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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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)

In this paper, a novel multi-objective artificial sheep algorithm (MOASA) is proposed. The basic search idea of MOASA inherits from the BASA, which is inspired by the social behavior of sheep herd, while some modifications are made to extend the algorithm to multi-objective problems. The Pareto-based theory is adopted in the MOASA along with external archive and leader selection mechanism to bring about multi-objective optimization. Furthermore, a novel neighborhood search method is proposed and applied to the external archive to enhance the performance of the algorithm. The proposed MOASA is then tested on 17 multi-objective benchmark problems to verify its efficiency and effectiveness by comparing with six powerful multi-objective optimization algorithms (MOAs). Experimental results show that the MOASA is generally superior to its competitors in solving those benchmark problems in terms of convergence and Pareto front distribution. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a new short-term multi-objective complementary scheduling problem for hydro-thermal-renewable power systems (HTRPSs). The economic/emission objectives, different from traditional economic/emission load dispatch problems, consider the on/off status of thermal power units as well as the dispatched load among engaged units as the optimization variables under various complicated nonlinear constraints. To solve the model with hybrid optimization variables, a multi-objective hybrid grey wolf optimization algorithm is proposed, in which continuous and discrete optimization variables are encoded and optimized synchronously. A daily scheduling simulation example of a hybrid power system consisting of cascade hydropower stations, thermal power units, and renewable energy (RE) power plants is studied to test the proposed model and algorithm. The results not only demonstrate that the proposed algorithm can achieve the best Pareto front for economic/emission bi-objectives compared to its competitors, but also confirm that the obtained scheduling schemes are completely within the feasible domain. Moreover, the impact of RE capacity on the power system is analyzed. Results indicate that the joint operation of RE and hydropower stations benefit both the economic and emission objectives, and the operational costs and pollution emissions decrease by 11.9% and 17.4%, respectively, when the RE capacity increases from 50% to 100% in the hybrid system. Highlights • A new problem of multi-objective complementary scheduling of hydro-thermal-RE power system is studied. • The on/off status of thermal power units and the load dispatch of thermal and hydropower units are optimized synchronously. • A multi-objective hybrid grey wolf optimizer is proposed for hybrid optimization of real and integral variables. • The proposed MOHGWO can get better Pareto front considering economic and environmental objective. • The impact of different RE capacity on the HTRPS is analyzed. [ABSTRACT FROM AUTHOR]

In order to improve the performance of the fuzzy clustering algorithm in fuzzy space partition in the identification of the Takagi–Sugeno (T–S) fuzzy model, a hyperplane prototype fuzzy clustering model is proposed. To solve the clustering objective function, which could not be handled by the gradient method as the traditional clustering method fuzzy c-means does, a newly developed excellent global search method, which is the gravitational search algorithm (GSA), is employed. Then, the GSA-based hyperplane clustering algorithm (GSHPC) is proposed and illuminated. GSHPC is used to partition the fuzzy space and identify premise parameters of the T–S fuzzy model, and orthogonal least squares is exploited to identify the consequent parameters. Comparative experiments are designed to verify the validity of the proposed clustering algorithm and the T–S fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature. [ABSTRACT FROM PUBLISHER]

Inter type-2 fuzzy model has been confirmed to be more effective in Takagi–Sugeno (T–S) fuzzy model identification compared to type-1 fuzzy model. It is indisputable that some algorithms based on inter type-2 fuzzy model have already been developed and shown remarkable modeling performance. To further improve the modeling accuracy, the optimization methods and the neural network are taken into consideration. In this paper, an evolving modified inter type-2 fuzzy c-regression model (MIT2-FCRM) algorithm based on gravitational search algorithm (GSA) and a consequent parameter identification method based on extreme learning machine algorithm with forgetting factor for processing online sequences (namely WOS-ELM) were proposed. Then a novel approach for T–S fuzzy modeling was presented, in which, the coefficients of the upper and lower hyperplanes were obtained by evolving MIT2-FCRM algorithm based on GSA, a hyper-plane-shaped membership function (MF) was utilized to identify the antecedent parameters of the T–S fuzzy model, and WOS-ELM was employed to identify the consequent parameters. The modeling results of six examples indicate that the proposed approach is superior to other studies in terms of identification accuracy, compact fuzzy rules and noise resistance ability. [ABSTRACT FROM AUTHOR]

For solving the parameter optimization problem of a hydraulic turbine governing system (HTGS) with a delayed water hammer (DWH) effect, a Mixed-Strategy-based Whale Optimization Algorithm (MSWOA) is proposed in this paper, in which three improved strategies are designed and integrated to promote the optimization ability. Firstly, the movement strategies of WOA have been improved to balance the exploration and exploitation. In the improved movement strategies, a dynamic ratio based on improved JAYA algorithm is applied on the strategy of searching for prey and a chaotic dynamic weight is designed for improving the strategies of bubble-net attacking and encircling prey. Secondly, a guidance of the elite's memory inspired by Particle swarm optimization (PSO) is proposed to lead the movement of the population to accelerate the convergence speed. Thirdly, the mutation strategy based on the sinusoidal chaotic map is employed to avoid prematurity and local optimum points. The proposed MSWOA are compared with six popular meta-heuristic optimization algorithms on 23 benchmark functions in numerical experiments and the results show that the MSWOA has achieved significantly better performance than others. Finally, the MSWOA is applied on parameter identification problem of HTGS with a DWH effect, and the comparative results confirm the effectiveness and identification accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

With increasing wind or solar farm and pump storage plant integrations, more and more large-scale renewable energy integration systems are taking shape gradually, which brings up challenges to optimized control these systems. Especially, due to the reversible properties of pumped storage hydro unit (PSHU), it is apt to fall into the ‘S’ characteristic area which can lead to a wide range of unit rotational speed oscillation under no-load condition at middle-low water head. In order to solve these unstable running problems effectively, an adaptively fast fuzzy fractional order PID (AFFFOPID) control method for PSHU is proposed in this paper. A combination of fuzzy logic controller and fractional order PID is adopted by the proposed control method, and error tracking element of PSHU guide vane opening is added to improve the regulation rate. Then, an accurate mathematical model of pump-turbine with complete characteristic curves is established for the PSHU regulating system (PSHURS). Meantime, a novel bacterial-foraging chemotaxis gravitational search algorithm (BCGSA) is used for optimized parameters selection of AFFFOPID method. The BCGSA which is based on the standard gravitational search algorithm accelerates convergence speed with a combination of the P best – G best -guided strategy and adaptive elastic-ball method. Chemotaxis operator of bacterial foraging algorithm is also added into the BCGSA which is devised to step out the local optimal with a certain probability. Furthermore, simulation tests under different running conditions are utilized to demonstrate the feasibility and robustness of the AFFFOPID. The results are analyzed in detail, which demonstrates the pump-turbine model and the proposed AFFFOPID method are practicable. Comparison with other methods clearly shows that AFFFOPID using BCGSA outperforms the others in most cases. [ABSTRACT FROM AUTHOR]

Abstract: For the purpose of further saving computing time, an improved algorithm about NSFOT is provided in this paper. That is, by introducing the simple operations such as preprocessing or after-processing, Haar and Walsh transforms are performed conveniently on the multiprocessor. As a result, one large size problem is divided into several small size sub-problems, load on every processor not only decreases greatly but also gets so uniform that much time is saved. Both the theoretical analysis and experimental results demonstrate the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Between Haar and Walsh, there exist other Haar-type orthogonal matrixes (HTOMs), which are rarely utilized in practice. In this paper, we introduce HTOMs, which have fast algorithm, to the mechanic signal analysis. Concretely speaking, the mechanic signals are transformed by various HTOMs, which can be generated easily by varying any one of two parameters in the same program, then the performance of the transform results is compared by viewing FDC as the evaluation criterion, and the most optimal HTOM is achieved, which provides guidance and reference for the HTOMs applied in the signal analysis. [Copyright &y& Elsevier]