Consideration of an appropriate TTC by probabilistic approach

Audomvongseree, Kulyos; Yokoyama, Akihiko

doi:10.1109/pes.2004.1373254

Cited by 15 publications

(22 citation statements)

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“…While in step 4 of the proposed solution algorithm, a particle with the best fitness sharing will instead take the position to guide the swarm into the next generation. And, accordingly, due to that the objective of the MDCP is to maximize the loading factor, with the fitness sharing of each particle calculated by (11), (13) and (14), the gbest in step 4 of the proposed solution algorithm is determined as follows: gbest = the pbest of the particle with the best fitness sharing (15) The setting of σ is determined on a basis of system case-by-case; in the study, with a certain number of simulations, the effects of various σ values to achieve the optimal solution are carefully inspected for the test systems.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Under a competitive environment, in [11], for reactive power devices installation, a tangent vector based loss sensitivity analysis was performed to indicate which buses are most necessary for reactive compensation. With the TCSC and UPFC installations and based on specific generation pattern, in [12] and [13], a sensitivity-based repetitive linear iterative approach (SRLIA) optimization algorithm was used to improve control performance and enhance real-time loadability.…”

Section: Introductionmentioning

confidence: 99%

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Fitness Sharing Particle Swarm Optimization Approach to FACTS Installation for Transmission System Loadability Enhancement

Chang¹

2013

Journal of Electrical Engineering and Technology

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-Proper installation of Flexible AC Transmission Systems (FACTS) devices in existing transmission networks can enable power systems to accommodate more power transfer with less network expansion cost. The problem to maximize transmission system loadability by determining optimal locations and settings for installations of two types of FACTS devices, namely static var compensator (SVC) and thyristor controlled series compensator (TCSC), is formulated as a mixed discrete-continuous nonlinear optimization problem (MDCP). For solving the MDCP, in the paper, the proposed method with fitness sharing technique involved in the updating process of the particle swarm optimization (PSO) algorithm, can diversify the particles over the search regions as much as possible, making it possible to achieve the optimal solution with a big probability. The modified IEEE-14 bus network and a practical power system are used to validate the proposed method.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fitness Sharing Particle Swarm Optimization Approach to FACTS Installation for Transmission System Loadability Enhancement

Chang¹

2013

Journal of Electrical Engineering and Technology

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“…In order to reduce linearization errors, load flows should be performed periodically. Probabilistic methods have considered the uncertainties of the system performance that could not be addressed in a deterministic way, and have been implemented to evaluate the TTC for various outages [5][6][7]. As all power system networks and the system components are exposed to nature, they are affected by the weather condition considerably, and the failure rates of transmission lines are increased due to weather conditions.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Assessment of Total Transfer Capability Using SQP and Weather Effects

Kim

Park

Rhee

et al. 2014

Journal of Electrical Engineering and Technology

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-This paper presents a probabilistic method to evaluate the total transfer capability (TTC) by considering the sequential quadratic programming and the uncertainty of weather conditions. After the initial TTC is calculated by sequential quadratic programming (SQP), the transient stability is checked by time simulation. Also because power systems are exposed to a variety of weather conditions the outage probability is increased due to the weather condition. The probabilistic approach is necessary to evaluate the TTC, and the Monte Carlo Simulation (MCS) is used to accomplish the probabilistic calculation of TTC by considering the various weather conditions.

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“…Many different methodologies have been applied for transfer capability studies and popular methods include: repeated power flow (RPF) [2,3]; continuation power flow (CPF) [4][5][6][7]; optimal power flow (OPF) [8,9]; DC load flow utilizing linear sensitivity factors [10]; and also some probabilistic approaches etc [11,12].…”

Section: Introductionmentioning

confidence: 99%