Recognition of Islanding and Operational Events in Power System With Renewable Energy Penetration Using a Stockwell Transform-Based Method

Kaushik, Rajkumar; Mahela, Om Prakash; Bhatt, Pramod Kumar; Khan, Baseem; Garg, Akhil Ranjan; Alhelou, Hassan Haes; Siano, Pierluigi

doi:10.1109/jsyst.2020.3020919

Cited by 29 publications

(14 citation statements)

References 37 publications

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“…The architecture of the ANN comprises of • Output layer: Usually it has one neuron, and its output ranges from 0 to 1, that is, greater than 0 and less than 1. But multiple outputs can be present [15,[21][22][23][24][25][26][27][28][29][30].…”

Section: Network Structurementioning

confidence: 99%

Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

Chane¹,

Gebru²,

Khan³

2021

J. Infor. Electr. Electron. Eng.

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This paper explains the load forecasting technique for prediction of electrical load at Hawassa city. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfill the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. Several models were developed and tested on the real load data of a Finnish electric utility at Hawassa city. The authors carried out short-term load forecasting for Hawassa city using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB and ETAP. Hourly load means the hourly power consumption in Hawassa city. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 1.5296% this paper was successfully carried out. This paper can be implemented by any intensive power consuming town for predicting the future load and would prove to be very useful tool while sanctioning the load.

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Section: Network Structurementioning

confidence: 99%

Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

Chane¹,

Gebru²,

Khan³

2021

J. Infor. Electr. Electron. Eng.

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“…It is effective even in the presence of non-linear loading conditions. Kaushik et al [20] introduced an IDM for identifying events such as islanding, grid integration, and outage of solar PV and wind generator (WG) plants in the grid by applying ST. Islanding events might be incidents with different nature of power injection in the grid. The algorithm is implemented to recognize the events by processing negative sequence components (NSC) of voltage using ST.…”

Section: A Related Workmentioning

confidence: 99%

Estimation of Islanding Events in Utility Distribution Grid With Renewable Energy Using Current Variations and Stockwell Transform

Mahela¹,

Sharma²,

Ali³

et al. 2021

IEEE Access

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This research work has designed an algorithm to identify islanding events using the current signals in a distribution grid interfaced with renewable energy (RE) sources and remote areas. A medianbased islanding recognition factor (MIRF) is designed by processing the current signal using Stockwell transform (ST). A current rate of change of islanding recognition factor (CRCIRF) is computed by differentiating the root mean square (RMS) current concerning time. The MIRF and CRCIRF are multiplied element by element to calculate the current-based islanding recognition factor (IRFC) used to recognize islanding events and non-islanding events. Simple decision rules are used to discriminate Islanding events from the faulty and the operational events by comparing peak magnitude of IRFC with pre-set threshold values. This IDM effectively recognizes islanding events in the presence of noise with 10 dB signal-to-noise (SNR) level. The performance of IDM is established on a practical distribution feeder. Developed work is executed in MATLAB/Simulink.

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“…Hence, in this study high penetration level of RE equal to 50% is considered. Load details used for this study are available in [17], [18]. However, loads connected to different nodes are included in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…However, loads connected to different nodes are included in Table 1. Description of feeders used in this study are available in [17], [18] and feeder lengths are included in Table 2. Details of the transformer employed at different locations of test utility grid are included in Table 3.…”

Section: Introductionmentioning

confidence: 99%

Identification of Islanding Events in Utility Grid With Renewable Energy Penetration Using Current Based Passive Method

Swarnkar

Mahela²,

Khan

et al. 2021

IEEE Access

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Renewable energy (RE) generation levels are increasing in modern power systems at a fast rate due to their advantages of clean and non-exhaustible nature of energy. However, this type of generation creates technical challenges in terms of operation and control due to uncertain and un-predictable nature of generation. Islanding is an operational scenario where there is a loss of grid and RE generators continue to feed power to the local load. This has harmful effects on the RE generators and operating personal. Hence, it is expected that islanding scenario is identified in minimum time and RE generators are disconnected within 2s duration after island formation. This paper designed an islanding identification scheme (IDS) by designing a current islanding detection indicator (CIDI) that combines the features computed by processing the current signals, negative sequence current (NSC) and negative sequence voltage (NSV) using the Stockwell transform (ST) and the Hilbert transform (HT). Information contained by the total harmonic distortions of voltage (T HD v ) and current (T HD i ) is also used while designing the CIDI. Islanding and non-islanding events of category-I & II are identified and discriminated from each other by comparison of peak magnitude of CIDI with the first threshold value (FTV) and second threshold value (STV). This IDS effectively recognizes the islanding events even in the noisy environment with minimum non-detection zone (NDZ) and minimum time. The efficiency is greater than 98% even with the noise of 20dB SNR (signal to noise ratio). The performance of proposed IDS is better compared to IDS using discrete wavelet transform (DWT), Empirical mode decomposition (EMD), Slantlet transform & Rdigelet probabilistic neural network (RPNN), and artificial neural network (ANN). The effectiveness of IDS is validated on IEEE-13 nodes test system using MATLAB software, practical distribution network and in real time scenario by use of real time digital simulator (RTDS). INDEX TERMSHilbert transform; Islanding; Renewable Energy; Stockwell transform; utility grid network.

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Recognition of Islanding and Operational Events in Power System With Renewable Energy Penetration Using a Stockwell Transform-Based Method

Cited by 29 publications

References 37 publications

Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

Estimation of Islanding Events in Utility Distribution Grid With Renewable Energy Using Current Variations and Stockwell Transform

Identification of Islanding Events in Utility Grid With Renewable Energy Penetration Using Current Based Passive Method

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