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The transient electromagnetic interference in practical existing engineering have measuring testability limitation. This limitation makes it difficult to assess electromagnetic effect. A multidimensional physical information based assessment method via physics-informed neural network (PINN) and Gaussian process regression (GPR) is proposed in this article. First, the physical topology of the disturbed equipment under electromagnetic interference is analyzed, and the partial differential equation of the space electric field is derived from the measurable electric quantity. Then, the electric field near the disturbed equipment is predicted by PINN which can characterize the physical information. Finally, combining calculated electric field data and measured magnetic field data, a two-dimensional GPR regression is used to assess the electromagnetic effect with multiple inputs and outputs, and the probabilities under each state are then calculated based on the assessed state. Expanded verification is conducted by combining different electromagnetic complexity calculation methods, proving the applicability and effectiveness of the proposed assessment model.

The P-class current transformer (CT) widely used at present creates the problem that transient saturation occurs and leads to the reduction of the reliability of the differential protection of the transformer or even false operation. This article proposes a novel CT saturation detection method to avoid differential protection false operation. First, based on the dynamic regional saturation J-A model, CT transient saturation characteristics are analyzed. These characteristics are extracted and represented using the improved Hausdorff distance algorithm, which constructs the detection scheme for CT transient saturation. The simulation tests demonstrate that the proposed scheme is feasible and rapid in various work conditions such as fault types, initial phase angle, voltage rising amplitude, and Gaussian noise signal, compared with the other saturation detection schemes. Finally, a series of fielded experiments up to 48 kA current are conducted on 330-kV protective CTs (P-class CTs). The large current dynamic experiments verify that the proposed CT saturation detection method is valid and applicable to practical engineering, which ensures that the transformer differential protection is reliable.

Capacitive voltage transformer (CVT) transient transform characteristics destroy the linear relationship between output voltage and input voltage, leading to a sharp increase in measurement error. To address this issue, this article proposed an indirect method to design an infinite impulse response (IIR) filter to suppress the voltage oscillation amplitude caused by the CVT transient transformation characteristics. The method derives the CVT transfer function using the cascade form. Determining the compensating reactor frequency response curve, the intermediate transformer frequency response curve, and the oscillatory components below 50 Hz caused by ferromagnetic resonance as the design targets, IIR filters were designed to suppress the voltage oscillation amplitude. The proposed method’s effectiveness is verified by simulation in suppressing the voltage oscillation amplitude when transient response and ferromagnetic resonance occur. The simulation results show that the output voltage amplitude can be suppressed below 0.5 V after 0.48 s in the transient response, and the voltage oscillation amplitude caused by ferromagnetic resonance can be suppressed well under different short-circuit durations. This method also reduces the output voltage oscillation time and improves the CVT voltage measurement accuracy.

This article presents output voltage drop compensation technology for high-voltage and high-power dc energy storage systems (DC-ESS). This technology is used to improve the output voltage stability of high-voltage high-power DC-ESS in high rate discharge. The proposed output voltage drop compensation technology includes an ESS architecture and a corresponding control method. The proposed ESS architecture adopts a dc dynamic voltage regulator (DC-DVR) to compensate for output voltage drop. DC-DVR adopts a six-phase interleaved parallel buck converter. This architecture not only maintains the system output voltage stability but also reduces the system cost. Moreover, the proposed ESS architecture does not change the original energy storage capacity. The dynamic power flow of the proposed ESS architecture is analyzed and a direct power control method based on full response power compensation is proposed. The steady-state power and dynamic power of the proposed ESS architecture are compensated so that the input power can quickly track the change of the output power. The proposed control method improves the stability and dynamic response speed in the system's dynamic process. Simulation and experiment verify the effectiveness of the proposed ESS architecture and control method.

To solve the problem of poor energy aggregation effect of existing time-frequency decomposition methods for electromagnetic interference (EMI) signals. A combined EMI signal processing method based on the generalized correntropy criterion (GCC) and statistical tenets is proposed in this article. And it combines two signal processing methods, variational mode decomposition (VMD) and generalized S-transform (GST). First, the statistical characteristics of the target signal are analyzed and a suitable distribution model to describe the target signal is constructed. VMD is selected to process the target signal so as to obtain the corresponding sub-modes. Second, the GCC formula for the correlation coefficient matrix is derived, and the fitness function is constructed to minimize the spectral correlation between sub-modes. Then, the optimal number of sub-modes K obtained from the fitness function was used for VMD decomposition and GST reconstruction to obtain the final time-frequency distribution. The method is modeled and its performance is tested by using two target EMI signals and one artificial composite signal in real scenarios. The effectiveness and correctness of the proposed method in this article is verified through the comparison of its test results with those of the other classical methods.

This article proposed a structure of self-powered modular high-voltage and high-current pulse generator for magnetron. This modular structure allows the designed generator to be power expansion according to the power requirements for different magnetrons. The self-powered auxiliary power supply is utilized to achieve active driving for the mosfet, and its input power is taken from capacitance of the generator itself. Meanwhile, analyzing the effect of parasitic inductance caused by high di/dt on the dynamic response of the generator, and snubber circuits are incorporated to suppress its effect. Finally, optical fiber is used to realize the transmission of the PWM signal, which ensures the consistency of the signal. This structure allows the generator to solve the insulation problem and its power can be scaled up as requirements.

Accurate current sharing is beneficial to realize uniform heat distribution and electrical stresses in multiphase converters. Most of the present current-sharing schemes need many current sensors which increase the cost and decrease the system reliability. To solve this problem, a sensorless current-sharing scheme is proposed to balance the phase currents of multiphase dc–dc boost converters in continuous conduction mode. By converting the inner resistances of inductors and switches to series resistances on the input side, the actual converter model is simplified. Regarding one duty cycle as reference, the relationship between duty cycle and series resistance ratio (SSR) is derived for equal current distribution. After that, two methods are provided to obtain the SSRs. One is based on duty disturbance and the other is based on current measurement. Actually, the phase with largest series resistance is chosen as reference to improve the transient response. Despite phase number, only output and input voltages are measured to achieve output voltage regulation and current balance in the overall control scheme. The validity of proposed scheme is verified on a three-phase dc–dc boost converter.

With the extensive application of primary–secondary fusion medium-voltage (MV) switch, the fault rate of acquisition circuit board of MV switch increases obviously. The maintenance staff highly suspects that the electromagnetic compatibility (EMC) of the MV switch has been damaged due to the short-range radiation interference. However, this kind of new switch is not equipped with electromagnetic field sensor to record the signal in real time. To solve this problem, first, electromagnetic interference (EMI) signals under two kinds of ultraserious current conditions (lightning current and inrush current) were found and obtained by the originality test in this article. Then, the finite integral time-domain (FITD) method was introduced into the primary–secondary fusion switch, and the radiated magnetic fields of two kinds of transient currents are obtained. Finally, the radiated magnetic field near the secondary equipment was calculated and analyzed, and the research results have a reference value for the resist of radiated magnetic field and the formulation of EMC standards of MV switch. The obtained radiated magnetic field can cause the sensitive equipment to malfunction.

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).

The state of charge(SOC) is an important index in Battery Management System(BMS) for smart grids and electric vehicles, whose accuracy is affected by model and sensor errors. In this paper, an online SOC estimation method based on EKF-AUKF algorithm with reference compensation for estimation results is proposed. Firstly, a joint estimation method based on extended Kalman filter(EKF) and adaptive unscented Kalman filter(AUKF) algorithm is adopted to estimate SOC under Beijing bus dynamic stress test(BBDST) condition. The result shows that the estimation error is within 2 %. Secondly, the characteristics of EKF-AUKF algorithm under voltage and current sampling errors are analyzed over the whole SOC range. The results indicate that voltage measurement error increases SOC estimation error. Then, an online reference is built by calculating the sum of the estimation result of ampere-hour counting(AHC) method and constant deviation, and then the difference between the estimation result of EKF-AUKF algorithm and online reference can be obtained. The absolute value of the difference is used to compare with limit deviation value and compensate the SOC of EKF-AUKF method by different compensation coefficients. Finally, the effectiveness of the proposed method is verified under different operating conditions. The experimental results show that the proposed method can greatly improve the SOC estimation accuracy under large voltage error.

Non-isolated bidirectional dc–dc converters (NBDCs) are widely used in energy storage systems. However, the zero-voltage switching (ZVS) for NBDCs is hard to realize by adding auxiliary circuits. The auxiliary current should be larger than the filtering inductor current. Then the auxiliary current causes high conduction losses, which have adverse effects on efficiency. In this paper, a ZVS NBDC with a transient current built-up technique is proposed, where the transient current is built in a very short time by an auxiliary circuit. The average value of the auxiliary current is quite small, which results in low conduction loss. The proposed auxiliary circuit does not result in high current or voltage stresses and does not affect the filtering inductor current. Experimental results show the correctness of the proposed zero-voltage switching method. They also show that the efficiency of the proposed NBDC is higher than that of the hard switching NBDC.

The surge in wind energy utilization, particularly within regions characterized by consistent wind patterns, has attracted substantial attention. Pakistan has several wind corridors with potential energy extraction, primarily located in Balochistan and Sindh. This study aims to predict wind speeds and assess wind energy feasibility across Pakistan using a novel hybrid NARX-RNN model. We analyzed wind data from five distinct zones over seven years to identify the most promising site for wind energy. Our hybrid model demonstrated superior performance, achieving a 20% reduction in RMSE and a 25% reduction in MAPE compared to recent leading-edge methods. The model maintained robust performance across various wind speed ranges, with notable improvements in high wind speed conditions. We identified Sujawal as the most suitable location due to its consistently high wind speeds, minimal directional variation, and strong economic profile. This study provides valuable insights for sustainable wind energy production in Pakistan, offering a more comprehensive and accurate prediction model crucial for effective wind energy planning and operation.

ABSTRACTHydroxychloroquine (HCQ) inhibits autophagy and therefore can sensitize some cancer cells to chemotherapy, but the high doses required limit its clinical use. Here we show that loading HCQ into liposomes (HCQ/Lip) decorated with a pH-sensitive TH-RGD targeting peptide (HCQ/Lip-TR) can concentrate HCQ in B16F10 tumor cells and lysosomes. HCQ/Lip-TR was efficiently internalized as a result of its ability to bind ITGAV-ITGB3/integrin αvβ3receptors highly expressed on the tumor cell surface and to undergo charge reversal from anionic at pH 7.4 to cationic at pH 6.5. Studies in vitro at pH 6.5 showed that the intracellular HCQ concentration was 35.68-fold higher, and lysosomal HCQ concentration 32.22-fold higher, after treating cultures with HCQ/Lip-TR than after treating them with free HCQ. The corresponding enhancements observed in mice bearing B16F10 tumors were 15.16-fold within tumor cells and 14.10-fold within lysosomes. HCQ/Lip-TR was associated with milder anemia and milder myosuppressive reductions in white blood cell and platelet counts than free HCQ, as well as less accumulation in the small intestine, which may reduce risk of intestinal side effects. In addition, co-delivering HCQ/Lip-TR with either free doxorubicin (DOX) or liposomal DOX improved the ability of DOX to inhibit tumor growth. Biochemical, electron microscopy and immunofluorescence experiments confirmed that HCQ/Lip-TR blocked autophagic flux in tumor cells. Our results suggest that loading HCQ into Lip-TR liposomes may increase the effective concentration of the inhibitor in tumor cells, allowing less toxic doses to be used.