Operation optimization for modern subway trains usually requires the speed curve optimization and speed curve tracking simultaneously. For the speed curve optimization, a multi-objective seeking issue should be addressed by considering the requirements of energy saving, punctuality, accurate parking, and comfortableness at the same time. But most traditional searching methods lack in efficiency or tend to fall into the local optimum. For the speed curve tracking, the widely applied proportional integral differential (PID) and fuzzy controllers rely on complicated parameter tuning, whereas robust adaptive methods can hardly ensure the finite-time convergence strictly, and thus are not suitable for applications in fixed time intervals of trains. To address the above-mentioned two problems, this paper presents a novel approach for speed curve seeking and tracking control. First, we present the random reinforcement genetic algorithm (GA) algorithm to avoid the local optimum efficiently. Then, a sliding mode controller is developed for speed curve tracking with bounded disturbance. The Lyapunov theory is adopted to prove that the system can be stabilized in the finite time. Finally, using the real datasets of Yizhuang Line in Beijing Subway, the proposed approach is validated, demonstrating its effectiveness and superiorities for the operation optimization.

Contactless fault diagnosis is one of the most important technique for fault identification of equipment. Based on the idea of contactless fault diagnosis, this paper presents a sound-based diagnosis method for railway point machines (RPMs). First, the sound signals are preprocessed using empirical mode decomposition (EMD). Entropy, time-domain and frequency-domain statistical parameters of the first 15 intrinsic mode functions (IMFs) are then extracted. Second, a two-stage feature selection strategy blending Filter method and Wrapper method is proposed, which can significantly reduce the dimension of features and select the optimal features. The superiority and effectiveness of the proposed feature selection strategy are verified by comparing with other feature selection methods. Third, a weighted majority voting (WMV)-based ensemble classifier optimized using particle swarm optimization (PSO) is developed and compared with single classifiers. And the ensemble patterns are discussed to select the most optimal ensemble pattern. The average diagnosis accuracies of 10 repeated trails of reverse-normal and normal-reverse switching processes reach 99% and 99.93%, respectively, which indicates the effectiveness and feasibility of the proposed method.

Microgrids, integrating user-side demand response and zero marginal cost renewable energies, are potential components for future smart grids to reduce carbon emissions and improve power system resilience. In this paper, a day-ahead microgrid energy management framework with demand response aggregator as an intermediate coordinator is developed. The corresponding scheduling strategy is obtained to maximize the social welfare of the microgrid system, with considering the privacy of end-users and the uncertainty of renewable energies. To this end, firstly, a accelerated distributed optimization method based on Alternating Direction Method of Multipliers, named as FAST-PP-ADMM, is developed to protect the end-users privacy and improve the scalability of the microgrid system. Secondly, a data-driven risk-adjusted uncertain set is constructed with a distributionally robust chance-constraints model to characterize the forecast error of renewable energies. Based on the constructed uncertain set, a two-stage robust microgrid-side energy management model is solved by using the column-and-constraint generation (C&CG) method. Finally, the effectiveness of the proposed energy management framework and scheduling strategy is verified by simulations.

Railway point machines (RPMs) is one of the most vital devices closely related to the efficiency and safety of train operation. Considering the advantages of contactlessness and easy-to-collect of sound signals, a novel sound-based fault diagnosis method for RPMs is proposed. First, fractional calculus is introduced to wavelet packet decomposition energy entropy (WPDE). Fractional WPDE (FWPDE) is then proposed, which is verified to be a more effective tool for fault feature representation. Second, coarse-grain process is firstly introduced to FWPDE. Novel feature named multi-scale FWPDE is developed, which can significantly improve fault diagnosis accuracy. Third, to select optimal feature set and optimize the hyperparameters of support vector machine (SVM) at the same time, a synchronous optimization strategy based on binary particle swarm optimization (BPSO) is presented, which can further improve the diagnosis accuracy. The superiority and effectiveness of the proposed method are verified by comparing to some existing fault diagnosis methods. The diagnosis accuracies of reverse-normal and normal-reverse switching processes reach 99.33% and 99.67%, respectively. Especially, the proposed method is suitable for diagnosis of similar faults, which can also provide reference for similar research fields.

The uncertainties arising from both renewable generation and load demand have brought challenges to the reliable and efficient operation of power systems. This paper presents a two-time-scale (i.e., day-ahead and intraday) microgrid energy management model for scheduling with low operational costs and high reliability against uncertainties. For the day-ahead scheduling, we propose a data-based distributionally robust chance-constrained (DRCC) energy dispatch model for grid-connected microgrids, to trade off the economic efficiency and operational risk. This model gains a robust and low conservative day-ahead scheduling solution against uncertainties by formulating the chance-constraint based on Wasserstein ambiguity set into a tractable convex constraint with conditional value-at-risk (CVaR) approximation. For the intraday scheduling, we blend the shorter-time scale prediction with a robust day-ahead scheduling plan as well as the model predictive control (MPC) rolling optimization method. This ensures accurate intraday dispatch solution and balanced supply-demand. Finally, the effectiveness and performance of the proposed method are verified via case studies.