Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm

Huang, Nantian; Peng, Haitao; Cai, Guowei; Chen, Jikai

doi:10.3390/en9110927

Cited by 34 publications

(31 citation statements)

References 37 publications

(51 reference statements)

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“…This chapter is divided into 2 parts: data description and experimental results. To demonstrate completely the performance of WindNet proposed in this paper, this chapter will also include comparisons of very popular and commonly used machine learning algorithms, such as support vector machine (SVM) [33][34][35][36][37][38], random forest (RF) [39][40][41][42][43][44], decision tree (DT) [45][46][47][48][49][50] and MLP.…”

Section: Resultsmentioning

confidence: 99%

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Huang

Kuo

2018

Energies

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To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed algorithm was compared to that of four other artificial intelligence algorithms commonly used in wind speed forecasting. Numerical testing results based on data from a designated wind site in Taiwan were used to demonstrate the efficiency of above-mentioned proposed learning method. Mean absolute error (MAE) and root-mean-square error (RMSE) were adopted as accuracy evaluation indexes in this paper. Experimental results indicate that the MAE and RMSE values of the proposed algorithm are 0.800227 and 0.999978, respectively, demonstrating very high forecasting accuracy.

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Section: Resultsmentioning

confidence: 99%

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Huang

Kuo

2018

Energies

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“…In order to fully demonstrate the performance of the EPNet proposed in this paper, this chapter includes comparisons between Support Vector Machine (SVM) [25][26][27][28][29][30], Random Forest (RF) [31][32][33][34][35][36], Decision Tree (DT) [37][38][39][40][41][42], MLP, CNN and LSTM. Figure 6 is the Electric Power Markets (PJM) Regulation Zone Preliminary Billing Data [43] used in this experiment, this data records the regulation market capacity clearing price of every half hour in 2017.…”

Section: Resultsmentioning

confidence: 99%

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

2018

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Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

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“…The classification rate for 12 types of three-phase simulated PQ disturbances was achieved as 99.59, and 107 features were selected, but no real data was evaluated in this paper. Optimal multi-resolution fast S-transform and cart algorithm were studied in [65]. Twelve types of single-phase simulated PQ disturbances were classified and a 98.92% classification rate was achieved.…”

Section: Performance Comparison With Published Articlesmentioning

confidence: 99%

Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems

et al. 2019

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The excessive use of power semiconductor devices in a grid utility increases the malfunction of the control system, produces power quality disturbances (PQDs) and reduces the electrical component life. The present work proposes a novel algorithm based on Improved Principal Component Analysis (IPCA) and 1-Dimensional Convolution Neural Network (1-D-CNN) for detection and classification of PQDs. Firstly, IPCA is used to extract the statistical features of PQDs such as Root Mean Square, Skewness, Range, Kurtosis, Crest Factor, Form Factor. IPCA is decomposed into four levels. The principal component (PC) is obtained by IPCA, and it contains a maximum amount of original data as compare to PCA. 1-D-CNN is also used to extract features such as mean, energy, standard deviation, Shannon entropy, and log-energy entropy. The statistical analysis is employed for optimal feature selection. Secondly, these improved features of the PQDs are fed to the 1-D-CNN-based classifier to gain maximum classification accuracy. The proposed IPCA-1-D-CNN is utilized for classification of 12 types of synthetic and simulated single and multiple PQDs. The simulated PQDs are generated from a modified IEEE bus system with wind energy penetration in the balanced distribution system. Finally, the proposed IPCA-1-D-CNN algorithm has been tested with noise (50 dB to 20 dB) and noiseless environment. The obtained results are compared with SVM and other existing techniques. The comparative results show that the proposed method gives significantly higher classification accuracy.

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Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm

Cited by 34 publications

References 37 publications

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems

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