A novel approach for early detection of impending voltage collapse events based on the support vector machine

Nguyen-Duc, Huy; Kamwa, Innocent; Dessaint, L.-A.; Cao-Duc, Huy

doi:10.1002/etep.2375

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…(18) f ∶ IR D → {+1, −1}. Now, let us derive the hyperplane and the margin [37,38]. Let x be an feature of data space IR D where IR D ⊆ IR In a general sense, the hyperplane contains no support vectors, i.e., the distance between +1 elements and −1 elements would be zero on the hyperplane.…”

Section: Machine Learning Classifiermentioning

confidence: 99%

Network Intrusion Detection: An Analytical Assessment Using Deep Learning and State-of-the-Art Machine Learning Models

Al-Imran

Ripon

2021

Int J Comput Intell Syst

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The internet connected devices are prone to cyber threats. Most of the companies are developing devices with built-in cyber threat protection mechanism or recommending prevention measure. But cyber threat is becoming harder to trace due to the availability of various tools and techniques to bypass the normal prevention measures. A data mining-based intrusion detection system can play a key role to handle such cyberattacks. This paper proposes a threefold approach to analyzing intrusion detection system. In the first phase, experiments have been conducted by applying SVM, Decision Tree, and KNN. In the second phase, Random Forest, and XGBoost are applied as lately they have been showing significant improved performance in supervised learning. Finally, deep learning techniques, namely, Feed Forward, LSTM, and Gated Recurrent Unit neural network are applied to conduct the experiment. Kyoto Honeypot Dataset is used for experimental purpose. The results show a significant improvement in IDS outperforming the state of the arts on this dataset. Such improvement strengthens the applicability proposed model in IDS.

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Section: Machine Learning Classifiermentioning

confidence: 99%

Network Intrusion Detection: An Analytical Assessment Using Deep Learning and State-of-the-Art Machine Learning Models

Al-Imran

Ripon

2021

Int J Comput Intell Syst

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“…The reactive power deficit has been calculated by assuming the power factor of generation and total load to be equal in [6], which is generally not correct as other sources also contribute to generating reactive power. Supervised learning‐based method using bus voltages as input features for determining the probability of generators hitting their overexcitation limits is presented in [7] for voltage stability monitoring. However, adequate emergency control should assure both frequency and voltage stability, following an event.…”

Section: Introductionmentioning

confidence: 99%

Event‐driven frequency and voltage stability predictive assessment and unified load shedding

Singh

Fozdar

2019

IET Generation, Transmission & Distribution

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Third zone protection to discriminate symmetrical fault and stressed system conditions

Venkatanagaraju

Jose

Biswal

et al. 2019

Int Trans Electr Energ Syst

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Summary Disastrous effects resulting from power system security issues have been pointed out in various blackout reports. In these reports, setting of the third zone reach being large was found to be a major reason. In addition to a fault, the apparent impedance measured by the relay can also enter the third‐zone setting for different stressed events, such as load encroachment, power swing, and voltage instability, and thus be misinterpreted as a three‐phase fault, leading to relay maloperation. To improve the functionality of the relay under such dire situations, a local end information‐based technique is proposed. The scheme uses the Teager energy‐based approach for developing an index criterion. As per the proposed method, after the entry of the impedance into the relay zone‐3 characteristic, the index is checked. If both the impedance and the index cross their respective thresholds, the disturbance event will be detected as a fault. Otherwise, it will be a no‐fault condition and undesired relay operation avoided. Reliability of the proposed technique is tested for various power system events by simulation on the New England 39‐bus, 10 machine test system.

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A novel approach for early detection of impending voltage collapse events based on the support vector machine

Cited by 5 publications

References 34 publications

Network Intrusion Detection: An Analytical Assessment Using Deep Learning and State-of-the-Art Machine Learning Models

Network Intrusion Detection: An Analytical Assessment Using Deep Learning and State-of-the-Art Machine Learning Models

Event‐driven frequency and voltage stability predictive assessment and unified load shedding

Third zone protection to discriminate symmetrical fault and stressed system conditions

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