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

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1280 ◽  
Author(s):  
Yue Shen ◽  
Muhammad Abubakar ◽  
Hui Liu ◽  
Fida Hussain
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Distribution System ◽  
Classification Accuracy ◽  
Distribution Systems ◽  
Principal Component ◽  
Original Data ◽  
Convolution Neural Network ◽  
Four Levels

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.

scholarly journals A new protection scheme for PQ events prediction and classification in distribution system: A SSO-ANN strategy

2019 ◽  
Vol 52 (5-6) ◽  
pp. 449-461 ◽  
Author(s):  
K Karthikumar ◽  
V Senthil Kumar ◽  
M Karuppiah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Quality ◽  
Distribution System ◽  
Swarm Optimization ◽  
Protection Scheme ◽  
Event Prediction ◽  
Power Quality Disturbances ◽  
Artificial Neural

Increased utilization of nonlinear loads and fault event on the power system have resulted in a decline in the quality of power provided to the customers. It is fundamental to recognize and distinguish the power quality disturbances in the distribution system. To recognize and distinguish the power quality disturbances, the development of high protection schemes is required. This paper presents an optimal protection scheme for power quality event prediction and classification in the distribution system. The proposed protection scheme combines the performance of both the salp swarm optimization and artificial neural network. Here, artificial neural network is utilized in two phases with the objective function of prediction and classification of the power quality events. The first phase is utilized for recognizing the healthy or unhealthy condition of the system under various situations. Artificial neural network is utilized to perceive the system signal’s healthy or unhealthy condition under different circumstances. In the second phase, artificial neural network performs the classification of the unhealthy signals to recognize the right power quality event for assurance. In this phase, the artificial neural network learning method is enhanced by utilizing salp swarm optimization based on the minimum error objective function. The proposed method performs an assessment procedure to secure the system and classify the optimal power quality event which occurs in the distribution system. At that point, the proposed work is executed in the MATLAB/Simulink platform and the performance of the proposed system is compared with different existing techniques like Multiple Signal Classification-Artificial Neural Network (MUSIC-ANN), and Genetic Algorithm - Artificial Neural Network (GA-ANN). The comparison results demonstrate the superiority of the SSO-ANN technique and confirm its potential to power quality event prediction and classification.

scholarly journals Perbandingan Convolution Neural Network Untuk Klasifikasi Kesegaran Ikan Bandeng Pada Citra Mata

2021 ◽  
Vol 8 (3) ◽  
pp. 601
Author(s):  
Eko Prasetyo ◽  
Rani Purbaningtyas ◽  
Raden Dimas Adityo ◽  
Enrico Tegar Prabowo ◽  
Achmad Irfan Ferdiansyah
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Animal Protein ◽  
Experimental Results ◽  
Convolution Neural Network ◽  
Food Ingredients ◽  
The People ◽  
Experimental Implementation

<p class="Abstrak">Ikan merupakan salah satu sumber protein hewani dan sangat diminati masyarakat Indonesia, dari survey bahan makanan yang diminati, bandeng peringkat keempat dibanding bahan makanan yang lain. Khususnya ikan bandeng, ikan ini menjadi satu dari enam ikan yang banyak dikonsumsi masyarakat selain tongkol, kembung, teri, mujair dan lele, maka ketelitian masyarakat ketika membeli ikan bandeng menjadi perhatian serius dalam memilih ikan bandeng segar. Deteksi kesegaran dengan menyentuh tubuh ikan dapat mengakibatkan kerusakan tanpa disengaja, maka deteksi kesegaran ikan harus dilakukan tanpa menyentuh ikan bandeng dengan memanfaatkan citra kondisi mata. Dalam riset ini, kami melakukan eksperimen implementasi klasifikasi kesegaran ikan bandeng sangat segar dan tidak segar berdasarkan mata menggunakan transfer learning dari empat CNN, yaitu Xception, MobileNet V1, Resnet50, dan VGG16. Dari hasil eksperimen klasifikasi dua kelas kesegaran ikan bandeng menggunakan 154 citra menunjukkan bahwa VGG16 mencapai kinerja terbaik dibanding arsitektur lainnya dimana akurasi klasifikasi mencapai 0.97. Dengan akurasi lebih tinggi dibanding arsitektur lainnya maka VGG16 relatif lebih tepat digunakan untuk klasifikasi dua kelas kesegaran ikan bandeng.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Fish, one source of animal protein, is an exciting food for Indonesia's people. From a survey of food-ingredients demanded, milkfish are ranked fourth compared to other food-ingredients. Especially for milkfish, this fish is one of the six fish consumed by Indonesia's people besides tuna, bloating, anchovies, tilapia, and catfish, so the exactitude of the people when buying is a severe concern in choosing fresh milkfish. Detection of freshness by touching the fish's body may cause unexpected destruction, so detecting the fish's freshness should be conducted without touching using the eye image. In this research, we conducted an experimental implementation of freshness milkfish classification (vastly fresh and not fresh) based on the eyes using transfer learning from several CNNs, such as Xception, MobileNet V1, Resnet50, and VGG16. The experimental results of the classification of two milkfish freshness classes using 154 images show that VGG16 achieves the best performance compared to other architectures, where the classification accuracy achieves 0.97. With higher accuracy than other architectures, VGG16 is relatively more appropriate for classifying two classes of milkfish freshness.</em></p>

scholarly journals Power quality event classification using complex wavelets phasor models and customized convolution neural network

2022 ◽  
Vol 12 (1) ◽  
pp. 22
Author(s):  
Likhitha Ramalingappa ◽  
Aswathnarayan Manjunatha
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Input Voltage ◽  
Convolution Neural Network ◽  
Training Time ◽  
Event Classification ◽  
Low Pass ◽  
Space Phasor

Origin and triggers of power quality (PQ) events must be identified in prior, in order to take preventive steps to enhance power quality. However it is important to identify, localize and classify the PQ events to determine the causes and origins of PQ disturbances. In this paper a novel algorithm is presented to classify voltage variations into six different PQ events considering the space phasor model (SPM) diagrams, dual tree complex wavelet transforms (DTCWT) sub bands and the convolution neural network (CNN) model. The input voltage data is converted into SPM data, the SPM data is transformed using 2D DTCWT into low pass and high pass sub bands which are simultaneously processed by the 2D CNN model to perform classification of PQ events. In the proposed method CNN model based on Google Net is trained to perform classification of PQ events with default configuration as in deep neural network designer in MATLAB environment. The proposed algorithm achieve higher accuracy with reduced training time in classification of events than compared with reported PQ event classification methods.

scholarly journals Detection and Classification of Recessive Weakness in Superbuck Converter Based on WPD-PCA and Probabilistic Neural Network

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 290 ◽  
Author(s):  
Chenhao Wu ◽  
Jiguang Yue ◽  
Li Wang ◽  
Feng Lyu
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Wavelet Decomposition ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Principal Component ◽  
Excellent Performance ◽  
Feature Vectors ◽  
Parseval’S Theorem

This paper proposes a detection and classification method of recessive weakness in Superbuck converter through wavelet packet decomposition (WPD) and principal component analysis (PCA) combined with probabilistic neural network (PNN). The Superbuck converter presents excellent performance in many applications and is also faced with today’s demands, such as higher reliability and steadier operation. In this paper, the detection and classification issue to recessive weakness is settled. Firstly, the performance of recessive weakness both in the time and frequency domain are demonstrated to clearly show the actual deterioration of the circuit system. The WPD and Parseval’s theorem are utilized in this paper to feature the extraction of recessive weakness. The energy discrepancy of the fault signals at different wavelet decomposition levels are then chosen as the feature vectors. PCA is also employed to the dimensionality reduction of feature vectors. Then, a probabilistic neural network is applied to automatically detect and classify the recessive weakness from different components on the basis of the extracted features. Finally, the classification accuracy of the proposed classification algorithm is verified and tested with experiments, which present satisfying classification accuracy.

Classification of Power Quality Events Using Wavelet Analysis and Probabilistic Neural Network

2016 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Pampa Sinha ◽  
Sudipta Debath ◽  
Swapan Kumar Goswami
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Power Quality ◽  
Distribution System ◽  
Reactive Power ◽  
Probabilistic Neural Network ◽  
Capacitor Switching ◽  
Set Up ◽  
Insulation Coordination

<p>Power quality studies have become an important issue due to widespread use of sensitive electronic equipment in power system. The sources of power quality degradation must be investigated in order to improve the power quality. Switching transients in power systems is a concern in studies of equipment insulation coordination. In this paper a wavelet based neural network has been implemented to classify the transients due to capacitor switching, motor switching, faults, converter and transformer switching. The detail reactive powers for these five transients are determined and a model which uses the detail reactive power as the input to the Probabilistic neural network (PNN) is set up to classify the above mentioned transients. The simulation has been executed for an 11kv distribution system. With the help of neural network classifier, the transient signals are effectively classified.</p>

scholarly journals Automated Diabetic Retinopathy Identification Using Convolutional Neural Network

2021 ◽  
Vol 10 (3) ◽  
pp. 1872-1882
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Convolution Neural Network ◽  
Phase Classification ◽  
Kappa Value

Classification phase is one of the important step for determining, analysing as well as diagnosing the diabetic retinopathy disorder. Nanostructures include red lesions, retinal hard macular exudates as well as Neovascularization would take up space aroundretina by the reason of devastation of veins. In order to computerise the technique pertaining to diabetic retinopathy phases categorization, a convolution neural network grounded method could be utilized. Colour fundus pictures of retina are collected during this work with aim of diabetic retinopathy classification among 5 phases by utilizing a convolution neural network. Convolution neural network with EfficientNet B5 network is employed for the phase classification of diabetic retinopathy disorder, a Kappa value (classification accuracy) of 88.48% is achieved.

Unit Template Based Control of PV-DSTATCOM

2020 ◽  
Vol 13 (1) ◽  
pp. 36-42
Author(s):  
Gunjan Varshney ◽  
Durg S. Chauhan ◽  
Madhukar P. Dave ◽  
Nitin
Keyword(s):  
Power Quality ◽  
Power Factor ◽  
Power Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Phase Distribution ◽  
Voltage Regulation ◽  
Photovoltaic Array ◽  
Quality Issues ◽  
Static Compensator

Background: In modern electrical power distribution systems, Power Quality has become an important concern due to the escalating use of automatic, microprocessor and microcontroller based end user applications. Methods: In this paper, power quality improvement has done using Photovoltaic based Distribution Static Compensator (PV-DSTATCOM). Complete simulation modelling and control of Photovoltaic based Distribution Static Compensator have been provided in the presented paper. In this configuration, DSTATCOM is fed by solar photovoltaic array and PV module is also helpful to maintain the DC link voltage. The switching of PV-STATCOM is controlled by Unit template based control theory. Results: The performance of PV-DSTATCOM has been evaluated for Unity Power Factor (UPF) and AC Voltage Control (ACVC) modes. Here, for studying the power quality issues three-phase distribution system is considered and results have been verified through simulation based on MATLAB software. Conclusion: Different power quality issues and their improvement are studied and presented here for harmonic reduction, DC voltage regulation and power factor correction.

scholarly journals High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1238
Author(s):  
Supanat Chamchuen ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Puripong Suthisopapan ◽  
Pirat Khunkitti
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Distribution System ◽  
Classification Accuracy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Electrical Distribution ◽  
Power Quality Disturbance ◽  
Optimal Feature Selection ◽  
Optimal Feature

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

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