Characteristic Study of Solar Photovoltaic Array under Different Partial Shading Conditions

© The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Photovoltaic (PV) systems are frequently exposed to partial or complete shading phenomena. Partial shading has a profound impact on the performance of solar power generation. The operational performance of PV arrays under partial shading shows multiple maximum power point peaks, therefore it is challenging to identify the actual maximum power point. This paper investigates the impact of partial shading location on the output power of solar photovoltaic arrays with various configurations. Multiple photovoltaic strings, in both parallel and series configurations, are considered. Different random shading patterns are considered and analyzed to determine which configuration has higher maximum power point. The sensitivity of the partial shading can change according to the partial shading types, shading pattern, and the configuration used to connect all PV modules. Moreover, the study also investigates the output of the PV array with shading two random models, two consecutive models, and three random and consecutive modules. Experimental results validate the analysis and demonstrate the effect of various partial shading on the eficiency and performance of the PV system.Peer reviewe

Diagnosis of Stator Turn-to-Turn Fault and Stator Voltage Unbalance Fault Using ANFIS

An induction machine is a highly non-linear system that poses a great challenge because of its fault diagnosis due to the processing of large and complex data. The fault in an induction machine can lead to excessive downtimes that can lead to huge losses in terms of maintenance and production. This paper discusses the diagnosis of stator winding faults, which is one of the common faults in an induction machine. Several diagnostics techniques have been presented in the literature. Fault detection using traditional analytical methods are not always possible as this requires prior knowledge of the exact motor model. The motor models are also susceptible to inaccuracy due to parameter variations. This paper presents Adaptive Neuro-fuzzy Inference system (ANFIS) based fault diagnosis of induction motors. The distinction between the stator winding fault and supply unbalance is addressed in this paper. Experimental data is collected by shorting the turns of a health motor as well as creating unbalance in the stator voltage. The data is processed and fed to an ANFIS classifier that accurately identifies the faulted condition and unbalanced supply voltage conditions. The ANFIS provides almost 99% accurate and computationally efficient output in diagnosing the faults and unbalance conditions.DOI:http://dx.doi.org/10.11591/ijece.v3i1.185

Inductive Transfer and Deep Neural Network Learning-Based Cross-Model Method for Short-Term Load Forecasting in Smarts Grids

In a real-world scenario of load forecasting, it is crucial to determine the energy consumption in electrical networks. The energy consumption data exhibit high variability between historical data and newly arriving data streams. To keep the forecasting models updated with the current trends, it is important to fine-tune the models in a timely manner. This article proposes a reliable inductive transfer learning (ITL) method, to use the knowledge from existing deep learning (DL) load forecasting models, to innovatively develop highly accurate ITL models at a large number of other distribution nodes reducing model training time. The outlier-insensitive clustering-based technique is adopted to group similar distribution nodes into clusters. ITL is considered in the setting of homogeneous inductive transfer. To solve overfitting that exists with ITL, a novel weight regularized optimization approach is implemented. The proposed novel cross-model methodology is evaluated on a real-world case study of 1000 distribution nodes of an electrical grid for one-day ahead hourly forecasting. Experimental results demonstrate that overfitting and negative learning in ITL can be avoided by the dissociated weight regularization (DWR) optimizer and that the proposed methodology delivers a reduction in training time by almost 85.6% and has no noticeable accuracy losses.Peer reviewe

Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).The modern-day urban energy sector possesses the integrated operation of various microgrids located in a vicinity, named cluster microgrids, which helps to reduce the utility grid burden. However, these cluster microgrids require a precise electric load projection to manage the operations, as the integrated operation of multiple microgrids leads to dynamic load demand. Thus, load forecasting is a complicated operation that requires more than statistical methods. There are different machine learning methods available in the literature that are applied to single microgrid cases. In this line, the cluster microgrids concept is a new application, which is very limitedly discussed in the literature. Thus, to identify the best load forecasting method in cluster microgrids, this article implements a variety of machine learning algorithms, including linear regression (quadratic), support vector machines, long short-term memory, and artificial neural networks (ANN) to forecast the load demand in the short term. The effectiveness of these methods is analyzed by computing various factors such as root mean square error, R-square, mean square error, mean absolute error, mean absolute percentage error, and time of computation. From this, it is observed that the ANN provides effective forecasting results. In addition, three distinct optimization techniques are used to find the optimum ANN training algorithm: Levenberg−Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The effectiveness of these optimization algorithms is verified in terms of training, test, validation, and error analysis. The proposed system simulation is carried out using the MATLAB/Simulink-2021a® software. From the results, it is found that the Levenberg−Marquardt optimization algorithm-based ANN model gives the best electrical load forecasting results.Peer reviewe

A Robust Grid-Tied PV System based Super-Twisting Integral Sliding Mode Control

In this paper, a super-twisting integral sliding mode controller (ST-ISMC) is proposed to enhance the performance of grid-tied Photovoltaic (PV) system based on voltage-oriented control (VOC). The three-phase two-stage grid connected voltage source inverter (VSI) based PV source is considered in this study. The ST-ISMC controller is designed and implemented at dc-link of the inverter to ensure better regulation of the capacitor voltage. Furthermore, two ST-ISMC controllers are implemented at the ac side to generate the control vectors for space vector modulation (SVM), while ensuring a lower total harmonic distortion (THD). Extensive simulation studies are carried out using MATLAB/Simulink software to evaluate the performance of the proposed controllers. The obtained results show the superiority of STISMC in term of dc-link voltage regulation and power quality improvement under different operating conditions

Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption

Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities during the training of the models. The lack of historical data for training and testing of developed models, considering security and privacy policy restrictions, is considered one of the greatest challenges to machine learning-based techniques. The paper proposes the use of homomorphic encryption, which enables the possibility of training the deep learning and classical machine learning models whilst preserving the privacy and security of the data. The proposed methodology is tested for applications of fault identification and localization, and load forecasting in smart grids. The results for fault localization show that the classification accuracy of the proposed privacy-preserving deep learning model while using homomorphic encryption is 97–98%, which is close to 98–99% classification accuracy of the model on plain data. Additionally, for load forecasting application, the results show that RMSE using the homomorphic encryption model is 0.0352 MWh while RMSE without application of encryption in modeling is around 0.0248 MWh

ANN-based system for a discrimination between unbalanced supply voltage and phase loss in induction motors

It is documented that almost 98% of all voltage generated by electric utilities has up to 3% unbalance. Single phasing fault deserves special attention since phase loss is considered the worst case of unbalanced supply voltage. This paper focuses on unbalanced supply condition diagnosis and discrimination between an unbalance in the supply and phase loss fault. The discrimination will be based on the ratio of third harmonic to fundamental Fast Fourier Transform (FFT) magnitude components (RTHF-FFT) of the three-phase stator line currents and supply voltages under different load conditions and using artificial neural network (ANN). The proposed approach achieves high accuracy in detecting the unbalanced supply voltage condition in induction motor and identifying the level of severity of the fault. In addition, the proposed algorithm will discriminate between the effects of unbalanced supply voltage and those due to phase losses fault. The paper proposed a reliable approach for detection and diagnosis of unbalanced supply voltage condition. Possible loss of winding insulation under different percentages of unbalanced supply voltages will be predicted which could help preventing sudden failure of the motor during operation. The approach will be proved through experimental validation.Scopu