Coordination of SVC and TCSC for Management of Power Flow by Particle Swarm Optimization

Jamnani, Jitendra G.; Pandya, Maulikkumar C.

doi:10.1016/j.egypro.2018.11.149

Cited by 41 publications

(29 citation statements)

References 8 publications

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“…Thus, the problem should be solved using a discrete optimization to rationally select the substation where the device should be installed, considering that numerous substations exist in the equivalent model. In this case, an artificial intelligence method can be adopted, such as genetic [22][23][24], particle swarm [25,26], or simulated annealing algorithms [27,28]. After researching various model algorithms, we found that a genetic algorithm is particularly suitable for this discrete optimization problem, because it can effectively manage any form of objective function and constraint.…”

Section: Combination Optimization Configuration Methods For Capacitancmentioning

confidence: 99%

Combination Optimization Configuration Method of Capacitance and Resistance Devices for Suppressing DC Bias in Transformers

Wang

Liu

2019

Energies

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The ground current of a high-voltage direct current (HVDC) transmission system can cause DC bias in transformers near the grounding electrode during monopole operations, which affects the alternating current (AC) power system operation. Owing to multiple bias current flow paths, a capacitance blocking device installed at the neutral point of a transformer may increase the DC bias in adjacent transformers, while suppressing the DC current in that transformer. This paper introduces the use of an effective bias current indicator to describe the effect of the grounding current on transformers in a network, considering the wiring characteristics of the autotransformers and the power system topology. Additionally, a combination optimization method for the capacitance and resistance is applied in order to determine the minimum number of installed devices that restrict the maximum effective bias current throughout the network to a permissible range. A genetic algorithm based on an improved roulette selection method is adopted to solve the optimal configuration problem. The method is validated by using a test case based on the Xizhe HVDC transmission receiving-end grid near the Jinsi grounding electrode. The configuration of the capacitance and resistance was optimized by the improved genetic algorithm. This method can achieve the desired level of DC bias management with fewer devices than the conventional method, which verifies the feasibility and superiority of the proposed optimization method.

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Section: Combination Optimization Configuration Methods For Capacitancmentioning

confidence: 99%

Combination Optimization Configuration Method of Capacitance and Resistance Devices for Suppressing DC Bias in Transformers

Wang

Liu

2019

Energies

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“…PSO was utilized to minimize the objective function of optimal placement and coordination of TCSC and SVC in [17]. The objective was the minimization of the cost associated with the total generation.…”

Section: Facts Device Real Power Flowmentioning

confidence: 99%

Optimal Placement, Sizing and Coordination of FACTS Devices in Transmission Network Using Whale Optimization Algorithm

et al. 2020

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Flexible AC Transmission Systems (FACTS) play an important role in minimizing power losses and voltage deviations while increasing the real power transfer capacity of transmission lines. The extent to which these devices can provide benefits to the transmission network depend on their optimal location and sizing. However, finding appropriate locations and sizes of these devices in an electrical network is difficult since it is a nonlinear problem. This paper proposes a technique for the optimal placement and sizing of FACTS, namely the Thyristor-Controlled Series Compensators (TCSCs), Shunt VARs Compensators (SVCs), and Unified Power Flows Controllers (UPFCs). To find the optimal locations of these devices in a network, weak buses and lines are determined by constructing PV curves of load buses, and through the line stability index. Then, the whale optimization algorithm (WOA) is employed not only to find an ideal ratings for these devices but also the optimal coordination of SVC, TCSC, and UPFC with the reactive power sources already present in the network (tap settings of transformers and reactive power from generators). The objective here is the minimization of the operating cost of the system that consists of active power losses and FACTS devices cost. The proposed method is applied to the IEEE 14 and 30 bus systems. The presented technique is also compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The findings showed that total system operating costs and transmission line losses were considerably reduced by WOA as compared to existing metaheuristic optimization techniques.

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“…P 1 and Q 1 , respectively, indicate the wind farm's active or reactive power before compensation. e compensation capacity is shown in the following [20,21]:…”

Section: Static Reactive Compensator Svg Mathematical Modelmentioning

confidence: 99%

“…P 1 and Q 1 , respectively, indicate the wind farm's active or reactive power before compensation. The compensation capacity is shown in the following [ 20 , 21 ]:

where cos ϕ 1 is the system power factor before compensation and cos ϕ 2 is the system power factor after compensation. According to Figure 4 , as the grid voltage reference value changes, the SVG voltage-current characteristic curve appears to fluctuate [ 18 ].…”

Section: Grid-connected Wind-solar Hybrid Energy Storage Systemmentioning

confidence: 99%

An Energy Storage Performance Improvement Model for Grid-Connected Wind-Solar Hybrid Energy Storage System

Zhu

Zhao

Wang

et al. 2020

Computational Intelligence and Neuroscience

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This study introduces a supercapacitor hybrid energy storage system in a wind-solar hybrid power generation system, which can remarkably increase the energy storage capacity and output power of the system. In the specific solution, this study combines the distributed power generation system and the hybrid energy storage system, while using the static reactive power compensation system and the conductance-fuzzy dual-mode control method to increase output power in stages. At the same time, the optimal configuration model of the wind-solar hybrid power generation system is established using MATLAB/Simulink software. The output power of the microgrid to the wind-photovoltaic hybrid power generation system is calculated by simulation, and the optimization process of each component of the system is simulated. This study mainly uses the static reactive power compensation system and the conductance-fuzzy dual-mode control method to optimize the wind-solar hybrid power generation system. Using MATLAB software simulation verifies the feasibility and rationality of the optimal configuration of the system.

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Coordination of SVC and TCSC for Management of Power Flow by Particle Swarm Optimization

Cited by 41 publications

References 8 publications

Combination Optimization Configuration Method of Capacitance and Resistance Devices for Suppressing DC Bias in Transformers

Combination Optimization Configuration Method of Capacitance and Resistance Devices for Suppressing DC Bias in Transformers

Optimal Placement, Sizing and Coordination of FACTS Devices in Transmission Network Using Whale Optimization Algorithm

An Energy Storage Performance Improvement Model for Grid-Connected Wind-Solar Hybrid Energy Storage System

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