is a need for further investigation of prognostic features within PD characteristics leading up to breakdown. In this work, a needle-plane test arrangement was set up using a hypodermic needle and pre-formed silicone rubber as test samples. The visual observations and tree growth measurements were made using a digital microscope. PD data was captured using a radio frequency (RF) sensor and analysed using PSA. The main idea of the PSA approach is the strong relationship between two consecutive pulses caused by PD activities, which can give an understanding of the local degradation processes [1]. As for electrical treeing, a breakdown indicator in PSA is the appearance of heavily clustered data points that lie diagonally in scatter plots of the differential ratio of voltage and time of consecutive charges (Un = Δun/Δtn) [2,3]. Figure 1 shows an example of a plot that changed to a diagonal line after 14 hours of aging time. This paper investigates the formation of the diagonal line based on the distribution of the plot from the start of electrical treeing until breakdown occurs. Finally, statistical features of the PSA plot are given and will be used for lifetime prediction of insulation samples in future work

On the use of probabilistic model-checking for the verification of prognostics applications

Prognostics aims to improve asset availability through intelligent maintenance actions. Up-to-date remaining useful life predictions enable the optimization of maintenance planning. Verification of prognostics techniques aims to analyze if the prognostics application meets the design requirements. Online prognostics applications depend on the data-gathering hardware architecture to perform correct prognostics predictions. Accordingly, when verifying prognostics requirements compliance, it is necessary to include the effect of hardware failures on prognostics predictions. In this paper we investigate the use of formal verification techniques for the integrated verification of prognostics applications including hardware and software components. Focusing on the probabilistic model-checking approach, a case study from the power industry shows the validity of the proposed framework

ADEPS: a methodology for designing prognostic applications

Prognostics applications predict the future evolution of an asset under study, by diagnosing the actual health state and modeling the future degradation. Due to rapidly growing interest in prognostics, different prediction techniques have been developed independently without a consistent and systematic design. In this paper we formalize the prognostics design process with a novel methodology entitled ADEPS (Assisted Design for Engineering Prognostic Systems). ADEPS combines prognostics concepts with model-based safety assessment, criticality analysis, knowledge engineering and formal verification approaches. The main activities of ADEPS include synthesis of the safety assessment model from the design model, prioritization of the system failure modes, systematic prognostics model selection and verification of the adequacy of the prognostics results with respect to design requirements. By linking system-level safety assessment models and prognostics results, design and safety models are updated with online information about different failure modes. This step enables system-level health assessment including prognostics predictions of different failure modes. The end-to-end application of the methodology for the design and evaluation of a power transformer demonstrates the benefits of the proposed approach including reduced design time and effort, complete consideration of prognostics algorithms and updated system-level health assessment

Prognostic modelling utilizing a high fidelity pressurized water reactor simulator

Within power generation, aging assets and an emphasis on more efficient operation of power systems and improved maintenance decision methods has led to a growing focus on asset prognostics. The main challenge facing the implementation of successful asset prognostics in power generation is the lack of available run-to-failure data. This paper proposes to overcome this issue by use of full-scope high-fidelity simulators to generate the run-to-failure data required. From this simulated failure data a similarity-based prognostic approach is developed for estimating the remaining useful life of a valve asset. Case study data is generated by initializing prebuilt industrial failure models within a 970 MW pressurized water reactor simulation. Such full-scope high-fidelity simulators are mainly operated for training purposes, allowing personnel to gain experience of standard operation as well as failures within a safe, simulated operating environment. This paper repurposes such a high-fidelity simulator to generate the type of data and affects that would be produced in the event of a fault. The fault scenario is then run multiple times to generate a library of failure events. This library of events was then split into training and test batches for building the prognostic model. Results are presented and conclusions drawn about the success of the technique and the use of high-fidelity simulators in this manner

Optimisation of the bidding strategy for wind power trading

The optimal bidding strategy for trading electricity from a wind farm is not always clear. This paper outlines a method for predicting whether the market will be long or short and uses this information to select the best quantile regression for the current market conditions. Results from a simulation with a 2.5MW turbine produced a savings of over £2,000 compared to using only a P50 forecaster

Online conditional anomaly detection in multivariate data for transformer monitoring

Retrofitting condition monitoring systems to aging plant can be problematic, since the particular signature of normal behavior will vary from unit to unit. This paper describes a technique for anomaly detection within the context of the conditions experienced by an in-service transformer, such as loading, seasonal weather, and network configuration. The aim is to model the aged but normal behavior for a given transformer, while reducing the potential for anomalies to be erroneously detected. The paper describes how this technique has been applied to two transmission transformers in the U.K. A case study of 12 months of data is given, with detailed analysis of anomalies detected during that time

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

Designing for reliability in wind turbine condition monitoring systems

For wind turbine condition-based maintenance to be economically advantageous over the more conventional periodic based maintenance, high reliability is required of the systems which carry out the monitoring. The use of condition monitoring systems (CMS) is becoming common place in offshore wind turbines, due to the high cost associated with maintenance; however for onshore wind turbines the benefits aren’t quite so obvious. Still, for both onshore and offshore wind turbine condition-based maintenance, it is essential that these systems can operate for long periods of time providing an accurate diagnosis of the state of the wind turbine’s health. Through the build and installation of one CMS and the design, build and installation of another the author aims to share experience and lessons learnt to aid the design of any new CMS. A brief description will be given of the first system installed followed by the issues that were experienced following its installation. The author will then go on to discuss the design of a new CMS which has been installed in a Vestas V42 wind turbine and the improvements that were made to it to increase its reliability and ruggedness

Investigation into pulse sequence analysis of PD features due to electrical tree growth in epoxy resin

Electrical trees developed using point-plane samples have been investigated under three different voltage conditions: AC, AC with positive DC bias, and AC with negative DC bias. Visual observations mainly indicate two types of electrical tree progression from initiation to breakdown: “forward and backward” (FB) trees and "forward" (F) trees. FB trees can be observed in AC tests, while F trees occur in AC with DC bias tests. The difference between AC with negative DC bias and AC with positive DC bias is the growth of a rapid long branch prior to breakdown under negative DC bias conditions. Based on the pulse sequence analysis (PSA) technique applied to the PD data associated with electrical tree growth, the findings confirm that PSA curves under different voltage tests have different regions and PSA features can be indicators of tree growth

A model-based approach for automatic validation of protection settings

The reliable operation of protection systems depends on the correct setting of protective devices. Due to the increasing network complexity and the large number of protective devices (and their associated setting parameters), it is extremely laborious for engineers to manually validate the settings. Existing model-based (MB) systems that are capable of performing the validation task require significant manual input for network models creation, relay models configuration, simulation result analysis, etc., which is both time consuming and subject to human errors. This paper presents a methodology that adopts the principle of model-based reasoning (MBR) for automated validation of protection settings. Such a methodology is demonstrated through the design and implementation of a prototype tool Model-Based protection setting Smart Tool (MBST), which is capable of automatically populating network models, configuring relay models with settings to be validated, creating credible system events, and simulating the relays’ behaviour under these events. The automated process is achieved by an interface layer within MBST that allows interaction with a commercially available simulation engine to leverage its internal data and functions for the settings validation task. The simulated results are automatically analysed using a rule-based (RB) approach. The key advantage of the work is the mechanism to automate the entire settings validation process. The design of the interface layer to interact with existing simulation engine and models also demonstrates a solution for rapid prototyping of intelligent systems dedicated to validation of protection settings