Steady-State Visual Evoked Potentials (SSVEP) have garnered significant attention due to their promising applications in brain-computer interfaces (BCI), medical diagnostics, and several other domains. Enhancing the characteristics of SSVEP signals through intricate signal processing has emerged as a pivotal research focus for more efficient signal extraction. In this work, we introduce a novel layered enhancement algorithm for SSVEP electroencephalogram (SSVEP-EEG) signals based on fractional-order differentiation operators. This innovative approach marries brain signal analysis with image processing methodologies. By utilizing fractional-order differentiation operators in tandem with the Laplace pyramid, the signal undergoes hierarchical enhancement. This amplified signal is then reconstructed, which facilitates an in-depth extraction of image intricacies and attributes, ultimately accentuating the distinctiveness of SSVEP features. To validate the efficacy of the proposed method, we applied it to three recognized target identification algorithms: Canonical Correlation Analysis (CCA), Filter Bank Canonical Correlation Analysis (FBCCA), and Task-Related Component Analysis (TRCA) using a publicly available dataset. Experimental outcomes underscore that, in contrast to contemporary techniques, our proposed algorithm not only effectively attenuates the trend components of SSVEP signals but also substantially elevates the recognition precision of CCA, FBCCA, and TRCA.

Affected by inverter fast control and commutation failure, most protection schemes for AC lines connected to 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 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.

Insulators are one of the key components in high-voltage power systems that prevent transmission lines from grounding. Since they are exposed to different kinds of harsh environments and climates, periodic inspection is indispensable for the safety and high quality of power grid. Nowadays, Unmanned Aerial Vehicle (UAV) inspection is more widely used, facilitating incorporation of CNN-based detectors in the insulator detection task. However, these methods are generally based on the assumption that the image samples are balanced among different categories and possess completely ideal annotations. The problem of sample imbalance or incomplete annotation is rarely investigated in depth for insulator defect detection. In this paper, we focus on insulator defect detection with imbalanced data and incomplete annotations. Our proposed framework, named Pi-Index, introduces Positive Unlabeled (PU) learning to solve the problem of incomplete annotation and designs a novel index the class prior, which is a key parameter in PU learning. Moreover, focal loss is integrated in our framework to alleviate the effect of sample imbalance. Experiment results demonstrate that the proposed framework achieves better performance than the baseline methods in situations of sample imbalance and missing annotation.

Localization in GNSS-denied environments for Unmanned Aerial Vehicles (UAVs) has recently gained significant interest from the research community. Most of the research is focused primarily on visual localization. This paper, examines an algorithm which employs Angle of Departure (AoD) and UAVs payload sensor data for UAV localization. First the algorithm uses multiple AoDs from a single base station and a travel calculated by applying dead-reckoning on the UAVs Inertial Measurement Unit (IMU), to compute UAV location in two-dimensional (2D) coordinates. The 2D location estimate is then fed into a modified Extended Kalman Filter (EKF), which employs the estimate, IMU and barometer data to compute the three-dimensional (3D) coordinates for UAV. For the simulation, we applied Simulation-in-the-Loop (SITL) accompanied by Arducopter and MAVLink to simulate different trajectories and collect the required data for the algorithm. We validated our algorithm by comparing the EKF estimates with IMU dead-reckoned positions. Three simulations were performed, consisting of linear, zigzag and curved trajectories. We achieved a 90th percentile error of 2.5m and 4m for the x-coordinate and y-coordinate, respectively, on the zigzag and curved trajectories. Interestingly, the linear trajectory showed a larger localization error in its y-coordinate.

The quasi-Z-source H-bridge grid-connected inverter (QHGCI) is well known with its advantages of the void of shoot-through problem and the high DC-voltage utilization. But the existence of the common-mode leakage current in the power frequency cycle, lower power density and higher thermal stress makes it hardly applicable to the grid-penetrating application. Thus with a purpose to conquer the problem relating to the QHGCI, an innovative transformerless Z-Source photovoltaic grid-connected inverter with coupled inductor coil (TZPGCI-CIC) is proposed. The circuitry topology and an unipolar SPWM strategy are firstly introduced in short. And thereafter the common-mode voltage in the whole working process is derived and evaluated through the detailed operating mode analysis, in which a constant value of it has been theoretically revealed. Lastly, a prototype platform of TZPGCI-CIC is setup and its good performance on leakage current suppression, lower thermal stress are validated with the experimental results.

The ion current generated during the operation of UHVDC (Ultra-high Voltage Direct Current) transmission lines has an increasingly prominent impact on the electromagnetic environment. At present, electric field line method and finite element method are commonly used to achieve fine steady-state solution of ion flow field, but the dynamic process of corona discharge is not analyzed. Therefore, based on the dilute species theory, the real-time motion state of charge is considered, and the behavior law of ions in complex flow field and electric field is emphasized. The effect of wind speed on space charge distribution is also analyzed. It provides reference for the electromagnetic environment prediction, control, and evaluation of UHVDC transmission projects. The results show that the microscopic process of positive and negative corona discharge is different. It also shows that the two have different mechanisms for maintaining self-maintained discharge. According to the established ±800kV UHVDC transmission line model, the maximum ion current density near the ground is 32.62nA/m2. Affected by the wind speed, the ground ion current density on the downwind side gradually decreases, while the upwind side gradually increases. When the wind speed is greater than or equal to 3m/s, the ground ion current density on the downwind side decreases to 0.

In order to solve the problems of the current wireless cable monitoring effect is not ideal and the prediction method is difficult to deal with the nonlinear data of cables, a power cable monitoring method based on UHF-RFID and deep learning in the edge computing environment is proposed. First, based on edge computing, a power cable monitoring system is designed to migrate the analysis of massive data to the edge of the network to improve the monitoring efficiency. Then, the temperature sensing chip and RFID chip were integrated to design a UHF-RFID temperature tag, which was fixed at the cable temperature measurement point to achieve passive wireless monitoring of the cable. Finally, the parameters of the GRNN model are optimized using the beetle antennae search algorithm, and the EEMD decomposed data is input into the BAS-GRNN model for learning to output temperature prediction results and determine whether it is over temperature. The proposed method was demonstrated, and results showed that the maximum error between UHF-RFID temperature tag temperature measurement results and the thermocouple was within 0.3℃. The average relative error of the proposed method was only 0.01, and the time was 3.59s, which can meet the actual usage requirements.

This study employs differential geometric algebra to offer a fresh perspective on voltage sag and swell analysis. By utilising differential geometry, simulated electrical signals can be visualised as curves. This is made possible by describing the instantaneous amplitude of a sinusoidal wave as a curve in Euclidean coordinates. This approach effectively represents the Frenet-Serret frame rotation at each point along the curve. In systems with derivative components, the velocity of the moving frame denotes the rate at which events change, as the Frenet structure is locally defined at every point along the curve. This mathematical representation, utilising the Frenet frame, enhances our understanding of phenomena such as sag and swell, in contrast to traditional approaches that rely on the Clark and Park transformations, which utilise two-dimensional forms to capture the details and portrayal of an occurrence. The work emphasises the depiction of voltage through curves and provides a geometric indicator of the pattern's evolution during operation.

To both enhance the flexibility of the power system and absorption capacity of renewable energy connected to a grid and achieve the efficient use and cooperation of multi-side complex type energy storage resources on the source, grid, and load sides, a collaborative optimal scheduling system architecture of source-grid-load-storage (SGLS) considering multiple energy storage types was constructed. Latin hypercube sampling and sample reduction based on k-medoids clustering were adopted to generate the SGLS scenarios, considering the emergency circumstances of the power system. The flexibility of the multi-scenario power system was evaluated, and the battery energy storage stations, pumped storage, and electric vehicles with sufficient capacity were configured on the source, grid, and load sides, respectively, to participate in the scheduling. A multi-scenario SGLS cooperative optimisation scheduling model that considers multiple energy storage capacity configuration types was constructed for economic and environmental protection. Based on data-driven, a multi-objective optimisation algorithm was proposed by using the Gaussian process regression algorithm and non-dominated sorting genetic algorithm II, combined with the manifold interpolation batch evolution mechanism. Finally, using actual regional power grid data for verification, the proposed strategy effectively reduces system operating cost, enhances energy storage battery life, and improves the renewable energy consumption capacity.

In recent years, extreme weather has occurred frequently in the world, and the contradiction between medium-voltage lines and wildfires has become more and more serious. In order to study the interaction between medium-voltage lines and wildfires, this paper builds a real fault simulation platform and carries out simulation experiments. The research is carried out from two aspects: the contact discharge between trees and wires to ignite trees to induce wildfires and the impact of smoke and flames caused by wildfires below the lines on the lines. The research results show that there is a high possibility that the medium-voltage line and the tree contact discharge will induce a mountain fire, and the accident must be discovered within at least 40 seconds to effectively prevent the further occurrence of the fire, and the relevant mechanism explanation is put forward. When smoke and flames are emitted from the line, a “particle bridge” will be formed. Under the ionization of gas and inorganic salts, conductive conditions will be formed together to cause discharge or breakdown. The breakdown field strength is about 57kV/m.

Phase-locked loop (PLL) technique is a critical control module applied in photovoltaic (PV) grid-connected control system to synchronize with grid voltage. The PLL error directly affects the response characteristic of PV grid-connected inverter under faults, leading to a mass of harmonic components in the output current of the PV inverter. Especially in the scenario of short-circuit fault with deep voltage sag, the PLL detection error is too significant to be ignored, posing challenges to transformer differential protection. This paper proposes an improved scheme that utilizes a digital computer algorithm to record and store pre-fault voltage, aiming to address the complexity and implementation challenges for phase-lock error in practical engineering applications. By utilizing the memory voltage, the PLL detection error is eliminated, mitigating harmonic distortion in the PV output current and ensuring the reliability of the transformer differential protection. However, introducing memory voltage may increase the short-circuit current of the PV output. Therefore, it is recommended to reduce the amplitude of the short-circuit current by multiplying the inverter port voltage reference value by an appropriate limiting coefficient k and outputting it to the physical system. Finally, the effectiveness of the proposed scheme is verified through MATLAB/Simulink simulation.

Vital signs such as heart rate (HR) and respiration rate (RR) are essential physiological parameters used to assess human health and bodily functions. These can be measured via methods that either require or do not require direct contact. A non-contact Doppler radar millimeter (mm)-wave sensing firmware utilizes a 76–81 GHz (W-band) electromagnetic wave over a short-range path to the human body. It then processes the reflected electromagnetic wave to filter and extract human heartbeat and breathing rhythm signals. The short-range sensor system proposed herein eliminates the need for electrodes, electric patches, photoelectric sensors, and conductive wires, as well as the requirement for direct contact with the human body when measuring physiological signals. The W-band Doppler mm-wave sensing firmware, paired with frequency-modulated continuous wave radar, enables continuous monitoring of HR and RR. Short-wavelength mm-waves are employed in short-range detection to deliver highly precise measurements of physiological signals with minimal noise interference. Consequently, experimental tests were conducted in a laboratory setting to measure the heartbeats and breathing rhythm signals of healthy young men. Their HR and RR were estimated through frequency- and time-domain analyses. The experimental results confirm the feasibility of the proposed mm-wave radar for continuous human vital sign detection.

The power characteristics of offshore wind power will change the regional power flow distribution and affect the regional voltage. In this paper, the direct-drive wind turbine generator controlled by unity power factor is selected as the research object, and the influence of change of wind power on voltage at the grid connection point is analyzed from the simple integration model. Based on the two critical states of the external equivalent power of the grid connection point, an analytical expression of the output of wind power in the corresponding state is obtained, and the relationship among the critical state outputs, the output of wind power and the parameters at the grid connection point is drawn, hence the related influence laws is analyzed. The influence of change of wind power on voltage and regional reactive power and the reactance distribution of transmission lines near the grid connection point is considered to analyze the influence of offshore wind power on the vicinity region of grid connection point. The rationality of the conclusion is verified by study the cases of the integration of offshore wind power into Guangdong power grid, and some suggestions are put forward for the planning of offshore wind power integration.

Faced with the uncertainty of wind and photovoltaic power output and load fluctuation caused by the increase of new energy penetration in active distribution network, the demand for operational flexibility and the construction demand for flexible resources of distribution network are gradually increasing. The flexible operation of active distribution network can be realized by coordinated planning of the soft open point integrated with energy storage system (ESOP) and flexible resources. Firstly, the flexibility resource adjustability evaluation and margin indicators are proposed for the response model of typical flexibility resources. Secondly, a two-stage distributionally robust coordinated planning model considering the coordination planning scheme of distributed generation, flexibility resource and ESOP as well as the comprehensive norm uncertainty of wind power and photovoltaic outputs multi-operation scenarios is established with the distribution network construction cost, annual operation cost and annual power sales revenue as the objective functions meanwhile the investment and flexibility resource operation as constraints. Finally, the column constraint generation algorithm is used to solve the problem, and the effectiveness of the proposed model is verified by the modified IEEE33 node system.

A recently developed linear convolution filter based on Hirschman theory has shown its advantage in saving computations compared with other convolution filters. In this paper, we ameliorate the Hirschman convolution filter with the usage of split-radix algorithm and explore its latency-reduced advantage for the first time. We present a comparison of hardware resource in FPGA for the proposed Hirschman-based filter and other convolution filters. Simulation results indicate that the split-radix Hirschman convolution filter achieves a promising reduction in latency by averagely 18.15% with an acceptable power consumption rise, compared with the main competitor using extended SRFFT. In the case of device capacity limited, the proposed Hirschman convolution filter is still computationally attractive as it performs small-size originator function, instead of larger Fourier transform required by other convolution filters.

State estimation is critical for railway power supply systems (RPSSs). Pseudo-measurement is commonly used in state estimation. However, the fluctuations of renewable generations and railway traction loads in RPSS may introduce data noise, which will jeopardize the accuracy of the generated pseudo-measurements and thus impact the state estimation. Additionally, when learning the historical measurement data sequences, the traditional pseudo-measurement model is likely to have overfitting, which will further impact the accuracy of pseudo-measurements, thereby affecting the accuracy of state estimation. To address these issues, this paper proposes a high-accuracy pseudo-measurement-based state estimation approach for RPSSs. Firstly, a denoising autoencoder (DAE)-based method is used to mitigate the impact of data noise on the accuracy of pseudo measurements, and a gated recurrent unit (GRU)-based method is used to adaptively learn the historical measurement data sequence, thereby improving the accuracy of pseudo measurements. Next, the pseudo-measurement weights are obtained by generating pseudo-measurement variances using the Gaussian mixture model. Finally, the pseudo measurements and real-time measurements are integrated by weighted least squares to realize the state estimation of RPSS. The effectiveness and accuracy of the proposed method are verified by simulation on a modified IEEE 33-node system which includes a railway traction substation and renewable generations.

The rapid growth in the number of electric vehicles, driven by the ‘double carbon’ target, and the impact of uncontrolled charging and discharging behavior and discharged battery losses severely limit electric vehicles’ low carbon characteristics. Existing research on systemic low carbon emissions and electric vehicle charging and discharging issues is usually determined by considering only carbon trading markets or charging and discharging management on the source side. In this regard, a coordinated and optimized operation model that considers the participation of electric vehicle clusters in deep peaking and the source network load and storage adjustable resources is proposed. The upper layer establishes a real-time price-based demand response model for the load side with the minimum net load fluctuation as the objective function; the middle layer establishes a comprehensive operation mechanism for the source and storage side that includes an orderly charging and discharging peaking compensation mechanism for electric vehicles and a deep peaking mechanism that takes into account clean emissions, and constructs an optimal operation model with the minimum comprehensive operating cost as the objective function; the lower layer establishes a distribution network loss minimization model for the network side that takes into account the orderly charging and discharging of electric vehicle as the objective function. The optimal tidal model with the objective function of minimizing the distribution network loss is established at the lower level. Finally, the original problem is transformed into a mixed integer linear programming problem, and the model’s effectiveness is verified by setting different scenarios.

Fractional slot concentrated winding (FSCW) permanent magnet (PM) machines are taken more attention in low-speed applications because of their great advantages. This paper proposes a new structure for an FSCW double rotor axial flux PM synchronous machine (DRAFPMSM) with counter-rotating rotors. In addition, an analytical formula for the Back EMF waveform of the proposed structure is presented. The accuracy of the formula is investigated by designing a 40 Watts sample machine. The rotors of this machine rotate with speeds of 600 and 428 rpm in opposite directions. The Back EMF of the sample machine is calculated using the suggested formula. In addition, the Finite Element Method (FEM) is used to determine the Back EMF waveform. Comparison of the obtained results confirms that the new formula yields the Back EMF precisely such that the results well match to those of FEM.