Comparison of Intelligent Algorithms with FACTS Devices for Minimization of Total Power Losses

Monshizadeh, Shohreh; Hegglid, Gunne John; Hagen, Svein Thore

doi:10.1007/978-3-030-15032-7_10

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Salman et al [12] employed GA to identify the best location for UPFC devices to optimize voltage profiles, reduce power losses, control power flow in overloaded transmission lines, and decrease power generation in the local Iraqi network (Diyala 132 kV). Two types of artificial algorithms, imperialist competitive algorithm (ICA) and PSO, were compared in [13], and FACTS devices effects are displayed to minimize network losses. It has been shown that the PSO algorithm with UPFC reduces both active and reactive power losses more than other FACTS device types and intelligent algorithms.…”

Section: Introductionmentioning

confidence: 99%

Loss reduction of transmission lines using PSO-based optimum performance of UPFC

2023

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Transmission line losses are one of the essential topics and issues in power systems research. Several methods and techniques have been used to reduce these losses, and one of these modern techniques is flexible alternating current transmission systems (FACTS). In this paper, one of the most important types of this technology, the unified power flow controller (UPFC), was used to reduce losses in the Iraqi national grid (ING) 400 kV. This paper presents an efficient method for minimizing losses of transmission lines in the ING system (400 kV) 46-bus approach. A particle swarm optimization (PSO)-based optimum proportional-integral (PI) controller with UPFC was proposed to obtain the optimal location of UPFC and optimum parameters of the PI controller to achieve the objective function of the research. MATLAB coded the algorithm. The Newton-Raphson method was employed to perform load flow analysis. The results showed that the best place for UPFC is buses (14-17) named BGE4 (Baghdad)-AMN4 (Baghdad), and the total active power and reactive power losses decreased from 727.4593 to 579.3874 MW and from 5155.9 to 3971.1 MVAR, respectively and also led to voltage regulation.

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Section: Introductionmentioning

confidence: 99%

Loss reduction of transmission lines using PSO-based optimum performance of UPFC

2023

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“…However, this power loss cannot be avoided, so the only thing that can be done is to minimize the loss. Some of the methods that have been offered in previous research to minimize the losses include power injection through flexible alternating current transmission systems (FACTS) [7,8], distributed generators (DGs) [9][10][11][12][13], and optimal sizing and placement of capacitors [14][15][16], so that the voltage profile can be corrected, which results in minimal power losses.…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Power Loss Minimization Based on Solar Irradiation Forecasting of Extreme Learning Machine

Rehiara¹,

Setiawidayat²,

Sumbung³

2023

Adv. sci. technol. eng. syst. j.

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Power losses exist naturally and have to be cared for in the operation of electrical power systems. Many researchers have worked on various methods and approaches to reduce losses by incorporating distributed generators (DG), particularly from renewable sources. These studies are based on the maximum unit penetration of the DGs, which is rarely achieved, resulting in inaccurate calculations. This paper proposes an advanced solution for calculating power losses by incorporating an Extreme Learning Machine (ELM) method for forecasting the solar irradiation. The ELM algorithm was used to create a model for forecasting solar radiation in the Manokwari region and its surroundings. Daily solar radiation in the region has been predicted using the model. NASA's 8016 data on temperature and solar irradiation were used to train the ELM model. With an MAE value of around 0.6392 and a training time of 4.4375 seconds, the test results demonstrate that the built model has good accuracy. The operation of a 1000 kWp solar power plant based on the ELM data forecasting can reduce the power loss of the existing distribution network around the location from 1.5095 kW/hour to 0.9068 kW/hour. Furthermore, the power plant operation can minimize the power loss by 39.9249 percent, from 36.2280 kW to 21.7640 kW.

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Comparison of Intelligent Algorithms with FACTS Devices for Minimization of Total Power Losses

Cited by 2 publications

References 5 publications

Loss reduction of transmission lines using PSO-based optimum performance of UPFC

Loss reduction of transmission lines using PSO-based optimum performance of UPFC

Day-Ahead Power Loss Minimization Based on Solar Irradiation Forecasting of Extreme Learning Machine

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