A combined mathematical morphology and extreme learning machine techniques based approach to micro-grid protection

Mishra, Manohar; Panigrahi, Rasmi Ranjan; Rout, Pravat Kumar

doi:10.1016/j.asej.2019.03.011

Cited by 41 publications

(16 citation statements)

References 31 publications

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“…In the fault location step, using a differential equation obtained from the equivalent microgrid model, the RLS method is employed to estimate the fault location. MM is used to act on current signals to extract a differential characteristics vector that will be used in an intelligent differential protection scheme for a microgrid system [33].…”

Section: Mathematical Morphologymentioning

confidence: 99%

Differential Analysis of Fault Currents in a Power Distribution Feeder Using abc, αβ0, and dq0 Reference Frames

2022

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This paper proposes a methodology to monitor the instantaneous value of the current and its derivative in the abc, αβ0, and dq0 reference frames to act in the detection of fault current in medium-voltage distribution systems. The method employed to calculate the derivative was Euler’s, with processing sampling rates of 10, 50, 100, and 200 μs. Using the MATLAB/Simulink platform, fault situations were analyzed on a real feeder of approximately 1.1132 km in length, fed by an 11.4 kV source, composed of 26 unbalanced loads and modeled as constant power. The simulation results show that the detection occurred in the different fault situations implemented in the feeder and that the detection speed is related to the value of the processing sampling rate (PSR) used. Considering all fault situations and regardless of the PSR value used, the total average detection time was 49 µs. Besides that, the joint action of the detection system with the Thyristor Controlled Series Capacitor (TCSC) limited the fault current in each situation. The average detection time for each fault situation analyzed was below the typical time for a recloser to act, regardless of the reference adopted for the analysis.

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Section: Mathematical Morphologymentioning

confidence: 99%

Differential Analysis of Fault Currents in a Power Distribution Feeder Using abc, αβ0, and dq0 Reference Frames

2022

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“…On Off Fig. 1 The utility integrated microgrid for reliable and eco-friendly power supply [1][2][3]. Microgrids can be defined as an interconnection of distributed power sources, loads, and storage devices with considered electric restrictions that act as an isolated controllable unit with reference to the main distribution grid as shown in Fig.…”

Section: Point Of Common Couplingmentioning

confidence: 99%

A Brief Analysis on Microgrid Control

Chandak

Sahoo

Rout

et al. 2021

Lecture Notes in Electrical Engineering

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Microgrids have been an innovative enhancement in the power sector to integrate distributed power sources. Having the capability to operate in two modes, the control of microgrid is an important aspect to be studied. The paper discusses the operational concept and challenges faced by microgrids in different modes of operation to achieve optimum stability. The study on microgrid's control hierarchy has been analyzed in this paper. A brief analysis of several challenges faced by microgrid control strategy till-date has been discussed. Several power sharing strategies, energy management, and load balancing strategies have been studied. Along with the challenges faced, the paper explores future aspects of research in the field of microgrid control. IntroductionThe shifting power demand and environmental combat have been a constant source of change and development in the growth of power sector. Integration of distributed power sources earlier was restricted to medium and high voltage levels of power systems. However, the innovations, developments, and improvements in the miniaturized scale of power generation and distribution have promoted the use of distributed power sources at low voltage levels. This idea of integrating distributed sources in distribution networks has prompted the setup of microgrids addressing the demand

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“…In the field of quality disturbance classification, commonly used feature extraction algorithms include Fourier transform [33], wavelet transform [34], S transform [35,36], Hilbert yellow transform [37,38], etc. Commonly used classification and recognition algorithms include extreme learning machine [8], support vector machine [39], BP neural network [40] and so on.…”

Section: Introductionmentioning

confidence: 99%

Disturbance Evaluation in Power System Based on Machine Learning

Sivakumar¹,

Ibrahim²

2022

Computers, Materials &Amp; Continua

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The operation complexity of the distribution system increases as a large number of distributed generators (DG) and electric vehicles were introduced, resulting in higher demands for fast online reactive power optimization. In a power system, the characteristic selection criteria for power quality disturbance classification are not universal. The classification effect and efficiency needs to be improved, as does the generalization potential. In order to categorize the quality in the power signal disturbance, this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality disturbances. Then, a multi-label classification algorithm based on rating is proposed to understand the relationship between the labels and identify the various power quality disturbances. The two algorithms are combined to construct a multi-label classification model based on a multi-level extreme learning machine, and the optimal network structure of the multi-level extreme learning machine as well as the optimal multi-label classification threshold are developed. The proposed method can be used to classify the single and compound power quality disturbances with improved classification effect, reliability, robustness, and anti-noise performance, according to the experimental results. The hamming loss obtained by the proposed algorithm is about 0.17 whereas ML-RBF, SVM and ML-KNN schemes have 0.28, 0.23 and 0.22 respectively at a noise intensity of 20 dB. The average precision obtained by the proposed algorithm 0.85 whereas the ML-RBF, SVM and ML-KNN schemes indicates 0.7, 0.77 and 0.78 respectively.

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A combined mathematical morphology and extreme learning machine techniques based approach to micro-grid protection

Cited by 41 publications

References 31 publications

Differential Analysis of Fault Currents in a Power Distribution Feeder Using abc, αβ0, and dq0 Reference Frames

Differential Analysis of Fault Currents in a Power Distribution Feeder Using abc, αβ0, and dq0 Reference Frames

A Brief Analysis on Microgrid Control

Disturbance Evaluation in Power System Based on Machine Learning

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