Erratum to: Advanced Smart Grid Functionalities Based on PowerFactory

González-Longatt, Francisco; Torres, José L. Rueda

doi:10.1007/978-3-319-50532-9_15

Cited by 14 publications

(22 citation statements)

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“…1 shows the process of collecting synchrophasor dataset within the control center of the power system. In this system, there exist the system load, automatic voltage regulators (AVRs), and synchronous generators, which have been developed in [23]. The synchrophasor datasets can be gathered from the system PMU devices that have been widely installed in the Wide Area Management Systems (WAMS) [24], [25].…”

Section: B Synchrophasor Spatiotemporal Data Preprocessingmentioning

confidence: 99%

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Zhang

2020

IEEE Access

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Deep learning techniques have been widely used for power system operations as a very hot topic in recent years. This paper proposed a spatiotemporal deep learning-based low frequency oscillation mode estimation method using synchrophasor data. The proposed deep learning method consists of the graph convolutional network (GCN) and the long short-term memory (LSTM). A graph network is used to model the power network in which the edges represent the connection relationships between system buses. Specifically, the GCN method is used to capture the topological structure of the power networks to obtain the spatial dependence. The LSTM model is used to capture the dynamic change of electrical variables that can be monitored by PMU devices through synchrophasor data to obtain the temporal dependence. Eventually, the proposed GCN-LSTM model is used to capture the spatiotemporal patterns and features of the system-wide synchrophasor data. To validate the effectiveness of the proposed method, we evaluate it on two classic IEEE simulation platform, i.e., IEEE 39-bus system and IEEE 118-bus system, by comparing with the Nonlinear Autoregressive Neural Network with External Input (NARX), the Gated Recurrent Unit (GRU) network, and single LSTM methods. It is demonstrated that the proposed GCN-LSTM method can achieve the best estimation results for different simulation platforms.

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Section: B Synchrophasor Spatiotemporal Data Preprocessingmentioning

confidence: 99%

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Zhang

2020

IEEE Access

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“…The increase of intermittency and uncertainty in power system as a consequence of integrating renewable energy would affect the equilibrium point of power system operations. The balance conditions dynamically change due to unpredictable power contribution from renewable energy based power generation [2].…”

Section: Introductionmentioning

confidence: 99%

Impact of Large-Scale Wind Power Penetration on Dynamic Voltage Stability of Interconnected Power System: An Indonesia Case Study

Krismanto¹,

Sulistiawati²,

Limpraptono³

et al. 2021

IJIES

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The increasing penetration of wind power plant introduces a various effect on power system stability, operation and control. Unpredictable, uncertain and fluctuating circumstances can significantly affect the performance of power system. Moreover, employment of novel technologies in wind power plant significantly alter the control characteristic and operation procedure of power system to deal with load variations and fast changing of generated power from wind-based power plant. One of the main focus in integrating large scale wind power plant is how to maintain voltage stability under transient and steady state scenarios. In this paper, effects of large-scale wind power plants on voltage stability of power system is investigated. Practical test system of South-West Sulawesi, Indonesia with integration of two large scale wind power plants are considered. Hence the novelty of this paper is to introduce new and practical test system for power system stability study considering renewable energy integration. The simulation results suggest that the additional power injection from wind power plant introduces beneficial effects on power system voltage stability performance. It was monitored that the increasing power injection from wind power plant enhanced the voltage profile (0.96 pu to 0.975 pu), voltage margin and load-ability (200 MW to 215 MW) of the system. Furthermore, it was also observed that additional power injection from wind power plant significantly improved the dynamic voltage stability performance of the power system, ensuring stable operation under transition stages when the power system was subjected to disturbances.

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“…Modern power system, involving Indonesia power system network, incorporates more and more uncertain and uncontrollable energy resources to fulfil the electricity demand [3]. As mainly influenced by weather and environmental factors, uncertain power injection from renewable based power generation potentially alter power flow, effects transmission lines congestion and influences voltage profiles of the power system.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Voltage Profiles Analysis of Power System with Large Scale Wind Power Integration

Krismanto¹

2021

IJIES

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One of the main focus in integrating large scale wind power plant is how to maintain voltage stability under different power injection from wind farm. In this paper, effects of large-scale wind power plants on voltage profile of power system is investigated. Practical test system of South-West Sulawesi, Indonesia with integration of two large scale wind power plants are considered. It was monitored that the increasing wind enhanced the voltage profile of the system. Impacts of wind power integration on power system voltage profile were investigated in this paper. Probability analaysis based on MCS were conduted to observe the impacts of uncertain power injection from wind farm on voltage fluctuation. It was clearly monitored that the probability distribution of bus voltage varied accordingly depending to location and capacity of wind farm. It was also monitored that enhancement of voltage profiles increased in proportion with power injection from wind farm. Thus, having more power production from wind farm results in better loadability and eventually improved voltage stability condition of power system as shown in bus 31 (the voltage magnitude increased from 0.942 to 0.952 pu).

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Erratum to: Advanced Smart Grid Functionalities Based on PowerFactory

Cited by 14 publications

References 0 publications

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Impact of Large-Scale Wind Power Penetration on Dynamic Voltage Stability of Interconnected Power System: An Indonesia Case Study

Probabilistic Voltage Profiles Analysis of Power System with Large Scale Wind Power Integration

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