Study of Different ANN Algorithms for Voltage Stability Analysis

Aydin, Faruk; Gümüş, Bilal

doi:10.1109/asyu50717.2020.9259817

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…Voltage stability [175]- [178] Security and cascading failure assessment [179]- [182] Transient stability [183]- [186] Building-level event detection Occupancy and activity [187]- [190] Fault diagnosis in appliances [191]- [194] Optimal power flow End-to-end learning [195]- [198] Learning-augmented [199]- [202] Energy management & control Demand side management Heating/Cooling loads [203]- [206] Demand-response [207]- [210] Buildings [211]- [214] Communities [215]- [218] Microgrids [219]- [222] Load frequency control [223]- [226] Voltage control [227]- [230] Grid variables estimation/ identification Phase [231]- [234] Topology and lines parameters [235]- [238] State estimation [239]- [242] Voltage calculation [243]- [246] FIGURE 9: Further decomposition of analytics services (II). Four indicative references are given for each analytics category.…”

Section: Analytics Cybersecuritymentioning

confidence: 99%

“…• Stability analysis: Data-driven methods that assess the ability of the distribution grid to maintain steady-state operation under various operating conditions. We split the literature into three services: voltage stability un-der various operating conditions [175]- [178], security and cascading failure assessment under fault conditions [179]- [182], and stability under transient conditions [183]- [186]. • Building-level event detection: Data-driven methods to detect and identify significant events or anomalies in the energy consumption patterns of individual buildings.…”

Section: Analytics Cybersecuritymentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis

Bachoumis,

Mylonas,

Plakas

et al. 2023

IEEE Access

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Data-driven machine learning-based methods have provided immense capabilities, revolutionizing sectors like the Buildings-to-grid (B2G) integrated system. Since the penetration rate of distributed energy resources increases towards a net-zero emissions power system, so does the need for advanced services that ensure B2G-integrated system reliability. The convergence of advancements in machine learning, computational resources at the entire cloud-edge continuum, and large datasets from sensing devices enable the development of these data-driven energy analytics services. This work conducts a systematic text-mining-based literature review to examine the diverse range and trends of machine learning methods used to enhance reliability in B2G-integrated systems. While traditional manual sampling and analysis approaches have limited the effectiveness of previous literature review papers in this field, this systematic literature review work aims to synthesize and summarize the existing body of research more efficiently and effectively. To achieve this, this study collected almost 10,500 papers from Scholar and Scopus databases. It employed text-mining-assisted BERTopic-based topic modelling and statistical trend analysis techniques to uncover semantic patterns and explore the temporal evolution of research themes. A two-dimensional taxonomy was derived to analyze the technical papers from a business and machine learning-related perspective. By quantifying the temporal trends within these topics, the study unveiled insights about the state-of-the-art analytics that ensure reliability in the B2G-integrated system domain while proposing future research directions.

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Section: Analytics Cybersecuritymentioning

confidence: 99%

Section: Analytics Cybersecuritymentioning

confidence: 99%

Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis

Bachoumis,

Mylonas,

Plakas

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The results indicated that the Radial Base ANN was able to accurately estimate the desired output, presenting an error of around 10 −4 . In [13], promising results were obtained in which an artificial neural network (ANN) reproduced the same results with high accuracy and speed compared to traditional voltage stability calculation methods. For this, the loading parameter and the voltage stability margin index were calculated using eight different input variables and fourteen different training functions.…”

Section: Introductionmentioning

confidence: 95%

“…In this context, it is desirable in engineering that even well-established energy systems analysis methods are revisited and eventually improved. In recent decades, an alternative formulation based on artificial neural networks (ANN) has been widely employed [12][13][14][15][16]. In [12], two artificial neural networks (ANN) were presented, the Multi-Layer Perceptron and the Radial-Based Perceptron, with the objective of estimating the magnitudes of voltages on the buses of electrical power systems, taking into account several parameters, such as the loading factor, the real and reactive power in the slack bus, and the contingent branch number.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Total Real and Reactive Power Losses in Electrical Power Systems via Artificial Neural Network

da Silva,

de Queiroz,

Garbelini

et al. 2024

ASI

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Total real and reactive power losses in electrical power systems are an inevitable phenomenon and occur due to several factors, such as conductor resistance, transformer impedance, line reactance, equipment losses, and phase unbalance. Minimizing them is crucial to the system’s efficiency. In this study, an artificial neural network, specifically a Multi-layer Perceptron, was employed to predict total real and reactive power losses in electrical systems. The network is composed of three layers: an input layer consisting of the variables loading factor, real and reactive power generated on the slack bus, a hidden layer, and an output layer representing the total real and reactive power losses. The training method used was backpropagation, adjusting the weights based on the desired output. The results obtained, using datasets from IEEE systems with 14, 30, and 57 buses, showed satisfactory performance, with a mean squared error of around 10−4 and a coefficient of determination (R2) of 0.998. In validation with 20% of the data that was not part of the training, the network demonstrated effectiveness, with a mean squared error around 10−3. This indicates that the network was able to accurately predict total power losses based on loads, generating estimates close to the desired values.

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“…Além disso, diversos trabalhos podem ser encontrados na literatura como métodos de domínio no tempo, ajuste de curva e critério das áreas iguais, para a classificação da estabilidade do sistema (Sobbouhi and Vahedi, 2021;Tan and Zivanovic, 2007;Wang et al, 2014). Recentemente, com o avanço do poder computacional, diversas pesquisas têm sido desenvolvidas utilizando técnicas de aprendizado de máquinas, como por exemplo as Redes Neurais Artificiais (do inglês Artificial Neural Network) (Alimi et al, 2020;Amjady, 2003;Aydin and Gumus, 2020;Calma and Pacis, 2021;Hagmar et al, 2021;Lotufo et al, 2007;Sawhney and Jeyasurya, 2006;Veerasamy et al, 2021).…”

Section: Introductionunclassified

Aplicação de Redes Neurais Artificiais para Estimação de Indicadores de Segurança Estática e Dinâmica de Sistemas Elétricos de Potência

SOUZA

Ferreira

Neto

2022

Proceedings IX Simpósio Brasileiro De Sistemas Elétricos

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Nowadays, with the increase of fear of blackout events due to society's increased dependence on electricity, the development of studies related to the power systems security analysis is one of the main activities to be carried out within the scope of planning and operation of electrical grids. Thus, this subject has been debated in the literature for a long period of time and with the advancement of computational power, lots of studies are developed using artificial intelligence techniques for the prediction of indicators related to the power systems security analysis. In this context, this article presents the development of a model, using artificial neural networks, which will work together with power system analysis software for prediction safety indicators, static and dynamic, of electrical power systems.Resumo: Atualmente, com o maior receio de ocorrências de blecautes em função do aumento da dependência da energia elétrica pela sociedade, o desenvolvimento de estudos relacionados à segurança dos sistemas elétricos de potência desponta como uma das principais atividades a serem realizadas no âmbito do planejamento e da operação dos sistemas elétricos. Em função disso, este assunto tem sido amplamente debatido na literatura e com o avanço do poder computacional, cada vez mais autores propõem a utilização de técnicas de inteligência artificial para a predição de indicadores relacionados à segurança dos Sistemas Elétricos de Potência. Neste contexto, este artigo apresenta o desenvolvimento de um modelo, utilizando redes neurais artificiais, que irá atuar em conjunto com softwares de análise de sistema de potência para a previsão de indicadores de segurança estática e dinâmica de sistemas elétricos de potência.

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Study of Different ANN Algorithms for Voltage Stability Analysis

Cited by 5 publications

References 20 publications

Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis

Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis

Estimation of Total Real and Reactive Power Losses in Electrical Power Systems via Artificial Neural Network

Aplicação de Redes Neurais Artificiais para Estimação de Indicadores de Segurança Estática e Dinâmica de Sistemas Elétricos de Potência

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