Electric Power Systems Research

Hong, Yi

doi:10.3390/en9100824

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…If the power system is subjected to a disturbance that does not lead to net change in power, machines can easily return to their original state. However, if such a disturbance causes an unbalance between the supply and demand, a new operating state is inevitable and could cause power angle oscillations above and beyond the generating unit capacity to damp [18]. The oscillations are reflected as fluctuations in generation and the power flow depending on the initial operating condition of the system that include the generator capability, line loading and the extent of the impact to which it is subjected.…”

Section: Power Angle Stabilitymentioning

confidence: 99%

Analysis of Rwanda’s Grid Point of Stability Loss

Bizimungu,

Njoka,

Saoke

et al. 2023

AEJ

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E Electricity stability is the key component in ensuring reliable power supply which is a major hurdle in most developing countries. Power angle being part of grid stability pillars, this work sought to theoretically and numerically investigate the maximum power angle variation that the power system in Rwanda could experience while maintaining transient stability at an acceptable range beyond which generators lose synchronism affecting overall system stability. A steady state and dynamic stability assessment of the lightly loaded Rwandan grid is conducted using PSS/E while MATLAB is used to obtain input values for calculating the system stability power angle. This involved collecting actual network parameters, creating an accurate model of the entire Rwandan grid and validating the model simulation results using regularly metered values at various critical busbars of the network. Results from the study show that a sudden drop in load of 11 MW and 6 MVAr leads to a variation of power angle of the largest power plant by 23o triggering transient stability problems leading to loss of synchronism. Both calculated and simulation results show that, a sudden power angle variation of between 23o to 23.5o at the swing bus power plant, causes the monitored generating units to trip triggering a long-term instability. This was done while also interrogating possible mitigation measures based on the developed and validated model. Based on the system status, the study revealed that the grid point of stability can be determined through simulation using software and to further obtain optimum stability, installation of active devices such as shunt reactors helps the grid to counter sudden variation of power angle due to line and load tripping. B

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Section: Power Angle Stabilitymentioning

confidence: 99%

Analysis of Rwanda’s Grid Point of Stability Loss

Bizimungu,

Njoka,

Saoke

et al. 2023

AEJ

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“…The electrical power system aims to generate electrical power and deliver it through the transmission and distribution system to customers' devices in a stable, secure, reliable, and sustainable manner [1]. However, nowadays, various electronic devices such as AC/DC converters, switching power supplies, and industrial non-linear load are becoming the factors responsible for the increasing PQ disturbances{XE "PQ"\t "Power Quality"} [2,3].…”

Section: Introductionmentioning

confidence: 99%

Clustering Methods for Power Quality Measurements in Virtual Power Plant

et al. 2021

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In this article, a case study is presented on applying cluster analysis techniques to evaluate the level of power quality (PQ) parameters of a virtual power plant. The conducted research concerns the application of the K-means algorithm in comparison with the agglomerative algorithm for PQ data, which have different sizes of features. The object of the study deals with the standardized datasets containing classical PQ parameters from two sub-studies. Moreover, the optimal number of clusters for both algorithms is discussed using the elbow method and a dendrogram. The experimental results show that the dendrogram method requires a long processing time but gives a consistent result of the optimal number of clusters when there are additional parameters. In comparison, the elbow method is easy to compute but gives inconsistent results. According to the Calinski–Harabasz index and silhouette coefficient, the K-means algorithm performs better than the agglomerative algorithm in clustering the data points when there are no additional features of PQ data. Finally, based on the standard EN 50160, the result of the cluster analysis from both algorithms shows that all PQ parameters for each cluster in the two study objects are still below the limit level and work under normal operating conditions.

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Electric Power Systems Research

Cited by 2 publications

References 22 publications

Analysis of Rwanda’s Grid Point of Stability Loss

Analysis of Rwanda’s Grid Point of Stability Loss

Clustering Methods for Power Quality Measurements in Virtual Power Plant

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