Multichannel detection of acoustic emissions and localization of the source by using a hybrid programming system

The detection of acoustic emissions with multiple channels and different kind of sensors (ultrasound electronic sensors and optical fiber sensors) is presented. The source localization based on the times of arrival is also carried out and different strategies for solving the location equations are compared. The most efficient strategy in terms of computational and complexity costs versus performance has been selected and the error propagation is analyzed. The errors of the acoustic emission source location (localization process) are evaluated from the errors of the times of arrival (detection process). For that, a hybrid programming architecture is proposed. It is formed by a virtual instrumentation system for the acquisition and the detection of multiple acoustic channels and an algorithms-oriented programming system for the implementation of the localization techniques (back-propagation and multiple-source separation algorithms could also be implemented in this system). Finally the communication between both systems is performed by a packet transfer protocol that allows remote operation (e.g. a local monitoring and a remote analysis and diagnosis).This work has been supported by the Spanish National Ministry of Science and Innovation, under the R&D project No. DPI 2009-14628-C03-01 and the scholarship FPI No. BES-2010-042083

Review of recent research towards power cable life cycle management

Power cables are integral to modern urban power transmission and distribution systems. For power cable asset managers worldwide, a major challenge is how to manage effectively the expensive and vast network of cables, many of which are approaching, or have past, their design life. This study provides an in-depth review of recent research and development in cable failure analysis, condition monitoring and diagnosis, life assessment methods, fault location, and optimisation of maintenance and replacement strategies. These topics are essential to cable life cycle management (LCM), which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies. The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications. The review concludes that the full potential of cable condition monitoring, condition and life assessment has not fully realised. It is proposed that a combination of physics-based life modelling and statistical approaches, giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses, such as water ingress, mechanical damage and imperfections left from manufacturing and installation processes, will be key to success in improved LCM of the vast amount of cable assets around the world

Classification of EMI discharge sources using time–frequency features and multi-class support vector machine

This paper introduces the first application of feature extraction and machine learning to Electromagnetic Interference (EMI) signals for discharge sources classification in high voltage power generating plants. This work presents an investigation on signals that represent different discharge sources, which are measured using EMI techniques from operating electrical machines within power plant. The analysis involves Time-Frequency image calculation of EMI signals using General Linear Chirplet Analysis (GLCT) which reveals both time and frequency varying characteristics. Histograms of uniform Local Binary Patterns (LBP) are implemented as a feature reduction and extraction technique for the classification of discharge sources using Multi-Class Support Vector Machine (MCSVM). The novelty that this paper introduces is the combination of GLCT and LBP applications to develop a new feature extraction algorithm applied to EMI signals classification. The proposed algorithm is demonstrated to be successful with excellent classification accuracy being achieved. For the first time, this work transfers expert's knowledge on EMI faults to an intelligent system which could potentially be exploited to develop an automatic condition monitoring system

An optic fiber sensor for partial discharge acoustic detection

Partial discharge (PD) is a very common problem in operating power transformers and is one of the factors that could lead to failure of power transformers, leading to power outage and expensive repairs. The acoustic wave induced by PD can be measured and used for monitoring, diagnosing, and locating potential failures in power transformers. The effects of the temperature of the transformer and transformer oil are one of the very important parameters in PD and these effects are investigated in detail. The Fast Fourier Transform (FF1\u27) is used to synthesize the measured data and results show that for periodic PD events, the dominant components of the energy of the PD shift to higher frequencies as the temperature increases. The experimental results are consistent with theoretical expectations. Fiber optic-based sensors have been shown to be attractive devices for PD detection because of a number of inherent advantages including small size, high sensitivity, electrical nonconductivity, and immunity to electromagnetic interference (EMI). A fiber optic sensor based on a Fabry-Perot interferometry is constructed by a simple micromachining process compatible with MEMS (Microelectromechanical system) technology. The sensor is used in a transformer to measure PD acoustic waves. The experimental results show that the sensor not only has an inherent high signal to noise capability, but is able to accurately localize the PD sources inside the transformer

Multichannel detection of acoustic emissions and localization of the source with external and internal sensors for partial discharge monitoring of power transformers

The detection of acoustic emissions with multiple channels and different kinds of sensors (external ultrasound electronic sensors and internal optical fiber sensors) for monitoring power transformers is presented. The source localization based on the times of arrival was previously studied, comparing different strategies for solving the location equations and the most efficient strategy in terms of computational and complexity costs versus performance was selected for analyzing the error propagation. The errors of the acoustic emission source location (localization process) are evaluated from the errors of the times of arrival (detection process). A hybrid programming architecture is proposed to optimize both stages of detection and location. It is formed by a virtual instrumentation system for the acquisition, detection and noise reduction of multiple acoustic channels and an algorithms-oriented programming system for the implementation of the localization techniques (back-propagation and multiple-source separation algorithms could also be implemented in this system). The communication between both systems is performed by a packet transfer protocol that allows continuous operation (e.g., on-line monitoring) and remote operation (e.g., a local monitoring and a remote analysis and diagnosis). For the first time, delay errors are modeled and error propagation is applied with this error source and localization algorithms. The 1% mean delay error propagation gives an accuracy of 9.5 mm (dispersion) and a maximum offset of 4 mm (less than 1% in both cases) in the AE source localization process. This increases proportionally for more severe errors (up to 5% reported). In the case of a multi-channel internal fiber-optic detection system, the resulting location error with a delay error of 2% is negligible when selecting the most repeated calculated position. These aim at determining the PD area of activity with a precision of better than 1% (less than 10 mm in 110 cm).This work was supported by the Spanish National Ministry of Science and Innovation, under the R&D project No. DPI 2009-14628-C03-01 and the scholarship FPI No. BES-2010-042083

A Sensor System for Detecting and Localizing Partial Discharges in Power Transformers with Improved Immunity to Interferences

The paper reports on the solution, principles, and application results related to a system for diagnosing main transformers in power plants via the radiofrequency method. The subject of the diagnostics is the occurrence of partial discharge activity in transformers. The technical solution of the system is characterized in the introductory section of the article. There then follows a description of the operating principle and the implemented novel advanced methods for signal detection and source localization. The results obtained from practical application of the system within the diagnostics of high-power transformers are presented as well. Because ambient electromagnetic disturbance was recognized as a major issue during the system development, novel detection methods were proposed, implemented, and verified. The principal approach utilizes an external radiofrequency sensor to detect outer impulse disturbance and to eliminate disturbance-triggered acquisitions, and it also ensures direct real-time visualization of the desired impulse signals. The ability of weak signal detection was verified via artificial impulse signal injection into the transformer. The developed detection methods were completed with localization techniques for signal source estimation. The desired impulse signal was detected and localized during full operation of the main transformer, despite the presence of strong electromagnetic interference

Gated pipelined folding ADC based low power sensor for large-scale radiometric partial discharge monitoring

Partial discharge is a well-established metric for condition assessment of high-voltage plant equipment. Traditional techniques for partial discharge detection involve physical connection of sensors to the device under observation, limiting sensors to monitoring of individual apparatus, and therefore, limiting coverage. Wireless measurement provides an attractive low-cost alternative. The measurement of the radiometric signal propagated from a partial discharge source allows for multiple plant items to be observed by a single sensor, without any physical connection to the plant. Moreover, the implementation of a large-scale wireless sensor network for radiometric monitoring facilitates a simple approach to high voltage fault diagnostics. However, accurate measurement typically requires fast data conversion rates to ensure accurate measurement of faults. The use of high-speed conversion requires continuous high-power dissipation, degrading sensor efficiency and increasing cost and complexity. Thus, we propose a radiometric sensor which utilizes a gated, pipelined, sample-and-hold based folding analogue-todigital converter structure that only samples when a signal is received, reducing the power consumption and increasing the efficiency of the sensor. A proof of concept circuit has been developed using discrete components to evaluate the performance and power consumption of the system

A review of techniques for RSS-based radiometric partial discharge localization

The lifespan assessment and maintenance planning of high-voltage power systems requires condition monitoring of all the operational equipment in a specific area. Electrical insulation of electrical apparatuses is prone to failure due to high electrical stresses, and thus it is a critical aspect that needs to be monitored. The ageing process of the electrical insulation in high voltage equipment may accelerate due to the occurrence of partial discharge (PD) that may in turn lead to catastrophic failures if the related defects are left untreated at an initial stage. Therefore, there is a requirement to monitor the PD levels so that an unexpected breakdown of high-voltage equipment is avoided. There are several ways of detecting PD, such as acoustic detection, optical detection, chemical detection, and radiometric detection. This paper focuses on reviewing techniques based on radiometric detection of PD, and more specifically, using received signal strength (RSS) for the localization of faults. This paper explores the advantages and disadvantages of radiometric techniques and presents an overview of a radiometric PD detection technique that uses a transistor reset integrator (TRI)-based wireless sensor network (WSN)

Effect of water on electrical properties of Refined, Bleached, and Deodorized Palm Oil (RBDPO) as electrical insulating material

This paper describes the properties of refined, bleached, deodorized palm oil (RBDPO) as having the potential to be used as insulating liquid. There are several important properties such as electrical breakdown, dielectric dissipation factor, specific gravity, flash point, viscosity and pour point of RBDPO that was measured and compared to commercial mineral oil which is largely in current use as insulating liquid in power transformers. Experimental results of the electrical properties revealed that the average breakdown voltage of the RBDPO sample, without the addition of water at room temperature, is 13.368 kV. The result also revealed that due to effect of water, the breakdown voltage is lower than that of commercial mineral oil (Hyrax). However, the flash point and the pour point of RBDPO is very high compared to mineral oil thus giving it advantageous possibility to be used safely as insulating liquid. The results showed that RBDPO is greatly influenced by water, causing the breakdown voltage to decrease and the dissipation factor to increase; this is attributable to the high amounts of dissolved water

Low Power Signal Processing Techniques for Radiometric Partial Discharge Detection in Wireless Sensor Network.

High-voltage infrastructure condition monitoring and diagnostics is essential for the continuous and uninterrupted supply of electricity to the grid. A common metric for establishing HV plant equipment condition is the identification and subsequent monitoring of insulation faults known as partial discharge. Traditional techniques for this require physical connection of sensors to plant equipment to be monitored, leading to potentially high cost and system complexity. A non-invasive and significantly less costly approach is the use of wireless receivers to measure the electromagnetic waves produced by partial discharge faults. Hence, a large-scale monitoring network can be realized to monitor equipment within a substation compound at relatively low cost. An issue with the use of commercially available wireless sensor technology, based around high-speed data conversion, is ensuring each node is low cost and low-power, whilst still being capable of detecting and measuring partial discharge faults within a reasonable distance. In order to reduce power consumption two signal processing techniques are proposed, a transistor-reset integrator and a gated high-speed analogue-to-digital converter, which provide a low power method of radiometric partial discharge measurement by avoiding continuous high-speed sampling. The measured results show that the transistor-reset integrator based system is capable of measuring partial discharge over a distance of 10 m with an error within 1 m, and performing location estimation to within 0.1 m as compared to estimation performed using high-speed sampling, at fraction of the power consumption. The folding ADC based system is able to sample a PD like signal, but requires additional development to improve performance and fully integrate it into a system. The overall results prove the operating principle of the partial discharge monitoring system, which has the potential to be developed into a viable solution