A new robust kernel ridge regression classifier for islanding and power quality disturbances in a multi distributed generation based microgrid

Chakravorti, Tatiana; Nayak, Niranjan; Bisoi, Ranjeeta; Dash, P.K.; Tripathy, Lokanath

doi:10.1016/j.ref.2018.12.002

Cited by 27 publications

(10 citation statements)

References 36 publications

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“…The disturbances are mostly created due to the introduction of non-linear loads, switching devices, rectifiers and inverters, etc. into the power system [7], [22], [23]. As explained by Wang et al [22], the continuous integrations of disturbing loads and RES are creating deterioration risks and malfunctioning of machineries at energy generation, transmission and consumption levels.…”

Section: Overview Of Power System Security and Stabilitymentioning

confidence: 99%

“…and they are capable of creating severe problems which include equipment malfunction, short life span and failures [24], [25]. According to EPRI, of all power supply outages crisis, PQ issues accounts for approximately 15% of the economic losses estimates of $104 billion to $164 billion annually [7]. Thus, the mitigation of PQD have continued to receive massive attention in the power system's research community.…”

Section: Overview Of Power System Security and Stabilitymentioning

confidence: 99%

“…In recent times, these instabilities and disturbances concerns have drawn the attention of the industrial and academic communities as they are leading causes of outages, economic losses, equipment malfunctioning and failures. According to Electrical Power Research Institute (EPRI), power supply outages crisis created an economic loss estimates of $104 billion to $164 billion annually [7]. Several studies in the literature have discussed the threats, effects and impacts of these disturbances, insecurities and instabilities [8]- [11].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Overview Of Power System Security and Stabilitymentioning

confidence: 99%

Section: Overview Of Power System Security and Stabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2020

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“…A kernel‐based classifier is further added to increase the reliability and tested in different grid conditions. Further, to reduce the complexity and increase the selectivity, an advanced AI‐based method is proposed in Chakravorti et al From the history of the faulty and healthy pattern, this algorithm prepares a pattern of probable events. As a result, it acts quickly and fulfills the operational and control need of the grid at the time of the fault.…”

Section: Protection Strategy For Microgridmentioning

confidence: 99%

Distributed generation hybrid AC/DC microgrid protection: A critical review on issues, strategies, and future directions

2020

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Summary The integration of distributed energy resources (DERs) with conventional systems emerges as an intelligent solution for providing uninterrupted and secure power even at times of high load demand. Better load management with a mature fault handling mechanism makes AC a viable option which has an efficiency of 78.24%. In contrast with less power loss and slightly better efficiency of 84.6%, DC microgrid is a reliable option in a low power environment. In order to accommodate all operating conditions and load types, a hybrid system can be designed with a theoretical efficiency of more than 90%. Bidirectional power flows, low inertia, the transition between different modes of operations are the challenges for the protection of alternating current (AC) and direct current (DC) microgrid systems. Power balance fluctuation, absence of zero‐crossing currents, selection of suitable grounding, and coordination between different rating devices restrict the hybrid system to achieve the said efficiency constantly. This paper reviews in detail of existing protection along with grid‐connected algorithms for both modes of operation. Finally, the limitation, major hurdles, and future course of action for a reliable, efficient, and secure hybrid grid system are figured out.

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“…The PQEs (voltage sag, swell, harmonics, transients, interruptions, unbalanced voltage and current, etc.) can influence the overall performance of MG operation and shorten the life time of power devices integrated into the MG system [ 2 , 3 ]. Therefore, to achieve improved PQ with safe and reliable operation of the MG power system, the source of PQEs must be detected and classified by means of applying advanced classification techniques [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network

Vinayagam¹,

Othman

Veerasamy

et al. 2022

PLoS ONE

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This study proposes SVM based Random Subspace (RS) ensemble classifier to discriminate different Power Quality Events (PQEs) in a photovoltaic (PV) connected Microgrid (MG) model. The MG model is developed and simulated with the presence of different PQEs (voltage and harmonic related signals and distinctive transients) in both on-grid and off-grid modes of MG network, respectively. In the pre-stage of classification, the features are extracted from numerous PQE signals by Discrete Wavelet Transform (DWT) analysis, and the extracted features are used to learn the classifiers at the final stage. In this study, first three Kernel types of SVM classifiers (Linear, Quadratic, and Cubic) are used to predict the different PQEs. Among the results that Cubic kernel SVM classifier offers higher accuracy and better performance than other kernel types (Linear and Quadradic). Further, to enhance the accuracy of SVM classifiers, a SVM based RS ensemble model is proposed and its effectiveness is verified with the results of kernel based SVM classifiers under the standard test condition (STC) and varying solar irradiance of PV in real time. From the final results, it can be concluded that the proposed method is more robust and offers superior performance with higher accuracy of classification than kernel based SVM classifiers.

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A new robust kernel ridge regression classifier for islanding and power quality disturbances in a multi distributed generation based microgrid

Cited by 27 publications

References 36 publications

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

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

Distributed generation hybrid AC/DC microgrid protection: A critical review on issues, strategies, and future directions

A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network

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