Development of an Automatic Reliability Calculation System for ...

Development of an Automatic Reliability Calculation System for Advanced Metering Infrastructure Shun-Yu Chan*

Shang-Wen Luan

Jen-Hao Teng Member IEEE Department of Electrical Engineering, I-Shou University, Kaohsiung, Taiwan

Abstract: The installation of Advanced Metering Infrastructure (AMI) is looked upon as a bridge to the construction of smart grids; especially, while most of the power meters are still mechanical ones without digitization. An AMI consists of smart power meter, communication technology, meter data management, and associated software and hardware. After AMI has been implemented, many advanced functions e.g. automatic and accurate reliability calculation systems can be achieved. Therefore, this paper tries to develop an automatic reliability system for AMI. A ZigBee-based smart power meter with outage recording function is designed first. The ZigBee-based power meters are used to transmit the detailed power consumption data and outage event data to rear-end processing system periodically. According to the detailed and accurate data stored in the rear-end database, an automatic and accurate reliability calculation system can be designed sequentially. The customer interruption costs can also be estimated by the proposed system. Experimental results show that the proposed system has great potential to be integrated into AMI to improve the accuracy of reliability calculation. Keywords: Advanced Metering Infrastructure, Smart Power Meter, Reliability Calculation System, ZigBee, Customer Interruption Cost

I. INTRODUCTION The distribution network is an important part of the total electrical supply system, as it provides the final link between the bulk transmission system and the customers. It has been reported that 80% of the customer service interruptions are due to failures in the distribution network. In order to improve service reliability, in many countries distribution automation has been applied to the distribution network to achieve significant and immediate improvement in reliability and hence service to the electricity customers [1-2]. Generally, distribution automation includes the functions of substation automation, feeder automation, automatic meter reading and automatic build-up of geographic information system and so on. Reliability worth assessment is currently receiving considerable attention as it provides an opportunity to incorporate the costs or losses incurred by utility customers as results of power failure [3-5]. Traditionally, historical interruption events occurring at the distribution feeders over specific time periods can be used to calculate the reliability indices of the distribution feeders. Using these historical data, behavior of the distribution feeders can also be predicted by combining component failure rates and the duration of repair, restoration, switching, and isolation activities.

978-1-4244-7299-4/10/$26.00 ©2010 IEEE

Lain-Chyr Hwang

*

Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung, Taiwan

However, due to the limits of mechanical power meters used in customers, the power consumption data and outage events’ data cannot be collected accurately; and therefore, the reliability indices cannot be calculated precisely either. For example, the customer loads, total load of distribution feeder and interruption duration etc. vary from time to time and from event to event; thus, in the past only estimated loads and duration can be obtained for an outage event. The concept of smart grids which features higher utilization of power grid, demand reduction, and extensive usage of renewable energy source, is accepted and implemented all over the world. Smart grids are used to accomplish an advanced power system with automatic monitoring, diagnosing, and repairing functions. The installation of Advanced Metering Infrastructure (AMI) is looked upon as a bridge to the construction of smart grids; especially, while most of the power meters are still mechanical ones without digitization. An AMI consists of smart power meter, communication technology, meter data management, and associated software and hardware. From the operational experiences of AMI in other countries, there are many advantages along with AMI, though the construction of AMI is not easy due to the complicated communications between millions power meters [6-10]. Recently, many governments deploy ubiquitous IT project, which aims to combine the latest wireless network and wide-band technologies etc. to accomplish a ubiquitous wireless communication network [11-15]. In power engineering applications, the use of wireless technology can profit customers by integrating wireless network into AMI, outage event recording, and the like. After AMI has been implemented, many advanced functions e.g. automatic and accurate reliability calculation systems can be achieved. Therefore, this paper tries to develop an automatic reliability system for AMI. A ZigBee-based smart power meter with outage recording function is designed first. The power consumption and outage data can be collected by the proposed smart meter and sent back to rear-end processing system periodically. According to the detailed and accurate data stored in the rear-end database, an automatic and accurate reliability calculation system can be designed sequentially. Experimental results demonstrate the validity of

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the proposed system. II. ZIGBEE-BASED OUTAGE RECORDING SYSTEM ZigBee has been designed to have general-purpose protocol, and low-cost and low-power- consumption wireless communications standard by ZigBee Alliance. The ZigBee application profile includes home automation, industrial plant monitoring, commercial building automation, automatic meter reading, telecom services/m-commerce, wireless sensor networks, personal home and hospital care and so on [11-17]. Fig. 1 shows the system architecture of the proposed ZigBee-based smart power meter and outage recording system. The full system can be divided into two parts: the ZigBee-based power meter and the rear-end processing system. The rear-end processing system is composed of a ZigBee coordinator and the software designed for the proposed smart power meter. The software of the proposed rear-end processing system is used to establish the power consumption and outage event database as well as to offer the inquiries of power consumption data and outage data recorded in the proposed smart power meter.

networking characteristic of ZigBee, smart power meter serving as a node apparatus will communicate with the ZigBee coordinator of rear-end processing system and ZigBee network can then be constructed to accomplish the meter-reading function. After the ZigBee network was constructed, the rear-end processing system can send the request commands to the ZigBee coordinators and receive the power consumption data and outage event data from the power meters. Therefore, the automatic meter reading can be accomplished. The concept of the proposed outage recording system is illustrated in Fig. 2.

Fig. 2. The Concept of ZigBee-based Outage Recording Systems

Fig. 1. System Architecture of the Proposed Smart Meter

The proposed smart power-meter and the rear-end processing system are equipped with a ZigBee device and a ZigBee coordinator, respectively. With the automatic

The data transmission format between power meter and rear-end processing system in the proposed system is shown in Table I. Fig. 3 shows the human-machine interface for real-time data acquisition. As illustrated in Fig. 3, the real-time measured data, power consumption data and outage event data can all be acquired, transmitted and displayed in the rear-end processing system. The detailed design and implementation of the proposed ZigBee-based outage recording system can be found in [13]. After those data was received by the rear-end processing system and stored in the rear-end database, the proposed automatic reliability calculation system can then be used to calculated reliability indices through the comprehensive and accurate power consumption and outage event data.

Table I Data Transmission Format between Power Meter and Rear-end Processing System Group 1: Real-time Measured Data (41 bytes) Power Meter ID

Date/Time

Voltage

Current

Power Factor

Frequency

16 bytes

9 bytes

4 bytes

4 bytes

4 bytes

4 bytes

Power Meter ID 16 bytes

Date/Time

Voltage

9 bytes

4 bytes

Group 2: Power Consumption Data (58 bytes) Power Date/Time for Power Current Frequency Factor Consumption 4 bytes 4 bytes 4 bytes 9 bytes Group 3: Outage Event Data (93 bytes)

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Real Power (kWh) 4 bytes

Reactive Power (kVARh) 4 bytes

No. of Event

Date/Time

Interruption Time

Restoration Time

Pre-outage Real Power (kW)

1 bytes

2 bytes

5 bytes x5

5 bytes x5

4 bytes x5

Fig. 3.

Pre-outage Reactive Power (kVAR) 4 bytes x5

Real-Time Data Acquisition for Rear-end Processing System

III. AUTOMATIC RELIABILITY CALCULATION SYSTEM The traditional reliability calculation system cannot calculate the reliability indices accurately, because it cannot obtain the detailed customers’ power consumption and interruption data of each outage. Using the proposed smart power meter and outage recording system, the detailed and accurate data can be transmitted to the rear-end database. The data transmitted as shown in Table I will be stored in the database sheets as shown in Table II. From Table II, it can be seen that three data sheets including customer information sheet, power consumption information sheet and outage event information sheet are designed and correlated in the rear-end database. Reliability indices can therefore be calculated accurately. TABLE II The Data Sheets Designed in Rear-End Processing System Database Customer Power Consumption Outage event Information Information Information Meter ID Meter ID Meter ID Name Month Interruption Time Telephone Monthly kWh Restoration Time Pre-outage real Record Address Monthly kVAh power (kW) data Pre-outage reactive Feeder ID power (kVAR) Starting Time User Sector

Visual Basic 2008 and Access 2003 are the tools used to design the proposed automatic reliability calculation system. The software of the proposed rear-end processing system is used to establish the power consumption and outage event database as well as to offer the inquiries of power consumption data and outage data recorded in the proposed automatic reliability calculation system. Fig. 4 shows the interface of automatic reliability calculation system. The users should choose the reporting year and the feeder no. first, the proposed reliability calculation system will then search the data in rear-end database, collect the required data and calculate the reliability indices after the “calculation” button

was pressed. The customer interruption cost can also be calculated and shown in the interface. The reliability indices can be calculated based on customers or load affected. The sustained interruption indices are based on the customer’s interruption frequency and duration time. Customer based sustained indices include [18]:     

System Average Interruption Frequency index (SAIFI) System Average Interruption Duration index (SAIDI) Customer Average Interruption Frequency index (CAIFI) Customer Average Interruption Duration index (CAIDI) Average Service Availability index (ASAI)

The definitions of customer based indices are given in (1)-(5).  N i  CI (1) SAIFI  NT NT SAIDI 

rN i

NT

CAIFI 

CAIDI 



N

rN N i

i

ASAI 

i

CMI NT i

CN i



SAIDI SAIFI

N T  8760   ri N i N T  8760

(2) (3) (4) (5)

The reliability indices can also be calculated based on load rather than customers affected. Load based sustained indices include [18]:    

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Average System Interruption Frequency Index (ASIFI) Average System Interruption Duration index, (ASIDI) Energy Not Supplied Index (ENS) Average Energy Not Supplied Index (AENS)

The definitions of load based indices are given in (6)-(9). ASIFI 

L

ASIDI 

rL

i

(6)

LT i

i

(7)

LT

ENS   ri Li

(8)

rL AENS  i

i

(9)

NT

The notations used in (1)-(9) are shown as follows:  i = Interruption Event  N T = Total number of customers served for areas

Fig. 4.

 N i = Number of interrupted customers for each sustained interruption event during the reporting period  ri = Restoration time for each interruption event  CI (  N ) = Customers interrupted i  CN = Total number of customers who have experienced a sustained interruption during the reporting period  CMI (  ri N i ) = Customers Minutes Interrupted  LT = Total connected kVA load served  Li = Connected kVA load interrupted for each interruption Event   Li = Total connected kVA of load interrupted (kVA) 

rL i

i

= Energy not supplies index (kVA-m)

Interface of Automatic Reliability Calculation

Note that the basic factors for reliability calculation have been already stored in the rear-end database and those factor can be easily searched and collected. For example, some of the basic factors can be summed up by the following steps:  CMI: The annual CMI is the summation of customer interruption duration time in the same feeder. It can be calculated from the outage event information sheet.  N T : NT denotes total number of customers connected in the same feeder. It can be obtained from the customer information sheet.  CI and CN: The CI factor is the number of events in the same feeder. It can be counted from outage event information sheet. The CN is number of the customers (number of meters in the database). It can be also counted from the outage event information sheet. i  LT : The factor can be calculated by (10). The FeederkVAh

is the feeder load of month i; it can be summed up from the power consumption information sheet.  Σ Li : The total connected kVA of load interrupted is the summation of interruption load in same feeder. Those data can be collected from the outage event information sheet.  ENS: The ENS is summation of the product of the interruption load and duration time in the same feeder. Those data can also be obtained from the outage event information sheet. 12

LT 

 Feeder

i kVAh

i 1

8760

(10)

Substituting the basic factors into (1)-(9), the reliability

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indices can be calculated easily. Since the accurate reliability indices can be obtained, the proposed system can also be used in competitive markets to increase the market value of the service. Utilities can also utilize the accurate information for further reliability improvement. IV. EXPERIMENTAL RESULTS A simple feeder with four secondary transformers and each transformer supplied 25 customers is used to test the proposed automatic reliability calculation system. All customers in the test feeder are residential customers. Due to limited budgets, some data required for the proposed system are generated randomly in this paper. The data of the monthly power consumption data and outage events are generated randomly and then stored in rear-end database. For Example, Table III shows the data received by an outage event occurred on a secondary transformer of the test feeder. From Table III, it can be seen that the meter ID, interruption time, restoration time and pre-fault kW and kVAR can all be recorded in the proposed smart power meter and then transmitted to the rear-end database. The reliability indices can then be calculated accurately according to those data. Those indices are shown in Fig. 4. The customer interruption cost can also be calculated based on those stored data and the customer interruption cost functions. The customer interruption cost functions are used to model the costs incurred by utility as results of power failure. The data represented in [4] is used to establish the residential customer interruption cost function. Fig. 5 shows the residential interruption cost curve. The residential customer interruption cost was established by interpolation and can be expressed as

CICR (t )  -0.0021  0.003  t  8.8756 10-5  t 2 -7.6832e 10  t  4.3229  10  t -8

3

-11

4

(11)

where CICR (t ) and t are the residential customer interruption cost and interruption duration, respectively.

V. CONCLUSIONS The installation of AMI is looked upon as a bridge to the construction of smart grids; therefore, after AMI has been implemented, many advanced functions should be designed and developed. An automatic and accurate reliability calculation system was designed and implemented in this paper. A ZigBee-based smart power meter with outage recording function was developed. The real-time measured data, power consumption data and outage event data can all be acquired, transmitted and stored in the rear-end processing system. According to the detailed data, reliability indices can be calculated effectively and accurately. The customer interruption cost can also be calculated in the proposed system. From the experimental results, it can be seen that proposed system can collect the detailed power consumption and outage event data effectively and then reliability indices can be calculated accurately. Therefore, an automatic reliability calculation system was successfully achieved in this paper. ACKNOWLEDGEMENTS This work was sponsored by National Science Council, Taiwan, under research grant NSC 96-2221-E-230-026-MY2. REFERENCES [1] [2] [3] [4]

[5] [6] [7] [8] [9] [10] [11]

Fig. 5. Residential Interruption Cost Curve

Based on the pre-fault power of each customer and customer interruption cost function, the customer interruption cost of each event can be calculated accurately and the annual customer interruption cost can be obtained by summing up the cost of each event. The annual customer interruption cost is also shown in Fig. 4.

[12]

[13]

[14]

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"IEEE Tutorial Course on Distribution Automation," IEEE Power Engineering Society, 88 EH0280-8 PWR. "IEEE Tutorial Course on Power Distribution Planning," IEEE Power Engineering Society, 92 EHO 381-6 PWR. Chowdhury, A. A., Koval, D.O., “Value-Based Power System Reliability Planning,” IEEE Transactions on Industry Applications, Vol. 35 No. 2, March-April 1999, pp. 305 –311. R. Billinton and P. Wang, “Distribution System Reliability Cost/Worth Analysis Using Analytical and Sequential Simulation Techniques,” IEEE Transactions on Power Systems Vol. 13, No. 4, pp. 1245-1250, Nov. 1998. Teng, J. H. and Lu, C. N., “Feeder Switch Relocation for Customer Interruption Costs Minimization,” IEEE Trans. on Power Delivery, Vol. 17, No. 1, pp. 254 – 259, 2002. "California Smart Grid Study," EPRI Aug. 2008 "San Diego Smart Grid Study Final Report," SAIC Smart Grid Team, Oct. 2006 Vojdani, A.; “Smart Integration,” IEEE Power and Energy Magazine, Vol. 6, Issue 6, November-December 2008, pp. 71 – 79 EU Smart Grids Framework “Electricity Networks of Future 2020 and beyond” Hart, D.G.; “Using AMI to Realize the Smart Grid,” IEEE PES General Meeting, July 2008, pp.1 - 2 Miaoqi Fang; Jian Wan; Xianghua Xu; Guangrong Wu; “System for Temperature Monitor in Substation with ZigBee Connectivity,” IEEE International Conference on 11th Communication Technology, Nov. 2008, pp. 25 - 28 Bo Chen; Mingguang Wu; Shuai Yao; Ni Binbin; “ZigBee Technology and its Application on Wireless Meter-reading System,” IEEE International Conference on Industrial Informatics, Aug. 2006, pp. 1257 - 1260 Shang-Wen Luan, Jen-Hao Teng, Shun-Yu Chan, Lain-Chyr Hwang, "Development of a Smart Power Meter for AMI Based on ZigBee Communication," IEEE 8-th International Conference on Power Electronics and Driver Systems, Taiwan, Paper No. 284, 2009 Cao, Liting; Tian, Jingwen; Liu, Yanxia; “Remote Wireless Automatic Meter Reading System Based on Wireless Mesh Networks and

Embedded Technology,” Fifth IEEE International Symposium on Embedded Computing, Oct. 2008, pp. 192 - 197 [15] Liting Cao; Jingwen Tian; Yanxia Liu; “Remote Real Time Automatic Meter Reading System Based on Wireless Sensor Networks,” 3rd International Conference on Innovative Computing Information and Control, June 2008, pp. 591 - 591 [16] “Wireless Medium Access Control (MAC) and Physical Layer (PHY)

Meter ID E007000001A31001 E007000001A31002 E007000001A31003 E007000001A31004 E007000001A31005 E007000001A31006 E007000001A31007 E007000001A31008 E007000001A31009 E007000001A3100A E007000001A3100B E007000001A3100C E007000001A3100D E007000001A3100E E007000001A3100F E007000001A31010 E007000001A31011 E007000001A31012 E007000001A31013 E007000001A31014 E007000001A31015 E007000001A31016 E007000001A31017 E007000001A31018 E007000001A31019

Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs),” IEEE 802.15.4-2003, New York, October 2003. [17] “ZigBee Specification,” ZigBee Alliance, ZigBee Document 053474r17, January 2008. [18] IEEE Std 1366™-2003, “IEEE Guide for Electric Power Distribution Reliability Indices”, New York, NY: IEEE, May 2004.

TABLE III One Outage Event Occurred on a Secondary Transformer Pre-outage Real Pre-outage Reactive Interruption Time Restoration Time Power (kW) Power (kVAR) 2008/01/03 09:50:00 2008/01/03 10:05:00 1.252368 0.6346576 2008/01/03 09:50:00 2008/01/03 10:05:00 0.3571904 0.07253058 2008/01/03 09:50:00 2008/01/03 10:05:00 0.3362202 0.2039452 2008/01/03 09:50:00 2008/01/03 10:05:00 0.3349663 0.1124576 2008/01/03 09:50:00 2008/01/03 10:05:00 0.9884161 0.5334905 2008/01/03 09:50:00 2008/01/03 10:05:00 0.2376192 0.1584439 2008/01/03 09:50:00 2008/01/03 10:05:00 0.7829035 0.1996128 2008/01/03 09:50:00 2008/01/03 10:05:00 1.054899 0.4300748 2008/01/03 09:50:00 2008/01/03 10:05:00 0.9205671 0.3893794 2008/01/03 09:50:00 2008/01/03 10:05:00 0.4878575 0.2593286 2008/01/03 09:50:00 2008/01/03 10:05:00 0.355315 0.1879017 2008/01/03 09:50:00 2008/01/03 10:05:00 0.578289 0.3583913 2008/01/03 09:50:00 2008/01/03 10:05:00 0.8339167 0.3823688 2008/01/03 09:50:00 2008/01/03 10:05:00 0.9685581 0.3704921 2008/01/03 09:50:00 2008/01/03 10:05:00 0.815845 0.05167578 2008/01/03 09:50:00 2008/01/03 10:05:00 0.9138056 0.6188363 2008/01/03 09:50:00 2008/01/03 10:05:00 1.034851 0.5099617 2008/01/03 09:50:00 2008/01/03 10:05:00 0.7165875 0.1255332 2008/01/03 09:50:00 2008/01/03 10:05:00 1.50225 0.6712567 2008/01/03 09:50:00 2008/01/03 10:05:00 0.8701254 0.6390083 2008/01/03 09:50:00 2008/01/03 10:05:00 0.7955741 0.2940021 2008/01/03 09:50:00 2008/01/03 10:05:00 0.8474123 0.5540101 2008/01/03 09:50:00 2008/01/03 10:05:00 0.308041 0.209409 2008/01/03 09:50:00 2008/01/03 10:05:00 0.4781895 0.1874848 2008/01/03 09:50:00 2008/01/03 10:05:00 0.6715352 0.27378 Total load interrupted (kVA) 20.4588

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