Abstract:To address the challenges posed by the large-scale integration of electric vehicles and new energy sources on the stability of power system operations and the efficient utilization of new energy, the integrated photovoltaic-energy storage-charging model emerges. The synergistic interaction mechanisms and optimized control strategies among its individual units have also become key issues urgently needing resolution in smart grid development. Due to the characteristics of integrated generation, load, and storage, mutual complementarity of supply and demand, and flexible dispatch, the photovoltaic-energy storage-charging (PV-ESS-EV) integrated station micro-grid (ISM) mode, incorporating "PV- PV-ESS-EV + intelligent building" features, has become a focal point for energy conservation, carbon reduction, and energy transition in China. In consideration of the challenges faced by the operational mode of microgrids, such as the strong uncertainty of distributed energy sources and the unclear interaction mechanisms during islanded and grid-connected operation, various aspects of the PV-ESS-EV ISM are reviewed, including its unit modules, key technologies, and operational states. Additionally, the current research status of PV-ESS-EV is summarized while future development trends are discussed, and the challenges that need to be addressed are examined. The research findings have important theoretical and practical implications for exploring the regulatory potential of various demand-response resources under economic incentives, ensuring the reliability of power grid supply, and serving as valuable references for both theory and practice.

Abstract:Environmentally friendly gases such as C4F7N, HFO1336mmz(Z), with low global warming potential (GWP) and high insulation strength, are expected to partially replace SF6 in high-voltage electrical equipment. This substitution is expected to drive the development of green, low-carbon, high-voltage equipment, aligning with the objectives of "Emission Peak and Carbon Neutrality". The characteristics of gas-solid interface discharge, the generation of solid deposits under abnormal conditions directly impact the operational safety of environmentally friendly insulation systems. Therefore, conducting thorough research before large-scale application is crucial. This paper reviews the current research progress on these topics, summarizing the global development and application of environmentally friendly insulation gases and related high-voltage equipment. It also analyzes the research progress on the charge accumulation at the gas-solid interface and the surface flashover characteristics. Additionally, it discusses the composition and formation process of solid deposits resulting from the deep decomposition, along with their impact on interface insulation and measures to inhibit their formation. This review provides valuable insights for the research and development of environmentally friendly gases and insulation equipment.

Abstract:The combination of cyber-physical-social systems and demand response assessment is reviewed. First, the history and framework of cyber-physical systems are analyzed. Then, summaries are provided for the existing research on demand response, including the significance, classification, and evaluation methods of demand response potential assessment, as well as the data sources for demand response capability assessment, including questionnaire surveys and operational data collection. In terms of the combination of cyber-physical-social systems and demand response, the physical domain, information domain and social domain foundation of demand response are analyzed respectively, and the corresponding modeling methods and research contents are introduced. Finally, prospects are provided for market assessment mechanisms, rapid simulation and modeling technologies, and demand response management under integrated energy systems.

Abstract:The intermittency and volatility of wind speed changes pose great challenges to the accurate prediction of wind power. Fully exploring the inherent laws of key factors such as wind power and wind speed is an effective way to improve the accuracy of wind power prediction. A method for ultra-short-term wind power prediction is proposed, which incorporates a temporal pattern attention (TPA) mechanism into a multi-layer stacked bidirectional long short-term memory network. Firstly, outlier detection for the wind power dataset is performed using a density-based noisy spatial clustering method (DBSCAN) and a linear regression algorithm, followed by data reconstruction of outlier points using k-nearest neighbor (KNN) interpolation. Next, the intrinsic correlations between wind power and various meteorological features are comprehensively considered, and the TPA mechanism is introduced into the MBLSTM network to properly allocate time step weights, capturing the underlying logical patterns of the wind power time series. Finally, the effectiveness of the proposed method is verified through experimental simulation data analysis. Results show that this method can fully explore the relationship between wind power and wind speed influencing factors, thereby improving its prediction accuracy.

Abstract:Wind turbine blades are susceptible to icing caused by frost or rime and other extreme weather conditions in winter, which directly affects the wind turbine output, even leads to safety problems such as ice accumulation and subsequent shedding. Therefore, it is necessary to investigate the anti /deicing technologies for wind turbine blades. In this paper, a deicing method for wind turbine blades based on microwave heating is proposed. To improve the microwave heating performance of the blade, carbon black and carbon fiber are incorporated into the blade composite. The study employs a combination of COMSOL and MATLAB to optimize the parameters of the filling medium within the blade. Subsequently, simulations are conducted to analyze the microwave heating and deicing performance of the optimized blade composite, examining the influences of microwave heating power and ambient temperature on deicing time. The results indicate that the optimum carbon black filling concentration is 3.98%. Under this condition, the carbon fibers within the composite form a sandwich structure?denser at the top and bottom and sparser in the middle, which effectively improves the microwave absorption rate of the composite. Furthermore, it is observed that increasing microwave power and ambient temperature significantly reduce the deicing time, with microwave power exerting a more pronounced effect compared to ambient temperature.

Abstract:In response to the integration of wind power into the multi terminal flexible DC transmission system, this paper proposes an additional adaptive frequency division control strategy on the basis of existing droop control, in order to fully utilize the wind power's participation in system frequency regulation ability and solve the voltage and frequency fluctuations caused by AC/DC grid connection faults in the DC system. The DC voltage deviation signal is taken as the input signal of the controller, and the input signal is divided into high-frequency fluctuation signal and low-frequency fluctuation signal through the first-order low-pass filter. According to the different frequency modulation capabilities of the wind power and DC transmission systems, the high-frequency fluctuation signal is added to the active power control loop of the converter at the rotor side of the wind turbine, and the low-frequency fluctuation signal is added to the external loop of the DC active power control, At the same time,voltage source converter （VSC） real-time power margin and DC voltage variation are introduced into frequency division control to adjust the time constant of the low-pass filter in real time and dynamically adjust the power output to improve system stability. A simulation model is built in PSCAD/EMTDC to verify the effectiveness of the proposed control strategy.

Abstract:Exploring the influence law of different photovoltaic penetration rates on the capacity allocation and operation of wind-solar-fire storage systems, a three-layer capacity optimization model considering penetration rate constraints and integrated control of thermal storage is constructed, using a wind-solar-thermal-storage combined generation system as an example. Firstly, introducing the golden search optimization (GSO) algorithm into the solution of capacity optimization configuration, the best capacity configuration of the system under different penetration rate constraints is provided, and the resulting data of system operation indicators is obtained. Then, the obtained results are subjected to a least squares curve fitting, yielding curves depicting the variations of system economics, reliability, and stability with different penetration rates. Finally, the main reasons for the trend changes in the curves are systematically analyzed, providing insights for the optimization and planning of capacity allocation and operation planning of renewable energy systems such as wind and solar.

Abstract:To address the operational quality assessment requirements of the high speed power line carrier communication (HPLC) unit in energy metering equipment, a multi-model integration-based assessment method utilizing KICA-CIM is proposed. Firstly, the main performance influencing factors of local internet of things (IoT) application scenarios and communication technologies of typical customer-side metering equipment are integrated and analyzed, and a universally applicable index library for performance evaluation of IoT scenarios is comprehensively constructed. Next, in the operational scenario with multi-source, heterogeneous, and high-dimensional data environment, on the one hand, kernel independent component analysis (KICA) is used to process nonlinear features and solve principal components, calculating individual weights of each indicator. On the other hand, component importance measure (CIM) model is utilized to distinguish and measure the different impact levels of each indicator evaluation result on the overall evaluation effect, assigning importance weights to the indicators to determine the functional weight of each indicator. Through the implementation of integrated weighting models, comprehensive assessment of operational quality is achieved. Finally, the feasibility and effectiveness of the proposed method are verified using data from energy metering equipment in a certain region, which contributes to improving the accuracy and rationality of the assessment results.

Abstract:Detecting user-side electricity theft accurately has long been a challenge for power supply companies, with traditional theft detection methods having certain limitations. Addressing the highly imbalanced positive and negative samples in the field of theft detection, and the poor performance of single classification models, this study proposes a theft detection method based on an improved Rotation Forest algorithm. The Rotation Forest algorithm uses Principal Component Analysis (PCA) for feature extraction, training each base classifier with all principal components of the original training set. Building upon the classical Rotation Forest algorithm, improvements are made in three aspects: balancing the positive and negative samples in the subset using the Synthetic Minority Oversampling Technique (SMOTE) algorithm, further sampling the training subset using Bootstrap sampling in the Bagging algorithm, and selectively integrating base classifiers based on accuracy. A case study using actual user data from a region in East China demonstrates that the proposed theft detection method achieves better results in multiple evaluation metrics compared to single classification models and existing ensemble learning strategies.

Abstract:To meet the requirements of accommodating new energy sources and balancing power supply and demand, a new type of power system dominated by new energy sources needs to maximize the load-side regulation capability. To address the problem of insufficient exploitation of load-side regulation capability, this study proposes an electricity load optimization method based on an interactive real-time electricity price mechanism. Firstly, a generalized benchmark new energy output curve is established based on the total new energy within a regional power grid. Secondly, a source-load similarity calculation method based on improved time series morphological similarity is proposed to calculate the similarity between electricity load and the generalized new energy benchmark curve, and a model for the correlation between load and electricity price is constructed. Then, an interactive real-time electricity price mechanism based on source-load similarity is proposed to guide user participation in response. Finally, a multi-objective optimization model is constructed for load optimization. Simulation results show that the proposed interactive real-time electricity price mechanism achieves better load optimization results, effectively increases user participation in demand-side response, and accurately measures the similarity between new energy and load curves. This approach stabilizes the net load power and promotes the high-quality integration of new energy.

Abstract:To eliminate the impact of spatial distribution uncertainty on the accuracy of ultra-short-term forecasting of electric vehicle charging load, a method based on the utilization rate of charging piles for electric vehicle charging load ultra-short-term forecasting is proposed. Firstly, the charging load power of each charging pile within the region is extracted from massive charging transaction data, and then quantified values of the utilization rate of charging piles are obtained through encoding. Then, the utilization rate of charging piles and charging load power data are merged to obtain training samples and test sets for long short-term memory (LSTM) neural networks, forming a deep learning model for ultra-short-term forecasting of electric vehicle charging load, with a time resolution of up to 0.5 h. Finally, the effectiveness and accuracy of the proposed method are validated in scenarios with different scales of charging load. The results indicate that compared to the unoptimized LSTM neural network load forecasting method, the proposed method achieves an increase in the average absolute percentage error of approximately 5%. This can provide significant support for the optimization operation of distribution grids under future vehicle-grid interaction.

Abstract:To address the "peak upon peak" phenomenon caused by unorganized charging of electric vehicles on a large scale, this study divides the distribution network into microgrids for residential, office, and commercial areas based on the location of electric vehicle charging. A multi-objective electric vehicle charging mode is proposed, considering peak-to-valley difference, time-of-use electricity prices, and user satisfaction. A dual-profit multi-objective optimization scheduling model is established to minimize the peak-to-valley difference for microgrid operators while minimizing user charging costs and maximizing charging satisfaction. Real mixed residential, office, and commercial complexes in Shanghai are used as a case study, and the MATLAB/NSGA-Ⅱ algorithm is employed to solve the load shaping degree. The particle swarm optimization algorithm is used to solve the optimal charging satisfaction for electric vehicle owners, guiding the timing and power of electric vehicle charging. Simulation results of the actual case demonstrate that this method effectively reduces the peak-to-valley difference in the distribution network, improves the efficiency of electric vehicle charging, and meets user charging demands.

Abstract:The metro stray current leaked during the train operation causes the surface potential fluctuation, resulting nearby transformer DC bias. Firstly, a equivalent resistance network model was established to realize actual time numerical calculation of stray current. Then, the stray current field equation was further derived, and the dynamic simulation of the geopotential potential distribution was realized during the train operation. Based on the potential distribution caused by stray current and the equivalent DC resistance model of AC power grid, the numerical calculation of DC bias current and excitation current of grounding transformer was realized. The index of neutral point current and waveform distortion rate is established, so the effective evaluation of the transformer DC bias could be realized. The simulation results show that the operating conditions and distance between the metro train have obvious effects on the transformer DC bias, so formulating a reasonable metro operation strategy could effectively restrain transformer DC bias interference for nearby substations.

Abstract:The floating crane supply system usually incorporates large-capacity multiple asynchronous motors. During the starting process of these motors, simultaneous activation can result in substantial current surges. Consequently, the transformer bears a heavy current burden, potentially triggering the false overcurrent protection and leading to further industrial production losses. This paper investigates a protection scheme for transformers supplying multiple high-capacity motors. Firstly, the mechanism of the overcurrent during motor starting is analyzed by an equivalent model. Subsequently, a random scenario generating algorithm is proposed to generate a large number of scenarios for analysis. Furthermore, the Hilbert-Huang transform technique is employed to examine the time-frequency domain characteristics of both fault current and normal current, leading to improvements in the conventional overcurrent protection scheme. Finally, The effectiveness of the proposed protection scheme is validated through a realistic floating crane supply project in Hunan province.

Abstract:The capacitance current to ground in a non-earthing system is increased due to cables, which results in the low-frequency nonlinear oscillation caused by the single-phase earthing fault and hence leads to the fuse of potential transformer (PT), seriously affect the safe operation. The principle of PT fuse caused by low-frequency nonlinear oscillation is analyzed, and a 10 kV neutral point ungrounded system is simulated with ATP-EMTP software. Calculations show that the maximum overvoltage of low-frequency nonlinear oscillation is less than 2.0 p.u.; after 0.1 s of fault occurse, the capacitance relative to the earth increases, the zero-sequence resistance decreases, and the fuse risk increases. Simulation results show the effectiveness of suppression measures for PT with four wires and the neutral point at its high voltage side in series with a varistor. Measurement errors of the zero-sequence voltage are also calculated. Finally, the platform of a 10 kV system for low-frequency nonlinear oscillation tests is built to verify the validity. It is valuable to improve the safety of PT operation in distribution network.

Abstract:It is one of the most important problems in power system reliability to detect the fault types and locations of transmission lines in time and accurately.Th is paper presents an approach for fault identification and location of transmission lines based on convolutional neural networks (CNN) paralled with extreme learning machine (ELM) based on fast Fourier transform (FFT). First, CNN is constructed with fault voltage sequence diagram as input. Then FFT is used to decompose the fault voltage data in time domain and extract the peak voltage and phase angle of each frequency band as fault feature samples. The ELM network is then constructed by taking the extracted fault feature sample set as input. Finally, the two neural networks are fused by the feature fusion layer to output the fault type and location results. Experimental results show that the accuracy of the method is 99.95%, the error of fault location is less than 500 m and the average error is 263.5 m; the reliability of the method is better than other models.

Abstract:When a single-phase-to-ground fault occurs in the small current system, its fault characteristics are easily affected by weak fault conditions such as the high grounding transition resistance and the small initial phase angle. Therefore, this paper presents a method of the single-phase-to-ground fault line selection based on an improved Hilbert?Huang transform-random forest. Firstly, the current transient signals of every lines are extracted. Then the pure transient electrical quantities are extracted by the improved Hilbert?Huang transform, and three kinds of eigenvectors such as standard deviations, energy entropy and amplitude distortion degrees are constructed. In the following, the eigenvectors are input into the random forest classifier to establish a fault line selection model, and the fault line selection problem is then transformed into a binary classification problem which realizing the automatic identification of fault lines. The simulation results show that the proposed method can effectively improve the accuracy of fault line selection by comprehensively using the amplitude, frequency and energy of transient signal; whatsmore it is not affected by weak fault condition and feeder structure, it hence has strong adaptability and reliability.

Abstract:After a fault occurs in the distribution network, active distribution network can realize the real-time power supply and demand balance of user side and meet the demand of load recovery after fault. Users' participation in the dispatching of distribution network is also one of the ways to improve the proportion of load recovery. The translational load has strong controllability, which improves the flexibility of active distribution network. The access of new controllable devices, such as soft open point, also makes it possible for power supply quickly. In this paper, a fault recovery model of active distribution network considering translatable load and soft open point is established. The importance coefficient of load is considered. Through the simulation analysis of the modified IEEE 33-node test system, three scenarios of single fault, multi-point fault and short-term fault are built, the effectiveness of the proposed method and the improvement of load recovery after fault by the controllable device are verified.

Abstract:In order to improve the efficiency of substation electromagnetic environment information collection and the accuracy of data measurement, an intelligent mobile monitoring device is designed and manufactured in this paper. Self-designed power frequency electric field probe can realize synchronous measurement of power frequency electric field in three directions. The power frequency magnetic field acquisition circuit with reset circuit can eliminate the abnormal problem caused by the interference of strong magnetic field to the magneto-resistive sensor. External temperature and humidity module are installed to ensure the accuracy of measurement. When the humidity is less than 70%, the measurement error is within 5%. The third-party test results show that the error of electromagnetic field measurement results is within 5%. The electromagnetic environment measuring results in actual 110 kV substation indicate that, this device can walk and measure accurately following the specified path. A three-dimensional model is established to simulate the spatial electromagnetic field distribution. The measured data and simulation data achieve the same change trend, which further verifies the measuring accuracy of the device.

Abstract:Direct current?based high?power charging (DC?HPC) technology can significantly reduce the charging time of electric vehicles and therefore alleviates concerns about charging duration. However, the rapid temperature increase of charging connectors under high voltage and current poses a challenge, impacting their lifespan and safety. This paper proposes a phase change cooling technology for the high?power super?charging connectors. Through a comparative numerical analysis of the single?phase and phase change cooling performance, the influence of coolant type, flow rate and casing thickness on the thermal behavior of the charging cable with active cooling are investigated. The results indicate that the cable temperature can be reduced to 69 ℃ when using 40 ℃ water as single?phase coolant at a 5 min charging time and a 600 A loading current. In contrast, the two?phase cooling coolant can further decrease the cable temperature to below 40 ℃. The charging cable temperature exhibits a decrease with increasing two?phase coolant flow and an increase with rising coolant sleeve thickness. The coolant sleeve thickness has a more significant effect on cable temperature than the coolant flow rate.

Abstract:Meteorological factors play an important role in the stable operation of transmission lines, which should be considered in risk assessment. This paper presents a method of transmission line meteorological risk early warning and protection. Considering the temporal and spatial power generation forecasting, equipment health and reliability assessment, probabilistic load forecasting and other aspects, a refined meteorological risk model is established, and a new risk measurement standard is proposed based on Meteorological hazards, grid vulnerability and post disaster recovery cost. In addition, aiming at the restoration of load interruption and the alleviation of power congestion, the online warning and defense strategy of meteorological disasters is proposed. Finally, the proposed method is tested and analyzed in the simulation to verify the effectiveness of the method.

Abstract:Aiming at the fluctuation of maintenance and repair costs in substation life cycle cost, a traditional grey model and an improved grey model are adopted respectively, to forecast the maintenance and repair costs of a substation in the next 3 years in order to optimize cost allocation strategy. Simulation results show that the prediction accuracy of both models is one grade; while both the average relative error and the posterior error ratio of the improved model are lower than those of the traditional one. The prediction accuracy of the improved model is hence higher than that of the traditional one, and can be suitable to predict the maintenance and repair costs of a substation. Finally, the improved grey model is used to predict the maintenance and repair costs during 2019 to 2021 of a specified substation in a city.

Abstract:To address the shortcomings of low DC voltage utilization and high insulated gate bipolar transistors (IGBT) loss in the three-phase voltage source inverter applied by the space vector pulse width modulation (SVPWM) drive method with constant switching frequency and rated DC bus voltage, permanent magnet synchronous motor (PMSM) and output cycle-based IGBT loss control model is established, based on which the optimal switching frequency and DC bus voltage for output cycle based IGBT loss are obtained by applying the dwarf mongoose optimization algorithm with the output current quality as the constraint and the switching frequency and DC bus voltage as the constraint variables. The proposed strategy is simulated and experimented to verify that the proposed strategy reduces loss and increases reliability while maintaining control system performance by comparing the output current total harmonic distortion (THD), current waveform and IGBT loss and junction temperature performance.

Abstract:In this paper, the oscillation mode of wind power through DC transmission system is analyzed, and the interaction mechanism between the circulating current zero sequence component of MMC system internal oscillation and capacitor voltage is revealed. The mechanism of the internal oscillation of MMC system caused by the interaction between the zero sequence component of the circulating current and the capacitor voltage of MMC is revealed. The dynamic relationship between the total energy stored by the equivalent capacitance of each arm of the MMC system and the zero-sequence current is constructed. The differential modulus and the common modulus of the total energy stored by the equivalent capacitance of each arm of the MMC system are derived. The zero-sequence current control strategy is proposed. The dynamic model of the control strategy is constructed, and the oscillation mode of MMC system with zero sequence current control strategy is analyzed after considering the intermediate variables of the model. The results show that the proposed control strategy can make the real part of the oscillation mode move left and increase the total damping. The electromagnetic transient simulation model of wind power through DC transmission system with zero sequence current control strategy is constructed. The simulation results show that the proposed strategy greatly increases the damping level of the system, improving the stability.

Abstract:Based on the VIENNA rectifier front-end circuit of the electric vehicle DC charging pile, a small signal method is proposed to model the VIENNA rectifier. This method derives the mathematical model of the VIENNA rectifier in the dq0 coordinate system by employing local linearization techniques and state-space averaging method. It obtains the relevant transfer function matrix, namely the linearized small-signal model of the VIENNA rectifier, which is then utilized for controller design. Subsequently, the voltage loop and current loop are designed separately using frequency domain methods. The effects of various parameters on the system are analyzed in detail using frequency domain methods and root locus plots. Additionally, curves describing the changes in the system's stability region with respect to various parameters are provided through curve fitting. Finally the correctness of the proposed method is verified through the experiments.

Abstract:With the permeability increase of renewable energy, the interaction between power electronic converters and power grid causes oscillation accidents frequently. In order to study the characteristics of oscillation induced by grid-connected new energy sources, this paper explains the mechanism that a single-frequency disturbance voltage as input to a single-phase inverter considering the PLL effect leads to double-frequency disturbance current as output, through the relationship of real number signal and complex vector. The mechanism of frequency coupling in weak power grid is further explained. The models of self-admittance and mutual-admittance are established, and the equivalent output admittance of inverter is then derived. Finally, the correctness of the self-admittance and mutual-admittance models are verified by MATLAB/Simulink. The equivalent output impedance of inverter can be utilized to judge the stability of an interconnected system according to the Nyquist stability criterion.

Abstract:Aiming at the open-circuit fault of the photovoltaic grid-connected three-phase voltage-type inverter,.a fault diagnosis method combining deep cascade mode-principal component analysis (DCM-PCA) and genetic algorithm-optimized BP(GA-BP) neural network is proposed. Firstly, the open-circuit fault of the inverter is analyzed and simulated, the three-phase current is determined as the fault signal, and 22 types of fault states are selected as the diagnosis objects, and the fault features are extracted through the deep cascade model with sparse representation classification as the basic operation unit, the DCM fault features are stratified based on the characteristics of hierarchical learning. The t-SNE method is used to verify that DCM has good feature extraction ability. PCA is used to reduce the redundancy of fault features, retain valuable principal components to improve the network mapping ability. Finally, the fault feature vector is used as the input of the GA-BP neural network to identify the fault and output the diagnosis result. The fault diagnosis accuracy of this method is 95.64% through simulation and experiments, compared with the DCM-PCA-BP, FFT-GA-BP and FFT-BP, the accuracy is increased by 8.71%, 20.64% and 51.70% respectively, indicating that the proposed method has better fault feature extraction capability and better fault diagnosis performance.

Abstract:In response to the phenomena of significant oscillations and incomplete noise reduction when dealing with partial discharge signals using traditional methods, a combined approach based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and tunable Q-factor wavelet transform (TQWT) is adopted to denoise the PD signals. Firstly, the CEEMDAN is employed to decompose the noisy transformer PD signals into multiple intrinsic mode functions (IMF), and the correlation coefficient is utilized to assess the correlation between the IMF components and the original signal. Those with weak correlations are considered inferior IMFs. They are decomposed using TQWT. Energy proportion and kurtosis indicators are utilized to select wavelet sub-bands, extracting effective detailed information from the IMF. Subsequently, inverse transformation of the TQWT is applied to obtain new IMF components. The IMFs with strong correlations are considered high-quality. They are reconstructed together with the transformed new IMF components to obtain the denoising result. Simulation and field signal analysis verify the effectiveness and practicability of the proposed method. Compared to the traditional empirical mode decomposition(EMD) method, the percentage of waveform distortion decreased by 44.94% after denoising simulated signals using the proposed method. Compared to using only CEEMDAN, the noise suppression ratio increases by 26.64% after denoising on-site signals using the proposed method.