A new global index for short term voltage stability assessment
- Authors: Alshareef, Abdulrhman , Shah, Rakibuzzaman , Mithulananthan, Nadarajah , Alzahrani, Saeed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 36114-36124
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- Description: The utility scale of non-conventional generators (NCGs), such as wind and photovoltaic (PV) plants, are competitive alternatives to synchronous machines (SMs) for power generation. Higher penetration of NCGs has been respondent of causing several recent incidents leading up to voltage collapse in power systems due to the distinct characteristics of NCGs under different operating conditions. Consequently, the so-called system strength has been reduced with higher NCGs penetration. A number of indices have been developed to quantify system strength from the short-term voltage stability (STVS) perspective. None of the indices capture the overall performances of power systems on dynamic voltage recovery. In this paper, an improvement in one of the STVS indices namely, the Voltage Recovery Index (VRI), is proposed to overcome shortcomings in the original index. Moreover, the improved index is globalized to establish a new index defined as system voltage recovery index (VRIsys) to quantify STVS at the system level. The amended VRI and developed VRIsys are used in systematic simulations to quantify the impact and interaction of various factors that could affect system strength. The assessment was conducted using time-domain simulation with direct connected induction motors (DCIMs) and a proliferation of converter-based technologies on both the generation and load sides, namely, NCGs and Variable Speed Drives (VSDs), respectively. © 2013 IEEE.
Examination of effective VAr with respect to dynamic voltage stability in renewable rich power grids
- Authors: Alzahrani, Saeed , Shah, Rakibuzzaman , Mithulananthan, N.
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 75494-75508
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- Description: High penetrations of inverter-based renewable resources (IBRs) diminish the resilience that traditional power systems had due to constant research and developments for many years. In particular, dynamic voltage stability becomes one of the major concerns for transmission system operators due to the limited capabilities of IBRs (i.e., voltage and frequency regulation). A heavily loaded renewable-rich network is susceptible to fault-induced delayed voltage recovery (FIDVR) due to insufficient effective reactive power (E-VAr) in power grids. Hence, it is crucial to thoroughly scrutinize each VAr resources' participation in E-VAr under various operating conditions. Moreover, it is essential to investigate the influence of E-VAr on system post-fault performance. The E-VAr investigation would help in determining the optimal location and sizing of grid-connected IBRs and allow more renewable energy integration. Furthermore, it would enrich decision-making about adopting additional grid support devices. In this paper, a comprehensive assessment framework is utilized to assess the E-VAr of a power system with a large-scale photovoltaic power. Plant under different realistic operating conditions. Several indices quantifying the contribution of VAr resources and load bus voltage recovery assists to explore the transient response and voltage trajectories. The recovery indices help have a better understanding of the factors affecting E-VAr. The proposed framework has been tested in the New England (IEEE 39 bus system) through simulation by DIgSILENT Power Factory. © 2013 IEEE.
Forced oscillation in power systems with converter controlled-based resources- a survey with case studies
- Authors: Surinkaew, Tossaporn , Emami, Koanoush , Shah, Rakibuzzaman , Islam, Syed , Mithulananthan, N.
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 150911-150924
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- Description: In future power systems, conventional synchronous generators will be replaced by converter controlled-based generations (CCGs), i.e., wind and solar generations, and battery energy storage systems. Thus, the paradigm shift in power systems will lead to the inferior system strength and inertia scarcity. Therefore, the problems of forced oscillation (FO) will emerge with new features of the CCGs. The state-of-the-art review in this paper emphasizes previous strategies for FO detection, source identification, and mitigation. Moreover, the effect of FO is investigated in a power system with CCGs. In its conclusion, this paper also highlights important findings and provides suggestions for subsequent research in this important topic of future power systems. © 2013 IEEE.
A new data driven long-term solar yield analysis model of photovoltaic power plants
- Authors: Ray, Biplob , Shah, Rakibuzzaman , Islam, Md Rabiul , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 136223-136233
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- Description: Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs). © 2013 IEEE.