A Novel Control Strategy for Compensation of Voltage Quality Problem in AC Drives

Suraya, Syed; Sujatha, P.; P, Bharat Kumar

doi:10.11591/ijpeds.v9.i1.pp8-16

Cited by 4 publications

(4 citation statements)

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“…A denial of service attack is an attempt to make a machine or network resource unavailable to its intended users [10,11], such as to temporarily or indefinitely interrupt or suspend services of a host connected to the Internet. This attack can be launched at different layers.…”

Section: Denial Of Service Attacks (Dos)mentioning

confidence: 99%

A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

Laqtib

Yassini

Hasnaoui

2020

IJECE

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Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network Inception-CNN, Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning methods in MANET.

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Section: Denial Of Service Attacks (Dos)mentioning

confidence: 99%

A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

Laqtib

Yassini

Hasnaoui

2020

IJECE

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“…also the transformations blocks that are utilized in SRF theory are not used. This prevents complex problematic calculations and makes the control technique facile [14][15][16]. The data of V a , V b , V c are obtained from the source voltage.…”

Section: Dynamic Voltage Restorermentioning

confidence: 99%

Multiple Voltage Disturbance Compensation in Distribution Systems using DVR

Suraya¹,

Irshad

Azeem

et al. 2020

Eng. Technol. Appl. Sci. Res.

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The subject of power quality is gaining much interest nowadays. With the sophisticated sensitive electrical devices available in the market, power stations should ensure qualified and eminent power to the point of utilization. Bad voltage conditions in a power system can cause a malfunction at the point of utilization. Dynamic voltage restorer (DVR) is a reliable power electronic offsetting device which when engaged in the power system is effectual in the operation of compensating voltage sag/swell. In this research work, a DVR for offsetting multiple voltage disturbances in a power system is presented and a novel control strategy to trigger power switches in DVR is proposed. The proposed novel control methodology is compared with Synchronous Reference Frame (SRF) theory-controlled DVR. The simulation model is designed and the analysis with MATLAB/SIMULINK is presented

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“…Another efficient technique is the use of flexible AC transmission system (FACTS) devices. These devices include static VAR compensators (SVC) and static synchronous compensators (STATCOM), which are widely used in voltage balancing [20]− [22]. Esfahani and Vahidi in [23], a combination of thyristorcontrolled reactor (TCR) and STATCOM was implemented to restore voltage balancing and reduce current harmonics.…”

Section: Introductionmentioning

confidence: 99%

Implementation of reactive compensator for voltage balancing using AI based models and novel performance index

Ragab

Ghaeb²

2022

IJPEDS

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Voltage-unbalance is one of the power quality deficiencies that degrades electrical power systems performance. In this work, voltage unbalance problem is tackled through two stages; evaluation using a novel performance index and mitigation using a thyristor-controlled reactor (TCR) compensator with artificial intelligent (AI) based models. Unlike standard performance indices that rely on voltages' root mean square (RMS) values, the proposed index depends on the space vector (SV) signal amplitude for voltage unbalance evaluation. This signal depends on the instantaneous values of the three-phase voltages and has twice the system frequency. Therefore, the proposed index entitled as space vector unbalance factor (SVUF) reflects the amount of voltage unbalance and reduces the time necessary for evaluation by half. Subsequently, advanced models based on several algorithms are proposed to generate the required firing angles for TCR compensator to restore voltage balance, including radial basis functions networks (RBFNs), hybrid-RBFNs (H-RBFNs), polynomials (PNs), and simplified neural networks (NNs). Models' structure, prediction capability, and response time are analyzed. Results show that the time required for voltage unbalance mitigation is reduced. Moreover, the models used to generate the firing angles are simplified significantly while maintaining high accuracy.

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A Novel Control Strategy for Compensation of Voltage Quality Problem in AC Drives

Cited by 4 publications

References 0 publications

A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

Multiple Voltage Disturbance Compensation in Distribution Systems using DVR

Implementation of reactive compensator for voltage balancing using AI based models and novel performance index

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