General Fuzzy C-Means Clustering Strategy: Using Objective Function to Control Fuzziness of Clustering Results

Zhao, Kaixin; Jia, Zhiyang; Ji, Yindong

doi:10.1109/tfuzz.2021.3119240

Cited by 10 publications

(6 citation statements)

References 27 publications

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“…[24] Because FCM is supported by fuzzy theory, it is mainly described by fuzzy partition matrix, which breaks the limitation that each data point in hard clustering can only be divided into one category. The final output fuzzy matrix reflects all the information of the data set, so the distribution and overall characteristics of the data set can be accurately examined [25]. Although, the traditional FCM algorithm has some weaknesses, such as initializing the cluster center, determining the optimal number of clusters and being sensitive to noise, and the realization of FCM depends on the value of the fuzzy index, and the results obtained by using different indexes may be different [26 ].…”

Section: Fuzzy C-means (Fcm) Methodsmentioning

confidence: 99%

Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

Xie¹,

Qiao²,

Xia³

et al. 2023

Preprint

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In recent years, the problem of fuzzy clustering has been widely concerned. The membership iteration of existing methods is mostly considered globally, which has considerable problems in noisy environments, and iterative calculations for clusters with a large number of different sample sizes are not accurate and efficient. In this paper, starting from the strategy of large-scale priority, the data is fuzzy iterated using granularballs, and the membership degree of data only considers the two granular-balls where it is located, thus improving the efficiency of iteration. The formed fuzzy granular-balls set can use more processing methods in the face of different data scenarios, which enhances the practicability of fuzzy clustering calculations.

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Section: Fuzzy C-means (Fcm) Methodsmentioning

confidence: 99%

Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

Xie¹,

Qiao²,

Xia³

et al. 2023

Preprint

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show abstract

“…FCM clustering algorithm is a method to describe and partition things with ambiguity or uncertainty (Zhao et al, 2022). When the traditional FCM algorithm is used to cluster wind turbines, the contribution degree of different clustering indexes to the clustering results is ignored, resulting in unreasonable clustering results.…”

Section: Clustering Of Dfig-based Wind Farm Based On Wfcm Algorithm 2...mentioning

confidence: 99%

Equivalent modeling and multi-parameter coupling optimization for DFIG-based wind farms considering SSO mode

Zhang

Jin

Zhang³

et al. 2023

Front. Energy Res.

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As a low-carbon and environmentally friendly renewable energy source, wind power has been globally recognized as the best solution to achieve energy saving and emission reduction and promote low-carbon economic growth. With the increase of wind power penetration, wind power has a great impact on sub-synchronous state stability and dynamic characteristics of the grid-connected system. Aiming at the fact that the correlation between clustering indexes and sub-synchronous oscillation (SSO) mode and the difference of the contribution to the clustering results are seldom considered in the current equivalent modeling of doubly-fed induction generator (DFIG)-based wind farm, this paper proposes a clustering method based on the index dimension reduction and weighted fuzzy C-means (WFCM) clustering algorithm. Besides, for the SSO study of the grid-connected system without sufficiently considering the coupling effects between controller parameters, a multi-parameter coupling optimization design strategy combining orthogonal experiment method (OEM) and response surface method is proposed. Firstly, the dominant variables of SSO mode of the DFIG-based wind farm connected to weak grid by series compensation system are taken as the initial clustering indexes. After dimension reduction by principal component analysis, the WFCM algorithm is utilized to cluster the wind farm. Then, the proportional and integral coefficients of the grid-side controller, rotor-side controller and phase-locked loop are optimized to achieve the simultaneous optimization of the SSO characteristics and dynamic characteristics of the system. Finally, the interaction between control parameters and the influence degree and trend on the system performance are quantitatively evaluated, and the optimal parameter combination is obtained. The proposed strategy can mitigate SSO more effectively while improving anti-interference than the particle swarm optimization based on OEM.

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“…The larger the membership degree is, the closer the sample point is to the clustering center. 10,11 The FCM clustering algorithm flowchart is shown in Figure 4.…”

Section: Generation Of Power Output and Market Clearing Price Scenari...mentioning

confidence: 99%

“…It is determined according to the distance between the sample point and the clustering center. The larger the membership degree is, the closer the sample point is to the clustering center 10,11 . The FCM clustering algorithm flowchart is shown in Figure 4.…”

Section: Scenario Modeling and Generationmentioning

confidence: 99%

CVaR‐based method for optimizing the contract bidding strategy of PV power stations

Xu¹,

Qiao²,

Chen³

et al. 2023

Engineering Reports

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With the rapid development of the new power system, the proportion of renewable energy participating in the electricity market is constantly increasing, and the uncertainty of renewable energy power output and market clearing price brings risks to its participation in long‐term contract bidding. Therefore, a method of contract bidding involving bidding amount and bidding price of PV power station is proposed. Firstly, the multi‐order weighted Markov chain model is used to forecast the distribution of weather states in the trading cycle, and the fuzzy C‐means (FCM) method is used to cluster the joint samples of power output indicators and market clearing price indicators, and then Latin hypercube sampling is used to generate power output scenarios and market clearing price scenarios. Then, an optimization method for long‐term contract bidding strategy of PV power station considering conditional value at risk (CVaR) and transaction probability is established, and the model is divided into two stages and solved in an alternate iterative way. Finally, an example is used to verify that the constructed model enables PV power stations to effectively avoid risks and obtain more stable revenue.

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General Fuzzy C-Means Clustering Strategy: Using Objective Function to Control Fuzziness of Clustering Results

Cited by 10 publications

References 27 publications

Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

Equivalent modeling and multi-parameter coupling optimization for DFIG-based wind farms considering SSO mode

CVaR‐based method for optimizing the contract bidding strategy of PV power stations

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