Due to the widespread use of traditional droop control in DC microgrids, the system suffers from rapid DC bus voltage changes, oscillations and large excursions during constant power load disturbances, which is detrimental to the proper operation of voltage-sensitive loads. To solve the above problems, the paper proposes an integrated control strategy for the DC bus voltage, consisting of the improved adaptive virtual capacitor control （IAVCC）, oscillation suppressor and voltage compensator. In particular, IAVCC adaptively adjusts the virtual capacitor according to the bus voltage change rate during load disturbance, enhancing system inertia of DC microgrid to slow bus voltage changes and improve system dynamic characteristics. On this basis, the oscillation suppressor significantly damps out the voltage oscillation by filtering out the high-frequency oscillation component of the bus voltage. In addition, the voltage compensator enables deviation-free regulation of the bus voltage, solving the problem of severe bus voltage dips when the load power increases. The proposed integrated control strategy achieves optimization of the dynamic characteristics of the DC bus voltage, oscillation suppression, and deviation-free regulation. Finally, the feasibility of the proposed strategy is proven by RT-LAB-based experiments.

The power electronic transformer （PET） based on modular multilevel converter （MMC） is prone to failures and disturbances under non-ideal operating conditions, which seriously affects the system power quality. Aiming at the poor dynamic and steady state performance of traditional control methods under non-ideal operating conditions, a continuous control set model predictive control strategy based on self-adaptive auto-disturbance rejection proportional integral controller is proposed in the MMC-PET rectifier stage. Firstly, a self-adaptive auto-disturbance rejection proportional integral controller is designed to solve the problems of poor signal tracking and disturbance rejection in the voltage outer loop. Secondly, the continuous control set model predictive control method is used in the current inner loop to improve the response speed and steady state performance of the system, and an improved carrier phase-shift modulation strategy is introduced to solve the problem of bridge arm current distortion. Finally, the MMC-PET system is compared by simulation and experiment under non-ideal operating conditions, such as sudden change of loads on the grid side, unbalanced grid voltage, and load input at the output stage, to verify the superiority of the proposed control strategy.

The sensitivity of tunnel magnetoresistance (TMR） current sensor applied in smart power grid is easy to change under the influence of ambient temperature, which will seriously affect the measurement accuracy of TMR current sensor. Based on the basic measurement principle of TMR current sensor, the sensitivity of the sensor chip is analyzed theoretically. Adding a periodic injection DC self-calibration loop below the sensor, the average value of the difference between the output voltage of the sensor before and after injection can be calculated, and the real value of the sensitivity under the current ambient temperature can be tracked and calibrated in real time, and the closed-loop operational control circuit is designed to adjust the sensor in real time. Finally, the DC injection experiment, temperature control experiment and linearity experiment are carried out on the experimental testing platform. The test results prove that bypass self-calibration technology can improve the accuracy and stability of open-loop TMR current sensor measurement.

To study the nanosecond pulsed breakdown process of nitrogen spark switch at atmospheric pressure, the discharge process in the gap is simulated using the particle-in-cell method and the transient physical images of the formation and propagation process of the streamer are obtained. The effect of the pulse voltage rise time on the discharge processes in the gap are comparatively analyzed. The results obtained are as follows. Firstly, the nanosecond pulsed breakdown process of the switch includes the formation and fast propagation of the streamer. Secondly, runaway electrons may be generated in the streamer head and the branches of the streamer channel may be formed due to the photoionization during the fast propagation stage of the streamer. Thirdly, the average propagation velocity of the streamer channel during the streamer fast propagation stage is larger than that during the streamer formation stage. Finally, for the increasing rise time of pulse voltage, the propagation velocity of streamer becomes smaller, the breakdown voltage becomes lower, and the breakdown time delay of the switch becomes longer, which is consistent with the measurement results.

With the widespread application of gas spark switches, choosing gas spark switches with stable operation and long service life has become an important guarantee for the stable operation of pulse power systems.At present, domestic and foreign scholars have conducted a large amount of research on gas switches, but most of them are based on the impact of discharge conditions on the ablation of gas spark switches.Therefore, starting from practical engineering needs, this paper comprehensively studies the distribution of self breakdown voltage, time delay jitter, and dispersion of gas spark switches under different working environments, as well as the phenomenon and mechanism of electrode ablation, as well as the change laws in macro/micro roughness.The results show that under the same gas pressure conditions, the dispersion of the breakdown voltage of the switch does not change significantly with the increase of electrode gap.As the working coefficient increases to 90%, the average value of the switch discharge delay remains basically unchanged, but shows fluctuations of several nanoseconds. When the gap distance is 10 mm and the working coefficient is below 60%, the initial value of jitter and its reduction rate are much higher than other gaps.As the electrode spacing increases, the impact on the ablation of the electrode surface is relatively small, and the ablation degree of long gaps at low gas pressure is more significant than that of short gaps at high gas pressure.

Aiming at the problems of background interference, low image resolution, and large scale variations of foreign objects in the detection of foreign objects on power transmission lines, a foreign object detection model of power transmission line based on improved YOLOv7 is proposed. Firstly, a new backbone network is constructed through space to depth conconvolution(SPD-Conv) and multidimensional collaborative attention(MCA) mechanism to enhance the model's ability to extract features from low-resolution images and to suppress background interference, thus the attention to small foreign objects is increased. Secondly, the output part of the efficient layer aggregation network(ELAN) module is improved by using ghost convolution (Ghost-Conv) to significantly reduce the model's computational complexity. Finally, based on the scalable intersection over union(SIoU) optimized loss function, the model's training speed and robustness are further improved. Experimental results show that the proposed model achieves a mean average precision (mAP) of 95.98% on the power transmission line foreign object detection dataset, which is higher than other mainstream comparative models. At the same time, the frames per second (FPS) reaches 64 to meet the real-time detection requirements of foreign objects of power transmission lines.

Transformer bushings account for a small proportion in infrared inspection images, and the characteristics of thermal defects are not obvious. Manual detection of thermal defects in bushings is easily affected by subjective judgments, and it is difficult to cope with the massive infrared images generated during inspections. In order to improve the detection efficiency of bushing thermal defects, a detection method for transformer bushing thermal defects combined with target detection algorithm and image skew correction is proposed. Firstly, the YOLOv7 object detection model is used to identify and locate bushing targets. The SimAM attention mechanism and efficient decoupled head are introduced to optimize the model, improving the recognition accuracy and recall rate of bushing targets. Then, image skew correction is performed on the positioned and cropped bushing target, and temperature feature information in the central area is extracted for thermal defect diagnosis. The experimental results show that the improved model has an accuracy rate of 95.50% for bushing target recognition, a recall rate of 97.14%, an average accuracy of 98.30%, and a detection FPS of 42 frames per second. The proposed method can accurately locate bushing targets and extract corresponding temperature curves, effectively improving the efficiency of bushing thermal defect detection.

To ensure the stability of units and systems under the new power system, a design method of interconnection and damping assignment passivity-based controller for steam turbine speed control system is proposed. Firstly, based on the theory of Hamiltonian system, the dynamic process of power system is described from the perspective of energy, and the influence of the structure of Hamiltonian system on the system energy is analyzed. Then, the variable gradient method is used to construct the system energy function including the speed control system. The damping matrix of the system is determined with the constraint of minimizing the derivative of injected energy. The difficulty of determining the system damping matrix in the design process of the interconnection and damping assignment passivity-based controller is overcome, and the design of the interconnection and damping assignment passivity-based controller is completed. Finally, the proposed method is used to build a model of single-machine infinity bus system in MATLAB/Simulink, and the effectiveness of the controller designed in this paper is verified.

Due to the complex trading scenarios and rich trading modes of blockchain, blockchain transactions are frequently threatened by illegal behaviors such as anonymous attacks, Ponzi schemes, phishing attacks, etc. These abnormal behaviors cause huge economic risks to the development of smart grid based on blockchain technology. Aiming at the problem of poor comprehensive performance of blockchain abnormal transaction detection, the characteristics of high data dimensions and imbalanced positive and negative samples of transaction data are analyzed. A blockchain abnormal transactions detection method based on deep principal component analysis （PCA） and Bayesian optimization is proposed. By designing the deep PCA model, linear and nonlinear dimensionality reduction of blockchain transaction data is achieved. The Bayesian optimization is employed to optimize the random forest hyper-parameters, and the optimized random forest classifier is utilized to effectively solve the problem of unbalanced positive and negative samples. And finally abnormal transactions are detected in the blockchain. The experiments based on Elliptic and power grid blockchain transaction datasets show that the proposed method improves the comprehensive performance of blockchain abnormal transaction detection.

The technical standard system is one of the core elements of the foundation of energy and electric power industry. To promote the development of new power system construction standard and to meet the demand of power system construction, the overall framework of the technical standard system for the new power system is studied considering the development direction and actual demand. Considering the national and industrial requirements on the preparation of technical standard system, the key technical areas involved in the new power system are examined from a strategic perspective. Existing standards and related standard systems are analyzed and selected from the perspective of adaptability. A flexible and extensible conceptual model of the new power system technical standard system is presented, which includes four parts: basic generality, physical basis, information support and value enhancement. The architecture is constructed which consists of four hierarchies, 14 technical domains, 138 categories, and several specific standards. On this basis, the future requirements of the standard system in all fields are analyzed, and the future standard layout planning suggestions are put forward.

In order to meet the demand for rapid frequency and voltage regulation of micro grid with high proportion of new energy, based on the understanding and recognition of the frequency/voltage dynamics of the new power supply system with high proportion of power electronic equipment, this paper introduces an amplitude-phase-locked-loop structure, and proposes a fast frequency/voltage support method for energy storage based on this structure to participate in the rapid frequency and voltage regulation of isolated micro grid. In the proposed method, the amplitude-phase-locked-loop can provide the frequency/voltage deviation and its differential signal of the power grid. The frequency and voltage regulation method is designed using these four signals to avoid the complex differential operation under the traditional control, while improving the dynamic support performance of the system, so that the energy storage can spontaneously respond to the changes of the system frequency and voltage. Finally, a micro grid model is built in RTDS to verify the effectiveness of the proposed control strategy.

Due to the randomness of renewable energy generation and the time series coupling characteristics of energy storage systems, it is necessary to properly model uncertain variables and develop optimization algorithms that can efficiently handle multi-objective problems when constructing economic scheduling models for microgrids. In this context, an efficient multi-time scale dispatching method for microgrids based on deep reinforcement learning and heuristic algorithms is proposed, which can take into account uncertain factors and achieve economic and environmental protection operation. The proposed method optimizes the microgrid from two time scales: day-ahead and intra-day. The day-ahead optimization phase utilizes short-term forecast data for initial decision making to minimize the operating cost. For the intra-day dispatching phase, it utilizes the day-ahead optimization scheme as a reference and revises the day-ahead operation scheme if necessary to cope with the real-time fluctuations of renewable energy. The process of intra-day optimization is decoupled into global and local two phases, the global stage is modeled as a non-convex nonlinear optimization problem and solved using heuristic algorithms, while the local stage is modeled as a Markov decision process and solved using deep reinforcement learning methods. Combining deep reinforcement learning with heuristic algorithms improves the training speed and convergence performance of reinforcement learning, avoiding the difficulty of designing reward functions in complex environments. Finally, the case analysis verifies that the proposed scheme achieves optimization of scheduling cost and computing speed, and is suitable for real-time scheduling of microgrids.

Aiming at the problems that the integrated energy system （IES） is difficult to optimize and scheduling due to the wide variety of equipment, and the utilization efficiency of comprehensive energy is low, an integrated energy system optimization scheduling Model based on generalized energy storage （GES） is proposed. Firstly, GES models of various loads are established, which simplify the information interaction between energy scheduling and consumption. Then, based on the GES model, day-ahead and intra-day optimal scheduling models are established to provide an effective way for IES energy flow analysis and optimization. Finally, IES simulation testing system is built which verifies the effectiveness of the proposed model. The simulation test results show that the optimization model can fully tap into the scheduling potential of the load, relieve the pressure of energy scheduling supply and demand balance, reduce the cost of power system, reduce the time for collaborative analysis and calculation, and improve the calculation efficiency and accuracy.

The safety assessment method of wind power system based on machine learning has become a hot spot at present, but the influence of sample noise is not fully considered. It is difficult to ensure the accuracy and reliability of system transient voltage stability assessment. In this paper, a noisy input multi-class Gaussian process （NIMGP） is constructed, which introduces a sparse Gaussian process and selects induction points instead of some original input points for training to reduce the complexity of model calculation. Secondly, additive Gaussian noise is introduced into the input data in the model to achieve anti-noise processing, and the Gaussian process with noisy input is approximated by Taylor series method, so that the input noise is converted into output noise and the model evaluation performance is improved. Finally, simulation is carried out in a New England 39-bus system with wind farms, including the stabilization of the transient voltage of the system, critical and unstable states and the stability margin of the stable sample for prediction. The comparison of simulation results under various working conditions show that NIMGP has strong generalization ability and good prediction accuracy under different working conditions.

With the increasing penetration of distributed power in distribution network, the mutual demand between transmission network and distribution network is increasing. Considering the issue of insufficient scheduling and allocation of resources to meet the regional autonomy needs in the optimization of active distribution network （ADN） within the framework of transmission and distribution networks collaboration, a multi-objective hierarchical active optimization model of ADN considering bidirectional cooperation of transmission and distribution networks is proposed. The model aims to improve the economy and safety of the overall operation of the transmission and distribution networks, with the power of the tie line as the coupling variables. The output of the thermal power units in the transmission network is adjusted through unit commitment, and the topology structure is configured with distribution network reconfiguration （DNR） as the main strategy to determine the scheduling strategy that supports the optimal operation mode of the system. Based on analytical target cascading （ATC） method, the multi-objective main and sub problems of the active distribution network with bidirectional cooperation of transmission and distribution networks are decoupled and solved hierarchically. Finally, the T6D2 and T118D5 test systems are taken as examples to verify the effectiveness of the proposed method. The results show that the model can deal with the phenomenon of “reciprocating flow”between transmission and distribution networks well, and improve the renewable energy consumption rate of the system, so that the transmission and distribution networks can obtain the maximum economic benefit.

Urban buildings are places that effectively promote the conservation and rational use of energy resources, which are conducive to accelerate the development of a circular low-carbon economy. A multi-objective optimization model for intelligent building load optimization from two aspects of economy and acceptability is proposed. Firstly, time/load-related indicators are established to describe the users′ acceptance of participating in load regulation for the transferable and reducible load. Then, with the goal of minimizing the peak-to-valley load difference in the statistical period, achieving the highest acceptability, and minimizing electricity costs, the building load combined mathematical model is established. Finally, it is proposed to solve the model based on continuous Hopfield neural network（CHNN）. The analysis of the calculation example shows that the multi-objective optimization model can effectively reduce the electricity cost and ensure the acceptability of electricity.