scholarly journals An Improved Cuckoo Search Clustering Method for Line Loss Data of Transformer District with DGs

2021 ◽  
Vol 2093 (1) ◽  
pp. 012017
Author(s):  
Lingang Yu ◽  
Dongwen Wu ◽  
Zhiqiang Hu ◽  
Aiqing Yu ◽  
Liang Zhu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Low Voltage ◽  
Cuckoo Search ◽  
Clustering Method ◽  
Voltage Transformer ◽  
Data Set ◽  
Maximum Information ◽  
Loss Data ◽  
Simulation Results ◽  
Line Loss

Abstract For the low-voltage transformer district with distributed generations (DGs), the traditional theoretical calculation method of line loss is not applicable. This paper presents a novel clustering method for line loss data of transformer district with DGs, which combined an improved Cuckoo Search algorithm and K-Means clustering algorithm. Firstly, the influence factors of line loss are screened based on the maximum information coefficient, and the line loss index system is established. Secondly, an improved cuckoo search clustering algorithm is proposed to cluster the sample data set to reduce the dependence on the initial clustering center. Finally, the simulation results of 410 samples from a certain area with photovoltaic power supply show the accuracy and effectiveness of the proposed method. The simulation results show that the proposed method is accurate and effective.

Ontology-Based K-Means Clustering Algorithm Analysis

2013 ◽  
Vol 380-384 ◽  
pp. 1290-1293
Author(s):  
Qing Ju Guo ◽  
Wen Tian Ji ◽  
Sheng Zhong
Keyword(s):  
Semantic Web ◽  
Clustering Algorithm ◽  
Algorithm Analysis ◽  
Clustering Method ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
Research Findings ◽  
Partition Clustering ◽  
Improved Algorithm

Lots of research findings have been made from home and abroad on clustering algorithm in recent years. In view of the traditional partition clustering method K-means algorithm, this paper, after analyzing its advantages and disadvantages, combines it with ontology-based data set to establish a semantic web model. It improves the existing clustering algorithm in various constraint conditions with the aim of demonstrating that the improved algorithm has better efficiency and accuracy under semantic web.

Rough ISODATA Algorithm

2013 ◽  
Vol 3 (4) ◽  
pp. 1-14 ◽  
Author(s):  
S. Sampath ◽  
B. Ramya
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Vital Role ◽  
Data Sets ◽  
Clustering Method ◽  
Data Set ◽  
Number Of Clusters ◽  
Real Life Data ◽  
Nonparametric Statistical

Cluster analysis is a branch of data mining, which plays a vital role in bringing out hidden information in databases. Clustering algorithms help medical researchers in identifying the presence of natural subgroups in a data set. Different types of clustering algorithms are available in the literature. The most popular among them is k-means clustering. Even though k-means clustering is a popular clustering method widely used, its application requires the knowledge of the number of clusters present in the given data set. Several solutions are available in literature to overcome this limitation. The k-means clustering method creates a disjoint and exhaustive partition of the data set. However, in some situations one can come across objects that belong to more than one cluster. In this paper, a clustering algorithm capable of producing rough clusters automatically without requiring the user to give as input the number of clusters to be produced. The efficiency of the algorithm in detecting the number of clusters present in the data set has been studied with the help of some real life data sets. Further, a nonparametric statistical analysis on the results of the experimental study has been carried out in order to analyze the efficiency of the proposed algorithm in automatic detection of the number of clusters in the data set with the help of rough version of Davies-Bouldin index.

scholarly journals Research on Predicting Line Loss Rate in Low Voltage Distribution Network Based on Gradient Boosting Decision Tree

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2522 ◽  
Author(s):  
Mengting Yao ◽  
Yun Zhu ◽  
Junjie Li ◽  
Hua Wei ◽  
Penghui He
Keyword(s):  
Decision Tree ◽  
Power Systems ◽  
Loss Rate ◽  
Low Voltage ◽  
Distribution Network ◽  
Gradient Boosting ◽  
Data Set ◽  
Economic Operation ◽  
Line Loss ◽  
Excessive Number

Line loss rate plays an essential role in evaluating the economic operation of power systems. However, in a low voltage (LV) distribution network, calculating line loss rate has become more cumbersome due to poor configuration of the measuring and detecting device, the difficulty in collecting operational data, and the excessive number of components and nodes. Most previous studies mainly focused on the approaches to calculate or predict line loss rate, but rarely involve the evaluation of the prediction results. In this paper, we propose an approach based on a gradient boosting decision tree (GBDT), to predict line loss rate. GBDT inherits the advantages of both statistical models and AI approaches, and can identify the complex and nonlinear relationship while computing the relative importance among variables. An empirical study on a data set in a city demonstrates that our proposed approach performs well in predicting line loss rate, given a large number of unlabeled examples. Experiments and analysis also confirmed the effectiveness of our proposed approach in anomaly detection and practical project management.

An Improving Algorithm Based on SOM Clustering and its Applications

2013 ◽  
Vol 655-657 ◽  
pp. 1000-1004
Author(s):  
Chen Guang Yan ◽  
Yu Jing Liu ◽  
Jin Hui Fan
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Network Clustering ◽  
Self Organizing Map ◽  
Clustering Method ◽  
Map Algorithm ◽  
Som Neural Network ◽  
Simulation Results ◽  
Som Clustering ◽  
Selection Of

SOM (Self-organizing Map) algorithm is a clustering method basing on non-supervision condition. The paper introduces an improved algorithm based on SOM neural network clustering. It proposes SOM’s basic theory on data clustering. For SOM’s practical problems in applications, the algorithm also improved the selection of initial weights and the scope of neighborhood parameters. Finally, the simulation results in Matlab prove that the improved clustering algorithm improve the correct rate and computational efficiency of data clustering and to make the convergence speed better.

scholarly journals Clustering Large-Scale Data Based On Modified Affinity Propagation Algorithm

2016 ◽  
Vol 6 (1) ◽  
pp. 23-33 ◽  
Author(s):  
Ahmed M. Serdah ◽  
Wesam M. Ashour
Keyword(s):  
Data Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Affinity Propagation ◽  
Clustering Method ◽  
Data Set ◽  
Local Cluster ◽  
Large Scale Data ◽  
Scale Data

Abstract Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most of them do not achieve clustering with high quality. Despite that Affinity Propagation (AP) is effective and accurate in normal data clustering, but it is not effective for large-scale data. This paper proposes two methods for large-scale data clustering that depend on a modified version of AP algorithm. The proposed methods are set to ensure both low time complexity and good accuracy of the clustering method. Firstly, a data set is divided into several subsets using one of two methods random fragmentation or K-means. Secondly, subsets are clustered into K clusters using K-Affinity Propagation (KAP) algorithm to select local cluster exemplars in each subset. Thirdly, the inverse weighted clustering algorithm is performed on all local cluster exemplars to select well-suited global exemplars of the whole data set. Finally, all the data points are clustered by the similarity between all global exemplars and each data point. Results show that the proposed clustering method can significantly reduce the clustering time and produce better clustering result in a way that is more effective and accurate than AP, KAP, and HAP algorithms.

scholarly journals Benchmarking Daily Line Loss Rates of Low Voltage Transformer Regions in Power Grid Based on Robust Neural Network

2019 ◽  
Vol 9 (24) ◽  
pp. 5565 ◽  
Author(s):  
Weijiang Wu ◽  
Lilin Cheng ◽  
Yu Zhou ◽  
Bo Xu ◽  
Haixiang Zang ◽  
...  
Keyword(s):  
Neural Network ◽  
Low Voltage ◽  
Voltage Transformer ◽  
Important Indicator ◽  
Benchmark Analysis ◽  
Comparison Results ◽  
Novel Method ◽  
Line Loss ◽  
Transmission And Distribution ◽  
Loss Rates

Line loss is inherent in transmission and distribution stages, which can cause certain impacts on the profits of power-supply corporations. Thus, it is an important indicator and a benchmark value of which is needed to evaluate daily line loss rates in low voltage transformer regions. However, the number of regions is usually very large, and the dataset of line loss rates contains massive outliers. It is critical to develop a regression model with both great robustness and efficiency when trained on big data samples. In this case, a novel method based on robust neural network (RNN) is proposed. It is a multi-path network model with denoising auto-encoder (DAE), which takes the advantages of dropout, L2 regularization and Huber loss function. It can achieve several different outputs, which are utilized to compute benchmark values and reasonable intervals. Based on the comparison results, the proposed RNN possesses both superb robustness and accuracy, which outperforms the testing conventional regression models. According to the benchmark analysis, there are about 13% outliers in the collected dataset and about 45% regions that hold outliers within a month. Hence, the quality of line loss rate data should still be further improved.

Sign in / Sign up

Export Citation Format

Share Document