emerging technologies and embedded intelligence in ...

Proceedings of the 4th World Congress on Engineering Asset Management Athens, Greece 28 - 30 September 2009

EMERGING TECHNOLOGIES AND EMBEDDED INTELLIGENCE IN FUTURE POWER SYSTEMS Johan J. Smit a, Dhiradj Djairam b, Qikai Zhuang b a

Delft University of Technology, P.O.Box 5031, 2600GA Delft, the Netherlands [email protected] b

Delft University of Technology, Mekelweg 4, 2628CD Delft, the Netherlands [email protected]; [email protected]

The replacement wave around 2030 will create a hybrid power system of old and new technologies of which in particular the latter part will provide eminent opportunities for the implementation of embedded intelligence. However, the investment in smart grids is a difficult decision because it concerns a composition of primary and secondary equipment which have different lifetimes and different levels of robustness. Integration of sensor technology, on/off-line diagnostic systems and advanced ICT solutions enable the monitoring of the health index of the grid and its components, provided a physical model can be devised. From an economical and environmental point of view, there is much to gain by smarter electrical power networks, because in principle they enable us to extend the useful lifetime and to delay large replacement investments. However, the emerging technologies for sensors specifically for high voltage equipment performance, interpretation tools and aging models needed for such smart power networks are still in a premature stage. A few emerging technologies have achieved robustness to some extent. Dedicated techniques for partial discharge detection in high voltages cables and gas-insulated switchgear can predict failures on the basis of incipient dielectric faults. Similarly, dissolved gas monitoring of power transformers to alleviate has also been relatively successful. In this paper, the expectations of the power equipment monitoring will be discussed. Key Words: asset management, smart grids, condition based maintenance, emerging diagnostics, electrical power systems 1

INTRODUCTION

Currently, the important economic driver is changing its valuation regarding high voltage network assets. Moreover, global warming and other customer concerns are increasing at such a pace that sooner or later the “real” price of the environment has to be taken into account. In that regard, preventing function loss of high voltage equipment is at stake at many utilities for a more sustainable power system. Many power system operators are more and more considering the introduction of an architecture of subsystems containing intelligent system interface technologies. These subsystems can be designed for independent operation and self-healing dynamics. The amount of data transfer at both local and general level will increase so rapidly that e.g. agent technologies should provide adequate solutions for processing the growing data streams in the autonomous grid sections. Clearly, minimizing the required manpower input and automating maintenance schedules will be essential to the future asset management policy. Sensors, computers and fast telecommunication through wired and wireless networks should enable remotely controlled operation and data processing for maintenance and control of the future electrical infrastructure mix, thereby preventing upcoming faults more efficiently.

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DRIVING FORCES

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From the historic and recent developments in various countries, we have learned that there are three prominent driving Efactors that are considered important: Engineering, Economics and Environment [1], see figure 1.

Engineering

Economics

Environment

Economics

Environment

Economics

Environment

Engineering

Engineering

Electrification

Mature situation

Future situation

Fig. 1. E-E-E drivers in power system development. Depending on the growth situation of the country or region, the ranking of these drivers will be different. In fast developing countries, where new power infrastructures are sometimes introduced for the very first time, the most important driver will be “Engineering”. In many industrialized countries, the focus will have changed to “Economics” as the primary object will be the economical utilization and improved delivery performance. Power trading, upgrading and lifetime extension of systems are examples of challenges that have induced a revolution in managing and operating the aged assets of the system. However, in the long run, the drivers “Economics” and “Engineering” are expected to interchange when the real pricing of the environment is taken into account. Extension and replacement of the existing subsystems will result in a hybrid electrical infrastructure of old and new technologies. Future technology should include more sustainable solutions for power processing, energy storage and social/societal demands. Environmental concerns are expected to impose higher requirements from society as well as from international treaties. Environmental advantages may be found by small/mid-scale decentralized power generation growing into the distribution system. Therefore, auxiliary diagnostic need to have more control over power flows and automatic transmission and distribution equipment to improve the self restoring capability of the system, a so-called “energy internet” concept [2].

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SENSORS AND MODELS

In order to get to the concept of “energy internet”, the actual health index of relevant components in the grid should be monitored which can be achieved by the integration of sensors for high voltage performance. Using the fixed and measured data and aging models, the actual condition or health state of the component can be assessed as input for advanced preventive maintenance methodologies like CBM, RCM, RBM which anticipate effectively on repair and replacement schedules. Also, by taking into account operational parameters and environmental conditions, the future health state can be predicted by using a predictive health model (PHM), which will be discussed in the next section. Two examples of components that are nowadays monitored closely are oil-filled cable systems and power transformers:

Fig. 2. Two examples of monitoring, an oil-filled cable system and a power transformer

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The stress factors that can act on a high voltage component will in the long run result in certain internal integrity changes in the component. These changes can be detects with certain sensors. Certain changes can be pinpointed for the component, while other changes can only be found for the component in general. This is summarized in Table 1.

Table 1. Assessment of high voltage equipment performance Assessment of HV performance Stress factors

Thermal Electrical Mechanical Ambient

Routine test

Sensor e.g.

Typical changes during aging

Range of assessment General Local condition Condition

Physical, intrinsic

dielectric absorption

+

-

Chemical reactions, corrosion, byproducts

dissolved gas analysis

+

-

Thermal distribution, expansion

spot temperature

-

+

Electrical losses and treeing

partial discharges

+

+

Mechanical interface formation

space charges

-

+

-

+

Functional property changes, e.g. withstand/inception voltages, vibration level, contact speed, resistance

contact performance

It must be noted that not all of these diagnostic methods are commercially available for all components or still being developed in the laboratory, see Table 2. For example, for gas/air insulated switchgear systems (GIS and AIS), the only sensors dedicated to high voltage performance available on the market are based on partial discharges and contact performance.

Table 2. Availability of market ready sensors. GIS = Gas Insulated Switch Gear, AIS = Air Insulated Switchgear and HVDC = High Voltage equipment. DIEL = Dielectric absorption, DGA = Dissolved Gas Analysis, Tspot = Spot temperature, PDloc = Partial discharge, SC = Space charges and CP = Contact performance. Development status of diagnostic sensors for integration in autonomous substations available on the market DIEL

DGA

Tspot

PDloc

SC

CP

TRANSFORMERS CABLE GIS AIS HVDC

However, merely having the availability of market ready sensors is not sufficient for adequate decision making. For this, we also require models for interpretation of which the composed availability is shown in Table 3. We can see that means that our options have been severely decreased and that for AIS and HVDC no options exist yet.

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Table 3. Availability of models for interaction and decision making. TR = Transformers, CAB = Cable, GIS = Gas Insulated Switch Gear, AIS = Air Insulated Switchgear and HVDC = High Voltage equipment. DIEL = Dielectric absorption, DGA = Dissolved Gas Analysis, Tspot = Spot temperature, PDloc = Partial discharge, SC = Space charges and CP = Contact performance. Development status of diagnostic sensors for integration in autonomous substations Models for interpretation and decision making DIEL

DGA

Tspot

PDloc

SC

CP

TR CAB GIS AIS HVDC

If we take all of this together, then we can see that in terms of overall readiness and robustness there are only three components for which there is exactly one suitable diagnostic method remaining as shown in Table 4. For transformers, only dissolved gas analysis (DGA) is available. For cables, only spot temperature is available and for GIS, only partial discharge location detection is available. Table 4. Overall readiness and robustness of the diagnostic sensors for integrations in autonomous substations Development status of diagnostic sensors for integration in autonomous substations Overall readiness and robustness DIEL

DGA

Tspot

PDloc

SC

CP

TR CAB GIS AIS HVDC

Therefore, it should be stressed that more research should be conducted to develop robust sensors and associated models that enable to monitor the actual aging state of the network component. This is even more necessary if an “energy internet” is to be developed by means of e.g. a predictive health model concept. 4

WIRELESS COMMUNICATION

In order to achieve autonomous operation of parts of the grid, information of the sensors needs to be collected for processing. As connecting all these sensors on and in all the components that might be present in e.g. a substation, the preferred method would be to use wireless technology, see figure 3. Currently, research is conducted on whether disturbances that frequently occur in substation, such as e.g. switching actions, corona and reflections. All these disturbances cause high frequency electromagnetic interference which can decrease the reliability and thus the applicability of wireless technology.

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Fig. 3. Implementation of wireless technology At this moment, the protocol that has been tested for transmitting and receiving in substation conditions is ZigBee which operates at 2,4 GHz. Using this protocol, it has been possible to reliably transmit information such as temperature, humidity and light intensity while reflections and corona were present [3]. Among the advantages of ZigBee are the low cost and the long battery life of the transmitters/receivers. Its main drawback is the low data rate which is still sufficient to transmit e.g. one read-out of the temperature, humidity and light intensity per second. However, as soon as voltages, currents, other operating parameters and environmental conditions need to be transmitted in the order of 50 to 100 times per second, the ZigBee protocol will fall short. This is because an effect occurs that is called Inter Symbol Interference (ISI), which occurs when the signal bandwidth is significantly higher than the coherence bandwidth of the transmission channel. Therefore, research is also conducted towards achieving high data transfer rates and one method of accomplishing that is by using Orthogonal Frequency Division Multiplexing (OFDM). This method basically divides a wideband signal into many parallel narrowband signals. If the bandwidths of these narrowband signals are smaller than the coherence bandwidth of the transmission channel, then ISI can be eliminated. This way, the path can be cleared to reliably transmit various parameters of many components in a substation to a central processing point after which the health states of the components can be determined. Subsequently, using predictive health modeling decisions can autonomously be made.

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PREDICTIVE HEALTH MODELLING

Due to all these advancements in measurement techniques and sensor technologies, a significant amount of technical information about the assets in the grid will become available. This information can be used for the optimization and the maintenance of power system equipment and thus become a valuable tool for asset management. In [4], a framework has been proposed for the modeling of the health state of power system equipment. This framework can be used to predict the effects and outcomes of different operating profiles, usage patterns and maintenance actions. This predictive health model is shown schematically in figure 4.

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Physical Equipment

Usage

Actions

Failure Rate (y)

Cumulative Stresses (x) Condition Parameters Monitoring Systems c Estimation of Cumulative Stresses (hx) xˆ e ud ua

Dynamic Stress Model (f)

xˆ

Failure Model (g)

yˆ

Fig. 4. Predictive health model (PHM) Using this model, the failure rate can be determined and therefore, steps can be taken to minimize the chance of a failure. In [5], this model has been applied in a simulation of the thermal effects in a power transformer, see figure 5. Using various models such as e.g. top-oil thermal model and hot-spot thermal model, it was possible to apply the predictive health model in such a way that the loading profile of the transformer was optimized. In this way, the temperature of the transformers could be maintained below the allowed limit.

Top-oil

load [pu × 10]

Hot-spot

140

140

120

120

100

100

Temperature (°C)

Temperature (°C)

Hot-spot

80 60 40

load [pu × 10]

80 60 40 20

20 0 0

Top-oil

60 120 180 240 300 360 420 480 540 600 660 720 time (minutes)

0 0

60 120 180 240 300 360 420 480 540 600 660 720 time (minutes)

Fig. 5. Optimized load control of a transformer using predictive health modeling. On the left side, the temperature of the transformers exceeds the maximum temperature 100 °C. On the right side, the optimizing algorithm decreases the load thereby reducing the temperature.

The method optimizes the utilization of the transformer by recommending load changes when required, thereby keeping the temperature within safe limits. Of course, in practice, the effectiveness of this method depends on the accuracy of the sensors and the reliability of the transmitted information. Also, it depends on the ability of the autonomous system to control the load patterns of the transformers.

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CONCLUSIONS

Because of the renewal wave the electricity grid will become a mix of old and new technology. The focus of the newer technology will be more on “Economics” and “Environment”, which ultimately means that the grids will need to be more

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sustainable. In order to achieve long-term performance, the health state of the assets in the grid needs to be determined advanced preventive maintenance and this can be done by the integration of sensors and the use of models for interpretations. It can be concluded that suitable sensors and/or models still do not exists for a number of assets and diagnostics methods. Even in the case that all these sensors and models would exist, this wealth of information cannot be transmitted using current wireless protocols such as ZigBee because of the limits on data rate. Nevertheless, this method has proven to be reliable in substation conditions with various forms of interference. In order to improve the data transfer rate, other protocols are being investigated of which Orthogonal Frequency Division Multiplexing looks promising. A predictive health model is currently being developed to collect and analyze the health state of the grid and its assets. Successful simulations have been carried out to maintain the temperature of a power transformer by optimizing the load profiles.

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REFERENCES

1

J. J. Smit, B.M. Pryor et al., (2003) Cigré Brochure 224, “Emerging Technologies and Material Challenges”.

2

J. J. Smit, E. Gulski (2007) Integral Decision Support System for Condition Based Asset Management of Electrical Infrastructures. Proc. World Congress on Engineering Asset Management, Harrogate.

3

A. Lou, D.Djairam, H. Nikookar, J.J. Smit (2009 ) Interference in the Wireless Channel. Internal graduation report, Delft University of Technology, Delft.

4

G. Bajracharya, T. Koltunowicz, R. R. Negenborn, Z. Papp, D. Djairam, B. D. Schutter, and J. J. Smit (2009) Optimization of maintenance for power system equipment using a predictive health model. in Proceedings of the 2009 IEEE Bucharest Power Tech Conference, Bucharest, Romania.

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G. Bajracharya, T. Koltunowicz, R.R. Negenborn, Z. Papp, D.Djairam, B. de Schutter, J.J. Smit (2009) Optimization of Condition-Based Asset Management using Predictive Health Model. International Symposium on High Voltage 2009, Cape Town, South Africa.

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