Luận văn thạc sĩ Kỹ thuật điện: Tối ưu hóa lưới điện phân phối 22KV áp dụng các tuyến dây thuộc tổng công ty điện lực Tp. HCM

ĈӇ khҳc phөc sӵ phӭc tҥp cӫa viӋF[iFÿӏQKFѭӡQJÿӝ GzQJÿLӋn thӵc tӃ, ta có thӇ xác ÿӏnh tәn thҩt ÿLӋn QăQJ theo ÿӗ thӏ phө tҧi bҵng cách biӇu diӉn sӵ biӃn thiên cӫa bình SKѭѫQJ Fѭӡng ÿӝ dòn

Chuyên QJjQK.Ӻ7+8Ұ7Ĉ,ӊ1

Mã ngành: 60 52 02 02

LUҰ19Ă17+Ҥ&6Ƭ

7S+ӗ&Kt0LQKWKiQJQăP

ĈҤI HӐC QUӔC GIA TP.HCM

&{QJWUuQKÿѭӧc hoàn thành tҥi: 7UѭӡQJĈҥi hӑc Bách Khoa ± Ĉ+4*-HCM

Cán bӝ Kѭӟng dүn khoa hӑc :

(Ghi rõ hӑ, tên, hӑc hàm, hӑc vӏ và chӳ ký) Cán bӝ chҩm nhұn xét 1 :

(Ghi rõ hӑ, tên, hӑc hàm, hӑc vӏ và chӳ ký) Cán bӝ chҩm nhұn xét 2 :

(Ghi rõ hӑ, tên, hӑc hàm, hӑc vӏ và chӳ ký) LuұQYăQWKҥFVƭÿѭӧc bҧo vӋ tҥL7UѭӡQJĈҥi hӑF%iFK.KRDĈ+4*7S+&0 QJj\WKiQJQăP Thành phҫn HӝLÿӗQJÿiQKJLiOXұQYăQWKҥFVƭJӗm: (Ghi rõ hӑ, tên, hӑc hàm, hӑc vӏ cӫa HӝLÿӗng chҩm bҧo vӋ luұQYăQWKҥFVƭ 1

2

3

4

5

Xác nhұn cӫa Chӫ tӏch HӝLÿӗQJÿiQKJLi/9Yj7Uѭӣng Khoa quҧn

lý chuyên ngành sau khi luұQYăQÿmÿѭӧc sӱa chӳa (nӃu có)

CHӪ TӎCH HӜ,ĈӖNG 75ѬӢNG +2$««««

ĈҤI HӐC QUӔC GIA TP.HCM CӜNG HÒA XÃ HӜI CHӪ 1*+Ƭ$9,ӊT NAM

75ѬӠ1*ĈҤI HӐC BÁCH KHOA Ĉӝc lұp - Tӵ do - Hҥnh phúc

_

NHIӊM VӨ LUҰ19Ă17+Ҥ&6Ƭ

,7Ç1Ĉӄ TÀI: TӔ,Ѭ8+Ï$/ѬӞ,Ĉ,ӊN PHÂN PHӔI 22KV ÁP DӨNG

CÁC TUYӂN DÂY THUӜC TӘ1*&Ð1*7<Ĉ,ӊN LӴC TP.HCM

NHIӊM VӨ VÀ NӜI DUNG:

Tìm hiӇu các tính toán vӅ tәn thҩWÿLӋQQăQJWURQJOѭӟLÿLӋn phân phӕi;

- Nghiên cӭu giҧi thuұt bài toán CAPO trong phҫn phӅP366$'(37ÿӇ [iFÿӏnh

vӏ WUtGXQJOѭӧng bù tӕLѭXWURQJOѭӟi phân phӕi thӵc tӃ ÿDQJTXҧn lý vұn hành;

- Nghiên cӭu giҧi thuұt bài toán TOPO trong phҫn phӅm PSS/$'(37ÿӇ [iFÿӏnh ÿLӇm dӯQJÿLӇPWiFKOѭӟi) tӕLѭXVDRFKR77Ĉ1FӫDOѭӟLÿLӋQÿDQJTXҧn lý vұn hành là thҩp nhҩt;

- ĈӅ xuҩWÿLӇm dӯQJÿLӇPWiFKOѭӟi) tӕLѭXWKHRGӵ báo phө tҧi cӫD7ĈĈ+7Ĉ

73+&0QăPVDRFKR77Ĉ1FӫDOѭӟLÿLӋQÿDQJTXҧn lý vұn hành là thҩp nhҩt

II NGÀY GIAO NHIӊM VӨ : 08/04/2019

III NGÀY HOÀN THÀNH NHIӊM VӨ: 15/08/2019

IV.CÁN BӜ +ѬӞNG DҮN: PGS.TS PHAN THӎ THANH BÌNH

/Ӡ,&Ҧ0Ѫ1

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&{QJW\ĈLӋQOӵF73+&0

7{L[LQFҧP ѫQFiFTXêWKҫ\F{ÿmJySêKѭӟQJGүQQӝLGXQJFKX\rQÿӅFӫDW{LÿӇW{LFyWKӇKRjQWKLӋQOXұQYăQFDRKӑFWӕWKѫQ

'RWKӡLJLDQFyKҥQYjNLQKQJKLӋPQJKLrQFӭXNKRDKӑFFKѭDQKLӅXQrQ OXұQ YăQ FzQ QKLӅX WKLӃX VyW UҩW PRQJ QKұQ ÿѭӧF ê NLӃQ JyS ê FӫD7Kҫ\&{YjFiFDQKFKӏKӑFYLrQ

- 3KѭѫQJWKӭFYұQKjQKOѭӟLÿLӋQSKkQSKӕLFӫDWX\ӃQGk\FҫQNKҧRViWÿѭӧFOӵDFKӑQOjSKѭѫQJiQFyWәQWKҩWÿLӋQQăQJOjQKӓQKҩW

- 7KӕQJNrSKөWҧLTXDQKLӅXQăPTXDÿyGӵEiRSKөWҧLWX\ӃQGk\WăQJFDRKѫQVRYӟLQăPWUѭӟF7ӯѭӟFOѭӧQJÿyFKҥ\OҥLEjLWRiQ7232ÿѭDUDNLӃQQJKӏFiFÿLӇPWiFKOѭӟLPӟLFӫDSKҫQPӅP366$'(37FyWәQWKҩWÿLӋQQăQJOjQKӓQKҩW

ĈӅWjLQJKLrQFӭXFKӫ\ӃXWұSWUXQJYjRKDLJLҧLSKiSOj[iFÿӏQKÿLӇPGӯQJWӕLѭXWK{QJTXDFKѭѫQJWUuQK366$'(37YjWKӕQJNrSKөWҧLTXDWӯQJQăPÿӇGӵEiRSKөWҧLWăQJEDRQKLrXVRYӟLQăPWUѭӟFĈӗQJWKӡLWK{QJTXDFKѭѫQJWUuQK366$'(37WD[iFÿӏQKWәQWKҩWF{QJVXҩWKLӋQKӳXWUrQOѭӟLÿLӋQSKkQSKӕLYӟLFiFWK{QJVӕOҩ\ÿѭӧFWӯNӃWTXҧYұQKjQKWKӵFWӃFӫDOѭӟLÿLӋQ WX\ӃQGk\FҫQNKҧRViW

- The method of operating the distribution grid of the surveyed line is selected

as the smallest power loss plan

- Statistics of load over the years, thereby forecasting line load increase 7% higher than the previous year From that estimation, rerun the TOPO problem to propose new meshing points of PSS / ADEPT software with minimal power loss

The research topic mainly focuses on two solutions: determining the optimal stop through the PSS / ADEPT program and the annual load statistics to predict how much the load will increase compared to the previous year At the same time, through the PSS / ADEPT program, we determine the loss of existing power on the distribution grid with the parameters obtained from the actual operating results of the wire network to be surveyed

/Ӡ,&$0Ĉ2$1

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7S+͛&Kt0LQKQJj\WKiQJQăP

êWrQYjJKLU}KӑWrQ

9®ĈӬ&1+Æ1

/Ӡ,&Ҧ0Ѫ1 iii

7Ï07Ҳ7 iv

ABSTRACT v

/Ӡ,&$0Ĉ2$1 vi

0Ө&/Ө& vii

'$1+6È&+&È&&+Ӳ9,ӂ77Ҳ7 xi

'$1+6È&++Î1+9Ӏ xii

DANH SÁCH BҦ1*%,ӆ8 xiv

&KѭѫQJ0ӢĈҪ8 1

1.1 /êGRFKӑQÿӅWjL 1

1.2 0өFÿtFKQJKLrQFӭX 2

1.3Nӝi duQJQJKLrQFӭX 2

1.4 KӃWTXҧÿҥWÿѭӧFFӫDÿӅWjL 3

1.5 *LӟLKҥQFӫDÿӅWjL 3

1.6 *LiWUӏWKӵFWLӉQ 3

&KѭѫQJ&Ѫ6Ӣ/é7+8<ӂ7 4

2.1 KiLQLӋPOѭӟLÿLӋQ 4

2.1.1 /ѭӟLKӋWKӕQJ 4

2.1.2 /ѭӟLWUX\ӅQWҧi: 4

2.1.3 /ѭӟLSKkQSKӕL 4

2.2 KiLQLӋPYӅWәQWKҩWÿLӋQQăQJ 6

2.2.1 &iFQJX\rQQKkQJk\UD77Ĉ1WUrQOѭӟLSKkQSKӕL 6

7әQWKҩWNӻWKXұW 7

7әQWKҩWWKѭѫQJPҥi: 8

3KѭѫQJSKiS[iFÿӏQKWәQWKҩWÿLӋQQăQJ 9

;iFÿӏQK77Ĉ1FӫDOѭӟLÿLӋQTXDWtQKWRiQWәQWKҩWNӻWKXұW 9

3KѭѫQJSKiSWKHRÿӗWKӏSKөWҧL 9

3KѭѫQJSKiSWKHRWKӡLJLDQWәQWKҩWF{QJVXҩWFӵFÿҥL 9

2.3.1.2.1 Tәn thҩWWUrQÿѭӡng dây 9

2.3.1.2.2 Tәn thҩt trong máy biӃn áp 10

2.3.2 ;iFÿӏQK77Ĉ1WKӵc hiӋn qua hӋ thӕQJF{QJWѫÿR ÿӃm 12

2.3.3 Nhұn dҥQJ 77Ĉ1 WKHR Wӯng cҩS ÿLӋn áp, tӯng khu vӵF OѭӟL ÿLӋn, tӯng xuҩt tuyӃn trung áp, tӯng trҥm biӃn áp công cӝng 13

2.4 Các biӋn pháp giҧm 77Ĉ1 13

2.4.1 BiӋn pháp quҧn lý kӻ thuұt-vұn hành: 13

2.4.2 BiӋn pháp quҧn lý quҧn lý kinh doanh: 15

2.4.3 Bài toán tái cҩXKuQKOѭӟi giҧm tәn thҩt công suҩt 17

3KѭѫQJSKiSNӃt hӧp Heuristic và tӕLѭX hóa 18

2.4.3.1.1 Thuұt toán kӻ thuұt vòng ktn cӫa Merlin vj Back 18

2.4.3.1.2 Thuұt toán khác 19

3KѭѫQJSKiS Heuristic 20

2.4.3.2.1 Thuұt toán kӻ thuұWÿәi nhánh cӫa Civanlar và các cӝng sӵ 20

2.4.3.2.2 Thuұt toán khác 20

2.4.3.3 Các giҧi thuұt dӵa trên trí tuӋ nhân tҥo 21

2.4.3.3.1 Thuұt toán di truyӅn ± Genetic Algorithm (GA) 21

2.4.3.3.2 3KѭѫQJSKiSORJLFPӡ - Fuzy logic 22

2.4.3.3.3 Mҥng thҫn kinh nhân tҥo ± Artificial Neural Network (ANN) 22

2.4.3.3.4 HӋ chuyên gia ± Expert System (ES) 23

2.4.3.3.5 Thuұt toán bҫ\ÿjn ± Particle Swarm Method (PSM) 23

2.4.3.3.6 Thuұt toán tӕLѭXNLӃn ± Ant Colony Optimization Method (ACO) 23

2.4.3.4 GiҧLSKiSÿӅ xuҩt bӣi phҫn mӅm PSS/ADEPT 25

2.4.4 Nhұn xét, kӃt luұn 27

2.5 %jLWRiQ[iFÿӏQKSKѭѫQJiQEWӕi ѭX 28

2.5.1 Lý thuyӃWEFKROѭӟi phân phӕi 28

2.5.1.1 ;iFÿӏQKGXQJOѭӧng bù tәng cho tӯng phát tuyӃn 28

2.5.1.2 ;iFÿӏnh vӏ WUtÿһt tө bù tӕLѭX 29

2.5.2 GiҧLSKiSÿӅ xuҩt bӣi phҫn mӅm PSS/ADEPT 31

2.5.2.1 3KѭѫQJ SKiS Wtnh bù kinh tӃ tӕL ѭX Fӫa công cө CAPO trong phҫn mӅm PSS/ADEPT .32

2.5.2.2 3KѭѫQJSKiS[iFÿӏnh tìm vӏ trt ÿһt bù tӕLѭXFӫa công cө CAPO trong phҫn

mӅm PSS/ADEPT 32

2.5.3 Nhұn xét, kӃt luұn 35

&KѭѫQJ7Ә1*48$19ӄ3+Ҫ10ӄ0366$'(37 37

3.1 Giӟi thiӋu tәng quan 37

3.2 Các cӱa sӕ ӭng dөng 39

3.2.1 Diagram View 40

3.2.2 Cӱa sә Equipment List View 40

3.2.3 Cӱa sә Progress View 40

3.2.4 Cӱa sә Report Preview 40

3.3 CáFEѭӟc thӵc hiӋn trong phҫn mӅm PSS/ADEPT 41

%ѭӟc 1 ThiӃt lұp thông sӕ mҥQJOѭӟi ÿLӋn 42

%ѭӟc 2 VӁ Vѫÿӗ Oѭӟi ÿLӋn 44

%ѭӟc 3 Chҥy các chӭFQăQJWtQK toán 44

%ѭӟc 4 Lұp báo cáo 46

 3KѭѫQJ SKiS [iF ÿӏQK ÿLӇm dӯQJ ÿLӇP WiFK Oѭӟi) tӕL ѭX Eҵng Modul TOPO Analysis cӫa phҫn mӅm PSS/ADEPT 5.0 49

3KѭѫQJSKiS[iFÿӏnh vӏ trí bù tӕLѭXFӫa phҫn mӅm PSS/ADEPT 5.0 51

3.6 Nhұn xét 55

&KѭѫQJӬ1*'Ө1*&+ѬѪ1*75Î1+PSS/ADEPT 56

TË1+%Ô&Ð1*68Ҩ73+Ҧ1.+È1*&+2;8Ҩ778<ӂ1.9 56

4.1 TәQJTXDQ&{QJW\ĈLӋn lӵc Bình Phú (PCBP) 56

4.1.1 Giӟi thiӋu vӅ OѭӟLÿLӋn PCBP 56

4.1.2 KhӕLOѭӧng quҧQOê&{QJW\ĈLӋn lӵc Bình Phú (PCBP) 58

4.2 7әQJTXDQWX\ӃQ&KLrX+RjQJ 58

4.2.1 6ѫÿӗOѭӟLÿLӋQ&KLrX+RjQJ 58

4.2.2 %LӇXÿӗSKөWҧLPD[WURQJQăPWX\ӃQ&KLrX+RjQJ 59

4.2.3 7K{QJVӕÿѭӡQJGk\&KLrX+RjQJ 60

4.2.4 7K{QJVӕSKөWҧLQJj\WҧLPD[QJj\ WX\ӃQGk\&KLrX+RjQJ%ҧQJ 7K{QJVӕSKөWҧLWX\ӃQ&KLrX+RjQJQJj\ 64

4.2.5 7K{QJVӕ0%7WX\ӃQ&KLrX+RjQJ 66

4.3 ThiӃt lұp thông sӕ các phҫn tӱ WUrQOѭӟi ÿLӋn 69

4.3.1 ThiӃWOұSWK{QJVӕQ~W QJXӗQ 69

4.3.2 ThiӃt lұp thông sӕ nút tҧi 70

4.3.3 ThiӃt lұp thông sӕ ÿѭӡng dây 70

4.3.4 ThiӃt lұp thông sӕ máy biӃn áp phân phӕi 71

4.3.5 ThiӃt lұp thông sӕ kinh tӃ FKRFKѭѫQJWUuQK PSS/ADEPT 72

4.4 ;iF ÿӏQK GXQJ Oѭӧng bù và vӏ trí bù tӕL ѭX FKR SKiW WX\Ӄn 482 Chiêu Hoàng 74

4.4.1 ĈӗWKӏSKө WҧL 74

4.4.2 ;iFÿӏnh vӏ trí bù tӕLѭX 77

ChѭѫQJ;È&Ĉӎ1+Ĉ,ӆ0'Ӯ1* 7Ӕ,Ѭ8%Ҵ1*3+Ҫ10ӄ0366$'(37 .79

&+2/ѬӞ,Ĉ,ӊ13+Æ13+Ӕ, 79

5.1 Giӟi thiӋu 79

5.2 ThiӃWÿһt thông sӕ kinh tӃ cho cho bài toán TOPO 80

Ĉһt các tùy chӑn cho bài toán TOPO 81

5.4 Chҥy bài toán TOPO cho tuyӃn dây 482 Chiêu Hoàng 82

&Kҥ\EjLWRiQ7232WX\ӃQ&KLrX+RjQJWKHRGӵEiRSKөWҧL 91

'ӵEiRF{QJVXҩWFKRQăP 91

&Kҥ\EjLWRiQ7232WX\ӃQ&KLrX+RjQJWKHRGӵEiRSKөWҧL 91

&KѭѫQJ.ӂ7/8Ұ19¬ +ѬӞ1*3+È775,ӆ1 100

6.1 KӃt luұn 100

6.2 Các nӝLGXQJFKѭDWKӵc hiӋn ÿѭӧc 101

+ѭӟng phát triӇn 101

7¬,/,ӊ87+$0.+ҦO 103

3+Ө/Ө& 105

3+Ҫ1/é/ӎ&+75Ë&+1*$1* HӐC VIÊN CAO HӐC 108

DANH SÁCH HÌNH VӀ

+uQK0{KuQKOѭӟLÿLӋQ 5

+uQK1JX\rQQKkQJk\WәQWKҩWÿLӋQQăQJ 6

+uQK6ѫÿӗ[iFÿӏQKWәQWKҩWÿLӋQQăQJEҵQJWKLӃWEӏÿR 13

+uQK/ѭXÿӗ[iFÿӏQKÿLӇPGӯQJWӕLѭXEҵQJSKҫQPӅP3SS/ADEPT 25

+uQK&ӱDVәJLDRGLӋQ366$'(37 39

+uQK%ӕQEѭӟFWKӵFKLӋQFӫDSKҫQPӅP366$'(37 41

+uQK7KLӃWOұSWK{QJVӕPҥQJOѭӟLÿLӋQ 42

+uQK;k\GӵQJWKXӝFWtQKOѭӟLÿLӋQ 43

+uQK7KLӃWOұSKҵQJVӕNLQKWӃ 44

+uQK&iFSKkQKӋWtQKWRiQWURQJ366$'(37 45

+uQK;iFÿӏQKGXQJOѭӧQJYjYӏWUtEWӕLѭX 46

+uQK7K{QJVӕKLӇQWKӏWUrQVѫÿӗ 47

+uQK7K{QJVӕKLӇQWKӏWUrQFӱDVәSURJUHVVYLHZ 48

+uQK&ӱDVәKLӇQWKӏNӃWTXҧ 49

+uQK+ӝSWKRҥLWKLӃWÿһWWK{QJVӕNLQKWӃFKREjLWRiQ&$32Yj7232 53

+uQK6ѫÿӗPLQL6&$'$ WX\ӃQGk\&KLrX+RjQJ 58

Hình  %LӇX ÿӗ SKө WҧL WURQJ QăP WX\ӃQ Gk\  &KLrX +RjQJ .59

+uQK%LӇXÿӗSKөWҧLQJj\WҧLPD[WURQJQăP WX\ӃQGk\&KLrX+RjQJ 59

+uQK7K{QJVӕQ~WQJXӗQYjP{KuQKQJXӗQ 69

+uQK7K{QJVӕQ~WWҧLYjP{KuQKQ~WWҧL 70

+uQK7KLӃWOұSWK{QJVӕGk\GүQ 71

+uQK7K{QJVӕPi\ELӃQiSSKkQSKӕLYjP{KuQK 72

+uQKĈӗWKӏSKөWҧLPD[QJj\QăP 74

+uQK;k\GӵQJÿӗWKӏSKөWҧLÿӇ[iFÿӏQKWtQK FKҩWWҧLFҫQE 75

+uQK3KөWҧLWKHRWKӡLÿLӇPWURQJQJj\ 76

+uQK7tQKGXQJOѭӧQJYjYӏWUtEWUrQOѭӟLWUXQJiS 77

Hình 5.1: 7K{QJVӕNLQKWӃFKREjLWRiQ7232 81

+uQKĈһWFiFW\FKӑQFKREjLWRiQ7232 81

+uQK6ѫÿӗOѭӟLÿLӋQWX\ӃQ&KLrX+RjQJ 82

Hình .ӃWTXҧFKҥ\EjLWRiQ7232 86

DANH SÁCH BҦNG BIӆU

%ҧQJéQJKƭDFiFWK{QJVӕNLQKWӃÿѭӧFWKLӃWÿһWWURQJ366$'(37 54

%ҧQJ7K{QJVӕÿѭӡQJGk\WX\ӃQ&KLrX+RjQJ 60

%ҧQJ7K{QJVӕ0%7ÿһWWUrQWX\ӃQGk\&KLrX+RjQJ 66

%ҧQJ9ӏWUtYjGXQJOѭӧQJEFӕÿӏQKYjӭQJÿӝQJFKROѭӟLWUXQJWKӃ 77

%ҧQJ7әQWKҩWF{QJVXҩWWUѭӟFYjVDXNKLE 78

%ҧQJ%ҧQJWUҥQJWKiLFiFNKyDÿLӋQWUѭӟFNKLFKҥ\EjLWRiQ7232 83

%ҧQJ%ҧQJWUҥQJWKiLFiFNKyDÿLӋQVDXNKLFKҥ\EjLWRiQ7232 87

%ҧQJ6ӕOѭӧQJFiFNKyDÿLӋQWKD\ÿәLWUҥQJWKiL 89

%ҧQJ'ӵEiRF{QJVXҩWVҧQOѭӧQJFKRQăP 91

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