Maintenance scheduling for a power system operating assets using Petri nets integration with ant colony optimization

2016 ◽  
Author(s):  
Hector A. Florez-Celis ◽  
Carlos A. Ruiz-Zea ◽  
German D. Zapata-Madrigal ◽  
Leon A. Martinez-Giraldo
Keyword(s):  
Power System ◽  
Petri Nets ◽  
Ant Colony Optimization ◽  
Maintenance Scheduling ◽  
Ant Colony ◽  
System Operating

scholarly journals DESAIN OPTIMAL POWER SISTEM STABILIZER PADA UNIT PEMBANGKIT BAKARU BERBASIS ANT COLONY OPTIMIZATION

Transmisi ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 70
Author(s):  
Muhammad Ruswandi Djalal ◽  
Herman Nawir ◽  
Sonong Sonong ◽  
Marhatang Marhatang
Keyword(s):  
Power System ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Power System Stabilizer ◽  
Optimal Power ◽  
System Stabilizer

Salah satu peralatan kontrol tambahan yang mampu meningkatkan kestabilan suatu system pada generator adalah Power System Stabilizer (PSS). Ketika terjadi osilasi gangguan pada generator, PSS memberikan sinyal tambahan ke peralatan eksitasi untuk memberikan redaman tambahan pada generator. Penggunaan PSS diperlukan koordinasi penentuan parameter yang tepat untuk mencapai kontrol kinerja yang bagus untuk sistem. Pada penelitian ini, metode kecerdasan buatan algoritma Ant Colony Optimization (ACO) digunakan untuk mengoptimasi parameter PSS. Dari hasil simulasi didapatkan parameter PSS yang optimal ditinjau dari respon osilasi overshoot dan sudut rotor. Kinerja sistem tanpa PSS didapatkan overshoot frekuensi sebesar -0,02242 s/d 0,005241 pu, kemudian PSS dengan Trial error sebesar -0,0196 s/d 0,003704 pu, PSS Bat sebesar -0.01394 s/d 0.0007533 pu, dan dengan metode ant colony didapatkan overshoot yang berkurang yaitu sebesar -0,0128 s/d 0,0003349 pu. Sedangkan untuk respon sudut rotor didapatkan tanpa PSS sebesar -4,71 s/d 4,486e-05 pu, PSS trial error sebesar -4,579 s/d 4,486e-05 pu, PSS Bat sebesar -4.71 s/d 4.486e-05, dan PSS ant colony sebesar -4,566 s/d 4,545e-05 pu. Implementasi metode cerdas sebagai metode penalaan PSS dapat memperbaiki kinerja generator dalam meredam osilasi sistem multimesin.

scholarly journals Enhancement of the power system distribution reliability using ant colony optimization and simulated annealing methods

2020 ◽  
Vol 17 (2) ◽  
pp. 877 ◽  
Author(s):  
Hadi Suyono ◽  
Rini Nur Hasanah ◽  
Panca Mudjirahardjo ◽  
M Fauzan Edy Purnomo ◽  
Septi Uliyani ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Power System ◽  
Ant Colony Optimization ◽  
Power Distribution ◽  
Distribution System ◽  
Ant Colony ◽  
Service Reliability ◽  
Power Distribution System ◽  
Reliability Indices ◽  
Base Reference

<span>The increasing demand of electricity and number of distributed generations connected to power system greatly influence the level of power service reliability. This paper aims at improving the reliability in an electric power distribution system by optimizing the number and location of sectionalizers using the Ant Colony Optimization (ACO) and Simulated Annealing (SA) methods. Comparison of these two methods has been based on the reliability indices commonly used in distribution system: SAIFI, SAIDI, and CAIDI. A case study has been taken and simulated at a feeder of Pujon, a place in East Java province of Indonesia, to which some distributed generators were connected. Using the existing reliability indices condition as base reference, the addition of two distributed plants, which were micro hydro and wind turbine plants, has proven to lower the indices as much as 0.78% for SAIFI, 0.79% for SAIDI, and 2.32% for CAIDI. The optimal relocation of the existing 16 sectionalizers in the network proved to decrease further the reliability indices as much as 43.96% for SAIFI, 45.52% for SAIDI, and 2.8% for CAIDI, which means bringing to much better reliability condition. The implementation of the SA method on the considered data in general resulted in better reliability indices than using the ACO method.</span>

scholarly journals Multi-Area Economic Dispatch by Using Differential Evolution Immunized Ant Colony Optimization (DEIANT)

2019 ◽  
Vol 9 (1) ◽  
pp. 3525-3530
Keyword(s):  
Power System ◽  
Differential Evolution ◽  
Ant Colony Optimization ◽  
Power Transmission ◽  
Economic Dispatch ◽  
Test System ◽  
Ant Colony ◽  
Area Network ◽  
Electrical Transmission ◽  
Reliable Technique

Economic dispatch is an important issue within the electrical system in meeting the lowest cost of the system and is subject to transmission and operating constraints.The power system operation and planning economic dispatch required a reliable technique to achieve minimal cost in economic dispatch otherwise the objective to minimize the generation cost fail. The electrical transmission network does not make complete electricity generation in Single-area economic dispatch. Thus, to complete the economic dispatch for the power transmission system, the multi-area network is proposed. The Multi-Area Economic Dispatch(MAED) connects two or more areas through a tie-line. Each region has a specific cost and load pattern. Tie-lines are used as connectors to enable power switching between regions. 31-Bus test system tested using an algorithm known as Differential Evolution Immunized Ant Colony Optimization (DEIANT) with different case studies with several trials taken to assess the consistency of results. Comparative studies with Pre-Optimization and Newton Rap son revealed that DEIANT technique is more reliable for multi-area power system network.

scholarly journals Perbaikan Level Tegangan dan Reduksi Rugi-Rugi Sistem Transmisi Sulbagsel Berbasis Ant Colony Optimization

2019 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ikhlas Kitta
Keyword(s):  
Power System ◽  
Ant Colony Optimization ◽  
Electric Power ◽  
Electric Power System ◽  
Ant Colony ◽  
Power Losses ◽  
The South ◽  
Optimal Amount ◽  
South Sulawesi

Abstract—The Ant Colony Optimization (ACO) method is used to determine the location and optimal amount of bank capacitors in the South Sulawesi electric power system (Sulbagsel). The purpose of employing ACO is to determine the ability of ACO as one method for optimization to improve voltage levels and reduce power losses in the electric power system. There are 5 scenarios carried out in this study, scenario 4 and scenario 5 are scenarios for applying the ACO method, the results of these two scenarios are the increase in voltage on the bus and the reduction of power losses in the Sulbagsel system.  

Study on the Application of Simulated Evolutionary Optimization Algorithm in Optimizing the Combination of Unit Commitments in Power System

2011 ◽  
Vol 48-49 ◽  
pp. 1186-1190
Author(s):  
Qing Song Liu ◽  
Jia Tong ◽  
Yi Feng Li
Keyword(s):  
Power System ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Unit Commitment ◽  
Evolutionary Optimization ◽  
Ant Colony ◽  
Feasible Region ◽  
Retrieval Process ◽  
Ant Colony Optimization Algorithm

This study adopted a simulated evolutionary optimization algorithm, ant colony optimization algorithm to find the optimal unit commitment operation. The concepts such as status, strategy, and path, etc. were introduced to devise the optimization of unit commitment operation by ant colony optimization algorithm mode, so that the optimal unit commitment operation could be found by ant colony optimization algorithm. To cope with different constraints by additional penalties and restrict the statuses not satisfying the constraints by tabu table, the retrieval of ant colony optimization algorithm could always be performed in feasible region and the retrieval process of the algorithm was effectively conducted. It is feasible and efficient to find the optimal unit commitment operation by ant colony optimization algorithm, which was proved by stimulation.

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