Short-term photovoltaic power prediction based on modal reconstruction and hybrid deep learning model

Bearing in mind European Green Deal assumptions regarding a significant reduction of green house emissions, electricity generation from Renewable Energy Sources (RES) is more and more important nowadays. Besides this, accurate and reliable electricity generation forecasts from RES are needed for capacity planning, scheduling, managing inertia and frequency response during contingency events. The recent three years have proved that Machine Learning (ML) models are a promising solution for forecasting electricity generation from RES. In this review, the 8-step methodology was used to find and analyze 262 relevant research articles from the Scopus database. Statistic analysis based on eight criteria (ML method used, renewable energy source involved, affiliation location, hybrid model proposed, short term prediction, author name, number of citations, and journal title) was shown. The results indicate that (1) Extreme Learning Machine and ensemble methods were the most popular methods used for electricity generation forecasting from RES in the last three years (2020–2022), (2) most of the research was carried out for wind systems, (3) the hybrid models accounted for about a third of the analyzed works, (4) most of the articles concerned short-term models, (5) the most researchers came from China, (6) and the journal which published the most papers in the analyzed field was Energies. Moreover, strengths, weaknesses, opportunities, and threats for the analyzed ML forecasting models were identified and presented in this paper.

/pdf/machine-learning-approaches-to-predict-electricity-fyczttjr.pdf

Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources

Accurate parameter estimation is crucial and challenging for the design and modeling of PV cells/modules. However, the high degree of non-linearity of the typical I–V characteristic further complicates this task. Consequently, significant research interest has been generated in recent years. Currently, this trend has been marked by a noteworthy acceleration, mainly due to the rise of swarm intelligence and the rapid progress of computer technology. This paper proposes a developed Mountain Gazelle Optimizer (MGO) to generate the best values of the unknown parameters of PV generation units. The MGO mimics the social life and hierarchy of mountain gazelles in the wild. The MGO was compared with well-recognized recent algorithms, which were the Grey Wolf Optimizer (GWO), the Squirrel Search Algorithm (SSA), the Differential Evolution (DE) algorithm, the Bat–Artificial Bee Colony Optimizer (BABCO), the Bat Algorithm (BA), Multiswarm Spiral Leader Particle Swarm Optimization (M-SLPSO), the Guaranteed Convergence Particle Swarm Optimization algorithm (GCPSO), Triple-Phase Teaching–Learning-Based Optimization (TPTLBO), the Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO), the quasi-Opposition-Based Learning Whale Optimization Algorithm (OBLWOA), and the Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO). The experimental findings and statistical studies proved that the MGO outperformed the competing techniques in identifying the parameters of the Single-Diode Model (SDM) and the Double-Diode Model (DDM) PV models of Photowatt-PWP201 (polycrystalline) and STM6-40/36 (monocrystalline). The RMSEs of the MGO on the SDM and the DDM of Photowatt-PWP201 and STM6-40/36 were 2.042717 ×10−3, 1.387641 ×10−3, 1.719946 ×10−3, and 1.686104 ×10−3, respectively. Overall, the identified results highlighted that the MGO-based approach featured a fast processing time and steady convergence while retaining a high level of accuracy in the achieved solution.

An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models

Water quality prediction plays a crucial role in pollution treatment. However, inaccurate long-term prediction resulting from complex information patterns and insufficient feature extraction may lead to unnecessary environmental costs. In this study, a novel Interval Decomposition Correlation Particle Swarm Optimization-Extreme Learning Machine (IDCPSO-ELM) model is proposed to improve long-term water quality prediction ability. We employ Multivariate Variational Mode Decomposition (MVMD), Variational Mode Decomposition (VMD), Sliding Correlation and Permutation Entropy to reconstruct features to reduce complexity. Seasonal and Trend decomposition using Loess-Empirical Wavelet Transform decomposition (STL-EWT) is applied to target variables to improve feature extraction ability. Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) is used to predict high-frequency components based on the reconstructive features, and Back Propagation Neural Network-Extreme Learning Machine (BPNN-ELM) is employed to predict the trend and lowest-frequency components. Grey Wolf Optimization Algorithm (GWO) is used to combine these results. Six stations with high potential pollution threats in the Haihe River basin of Beijing from 2021 to 2022 are taken into model competition. The results show that: (1) In short-term prediction, average MAPE, RMSE and NSE of IDCPSO-ELM model reach 0.0934, 0.0410 and 0.8061, respectively, which are better than Genetic Algorithm-Elman Network (GA-ELMAN), Cuckoo Search Algorithm-Back Propagation Neural Network (CS-BPNN), PSO-ELM and Sparrow Search Algorithm-Kernel Extreme Learning Machine (SSA-KELM) short-term prediction models. (2) In long-term prediction, average MAPE, RMSE and NSE of IDCPSO-ELM model reach 0.1114, 0.0420 and 0.8268, respectively, which are better than the Radial Basis Function Neural Network (RBFNN), Grey Wolf Optimization-Support Vector Machine Regression (GWO-SVR), Whale Optimization Algorithm-Deep Extreme Learning Machine (WOA-DELM), Wavelet Transform decomposition-GWO-SVR-BPNN (WT-GWO-SVR-BPNN) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-PSO-ELM-Long Short-Term Memory (CEEMDAN-PSO-ELM-LSTM) long-term prediction models. IDCPSO-ELM model is considered competitive and promising for long-term water quality prediction, particularly in areas with high potential pollution threats. 

A novel Interval Decomposition Correlation Particle Swarm Optimization-Extreme Learning Machine model for short-term and long-term water quality prediction

A two-phase-based deep neural network for simultaneous health monitoring and prediction of rolling bearings

Intelligent fault diagnosis of photovoltaic array based on variable predictive models and I–V curves

Fault diagnosis plays a crucial role in the operation and maintenance (O&M) of photovoltaic (PV) arrays, and reasonable feature extraction is a prerequisite for effective fault diagnosis. In this paper, a feature extraction and fault diagnosis method based on current–voltage (I–V) conversion is proposed. First, the PV array modeling method based on the double diode model (DDM) and the reverse bias model (RBM) is proposed. This modeling method can simulate the I–V curves under different states and provide data foundation for feature extraction and fault diagnosis. Next, three procedures for correcting I–V curves and three feature enhancement methods are compared to select the optimal program for I–V conversion. The converted feature matrix is dimensionalized using T-distributed stochastic neighbor embedding (T-SNE) to achieve feature extraction. Finally, ten classification models for fault diagnosis are adopted to verify the effectiveness of the proposed feature extraction method. Experimental results demonstrate that the proposed methods perform well on simulation data and provide satisfactory fault diagnosis results for the measured I–V curves. Among the classification models tested, the variable prediction model (VPM) shows the optimal comprehensive performance, with the computational time of 0.17 s and the accuracy of 99.4%. 

Feature extraction and fault diagnosis of photovoltaic array based on current–voltage conversion

Health status evaluation of photovoltaic array based on deep belief network and Hausdorff distance

Research on real-time identification method of model parameters for the photovoltaic array

Partial shading (PS) is a prevalent fault during photovoltaic (PV) array operation, significantly impacting power generation performance. An effective approach of PS recognition and fault severity diagnosis for PV array is proposed in this paper. This paper primarily includes current–voltage (I–V) data preparation, PV array modeling, and PS fault diagnosis. Data preparation involves preprocessing and conversion procedures for I–V curves, providing a dependable data source for subsequent studies. The converted I–V curves are the basis of the PV array modeling, which can simulate normal and fault I–V characteristics under target ambient conditions. PS fault diagnosis comprises pre-diagnosis, PS recognition and fault severity diagnosis. Pre-diagnosis is realized by quantifying the power degradation of the PV array. PS recognition and fault severity diagnosis are based on the number and position of the steps on the I–V curve. The experiments validate the effectiveness of the PV array modeling method and the PS fault diagnosis method. The root mean square error (RMSE) of the measured and simulated currents is employed to assess the modeling accuracy, within 0.03A. PS recognition accuracy surpasses 96%, and fault severity diagnosis achieves over 80% accuracy. Therefore, the proposed methods for PV array exhibits strong reliability and practicality. 

Fast simulation modeling and multiple-PS fault diagnosis of the PV array based on I–V curve conversion

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