Xiaowei Wang

Existing protection schemes often fail to detect cross-line successive single-phase-to-ground (SSPG) faults in resonant-grounded active distribution networks, due to the severely weakened transient signature that results when a secondary fault (f₂) superimposes on an uncleared primary fault (f₁). To address this, a novel detection method based on inner-product analysis is proposed. First, a transient equivalent circuit for the f₂ fault is established. Expressions for the transient zero-sequence currents in healthy, primary, and secondary fault feeders, as well as for the bus zero-sequence voltage, are subsequently derived and analyzed. Then, the inner-product is applied to reveal the inherent correlation between healthy and faulty feeders during an f₁ fault. For the f₂ fault, distinct dynamic patterns, specifically in the amplitude and polarity of the inner products, are revealed for the primary, and secondary fault feeders. This analysis provides the theoretical foundation for SSPG fault detection. Furthermore, a differential preprocessing algorithm is introduced to enhance transient signatures, thereby improving the reliability of the inner-product based detection. Simulation and field test results confirm that the proposed method accurately detects both the f₁ and f₂ faults under challenging conditions, including short fault intervals, diverse grounding resistance combinations, distributed generation integration, arc faults, and strong noise interference.

In non-effectively grounded system, the rise in phase voltage during single-phase grounding (SPG) fault poses a significant threat to the system’s insulation integrity. The increasing cabling rate in distribution network has led to a rising proportion of cross-line double successive single-phase grounding (SSPG) faults triggered by initial fault. To address the challenges of weak characteristic and difficult detection of such successive fault, this paper proposes a SSPG detection method based on zero-sequence capacitance variation. First, the dynamic zero-sequence equivalent network during successive faults is simplified, and derive the zero-sequence current (ZSC) expressions for each feeder under both overdamped and underdamped conditions. Subsequently, the singular value decomposition-total least squares extended Prony algorithm is employed to fit the zero-sequence voltage (ZSV) and ZSC, enabling the calculation of zero-sequence capacitance for both initial and successive faults in the complex frequency domain. A detection criterion is then constructed using the variation of capacitance. Finally, digital simulations and recorded field data validate the correctness and feasibility of the theoretical analysis.

For the multi terminal flexible DC distribution network based on modular multilevel converters, if the line fails and is not cut off in time, it will damage power electronic devices and may burn out the converter station. It is difficult to accurately distinguish between internal high resistance faults and external low resistance faults when faults occur in multi terminal flexible DC distribution networks. Analyzed the equivalent model and equation of zero-mode current at each measurement point in the time domain and complex frequency domain when a grounding fault occurs in the system. Based on this, a flexible DC distribution network grounding protection method based on time domain skewness and integral energy is proposed. Using the zero-mode current skewness symbol at each measuring point to characterize directional characteristics, and the integrated energy of the line to characterize amplitude characteristics. Using integrated energy as a supplement to skewness criteria can accurately extract fault features and perform fault identification in extreme cases where a certain line is long. Finally, a four-terminal flexible DC power grid model was built on the PSCAD/EMTDC simulation platform to verify the adaptability and superiority of the protection method.

When cross-line same-phase successive faults (CSSF) occur in a distribution network, the second fault makes the zero-sequence voltage consistently exceed 15% of the phase voltage, resulting in the existing detection devices not being able to return, which makes the fault persist for a long time and endangers the stable operation of the power grid. To address this problem, we propose a fault detection method based on vertical and horizontal double criteria. Firstly, zero-sequence current and voltage features are analyzed for a flexible grounding system when the grounding mode changes during the CSSF period. Secondly, constructing two boundary cases calculates the zero-sequence admittance modulus of the first and second faulty feeders. Finally, vertical criterion 1 is constructed because the fault feeder’s zero-sequence admittance modulus is greater than the healthy feeder. Horizontal criterion 2 is constructed because the faulty feeder’s zero-sequence admittance modulus ratio is greater than the healthy feeder. PSCAD simulations and field tests show that the method can accurately detect CSSF in flexible grounding systems without being affected by current transformer reversal, missing data, strong noise and intermittent arcing.

In the context of global industrialization, integrated energy systems (IES) have emerged as a crucial means of facilitating green and low-carbon transformation. To comprehensively explore the potential for carbon reduction through source-load synergy and achieve low-carbon and high-efficiency operation of the system, an IES optimal scheduling method that incorporates a reward and punishment mechanism for dynamic electricity and thermal carbon emission factors is proposed. Firstly, the concepts of energy coupling and thermal energy cascade utilization are considered, and an IES-coupled equipment cascade utilization model with carbon capture is established with the objective of realizing high-grade use of thermal energy in combination with thermal load demand. Secondly, an improved IES dynamic electricity and thermal carbon emission factors model is proposed to characterize the carbon emission intensity and renewable energy consumption capacity. Based on this, an integrated demand response (IDR) mechanism driven by electricity, thermal reward, and carbon price is established to guide users toward low-carbon behaviors and renewable energy consumption. Finally, a two-stage optimal scheduling model is constructed. In the pre-scheduling phase, the initial scheduling plan is solved, and the load distribution is adjusted by the integrated demand response. In the re-scheduling phase, the load curve is updated to obtain the final scheduling plan. Through simulation verification, the proposed scheduling model reduces carbon emissions by 7.7 %, improves the renewable energy consumption rate by 4 %, and reduces the comprehensive operating cost of the system by 4.3 %, which indicates that the method proposed in this paper can effectively promote the emission reduction and consumption and reduce the operating cost of the system.

The barrier dielectric material and carrier gas are the two crucial factors affecting the discharge characteristics and application effect of Ar–NH $_{3}$ dielectric barrier discharge (DBD). This study aims to experimentally investigate the impact of dielectric constant and NH $_{3}$ concentration on NH $_{3}$ decomposition for hydrogen production in a DBD reactor. By analyzing the current waveform, discharge image and optical emission spectrum (OES) of plasma, it is found that the number of discharge current pulses and discharge power increase with increasing the discharge voltage. As the percentage of NH $_{3}$ increases, the discharge mode transitions from Townsend to glow and filamentary, leading to a gradual decrease in the percentage of NH, NH $_{2}$ , Ar, and other intermediate expressed by their OES intensity. In addition, it is obtained that while both ignition voltage and extinguishing voltage initially decrease then increase with increasing NH $_{3}$ percentage; however, the former always remains higher than the latter throughout this process. At low NH $_{3}$ percentages, the Al $_{2}$ O $_{3}$ reactor exhibits the highest plasma OES intensity due to catalysis effects; however, when using aluminum nitride (AlN) or epoxy resin (EP) as barrier dielectric, factors, such as material surface traps significantly affect the OES intensity of plasma. Furthermore, an increase in dielectric constant results in a corresponding rise in current pulse amplitude while causing the discharge mode to gradually change from Townsend to glow and filamentary. Simultaneously, the line intensity of the products, such as NH and NH $_{2}$ , also increases with increasing the dielectric constant: high dielectric constant group (ZrO $_{2}$ , $\varepsilon_{\text{r}}$ $=$ 37.1) $>$ the middle dielectric constant group (Al $_{2}$ O $_{3}$ , $\varepsilon_{\text{r}}$ $=$ 10.2 and AlN, $\varepsilon_{\text{r}}$ $=$ 9.8) $>$ the low dielectric constant group (EP, $\varepsilon_{\text{r}}$ $=$ 6.6 and quartz, $\varepsilon_{\text{r}}$ $=$ 4.7).

This paper presents a novel approach for detecting high impedance faults (HIF) in active distribution networks that operate in resonant grounding mode. The method leverages the impedance characteristics of the network. It builds on previous research showing that healthy feeder currents have lower amplitude and opposite phases compared to faulty feeder currents only within a characteristic frequency band (CFB). The proposed method utilizes empirical wavelet transform (EWT) with comprehensive voting theory. The CFB is first determined based on the variation of zero-sequence impedance, and EWT is then applied to extract transient current features within the CFB accurately. Three complementary criteria are then constructed to measure transient currents’ amplitude, polarity, and waveform variation trend: feature energy ratio, feature integral area, and feature cross-correlation coefficient. Finally, a comprehensive voting mechanism is employed, following the "minority obeying majority" principle to achieve fault detection. This method has been tested in active distribution networks with distributed generators (DGs) considering various conditions. Results show that the process is highly adaptable and effective even under significant noise interference (up to 3dB). Additionally, the method’s effectiveness has been validated through testing in a 10kV real-model experiment and on-site recording data.

When a high-impedance fault (HIF) occurs in active distribution networks, the features of the arc current are weak and can be easily confused with the features of the capacitor switching current or load switching current. Meanwhile, fault currents are easily affected by the harmonics and noises of distributed generators, which may result in low detection accuracy for existing HIF detection methods. To accurately detect high-impedance arc faults, an HIF detection method based on the arc changing tendency and nonlinear least squares (NLSs) are proposed. In the proposed method, variational mode decomposition and windowed fast Fourier transform are first employed to extract current amplitudes. Then, NLSs method is introduced to quantitatively estimate the changing tendency of the current amplitudes. Finally, an HIF detection criterion is developed based on the sign of the coefficient in the nonlinear function. Both simulation results and field test results confirm the effectiveness and applicability of the proposed HIF detection method. The inspiration for the proposed method is drawn from the physical characteristics during the initial stage of HIFs. These characteristics remain unaffected by network operation conditions. Comparative analysis reveals that the proposed method offers higher detection accuracy and reliability compared to existing conventional methods.

Crosslinked polyethylene (XLPE) insulated cables have been widely developed in transmission lines and urban distribution networks due to their advantages, such as being lightweight, high working temperature, and high transmission power. Partial discharge (PD) detection is the primary means to evaluate the insulation status of XLPE cables. This article proposes a denoising method based on image information entropy (IIE) and a novel adaptive multivariate variational mode decomposition (MVMD) to address the issues of white noise, periodic narrowband interference, and weak adaptability. The method first decomposes the signal based on MVMD, reconstructs and converts it into a grayscale, and then calculates the information entropy. Considering the efficiency of execution, the modal parameters of the algorithm are optimized by combining the correlation coefficient with IIE. Second, the PD feature information is distinguished from the noise interference component by calculating the kurtosis of each intrinsic mode function (IMF) to determine its dominant component’s property characteristics and utilizing the kurtosis’s sensitivity to noise. Then, the noise interference component is subjected to $3\sigma $ criteria filtering. Finally, the denoised signal is obtained through a novel, improved wavelet threshold algorithm. The denoising effect of this method is validated by comparing it with several existing methods. The results show that this method has good noise suppression performance for on-site PD signals, with low time consumption, high execution efficiency, and high engineering application value.