ARIMA-Based Time Series Model of Stochastic Wind Power Generation

Chen, Peiyuan; Pedersen, Troels; Bak‐Jensen, Birgitte; Chen, Zhe

doi:10.1109/tpwrs.2009.2033277

Cited by 319 publications

(148 citation statements)

References 9 publications

(14 reference statements)

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“…step 2: Generate AOI(t) during a time period of t. step 3: Determine the characteristics of AOI(t) combining the autoregressive integrated moving average model (ARIMA) model [27]. step 4: Obtain the current RUL for a generator.…”

Section: Prediction Modelmentioning

confidence: 99%

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

et al. 2017

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Abstract:The fast-growing wind power industry faces the challenge of reducing operation and maintenance (O&M) costs for wind power plants. Predictive maintenance is essential to improve wind turbine reliability and prolong operation time, thereby reducing the O&M cost for wind power plants. This study presents a solution for predictive maintenance of wind turbine generators. The proposed solution can: (1) predict the remaining useful life (RUL) of wind turbine generators before a fault occurs and (2) diagnose the state of the wind turbine generator when the fault occurs. Moreover, the proposed solution implies low-deployment costs because it relies solely on the information collected from the widely available supervisory control and data acquisition (SCADA) system. Extra sensing hardware is needless. The proposed solution has been deployed and evaluated in two real-world wind power plants located in China. The experimental study demonstrates that the RUL of the generators can be predicted 18 days ahead with about an 80% prediction accuracy. When faults occur, the specific type of generator fault can be diagnosed with an accuracy of 94%.

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Section: Prediction Modelmentioning

confidence: 99%

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

et al. 2017

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“…For the latter case, various techniques have been developed, such as Markov Chain and Auto Regressive Moving Average (ARMA) models [10]. Since multiple wind farms are integrated, the spatial dependence among different wind sits should also be taken into consideration.…”

Section: Methodology For Optimal Siting and Sizingmentioning

confidence: 99%

Optimal siting and sizing of Energy Storage System for power systems with large-scale wind power integration

Zhao

Huang

et al. 2015

2015 IEEE Eindhoven PowerTech

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Abstract-This paper proposes algorithms for optimal siting and sizing of Energy Storage System (ESS) for the operation planning of power systems with large scale wind power integration. The ESS in this study aims to mitigate the wind power fluctuations during the interval between two rolling Economic Dispatches (EDs) in order to maintain generation-load balance. The charging and discharging of ESS is optimized considering operation cost of conventional generators, capital cost of ESS and transmission losses. The statistics from simulated system operations are then coupled to the planning process to determine the optimal siting and sizing of storage units throughout the network. These questions are investigated using an IEEE benchmark system.

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“…statistical properties are time-invariant. In order to achieve that, time-series differentiation [34] has been employed in the case of wind power data, while for inflexible 8 demand data, mean subtraction and division by the standard deviation -to remove the diurnal component-has preceded the differentiation process. In order to meaningfully compare the solution efficiency of the seven implemented models (six models corresponding to the six different scenario trees and one model corresponding to the extended SDDP approach, which is denoted SDDPe in the remainder), the first-stage decisions taken by each model are used for Monte Carlo validation i.e.…”

Section: B 6h Operating Horizon Case Studymentioning

confidence: 99%

Stochastic Dual Dynamic Programming for Operation of DER Aggregators Under Multi-Dimensional Uncertainty

Fatouros

Konstantelos

Papadaskalopoulos

et al. 2019

IEEE Trans. Sustain. Energy

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Abstract--The operation of aggregators of distributed energy resources (DER) is highly complex, since it entails the optimal coordination of a diverse portfolio of DER under multiple sources of uncertainty. The large number of possible stochastic realizations that arise, can lead to complex operational models that become problematic in real-time market environments. Previous stochastic programming approaches resort to two-stage uncertainty models and scenario reduction techniques to preserve the tractability of the problem. However, two-stage models cannot fully capture the evolution of uncertain processes and the a priori scenario selection can lead to suboptimal decisions. In this context, this paper develops a novel stochastic dual dynamic programming (SDDP) approach which does not require discretization of either the state space or the uncertain variables and can be efficiently applied to a multi-stage uncertainty model. Temporal dependencies of the uncertain variables as well as dependencies among different uncertain variables can be captured through the integration of any linear multidimensional stochastic model, and it is showcased for a porder vector autoregressive (VAR) model. The proposed approach is compared against a traditional scenario-tree-based approach through a Monte-Carlo validation process, and is demonstrated to achieve a better trade-off between solution efficiency and computational effort.Index Terms--Aggregator, distributed energy resources, multidimensional uncertainty, stochastic dual dynamic programming, vector autoregressive modeling. I. NOMENCLATURE A. IndexesIndex of time periods, running from 1 to . Index of energy storage (ES) units, running from 1 to . Index of flexible loads (FL), running from 1 to . Index of wind turbines (WT), running from 1 to . Index of micro-generators, running from 1 to . B. ParametersEnergy market price at period . Operating cost of micro-generator . Cost of demand shedding. C. VariablesPower sold to (positive)/bought from (negative) the market at period . ,Power output of micro-generator at period . Demand shed at period ., Energy level of ES unit at period . ,Power input (positive) / output (negative) of ES unit at period . , ℎChange of demand of FL at period due to load shifting. ,Demand of FL at period after load shifting., Dispatched wind power output of WT at period . II. INTRODUCTIONA. Background FUNDAMENTAL feature of the emerging Smart Grid paradigm involves the integration of a large number of distributed energy resources (DER) storage units, in order to support the economic operation of the future low-carbon power system [1]- [2]. However, the large number, small individual size and inherent stochasticity characterizing these DER have complicated system scheduling and market coordination. Furthermore, driven by the wide integration of renewable generation in power systems, there is a general consensus to move energy trading as close as possible to real-time [3]- [4], which intensifies the complexity of DER coordination. These challenges have...

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ARIMA-Based Time Series Model of Stochastic Wind Power Generation

Cited by 319 publications

References 9 publications

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

Optimal siting and sizing of Energy Storage System for power systems with large-scale wind power integration

Stochastic Dual Dynamic Programming for Operation of DER Aggregators Under Multi-Dimensional Uncertainty

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