Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods

Cui, Jia; Yu, Renzhe; Zhao, Dongbo; Yang, Junyou; Ge, Weichun; Zhou, Xiaoming

doi:10.1016/j.apenergy.2019.03.163

Cited by 33 publications

(13 citation statements)

References 33 publications

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“…Content may change prior to final publication. refers to the latest research about [39,40]. Through the research on the de-noised signal in the actual experimental environment, under the acceleration signals of five different states, the decomposition number of VMD is defined as 3.…”

Section:  mentioning

confidence: 99%

A Fault Diagnosis Method Based on Improved Adaptive Filtering and Joint Distribution Adaptation

2020

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In the real environment of industrial equipment, the vibration signals of essential components show deviations due to the fault and noise. Notably, the noise in the signal will interfere with the diagnosis process of the signal and reduce the accuracy of fault diagnosis. Based on the above problem, adaptive filtering (AF) is used as an excellent method to attenuate noise without specifying the noise type. However, how to define the most appropriate length and type of morphological filter element is the most inherent problem which needs to be solved first. This paper proposed a cooperative diagnosis method of rolling bearings vibration signal based on improved adaptive filtering and joint distribution adaptation (JDA). First, the kurtosis under different element types and lengths is calculated as an index. The structural element corresponding to maximum kurtosis is selected as the most suitable morphological filter element because the different morphological filter elements reflect the effect of feature extraction. Then, JDA aims to improve both the marginal distribution and the conditional distribution to solve the chaotic distribution of time-domain features under variable working conditions. Finally, the improved least squares support vector machine (LSSVM) verified the effectiveness and improvement of the proposed method under bearing acceleration signal. At the same time, the comparative experiment proved that the proposed method not only directly corrects the most appropriate elements greatly optimizes the feature structure, but also enhances the accuracy of fault diagnosis. INDEX TERMS adaptive filters, morphological operation, time domain features, transfer learning, joint distribution adaptation, LSSVM, fault diagnosis.

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Section:  mentioning

confidence: 99%

A Fault Diagnosis Method Based on Improved Adaptive Filtering and Joint Distribution Adaptation

2020

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“…In the last few decades, energy demand forecasting has turned into an active research area due to its significant impact on the energy security and socioeconomic development of a country [13,14]. A series of prominent research studies have reported on energy demand forecasting, including factor selection [7,15,16] and method determination [13,17].…”

Section: Energy Demand Influencing Factorsmentioning

confidence: 99%

A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm

2020

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Accurate forecasting of the energy demand is crucial for the rational formulation of energy policies for energy management. In this paper, a novel ensemble forecasting model based on the artificial bee colony (ABC) algorithm for the energy demand was proposed and adopted. The ensemble model forecasts were based on multiple time variables, such as the gross domestic product (GDP), industrial structure, energy structure, technological innovation, urbanization rate, population, consumer price index, and past energy demand. The model was trained and tested using the primary energy demand data collected in China. Seven base models, including the regression-based model and machine learning models, were utilized and compared to verify the superior performance of the ensemble forecasting model proposed herein. The results revealed that (1) the proposed ensemble model is significantly superior to the benchmark prediction models and the simple average ensemble prediction model just in terms of the forecasting accuracy and hypothesis test, (2) the proposed ensemble approach with the ABC algorithm can be employed as a promising framework for energy demand forecasting in terms of the forecasting accuracy and hypothesis test, and (3) the forecasting results obtained for the future energy demand by the ensemble model revealed that the future energy demand of China will maintain a steady growth trend.

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“…As in a DS, load variation is random at different time of the day, month, or season; therefore, analyzing the power system performance by fixing the demand would give misleading results. 26,50 Therefore, modeling of the stochastic behavior of load demand is important. For probabilistic modeling, it is necessary to observe the power consumption pattern of various types of loads over a sufficient period.…”

Section: Probabilistic Load Modelmentioning

confidence: 99%

Estimating the impact of uncertainty on optimum capacitor placement in wind‐integrated radial distribution system

Das

Malakar

2020

Int Trans Electr Energ Syst

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The unpredictable load variations and raising presence of wind farms have caused modern-day distribution system operation and planning extremely complex. This paper puts an earliest effort toward the optimal allocation of the shunt capacitors in radial distribution system (RDS) under uncertain load and wind power generation. The authors aim to minimize the annual operating cost (AOC) for RDS by deciding the optimum placements of shunt capacitors. Here, three-point estimate method is applied to estimate the uncertainties in the load demand and power output from the wind generator. The stochastic behavior of wind generators and load demands are modeled by using Weibull and normal distribution functions, respectively. An opposition-based competitive swarm optimizer (OCSO) algorithm is applied to minimize the AOC, and the optimization problem is solved for both probabilistic and deterministic approach. The Cornish-Fisher expansion is used in this work to provide more accurate probability distribution functions of AOC. Furthermore, the impact of VAr injection on AOC values and power losses are also studied in this work. Moreover, the results obtained from the OCSO algorithm are compared with the original competitive swarm optimizer results. The proposed problem is tested on 69 bus RDS with different loading conditions, and results are analyzed for each scenario.competitive swarm optimizer, load uncertainty, optimum capacitor allocation, point estimate method, wind generation uncertainty | INTRODUCTIONShunt capacitors (SC) are being used in power distribution system (DS) conventionally to reduce power loss, improve the system voltage, and power factor. The power loss in DS is higher than that of the transmission system because of its List of Symbols and Abbreviations: V W , wind speed; P WT , power output of WGS; Pr, rated power output of WGS; Vr, rated wind speed; Vci, cut-in wind speed; Vco, cut-out wind speed;V w , mean of the wind speed; σ w , SD of the wind speed; P Li , real power demand at the bus "i"; Q Li , reactive power demand at the bus "i"; μ PLi , mean of real power demand; μ QLi , mean of reactive power demand; σ PLi , SD of real power demand; σ QLi , SD of reactive power demand; P μ loss , mean active power loss; Q μ ci, , mean VAr supply cost; nl, number of branches; cb, number of candidate bus; nb, number of load bus; P ss , real power of the substation bus; Q ss , reactive power of the substation bus; P μ w , mean power output from WGS; Q μ ci , mean VAr injection at bus "i"; P μ di , mean demands for real power at bus "i"; Q μ di , mean demands for reactive power at bus "i"; P μ lossj , mean real power loss at branch "j"; Q μ lossj , mean reactive power loss at branch "j"; Q cn , VAr injection at bus "n"; Q cn min , minimum values of Q cn ; Q cn max , maximum values of Q cn ; S li , complex power flow through line "i"; S li max , maximum line limit through line "i".

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Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods

Cited by 33 publications

References 33 publications

A Fault Diagnosis Method Based on Improved Adaptive Filtering and Joint Distribution Adaptation

A Fault Diagnosis Method Based on Improved Adaptive Filtering and Joint Distribution Adaptation

A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm

Estimating the impact of uncertainty on optimum capacitor placement in wind‐integrated radial distribution system

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