Classification of voltage sag disturbance sources using fuzzy comprehensive evaluation method

Li, Chenyi; Yang, Jiali; Xu, Yan; Wu, Ying; Wei, Pengfei

doi:10.1049/oap-cired.2017.0776

Cited by 15 publications

(11 citation statements)

References 5 publications

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“…According to the index system, it evaluates from low level to high level, and shows the influence of various factors on evaluation objects through different levels. The specific evaluation principle can be referred to [24], [25].…”

Section: : Single Decision Evaluation Methods Based On Fuzzy Comprehementioning

confidence: 99%

Economic Operation Evaluation of Active Distribution Network Based on Fuzzy Borda Method

Zhang

2020

IEEE Access

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In order to make up for the shortcomings of the development of traditional distribution networks, and promote the diversification of energy structure, active distribution network (ADN) came into being. With the large amount of distributed energy resources (DER) connected to the grid, the economic operation evaluation of the active distribution network has been greatly restricted. According to the characteristics of the active distribution network, 7 second-level evaluation indicators such as DER penetration rate are defined in this paper. At the same time, combined with the indicators of the traditional distribution network, a new active distribution network economic operation evaluation index system is constructed, which includes 7 first-level indicators and 21 second-level indicators, and a comprehensive evaluation model based on fuzzy borda method is proposed. This comprehensive evaluation model is put forward due to inconsistent results of evaluation methods, in addition, Kendall-w coefficient is used for the prior consistency test, fuzzy borda method is used for the combination evaluation of different results of several evaluation methods. By considering the difference of evaluation results, the evaluation results are closer to the reality. Finally, the simulation and sensitivity analysis of the last example verify the rationality and effectiveness of the index system and the evaluation model. INDEX TERMS ADN, economic operation, Kendall-w coefficient, fuzzy borda method, combined evaluation.

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Section: : Single Decision Evaluation Methods Based On Fuzzy Comprehementioning

confidence: 99%

Economic Operation Evaluation of Active Distribution Network Based on Fuzzy Borda Method

Zhang

2020

IEEE Access

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“…Thus, the mutual information value can be transformed into the SU value to compensate for the offset of the mutual information method. The calculation of SU is 2 ( , ) ( , ) ( (16) The redundancy between any two indices and the relevance between each index and fault type are obtained based on the SU value. Then, a set of feature indices is screened out by the MRMR method [26].…”

Section: B Feature Index Selection Based On Su-mrmrmentioning

confidence: 99%

“…In the field of fault source identification, there are mainly two methods: non-artificial intelligence methods and artificial intelligence methods. In non-artificial intelligence methods, fault sources are identified based on the similarity or transform of fault waveform [15], [16]. However, it is difficult to identify the fault sources of wind farm HVTOs because there are many kinds of fault waveforms considered.…”

Section: Introductionmentioning

confidence: 99%

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Complex Fault Source Identification Method for High-Voltage Trip-Offs of Wind Farms Based on SU-MRMR and PSO-SVM

et al. 2020

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Large-scale high-voltage trip-offs (HVTOs) of wind farms are serious incidents afflicting power systems that can lead to voltage instability, power deficiency, and frequency fluctuation. In order to reduce the influence of HVTOs, it is necessary to efficiently identify the fault source after an HVTO at a wind farm. A fault source identification method for wind farm HVTOs is proposed in this work. First, the fault tree analysis (FTA) method is used to summarize the causal and logical relationships among the different factors that lead to HVTOs at wind farms. An index system is constructed according to simulations of wind farm HVTOs under multiple scenarios. Second, a set of feature indices are selected from the original index system as key criteria for wind farm HVTOs based on the symmetrical uncertainty of mutual information and the maximum relevance and minimum redundancy (SU-MRMR) method. Finally, the particle swarm optimization (PSO) method is effectively utilized in the parameter optimization of support vector machine (SVM) method, and the optimized SVM with good performance is further employed for fault source identification based on the feature indices. Both single fault source and compound fault source are identified in an actual power system, and the proposed method is verified as a reliable solution for complex fault source identification for wind farm HVTOs based on the statistical identification accuracy. INDEX TERMS High-voltage trip-offs (HVTOs), fault source identification, SU-MRMR method, feature index, PSO method, SVM method.

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“…Among them, the more successful methods are Holt winters exponential (HWE) smoothing [6], autoregressive integrated moving average (ARIMA) [7], and linear regression (LR) [8] based on machine learning. In recent years, with the rapid development of computing technology, artificial neural network (ANN) [9], fuzzy comprehensive evaluation (FCE) [10]- [12], wavelet analysis (WA) [13]- [16] and support vector machine (SVM) [17]- [19] are widely used in short-term time series prediction. Many researchers successfully applied artificial neural networks in the field of classification [20]- [24] and regression [25]- [29], but it is easy to fall into the dilemma of local optimum.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Method for Short-Term Time Series Forecasting Based on EEMD

Yang

2020

IEEE Access

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To improve the prediction effect of time series, we make a systematic study of various time series prediction methods based on statistics and machine learning in this paper. In the experiment, we compare the prediction results of several prediction methods. In particular, much research has been done on the selection of experimental data because representative time series data can better test the effectiveness and practicability of the prediction method. Based on the idea of divide and conquer of complex problems and the strategy of continuous optimization of machine learning, we proposed the prediction methods of LSTM-TFE, LR-TFE, and BR-TFE combined the EEMD, LSTM, LR, and BR methods in this paper. These methods use EEMD to decompose complex time series into several relatively milder, more regular and stable subsequences. Then the prediction model of each subsequence based on machine learning is carried out by using the LSTM, LR, or BR methods. We use these prediction models to predict the value of each subsequence. Finally, the value of multiple subsequences is fused to form the prediction results of the original complex time series. To verify the proposed method comprehensively, we select three representative time series data to test this paper. From the experimental results, we found that the proposed method has a good effect. INDEX TERMS LSTM, short-term forecasting, EEMD, price prediction, time series.

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Classification of voltage sag disturbance sources using fuzzy comprehensive evaluation method

Cited by 15 publications

References 5 publications

Economic Operation Evaluation of Active Distribution Network Based on Fuzzy Borda Method

Economic Operation Evaluation of Active Distribution Network Based on Fuzzy Borda Method

Complex Fault Source Identification Method for High-Voltage Trip-Offs of Wind Farms Based on SU-MRMR and PSO-SVM

Hybrid Method for Short-Term Time Series Forecasting Based on EEMD

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