Optimal reactive power planning using oppositional grey wolf optimization by considering bus vulnerability analysis

Babu, Rohit; Raj, Saurav; Dey, Bishwajit; Bhattacharyya, B.

doi:10.1049/enc2.12048

Cited by 20 publications

(15 citation statements)

References 49 publications

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“…And the current through line connected between two buses is expressed using Kirchhoff Voltage Law (KVL), Substituting ( 11) into ( 10) and ( 10) into (11), Re-arrange ( 12) and ( 13),…”

Section: Methodsmentioning

confidence: 99%

“…VSIs are not only competent in identifying the critical lines and buses under both online and offline modes through static analysis or phasor measurement units, but also provide information on voltage instability or the proximity of a collapse under various loading conditions and contingencies, such as loss of generators or lines, and indicate real-time information for voltage instability through phasor measurement unit wide area measurement system (PMU-WAMS) [6][7][8][9]. The indices are also widely used as the objective function to solve various applications, such as determining the optimal location and sizing reactive power compensation (RPC) [10][11][12][13], optimal distributed generation allocation (DG) [14][15], optimal power flow [16][17][18][19], optimal reactive power dispatch in [20], optimal network reconfiguration [21], and optimal locations of PMUs [22]. The reviewed works on various applications of VSIs can be found in [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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New Line Voltage Stability Index (BVSI) for Voltage Stability Assessment in Power System: The Comparative Studies

et al. 2022

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Modern power utilization has been forced to operate close to their stability limits, which may lead to voltage instability or collapse owing to the stressed operating conditions of networks. This raises the need to develop a new technique to predict the level of voltage security under different operating conditions to detect critical lines or buses that operate close to its stability limit. This study proposes a new line voltage stability index (BVSI) to identify weak lines and buses for various loading conditions and network configurations. Comparative studies between the existing voltage stability indices (VSIs) and the proposed index have been performed to comprehensively highlight the indices' foundation, performance, and overall behavior for several different IEEE benchmark test systems; 14-bus, 30-bus, 118-bus test systems and 33bus radial distribution system. The sensitivity towards different loading conditions due to variables such as active power, reactive power, angular difference between sending and receiving bus voltage, line resistance, and shunt admittance, which have been neglected in the formulation of VSIs, are briefly discussed. The findings of this work are aimed to provide a general guideline for other researchers in selecting the appropriate VSIs for various applications, particularly in solving optimization problems such as distributed generation (DG) and reactive power compensation (RPC) placement, optimal power flow, optimal reactive power dispatch, optimal network reconfiguration for various loading occasions, and different networks. Lastly, the application of the proposed index has been adopted as an analytical approach for solving optimal location and sizing of DG(s) problems. The results prove the effectiveness of the proposed index in accessing the voltage stability state and stress condition of lines. Besides, the proposed approach also capable of determining the potential candidate bus for single DG location and sizing effectively.

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“…And the current through line connected between two buses is expressed using Kirchhoff Voltage Law (KVL), Substituting ( 11) into ( 10) and ( 10) into (11), Re-arrange ( 12) and ( 13),…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Line Voltage Stability Index (BVSI) for Voltage Stability Assessment in Power System: The Comparative Studies

et al. 2022

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“…Metaheuristic approaches have been employed to handle other technical optimization problems. References [27]- [31] used a combination of bio-inspired approaches to solve the reactive power-source planning problem. Reference [32] presented a hybrid metaheuristic approach to reduce the cost of microgrid systems.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems

Kaushik

Al-Raweshidy

2022

IEEE Access

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Fifth-generation (5G) empowered internet of things (IoT) edge networks suffer from latency in delay-sensitive applications. To fulfil the low latency requirements of beyond fifth-generation (B5G)-IoT applications and provide quality of service (QoS) to IoT-edge communication, it is important to minimize server delay. Furthermore, 5G-IoT systems consume more power than their predecessors, which is a concern given the growing size of future IoT networks. This research presents a hybrid latency and power-aware approach for B5G-IoT networks (HLPA B5G-IoT) that minimizes latency with minimum overhead on battery-constrained IoT nodes while simultaneously providing a power-efficient solution for B5G-IoT-edge networks. HLPA B5G-IoT has a novel algorithm classifier tool (ACT) for selecting appropriate optimization algorithms based on the characteristics and requirements of B5G-IoT systems. The ACT matrix not only parametrically compares HLPA B5G-IoT with existing approaches but also identifies crucial parameters that enable algorithm selection for load balancing and energy efficiency. In this paper, metaheuristic algorithms, i.e., biogeography-based optimization (BBO) and grey wolf optimization (GWO), are tailored to meet the requirements of load balancing and power efficiency in IoT-edge systems. The proposed load-balancing algorithm reduces latency and improves overall network performance by 33.33%, 27.45%, 23.52%, 21.56%, 13.72%, 11.76%, and 7.84% compared with simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), bacteria foraging algorithm (BFA), ant colony optimization (ACO), bat algorithm (BA), and genetic SA PSO (GSP), respectively. The power-efficiency algorithm consumes 46.6%,

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“…It is applied to the IEEE 30, IEEE-57, and IEEE-118 bus systems. Babu et.al [8] have suggested a grey wolf optimization algorithm for optimal reactive power planning issues by installing the reactive compensation at the weak bus. The weak buses are first detected by using three indexes and then the reactive sources are installed on these buses.…”

Section: Literature Surveymentioning

confidence: 99%

“…Meta-heuristic Techniques Minimum Power Losses MWAO 22.2876 SCA [10] 24.0500 ABC [4] 24.1025 ISMA [5] 24.5856 MJAYA [6] 23.4705 MFOM [7] 24.2529 BBO [1] 24.5400 GSA [2] 24.4300 SOA [3] 24.2654 BSO [29] 24.3700 OGWO [20] 24.7200 OGWO-VCPI [8] 24.7500 HHO-PSO [9] 24.4600…”

Section: Table 3: Comparison Of Best Value Of Active Power Loss Of Ie...mentioning

confidence: 99%

Statistical Analysis of Modified Whale Optimization Algorithm for Active Power Loss Minimization

Sahay

Upputuri

Kumari

et al. 2022

Preprint

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Optimal reactive power dispatch has an influential impact on improving the power quality of different utilities connected to the power system. It is a non-linear, non-convex, mixed-integer type problem formulated to minimize the active power loss considering generator voltage, tap setting value, and reactive power compensation as a control variable. In this article, a novel modified whale optimization algorithm is applied to solve this problem. A non-linear adaptive weight and an improved golden sign operator are being used to modify the basic whale algorithm. It improves the randomness due to parameter variation and enlarges the search area for accurate global solutions. It prevents the algorithm from local convergence. It also improves the convergence speed as compared to the basic whale optimization algorithm. It is tested and verified on the standard IEEE-57 bus system and IEEE-118 bus system. The solutions given by this method are compared with that of recently developed techniques for proving its dominance over the other techniques. The power losses of the IEEE-57 bus system are reduced to 1.78% and for the IEEE-118 bus system are reduced to 2.66%. Additionally, the comparative statistical analysis is performed in terms of the probability density function and cumulative density function along with the Wilcoxon rank test on the above test systems for 30 independent runs. The statistical analysis results prove that this algorithm is highly efficient in solving this problem compared to other techniques.

show abstract

Optimal reactive power planning using oppositional grey wolf optimization by considering bus vulnerability analysis

Cited by 20 publications

References 49 publications

New Line Voltage Stability Index (BVSI) for Voltage Stability Assessment in Power System: The Comparative Studies

New Line Voltage Stability Index (BVSI) for Voltage Stability Assessment in Power System: The Comparative Studies

A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems

Statistical Analysis of Modified Whale Optimization Algorithm for Active Power Loss Minimization

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