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

Moustafa, Ghareeb; Tolba, Mohamed A.; El-Rifaie, Ali M.; Ginidi, Ahmed; Shaheen, Abdullah M.; Abid, Slim

doi:10.3390/biomimetics8040332

Cited by 6 publications

(4 citation statements)

References 64 publications

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“…The SABT methodology was based on mathematical concepts, including average values, shifts in seeking indicative sites, and the direction of the variance between two objectively quantifiable quantities. The method used by the SABT to calculate the arithmetic average is wholly original because it relies on a certain functional called the "v-subtraction operator" [36]. Therefore, each vector-based response in the SABT group was altered to comply with the following equation.…”

Section: Asabt For Optimizing Pv Units and Shunt Capacitors In Distri...mentioning

confidence: 99%

Optimizing PV Sources and Shunt Capacitors for Energy Efficiency Improvement in Distribution Systems Using Subtraction-Average Algorithm

Smaili,

Almalawi,

Shaheen

et al. 2024

Mathematics

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This work presents an optimal methodology based on an augmented, improved, subtraction-average-based technique (ASABT) which is developed to minimize the energy-dissipated losses that occur during electrical power supply. It includes a way of collaborative learning that utilizes the most effective response with the goal of improving the ability to search. Two different scenarios are investigated. First, the suggested ASABT is used considering the shunt capacitors only to minimize the power losses. Second, simultaneous placement and sizing of both PV units and capacitors are handled. Applications of the suggested ASAB methodology are performed on two distribution systems. First, a practical Egyptian distribution system is considered. The results of the simulation show that the suggested ASABT has a significant 56.4% decrease in power losses over the original scenario using the capacitors only. By incorporating PV units in addition to the capacitors, the energy losses are reduced from 26,227.31 to 10,554 kW/day with a high reduction of 59.75% and 4.26% compared to the initial case and the SABT alone, respectively. Also, the emissions produced from the substation are greatly reduced from 110,823.88 kgCO2 to 79,189 kgCO2, with a reduction of 28.54% compared to the initial case. Second, the standard IEEE 69-node system is added to the application. Comparable results indicate that ASABT significantly reduces power losses (5.61%) as compared to SABT and enhances the minimum voltage (2.38%) with a substantial reduction in energy losses (64.07%) compared to the initial case. For both investigated systems, the proposed ASABT outcomes are compared with the Coati optimization algorithm, the Osprey optimization algorithm (OOA), the dragonfly algorithm (DA), and SABT methods; the proposed ASABT shows superior outcomes, especially in the standard deviation of the obtained losses.

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Section: Asabt For Optimizing Pv Units and Shunt Capacitors In Distri...mentioning

confidence: 99%

Optimizing PV Sources and Shunt Capacitors for Energy Efficiency Improvement in Distribution Systems Using Subtraction-Average Algorithm

Smaili,

Almalawi,

Shaheen

et al. 2024

Mathematics

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“…The presented GA was incorporated with dual mutation probability in order to enhance the available transfer capability in power systems. In [34], a modified version of SAA is presented for the allocation of TCSC for reducing losses in electrical power grids. This study incorporates a cooperative learning technique based on the leader solution into the standard SAA.…”

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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“…The dwarf mongoose optimizer (DMO) is created by studying the foraging behavior of dwarf mongoose animals (DMAs). In the presented meta-heuristic technique (DMO), the population of DMAs is initially generated as follows [56,58]:…”

Section: Proposed Mdmo For Optimal Modeling Of Spv Systems and Parame...mentioning

confidence: 99%

Dwarf Mongoose Optimizer for Optimal Modeling of Solar PV Systems and Parameter Extraction

Moustafa,

Smaili,

Almalawi

et al. 2023

Electronics

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This article presents a modified intelligent metaheuristic form of the Dwarf Mongoose Optimizer (MDMO) for optimal modeling and parameter extraction of solar photovoltaic (SPV) systems. The foraging manner of the dwarf mongoose animals (DMAs) motivated the DMO’s primary design. It makes use of distinct DMA societal groups, including the alpha category, scouts, and babysitters. The alpha female initiates foraging and chooses the foraging path, bedding places, and distance travelled for the group. The newly presented MDMO has an extra alpha-directed knowledge-gaining strategy to increase searching expertise, and its modifying approach has been led to some extent by the amended alpha. For two diverse SPV modules, Kyocera KC200GT and R.T.C. France SPV modules, the proposed MDMO is used as opposed to the DMO to efficiently estimate SPV characteristics. By employing the MDMO technique, the simulation results improve the electrical characteristics of SPV systems. The minimization of the root mean square error value (RMSE) has been used to compare the efficiency of the proposed algorithm and other reported methods. Based on that, the proposed MDMO outperforms the standard DMO. In terms of average efficiency, the MDMO outperforms the standard DMO approach for the KC200GT module by 91.7%, 84.63%, and 75.7% for the single-, double-, and triple-diode versions, respectively. The employed MDMO technique for the R.T.C France SPV system has success rates of 100%, 96.67%, and 66.67%, while the DMO’s success rates are 6.67%, 10%, and 0% for the single-, double-, and triple-diode models, respectively.

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

Cited by 6 publications

References 64 publications

Optimizing PV Sources and Shunt Capacitors for Energy Efficiency Improvement in Distribution Systems Using Subtraction-Average Algorithm

Optimizing PV Sources and Shunt Capacitors for Energy Efficiency Improvement in Distribution Systems Using Subtraction-Average Algorithm

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

Dwarf Mongoose Optimizer for Optimal Modeling of Solar PV Systems and Parameter Extraction

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