Design of static synchronous series compensator based damping controller employing invasive weed optimization algorithm

Ahmed, Ashik; Al-Amin, Md Rasheduzzaman; Amin, Ruhul

doi:10.1186/2193-1801-3-394

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Selection of a method depends on the characteristics of the data and the distribution of the groundwater spring indexes in a histogram (Ayalew and Yamagishi, 2005). If the indexes have a positive or negative skewness, the quantile or natural break classification is suitable for index classification (Akgun, 2012). In this research, the histogram was checked and the results revealed that the quantile method was better than other methods for index classification.…”

Section: Generation Of Groundwater Spring Potential Maps Using Anfis mentioning

confidence: 92%

“…Some of the advantages of IWO include the manner of reproduction, spatial dispersal, and competitive exclusion (Mehrabian and Lucas, 2006) as well as the fact that seeds and their parents are ranked together and that those with better fitness survive and become reproductive (Ahmed et al, 2014). This algorithm can benefit from combined advantages of retaining the dominant poles and the error minimization (Abu-Al-Nadi et al, 2013).…”

Section: Advantages/disadvantages Of the Models And Performance Analysismentioning

confidence: 99%

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Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization

Khosravi

Panahi

Bui

2018

Hydrol. Earth Syst. Sci.

142

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Abstract. Groundwater is one of the most valuable natural resources in the world (Jha et al., 2007). However, it is not an unlimited resource; therefore understanding groundwater potential is crucial to ensure its sustainable use. The aim of the current study is to propose and verify new artificial intelligence methods for the spatial prediction of groundwater spring potential mapping at the Koohdasht–Nourabad plain, Lorestan province, Iran. These methods are new hybrids of an adaptive neuro-fuzzy inference system (ANFIS) and five metaheuristic algorithms, namely invasive weed optimization (IWO), differential evolution (DE), firefly algorithm (FA), particle swarm optimization (PSO), and the bees algorithm (BA). A total of 2463 spring locations were identified and collected, and then divided randomly into two subsets: 70 % (1725 locations) were used for training models and the remaining 30 % (738 spring locations) were utilized for evaluating the models. A total of 13 groundwater conditioning factors were prepared for modeling, namely the slope degree, slope aspect, altitude, plan curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distance from fault, distance from river, land use/land cover, rainfall, soil order, and lithology. In the next step, the step-wise assessment ratio analysis (SWARA) method was applied to quantify the degree of relevance of these groundwater conditioning factors. The global performance of these derived models was assessed using the area under the curve (AUC). In addition, the Friedman and Wilcoxon signed-rank tests were carried out to check and confirm the best model to use in this study. The result showed that all models have a high prediction performance; however, the ANFIS–DE model has the highest prediction capability (AUC = 0.875), followed by the ANFIS–IWO model, the ANFIS–FA model (0.873), the ANFIS–PSO model (0.865), and the ANFIS–BA model (0.839). The results of this research can be useful for decision makers responsible for the sustainable management of groundwater resources.

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Section: Generation Of Groundwater Spring Potential Maps Using Anfis mentioning

confidence: 92%

Section: Advantages/disadvantages Of the Models And Performance Analysismentioning

confidence: 99%

Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization

Khosravi

Panahi

Bui

2018

Hydrol. Earth Syst. Sci.

142

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“…The IWO, suggested by Mehrabian and Lucas in 2006, is used to solve general, multi-dimensional, linear and non-linear optimization problems [22]. Weeds tend to colonize for growth and reproduction, and find an appropriate place [23]. Some distinct characteristics of IWO compared to other evolutionary algorithms are reproduction, spatial distribution and the competitive exclusion method.…”

Section: Invasive Weed Optimization (Iwo)mentioning

confidence: 99%

Optimal Placement of Wind Turbines Using Novel Binary Invasive Weed Optimization

2019

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Wind turbines -which are significant in terms of clean energy production globally -are environmentally friendly, consistent and economical systems. Wind turbines, due to developing technology, have become one of the most widely used renewable energy resources, and every country has worked to satisfy its electricity demands with the help of wind energy. As the importance of wind energy increases all around the world, the importance of wind turbine placement also rises. In this study, the aim was to position wind turbines over a certain area of a wind farm to obtain maximum turbine power with minimum investment cost, thereby achieving the highest power efficiency. The experimental studies were conducted over a 2×2 km area; this area was divided into a 10×10 grid, and a 20×20 grid for more efficient placement. Because these operations occurred in a binary search space, Invasive Weed Optimization (IWO) -normally used to solve unceasing optimization problems -was used in this study by obtaining fourteen different binary Invasive Weed Optimization (BIWO1 to BIWO14) algorithms with the help of ten different transfer functions (four from the sshaped family, four from the v-shaped family, two based on modulo 2, ceil, ceil-round, ceil-floor and round-floor). The proposed method was compared with other studies carried out in the binary search space found in published literature. As a result, it was seen that the proposed algorithm was an efficient algorithm for solving the problem of wind turbine placement to achieve an optimal placement.

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“…Extensive studies were performed on the effect of SSSC to dampen low frequency electromechanical oscillations (LFEOs) and ensure the dynamic stability of large grid. A variety of conventional regulators have been notified in the scientific publications comprising lead-lag [8] and proportional integral (PI) [9]. Several studies have proposed new techniques for designing SSSC-based  ISSN: 2302-9285 damping controllers.…”

Section: Introductionmentioning

confidence: 99%

Optimal fractional SSSC auxiliary controller for power system low frequency oscillations damping improvement

Benayad¹,

Zabaiou²,

Bouafassa³

2023

Bulletin EEI

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This work presents a new controller design to improve inter-area low frequency electromechanical oscillations (LFEOs) damping and enhance the overall stability of the electrical network. The control strategy used the static synchronous series compensator (SSSC) based flexible AC transmission systems (FACTS) series-type device mainly performed for power flow and voltage regulation. Then, a fractional order proportional integral derivative (FOPID) is introduced as an auxiliary controller for the SSSC using the difference of rotor speed deviations of generators as input signal to improve the oscillations damping. Moreover, genetic algorithm (GA) is applied to seek for optimum controller gains. The proposed control approach is examined on two-area four-machine (2A4M) test system. The FOPID performance is compared with the integer order proportional integral derivative (PID). Obtained results show that the proposed SSSC-based FOPID controller achieves high performance for enhancement of inter-area low frequency oscillations damping.

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Design of static synchronous series compensator based damping controller employing invasive weed optimization algorithm

Cited by 6 publications

References 16 publications

Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization

Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization

Optimal Placement of Wind Turbines Using Novel Binary Invasive Weed Optimization

Optimal fractional SSSC auxiliary controller for power system low frequency oscillations damping improvement

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