Optimal scheduling of electricity and hydrogen integrated energy system considering multiple uncertainties.

The spread of renewable energy (RE) generation not only promotes the economy and environmental protection, but also brings uncertainty to the power system. As the integration of hydrogen and electricity can effectively mitigate the fluctuation of RE generation, an electricity-hydrogen integrated energy system is constructed. Then, this paper studies the source-load uncertainties and corresponding correlation as well as the electricity-hydrogen price uncertainties and corresponding correlation. Finally, an optimal scheduling model considering economy, environmental protection, and demand response (DR) is proposed. The simulation results indicate that the introduction of the DR strategy and the correlation of electricity-hydrogen price can effectively improve the economy of the system. After introducing the DR, the operating cost of the system is reduced by 5.59%, 10.5%, and 21.06% in each season, respectively. When considering the correlation of EP and HP, the operating cost of the system is reduced by 4.71%, 6.47%, 1.4% in each season, respectively.

Many-objective bi-level energy scheduling method for integrated energy stations based on power allocation strategy.

The integrated energy station of new energy vehicle hydrogenation/charging/power exchange is proposed, which also includes hydrogen production, hydrogen storage, electricity sales to users and the grid (WPIES). To address the efficiency of renewable energy use, this paper proposes a future value competition strategy for wind and photovoltaic (PV) allocation based on goal optimization (FVCS). In order to better realize the distribution of wind power/PV in the integrated energy station and improve the energy utilization efficiency of the integrated energy station, a two-layer optimization model of FVCS-WPIES is proposed, in which the upper layer model aims to maximize the expected income. The goals of the lower-level model are to maximize total profit, minimize battery losses, and minimize pollutant emissions. The model also considers the hydrogen power constraint and the upper-level model penalty. The comparison results show that the Pareto solution set is superior to the traditional model.

An overview of deterministic and probabilistic forecasting methods of wind energy.

In recent years, a variety of wind forecasting models have been developed, prompting necessity to review the abundant methods to gain insights of the state-of-the-art development status. However, existing literature reviews only focus on a subclass of methods, such as multi-objective optimization and machine learning methods while lacking the full particulars of wind forecasting field. Furthermore, the classification of wind forecasting methods is unclear and incomplete, especially considering the rapid development of this field. Therefore, this article aims to provide a systematic review of the existing deterministic and probabilistic wind forecasting methods, from the perspectives of data source, model evaluation framework, technical background, theoretical basis, and model performance. It is expected that this work will provide junior researchers with broad and detailed information on wind forecasting for their future development of more accurate and practical wind forecasting models.

Intelligent fault monitoring and diagnosis of tunnel fans using a hierarchical cascade forest.

Tunnel fan is critical fire-fighting equipment, and its safe and stable operation is very important for the efficiency and safety of tunnel traffic. Existing studies commonly train the fault diagnosis methods with the goal of minimizing mean error which ignores the difference between classes in feature distribution. To solve the problem of inaccurate prediction caused by mean error evaluation, this paper presents a non-neural deep learning model, namely hierarchical cascade forest, which has three characteristics: (1) A hierarchical cascade structure is constructed, of which the output comes from each layer; (2) Each fault class is evaluated and recognized independently, the result of fault classes that are easy to distinguish is output earlier; (3) A confidence-based threshold estimate method is proposed in HCF and used to improve the training method to increase the reliability of HCF. Based on these, HCF improves the cascade forest structure and implements the proper matching of different depth of feature and fault patterns. The effect of HCF is verified through experiments based on the tunnel fans testing rig. Experimented results show that, compared to Deep Forest, the accuracy of HCF increases by 0.6% to 10.8%, and the training time of HCF is reduced 33.24%.

Deep balanced cascade forest: An novel fault diagnosis method for data imbalance.

Data imbalance is a common problem in rotating machinery fault diagnosis. Traditional data-driven diagnosis methods, which learn fault features based on balance dataset, would be significantly affected by imbalanced data. In this paper, a novel imbalanced data related fault diagnosis method named deep balanced cascade forest is proposed to solve this problem. Deep balanced cascade forest is a multi-channel cascade forest, in which, each of its channels adaptively generates deep cascade structure and is trained on independent data. To enhance the performance of imbalance classification, the deep balanced cascade forest is promoted from both aspects of resampling and algorithm design. A hybrid sampling method, namely Up-down Sampling, is proposed to provide rebalanced data for each cascade forest channel. Meanwhile, a new type of balanced forest with an improved balanced information entropy for attribute selection is designed as the basic classifier of cascade forest. The good synergy of these two methods is the key to the deep balanced cascade forest model. This good synergy makes deep balanced cascade forest achieve the fusion of data-level methods and algorithm-level methods. Comparative experiments on sufficient imbalanced datasets have been designed to verify the performance of the proposed model, and results confirm that deep balanced cascade forest is much more stable and effective in handling imbalance fault diagnosis problem compared to the popular deep learning methods.

Deep Learning Method Based on Gated Recurrent Unit and Variational Mode Decomposition for Short-Term Wind Power Interval Prediction.

Wind power interval prediction (WPIP) plays an increasingly important role in evaluations of the uncertainty of wind power and becomes necessary for managing and planning power systems. However, the intermittent and fluctuating characteristics of wind power mean that high-quality prediction intervals (PIs) production is a challenging problem. In this article, we propose a novel hybrid model for the WPIP based on the gated recurrent unit (GRU) neural networks and variational mode decomposition (VMD). In the hybrid model, VMD is employed to decompose complex wind power data into simplified modes. Basic GRU prediction models, comprising a GRU input layer, multiple fully connected layers, and a rank-ordered terminal layer, are then trained for each mode to produce PIs, which are combined to obtain final PIs. In addition, an adaptive optimization method based on constructed intervals (CIs) is proposed to build high-quality training labels for supervised learning with the hybrid model. Several numerical experiments were implemented to validate the effectiveness of the proposed method. The results indicate that the proposed method performs better than the traditional interval prediction models with much higher quality PIs, and it requires less training time.

Fault Diagnosis of Rolling Element Bearings with a Two-Step Scheme Based on Permutation Entropy and Random Forests.

This study presents a two-step fault diagnosis scheme combined with statistical classification and random forests-based classification for rolling element bearings. Considering the inequality of features sensitivity in different diagnosis steps, the proposed method utilizes permutation entropy and variational mode decomposition to depict vibration signals under single scale and multiscale. In the first step, the permutation entropy features on the single scale of original signals are extracted and the statistical classification model based on Chebyshev's inequality is constructed to detect the faults with a preliminary acquaintance of the bearing condition. In the second step, vibration signals with fault conditions are firstly decomposed into a collection of intrinsic mode functions by using variational mode decomposition and then multiscale permutation entropy features derived from each mono-component are extracted to identify the specific fault types. In order to improve the classification ability of the characteristic data, the out-of-bag estimation of random forests is firstly employed to reelect and refine the original multiscale permutation entropy features. Then the refined features are considered as the input data to train the random forests-based classification model. Finally, the condition data of bearings with different fault conditions are employed to evaluate the performance of the proposed method. The results indicate that the proposed method can effectively identify the working conditions and fault types of rolling element bearings.

A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm.

As crucial equipment during industrial manufacture, the health status of rotating machinery affects the production efficiency and device safety. Hence, it is of great significance to diagnose rotating machinery faults, which can contribute to guarantee the running stability and plan for maintenance, thus promoting production efficiency and economic benefits. For this purpose, a hybrid fault diagnosis model with entropy-based feature extraction and SVM optimized by a chaos quantum sine cosine algorithm (CQSCA) is developed in this research. Firstly, the state-of-the-art variational mode decomposition (VMD) is utilized to decompose the vibration signals into sets of components, during which process the preset parameter K is confirmed with the central frequency observation method. Subsequently, the permutation entropy values of all components are computed to constitute the feature vectors corresponding to different kind of signals. Later, the newly developed sine cosine algorithm (SCA) is employed and improved with chaotic initialization by a Duffing system and quantum technique to optimize the support vector machine (SVM) model, with which the fault pattern is recognized. Additionally, the availability of the optimized SVM with CQSCA was revealed in pattern recognition experiments. Finally, the proposed hybrid fault diagnosis approach was employed for engineering applications as well as contrastive analysis. The comparative results show that the proposed method achieved the best training accuracy 99.5% and best testing accuracy 97.89%. Furthermore, it can be concluded from the boxplots of different diagnosis methods that the stability and precision of the proposed method is superior to those of others.

Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings.

This paper proposes a hybrid system named as HGSA-ELM for fault diagnosis of rolling element bearings, in which real-valued gravitational search algorithm (RGSA) is employed to optimize the input weights and bias of ELM, and the binary-valued of GSA (BGSA) is used to select important features from a compound feature set. Three types fault features, namely time and frequency features, energy features and singular value features, are extracted to compose the compound feature set by applying ensemble empirical mode decomposition (EEMD). For fault diagnosis of a typical rolling element bearing system with 56 working condition, comparative experiments were designed to evaluate the proposed method. And results show that HGSA-ELM achieves significant high classification accuracy compared with its original version and methods in literatures.

Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing.