Power system reliability evaluation using a state space classification technique and particle swarm optimisation search method

Benidris, Mohammed; Elsaiah, Salem; Mitra, Joydeep

doi:10.1049/iet-gtd.2015.0581

Cited by 35 publications

(23 citation statements)

References 29 publications

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“…Other approaches that incorporate a deterministic approach with a probabilistic approach were proposed in [33,124]. State space classification techniques were proposed in [125,126]. A least-squares support vector machine classifier was combined with MCS in [25] to achieve an accurate and computation-efficient simulation.…”

Section: Studies On Evaluation Efficiency and Systemsmentioning

confidence: 99%

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A Systematic Review of Reliability Studies on Composite Power Systems: A Coherent Taxonomy Motivations, Open Challenges, Recommendations, and New Research Directions

et al. 2018

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Power systems has been subjected to significant upgrades in terms of structure and capacity. Reliability evaluation of composite power systems has surfaced as an essential step in operation and planning stages of the modern power system. It is an effective tool to investigate the ability of power systems to supply customers with reliable power service. The purpose of this review is to enhance the knowledge of reliability studies conducted on composite power systems by providing a critical and systematic review. This work investigates peer-reviewed articles published between 2007 and 2017 in three reliable databases. The findings reveal that the reliability of composite power systems has received considerable attention over the last few years. Secondly, investigation studies demonstrated a crucial role in verifying the impact of adopting new technologies. Third, studies on this topic have been intensively conducted in Asia, which highlights the promising sectors in these regions. However, researchers have generally focused on developing several aspects (e.g., evaluation speed and wind power integration) at the expense of others (e.g., realistic studies and other renewable energy resources). The lack of practical applications is evident in the surveyed publications. These findings imply a potential incoordination between the needs of the real applications and researchers’ tendencies. Future reliability evaluation scholars are advised to consider the findings of this systematic review including concentrating on insufficiently covered topics and enhance the coordination among the efforts devoted in this area.

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Section: Studies On Evaluation Efficiency and Systemsmentioning

confidence: 99%

“…The increase in the scale of modern power systems stimulates actions to improve simulation efficiency and overcome computational burdens. Many studies reduced simulation time to facilitate reliability evaluation of large-scale applications [17,19,124,126,132,139,140,149].…”

Section: Motivations Related To Power System Developmentmentioning

confidence: 99%

A Systematic Review of Reliability Studies on Composite Power Systems: A Coherent Taxonomy Motivations, Open Challenges, Recommendations, and New Research Directions

et al. 2018

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“…Compared with the traditional optimization algorithm, it has faster computing speed and better global search ability [12]. Therefore, PSO is applied to the optimization of K-means clustering algorithm to solve the problem that K-means algorithm is easy to fall into local optimal problem improving the final clustering accuracy.…”

Section: Linear Decreasing Weight Psomentioning

confidence: 99%

A Method of Fault Line Selection in Small Current Grounding System Based on VMD Energy Entropy and Optimized K-means Clustering

Sun

et al. 2017

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. When single-phase to ground fault occurs in small current grounding system, the zero-sequence transient current contains a lot of complex fault information. In order to extract characteristic from the zero-sequence current accurately as the criterion of fault line selection, this paper puts forward a method of fault line selection based on VMD energy entropy and optimized K-means clustering. Firstly, the zero-sequence current of each line is decomposed by VMD (variational mode decomposition, VMD), and each sub mode of the original current is obtained. Secondly, the energy entropy of each sub mode is calculated as the characteristic quantity of each line state. Finally, the optimized K-means clustering algorithm is used to cluster the fault feature to realize fault line selection. In this paper, Simulink is used to simulate the single-phase to ground fault in resonant grounded system and the results show that the method that has high accuracy can provide reference for practical engineering application. IntroductionIn recent years, there have been a lot of fault line selection methods, mainly divided into the line selection method applying the steady-state signal and the transient signal [1]. The transient current signal has large intensity, and is not affected by the arc suppression coil, so the method of line selection applying transient signal is more suitable for the identification of faults [2][3]. A method of Hilbert transform and probabilistic neural network routing is proposed in [4]. In the proposed method, it is shown that the zero-sequence transient current of each line is decomposed into a series of intrinsic mode functions by the empirical mode decomposition (EMD) and the intrinsic mode function energy is calculated as the input of PNN network to select the fault line. However, the empirical mode decomposition is prone to the phenomenon of modal aliasing, which cannot accurately extract the fault information [5].In view of the problem of mode mixing in EMD, the Hilbert transform is improved by ensemble empirical mode decomposition (EEMD), and the fault feature components are extracted to identify the fault line [6]. However, EEMD is affected by the addition of white noise amplitude and integration times, and the computational complexity is large. Variational modal decomposition [7] is a new signal processing method that decomposes the original signal into k finite bandwidths with different center frequencies. This method not only effectively avoids the modal aliasing problem of EMD, but also greatly reduces the computational complexity and improves the computational efficiency relative to EEMD. K-means algorithm is a widely used clustering method [8], usually in European distance as a similarity measure. The algorithm is simple and has a fast convergence rate.

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“…According to the proposed method, the impact of consumer participation demand response (DR) on system reliability is analyzed by using the conditional value-at-risk (CVaR) method. In [9], an intelligent particle swarm optimization (PSO) based search method is proposed. The reliability of the Institute of Electrical and Electronics Engineers reliability test system (IEEE RTS) was calculated by using the PSO search method.…”

Section: Introductionmentioning

confidence: 99%

A Method for Load Classification and Energy Scheduling Optimization to Improve Load Reliability

Ren

Yang

et al. 2018

Energies

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With the large amount of distributed generation in use, the structure of the distribution system is increasingly complex. Therefore, it is necessary to establish a method to improve load reliability. Based on the reliability model of distributed generation, this paper investigates the time sequential simulation of a wind/solar/storage combined power supply system under off-grid operation. After classifying the load by power supply region, the load weight coefficient is established, which modifies the reliability index of the load point and system. The modified expected energy not supplied (EENS) is adopted as the objective function, and the particle swarm optimization algorithm is used to solving the optimal energy scheduling for improving the load reliability. Finally, the load reliability is calculated with a hybrid method. Using the IEEE-RBTS Bus 6 system as an example, the correctness and validity of the proposed method are verified as an effective way to improve load reliability.

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Power system reliability evaluation using a state space classification technique and particle swarm optimisation search method

Cited by 35 publications

References 29 publications

A Systematic Review of Reliability Studies on Composite Power Systems: A Coherent Taxonomy Motivations, Open Challenges, Recommendations, and New Research Directions

A Systematic Review of Reliability Studies on Composite Power Systems: A Coherent Taxonomy Motivations, Open Challenges, Recommendations, and New Research Directions

A Method of Fault Line Selection in Small Current Grounding System Based on VMD Energy Entropy and Optimized K-means Clustering

A Method for Load Classification and Energy Scheduling Optimization to Improve Load Reliability

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