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Wireless Condition Monitoring Technique of Power Transformer and Formulation of Health Index

In this paper remote condition monitoring system is developed using single board computer and data acquisition system which senses different parameters and communicated to the remote end using internet. The economical aspect of transformer with remote condition monitoring system which gives necessary information of operating condition is analyzed. The performance of remote condition monitoring system is tested on 16 KVA transformer in the laboratory. Transformers with remote condition monitoring system can be provided maintenance as and when required.


Introduction
Transformer is a static piece of equipment with a complicated electromagnetic circuit inside. Although the transformer is a complex piece of equipment, proper designing of an insulation system, magnetic circuit, structure and selection of proper transformer auxiliaries make transformer extremely reliable equipment. Utilities are very much keen to control and monitor the status and condition of all transformers fleet so they can intervene before a failure or malfunction of it. Many of the transformers in use at utilities have already exceeded design life. Most power transformer will encounter emergency overloads on occasions, with subsequent loss of life. The load peaks may be predictable or non predictable, unexpected generates high temperatures which shortens transformer lifetime. Presently the load and the age of the apparatus is increasing and therefore the monitoring and diagnosis of power transformers becomes more and more important, whereby monitoring is the collection of relevant data during service (online) or during maintenance or test periods (offline) and diagnostic is the technical evaluation and interpretation of the recorded data. Monitoring improves the reliability of the assets by continuously keeping a watchful eye on the most critical transformer components. Inordinate temperature rise in a power transformer due to load current have known to be the most important factor in causing rapid degradation of its insulation and decides the optimum load carrying ability or the load ability at a transformer.
In [1] hourly load and ambient temperatures obtained through condition monitoring are used to assess the operating profile of the equipment. The only inputs to the IEEE life consumption models to assess the consumed life of insulation estimated are restricted to load factors and ambient temperatures. However, this approach needs to be extended to include the long-term shift effects in load and ambient temperature variations. A laboratory controlled thermal aging experimental method has been described in [2] to obtain information on the status of insulation by diagnostic testing and condition monitoring has been suggested. However, to correct the results to field conditions, a correction factor is required for certain diagnostic parameters. The monitoring methods presented in [3] are based on the existing IEC and IEEE standards and neural-network analysis. These methods are used in to calculate the top-oil and hot-spot temperature as well as the loss of life of a transformer. This requires measured data from the studied or same type of transformer supplying similar loading.
Article [4], classified the defect sources using phase-resolved partial discharge patterns. But, this requires an understanding of the traits associated with, and relationship between, observed partial discharge activity and responsible defect sources. The application of Fiber Bragg Grating (FBG) sensors for monitoring power transformer hot-spot temperatures has been demonstrated in [5]. However, the temperature sensors are based on proprietary encapsulated FBG sensors and the optical interrogation unit requires a special designed narrowband high power broadband fiber conducted in this work, a further investigation needs to be undertaken to verify source. Model-based identification approach has been developed in [6] to determine parameters of a demagnetized transformer core such as magnetic permeability and electrical conductivity, of power transformers on the basis of frequency response analysis (FRA). However, only simulation studies are the proposed identification approach with experimental measurements. In [7] an evidential reasoning ER-based approach to transformer winding condition assessment using FRA is proposed to formalize a frequency response analysis (FRA) evaluation process for the first time. In this study, only the experimental FRA data from a normal transformer is used as a winding condition evaluation. Much research needs to be carried out to establish more reliable interpretation features with regard to different FRA diagnoses using experimental FRA data related to various winding failure conditions from real transformers. The technique of Conditional Anomaly Detection and its application to transformer monitoring described in [8]. However, various system components are needed to provide a solution that supports engineers in decision making. The main components of power transformer are the windings, bushings, transformer oil, core, tank, cooling system, and tap changer.
Failure analysis of large power transformers can be beneficial in determining which component is more important in evaluating the condition of transformers. Statistics [9] show that the most frequent causes followed by long outage damages are in tap changer, active component and in bushings, as shown in table I. Power organizations today use either or both of the following traditional maintenance models. On the one hand, "failure-based" or "reactive" maintenance occurs when equipment is left un-monitored until one or more failure modes have been observed. On the other hand, "preventive" maintenance approach involves physically visiting an asset on a regularly scheduled basis, assessing the performance of that asset and then applying necessary maintenance to ensure optimal performance.
On the bases of CIGRE report, the failure pattern of power transformers follows a "bath-tub" curve for 400 transformers, as shown in Fig. 1 [10]. The first part of the curve is failure due to infant mortality; the second part of the curve is the constant failure rate; and the last part of the curve is failure due to aging. The numbers of transformer failures in the second part is greater than the last part. Electrical stresses, Electromagnetic stresses, Dielectric stresses, Thermal stresses, and Chemical stresses can cause premature failure. The accuracy of the predictions is not always as good as are desired. Unacceptable temperature rise may occur due to several fault conditions other than overloading, and hence the need of an online monitoring of the transformer becomes more prominent. The accuracy of the predictions is not always as good as are desired. Unacceptable temperature rise may occur due to several fault conditions other than overloading, and hence the need of an online monitoring of the transformer becomes more prominent. Transformer condition monitoring techniques available in literature are mainly used for power transformers. All monitoring techniques are applied periodically on oil power transformer for full assessment. The result if these procedures are used to evaluate the most cost effective option when transformer is in service. or under maintenance or under repair. It helps to take replacement decision also. There is a need of developing a remote condition monitoring system incorporating more parameters like temperature, oil level, humming noise etc. Advancements in information technology and communication can aid such a system effectively.

Proposed Model: Introduction
It is evident that an RCMS for transformer is essential. A proposed model for remote condition monitoring of transformer is presented here. It includes both hardware and software implementation. The communication based on GSM or GPRS is not taking the data continuously, which again is a drawback. In the new model proposed different parameters like temperature and oil level that directly acts as a health monitor are incorporated. The monitoring system is explained by going through different aspects like data acquisition, processing, communication and the applications.

A) System Architecture
The proposed model is explained using the functional diagram as shown in Fig2. It consists of data acquisition, power supply for different components, processing, communication and a Human Machine Interface (HMI). Different parameters are sensed using sensors and passed to processing unit, which is Single Board Computer (SBC) Friendly ARM Mini2440. Then it is communicated to the remote end where decision needed to be taken regarding maintenance and protection. Computational Algorithm is developed and implemented in RCMS.  (1) 6) The value of HI can be used to identify health status of transformer and is shown in application.

C) Experimental Setup
A transformer is constructed for testing this system. Special arrangements have been made for incorporating all sensors and to change the internal conditions. The parameters are varied to test RCMS. All electrical parameters can be sensed from energy meter data read out which is a serial port using RS232. Voltages, currents, power, power factor and other tampering data are available in energy meter system memory. An L & T energy meter is used for this purpose. It is interfaced with SBC and then communicated to the remote end. Temperature of oil in the top of the tank is sensed with a thermistor type sensor (LM3). The output of the sensor that will be a voltage in accordance with temperature can be interfaced with SBC after passing through ADC. Temperature as well as oil level is interfaced with SBC using the PIC16F877A microcontroller. Its ADC's are used for converting the analog signal output of sensors to digital values. Then serial communication using RS232 passes this data to SBC When float moves, resistance varies and thereby PIC ADC senses the variation in output voltage. Interfacing with SBC is same as shown for temperature sensor. Both temperature and oil level are sent using same serial port from PIC. The application software developed consists of two windows. In first window, parameter values are shown and in second window, analysis is done. Health index algorithm is written in MATLAB but the results are shown in first window. Humming noise analysis consists of frequency spectrum of noise. This MATLAB figure will be shown on the application window.

Experimental Result
Detailed test results prove that RCMS can be used for transformers. Different parameter variations are discussed and corresponding HI is also calculated using program written in MATLAB. So it is evident that this system is efficient in determining the health status of Transformer. Operator will be able to make a decision without inspecting the transformer on site.

Conclusion
A remote condition monitoring system is developed and tested. The analytical method used is based on health index, which is found to be effective for transformers.
Condition based maintenance and control of transformers is achieved through this work. This new system is monitoring other internal parameters also which directly influence the internal condition of the transformer and thereby ageing. RCMS implementation can reduce failure and ageing rate of transformers. It is a completely online system where instantaneous parameter variations are monitored.