Operating Analysis and Data Mining System for Power Grid Dispatching

doi:10.4236/epe.2013.54B119

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Energy and Power Engineering, 2013, 5, 616-620

doi:10.4236/epe.2013.54B119 Published Online July 2013 (http://www.scirp.org/journal/epe)

Operating Analysis and Data Mining System for Power

Grid Dispatching

Haiming Zhou1, Dunnan Liu2, Dan Li1, Guanghui Shao3, Qun Li3

1China Electric Power Researc h I n s t itute, Beij i n g, China

2School of Economics and Management, North China Electric Power University

3Northeast China Grid Company, Shenyang, China

Email: haimingzhou@ 163.com, [email protected], lidan@163. com

Received January, 2013

ABSTRACT

The dispatching center of power-grid companies is also the data center of the power grid where gathers great amount of

operating information. The valuable information contained in these data means a lot for power grid operating manage-

ment, but at present there is no special method for the management of operating data resource. This paper introduces the

operating analysis and data mining system for power grid dispatching. The technique of data warehousing online ana-

lytical processing has been used to manage and analysis the great capacity of data. This analysis system is based on the

real-time data of the power grid to dig out the potential rule of the power grid operating. This system also provides a

research platform for the dispatchers, help to improve the JIT (Just in Time) management of power system.

Keywords: Power Grid Dispatch; Index System; Data Mining

1. Introduction

Network control center is th e center of the grid operating

data, a large amount of data that produced from SCADA,

EMS, CPS and other systems contains a grid in the op-

eration and safety of all o f the information. However, th e

current dispatch center information system is mainly for

data collection, storage and simple summary of query of

daily operations, and not specifically for a lot of histori-

cal information for effective category management and

correlation analysis, cannot effectively use the data from

the valuable resource the data extract useful knowledge

paired scheduling management.

Data mining techniques [1] is a large database from

extracting previously unknown information about the

operability of the knowledge discovery process, includ-

ing data warehouse [2], online analytical processing

OLAP [3], and data visualization of the new information

science technology. Data mining is widely used in avia-

tion, finance, telecommunications and other areas, which

have greatly improved data management and deci-

sion-making; in the power industry, data mining system

has been in power market analysis, supply market analy-

sis and other fields to be effectively applied. Therefore, it

is technologically advanced and practical feasibly for

data mining techniques applied to power scheduling

analysis.

The measures of establishing dispatching operation

analysis and data mining systems, effectively manage-

ment and scientific analysis for power grid data, do goo d

use for the summed experience of power grid, the explo-

ration of the power grid law in order to provid e the anal-

ysis platform for the dispatchers and improve the level of

dispatching and scientific decision-making.

This paper first describes the content of the dispatch-

ing operation analysis and the system of the index evalu-

ation, and then describes the data warehouse platform

and the analysis techniques of multidimensional data of

data analysis, finally describes examples of data mining

with the act ua l needs of dispatchi ng operat ion.

2. Dispatching Content

It is supposed to monitor and compute the status of all

aspects of dispatching operation, in order to facilitate the

refinement of grid operation management, accumulate

data and experience under the conditions of the electric-

ity market, continuously improve network security, qual-

ity, economic operation, and ensure grid security and

stability.

Dispatching indicator system is to quantitatively re-

flect the operations of all aspects of the grid, and make

comprehensive and clear understanding on the main fea-

tures of network parameters for the scheduling staff

through the calculation and analysis of a series of core

indicators.

According to State Grid Corporation of scheduling

system [4] requirements, grid dispatching departments

H. M. ZHOU ET AL. 617

need to analyze and evaluate the power grid control class,

scheduling classes, statistical analysis class, with the

purpose of forming the closed-loop feedback mechanism

of the accuracy of network control and the rationally

scientific operation mode, and to achieve meticulous

management of the power grid.

2.1. Run Control Class Indicators

Categories grid operation control targets should real-time

record automatically generated indicators report and

power grid index quantification statistics accord ing to the

EMS and other automated systems for the inspection of

the relevant analysis.

Scheduling on duty should be able to real-time view

the running control class indi cators and the basic content,

in order to adopt appropriate control measures to con-

tinuously improve the level of the grid control. These

include the follo wing ind icators:

1) Frequency indicators:

Include the highest power, lowest frequency, out of 50

± 0.2 Hz of the frequency of the more limited running

time, 50 ± 0.1 Hz of freq uency pass rate.

2) Tie-line indicator:

Including the tie-line A1/A2 or CPS1/CPS2 more lim-

ited number.

3) Important trend s of b ound cross-sec t i on:

Include the cumulative time and the percentage of total

calendar time of an important trend over stable section

running limits, the important trend of cross-section limit

of 90% to 100% of the total time and percentage of total

calendar time.

4) Standby indicator:

Include: real-time spinning reserve capacity and day

96 load ratio of the percentage of the maximum and

minimum values, the ratio of the percentage of an FM

unit Number of units and operating units Number of units,

the total available capacity of investment in an FM unit,

minimum spinning reserve capacity during peak hours,

spinning down maximum output capacity during low

hours.

5) Voltage indicators:

Include the voltage passing rate of the plant site under

the jurisdiction, the proportion of the total number of

stations under the jurisd iction of plants of the lower limit

of the peak voltage, the proportion of the number of ju-

risdiction of plants under the total number of stations in

the low hours of maximum voltage.

2.2. Scheduling Class Indicators

1) Load characteristics of indicators:

Inclu de: max imum / minimu m load , maximum l oad at

the same time rate, which is the whole network maxi-

mum load and net provincial, city (region or control area)

ratio of the sum of the maximum load; net provincial,

city (region or control area), maximum rate of change on

rise and decrease of 96 points load in the more than 220

kV substation, the dev iates of 96 points on the load fore-

cast and the actual load value more than ±2% of the

points and so on in net provinces (or control area).

2) Power balance indicators

Include: the completion of day, month power supply;

the completion of selected power plant project.

3) Standby indicator:

Include: spinning reserve to meet the rules of order, 10

minutes to bring up the emergency and alternative situa-

tion, 30 minutes to control the load standby conditions.

4) Maintenance plan targets:

Include: the completion of scheduled maintenance and

the rationality of repairing plan.

5) Indicators of low-fr equency load shedding:

Include: real cut capacity of low-frequency load shed-

ding partition, the real fault cut capacity to meet the var-

ious regions of the maximum possible N-1 components.

6) System automatic safety device index:

Include: the adaptability of installation strategies, the

rationality of device configuration.

7) Voltage indicators:

Include: the rationality of the central point reactive

power arr angemen t, th e ave rage in the p lant statio n unde r

the jurisdiction of the peak voltage, low hours.

8) Important indicators of the transmission section:

Include: the number and total time of the runn ing trend

over stable limit, the number and total time of the run-

ning trend in the 80% to 100%.

2.3. Use of KPI

Once we’ve identified the Key Performance Indices for

the business, it is ready to filter these down to the ana-

lyzers. The KPI usually have these three functions:

1) Reflect the Targets

Where possible offer incentives linked to targets, and

encourage analyzers to involve staff in the setting of their

targets. For example, a sales analyzer may set the sales

team targets of a certain number of new contacts per

week or visits per month. A production manager might

set targets for output, reject rate or work breaks.

2) Find the Cause

Set review dates and compare against previous figures,

your business plan, budget or other agreed standard. If

you spot an anomaly or problem, the KPI will help to

backtrack and pinpoint the cause.

3) Report regularly

Communicate performance figures regularly. Keeping

employees updated will encourage them to focus on

meeting or exceeding their KPI.

H. M. ZHOU ET AL.

618

2.4. Statistical Analysis of Categories of

Indicators

Statistical analysis indicator refers to the statistical anal-

ysis of the previous two categories of indicator s during a

period of time.

1) Total statistics:

The purpose is that to stat the cumulative quantity of

some indicators over a period of time. Such as: failure

time (seconds) of frequency, the unqualified cumulative

number of days and failure time, the maintenance ticket

number and the operating ticket number performed

monthly, power grid network losses, unplanned generator

outage times, and the frequency of fault trip of power

lines, bus, together (main).

2) Mean / extreme value statistics:

The purpose is that to stat the maxi mum, the minimum

or the average indicators over a period of time.

Such as: the largest number of operating ticket in sin-

gle-day, the 96-points load characteristic curve, together

(main) maximum load change rate, water consumption

rate of direct transfer hydropower plants, thermal power

plant coal consumption rate of direct transfer, high- vol-

tage transmission network net loss, 220 kV and more

rapid protection line fault removal rate, times of power

cuts and the average daily capacity and so on.

3) Percentage statistics:

The purpose is that to stat the ratio between the two

types of indicators of the total in a period of time. Such

as: the correct rate of the moves of security control de-

vice, the tie-line bias and frequency control passing rate,

voltage passing rate, load forecasting accuracy, the pass-

ing rate of the low-frequency load shedding control ca-

pacity, the operation rate of unit PSS, the operation rate

of unit AGC, the qualified rate of the operating vote.

3. Scheduling Operaion Analysis Data

Warehouse Platform

3.1. Scheduling Integrated Data Warehouse

Platform

All the available data relative to the supplier’s bidding

can be divided into three classes:

Data Hub is an onlin e system that is designed for Stats

analysis and decision support applications. It could meet

the decision support and online analytical applications

require all. This data is called data warehouse platform.

The establishment of dispatching and comprehensive

analysis platform for data mining is responsible for col-

lecting all kinds of the required scheduling run data indi-

cators in regular time.

1) The basic information of network parameters, pow-

er plants and units.

2) Power generation, by the power, the load plan and

actual data.

3) Power line trend data, the node voltage data.

4) Local power plants planned and actual data.

5) AGC unit and various indicators of data assessment.

6) Market transactions and load forecast data.

7) Reporting of transactions, transaction data.

8) All kinds of i nf ormation in regional p ower market .

9) Regional power grid parameters and inter-provin-

cial trade data.

Data warehouse is a new application of a database

technology, and so far, the data warehouse is a relational

database management system to manage the data.

4. Olap Multidimensional Data Analysis and

Reporting System

Data warehouse contains a lot of data extracted from a

number of databases, but these data could play its proper

value only by using the right tools and being used effec-

tively.

The methods of data mining for the data in the data

warehouse to be analyzed include online analytical proc-

essing (OLAP), association rule mining, decision tree

analysis, cluster analysis and other. Shanghai power sys-

tem mainly uses online analytical processing (OLAP)

technology.

4.1. Dispatching Multi-dimensional Analysis and

Processing

Multi-dimensional data ex traction and OLAP data analy-

sis could be done by the use of all kinds of data in the

dispatching data warehouse. As shown in Figur e 1:

1) Time dimension: Data classification according to

year, quart e r, month , week, day, ho ur and minute.

2) Period dimension: Data classification according to

the three period-peak, trough , and waist load.

3) Regional dimension: Data classification according

to different regions.

4) Plant dimensions: Data classification according to

power plant, the type (bid/peaking/FM/self) and Power

Generatio n Gr oup.

5) Unit dimensions: Data classification depending on

the capacity of the unit.

6) Line dimensions: data analysis depending on the

line, the line type.

7) Substation dimensions: Data classification accord-

ing to the type of substation and sub station.

8) Weather dimensions: Data classification according

to the temperature, humidity, the sunny weather or the

cloudy weather and other standard.

9) Custom dimension: We could create new data clas-

sification flexibly according to the needs of scheduling

Analysis, such as the level of the plant load factor, power

frequency, peak and valley levels and any other levels of

parameters.

H. M. ZHOU ET AL.

619

Year /quarter/ month/ week / day

Date dimension

Peak / waist /Valley

Period dimension

Custom Category

Custom dimension

Different transmission line

Line dimensions

Basic business data

Various types of indicators

Hours / minutes

Time dimensi on

Teenage/temperature/humidity

Weather dimensions

Province/local /county

Regi onal dimension

Group / plant

Plant dimensions

Figure 1. Multi-dimension analysis for power grid data.

4.2. OLAP-based Reporting System 3) Annual analysis: The aim is to make suggestions

and comments for company planning and technical in-

novation mainly through the analysis of statistical analy-

sis indicators combined with annual operational mode.

Then it provides support for the operational mode in the

next year.

The previous po wer system analysis is based on a variety

of control model calculations in advance of the simula-

tion analysis, the schedule data warehouse platform es-

tablished by the project, aims to statistically analyze and

data excavate a large number of historical data, and find

the potential operational law from the long-term accu-

mulation. These all cannot be achieved in any previous

analysis methods and models.

The operator is supposed to strengthen communication

and coordination with the company development of in-

frastructure, production, safety supervision, marketing,

trading and other relevant departments, and implement

corrective measures according to the important issues

reflected in the power grid power system analysis.

Power grid analysis is divided into day, monthly and

yearly analysis, in accordance with the Requirements of

refinement of grid operation. Scheduling classes, statis-

tical analysis of categories of indicators should be

months, years for analysis. The reporting system should

be based on real-time automation systems such as EMS

automatic generation of indicators recorded statements

and quantify the power grid index statistics for the in-

spection of the relevant power system analysis. Dis-

patchers on duty should be able to view real-time basic

and operational control content class indicators, in order

to adopt appropriate control measures to continuously

improve the level of the grid control.

The purpose is to get th e patterns and trends related to

grid operation through the power grid power system

analysis statistics and analysis, as the power grid sum-

mary information and to make decision thought on the

future operation mode of preparation and decision.

5. Dispatching Data Mining Analysis System

5.1. Dispatching Data Mining Analysis Methods

Data Mining (Data Mining) is to extract the implicit in

the work, people do not know in advance, but is poten-

tially useful information and knowledge from a large

number of, incomplete, noisy, fuzzy, random data.

1) Day analysis: The aim is to find grid operation con-

trol problems and deviations of yesterday, and find out

the reasons, and to remedy the problems in the next day's

operation, to continuously improve the level of opera-

tional contro l. The methods and procedures of data mining for the

data of grid dispatching are as follows:

1) Statistical analysis of indicators

2) Monthly analysis: Mainly through the analysis of

the type of operation plan, statistical analysis class indi-

cators to verify the adaptab ility of operating mode on the

month, in-depth study of the problems and deviations,

and improve it in the second month operation mode ar-

rangements, at the same time, to make recommendations

and comments for other departments and continuously

improve the scheduling refinement level.

It can create various indicators to analyze according to

the needs of data analysis, inclu ding:

a) Indicators statistics: Aggregate value, maximum /

minimum / average, expectations, variance, standard de-

viation.

b) Indicators score and early warning: You can score

all types of indicators, based on operating experience,

H. M. ZHOU ET AL.

620

determine early warning threshold on the basis of statis-

tical indicators.

2) Multivariate correlation analysis

For any amount of view two or more groups, you can

associate the following methods analyzed in order to

study the association and mutual influence:

a) Qualitative analysis: You can observe the changes

of the curve and relevance by the use of data visualiza-

tion technology, the multiple dimensions of data accord-

ing to different categories, in 2D or 3D graphics on dis-

play, rotation, and perspective.

b) Quantitative analysis: Relevant statistics, analysis

and factors affecting the amount of view between the

correlation coefficient, cov ariance .

3) Other analytical methods

Data mining itself reflects a thinking that with the ap-

propriate mathematical methods to explore the law from

the data. Data mining does not require one or a few me-

thods to analyze data. In contrast, data mining method

broadly define to include all kinds of statistics, signal

processing, information science, computer graphics, arti-

ficial intelligence and other areas of different methods,

depending on the data content, you can use different me-

thods.

Commonly used methods include: cluster analysis,

classification, prediction, regression, power system analy-

sis the evolution, correlation analysis, correlation analy-

sis and other methods.

5.2. The Practical Application of Dispatching

Data Mining

Power dispatching data mining can be applied to the

following practical issues.

1) The analysis of the reasonableness of the electric

power grid power system.

Analysis methods: analysis of local power generation

planning and load correlation, analyze tie-line planning

and load correlation.

2) Analysis of electricity contract in real-time

Analytical purposes: to ensure the fairness of the pow-

er plant contract implementation and scheduling.

Analysis methods: monitoring all electricity plants

contract in real-time completion, monitoring all power

plants project plan and the actual deviation; by Dispatch-

ers three shifts and each team for the electricity comple-

tion of sequencing analysis.

3) Analysis of various factors in CPS

Purpose of analysis: analyze various factors on the

impact of CPS indicators, to improve scheduling policy.

Analysis methods: To list CPS-related factors, to stat

the correlation between the factors and CPS indicators by

the time dimension, to sort the factors relation, and to

give scheduling control recommendations to improve

CPS.

6. Conclusions

Dispatching operational analysis and data mining system

described in this paper is the implementation and en-

forcement to the schedule of dispatching operational

analysis. The calculation and analysis of the dispatching

indicators gives the power grid dispatchers a more com-

prehensive, clear, quantitative understanding. The data

mining of the historic data can facilitate the understand-

ing of some complex related issues in the dispatching

operation. The author hopes that this research practice

could make contributions to the work of dispatching op-

erational analysis.

REFERENCES

[1] J. Q. Zhao, S. Yan, X. Xiao and Y. Z. Zhou, “Design and

Implementation of Double-core Redundant Power Grid

Dispatch Automation System,” Automation of Electric

Power Systems, Vol. 33, No. 21, 2009, pp. 101-103.

[2] S. M. Wang, S. N. Wu, D. L. Zhou and W. C. Wu, “Re-

search on Dispatch Training Base Construction Scheme

for Jiangxi Power Grid,” Electric Power, Vol. 42, No. 4,

2009, pp. 70-74.

[3] “ISO Market Monitoring & Information Protocol,” Cali-

fornia Independent System Operator Corporation FERC

Electric Tariff First Replacement, Vol. 2, No. 497.

[4] H. L. Jin and H. Liu, “Research on visualization tech-

niques in data mining,” Proceedings of the 2009 Interna-

tional Conference on Computational Intelligence and

Software Engineering, 2009, p. 3.

[5] A. Koretsune, S. Aoki, T. Konzo, H. Tsuji, S. Shimano

and E. Mimura, “DEA-based data mining for energy

consumption,” 10th IEEE International Conference on

Emerging Technologies and Factory Automation, Vol. 1,

2005, p. 4.

doi:10.1109/ETFA.2005.1612646