On-line transformer condition monitoring through diagnostics and anomaly detection

This paper describes the end-to-end components of an on-line system for diagnostics and anomaly detection. The system provides condition monitoring capabilities for two in- service transmission transformers in the UK. These transformers are nearing the end of their design life, and it is hoped that intensive monitoring will enable them to stay in service for longer. The paper discusses the requirements on a system for interpreting data from the sensors installed on site, as well as describing the operation of specific diagnostic and anomaly detection techniques employed. The system is deployed on a substation computer, collecting and interpreting site data on-line

Embedded intelligence for electrical network operation and control

Integrating multiple types of intelligent, mulitagent data analysis within a smart grid can pave the way for flexible, extensible, and robust solutions to power network management

Evaluation of Lightning Impulse Test by Frequency Response Analysis

In this work are presented the basis for improving the interpretation of transformer lightning impulse test and the development of a graphical user interface system, which allows comparisons of time domain data and frequency response. The frequency response is obtained from deconvolution of voltage and neutral current records. A quantitative comparison of frequency response is performed using the techniques applied to displacement detection through Frequency Response Analysis, such as correlation and spectral deviation. The system is implemented using 8 bit digitizers to acquire the voltage and neutral current records. The quantization error and reliability of the frequency response obtained is handled through the use of the coherence function and tolerance bands. The system is thoroughly tested applying a lightning impulse test to a dry type distribution transformer, simulating an interdisc fault with a spark gap. Failure detection is confirmed

Fault Diagnosis of Electric Transmission Lines Using Modular Neural Networks

"(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."This paper proposes a new method for fault diagnosis in electric power systems based on neural networks. With this method the diagnosis is performed by assigning a neural module for each type of component of the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up. The neural module for transmission lines also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents, as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network, nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system..Flores, A.; Quiles Cucarella, E.; García Moreno, E.; Morant Anglada, FJ. (2016). Fault Diagnosis of Electric Transmission Lines Using Modular Neural Networks. IEEE Latin America Transactions. 14(8):3663-3668. doi:10.1109/TLA.2016.7786348S3663366814

A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies

CBM (Condition Based Maintenance) solutions are increasingly present in industrial systems due to two main circumstances: rapid evolution, without precedents, in the capture and analysis of data and significant cost reduction of supporting technologies. CBM programs in industrial systems can become extremely complex, especially when considering the effective introduction of new capabilities provided by PHM (Prognostics and Health Management) and E-maintenance disciplines. In this scenario, any CBM solution involves the management of numerous technical aspects, that the maintenance manager needs to understand, in order to be implemented properly and effectively, according to the company’s strategy. This paper provides a comprehensive representation of the key components of a generic CBM solution, this is presented using a framework or supporting structure for an effective management of the CBM programs. The concept “symptom of failure”, its corresponding analysis techniques (introduced by ISO 13379-1 and linked with RCM/FMEA analysis), and other international standard for CBM open-software application development (for instance, ISO 13374 and OSA-CBM), are used in the paper for the development of the framework. An original template has been developed, adopting the formal structure of RCM analysis templates, to integrate the information of the PHM techniques used to capture the failure mode behaviour and to manage maintenance. Finally, a case study describes the framework using the referred template.Gobierno de Andalucía P11-TEP-7303 M

Technical and vocational skills (TVS): a means of preventing violence among youth in Nigeria

Technical and vocational skills are an important tool for reducing violence among youth, especially in Nigeria, who face security challenges due to different kinds of violence. This paper focusses on the policies and programmes intended to provide youth with skills that can help them improve their life instead of engaging in violence. The paper also studies youth participation in violence. The study shows that youth in Nigeria participate in violence because of unemployment and economic pressure. These youth are mostly from poor families and are mostly used by others to achieve their own unlawful ambition. The data were collected from various secondary sources such as textbooks, journals and conference papers that were carefully reviewed. The results obtained from the literature revealed that youth are not committed, sensitised and mobilised to taking advantage of the opportunities available to them. The results also revealed that almost all the programmes meant to provide youths with skills have failed. Poverty alleviation programmes established to create jobs, self-employment and self-reliance have been unsuccessful. Therefore, alternatives must be provided to help the younger generations. Based on the literature reviewed, the paper discusses related issues and outcomes and ends with recommendations to improve the situation

Novel Formulation using Artificial Neural Networks for Fault Diagnosis in Electric Power Systems -A Modular Approach

This work proposes a new method for fault diagnosis in electric power transportation systems based on neural modules. With this method, the diagnosis is performed by assigning a generic neural module for each type of element conforming a transportation system, whether it be a transportation line, bar or transformer. A total of three generic neural modules are designed, one for each type of element. These neural modules are placed in repeating groups in accordance with the element to be diagnosed, taking into consideration its circuit breakers and relays, both internal and backup. For the diagnosis of a transportation line, this method is further reinforced by taking into consideration the corresponding waveforms of fault voltages and currents as well as the frequency spectrums of these waveforms, through a neural structure, in order to verify if the line had in fact been subjected to a fault, and at the same time to determine which type of fault ( LT, LLT, LL, LLL, LLLT ). The most important and innovative aspect of this method is that only three neural modules will be used, one for each type of element, and these can be employed for a diagnosis as one function, the instant any change of status is detected in the internal and/or backup relays relating to the element subjected to diagnosis. Keywords: Modular Neural Network, Fault Diagnosis, waveforms of fault voltages and currents, frequency spectrums of fault voltages and currents

HVAC SYSTEM REMOTE MONITORING AND DIAGNOSIS OF REFRIGERANT LINE OBSTRUCTION

A heating, ventilation, and air conditioning (HVAC) system of a building includes a refrigerant loop. A monitoring system for the HVAC system includes a monitoring device installed at the building. The monitoring device is configured to measure a first temperature of refrigerant in a refrigerant line located between a filter - drier of the refrigerant loop and an expansion valve of the refrigerant loop. The monitoring system includes a monitoring server, located remotely from the building. The monitoring server is con figured to receive the first temperature and, in response to the first temperature being less than a threshold, generate a refrigerant line restriction advisory. The monitoring server is configured to, in response to the refrigerant line restriction advisory, selectively generate an alert for transmission to at least one of a customer and an HVAC contractor