Employing Machine Learning for Enhancing Transient Stability of Power Synchronization Control During Fault Conditions in Weak Grids

Sepehr, Amir; Gomis‐Bellmunt, Oriol; Pouresmaeil, Edris

doi:10.1109/tsg.2022.3148590

Cited by 24 publications

(11 citation statements)

References 30 publications

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“…The techniques used are K-means (R. Liu et al, 2018;Naderi et al, 2023;Pérez-Londoño et al, 2017;Y. Wang, Silva-Saravia, & Pulgar-Painemal, 2019), Growing hierarchical self-organizing map (GHSOM) , Auto Encoders ( (Mahdi & Genc, 2018;Sepehr et al, 2022;Tan et al, 2017)), Generative adversarial network (GAN) (Ren & Xu, 2019), Complete Linkage Clustering (Ortiz-Villalba et al, 2020;Rahmann et al, 2017) and Bayesian Neural Network (T. Yousefian & Kamalasadan, 2016). Five works that use Reinforcement Learning (RL) were found (Y.…”

Section: Types Of Techniquementioning

confidence: 99%

“…This phenomenon can be a fast or slow interaction (Hatziargyriou et al, 2021). Since this phenomenon has gained importance recently, only two works address this issue with AI techniques (Sepehr et al, 2022) and (Y. Wang & Pal, 2023).…”

Section: Converter-driven Stabilitymentioning

confidence: 99%

“…Using DL enabled it to extract spatial and temporal features from the input data and combine local features with global ones, which ML algorithms such as SVM or simpler ANN structures could not. MPL applications with more than one hidden layer only started to become prevalent in recent years, as seen in (Abbass, Lis, & Mushtaq, 2023;Ali, El-Amary, Ibrahim, & Mekhamer, 2015;Anderson et al, 2023;Bahmanyar & Karami, 2014;Gurung, Naetiladdanon, & Sangswang, 2021;Hashiesh, Mostafa, Khatib, Helal, & Mansour, 2012;Hossain & Kumar, 2023;Krishnan & Thampatty, 2020;Naderi et al, 2023;Ren & Xu, 2019;Sepehr, Gomis-Bellmunt, & Pouresmaeil, 2022;Shah & Verma, 2016;Tang et al, 2017;Y. Wang & Pal, 2023;R.…”

Section: Types Of Techniquementioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence Techniques for Dynamic Security Assessments – A Survey

Cuevas,

Alvarez-Malebrán,

Rahmann

et al. 2024

Preprint

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The rise of converter interface generation (CIGs) technologies with fast response times has introduced new challenges for the stability of electrical power systems. Traditional dynamic security assessment (DSA) methods of power systems are based on dynamic simulations within a time domain. Because of the high computational efforts and human resources required to carry out these kinds of simulations, they are performed for a limited number of adverse (critical) operational conditions in terms of their stability. However, further incorporation of CIGs in power systems not only results in a shift of the critical operating conditions that may threaten the system’s stability, but also the number of critical conditions increases. Consequently, there is an urgent need to develop new methods for evaluating system stability in a wide range of operating conditions with a reasonable amount of human and computational effort. A complementary 1 strategy to the traditional methods for DSA is to use artificial intelligence (AI) techniques. Unlike traditional methods, AI techniques use a data set that captures the non-linear relationships between the system’s operational conditions and their stability, without needing to solve the algebraic-differential equations modeling the power system. Once these relationships are established, the system’s stability can be evaluated for other operational conditions much faster (within the order of the hundreds of milliseconds) and accurately. This, in turn, allows for the consideration of an extended range of operational conditions for DSA studies. This article surveys the state of the art regarding the use of AI techniques for DSA in electrical power systems. Focus is made on algorithms, the kind of stability being addressed, data processing and applications. Finally, limitations, challenges, and future trends are discussed.

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Section: Types Of Techniquementioning

confidence: 99%

Section: Converter-driven Stabilitymentioning

confidence: 99%

Section: Types Of Techniquementioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Techniques for Dynamic Security Assessments – A Survey

Cuevas,

Alvarez-Malebrán,

Rahmann

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Data A notable characteristic of modern BEPS is the extensive use of converters and highvoltage converter equipment. In [76], the control of converters is suggested as a means to maintain EPS TS. For the numerical experiment, a mathematical model of an EPS comprising two nodes, a transmission line, and an inverter was utilized.…”

Section: Providing Transient Stabilitymentioning

confidence: 99%

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Senyuk,

Beryozkina,

Safaraliev

et al. 2024

Energies

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Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research.

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“…By filtering the data, it is found that some characteristics have a certain correlation with the fault. In order to further improve the prediction accuracy of the fault, the mined failure factors and related characteristics are combined to form a time series event, so as to achieve the purpose of early warning of the accident [19]. Combining multi-source data with time sequence similarity fault matching method, a fault early warning mode is established, and the fault factors are predicted by using the multi-source analysis method of fault factors, so as to enhance the comprehensive ability of the fault and the prognosis ability of the fault.…”

Section: Measuring Equipmentmentioning

confidence: 99%

Research on fault early warning of power transmission line crossing and collision based on multi-source data

Chang

Zheng

Shang

et al. 2023

Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022)

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The existing transmission line fault early warning can’t automatically identify the abnormal area, resulting in poor early warning effect for the line crossing and line touching fault. Based on this, an early-warning method of power transmission line crossing and collision fault based on multi-source data is proposed. Combined with the feature extraction method of multi-source data fusion, based on the analysis of the characteristics of the stock index of power grid transmission line crossing and touching, the leakage current acquisition model is constructed. Based on the model, the abnormal data of power grid are collected, and the characteristics of power grid transmission line crossing and touching are mined and identified. Then the texture features are obtained by using the local binary pattern operator to simplify the early warning steps of power grid transmission line crossing and touching. The experimental results show that the multi-source data-based power grid transmission line crossing and collision fault early warning is more practical than the traditional methods. In the process of practical application, the effect of abnormal data prediction and collection is significantly better. It can carry out fault early warning more quickly and accurately, and fully meet the research requirements.

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Employing Machine Learning for Enhancing Transient Stability of Power Synchronization Control During Fault Conditions in Weak Grids

Cited by 24 publications

References 30 publications

Artificial Intelligence Techniques for Dynamic Security Assessments – A Survey

Artificial Intelligence Techniques for Dynamic Security Assessments – A Survey

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Research on fault early warning of power transmission line crossing and collision based on multi-source data

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