A Comparative Analysis of Decision Trees, Support Vector Machines and Artificial Neural Networks for On-line Transient Stability Assessment

Baltas, Gregory N.; Mazidi, Peyman; Ma, Jin; Fernández, Francisco de Asís; Rodríguez, Pedro

doi:10.1109/sest.2018.8495872

Cited by 37 publications

(24 citation statements)

References 21 publications

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“…The power grid stability assessment-which comprises transient stability, frequency stability, small signal stability, and voltage stability [97,98]-is fundamental for ensuring the reliability and security of the power system. Power system stability is the ability to stay at an equilibrium operation state or quickly reach a new equilibrium state of operation after a perturbation [99]. Traditional models [92,[100][101][102] for stability assessments are complex and require significant computing resources because they heavily rely on accurate real-time dynamic power system models [98].…”

Section: Power Grid Stability Assessmentmentioning

confidence: 99%

Artificial Intelligence Techniques in Smart Grid: A Survey

2021

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The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

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Section: Power Grid Stability Assessmentmentioning

confidence: 99%

Artificial Intelligence Techniques in Smart Grid: A Survey

2021

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“…The generation of input vector sets is identified as the first and the most important step in establishing reliable TSA model analysis [88]. Widespread use of phasor measurement units (PMU)-based wide area measurement system (WAMS) have assisted in the acquisition of synchronized measurements thus allowing the possibility of implementing advanced wide-area protection, decision making and control operations.…”

Section: ) Feature Generationmentioning

confidence: 99%

“…Deploying a different approach. Baltas et al [88] presented a comparative study using three different algorithms (DT, SVM and ANN) with the aim of suggesting which algorithms is more suited for the deployed data.…”

Section: ) Classification/predictionmentioning

confidence: 99%

A Review of Machine Learning Approaches to Power System Security and Stability

2020

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Increasing use of renewable energy sources, liberalized energy markets and most importantly, the integrations of various monitoring, measuring and communication infrastructures into modern power system network offer the opportunity to build a resilient and efficient grid. However, it also brings about various threats of instabilities and security concerns in form of cyberattack, voltage instability, power quality (PQ) disturbance among others to the complex network. The need for efficient methodologies for quicker identification and detection of these problems have always been a priority to energy stakeholders over the years. In recent times, machine learning techniques (MLTs) have proven to be effective in numerous applications including power system studies. In the literature, various MLTs such as artificial neural networks (ANN), Decision Tree (DT), support vector machines (SVM) have been proposed, resulting in effective decision making and control actions in the secured and stable operations of the power system. Given this growing trend, this paper presents a comprehensive review on the most recent studies whereby MLTs were developed for power system security and stability especially in cyberattack detections, PQ disturbance studies and dynamic security assessment studies. The aim is to highlight the methodologies, achievements and more importantly the limitations in the classifier(s) design, dataset and test systems employed in the reviewed publications. A brief review of reinforcement learning (RL) and deep reinforcement learning (DRL) approaches to transient stability assessment is also presented. Finally, we highlighted some challenges and directions for future studies.

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“…In the machine learning literature, several techniques have been proposed to reduce the number of features. In power systems applications, the 'Relief' method, which is is a filter-based method, has been used alone [22], [26], or combined with a PCA to reduce even more the number of features [27], [28]. Variants of this method such as 'Relief-F' have also been used [29]- [33], mostly to improve the predictions of randomised learning algorithms such as extreme learning machines.…”

Section: B Data Pre-processingmentioning

confidence: 99%

“…Since many learning algorithms exist, all with their advantages and disadvantages, comparing several algorithms with your dataset is the best way to know which algorithm is more suitable for your application. In [28], a decision tree model is compared to SVMs and neural networks for transient stability assessment. The same algorithms are compared in [58], but this time considering also the random forest algorithm.…”

Section: Learning a Modelmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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A Comparative Analysis of Decision Trees, Support Vector Machines and Artificial Neural Networks for On-line Transient Stability Assessment

Cited by 37 publications

References 21 publications

Artificial Intelligence Techniques in Smart Grid: A Survey

Artificial Intelligence Techniques in Smart Grid: A Survey

A Review of Machine Learning Approaches to Power System Security and Stability

Recent Developments in Machine Learning for Energy Systems Reliability Management

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