A real time PMU data and neural network approach to analyze voltage stability

Innah, Herbert; Hiyama, Takashi

doi:10.1109/apap.2011.6180572

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…While the vcaRVSA index predicts the margin to collapse from the maximum available reactive power of the generators, the TVSI quantifies the magnitude of the voltage violation and the associated duration time. IS for evaluating long-term (small-disturbance) voltage stability are proposed in (Adewole & Tzoneva, 2017;Diao et al, 2009;Innah & Hiyama, 2011;Jain et al, 2003;Malbasa et al, 2017;H. Mohammadi et al, 2016;Nandanwar et al, 2018;Nandanwar & Warkad, 2016;Nie et al, 2017;Shah & Verma, 2016;Suganyadevi et al, 2016;C.…”

Section: Voltage Stabilitymentioning

confidence: 99%

“…Small test systems used in the literature are the IEEE 4-bus test system (Abbass et al, 2023), the IEEE 9-bus test system (Bahbah & Girgis, 2004;Goh et al, 2015;Hossain & Kumar, 2023;C. Xu et al, 2005), the IEEE 14bus power system (Adewuyi, Shigenobu, Ooya, Senjyu, & Howlader, 2019;Ali et al, 2015;Dharmapala et al, 2020;Goh et al, 2015;Innah & Hiyama, 2011;Jayasankar et al, 2010;Ortiz-Villalba et al, 2020;Pannell et al, 2018;Pérez-Londoño et al, 2017;, the IEEE 24-bus power system (Y. , the IEEE 30-bus power system (Ali et al, 2015;Jain et al, 2003;Mukherjee & De, 2020;Nandanwar et al, 2018;Pérez-Londoño et al, 2017;Suganyadevi et al, 2016;Suliman & Rahman, 2010;, the IEEE 39-bus New England (Adewole & Tzoneva, 2017;Azman et al, 2020;Bahmanyar & Karami, 2014;Bo et al, 2014;Cepeda et al, 2014;Chen et al, 2019;Echeverría et al, 2017;Frimpong et al, 2017;Gomez et al, 2011;Gu & Li, 2013;Hashiesh et al, 2012;Hossain & Kumar, 2023;Hu et al, 2019;F. Li et al, 2021;Y.…”

Section: Test Systemsmentioning

confidence: 99%

“…As indicated earlier, many works only focus on evaluating system stability, without triggering any actions. We identified following works that falls into this case: (Abbass et al, 2023;Ali et al, 2015;Anderson et al, 2023;Azman et al, 2020;Bahbah & Girgis, 2004;Bahmanyar & Karami, 2014;Bashiri Mosavi et al, 2018;Bo et al, 2014;Cepeda et al, 2014;Chen et al, 2019;Dharmapala et al, 2020;Diao et al, 2009;Echeverría et al, 2017;Frimpong et al, 2017;Gao et al, 2023;Geeganage et al, 2015;Goh et al, 2015;Gomez et al, 2011;Gu & Li, 2013;, 2014Hashiesh et al, 2012;Hu et al, 2019;Innah & Hiyama, 2011;Jain et al, 2003;Jayasankar et al, 2010;Kaci et al, 2014;Kamwa et al, 2010;Karapidakis, 2007;F. Li et al, 2021;Y.…”

Section: Type Of Actionmentioning

confidence: 99%

“…The only difference with an ELM is that an RVFL has its output layer connected to the input layer. Another specific type of ANN is the Radial Basis Function neural network (RBFNN), in which the response of neurons is given by a radial basis function (Bahbah & Girgis, 2004;Innah & Hiyama, 2011;Jain, Srivastava, & Singh, 2003;Siddiqui, Verma, Niazi, & Fozdar, 2018;Velayati, Amjady, & Khajevandi, 2015;Y. Xu et al, 2011Y.…”

Section: Types Of Techniquementioning

confidence: 99%

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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: Voltage Stabilitymentioning

confidence: 99%

Section: Test Systemsmentioning

confidence: 99%

Section: Type Of Actionmentioning

confidence: 99%

Section: Types Of Techniquementioning

confidence: 99%

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Artificial Intelligence Techniques for Dynamic Security Assessments – A Survey

Cuevas,

Alvarez-Malebrán,

Rahmann

et al. 2024

Preprint

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“…Thus, the power system is simulated with distinct operating conditions, including sometimes contingencies. These simulated conditions are used to obtain other system parameters such as branch currents, line flows, real and reactive power and different voltage stability indicators such as line voltage stability indices, bus voltage stability indices and Voltage Stability Margin (VSM) or Loading Margin (LM) [24][25][26]. Although ANNs have been extensively used by researchers, they exhibit some shortcomings, mainly related to the long time required in their training process, and the fact that its learning is highly dependent on the number of training data.…”

Section: Introductionmentioning

confidence: 99%

Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach

2020

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This paper presents a novel approach for Voltage Stability Margin (VSM) estimation that combines a Kernel Extreme Learning Machine (KELM) with a Mean-Variance Mapping Optimization (MVMO) algorithm. Since the performance of a KELM depends on a proper parameter selection, the MVMO is used to optimize such task. In the proposed MVMO-KELM model the inputs and output are the magnitudes of voltage phasors and the VSM index, respectively. A Monte Carlo simulation was implemented to build a data base for the training and validation of the model. The data base considers different operative scenarios for three type of customers (residential commercial and industrial) as well as N-1 contingencies. The proposed MVMO-KELM model was validated with the IEEE 39 bus power system comparing its performance with a support vector machine (SVM) and an Artificial Neural Network (ANN) approach. Results evidenced a better performance of the proposed MVMO-KELM model when compared to such techniques. Furthermore, the higher robustness of the MVMO-KELM was also evidenced when considering noise in the input data.

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