Reactive Power Support in Radial Distribution Network Using Mine Blast Algorithm

Shahzad, Mohsin; Shafiullah, Qazi; Akram, Waseem; Arif, Muhammad; Ullah, Barkat

doi:10.5755/j02.eie.28917

Cited by 12 publications

(10 citation statements)

References 34 publications

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“…In the optimal generation allocation of conventional sources or optimal dispatch problem, the sizing and siting of the sources is improved, which optimizes various parameters of microgrids, whereas in the power scheduling problem, researchers focused on scheduling the demanded power in microgrid generators in such a way that it optimizes different parameters such as power losses, generation cost, total operation cost, and so on. The authors in [31][32][33] have discussed the optimal allocation problem considering the sizing and siting of various sources to minimize cost, power losses, emissions, and more in microgrids. The authors in [34] proposed a stochastic multi-objective optimization model to reduce the voltage deviation and operational cost in grid-connected mode for energy management.…”

Section: Related Workmentioning

confidence: 99%

A Cost-Effective Multi-Verse Optimization Algorithm for Efficient Power Generation in a Microgrid

et al. 2023

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Renewable energy sources (RESs) are a great source of power generation for microgrids with expeditious urbanization and increase in demand in the energy sector. One of the significant challenges in deploying RESs with microgrids is efficient energy management. Optimizing the power allocation among various available generation units to serve the load is the best way to achieve efficient energy management. This paper proposes a cost-effective multi-verse optimizer algorithm (CMVO) to solve this optimization problem. CMVO focuses on the optimal sharing of generated power in a microgrid between different available sources to reduce the generation cost. The proposed algorithm is analyzed for two different scale microgrids (IEEE 37-node test system and IEEE 141-node test system) using IEEE test feeder standards to assess its performance. The results show that CMVO outperforms multi-verse optimizer (MVO), particle swarm optimization (PSO), artificial hummingbird algorithm (AHA), and genetic algorithm (GA). The simulation results emphasize the cost reduction and execution time improvement in both IEEE test systems compared with other meta-heuristic algorithms.

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Section: Related Workmentioning

confidence: 99%

A Cost-Effective Multi-Verse Optimization Algorithm for Efficient Power Generation in a Microgrid

et al. 2023

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“…Initially every particle has its own position X k i and moves in a Ddimensional search space with random velocity Vel k i and searches for optima. Each particle modifies its velocity Vel k+1 i and position X k+1 i , according to ( 14) and (15), respectively. All particles update their personal best position P P best in every iteration.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Recently, researchers have paid more attention to the optimal capacitor allocation problem-a combinatorial problem used to determine heuristic approaches [15]. An optimization technique called grasshopper optimization algorithm (GHOA) is used to allocate a capacitor bank in a distribution system optimally [16].…”

Section: Introductionmentioning

confidence: 99%

Annual Cost and Loss Minimization in a Radial Distribution Network by Capacitor Allocation Using PSO

et al. 2021

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Increasing power demand from passive distribution networks has led to deteriorated voltage profiles and increased line flows. This has increased the annual operations and installation costs due to unavoidable reinforcement equipment. This work proposes the reduction in annual costs by optimal placement of capacitors used to alleviate power loss in radial distribution networks (RDNs). The optimization objective function is formulated for the reduction in operation costs by (i) reducing the active and reactive power losses, and (ii) the cost and installation of capacitors, necessary to provide the reactive power support and maintain the voltage profile. Initially, the network buses are ranked according to two loss sensitivity indices (LSIs), i.e., active loss sensitivity with respect to node voltage (LSI1) and reactive power injection (LSI2). The sorted bus list is then fed to the particle swarm optimization (PSO) for solving the objective function. The efficacy of the proposed work is tested on different IEEE standard networks (34 and 85 nodes) for different use cases and load conditions. In use case 1, the values finalized by the algorithm are selected without considering their market availability, whereas in use case 2, market-available capacitor sizes close to the optimal solution are selected. Furthermore, the static and seasonal load profiles are considered. The results are compared with recent methods and have shown significant improvement in terms of annual cost, losses and line flows reduction, and voltage profile.

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“…Despite the free availability and other attractive qualities, PV systems also face challenges, such as reliability, high initial cost, fault sensitivity, and uncertainty [ 3 ]. Energy from renewable sources results in overstressed power transmission networks with compromised power quality [ 4 ], a poor voltage profile [ 5 ], and increased losses [ 6 , 7 ] due to the fact that these are usually connected to medium- or low-power networks. Similarly, physical, environmental, or electrical circumstances can cause faults in a PV system [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Robust Classification Method for PV Panel Faults

Memon

Javed

Kim

et al. 2022

Sensors

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Renewable energy resources have gained considerable attention in recent years due to their efficiency and economic benefits. Their proportion of total energy use continues to grow over time. Photovoltaic (PV) cell and wind energy generation are the least-expensive new energy sources in most countries. Renewable energy technologies significantly contribute to climate mitigation and provide economic benefits. Apart from these advantages, renewable energy sources, particularly solar energy, have drawbacks, for instance restricted energy supply, reliance on weather conditions, and being affected by several kinds of faults, which cause a high power loss. Usually, the local PV plants are small in size, and it is easy to trace any fault and defect; however, there are many PV cells in the grid-connected PV system where it is difficult to find a fault. Keeping in view the aforedescribed facts, this paper presents an intelligent model to detect faults in the PV panels. The proposed model utilizes the Convolutional Neural Network (CNN), which is trained on historic data. The dataset was preprocessed before being fed to the CNN. The dataset contained different parameters, such as current, voltage, temperature, and irradiance, for five different classes. The simulation results showed that the proposed CNN model achieved a training accuracy of 97.64% and a testing accuracy of 95.20%, which are much better than the previous research performed on this dataset.

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Reactive Power Support in Radial Distribution Network Using Mine Blast Algorithm

Cited by 12 publications

References 34 publications

A Cost-Effective Multi-Verse Optimization Algorithm for Efficient Power Generation in a Microgrid

A Cost-Effective Multi-Verse Optimization Algorithm for Efficient Power Generation in a Microgrid

Annual Cost and Loss Minimization in a Radial Distribution Network by Capacitor Allocation Using PSO

A Machine-Learning-Based Robust Classification Method for PV Panel Faults

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