frankfurt (germany), 6-9 june 2011 motivation visual overlay data mining example event analysis...

Frankfurt (Germany), 6-9 June 2011

Motivation

Visual overlay

Data mining

Example event analysis

Gerhard Krost – Germany – Session 2 Block 3 – Paper ID 417

Systematic Power Quality Monitoring in Municipal Power Grid


Power quality is a key factor for solid energy delivery

Capable and affordable PQ recorders available

Continuous monitoring provides

- transparency of system conditions

- increased customer satisfaction


Motivation

But: systematic data treatment required


60 PQ recorders firmly installed in municipal grid: - 7 on 110 kV - 8 on 25 kV - 45 on 10 kV

Parameterized according to common standards (mainly EN 50 160)

Warning if 75% of admissible levels are exceeded

Linked to central server in utility headquarters


Installation



Original recorder survey (WinPQ)

(a-eberle)



Geographic visual overlay

No violation

Warnig limit(s) exceeded

EN 50160 limit(s) violated

110 kV 25 kV 10 kV



Central database

But: multiple SQL queries for survey of complete grid

Queries: commands

(a-eberle)



Solution for integral database access

MySQL Workbench

Free and multi-functional database tool

Generation, treatment and administration of

complex SQL queries

Stored query procedures of any complexity

Various export formats: HTML, XML, CSV



Example MySQL Workbench query DELIMITER $$

DROP PROCEDURE IF EXISTS Example procedure;

CREATE PROCEDURE Example procedure( IN name_part VARCHAR(2056), params_what VARCHAR(2056), params_where VARCHAR(2056))

Begin

Declare t_n VARCHAR(1024);

Declare done int default 0;

Declare looper int default 0;

Declare TableCursor Cursor for SELECT table_name FROM information_schema.tables WHERE table_schema = ‘Example database' and table_name LIKE CONCAT('%',name_part,'%');

DECLARE CONTINUE HANDLER FOR NOT FOUND SET done = 1;

SET @s := "";

Open TableCursor;

read_loop: LOOP

FETCH TableCursor INTO t_n;

IF done THEN

LEAVE read_loop;

END IF;

IF looper THEN

SET @s := CONCAT(@s," UNION SELECT '",t_n,"' as table_n , ",params_what," FROM ", t_n , " ",params_where);

ELSE

SET @s := CONCAT(@s,"SELECT '",t_n,"' as table_n , ",params_what," FROM ", t_n , " ",params_where);

SET looper=1;

END IF;

END LOOP;

PREPARE stmt FROM @s;

EXECUTE stmt;

Close TableCursor;

END $$

DELIMITER ;

Only red variables individually adapted

Blue query text firmly stored

CALL Example procedure(„Data class“, „Measure values“, „WHERE criterion x > y“)


Messort AVG(H3_MAX) AVG(H5_MAX) AVG(H7_MAX) AVG(H9_MAX) AVG(H11_MAX) AVG(H13_MAX)qb1uw02ka04 0.40 2.04 0.66 0.06 0.44 0.22qb2uw02kb02 0.41 1.72 0.62 0.04 0.25 0.11qc1uw03kb03 0.61 1.99 0.88 0.12 0.33 0.19qc2uw03ka04 0.44 1.90 1.15 0.10 0.59 0.58qe1uw07ka07 0.36 1.99 0.71 0.07 0.41 0.16qe2uw07ja07 0.33 1.82 0.98 0.03 0.12 0.16qe3uw07ka20 0.34 1.84 0.81 0.09 0.65 0.48

qe4uw07ja01 0.41 1.83 0.85 0.06 0.40 0.31qe5uw07ec04 0.25 0.60 0.74 0.04 0.51 0.27qe6uw07ea04 0.33 1.83 0.85 0.15 0.18 0.26qf1uw08ka06 2.20 1.81 0.97 0.03 0.17 0.17qf2uw08ka10 0.52 1.76 0.68 0.11 0.45 0.44qg1uw09kb02 0.41 2.33 0.64 0.07 0.44 0.17qg2uw09ka07 0.36 1.41 0.51 0.08 0.36 0.25qj1uw63ka07 0.51 1.61 0.58 0.08 0.31 0.24qj2uw63ka03 0.59 1.97 0.60 0.11 0.38 0.13qj3uw63ja15 0.35 1.86 0.71 0.04 0.20 0.07qj4uw63ja12 0.38 1.10 0.49 0.05 0.18 0.11qk1uw64ka13 0.46 2.80 0.44 0.06 0.26 0.10qk2uw64ka06 0.44 1.67 0.95 0.08 0.19 0.06qk3uw64ea04 0.29 0.81 0.67 0.05 0.18 0.17qk4uw64ec01 0.34 1.85 1.02 0.06 0.21 0.07ql1uw30ec01 0.36 1.88 1.04 0.06 0.21 0.06ql2uw32ec02 0.28 1.50 0.89 0.05 0.17 0.05qo1uw33ea03 0.33 1.80 1.01 0.06 0.20 0.06qp1uw34ka12 0.40 1.81 0.48 0.06 0.26 0.22qp2uw34kb06 0.37 1.81 0.59 0.05 0.27 0.22qr1uw36ka18 0.36 1.94 0.86 0.04 0.29 0.28qr2uw36kb06 0.37 2.16 0.96 0.04 0.35 0.13qs1uw37ka05 0.39 1.85 0.47 0.08 0.32 0.28qs2uw37ka11 0.35 1.86 0.87 0.05 0.24 0.21qu1uw52kb12 0.09 0.19 0.12 0.01 0.02 0.01qu2uw52kb08 0.44 1.78 0.39 0.08 0.29 0.18qv1uw53ka01 0.40 2.11 0.62 0.05 0.42 0.24qv2uw53kb01 0.40 2.11 0.62 0.05 0.42 0.24qv3uw53ka11 0.40 1.92 0.71 0.04 0.44 0.19qv4uw53kb11 0.40 1.92 0.71 0.04 0.44 0.19


Example MySQL result in Exceldaily maxima of voltage harmonics orders 3, 5, 7, 11, 13

Table: not useful

Histogram: gradually betterKiviat diagram: best survey



Example PQ evaluation Feb 2011

LocationTemporary overvoltages



Excerpt of 10 ms-recorders

Intermittent single phase earth fault ...

19:49:05 h 19:49:25 h



Excerpt of 10 min data

19:00 h 22:00 h

... subsequent continuous single phase earth fault

phase –ground

between conductors

Voltages



Conclusions

High transparency of system status

Easy detection and analysis of faults

Flexible mining system for numerous data

Visual survey



Thank you !

frankfurt (germany), 6-9 june 2011 motivation visual overlay data mining example event analysis...

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