Decentralized Stochastic Disturbance Observer-Based Optimal Frequency Control Method for Interconnected Power Systems With High Renewable Shares

Haesalhelou, H.; Parthasarathy, Harish; Nagpal, Neelu; Agarwal, Vijayant; Nagpal, Hardik; Siano, Pierluigi

doi:10.1109/tii.2021.3107396

Cited by 24 publications

(5 citation statements)

References 33 publications

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“…2). The Simulink model, and the script file of the area-parameters are developed using dynamics given in (1) and values of different parameters used are referred from [37]. For investigation, the simulation experiment environment is created and presented in Table 3.…”

Section: Simulation Study and Results Analysismentioning

confidence: 99%

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Decentralized Optimized Integral Sliding Mode-Based Load Frequency Control for Interconnected Multi-Area Power Systems

et al. 2023

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In this paper, a decentralized load frequency control (LFC) is proposed for the frequency regulation of multi-area power systems applying optimized integral sliding mode control (OISMC) scheme. A modified particle swarm optimization (MPSO) algorithm is utilised to optimize the control variables of the proposed controller for improving the performance of frequency response. A comprehensive model of a multi-area power system is developed where each area is considered to power from conventional sources, renewable energy resources (RERs), and electrical vehicle (EV) aggregators. Different types of interconnectors have been considered to tie different power regions. This system is subjected to random generation and loading conditions, as well as model uncertainties, which cause its frequency and area control error (ACE) to deviate from their nominal values. A decentralised OISMC is implemented for the proposed model to keep the frequency variation within the nominal range. Further, a validation study investigates the performance of the proposed control technique in MATLAB simulation environment to mimic the real power system operation. This study includes different test scenarios considering the dynamic events such as variations in load demand, amount of generated power, participation factor of EV reserve, system parameters, disturbances, and time delay. The simulated results support better frequency regulation with the suggested OISMC compared to other alternative control approaches.

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Section: Simulation Study and Results Analysismentioning

confidence: 99%

“…||d i (t)|| ≤ di , and || ḋi (t)|| ≤ di . In this work, it is assumed that d i (t) is accurately predictable with the convergence of disturbance estimation error to sufficiently close to zero with time by any of methods as described in [24], [28], and [37].…”

Section: System Model and Dynamicsmentioning

confidence: 99%

Decentralized Optimized Integral Sliding Mode-Based Load Frequency Control for Interconnected Multi-Area Power Systems

et al. 2023

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“…The recommendation to add additional renewable generation sources can cause issues that are generally identified during detailed electrical engineering analyses. A variety of methods exist in the literature to analyze potential electrical engineering issues associated with introducing renewables to microgrids [74][75][76][77][78]. Future work could include linking the proposed method with existing electrical engineering methods.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Defense Installation Energy Resilience for Changing Operational Requirements

Mallery

Bossuyt

Pollman

2022

Designs

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We propose a methodology to determine the impact of different potential mission scenarios upon energy resilience for mission-critical loads attached to a military base’s microgrid infrastructure. The proposed methodology applies to any installation with changing operational states that has energy-resilience requirements. The proposed methodology may be used by energy managers to account for potential mission scenarios that a base may be part of, followed by assessing the microgrid energy resilience to supply the critical loads for said mission scenarios, especially where the external grid power may be unavailable and/or damage to microgrid components may be present. In the event a microgrid design is unable to provide sufficient electrical energy, distributed energy resources and energy storage systems including renewable energy resources may be added to improve energy resilience. A case study is conducted on a fictitious representative military base, microgrid design, and changing mission demands to demonstrate the application of the proposed methodology. This article contributes a methodology for energy managers to evaluate energy resilience using microgrids by accounting for potential mission scenarios, their energy requirements, resulting energy preparedness, and recommendations for improvement, as necessary.

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“…PQDs are simulated in MATLAB using numerical models imitating the PQ disturbances and these simulated PQDs are very close to the real PQ disturbances that could be used for training and testing of deep neural networks. Further to accommodate the modelling uncertainty from real signal, white Gaussian noise (WGN) is added to these signals that is a general practice in power system modelling [41], [42], [43]. Single PQD as well as multiple PQDs are developed using the numerical models [44] which are presented in Table 1.…”

Section: A Pqd Dataset Generationmentioning

confidence: 99%

Power Quality Disturbances Detection and Classification Based on Deep Convolution Auto-Encoder Networks

et al. 2023

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Power quality issues are required to be addressed properly in forthcoming era of smart meters, smart grids and increase in renewable energy integration. In this paper, Deep Auto-encoder (DAE) networks is proposed for power quality disturbance (PQD) classification and its location detection without using complex signal processing techniques and complex classifiers. In this technique, Gabor filter is used to extract a set of general Gabor features from the convolution of PQD image. Subsequently, through sparse based DAE network, essential and optimal features are extracted and learnt which are used by a simple classifier (SoftMax) to classify the PQD type. Furthermore, the temporal information of the PQD is obtained using the PQD image to correctly locate the disturbance's initiating and terminating instants. The proposed DAE network has the benefits of Deep Learning-based networks in terms of automatic feature selection, but it requires smaller data sets. The issue of obtaining optimised, robust, and strong features from the PQD signal is thus resolved. Excellent classification accuracy of PQD is obtained with appropriate network parameter setting of the proposed DAE network. The proposed technique is compared with three other methods i.e. support vector machine (SVM), stacked auto-encoder (SAE) and principal component analysis (PCA) for PQD classification by implementing all the four techniques on python platform using the same data set. It has an overall classification accuracy of more than 97% at a signal to noise ratio (SNR) of 20dB, which is on the higher side when compared to other methods of PQD detection under noisy environment. Additionally, this method requires less computation time with the same data set than alternative approaches like SVM. Thus, the proposed method outperforms existing methods for PQD classification and detection of single disturbance and multi-disturbance in terms of greater accuracy and reduced computation complexity and computation time.INDEX TERMS Power quality monitoring, power quality disturbance, deep auto-encoders, optimal feature extraction, power quality event detection.The associate editor coordinating the review of this manuscript and approving it for publication was Nagesh Prabhu .

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Decentralized Stochastic Disturbance Observer-Based Optimal Frequency Control Method for Interconnected Power Systems With High Renewable Shares

Cited by 24 publications

References 33 publications

Decentralized Optimized Integral Sliding Mode-Based Load Frequency Control for Interconnected Multi-Area Power Systems

Decentralized Optimized Integral Sliding Mode-Based Load Frequency Control for Interconnected Multi-Area Power Systems

Defense Installation Energy Resilience for Changing Operational Requirements

Power Quality Disturbances Detection and Classification Based on Deep Convolution Auto-Encoder Networks

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