BIG DATA FOR MANAGEMENTMBA 748A

Department of Industrial and Management Engineering

INDIAN INSTITUTE OF TECHNOLOGY KANPUR

DETAILED PROJECT PROPOSAL

POWER PLANT INTELLIGENT MAINTENANCE SYSTEM

BY:

SHIVAM GUPTA -16125039

MAHENDRA KUMAR-16114016

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A. INTRODUCTION

There have been several technological breakthroughs in power generation since its advent in 1882 when the first thermal power plant was set up by Thomas Edison in England. Since than power technology has both expanded in fuel sources and power production methods. This complex ecosystem involved Fuel Production Units (eg. CIL), Logistics for Fuel, Power Producers (e.g. Adani Power), Power Equipment Suppliers (eg L&T), Grids (eg. PGCIL), DISCOMs (e.g. Torrent Power), Users, Financial Institutions (e.g. PFC) and Environmental Protection Institutions (eg. NGT).

The problem which we try to address here is that of Power Plant Maintenance. Power Plant has number of critical equipment which are necessary for smooth operations of power plant. As power plant ages, the failure rate in these equipment increases leading to continuous power plant shut downs. The problems of the system are sometimes unrecognizable and needs OEM’s expertise who go through stored operational data in power plant servers to recognize the problems. Services of these critical part service engineers like that of STG & Boiler package varies almost from Rs 10000 to 50000 per day and since they are only informed when situation is grave hence it takes almost 4to 7 days in case of domestic and 30-40 days in case of international sites. Other factors also matter such as support labor and spares availability.

Power Plant operation involves continuous monitoring of over 1000+ data which is being relayed directly from sensors on critical equipment. These are skillfully monitored by power plant operation engineer who varies air, fuel, water etc. on basis of transmitter readings and experience for getting required power output.

But due to wear & tear in moving parts, some of the sensor readings become bad and if operator is unable to recognize reason behind these bad readings, it might lead to equipment breakdown. Further, it might be possible that bad pattern of system readings keeps on repeating itself which is also difficult to recognize. And even if pattern is recognized, the damage to design life cannot be predicted.

The objective of this project proposal is to design Power Plant Intelligent Maintenance System (IMS) which has following aims:

1. Preventive Maintenance for avoiding complete maintenance2. Reduce Power Plant Breakdowns to almost zero.

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B. DATA FLOW DIAGRAM

Fig1. Data Flow Diagram for Power Plant Intelligent Maintenance System

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STORAGE OF PRIMARY INPUTS EQUIPMENT WISE ON SERVERS

NOYES

Is Spares Available?

Is Expected Life < Design Life of Equipment?

Is Frequency of BAD POINTS occurring

beyond Acceptable limit?

REPORT GENERATION

ANALYSIS OF BAD POINTS

PRE-PROCESSING DATAAND ANALYSIS

DATA ACQUISITION

USING TRANSMITTER

S/SENSORS

COMPARSION OF Primary Data with

Secondary Data

SECONDARY INPUTS

From OEM Specification

1. Operating Temperature Range2. Operating Pressure Range3. Operating Vibration Range4. Operating RPM and Flow5. Maximum Current & Voltage

Readings6. Weather

BAD GOOD

PRIMARY INPUTS

1. Equipment Temperature 2. Equipment Pressure 3. Equipment Vibration4. RPM5. Equipment Material Flow

Rate6. Motor Current and Voltage7. Operator Inputs8. Time

EQUIPMENT CONTROL PANELS(Equipment Secondary Data embedded in Control loops)

Original Equipment Manufacturer

NO

POWER PLANT SHUT DOWN FOR MAINTENANCE

YES

YES

ANALYSIS FOR PREVENTIVE

MAINTENANCE

SECONDARY INPUTSHistoric Temperature, Pressure readings etc. for

Equipment from OEMDesign Life Period

YES

PRIMARY INPUTS

ERP Levels of Spares /Consumables Inventory

ANALYSIS FOR EXPECTED LIFE OF

PARTS OF EQUIPMENT

POWER PLANT CONFIRMATION

FOR MAINTENANCEAuto Calibrate Inputs using

Historical GOOD DATA Points such that Expected Plant

life>=Design Life

C. WHAT DATA WILL YOU REQUIRE FOR YOUR APPLICATION?

Data Type of Data

Style of Data

Source of Data Volume Data Req

Weather Secondary Structured Meteorological Dept./Forecasting Agencies

Large Volume

6TB/day

Safe Running Parameters of Equipment

1. Operating Temperature Range

2. Operating Pressure Range

3. Operating Vibration Range

4. Operating RPM and Flow

5. Maximum Current & Voltage Readings

Secondary Structured OEM Technical Data Sheet

Small Volume

50 GB

Equipment Readings

1. Equipment Temperature

2. Equipment Pressure

3. Equipment Vibration

4. RPM5. Equipment Material

Flow Rate6. Motor Current and

Voltage7. Time

Primary Structured Equipment Transmitters/Sensors

Large Volume

4-5 TB/day

Operator/OEM Maintenance Engineer Calibration Inputs

Primary Semi-Structured

Power Plant Servers Large Volume

1 TB/day

Spares Inventory Secondary Structured ERP Database Large Volume

500 GB/day

Historic Data for Equipment

Secondary Structured Original Equipment Manufacturer

Medium Volume

40TB-50TB/ 6 month

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D. HOW WILL YOU INGEST THE DATA? Data Ingestion takes in to the account whether data has volume, variety and velocity. The data considered above has following attributes:

Volume:

a. Data for weather will require large storage space. b. Equipment safe running parameter range of temperature, pressure and other readings

for equipment’s from OEM will require very less volume as data set is fixed c. While data for Equipment Temperature, Equipment Pressure, Equipment Vibration,

RPM, Equipment Material flow rate, Motor current and voltage, Operator inputs & Time requires large amount of storage space since it is continuously being relayed at different refresh rates. (Assuming refresh rate of 1 per sec)

d. Equipment Historical Data can be taken from OEM itself or can be bought from other power plant using same equipment. The data is of small volume.

e. Inventory Data is collected from SAP system and is of small volume.

Velocity:

a. Data for weather is continuously being relayed by Meteorological with high refresh rates.b. Equipment safe running parameter range of temperature, pressure and other readings

for equipment’s from OEM will be fixed data setc. While data for Equipment Temperature, Equipment Pressure, Equipment Vibration,

RPM, Equipment Material flow rate, Motor current and voltage, Operator inputs & Time is also being relayed continuously from local equipment transmitters. Refresh rate is very high in these cases.

d. Equipment Historical Data can be taken from OEM itself and is a fixed data which can be bought once in 6 months.

e. Inventory Data is collected from SAP system is also being relayed continuously for equipment but with (4-5 refresh rates per day).

Variety

a. Variety of Data is mostly structured in form of temperature, pressure readings form local transmitters/sensors etc. and also historical data bought from OEM/other power plants.

b. Only semi-structured data is input from operator which can give multiple type of inputs for power plant operations and hence classified as semi-structured data.

We can use any of Apache framework for ingestion of structured and unstructured data which are capable for handling huge volume of data.

E. WHERE AND HOW WILL YOU STORE IT?

Since the data storage requirement is limited, therefore we should use distributed node storage architecture.

The database would be stored in cloud since it requires access from multiple agencies and cloud storage is best for this purpose. We can use Google Cloud TM storage.

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F. HOW WILL YOU GET IT? WILL YOU NEED LEGAL PERMISSIONS?Power Plant operational data is already being stored in internal servers of power plants. For this purpose, we need to take legal permission of power producers to store and process the same on cloud and providing them insight into their operating data.

We also need to consider weather data which can be bought from National Data Centre and also airline data is open source which can be taken from sources such as openflight.com

Historical data needs to be taken for design life from OEM or data can also be taken from other power plant servers who use same equipment. Since they are direct beneficiaries from this process we can sell them the data for marginal profit and get the data from them

G. HOW WILL YOU ENSURE QUALITY OF THE DATA?Parameters of Data Qualities are as follows:

i. Accuracy

Power Plant operators uses the day to day power plant data for monitoring healthiness of the system. The data provided by these critical equipment’s is highly accurate. Further to increase reliability of readings we can increase no of measuring devices. Also, we need to have similar unit for e.g. Pressure in ATA and bar is not comparable.

ii. Timeliness

The sensors installed on critical equipment’s in power plants varies from a signal refresh rate of 1 per second to 1 per 5min

iii. Completeness

Data suitable for operations might not be suitable for taking decision regarding effective life of the equipment. We need historical data for operations from OEM/ other power plant for calculating life against it. There can be missing data due to different refresh rate for a data set for making decision.

We will use Talend TM Data Quality software package for end-to-end data profiling and monitoring improves the completeness, accuracy and integrity of data, so you have more confidence in the decisions you make.

It has following features:

1. Advanced data profiling2. Customizable assessment3. Graphical charts with drilldown data4. Fully open source

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H. HOW WILL SCALE UP DATA VOLUME, VARIETY AND VELOCITY?A scalable application platform not only accommodates rapid growth in traffic and data volume, variety and velocity (scaling up) but also adapts to decreases in demand (scaling down).

The data volume can be scaled up when users of smaller equipment suppliers starts integrating with this application to have insight of their product usage.

Also, if we need to reliability in determining age of equipment, we will need a lot larger data from OEMs..

I. WHAT TECHNOLOGY WILL BE USED FOR YOUR APPLICATION? JUSTIFY YOUR CHOICE.Following technologies are being used at different stages of data flow:

1. Data Collection: Local Transmitters and Sensors: These are used for collecting equipment running data

2. Data IngestionApache Kafka can be used for capturing streaming data sets

3. Data Storage:We can use Google Cloud TM storage

4. Data AnalyticsApache Hadoop, MapReduce

We will be using Apache Hadoop databases. Since, we are using both structured and semi-structured data hence we would need application for converting unstructured data (visa regulations for different countries) to structured data (days required for visa process) and process the same in Apache Hadoop databases.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

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J. WHAT WILL BE THE BUDGET OF YOUR PROJECT? The cost of handling data depends upon the cost of storage, maintaining databases, software, hardware and data security. The average cost of the project would be $1000/TB of data.

Data which needs to be handled - 10TB-15TB/day

Cost of Project - $15000/day

Cost for 450 Industrial Power Plants - $5.47 million/year/user

Indian Coal Fired PP (Capacity) -188967.88 MW

Days which can be saved per year due to -5 days out of 10 days forced shutdown

Forced shutdown

Loss of Revenue -188967.88MW*1000*(3600*24*5)* Rs 1.5/70

-$ 1.7 trillion

Since the losses of revenue due to forced shutdown are huge hence the power producers will choose big data application for arranging maintenance engineer and advisory function for prevention of forced Shutdowns.

K. REFERENCES

1. “All India Installed Capacity (IN MW) OF Power Stations”, CEA, Ministry of Power, GOI.

http://www.cea.nic.in/reports/monthly/installedcapacity/2016/installed_capacity-12.pdf

2. “Data Processing, Product Generation and Distribution at the NWS National Centers for Environmental Prediction”, NCEP

https://www.nist.gov/sites/default/files/documents/itl/ssd/is/Big-Data_NCEP.pdf

3. “Big data storage architecture: Categories, strengths and use cases”, Phil Goodwin, Search Storage.

http://searchstorage.techtarget.com/feature/Big-data-storage-architecture-Categories-strengths-and-use-cases

4. “Data Quality Concepts | Data Quality Tutorial”, Data Warehousing Tutorial, Edureka.

https://www.youtube.com/watch?v=HWaBdqmTqEA

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5. “List of Coal Based Thermal Power Stations in India up to 2016”, ENVIS Centre on Flyash, Ministry of Environment, Forest & Climate Change, GOI

http://cbrienvis.nic.in/Thermal%20Power%20Station%20in%20India%202016.pdf

6. “How Hadoop cuts big data costs”, Jeff Bertolucci, Information Week.

http://www.informationweek.com/software/how-hadoop-cuts-big-data-costs/d/d-id/1105546

7. https://www.talend.com/download/talend-open-studio/

L. CONTRIBUTORS

1. SHIVAM GUPTAR. No.-16125039MBA, DIMEIIT KANPUR

1. MAHENDRA KUMARR. No.-16114016M.Tech, DIMEIIT KANPUR

SPECIAL THANKS TO

ALOK TRIVEDI Asst. ManagerIsgec Heavy Engineering LtdNoida-20130

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