maintenance improvement and cost reduction of large … … · maintenance improvement and cost...

Maintenance Improvement and Cost Reduction of Large Scale Systems Using Remote Monitoring and Supervision

ALEKSANDAR NIKOLIC, BRANKO PEJOVIC, BRANKA DJURIC, JELENA JANKOVIC,

KSENIJA DRAKIC Electrical Engineering Institute Nikola Tesla, Department for Electrical Measurements

University of Belgrade Koste Glavinica 8a, 11000 Belgrade

SERBIA [email protected] http://www.ieent.org

Abstract: - Importance of remote monitoring and supervision of large scale systems like thermal power plants is presented and explained in the paper. High production and maintenance costs of high power electrical equipment, mainly generator power transformers leads to moving to the preventive and predictive maintenance. Example of on-line monitoring system, installed on thermal power plant’s generator transformer and supervised remotely through Internet, proves those statements. Final results of a proper decision making based on the on-line monitoring system indicate several features. Some of them are reducing the risk and costs of unexpected failure, actual conditions drive maintenance and repair, extending life of assets and reducing costs of maintenance. Key-Words: - Systems, Remote Monitoring, Supervision, Maintenance 1 Introduction

Generator power transformer is the largest unit in power plants, since its capacity could goes up to 1400MVA. These transformers are very important for electric power system due to the fact that nowadays it should be wait more than two years for production of new generator transformer. That is reason why it is necessary to continuously supervise transformer operation.

In order to minimize system outages, many devices have evolved to monitor the serviceability of power transformers. These devices, such as, Buchholz relays or differential relays, respond only to a severe power failure requiring immediate removal of the transformer from service, in which case, outages are inevitable. Thus, preventive techniques for early detection faults to avoid outages would be valuable [1].

Transformer replacement before failure can be motivated by several legitimate reasons. These include environmental and fire safety regulations, changes in the load or the voltage level, an increased risk of failure due to transformer ageing, or the aim to improve the energy efficiency. This last motivation is less common. This is unfortunate, because replacing a transformer with a new one with higher energy efficiency will in many cases lead to a lower life cycle cost of the device [2].

Thermal management is one of the essential approaches for on-line monitoring of power

transformer. Mostly spread method is based on calculating the transformer’s highest temperature (i.e. hot-spot temperature) using the measured transformer’s top oil temperature and load current [3], [4].

Fault gases in transformers are generally produced by oil degradation and other insulating materials, e.g., cellulose and paper. Theoretically, if an incipient or active fault is present, the individual dissolved gas concentration, gassing rate, total combustible gas (TCG) and cellulose degradation are all significantly increased. By using gas chromatography to analyze the gas dissolved in a transformer's insulating oil, it becomes feasible to judge the incipient fault types [5].

The analysis of the failure modes of the various components leads to a review of the inspection and maintenance procedures of power transformers. On-line diagnostic condition assessment addressing common failure modes:

- Multiple sensors, - Multiple on-line models, - All parameters are recorded automatically and

continuously, - Trend and limit alarms.

Early detection of problems, at the incipient stage, will help extend the life of the transformers. Detection of these problems is accomplished with several models, which rely on various sensors installed on the transformer combined with other

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parameters manually entered. The models focused on the main transformer tank and the cooling system. These capabilities increase the useful data while significantly reducing the shear volume of data. This data is then fed into industry standard and accepted models, which calculate the various outputs. Finally, these outputs are displayed and trended both in the plant control room and externally in the supervising accredited laboratory. 2 System description

In this paper, realized system in one large thermal power plant is presented, with on-line monitoring system of 725MVA generator power transformer. Since the system is remotely supervised from the laboratory, several improvements could be addressed regarding maintenance, cost reduction and forehand decision. 2.1 System for thermal monitoring

Solution presented in this paper is based on calculation of the hot-spot temperature using measured transformer oil on the top of housing, ambient temperature and load current. Real-time algorithm is developed using concept with differential equation from IEC 60076-7 standard, shown in Fig. 1, since it is suitable for on-line monitoring.

Fig. 1: Algorithm for calculating hot-spot

temperature Modern on-line systems for calculating

transformer hot-spot temperature are based on microprocessor devices that use differential equation from the Fig. 1. Input signals are measured oil temperature from the sensor in upper part of transformer oil tank (top oil temperature), ambient temperature and load current, usually measured on one phase only. Hot spot factor is normally presented between 1.1 to 1.5 depending on winding design. In Fig. 2 principle of temperature distribution through transformer is shown, including part of a cooling system.

Fig. 2: Transformer temperature identification Pt100 based temperature sensor is positioned on

the top of the transformer tank partially immersed in the oil. Since most part of the heat could be expected in that part of the tank, hot-spot temperature of the winding could be detected using previously explained algorithm with a satisfied accuracy. Position of the temperature sensor on the transformer is shown in Fig. 3.

Fig. 3: Transformer top oil temperature sensor


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Since the system controls transformer cooling, it is wise to put one sensor at the input of cooling device and the other on the output. Temperature sensors are mounted on the pipes where transformer oil is circulating. In that case, besides cooling control, more information about operation of cooling device could be obtained, like malfunction of fan if temperatures on the input and output are near the same value.

2.2 System for monitoring gases in oil Dissolved Gas Analysis (DGA) monitors

measure gases dissolved in transformer insulating oil. These gases are generated as oil breaks down naturally, or when fault conditions cause oil to break down. Monitoring provides early indication of faults and can prevent transformer failures.

On-line DGA monitors range from single gas monitors (most commonly hydrogen) to multi gas monitors measuring 8 or more gases considered to be “fault gases”. The levels of gases, and the ratios of multiple gases, are indicative of the type of transformer fault. Basic principle of DGA is shown in next figure.

Fig. 4: Gas chromatography on power transformer

Numerous diagnostic tools exist to aid in the

interpretation of DGA results. Gas Chromatography – or “GC” – is the only method approved by IEEE / IEC standards [5], though other methods do exist. In the past 10 years, Online DGA has become increasingly common, and is now typically a standard tool for asset management of generation and transmission transformers.

Mounted equipment and transformer upper oil valve with temperature and moisture sensor is shown in Fig. 5.

Presented industrial solution is based on gas chromatograph (GC) and supported industrial computer, both mounted in IP65 protected cabinet. Oil from transformer is taken from upper valve and introduced to GC. In Fig.5 next to the GC monitor are junction box and helium cylinder. Helium is used to move fault gases into the stationary phase (GC column). Oil sampling is done every 4 hours, while gas analysis last one hour. In case of error (gas increase), sampling could be on every hour. 2.3 Communication system

Communication between system parts located on and near transformers and power-plant control room could be realized either using Ethernet and/or Fiber optic cables or even wirelessly [7]. Although power plants are noisy industrial environments, a new solution based on wireless communication is proposed since cabling costs were too high due to complicated installation requirements [8]. Such a solution is done via Zigbee wireless protocol using real-time application running on Programmable Automation Controller (PAC).

Complete on-line monitoring system is connected via Ethernet to the power plant’s LAN. It could be remotely programmed and supervised via the Internet, reducing maintenance time and costs.

Fig. 5: GC analyzer with equpment (left) and upper oil valve with temperature and moisture sensor (right)


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3 Improvements In this chapter several improvements of on-line

monitoring system are addressed, regarding maintenance and cost reduction.

3.1 Maintenance improvements Results of proper transformer monitoring system

are taken through installed application on the panel PC computer in thermal power plant control room. Application receives every ten seconds updated data from real-time gateway and stores it into the SQL database.

Further merit is that application is reachable from outside of the plant through Internet via protected VPN channel. In that case system could be monitored remotely and finally application could be even updated and replaced without necessity to visit the plant. That significantly simplifies system maintenance and reduces additional costs.

In Fig. 6 main application screen is given, where all temperature and status data are given per transformer: 1. Measured transformer load current, 2. Measured top oil temperature, 3. Measured cooling group input temperature, 4. Measured cooling group output temperature, 5. Calculated hot-spot temperature, 6. Command for activating of cooling fan (green

square means ON state, gray OFF state), 7. Information about real cooling fan ON or OFF

state (green square means ON state, gray OFF state).

Fig. 6: Real data taken from monitoring system In Fig. 7 one part of monitoring system is given

for one transformer, with transformer scheme and places where temperature sensors are mounted

which is very important for plant maintenance department.

Fig. 7: Monitoring system data for transformer taken

from temperature sensors In Fig. 8 a graphical trend during one day is

shown. Upper part of figure represents load current, while lower part shows both top oil and hot-spot temperatures.

Fig 8: Graphical trend of transformer load current

and oil temperatures In the past 10 years, on-line DGA has become

increasingly common, and is now typically a standard tool for asset management of generation and transmission transformers. Comparing with laboratory measurements, on-line accuracy is also around ±5%. Laboratory readings can be highly accurate, but the sample being measured is different from on-line sample. Some differences could be also due to the unequal way of calibration and reference gases concentration. Also, different principles of measurement of absolute content of moisture in oil could give different results. Comparisons are


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summarized in the next table. Results are taken from the oil samples from same transformer both in the laboratory and using presented on-line system. Table 1: Comparative analysis of gases dissolved in oil of 725MVA transformer

On-line system

Laboratory

Temperature [°C] 45.5 54

Gases [ppm]

H2 6 14 CH4 271 301 C2H2 0 0 C2H4 294 362 C2H6 168 118 CO 964 851 CO2 20662 16511 O2 1187 2272 N2 52889 62648

Moisture RS, % 5 - Moisture cal [ppm] 7 7

Maintenance improvements rely on proper

decisions when some error is predicted using on-line monitoring system. In such a case, supervising laboratory initiate further review and call plant maintenance department to take additional oil samples that are checked in the laboratory. Also, taken results both from on-line monitoring system and laboratory are compared with similar test performed in the past and recorded in database. This gives opportunity to make a proper decision regarding transformer operation and regular (periodical) maintenance, what is important especially for aged transformers.

Remote supervising of the system provides also simpler and faster application repair and update, since it is not necessary for IT team to come to the plant and make changes. All software updates and software changes could be done remotely without shutdown of the whole system, so real-time part of application still could run on PLC. That provides continuous operation of main part of the system, while reducing cost of transportation and also reduces time needed for forehand reaction of maintenance staff.

3.2 Cost analysis Regarding power transformers installed in

thermal power plants like this presented in the paper, dominant costs are downtime and maintenance costs. Downtime costs are mostly

connected to the power outage, while maintenance costs could be significantly reduced using proposed on-line monitoring system and supervisory laboratory services for preventive maintenance. Some electric utility power companies prove that they have reduced maintenance costs by 30% by moving from the concept of corrective maintenance to the predictive maintenance. Duration of preventive maintenance is shorter, requires less number of maintenance staff and needs less investment. Furthermore, time between equipment checks is also extended. In the following figure qualitative dependence of annual costs for defect rejection and maintenance regarding interval between two preventive maintenance actions is shown.

annual costs

total costs

costs for defect rejection

costs for preventive maintenance

preventive maintenance interval

Fig. 9: Dependence of operational costs from

interval of preventive maintenance From the Fig. 9 several important conclusions

could be observed: - Repair or replacement costs are increasing in

time due to the ageing, different degradation processes or inadequate maintenance;

- Preventive maintenance costs are inversely proportional to the period between two succeeding preventive maintenance;

- For the given reliability indicators of equipment it is possible to calculate the most favorable length of the maintenance period (the value of which is the lowest exploitation costs).

4 Conclusion

Importance and features of remote monitoring and supervision for large power plants are shown in the paper. High operational and maintenance costs for power transformers in the power plants could be significantly reduced using on-line monitoring systems. Furthermore, if such a system is supervised from external laboratory, additional cost reductions could be made. In that case, forehand decisions


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about transformer operation, disconnection and failure prevention could be made on time. References: [1] C.E.Lin, J.M.Ling, C.L.Huang, An Expert

System for Transformer Fault Diagnosis Using Dissolved Gas Analysis, IEEE Transactions on Power Delivery, Vol. 8, No. 1, Jariuary 1993, pp. 231-238.

[2] Bruno De Wachter, Transformer Replacement Decisions, Application note, ECI Publication, No Cu0185, November 2013.

[3] L.W.Pierce, Hottest stpot temperatures in ventilated dry type transformers, IEEE Transactions on Power Delivery, Vol. 9, No. 1, January 1994, pp. 257-264.

[4] J.Li, T.Jiang, S.Grzybowski, Hot spot temperature models based on top-oil temperature for oil immersed transformers, IEEE Conference on Electrical Insulation and Dielectric Phenomena, 2009. CEIDP '09., 18-21 October 2009, pp. 55.

[5] Rogers, R.R., IEEE and IEC Codes to Interpret Incipient Faults in Transformers Using Gas in Oil Analysis, IEEE Transactions on Electrical Insulation, Vol. EI-13 No. 5, 1978, pp. 349–354.

[6] B.Flynn, Case Studies regarding the integration of monitoring & diagnostic equipment on aging transformers with communications for SCADA and maintenance, DistribuTECH 2008 Conference and Exhibition, January 22-24, 2008, Tampa FL, USA.

[7] Industrial Ethernet Book, The Journal of Industrial Network Connectivity, Issue 70, May 2012.

[8] A. Nikolic, A.Zigic, N.Miladinovic, Wireless Sensor Network Based Monitoring System for High Power Transformers, 15th International Power Electronics and Motion Control Conference (EPE/PEMC), 2012, pp.LS4e.4-1,LS4e.4-5, 4-6 Sept. 2012.

[9] Bruno De Wachter, Life Cycle Costing – The Basics Application note, ECI Publication, No Cu0146, February 2012.


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