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Abstract When some new‐type sensors are subjected electromagnetic interference (EMI), the traditional standard can no longer serve as the guidance for their EMI protection. Therefore, it is necessary to evaluate the complexity of the new sensing equipment's suffering from electromagnetic interference. A hybrid method of feature extraction and complexity evaluation of Electromagnetic Interference signals based on generalised correntropy criterion (GCC) is proposed. First, a variational mode decomposition algorithm for adaptive extraction of decomposed signals is constructed using GCC, and Generalised S‐transform is used for processing and reorganisation. Then, according to the time‐frequency space model, a new quantitative evaluation criterion interference factor is proposed, which overcomes the shortcomings of the traditional qualitative classification and the inability to solve the cross classification of indicators. Then, the Gated Recurrent Unit is introduced, and the GCC‐GRU evaluation model with GCC as the loss function is derived. The authors conduct EMI tests and measured signal evaluation with Medium Voltage and High Voltage switches, and the results show that the method proposed is correct and effective.

Abstract Affected by inverter station commutation failure and fast control, line fault characteristics are obviously different from traditional AC transmission line fault characteristics. The existing protection methods for AC line connected to line commutated converter based high voltage DC (LCC‐HVDC) inverter station (ACLs) have the problems of complex setting, weak anti‐noise and high demand for data synchronization. This paper proposes a pilot protection scheme based on high‐frequency transient current waveform similarity. Firstly, with analyzing the fault traveling wave propagation process, the obvious differences are found in the transient current waveforms at both ends of the line when internal and external faults occur. Then the similarity of transient current waveforms is measured with the improved longest common similar subsequence (LCSS) method. And the weak high‐frequency current is extracted with the improved synchronous squeezed S‐transform (SST) method. Finally, a large number of power systems computer aided design (PSCAD) simulation results show that the proposed protection method does not require strict data synchronization, has strong anti‐noise performance, and is tolerant to the influence of abnormal point fluctuations.

The DC voltage level of photovoltaic and PEDF applications have continuously advanced, reaching up to DC3000V. This presents new requirements for DC circuit breakers operating at higher voltages (>DC1500V) to ensure effective fault protection in the DC side. Based on this, a self-powered hybrid DC circuit breaker with synergistic effect of step-down and divided current is proposed. This innovative design utilizes the voltage of main branch to trigger IGBT conduction, eliminating the need for an additional trigger circuit. The interruption characteristics is analyzed by simulation. The reactor and shunt resistance effectively reduce the peak currents of both main and transfer branches. This approach significantly mitigates the voltage stress on the mechanical switch, reduces the risk of post-arc reignition, and shortens the interruption time. The circuit breaker topology proposed enables the DC arc interruption at larger current and higher voltage, serving as the valuable guide for the implementation of hybrid DC circuit breakers in renewable energy power systems.

In order to improve the efficiency of energy utilization, the integrated energy system (IES) has emerged. The IES typically acts as a whole system during operations, the subsystems are separated, and the interests of each system are independent. In this paper, considering the relationship between the various energy systems, non-cooperative game theory is used to establish the optimal dispatch model. The proposed model mainly relies on the relationship between the cooperation and competition among various subsystems to obtain the maximum benefit they can accept. Furthermore, the basic definition is combined with the particle swarm optimization algorithm to solve the problem. The results show that the optimization strategy proposed in this paper can operate safely and reliably, and effectively distribute the benefits of each energy system.

Electricity consumption forecasting (ECF) plays a crucial role in the new open electricity market nowadays, and in order to pass the ECF test, the ECF error is required to be less than 5%. However, for historical electricity consumption (EC) data and the error distribution of ECF is usually nonlinear and non-Gaussian distributed, and some random events and data collection errors have a great impact on the forecasting accuracy. Therefore, the traditional forecasting methods designed based on mean squared error (MSE) have the problem of unstable forecasting results and difficult to improve the accuracy. To solve this problem, this paper first uses the Generalized Maximum Correntropy Criterion to optimize the Kernel Extreme Learning Machine, and proposes a new forecasting model of KELM-GMCC. Finally, the historical EC and corresponding temperature data of a commercial customer in China in 2018 and 2019 are used to test and guide the electricity prediction in 2020 and 2021. The model proposed in this paper has a mean absolute percentage error of 1.46% in forecasting daily EC using annual data. The results show that the proposed model is robust and has high forecasting accuracy compared with existing models such as Back Propagation (BP), Support Vector Regression (SVR), and Extreme Learning Machine (ELM).

State-of-charge balance is vital for allowing multiple energy storage units (ESUs) to make the most of stored energy and ensure safe operation. Concerning scenarios wherein boost converters are used as the interfaces between ESUs and loads, this paper proposes a balancing strategy for realizing consistent state-of-charge (SoC) levels and equal currents among different ESUs. This strategy is valid for both parallel and series applications. Its advantages also include its high precision of SoC equalization without extra sensors and fast convergence. A common outer voltage loop is used to accomplish tight voltage regulation, while multiple inner current loops are utilized to achieve current control and SoC balance simultaneously. Firstly, by introducing SoC-based current distribution ratios (CDRs) to modify current references online, the currents are gradually adjusted to eliminate SoC deviations. Secondly, to expedite the balancing process, current saturations are further adopted. Thirdly, the influences of accelerating factor and current limits in CDR expressions are analyzed, and their selection guidelines are subsequently provided. Fourthly, the controller design, consisting of a dual loop, is illustrated to guarantee sufficient stability margins. Fifthly, an experimental platform consisting of three battery ESUs is developed to verify the proposed strategy.

As a significant approach to local utilization of clean renewable energy, the dispersed wind power (DWP) is becoming more and more widespread in engineering applications. With the fluctuating wind power widely and dispersedly integrated into distribution networks, it is urgent and pressing to effectively enhance the penetration rate of wind power based on the safe operation of distribution networks. This paper takes full advantage of the power regulation ability of wind turbines to actively participate in the operation of distribution networks, and then an optimal operation model of distribution networks is established with the optimization objectives of high penetration of DWP and maximum correntropy of node voltage. Aiming at the characteristics of the proposed model, the multi-objective brain storm optimization algorithm is adopted to solve the model to achieve the Pareto solution set, and the fuzzy membership function is used to determine the optimal operation scheme from the solution set. The simulation results on the expanded IEEE 33 bus system indicate the rationality of the proposed optimal operation strategy for distribution networks with high penetration of DWP. Meanwhile, the feasibility of the optimal operation scheme is verified through the case of a practical demonstration project.

As the structures of multiple branch lines (MBLs) will be widely applied in the future flexible DC distribution network, there is a urgent need for improving system reliability by tackling the frequent non-permanent pole-to-pole (P-P) fault on distribution lines. A novel fault restoration strategy based on local information is proposed to solve this issue. The strategy firstly splits a double-ended power supply network into two sin-gle-ended power supply networks through the timing difference characteristics of a hybrid direct current circuit breaker (HDC-CB) entering the recloser. Then, a method based on the characteristic of the transient energy of fault current is proposed to screen the faulty branch line in each single-ended power supply network. Also, a four-terminal flexible DC distribution network with MBLs is constructed on PSCAD to demonstrate the efficacy of the proposed strategy. Various factors such as noise, fault location, and DC arc equivalent resistance are considered in the simulation model for testing. Test results prove that the proposed strategy for fault restoration is effective, and features high performance and scalability.

Owing to low voltage stresses, wide range of input voltage and the same polarity of output voltage and input voltage, double-switch buck-boost converter (DSBBC) is a popular choice to achieve dc/dc conversion in photovoltaic generations (PV), power factor correction (PFC), portable devices and so on. Combined control (CC) makes DSBBC operate efficiently, but large ripples of output voltage are unavoidable during mode transition. With two equal duties, interleaved modulation (IM) makes the inductor ripple current decrease remarkably compared with synchronous modulation (SM) while inductor average current is still high. By introducing duty offset, inductor average current declines and efficiency is enhanced obviously under interleaved modulation with duty cycle offset (IMDO). However, with 180-degree phase-shifted angle, the ripple current is not low enough in some cases. Thus, to make further improvement on ripple current, an interleaved modulation scheme with optimized phase shifting (IMOPS) is proposed for DSBBC in this paper. Current features, optimal phase-shifted angle (OPSA) and operating region under IMOPS are described meticulously. Besides, theoretical and quantitative comparisons of different modulation schemes are presented, including inductor average current, inductor ripple current and ratio of direct power transfer (DPT). Based on a 150W prototype, the feasibility and effectiveness of IMOPS are demonstrated by experimental results.

In order to reduce the current ripple and improve the power density of the system, the multiple structure design is generally adopted by the traditional bidirectional DC/DC converter. However, the fixed multiplicity design can’t make the DC/DC power converter always output the smallest current ripple under different duty ratios. Through this research, it is found that the current ripple is related to duty cycle and parallel multiplicity, and then a variable multiplicity bidirectional DC/DC power converter is proposed. Firstly, the relationship between the current ripple and parallel multiplicity and duty cycle is deduced, and the basic topology of variable multiplicity bidirectional DC/DC power converter is determined; Secondly, the average value model and AC small signal model of the system are established based on the topological structure, and then the state equation is obtained. Thirdly, the current compensation control method is designed based on the state equation. Finally, the experimental platform of variable multiplicity bidirectional DC/DC power converter is built.

Power electronics technology is playing an increasingly important role in modern energy utilization systems [...]

In this paper, a backstepping sliding mode controller based on a nonlinear disturbance observer (NDO-SMC) is proposed to realize the high-performance speed control of a permanent magnet synchronous motor (PMSM). This paper compares the advantages and disadvantages of the traditional backstepping sliding mode control algorithm (SMC) and integral backstepping sliding mode control algorithm (I-SMC) in the face of mismatched disturbances. In view of the shortcomings of these two algorithms, the idea of using a disturbance observer to observe disturbance and carry out dynamic compensation is proposed, and the composite controller is designed. The overshoot and settling time is improved by 30% and 8 s, respectively, for the proposed NDO-SMC controller compared with the SMC controller. The simulation and experimental results illustrate that the designed controller not only effectively solves the torque jitter problem of SMC, but also improves the overshoot problem caused by the integral module of I-SMC. There is also a better matching degree between the theoretical derivation, the simulation results, and experimental data. It also proves that the composite control algorithm proposed in this paper provides a meaningful solution to the operation disturbance suppression problem of the permanent magnet synchronous motor.

High voltage ride through (HVRT) control strategies for doubly-fed induction wind turbines (DFIG) have mostly focused on the rotor side converter, while the reactive power compensation capability of the grid side converter and the impact of grid side converter current transients have often been ignored on the DC bus voltage and reactive power. Therefore, a control strategy based on grid side current dynamic change characteristics is proposed, which resets the reference values of the grid side active and reactive currents for wind turbine HVRT to ensure partial absorption of reactive power on the grid side. Secondly, the key parameter in the proposed control strategy is optimization to get the most suitable DC bus voltage value with the grey wolf algorithm. Finally, the grid-integrated wind turbine simulation model is built on the MATLAB/SIMULINK and RT-Lab platforms. The simulation test results show that the proposed HVRT control strategy and its parameter optimization method are effective, DFIG can achieve HVRT when the wind turbine voltage rises to 1.3 pu.

The isolated three-level DC/DC converter (ITLDC) can be used to charge electric vehicles. During the constant current charging stage, the ITLDC can be designed to realize nature zero voltage switching (ZVS). However, during the constant voltage charging stage, the charging current is small; thus, nature ZVS cannot be realized. This paper presents an active commutation auxiliary circuit (ACAC) for the ITLDC to realize the full load range ZVS. With the proposed ACACs, all the main switches achieve zero-voltage turn-on and quasi zero-voltage turn-off, and the auxiliary switches realize zero current turn-on and zero-voltage turn-off; thus, the efficiency will be high. The auxiliary currents generated by the ACACs are controllable. During the constant current charging stage, the ITLDC realizes nature ZVS and the auxiliary currents are controlled to zero; thus, the ACACs do not result in high current stress or bring in additional losses, and the efficiency will be high. During the constant voltage charging stage, the charging current decreases with charging time and the charging current is too small to realize nature ZVS. Thus, the ITLDC can work with the proposed ACACs and the auxiliary currents can be controlled within a suitable value to realize ZVS. With the proposed ACACs, the ITLDC can realize ZVS during the whole charging process; thus, the efficiency will be high. The structure and operating principle of the ITLDC with ACACs are introduced and the performance of the proposed TLDC is experimentally verified on a 1.5 kW prototype converter.

The exploitation and utilization of clean energy such as wind and photovoltaic power plays an important role in the reduction in carbon emissions to achieve the goal of “emission peak and carbon neutral”, but such a quantity of clean energy accessing the electric system will foster the transition of the electric power system structure. The intelligentization of power equipment will be an inevitable trend of development. High breaking performance, remote control and a digital detection platform of miniature circuit breaker, a protective equipment of a power distribution system, have also been inevitable requirements of the power Iot system. Based on the above, this paper studies three aspects: high-performance AC and DC general switching technology, remote control technology and operation status’ digital monitoring. A new DC non-polar breaking technology is proposed, which improves the short circuit breaking ability. An experimental prototype using the above techniques was fabricated and passed the DC 1000 V/10 kA short-circuit breaking test. On the basis of the above, an intelligent circuit breaker is developed, which contains multiple functions: remote switching, real-time temperature detection, energy metering and fault warning. Moreover, a software for digital condition monitoring and remote control is developed. This work has certain theoretical and practical significance for the development of the power Internet of things.

Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. However, the optimization of the free parameters is still a key problem for improving the performance of the RVM. Taking advantage of the strong global search capability, good stability, and fast convergence rate of the sparrow search algorithm (SSA), this paper optimizes the parameters of the RVM by using the SSA to develop an excellent RVM (called SSA-RVM). Consequently, a novel hybrid PV power forecasting model via the SSA-RVM is proposed to perform ultra-short-term (4 h ahead) prediction. In addition, the effects of seasonal distribution and weather type on PV power are fully considered, and different seasonal prediction models are established separately to improve the prediction capability. The benchmark is used to verify the accuracy of the SSA-RVM-based forecasting model under various conditions, and the experiment results demonstrate that the proposed SSA-RMV method outperforms the traditional RVM and support vector machine models, and it even shows a better prediction effect than the RVM models with other optimization approaches.

At present, the small resistance to ground system (SRGS) is mainly protected by fixed-time zero-sequence overcurrent protection, but its ability to detect transition resistance is only about 100 Ω, which is unable to detect single-phase high resistance grounding fault (SPHIF). This paper analyzes the zero-sequence characteristics of SPHIF for SRGS and proposes a SPHIF feeder detection method that uses the current–voltage phase difference. The proposed method is as follows: first, the zero-sequence current phase of each feeder is calculated. Second, the phase voltage root mean square (RMS) value is used to determine the fault phase and obtain its initial phase as the reference value. The introduction of the initial phase of the fault phase voltage can highlight the fault characteristics and improve the sensitivity and reliability of feeder detection, and then CVPD is the difference between each feeder ZSC phase and the reference value. Finally, the magnitude of CVPD is judged. If the CVPD of a particular feeder meets the condition, the feeder is detected as the faulted feeder. Combining the theoretical and practical constraints, the specific adjustment principle and feeder detection logic are given. A large number of simulations show that the proposed method can be successfully detected under the conditions of 5000 Ω transition resistance, –1 dB noise interference, and 40% data missing. Compared with existing methods, the proposed method uses phase voltages that are easy to measure to construct SPHIF feeder detection criteria, without adding additional measurement and communication devices, and can quickly achieve local isolation of SPHIF with better sensitivity, reliability, and immunity to interference.

In this paper, a novel multiobjective lightning flash algorithm (MOLFA) is proposed to solve the multiobjective optimization problem. The charge population state of the lightning flash algorithm is defined, and we prove that the charge population state sequence is a Markov chain. Since the convergence analysis of MOLFA is to investigate whether a Pareto optimal solution can be reached when the optimal charge population state is obtained, the development of a charge population state is analyzed to achieve the goal of this paper. Based on the martingale theory, the MOLFA convergence analysis is carried out in terms of the supermartingale convergence theorem, which shows that the MOLFA can reach the global optimum with probability one. Finally, the effectiveness of the proposed MOLFA is verified by a numerical simulation example.

For avoiding the occurrence of large-scale blackouts due to disconnected nodes in the power grid, a modified PageRank algorithm is proposed to identify key nodes by integrating the topological information and node type. The node betweenness index is first introduced based on complex network theory, which is modified to reflect the node topological information in the power grid. Then, according to the characteristics of different node types in the power grid, a modified PageRank algorithm is proposed to rapidly identify key nodes, which takes the generator nodes, load nodes, and contact nodes into account. IEEE 39-Bus system and IEEE 118-Bus system are used for the simulations. Simulation results showed that the network transmission efficiencies of the power grid are reduced from 64.23% to 5.62% and from 45.4% to 5.12% in the two simulation systems compared with other methods. The proposed identification algorithm improved the accuracy, and a provincial power grid simulation system in China is used to verify the feasibility and validity. The identified nodes are removed, which split the power grid according to importance index values. The proposed method in this paper is helpful to prevent the occurrence of cascading failure in the power system, and it can also be used to power systems with renewable energy sources and an AC/DC hybrid power grid.

In order to solve the system identification problems, the normalized least mean absolute deviation (NLMAD) algorithm was developed as an effective and robust method. In this paper, aiming at the system identification problems with sparsity characteristic, and taking the advantage of the NLMAD algorithm to suppress impulsive output measurement noise interference, we introduce the L1-norm as a sparse penalty constraint into the NLMAD algorithm to design a robust sparse adaptive filtering algorithm. Furthermore, considering the biased estimation caused by the input noise, we employ an unbiasedness criterion to derive an effective bias-compensated vector which can compensate the bias efficiently for the proposed sparse NLMAD algorithm. The desirable performance of the new method is measured with simulations of two stages. The proposed bias-compensated sparse NLMAD algorithm achieves better performance compared to other existing methods in both stages. Simulation results demonstrate the excellent performance of the proposed algorithm in solving sparse system identification problems. The promising results in this paper suggest that the bias-compensated sparse NLMAD algorithm may become a useful tool for system identification with noisy input and impulsive output noise.

The development of the smart grid requires the distribution switch to not be limited to the original breaking function. More functional requirements lead to more complex switch structures, especially the intelligent processing unit on the secondary side. A technology called primary and secondary integration optimizes the structure of the switch, which greatly increases the intelligence level of the switch, but also has disadvantages. The secondary intelligent unit is arranged close to the primary high-voltage electromagnetic environment, and the distribution switch is prone to failure due to electromagnetic interference. In order to explore the influence of electromagnetic interference on it, a transient electromagnetic interference simulation test platform was built for a 10 kV intelligent distribution switch based on the principle of spherical gap arc discharge, and the interference signal of the intelligent distribution switch was measured; the law of the spatial magnetic field near the electronic transformer is mainly studied in this paper. The shielding effectiveness of the distribution terminal of the switch was analyzed, and the interference of the power line of the sensor merging unit circuit board was calculated. The results show that the electronic transformer may have serious faults under continuous strong transient electromagnetic interference. The electromagnetic transient simulation test system studied in this paper can evaluate the anti strong electromagnetic interference ability of the electronic transformer.

Aiming at the problem of insufficient flexibility of the power system caused by large-scale wind power grid integration, a flexible economic dispatch model of the electricity-gas integrated system that considers power-to-gas and demand responses is proposed. First, it elaborates on the scheduling flexibility and demand response model. Secondly, the power system and the natural gas system are regarded as different stakeholders; with the goal of minimizing their respective operating costs, a two-tier distributed coordination optimization model of electricity and gas system is established. In order to achieve the coordination and optimization of the upper and lower systems, slack variables are introduced to describe the infeasible part of the power system scheduling results in the natural gas system and are used for interactive iterative solution of the model. The numerical results of the revised IEEE 30-node power system and 10-node natural gas system illustrate the effectiveness and necessity of the proposed model, as well as the superiority of comprehensively considering power-to-gas and demand response in improving the flexibility and economy of the power system.

A diffusion general mixed-norm (DGMN) algorithm for distributed estimation over network (DEoN) is proposed. The standard diffusion adaptive filtering algorithm with a single error norm exhibits slow convergence speed and poor misadjustments under specific environments. To overcome this drawback, the DGMN is developed by using a convex mixture of p and textit q norms as the cost function to improve the convergence rate and substantially reduce the steady-state coefficient errors. Especially, it can be used to solve the DEoN under Gaussian and non-Gaussian noise environments, including measurement noises with long-tail and short-tail distributions, and impulsive noises with α-stable distributions. In addition, the analysis of the mean and mean square convergence is performed. Simulation results show the advantages of the proposed algorithm with mixing error norms for DEoN.

Aiming at the problem of large number of power grids and the difficulty of on-line monitoring, a method of on-line search of typical transmission section considering geographical attributes is proposed. Firstly, the initial partition is divided by the geographical attributes of the station nodes, and the boundary nodes in each partition are further optimised and rationally evaluated to obtain the simplified structure of the grid. Then, the cut-set search algorithm based on matrix operations of graph theory is used to quickly identify the transmission section in the power grid, and the power transmission section with heavy load and low margin is obtained by using the safety margin index of the power grid, which is regarded as a typical transmission section and used as the key monitoring object of power grid. Finally, an actual power grid and local grid are taken as an example to search for the typical transmission section of the system, which can not only find the section given by the operation mode expert but also find the new section, which verifies the practicability and effectiveness of the method.

State of charge (SOC) estimation plays a crucial role in battery management systems. Among all the existing SOC estimation approaches, the model-driven extended Kalman filter (EKF) has been widely utilized to estimate SOC due to its simple implementation and nonlinear property. However, the traditional EKF derived from the mean square error (MSE) loss is sensitive to non-Gaussian noise which especially exists in practice, thus the SOC estimation based on the traditional EKF may result in undesirable performance. Hence, a novel robust EKF method with correntropy loss is employed to perform SOC estimation to improve the accuracy under non-Gaussian environments firstly. Secondly, a novel robust EKF, called C-WLS-EKF, is developed by combining the advantages of correntropy and weighted least squares (WLS) to improve the digital stability of the correntropy EKF (C-EKF). In addition, the convergence of the proposed algorithm is verified by the Cramér–Rao low bound. Finally, a C-WLS-EKF method based on an equivalent circuit model is designed to perform SOC estimation. The experiment results clarify that the SOC estimation error in terms of the MSE via the proposed C-WLS-EKF method can efficiently be reduced from 1.361% to 0.512% under non-Gaussian noise conditions.

The voltage violation phenomenon can be very prominent in the condition of dispersed wind power (DWP) widely integrated into network, which affects the safe operation during integration and effective consumption of wind power. In this study, a self-discipline control strategy of DWP is proposed to solve the voltage violation problem with high penetration. First, the influence of system voltage after the DWP integrated into network is analysed, which raises the feasibility of self-discipline control strategy. Then, according to the proposed strategy, the object of optimisation model is designed to better the voltage quality of the grid connection point of wind power generator and to maximise the active power outputs of wind power. Considering the non-linear and multivariable characteristics of the optimisation model, this study used the particle swarm optimisation to obtain the reactive power and active power regulations of wind power. Finally, the proposed algorithm is tested and analysed on the IEEE 33-node distribution system, and verified by a practical project. The results show that the self-discipline control strategy can effectively eliminate the voltage violation caused by the DWP integration. Moreover, the wind power can be fully utilised.

The electricity consumption forecasting (ECF) technology plays a crucial role in the electricity market. The support vector regression (SVR) is a nonlinear prediction model that can be used for ECF. The electricity consumption (EC) data are usually nonlinear and non-Gaussian and present outliers. The traditional SVR with the mean-square error (MSE), however, is insensitive to outliers and cannot correctly represent the statistical information of errors in non-Gaussian situations. To address this problem, a novel robust forecasting method is developed in this work by using the mixture maximum correntropy criterion (MMCC). The MMCC, as a novel cost function of information theoretic, can be used to solve non-Gaussian signal processing; therefore, in the original SVR, the MSE is replaced by the MMCC to develop a novel robust SVR method (called MMCCSVR) for ECF. Besides, the factors influencing users’ EC are investigated by a data statistical analysis method. We find that the historical temperature and historical EC are the main factors affecting future EC, and thus these two factors are used as the input in the proposed model. Finally, real EC data from a shopping mall in Guangzhou, China, are utilized to test the proposed ECF method. The forecasting results show that the proposed ECF method can effectively improve the accuracy of ECF compared with the traditional SVR and other forecasting algorithms.

This paper proposed a modularized discharge-type topology for the voltage balance of series-connected super capacitor (SC) string. The proposed topology consists of cascaded converter modules and a boost converter. The cascaded converter modules discharge the higher voltage SCs directly with the ideal output current to realize a fast balancing speed and the boost converter feedbacks the extra energy from the higher voltage SCs to the super capacitor energy storage system (SCESS). The modular design of the cascaded converter modules makes the balancing system suitable for different voltage levels of SCESS. Unlike the charge-type topologies which discharge the higher voltage SCs indirectly, the proposed topology discharges the higher voltage SCs directly with a big current, and the over voltage phenomenon of SCs is then avoided, which means the reliability of the SCESS can be improved. The voltage stress of the switches inside the cascaded converter modules is low, which is different from the existing modularized discharge-type balancing topology. What is more, the control of cascaded converter modules and the boost converter can be implemented by analog devices which will simplify the control of the whole system. The control degree of freedom is high and the voltage of each cell can be controlled. An in-depth comparison analysis with the charge-type balancing topology is performed from the perspective of balancing speed and round-trip energy efficiency. The proposed topology and the balancing performance are confirmed by experimental results.

To address the sparse system identification problem under noisy input and non-Gaussian output measurement noise, two novel types of sparse bias-compensated normalized maximum correntropy criterion algorithms are developed, which are capable of eliminating the impact of non-Gaussian measurement noise and noisy input. The first is developed by using the correntropy-induced metric as the sparsity penalty constraint, which is a smoothed approximation of the ℓ 0 norm. The second is designed using the proportionate update scheme, which facilitates the close tracking of system parameter change. Simulation results confirm that the proposed algorithms can effectively improve the identification performance compared with other algorithms presented in the literature for the sparse system identification problem.

In recent years, with the deepening of China’s electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we propose a novel prediction scheme based on the least-square support vector machine (LSSVM) model with a maximum correntropy criterion (MCC) to forecast the electricity consumption (EC). Firstly, the electricity characteristics of various industries are analyzed to determine the factors that mainly affect the changes in electricity, such as the gross domestic product (GDP), temperature, and so on. Secondly, according to the statistics of the status quo of the small sample data, the LSSVM model is employed as the prediction model. In order to optimize the parameters of the LSSVM model, we further use the local similarity function MCC as the evaluation criterion. Thirdly, we employ the K-fold cross-validation and grid searching methods to improve the learning ability. In the experiments, we have used the EC data of Shaanxi Province in China to evaluate the proposed prediction scheme, and the results show that the proposed prediction scheme outperforms the method based on the traditional LSSVM model.