Electrical Power Systems Quality, Second Edition phần 8 pps

overtravel and sympathetic tripping, since this can circumvent mea￾sures taken specifically to improve power quality.

Circuit breaker with relay. A circuit breaker will schedule an opening

event if its currents, adjusted by the associated current transformer

ratio, exceed the associated relay pickup setting. If the relay has an

instantaneous setting and the current exceeds that level, the event

time will be the relay instantaneous pickup time plus the breaker

clearing time. Otherwise, the event time will depend on the relay’s

time-current characteristic. If the relay is of the definite-time type, this

will be a constant relay setting plus breaker clearing time. If the relay

is of the inverse type, this will be a current-dependent time plus the

breaker clearing time. We use approximate time-current curves for

both relays and reclosers.

If the fault current is removed before the breaker opens, an internal

relay travel state variable is updated. This may produce a sympathetic

trip due to relay inertia. If no sympathetic trip is predicted, an event

for full reset is then pushed onto the priority queue.

The circuit breaker may have one or two reclosure settings. If the

breaker has opened, it will schedule a closing operation at the appro￾priate time. In case there are subsequent events from other devices, the

breaker model must manage an internal state variable of time accu￾mulated toward the reclose operation. The time between opening and

reclosing is a constant. Once the breaker recloses, it follows the defined

fault-clearing behavior. There may be two reclosings, at different time

settings, before the breaker locks out and pushes no more events.

Fault. A permanent fault will not schedule any events for the priority

queue, but will have an associated repair time. Any customers without

power at the end of the fault simulation will experience a sustained

interruption, of duration equal to the repair time.

A temporary fault will schedule a clearing event whenever its voltage

is zero. Whenever the fault is reenergized before clearing, any accu￾mulated clearing time is reset to zero. Upon clearing, the fault switch

state changes from closed to open, and then the fault simulation must

continue to account for subsequent device reclosures.

Fuse. A fuse will open when the fault current and time applied pene￾trate the minimum melting curve, or when the I2t product reaches the

minimum melting I2t. We use minimum melt rather than total clearing

time in order to be conservative in studies of fuse saving; this would not

be appropriate for device coordination studies. Expulsion fuses are

modeled with a spline fit to the manufacturer’s time-current curve,

while current-limiting fuses are modeled with I2t. In both cases, if the

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fault is interrupted before the fuse melts, an internal preheating state

variable is updated in case the fault is reapplied. However, we do not

specifically track possible fuse damage during the simulation.

If the fuse currents will penetrate the time-current curve or mini￾mum melting I2t, then a fuse melting time is pushed onto the priority

queue. If the fuse currents are too low to melt the fuse, no event is

pushed. Once the fuse opens, downstream customers will experience a

sustained interruption equal to the fuse repair time.

Recloser. The recloser model is very similar to the circuit breaker with

relay model previously discussed. The main differences are that the

recloser can have up to four trips during the fault sequence, and two

different time-current curves can be used.

Sectionalizer. A sectionalizer will count the number of times the cur￾rent drops to zero and will open after this count reaches a number that

can vary from 1 to 3. The device will not open under either load or fault

current.

8.8.6 Customer damage costs

Customer damage costs are determined by survey, PQ contract

amounts, or actual spending on mitigation. In terms of kilowatthours

unserved, estimates range from $2/kWh to more than $50/kWh. A typ￾ical cost for an average feeder with some industrial and commercial

load is $4 to $6/kWh. For approximating purposes, weighting factors

can be used to extend these costs to momentary interruptions and rms

variations assuming that the event has caused an equivalent amount

of unserved energy. Alternatively, one can use a model similar to the

example in Sec. 8.5, which basically is based on event count. Average

costs per event for a wide range of customer classes are typically stated

in the range of $3000 to $10,000.

With such high cost values, customer damage costs will drive the

planning decisions. However, these costs are very uncertain. Surveys

have been relatively consistent, but the costs are seldom “verified” with

customer payments to improve reliability or power quality. For exam￾ple, aggregating the effect on a large number of residential customers

may indicate a significant damage cost, but there is no evidence that

residential customers will pay any additional amount for improved

power quality, in spite of the surveys. There may be a loss of goodwill,

but this is a soft cost. Planning should focus on high-value customers

for which the damage costs are more verifiable.

Costs for other types of PQ disturbances are less defined. For exam￾ple, the economic effect of long-term steady-state voltage unbalance on

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motors is not well known, although it likely causes premature failures.

Likewise, the costs are not well established for harmonic distortion and

transients that do not cause load tripping.

The costs may be specified per number of customers (residential,

small commercial), by energy served, or by peak demand. If the cost is

specified by peak demand, it should be weighted using a load duration

curve. For steady-state voltage, harmonic distortion, and transients,

the load variation should be included in the electrical simulations, but

this is not necessary for sustained interruptions and rms variations.

Several examples and algorithm descriptions are provided in the

EPRI Power Quality for Distribution Planning report19 showing how

the planning method can be used for making decisions about various

investments for improving the power quality. We’ve addressed only the

tip of the iceberg here but hopefully have provided some inspiration for

readers.

8.9 References

1. EPRI TR-106294-V2, An Assessment of Distribution System Power Quality. Vol. 2:

Statistical Summary Report, Electric Power Research Institute, Palo Alto, Calif.,

May 1996.

2. M. McGranaghan, A. Mansoor, A. Sundaram, R. Gilleskie, “Economic Evaluation

Procedure for Assessing Power Quality Improvement Alternatives,” Proceedings of

PQA North America, Columbus, Ohio, 1997.

3. Daniel Brooks, Bill Howe, Establishing PQ Benchmarks, E Source, Boulder, Colo.,

May 2000.

4. EPRI TR-107938, EPRI Reliability Benchmarking Methodology, EPRI, Palo Alto,

Calif., 1997.

5. IEEE Standard 1366-1998, IEEE Guide for Electric Power Distribution Reliability

Indices.

6. D. D. Sabin, T. E. Grebe, M. F. McGranaghan, A. Sundaram, “Statistical Analysis of

Voltage Dips and Interruptions—Final Results from the EPRI Distribution System

Power Quality Monitoring Survey,” Proceedings 15th International Conference on

Electricity Distribution (CIRED ’99), Nice, France, June 1999.

7. IEEE Standard 1159-1995, IEEE Recommended Practice on Monitoring Electric

Power.

8. Dan Sabin, “Indices Used to Assess RMS Variations,” presentation at the Summer

Power Meeting of IEEE PES and IAS Task Force on Standard P1546, Voltage Sag

Indices, Edmonton, Alberta, Canada, 1999.

9. D. L. Brooks, R. C. Dugan, M. Waclawiak, A. Sundaram, “Indices for Assessing

Utility Distribution System RMS Variation Performance,” IEEE Transactions on

Power Delivery, PE-920-PWRD-1-04-1997.

10. IEEE Standard 519-1992, IEEE Recommended Practices and Requirements for

Harmonic Control in Electrical Power Systems.

11. A. E. Emanuel, J. Janczak, D. J. Pileggi, E. M. Gulachenski, “Distribution Feeders

with Nonlinear Loads in the NE USA: Part I. Voltage Distortion Forecast,” IEEE

Transactions on Power Delivery, Vol. 10, No. 1, January 1995, pp. 340–347.

12. Barry W. Kennedy, Power Quality Primer, McGraw-Hill, New York, 2000.

13. M. F. McGranaghan, B. W. Kennedy, et. al., Power Quality Standards and

Specifications Workbook, Bonneville Power Administration, Portland, Oreg., 1994.

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14. Andy Detloff, Daniel Sabin, “Power Quality Performance Component of the Special

Manufacturing Contracts between Power Provider and Customer,” Proceedings of the

ICHPQ Conference, Orlando, Fla., 2000.

15. Shmuel S. Oren, Joseph A. Doucet, “Interruption Insurance for Generation and

Distribution of Power Generation,” Journal of Regulatory Economics, Vol. 2, 1990,

pp. 5–19.

16. Joseph A. Doucet, Shmuel S. Oren, “Onsite Backup Generation and Interruption

Insurance for Electricity Distribution,” The Energy Journal, Vol. 12, No. 4, 1991, pp.

79–93.

17. Mesut E. Baran, Arthur W. Kelley, “State Estimation for Real-Time Monitoring of

Distribution Systems,” IEEE Transactions on Power Systems, Vol. 9, No. 3, August

1994, pp. 1601–1609.

18. T. E. McDermott, R. C. Dugan, G. J. Ball, “A Methodology for Including Power

Quality Concerns in Distribution Planning,” EPQU ‘99, Krakow, Poland, 1999.

19. EPRI TR-110346, Power Quality for Distribution Planning, EPRI, Palo Alto, CA,

April 1998.

20. M. T. Bishop, C. A. McCarthy, V. G. Rose, E. K. Stanek, “Considering Momentary and

Sustained Reliability Indices in the Design of Distribution Feeder Overcurrent

Protection,” Proceedings of 1999 IEEE T&D Conference, New Orleans, La., 1999, pp.

206–211.

21. V. Miranda, L. M. Proenca, “Probabilistic Choice vs. Risk Analysis—Conflicts and

Synthesis in Power System Planning,” IEEE Transactions on Power Systems, Vol. 13,

No. 3, August 1998, pp. 1038–1043.

8.10 Bibliography

Sabin, D. D., Brooks, D. L., Sundaram, A., “Indices for Assessing Harmonic Distortion

from Power Quality Measurements: Definitions and Benchmark Data.” IEEE

Transactions on Power Delivery, Vol. 14, No. 2, April 1999, pp. 489–496.

EPRI Reliability Benchmarking Application Guide for Utility/Customer PQ Indices,

EPRI, Palo Alto, Calif., 1999.

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373

Distributed Generation

and Power Quality

Many involved in power quality have also become involved in distrib￾uted generation (DG) because there is considerable overlap in the two

technologies. Therefore, it is very appropriate to include a chapter on

this topic.

As the name implies, DG uses smaller-sized generators than does the

typical central station plant. They are distributed throughout the

power system closer to the loads. The term smaller-sized can apply to a

wide range of generator sizes. Because this book is primarily concerned

with power quality of the primary and secondary distribution system,

the discussion of DG will be confined to generator sizes less than 10

MW. Generators larger than this are typically interconnected at trans￾mission voltages where the system is designed to accommodate many

generators.

The normal distribution system delivers electric energy through

wires from a single source of power to a multitude of loads. Thus, sev￾eral power quality issues arise when there are multiple sources. Will

DG improve the power quality or will it degrade the service end users

have come to expect? There are arguments supporting each side of this

question, and several of the issues that arise are examined here.

9.1 Resurgence of DG

For more than 7 decades, the norm for the electric power industry in

developed nations has been to generate power in large, centralized gen￾erating stations and to distribute the power to end users through trans￾formers, transmission lines, and distribution lines. This is often

Chapter

9

Source: Electrical Power Systems Quality

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