Security Enhancement in Power System Using FACTS Devices and Atom Search Optimization Algorithm

Amarendra, A.; Srinivas, L. Ravi; Rao, R. Srinivasa

doi:10.4108/eai.16-4-2021.169335

Cited by 8 publications

(8 citation statements)

References 32 publications

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“…The constraints of the optimal placement of FACTS devices are represented by the energy balance, the network security constraints and the constraints related to the FACTS devices [78][79][80][81], such as:…”

Section: Constraints Of the Problemmentioning

confidence: 99%

“…These methods are easier to find the best solution to problems than traditional methods. This group can be classified into four categories [18], (i)Evolutionary algorithms like Genetic Algorithms (GA) [19], Evolution Strategy (ES) [20], Evolutionary Programming (EP) [21], Genetic Programming (GP) [22], (ii) Physics-based algorithms contain Ant Lion Optimization (ALO) technique [23], Biogeography Based Optimizer (BBO) [24], Curved Space Optimization (CuSO) [25], Flower Pollination Algorithm (FPA) [26], Galaxy-based Search Algorithm (GBSA) [27], Gravitational Search Algorithm (GSA) [28], Harmony Search Algorithm (HAS) [29], Multi-Verse Optimization (MVO) Algorithm [30], Simulated Annealing (SA) [31], Atom Search Optimization (ASO) Algorithm [32], etc. (iii) Swarm Based algorithms such as Particle Swarm Optimization (PSO) [33], Whale optimization algorithm (WOA) [34], Artificial Bee Colony (ABC) [35], Chemical Reaction Optimization (CRO) algorithm [36], Crow Search Algorithm (CSA) [37], Cat Swarm Optimization (CaSO) algorithm [38], Cuckoo search (CS) [39], Dragonfly Algorithm (DA) [40], Bats Algorithm (BA) [41], Firefly algorithm (FFA) [42], Grasshopper optimization algorithm (GOA) [43], Grey Wolf Optimizer (GWO) [44], Honey-Bee Mating Optimization (HBMO) [45], Moth-Flame Optimization (MFO) algorithm [46], Bacterial Swarm Optimization (BSO) [47], Immune Algorithm (IA) [48], Symbiotic Organism Search (SOS) Algorithm [49], etc.…”

Section: Introductionmentioning

confidence: 99%

“…ASO algorithm A Amarendra et al [32] have been presented a novel physics-inspired metaheuristic optimization algorithm, namely ASO for efficiently predicting the optimal placements of FACTS controllers containing TCSC,SVC,UPFC and IPFC. In order to obtain minimization in investment costs, fuel costs, real power loss, voltage deviation, severity index, Line Overload Sensitivity Index (LOSI), a regular standard IEEE test system such as 30-bus,118-bus and 300-bus have been considered in this work.…”

mentioning

confidence: 99%

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Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Ismail¹,

Guesmi²,

Alshammari³

et al. 2023

Preprint

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Using flexible AC transmission system (FACTS) devices in power systems while adhering to some equality and inequality constraints, researchers around the world sought to address this issue with the objectives of improving the voltage profile, reducing power losses in transmission lines, and increasing system reliability and safety. The recent development of FACTS controllers opens up new perspectives for safer and more efficient operation of electrical power networks by continuous and rapid action on power systems parameters, such as phase angle shifting, voltage injection and line impedance compensation. Thus, an improvement on voltage profile and enhancement of power transfer capability can be obtained. It is for that, the idea behind the FACTS concept is to enable the transmission system to be an active element in increasing the flexibility of power transfer requirements and in securing stability of integrated power system. It may also be effective in transient stability improvement, power oscillations damping and balancing power flow in parallel lines. The primary issue that has significantly piqued the interest of a number of researchers working in this field is the FACTS optimization problem, which involves determining the optimal type, location, and size of FACTS devices in electrical power systems. For solving this mixed integer, nonlinear and non-convex optimization problem, this paper provides an in-depth and comprehensive review of the various optimization techniques covered in published works in the field. In this review, a classification of optimization techniques in five main groups that are widely used, such as classical optimization techniques or conventional optimization approaches, Meta heuristic methods, analytic methods or sensitive index methods and mixed or hybrid methods, is summarized. In addition, a performance descriptions and comparison of these different optimization techniques are discussed in this study. Finally, some advice is offered for future research in this field.

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Section: Constraints Of the Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Ismail¹,

Guesmi²,

Alshammari³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…where M x i ð Þ as in Equation ( 30), refers to the average positions of the ith solution as determined by Equation (31). UB (j) And LB (j) are the lower and upper boundaries in the jth position, correspondingly.…”

Section: Reptile Search Algorithmmentioning

confidence: 99%

Localisation of facts compensator using reptile search algorithm for enhancing power system security under multi‐contingency conditions

Punam,

Kumar

2023

Cognitive Comp and Systems

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Due to the improvement in power demand usage, the system causes more stress. Suitable placement of FACTS is introduced to improve power flows, stability, and power system security. An advanced optimal method has been introduced to resolve optimal power flow under various conditions. Five varied FACTS devices were positioned inside the power system to improve system safety at the least cost. Selecting the suitable location and size of FACTS device enabling high security and less cost was proposed using RSA. The objective functions were mitigated based on the RSA's inspiration to improve system security. The placement of the FACTS compensator was based on objective functions like LOSI, voltage deviation, real power loss, investment cost, sensitivity index, fuel costs, and constraints. The proposed model was validated under three conditions, namely generator outage, line outage, and both outage in IEEE 118 and IEEE 30 bus‐systems. In the IEEE 30 bus system, TCSC provides better security of 1.4 severity at normal conditions and 1.3 severity in contingency conditions. In the IEEE 118 bus system, UPFC has less severity of 2.4 at normal conditions, and STATCOM has the least severity of 3 at contingency conditions. The proposed model provides enhanced security in all circumstances and reduces overall costs.

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“…To have a good voltage performance, the voltage deviation (VD) at each bus must be made as small as possible [28] as given by:…”

Section: Objective Function Formulation (1) Minimization Of Bus Volta...mentioning

confidence: 99%

Security Enhancement of Power Systems through Interline Power Flow Controller (IPFC) under Contingency Condition: A Case Study and Analysis‐EEP 400 kV System

Woldesemayat

Tantu

2022

Journal of Electrical and Computer Engineering

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Increasing contingency issues result in inevitable bus voltage violations and force the system to operate around its limit. This situation creates strains that are faced by the security of the Ethiopian Electric Power (EEP) 400 kV system. In this paper, the static security assessment (SSA) of the system under the base and N-1 contingency scenarios was examined by using Newton-Raphson (NR) method. The Bus and line loading-based composite severity indices (CSI) were utilized to determine the state of the system and to get an optimal location for interline power flow controller (IPFC) placement. In this research, the optimal sizing of IPFC was done by using Grey Wolf Optimization (GWO) algorithm. From N-1 contingency analysis, Gelan to Sebeta and Gelan to Holeta branches were found as optimal places for IPFC placement. The IPFC optimal parameters were the series voltage and angle injection found as Vse1 = 0.1101pu, Vse2=0.1096pu, δse1=-0.1848rad, and δse2=2.7682rad with the optimal size of 50 MVA for each IPFC. The average bus voltage severity index (BVSI) without and with IPFC cases was found as 217.243% and 2.641% respectively. The average line severity index (LSI) without and with IPFC cases were found as 39.448% and 15.283% respectively. Moreover, the total line loss under severe N-1 contingency scenarios without and with IPFC integration was 437.783 MW and 63.136 MW respectively. From analysis, it was observed and concluded that the static security of the EEP 400 kV system under N-1 contingency conditions is effectively improved by the integration of IPFC.

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Security Enhancement in Power System Using FACTS Devices and Atom Search Optimization Algorithm

Cited by 8 publications

References 32 publications

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Localisation of facts compensator using reptile search algorithm for enhancing power system security under multi‐contingency conditions

Security Enhancement of Power Systems through Interline Power Flow Controller (IPFC) under Contingency Condition: A Case Study and Analysis‐EEP 400 kV System

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