To address saturation issues in current measurement devices under high currents, a winding wound on the core, driven by a power amplifier, is utilized to counterbalance the magnetic field generated by the measured current in the traditional method. However, this method consumes a significant amount of energy, and the power consumption increases under higher currents, limiting its applicability. To address this vital problem, a wide-range self-powered current measurement method is proposed in this work. The dual-core structure-based self-powered current measurement system integrates a magnetic field energy harvester (MFEH) and a current measurer (CM). In this system, the windings of the MFEH and the CM are connected in series, and the induced current of the MFEH is multiplexed. On one hand, it flows through the load for energy harvesting. Simultaneously, it flows into the CM, reducing the magnetic field within the air gap, and thereby broadening the range of current measurements. Besides, the equivalent circuit model of the dual-core structure is established to analyze the principles of current measurement and energy harvesting. Subsequently, a voltage regulation circuit based on an active rectifier is implemented, which can mitigate the saturation of the magnetic core over a wide current range. A laboratory prototype is built to verify the effectiveness of the proposal. The results show that the output voltage can be maintained at 3.3 V, ensuring stable voltage output. At 300 Arms, the maximum harvesting power reaches 1.83 W. Additionally, the upper limit of the measured current can reach 600 Arms, with a maximum error of 0.54%.

The integrated transmission of power and data is a promising technology in various wireless power transfer (WPT) application scenarios. In this work, a novel simultaneous power and data transfer (SPDT) technology for domino-resonator WPT systems is proposed. The frequency-shift keying power modulation method is adopted in dual-resonance-band circuits to maintain high system transfer efficiency at two different frequencies. Considering the cross-coupling between nonadjacent resonators, the system operating frequencies shift slightly away from the circuit resonant frequencies, and the same constant output voltage characteristics can be always guaranteed with different data sequences. Subsequently, a compact coil structure is developed to accommodate the tight space constraint inside the insulator. Finally, aiming at the 35-kV suspension composite insulator with five embedded domino resonators, a 15-W experimental prototype is built to realize SPDT and constant voltage output. The experimental results show that the fluctuation of output voltage is less than 5%, and the overall system transfer efficiency is higher than 75%.

Regenerative braking energy (RBE) utilization plays a vital role in improving the energy efficiency of electrified railways. To date, various power flow control-based solutions have been developed to recycle the RBE for utilization within railway power systems (RPSs). In this paper, an overview of the state-of-the-art power flow control-based solutions for RBE utilization in AC electrified railways is presented. It provides a technical analysis of four primary power flow control-based solutions for RBE utilization, including power sharing-based, energy feedback-based, energy storage-based, and composite solutions. The critical architectures of power flow conditioners for each solution are analyzed in depth. Meanwhile, the power flow control strategies for these solutions are reviewed from the perspectives of power flow management and converter control. From the industrial point of view, the critical challenges associated with fault protection, economy, and environmental impact are discussed. In addition, future trends are comprehensively elaborated from internal and extended improvements. This comprehensive review provides an insightful understanding of the technology readiness, constraints, and perspectives regarding the power flow control-based RBE utilization in electrified railways, contributing to bridging the gaps between academic research and industry implementation.

Flexible direct current (dc) distribution networks are the development trend for the future distribution system. However, the protection and control of these networks are vulnerable to complicated dc faults, accurate fault analysis is required. In this article, we propose a three-stage transient fault analysis method to improve the accuracy of fault characteristic calculation for the voltage source converter based dc distribution network with multi distributed generators, which can unify and simplify the calculation process of different transient fault stages. An impedance parameter decoupling approach is proposed based on the dc line parameters, which can avoid the influence of the coupling impedance on fault response. The decoupled equivalent fault circuits are established for accurate fault analysis with flexibility and independence. In addition, a three-stage fault analysis is proposed, which is able to unify the calculation process. Finally, numerical simulations based on PSCAD/EMTDC have been carried out, which well demonstrates the effectiveness of the proposed impedance decoupling approach and the accuracy of the proposed method for fault analysis. Compared with the traditional fault analysis without decoupling, the proposed method stands out the conciseness and correctness.

In the context of increasing construction of new railways and uninterrupted operation of existing railways in China, maintenance supervisors are facing great challenges to achieve as much maintenance workloads as possible in an efficient and economical way within the required maintenance cycle. Therefore, a cost-effectiveness oriented intelligent maintenance scheduling optimization method for the traction power supply system (TPSS) of high-speed railways (HSRs) is proposed. First, the skill levels of maintenance operators are evaluated by the skill evaluation model, and the workflow model of TPSS maintenance tasks is established to describe the relation between the operator deployment and the maintenance efficiency. Then the bi-objective optimization model which simultaneously maximizes maintenance workload and minimizes cost subject to available resources during a prescribed period is established. A modified bi-objective genetic algorithm is applied to obtain the Pareto solutions of the model and thus optimized maintenance scheduling strategies are formulated. A case study dealing with the maintenance scheduling optimization of a catenary system in China shows that, compared with the current scheme, the proposed strategy can effectively improve maintenance efficiency and meanwhile reduce cost. Furthermore, a prototype of an intelligent maintenance scheduling system (IMSS) for HSR TPSS is developed. The application of IMSS with field data indicates it can help operators formulate highly efficient yet economical maintenance strategies, and meanwhile motivate the transition of current maintenance decision-making policy from traditional experience-based strategies towards scientific “cost-effective” ones.

Situation awareness (SA) has been recognized as a critical guarantee for the stable and secure operation of electric power systems, especially under complex uncertainties after renewable energy integration. In this article, an artificial-intelligence-powered solution is presented to reach a full realization of SA covering perception, comprehension, and prediction, the last of which is more advanced but challenging and hence has not been discussed in any literature before. A novel SA model is proposed by aggregating two powerful deep learning structures: convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network. The proposed CNN-LSTM model has superiority to achieve collaborative data mining on spatiotemporal measurement data, i.e., to learn both spatial and temporal features simultaneously from phasor measurement units data. Two functional branches are designed within the SA model: a contingency locator to detect the exact fault location at present and a stability predictor to predict stability status of the system in the future. Test results have shown high performance (accuracy) of the model even on a low level of data adequacy. The proposed SA model can promisingly facilitate very fast postfault actions by the system operators to prevent the power system from any unstable operational status.

Current maintenance mode for high-speed railway (HSR) power equipment is so outdated that can hardly adapt to the high-standard modern HSR. Therefore, a new possibility is proposed in this article to update the obsoleting maintenance mode of the HSR power equipment by adopting both predictive maintenance and proactive maintenance. With the combination of data-driven (predictive) and model-based (proactive) approaches, two principal constituents-the sample generator and the maintenance predictor-are designed. The maintenance predictor which is powered by the long short-term memory recurrent neural network is developed to realize the goal of predictive maintenance. The sample generator which is formulated by the physical degradation and failure model of HSR power equipment is proposed toward the goal of proactive maintenance. Test results on a gas-insulated switchgear have shown the powerful collaboration between the generator and the predictor, to not only accurately predict future maintenance timing of the switchgear based on historical sample data, but also enrich the data supply proactively to deal with potential data deficiency problems.

The upcoming comprehensive operation and maintenance period of high-speed railway (HSR) in China is being severely challenged by the existing reactive maintenance of a traction power supply system (TPSS). In this article, an intelligent proactive maintenance system (IPMS) is presented for supporting the field maintenance work of the HSR TPSS. The IPMS is essentially a distributed platform integrated with well-designed hardware and software. Under the conceptual framework imitating the biological immune system, two core functionalities of the IPMS-health assessment and maintenance decision-are designed. Based on the presented hardware architecture and software algorithms, a prototype of the IPMS is developed and has already been put into operation in Beijing-Shenyang HSR. With the help of the IPMS, massive data in the TPSS can be efficiently managed, and the health status of each equipment can be precisely perceived. Most importantly, it provides maintenance supervisor and maintenance personnel with the optimal maintenance strategies, to facilitate appropriate and efficient maintenance activities, and hence to significantly save the maintenance time and cost, by its field application in the HSR TPSS.

Dynamic wireless power transfer (DWPT) is a promising solution to address electric vehicle range anxiety and to reduce the capacity and the cost of the on-board batteries. In the traditional DWPT system, the mutual inductances among the transmitters make the design of compensation networks very complex. Besides, the power null phenomenon and the power pulsation phenomenon cause a fluctuating dc output voltage or current on the receiver side. To address these issues, this paper proposes a new magnetic coupler. Unipolar and bipolar coils are laid alternately to form segmented transmitters, which are turned on or off according to the position of the overhead receiver coil. LCC compensations, whose inputs are connected in parallel to a common inverter for cost reduction, are adopted. At the receiver side, unipolar and bipolar coils are overlapped in the same plate in order to effectively smooth out the mutual inductance variations and, hence, reduce the output voltage or current fluctuations. Also, a configurable resonant circuit is designed on the receiver side to achieve constant voltage charging and constant current charging. Moreover, an optimization design by using finite-element analysis software Maxwell is developed to choose the best turns of the receiver coil to further improve the output quality. A laboratory prototype with 4-A charging current and 96-V charging voltage, using 85 kHz operation frequency, is constructed to verify the proposed DWPT system. The experimental results show that constant and stable output voltage and current can be achieved with only ±2% fluctuation, and the overall efficiency is 90.37%.

In the advanced manufacturing industry, planar switched reluctance motors (PSRMs) have proved to be a promising candidate due to their advantages of high precision, low cost, low heat loss, and ease of manufacture. However, their inverse force function, which provides vital phase current command for precise motion, is highly nonlinear and hard to be accurately modeled. This paper proposes a novel inverse force function using sparse least squares support vector machines (LS-SVMs) to achieve nonlinear modeling for precise motion of a PSRM. The required training and testing sets of sparse LS-SVMs are first obtained from experimental measurement. A sparse LS-SVMs regression is further developed using training set to accurately model the inverse force function. Accordingly, the function is tested via the testing set to assess its feasibility. Finally, the proposed approach is applied to the PSRM system with dSPACE controller for trajectory tracking, and its effectiveness and superior performance are verified through experimental results.

An individual-activation-factor memory proportionate affine projection algorithm (IAF-MPAPA) is proposed for sparse system identification in acoustic echo cancellation (AEC) scenarios. By utilizing an individual activation factor for each adaptive filter coefficient instead of a global activation factor, as in the standard proportionate affine projection algorithm (PAPA), the adaptation energy over the coefficients of the proposed IAF-MPAPA can achieve a better distribution, which leads to an improvement of the convergence performance. Moreover, benefiting from the memory characteristics of the proportionate coefficients, its computational complexity is less than the PAPA and improved PAPA (IPAPA). In the context of AEC and stereophonic AEC (SAEC) for highly sparse impulse responses, simulation results indicate that the proposed IAF-MPAPA outperforms the PAPA, IPAPA, and memory IPAPA (MIPAPA) in terms of the convergence rate and tracking capability when the unknown impulse response suddenly changes.

A novel complex-valued non-linear equaliser-based pipelined decision feedback recurrent neural network (CPDFRNN) is proposed in this study for non-linear channel equalisation in wireless communication systems. The CPDFRNN with low computational complexity, a modular structure comprising a number of modules that are interconnected in a chained form, is an extension of the recently proposed real-valued pipelined decision feedback recurrent neural equalisers. Each module is implemented by a small-scale complex-valued decision feedback recurrent neural network (CDFRNN). Moreover, a decision feedback part in each module can overcome the unstable characteristic of the complex-valued recurrent neural network (CRNN). To suit the modularity of the CPDFRNN, an adaptive amplitude complex-valued real-time recurrent learning (CRTRL) algorithm is presented. Simulations demonstrate that the CPDFRNN equaliser using the amplitude CRTRL algorithm with less computational complexity not only eliminates the adverse effects of the nesting architecture, but also provides a superior performance over the CRNN and CDFRNN equalisers for non-linear channels in wireless communication systems.

This correspondence presents an extended pipelined second-order Volterra (EPSOV) filter for active control of nonlinear noise processes. The corresponding nonlinear filtered-x algorithms using the filter bank implementation are also suggested. Compared to the standard SOV filter using the filtered-x least mean square (SOVFXLMS), those modules of the EPSOV filter can be performed simultaneously in a pipelined parallelism fashion, and this would lead to a significant improvement in its total computational efficiency. Results obtained from computer simulations for nonlinear noise processes demonstrate that the proposed method outperforms the SOV.

To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models.

The diagnosis system of Maglev train is introduced in this paper. The system structure and diagnosis method are analyzed and discussed in detail. The disadvantages of diagnosis system are proposed. In virtue of the theory of ADS (autonomous decentralized system), some basic ideas of ADS are applied in new diagnosis system. The structure, component parts and diagnosis method of new diagnosis system are proposed, designed and discussed in detail. The analysis results show that new diagnosis not only embodies some ADS' ideas but also better meet the demands of Maglev train diagnosis system such as the reliability, real-time and autonomous property.

In this paper we introduce a methodology to predict IP traffic in IP-based next generation network (NGN). By using Netflow traffic collecting technology, we've collected some traffic data for the analysis from an NGN operator. To build wavelet basis neural network (NN), we replace Sigmoid function with the wavelet in NN, and use wavelet multiresolution analysis method to decompose the traffic signal and then employ the decomposed component sequences to train the NN. By using the methods, we build a NGN traffic prediction model by which to predict one day's traffic. The experimental results show that the traffic prediction method of wavelet NN (WNN) is more accurate than that without using wavelet in the NGN traffic forecasting.