Intentional Islanding Solution Based on Modified Discrete Particle Swarm Optimization Technique

Saharuddin, N. Z.; Abidin, Izham Zainal; Mokhlis, Hazlie

doi:10.1109/pecon.2018.8684097

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Figure13. Comparative results of Machine learning with AI based algorithms Furthermore, ANN identifies faults and generates '0' at 0.05 ms for all switches, but it again generates '1' after the fault is isolated.…”

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confidence: 96%

“…Few papers provide load shedding and battery energy storage systems solutions for intentional islanding in AC systems [10][11]. Additionally, few papers present optimal algorithms based on genetic algorithms and particle swam optimization (PSO) algorithms [12][13]. A recent article shows a fault location, isolation and service restoration (FLISR) application along with integration of an open-source standards-based platform for ADMS application development in AC distribution systems [14].…”

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Machine Learning Based Intentional Islanding Algorithm for DERs in Disaster Management

Kumar

Laxmi

2021

IEEE Access

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Currently, research work is primarily dependent on the collection of large sets of data from systems and making predictions based on the knowledge obtained from the data, which is generally termed as 'data mining'. These data mining algorithms are of great importance in improving the performance of different applications. In this regard, Machine Learning (ML) algorithms have been demonstrated to be excellent tools to cope with difficult problems. In this paper, a classification learner based supervised ML algorithm is proposed for intentional islanding of DERs based on the live data collected from supervisory control and data acquisition (SCADA) system in post disaster situations. Literature presents various islanding detection techniques and also intentional islanding algorithms to address different problems in AC networks. These algorithms majorly work based on the control of current source or voltage source inverters. On the other hand, a low voltage DC distribution system allowing the removal of inverter is proposed, which is supposed to be more advantageous by reducing losses and is also economical when working with DERs. In this paper, ML based intentional islanding algorithm for DERs based low voltage DC distribution system is proposed by considering the effects of natural disasters. The learner models trained are fine tree, linear SVM, quadratic SVM and Gaussian SVM. The training of fine tree model is achieved with higher accuracy of 99.8%. The main objective of this work is to achieve a faster and accurate decision making. The performance of the ML based intentional islanding algorithm is compared with the earlier proposed artificial intelligence (AI) based intentional islanding algorithms. The AI algorithms proposed earlier are fuzzy inference systems (FIS), artificial neural networks (ANN) and adaptive network based fuzzy inference system (ANFIS). The comparison shows that, the decision making with ML based intentional islanding algorithm is faster and accurate than all other algorithms.

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confidence: 96%

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Machine Learning Based Intentional Islanding Algorithm for DERs in Disaster Management

Kumar

Laxmi

2021

IEEE Access

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Optimal operation of under-frequency load shedding relays by hybrid optimization of particle swarm and bacterial foraging algorithms

Awad

Hafez

2022

Alexandria Engineering Journal

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Intentional Islanding of Electricity Grids Using Binary Genetic Algorithm

Ivanova

Paradell

Domínguez‐García

et al. 2020

2020 2nd Global Power, Energy and Communication Conference (GPECOM)

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Nowadays, if a fault happens, the system operator has to manage the fault manually to solve the problem. The aim of this article is to provide a method of self-healing and islanding for the electric grid, when a fault appears, due to an electric problem or not. The presented tool has been developed to provide the best configuration of a grid when a problem occurs, with the least amount of changes to the grid and maintaining the power supply to the maximum number of users possible. The tool relies on the Binary Genetic Algorithm.

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Intentional Islanding Solution Based on Modified Discrete Particle Swarm Optimization Technique

Cited by 4 publications

References 9 publications

Machine Learning Based Intentional Islanding Algorithm for DERs in Disaster Management

Machine Learning Based Intentional Islanding Algorithm for DERs in Disaster Management

Optimal operation of under-frequency load shedding relays by hybrid optimization of particle swarm and bacterial foraging algorithms

Intentional Islanding of Electricity Grids Using Binary Genetic Algorithm

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