Pattern identification for wind power forecasting via complex network and recurrence plot time series analysis

Charakopoulos, Avraam; Karakasidis, Theodoros E.; Sarris, Ioannis E.

doi:10.1016/j.enpol.2019.110934

Cited by 29 publications

(11 citation statements)

References 43 publications

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“…Among them, the node variables on path 1 and path 2 need to be monitored, and path 3 and path 4 have less profound impact on the subsequent node propagation than path 1 and path 2; For path 3 and path 4, the propagation nodes overlap, which indicates that the node has been affected many times, and this type of node should be monitored. Finally, we can get two sets of loop and node variables for key monitoring [9,8,7,21,12,18].…”

Section: Tracing Algorithm For Complex Network Of Fault Propagationmentioning

confidence: 99%

“…For the problems caused by these factors, complex network can realize the topology analysis of device variable attributes and complex interaction system. Complex networks, as a powerful tool to describe the relationship between multiple process variables in complex systems, are widely used in psychology, electricity, social networking, disease transmission, meteorology and other aspects [7][8][9][10][11][12]. In practice, most of the research on complex networks is to simply abstract many individuals into a single node, so as to discuss their various nonlinear functions and connections [13]; This method cannot reflect the correlation structure between them in time and space, and multi-layer network can meet this requirement.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis and Fault Propagation Traceability of Chemical Process Based on Complex Network Method

2023

IEIM

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In order to ensure the safe production of chemical handicrafts, it is necessary to monitor the process variables of the chemical system in order to deal with the failures. In order to improve the accuracy and accuracy of the monitoring process, the whole process is modeled to find out the key variables that cause the fault, and at the same time, the impact of the key variables in the system is analyzed to find out the propagation path of the key variables. Based on the complex network theory, the fault node set is obtained by combining the measured data with the horizontal visibility map; At the same time, the fault propagation complex network is constructed, and the fault propagation source node is identified according to the topology characteristics of the complex network. A traceback algorithm is proposed to determine the propagation path of the source node; The importance ranking (IR) is used to quantify the impact of the propagation path on the system and focus on monitoring the key nodes in the path. By taking the Tennessee Eastman process as the verification object, the results show that the method can identify the occurrence of the fault and control the propagation path of the fault, which proves the effectiveness of the method.

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Section: Tracing Algorithm For Complex Network Of Fault Propagationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis and Fault Propagation Traceability of Chemical Process Based on Complex Network Method

2023

IEIM

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“…The determinism (DET) quantifies the percentage of recurrence points forming the diagonal lines parallel to the LOI. Fundamentally, these diagonal line structures are graphic representation of the deterministic component in system dynamics (Charakopoulos and Karakasidis, 2019)

DET = \frac{\sum_{l = l_{\min}}^{N} italiclP ((), l)}{\sum_{ij}^{N} R_{ij}} .

…”

Section: Recurrence Analysismentioning

confidence: 99%

Dynamic analysis of meteorological time series in Hong Kong: A nonlinear perspective

Yan¹,

Chan

et al. 2021

Intl Journal of Climatology

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Many meteorological systems are chaotic in nature, which inevitably limits its predictability. Accurate prediction of meteorological variables depends primarily on properly diagnosing the complex underlying dynamics. Nonlinear dynamic analysis has shown to be particularly useful for such purpose. In this study, the concept of recurrence analysis was extended to investigate and characterize the underlying dynamics of meteorological time series (i.e., wind speed, temperature, pressure and relative humidity) based on daily observation in Hong Kong during a period from 1998 to 2018. The existence of chaos was clearly identified based on the phase space reconstruction diagram and the recurrence plot. It was shown that the underlying dynamics associated with wind speed appears to indicate higher level of complexity as compared to those of pressure and temperature, which, in consequence, may lead to more irregular time‐dependent behaviour and lower predictability. Moreover, season‐to‐seasonal variability in the dynamics of meteorological time series was evident, particularly for wind speed and temperature. Overall, this study shows that the recurrence analysis can be well applied as a useful diagnostic tool to investigate the dynamics of meteorological systems, which is expected to provide a new avenue regarding the modelling and prediction of the behaviour of meteorological parameters.

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“…Moreover, Recurrence Plots and Recurrence Quantification Analysis with epoqs seem to be a useful tool for detecting transitions during the evolution of the system [17]. Since 1989, the RP methodology and also the RQA have been successfully employed in many systems, giving satisfactory results regarding the comprehension of various systems' dynamics during their time evolution, such as biology [18], physiology [19], engineering [20], environmental studies [21][22][23][24], climate change [25][26][27], biology [28], finance [29], biological data [30], chemical processes [31], transportation [32], complex networks [33] and medical data [34], among others.…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Analysis of River System Dynamics Using Recurrence Quantification Analysis

et al. 2022

Self Cite

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Understanding the underlying processes and extracting detailed characteristics of rivers is critical and has not yet been fully developed. The purpose of this study was to examine the performance of non-linear time series methods on environmental data. Specifically, we performed an analysis of water level measurements, extracted from sensors, located on specified stations along the Nestos River (Greece), with Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA) methods. A more detailed inspection with the sliding windows (epoqs) method was applied on the Recurrence Rate, Average Diagonal Line and Trapping Time parameters, with results showing phase transitions providing useful information about the dynamics of the system. The suggested method seems to be promising for the detection of the dynamical transitions that can characterize distinct time windows of the time series and reveals information about the changes in state within the whole time series. The results will be useful for designing the energy policy investments of producers and also will be helpful for dam management assessment as well as government energy policy.

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Pattern identification for wind power forecasting via complex network and recurrence plot time series analysis

Cited by 29 publications

References 43 publications

Fault Diagnosis and Fault Propagation Traceability of Chemical Process Based on Complex Network Method

Fault Diagnosis and Fault Propagation Traceability of Chemical Process Based on Complex Network Method

Dynamic analysis of meteorological time series in Hong Kong: A nonlinear perspective

Non-Linear Analysis of River System Dynamics Using Recurrence Quantification Analysis

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