Optimal placement of power factor correction capacitors in power systems using Teaching Learning Based Optimization

Bouaraki, Mouloud; Recioui, Abdelmadjid

doi:10.51485/ajss.v2i2.37

Cited by 7 publications

(4 citation statements)

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“…To deal with the OPFI, several population-based algorithms such as the EM approach [14], SAA [15], TLBO [16], GA [17], GWO and DE [18], CSSO [19], GBOA [20], BBO [21], PSO [22], WCEMFT [23], and QMFT [24] are utilized. In addition, in [25], the TLBO approach was created and used to solve the allocation optimization problem of capacitor devices in electrical systems for the purpose of power factor adjustment.…”

Section: B) Literature Reviewmentioning

confidence: 99%

Optimal Allocation of TCSC Devices in Transmission Power Systems by a Novel Adaptive Dwarf Mongoose Optimization

Alnami,

El-Rifaie,

Moustafa

et al. 2024

IEEE Access

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This paper introduces a novel Improved Dwarf Mongoose Optimizer (IDMO) based on an Alpha-Directed Learning Process (ADLP) for dealing with different mathematical benchmark models and engineering problems. The dwarf mongoose's foraging behavior motivated the DMO's primary design. Three social groupings are used: the alpha group, babysitters, and scouts. The unique suggested solution includes an upgraded ADLP to boost searching abilities, and its upgrading mechanism is substantially led by the improved alpha. First, the IDMO and DMO are put through their paces using CEC 2017 single objective optimization benchmarks. Also, several recent optimization techniques are taken into contrast, including artificial ecosystem optimization (AEO), aquila optimization (AQU), equilibrium optimization (EO), enhanced slime mould algorithm (ESMA), Gorilla troops optimization (GTO), red kite optimization (RKO), subtraction-average-based algorithm (SAA) and slime mould algorithm (SMA). Further, their application validity is examined for optimal allocation of Thyristor Controlled Series Capacitor (TCSC) devices in transmission power systems. The simulations are implemented on two different IEEE power systems of 30 and 57 buses, and considering different numbers of TCSC devices. The suggested IDMO and DMO are compared to several different current and popular techniques for all applications. The findings from the simulation demonstrate that, in relation to efficiency and effectiveness, the suggested DMO beats not only the standard DMO but also a large number of other contemporary solutions. For the first system, considering three TCSC devices to be optimized and based on the mean acquired losses, the proposed IDMO accomplishes 5.65%, 0.68%, 3.72%, 16.44%, and 5.88% reduction in power losses in compared to DMO, SAA, AEO, Grey Wolf Optimizer (GWO) and AQU, respectively. Similarly, for the second system, the proposed IDMO achieves improvement reduction 28.96%, 54.20%, 9.44%, 60.99% and 48.54%, respectively, compared to the obtained results by the DMO, SAA, AEO, GWO and AQU.INDEX TERMS Alpha-directed learning process, Dwarf Mongoose Optimizer, CEC 2017 benchmark models, TCSC technology, power trans-mission systems.

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Section: B) Literature Reviewmentioning

confidence: 99%

Optimal Allocation of TCSC Devices in Transmission Power Systems by a Novel Adaptive Dwarf Mongoose Optimization

Alnami,

El-Rifaie,

Moustafa

et al. 2024

IEEE Access

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“…There are diverse population-based heuristics that are used to solve the OPFI, such as the electromagnetism-like mechanism [ 37 ], simulated annealing optimization [ 38 ], Particle Swarm Optimization (PSO) [ 39 ], Gradient-Based Optimization Algorithm (GBOA) [ 40 ], and Quantum computing with Moth Flame Technique (QMFT) [ 41 ]. In addition, in [ 42 ], the TLBO technique has been developed and adopted for solving the allocation optimization problem in power systems of capacitors for the sake of power factor correction. To suitably increase the incorporation of dispersed sources of energy in low-inertia electrical networks, an updated priority-list approach with a Boolean inference coding/decoding method and a feed-forward neural network for determining the subsequent function assessment was hybridized [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

A Subtraction-Average-Based Optimizer for Solving Engineering Problems with Applications on TCSC Allocation in Power Systems

et al. 2023

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The present study introduces a subtraction-average-based optimization algorithm (SAOA), a unique enhanced evolutionary technique for solving engineering optimization problems. The typical SAOA works by subtracting the average of searcher agents from the position of population members in the search space. To increase searching capabilities, this study proposes an improved SAO (ISAO) that incorporates a cooperative learning technique based on the leader solution. First, after considering testing on different standard mathematical benchmark functions, the proposed ISAOA is assessed in comparison to the standard SAOA. The simulation results declare that the proposed ISAOA establishes great superiority over the standard SAOA. Additionally, the proposed ISAOA is adopted to handle power system applications for Thyristor Controlled Series Capacitor (TCSC) allocation-based losses reduction in electrical power grids. The SAOA and the proposed ISAOA are employed to optimally size the TCSCs and simultaneously select their installed transmission lines. Both are compared to two recent algorithms, the Artificial Ecosystem Optimizer (AEO) and AQuila Algorithm (AQA), and two other effective and well-known algorithms, the Grey Wolf Optimizer (GWO) and Particle Swarm Optimizer (PSO). In three separate case studies, the standard IEEE-30 bus system is used for this purpose while considering varying numbers of TCSC devices that will be deployed. The suggested ISAOA’s simulated implementations claim significant power loss reductions for the three analyzed situations compared to the GWO, AEO, PSO, and AQA.

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“…Similarly, an inductive load causes the current wave to lag behind the voltage wave as it takes time to establish its magnetic field when voltage is applied. Therefore, the PF of an inductive load is lagging (Blume, 2007;Che Soh et al, 2014). The PF in an electrical power system is defined as the cosine of the angle between the current and voltage (Che Soh et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Optimal capacitor planning for power factor improvement using hybrid particle swarm and harmony search optimization

Ibrahim,

Ashigwuike,

Oluyombo

et al. 2023

Nig. J. Technol. Dev.

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Industrial loads reduce the Power Factor (PF) of supply systems, causing increases in power losses, damaging equipment and higher utility bills. Optimization techniques are used in planning reactive sources to improve PF of power systems. However, conventional techniques suffer difficulties in passing over local optimal, divergence risk, constraints handling or computing higher order derivatives. Herein, the hybridization of Particle Swarm and Harmony Search Algorithm (PS – HSA) is developed for optimal capacitor planning to improve PF, and comparison is made with the Enhanced Particle Swarm Optimization (EPSO) and Improved Adaptive Harmony Search Algorithm (IAHSA). The test systems are the Modified IEEE 6 and 16 buses and nodes respectively. To create semblance of industrial load dominated power systems, the test networks were modified by increasing the reactive load demand at all buses of the IEEE 6 and 16 by 50% and 70% respectively. The capacitor is modelled as static shunt-controlled element deployed to inject reactive power at buses/nodes. Results show that for IEEE 6 buses, PF improved from 0.68 to 0.8983, 0.8986 and 0.8992 with EPSO, IAHSA and hybrid PS – HSA respectively. Similarly, in IEEE 16 nodes, PF improved from 0.76 to 0.9439, 0.943, and 0.944 with EPSO, IAHSA and hybrid PS – HSA respectively. Furthermore, real power losses reduced from 16.94 MW to 14.03 MW in IEEE 6 buses, translating to 17.2% reduction with the hybrid PS - HSA. While in IEEE 16 nodes, reduction is from 0.719 MW to 0.69 MW accounting for 4% reduction, also with the hybrid PS - HSA.

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Optimal placement of power factor correction capacitors in power systems using Teaching Learning Based Optimization

Cited by 7 publications

References 0 publications

Optimal Allocation of TCSC Devices in Transmission Power Systems by a Novel Adaptive Dwarf Mongoose Optimization

Optimal Allocation of TCSC Devices in Transmission Power Systems by a Novel Adaptive Dwarf Mongoose Optimization

A Subtraction-Average-Based Optimizer for Solving Engineering Problems with Applications on TCSC Allocation in Power Systems

Optimal capacitor planning for power factor improvement using hybrid particle swarm and harmony search optimization

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