High voltage direct current transmission plays a crucial role in the coordinated allocation of power resources in China, and ensuring its stable operation is of great significance. At present, the research hotspot on small disturbance stability of high voltage direct current systems mainly focuses on the modeling of inverters, while the modeling of transmission lines only uses centralized parameters for equivalence. However, centralized parameter models cannot accurately reflect the harmonic transmission characteristics of transmission lines in the high-frequency range. Firstly a distributed parameter model for overhead lines that considers frequency variation characteristics is proposed，which is simplified by eliminating ground wire effects and inter pole coupling effects. Then, the model is applied to the AC side admittance model of high-voltage DC systems. Finally, the correctness and effectiveness of the proposed model is verified on the PSCAD/EMTDC simulation experimental platform.

At the national level, the Electromagnetic Pulse Committee has been established and the strong electromagnetic pulse defense plan has been actively promoted. China Southern Power Grid attaches great importance to the risk of power grid equipments being attacked by strong electromagnetic pulse. In order to strengthen the security defense of power grid and improve the reliability of power supply, it is urgent to establish strong electromagnetic pulse test platform to carry out relevant experimental research. Firstly, the functional positioning and general layout of the strong electromagnetic pulse test platform have been studied. Then, research and construction have been carried out from field power consumption and test power supply, electromagnetic shielding chamber and control room, substation equipment and transmission line, ground network design, fire protection and water supply. Finally, field debugging and electromagnetic environment testing are carried out. The strong electromagnetic pulse platform for power grid equipment is established for the first time in China, fully meeting the needs of various tests of strong electromagnetic pulse and providing platform support for research on the generation and propagation characteristics of strong electromagnetic pulse, the influence law on power grid main equipment and control & protection equipment, and the research and development of defense devices.

In order to solve the problem of low accuracy of fault diagnosis of oil-immersed transformers, a transformer fault diagnosis method of kernel principal component analysis (KPCA) with improved pelican optimization algorithm (IPOA) optimized least squares support vector machine (LSSVM) is proposed. Firstly, KPCA is used to extract features from multidimensional transformer fault data, reducing computational complexity. Secondly, logistic chaotic mapping, adaptive weight strategy, and lens imaging reverse learning strategy are introduced to improve the pelican optimization algorithm (POA). Finally, the KPCA-IPOA-LSSVM fault diagnostic model is established, and the diagnostic accuracy is 94.24%. Compared with the PCA-IPOA-SVM, KPCA-IPOA-SVM, KPCA-WOA-LSSVM, and KPCA-POA-LSSVM fault diagnostic models, the accuracy is improved respectively by 18.31%, 11.53%, 11.87%, 7.46%. The results show that the transformer fault diagnosis model proposed in this paper effectively improves the accuracy of fault diagnosis, proving that the diagnostic model has certain significance in theoretical research and practical engineering application.

To accurately identify abnormal conditions in gas insulated switchgear (GIS) equipment, a voiceprint recognition algorithm is proposed based on weighted Mel frequency cestrum coefficient-one class support vector machine (MFCC-OCSVM) and Bayesian optimized bidirectional gate recurrent unit (BiGRU). Firstly, weighted extractions of voiceprint data are performed using MFCC based on the F-statistic, highlighting important features and reducing the influence of noise. Subsequently, OCSVM is utilized to detect anomalies and remove anomalous values from the weighted features to improve data quality. To address the issue of sample imbalance, synthetic minority over-sampling technique (SMOTE) is employed to balance voiceprint samples. Finally, voiceprint recognition is carried out using a BiGRU model based on Bayesian optimization. Taking a certain GIS equipment as an example, sound samples from 20 different operating conditions are collected and compared with various classical classification models. The results demonstrate that the proposed algorithm achieves the highest average recognition accuracy of 92.8%, resulting in improvements of 30.1%, 14.7% and 11.5% compared to adaptive boosting, Naïve Bayes, and linear discriminate analysis, respectively. Ablation study further assesses and validates the practical effects and performance impacts of each process in the proposed algorithm. Research results provide an efficient technical approach for voiceprint recognition of anomalous conditions in GIS.

With the increasing scale of distributed photovoltaic connected to the distribution network, conducting research on distributed photovoltaic siting and sizing is of great significance for improving the economy and security of the distribution network. Currently, most research on photovoltaic siting and sizing planning adopts conventional single period programming method for problem modeling, ignoring the medium- and long-term loads growth property of the distribution network, which will have a negative impact on the actual application effect of the model. Therefore, a bi-level multi-stage programming model for multi-objective photovoltaic siting and sizing planning that considers load classification growth is proposed. Firstly, the dynamic time warping （DTW） and LightGBM algorithm are combined to achieve classification and prediction of medium- and long-term loads. Then, a two-stage dynamic planning model is constructed for multi-objective photovoltaic siting and sizing planning considering load classification growth, and the multi-objective bi-level crisscross optimization algorithm （MOBL-CSO） is introduced for solving. The experimental results in the modified IEEE 33-bus system indicate that the proposed scheme can comprehensively consider the load changes throughout the entire planning period for optimization, and can obtain a photovoltaic siting and sizing planning scheme that is more long-term safe and economic compared to traditional methods.

Scenarios sequence generation is the basis of scenario analysis and optimization problem, and its accuracy directly affects the effectiveness of related analysis and optimization calculation. Based on this, a new method of generating new energy output scenarios with load level constraints based on artificial intelligence is proposed, significantly improving the accuracy of the generated scenarios and overcoming the difficulties without effective methods faced by the dispatch center. The process of the new method is as follows: firstly, the historical data expansion strategy based on Kalman gain information fusion technology is adopted，realizing the effective data expansion of corresponding historical samples. Then, based on the self-organizing mapping network, the historical daily load curves are clustered to obtain multiple daily load type clusters, and each of the new energy output sequences on the same day as the daily load curves within each load cluster is corresponding classified into clusters, and the source load clusters with the specific load level constraint are achieved. Finally, a state transfer matrix based on Markov chain （MC） daily type transformation relationship is constructed, and then a sequence of daily types in the near future is generated by rolling sampling. And a generative adversarial network （GAN） already trained on each of source-load cluster data is applied to generate 96-point source-load scenarios sequences for the corresponding daily types. The numerical experiments verify the effectiveness and advancement of the proposed method.

The application of data-driven models for rapid static security analysis in new power system is a research area worth exploring. Enhancing the generalization ability of data-driven models to operation condition changes and the adaptability to power system topology variations is one of the key technical challenges. A graph learning model for static security analysis of the power system based on power flow embedding and the min-cut pooling is proposed. At first, a power flow embedding module directed by the node voltage restoration is designed to improve the model's generalization ability, which converts the topological differences in N-1 contingency scenarios into node feature differences. Secondly, based on the concept of community partitioning, a min-cut pooling technology is employed to dynamically reduce node scale and node feature dimensions, which enables the model to adapt to topological changes. Verification tests and visualization analyses conducted on IEEE 39-bus and IEEE 118-bus systems demonstrate that the model can achieve high accuracy, second-level evaluation speed, and good adaptability to variation of the power grid scale and topology.

False data injection attacks disrupt the stability of power systems by tampering with the data collected by data acquisition and monitoring control systems. Traditional methods for detecting false data injection attacks are unable to locate the attacked location or have low accuracy. Firstly, an improved method for detecting false data injection attacks using seagull optimized convolutional neural networks is proposed. The proposed method uses a convolutional neural network with shared weights and local connectivity to efficiently extract and classify features from high-dimensional historical measurement data. Secondly, an improved seagull optimization algorithm with balanced global and local search capabilities is introduced to perform hyperparametric optimization to obtain a highly matched network structure for false data detection. The network structure is then used to detect and locate bad data. Finally, the effectiveness of the proposed method is verified through extensive attack detection experiments on IEEE-14 and IEEE-57 node systems, and compared with various other detection methods to verify that the proposed method has better classification performance, higher accuracy, precision, recall, and F1 value.

With the increasing penetration rate of power electronic equipment in power grid, the resonance phenomenon of power grid is becoming more and more serious, which seriously affects the normal operation of power equipment and the safe operation of power system. A resonance frequency sensitivity analysis method for power grid based on singular value decomposition (SVD) is proposed to address the limited applicability of existing modal frequency sensitivity calculation methods. The basis and process of the proposed method are given in detail. The non-homogeneous formula is used to solve the key variable expression, which avoids the influence of frequency resolution, improves the calculation accuracy, and effectively overcomes the deficiency that the modal frequency sensitivity cannot analyze the characteristics of branch current resonance. Numerical examples analysis shows that the proposed method can analyze the influence of network component parameters on the resonant frequency and quantize the influence of component parameters on the resonant frequency in two cases of low frequency and medium-high frequency resonance.

Aiming at the problems of low efficiency and high cost of multi-target point path planning for robotic arms in complex transmission environments, the improved artificial potential field-informed rapidly-exploring random trees star (IAPF-IRRT*) algorithm is proposed to enhance path planning performance. Firstly, a cuboid repulsive field model is introduced to improve the spherical repulsive field model in traditional artificial potential field, and a repulsive field is established for complex obstacles in the transmission environment. Then, ellipsoidal regions are adopted with uniformly distributed positions to enhance the ellipsoidal regions in the IAPF-IRRT* algorithm. Local redundancies are avoided in sampling points in complex transmission environments and search efficiency is improved. Finally, the redundant nodes in the path are optimized by using a triangular optimization method and the path is smoothed by using cubic spline interpolation. Validation is conducted on three sets of obstacle maps of different complexity: simple 3D, complex 3D, and complex transmission environments. The results show that the time efficiency of IAPF-IRRT* algorithms compared with the standard RRT and RRT* algorithms is improved by 44.8%~83.8%, 68.3%~95.2%, and 26.5%~71.8%, respectively. The cost of the path is reduced by 15.5%~35.0%, 14.1%~35.3%, and 31.5%~43.5%, respectively. The number of nodes in the path is reduced by 75.6%~78.8%, 75.0%~78.0%, and 70.4%~72.0%, respectively.

With the promotion of "Internet +" construction, as an important application of power Internet of Things （IoT） construction, multi-station integration needs to study its modular configuration scheme suitable for different regions. Considering the life cycle attenuation of energy storage and the economic requirements of multi-station integration operators, this paper proposes a bilevel stochastic programming model considering the operation characteristics of each functional substation. Firstly, the output or load model under normal operation is determined according to the operation characteristics of functional substations that need to be fused. Secondly, considering the uncertainty of source load during long-term operation, the corresponding uncertainty characterization and processing method are adopted according to the load characteristics. The two-layer stochastic optimization model is used to solve the optimal configuration model in a long-time scale, and the operation results in the whole life cycle are equivalent to the expected values of several typical operation scenarios. Finally, the model is validated in a typical 220 kV substation scenario.

In order to reduce the curtailment of renewable energy, firstly the operating characteristics of the high proportion renewable energy power system is analyzed, and a flexibility demand model of the power system is proposed for units maintenance. Then, the differences in the adjustment ability of different units are analyzed, and the concept of unit flexibility is put forward. Considering comprehensively the adjustable capacity, minimum running time and outage time, operating cost and other indicators of the unit, the flexibility supply model of the unit is established. Based on the principle of equal flexibility of the power system at all times of the year, a unit maintenance model is established considering flexibility, and a unit maintenance strategy is formed based on the equal flexibility method. The results show that compared with the unit maintenance strategy of the currently widely used equal reserve method, the maintenance strategy of the equal flexibility method proposed can significantly reduce the annual renewable energy curtailment and increase the renewable energy consumption rate, which verifies the effectiveness of the model and method proposed.

Aiming at the capacity optimization configuration method for the hybrid energy storage system （HESS） in microgrid, based on an improved complete ensemble empirical mode decomposition with adaptive noise （ICEEMDAN）, is proposed to address the issue of power fluctuation in the tie-line caused by the variability of renewable energy output and electricity load in grid-connected microgrids. The method decomposes the unbalanced power signals in the microgrid into high-frequency and low-frequency components, achieving power signal reconstruction. Considering the technical characteristics of different energy storage systems, sodium-sulfur batteries are used to suppress low-frequency components, and supercapacitors are used to suppress high-frequency components. Furthermore, by establishing a HESS capacity optimization configuration model aiming at minimizing initial investment and maintenance costs, the mixed energy storage configuration scheme is solved using the commercial solver GUROBI. A case study based on a certain grid-connected microgrid shows that the configured HESS can effectively smooth out the power fluctuation of the microgrid's tie-line, and the method demonstrates good economic performance. The effectiveness and feasibility of the proposed method are thus validated.

Most of the interconnecting converters in island hybrid microgrids are grid-following converters, which change their control strategies to switch operation modes by means of remote communication order. When the system operation mode suddenly switches and communication failure or delay happens, the grid-following converters cannot quickly change the control strategies, resulting in voltage and frequency collapse. To solve the above problems, a new type of interconnected converter called grid-forming converter is introduced, which consists of a pair of back-to-back DC/DC and DC/AC converters. The grid-forming converter only needs local communication, and can complete seamless switching of operation modes by measuring and controlling the DC voltage on the DC-DC side, the voltage of interconnected capacitors and the AC frequency on the DC-AC side, and improves the resilience of hybrid microgrids operation. The small size magnitude drop of voltage or frequency will happen after microgrids operation modes switching due to the grid-forming converter control strategy supported by photovoltaic (PV) generations. So a coordinated control strategy between the grid-forming converter and the virtual DC motor energy storage is proposed. The simulation results show that the frequency of AC side and the voltage of DC side can be quickly stabilized at the rated value after the sudden failure of either side of AC/DC voltage sources in isolated island hybrid microgrids under the synergistic action of the grid-forming converter and virtual DC motor energy storage.

With the large-scale integration of photovoltaic (PV) into the distribution network, energy storage (ES), as a flexible and economically controllable resource, becomes an important choice of investment for distribution network planning to smooth out the fluctuation of PV generation and improve the economic efficiency of distribution network operation. The traditional methods identify the optimal allocation ratio of PV and ES in distribution network planning and analyze the economic benefits of distribution network planning by solving a large number of optimal distribution network planning problems. However, these methods are computationally inefficient and cannot explicitly quantify the impact of ES capacity on the overall economic efficiency, making it difficult to guide distribution network planning. Therefore, an explicit characterization and quantitative analysis method for the optimal allocation ratio of photovoltaic and energy storage for distribution network planning is proposed. First, a distribution network planning model is constructed, which contains constraints of PV generation and physical operating characteristics of ES integrated into the distribution network and aims at the highest overall economic efficiency of PV and ES planning in the distribution network; on the basis of this, the model is converted into convex by pre-solving method. Then, based on the multi-parameter planning method with PV capacity as the planning parameter, the analytical expressions between the optimal planning capacity of ES and the PV capacity of distribution network, the cost of distribution network planning and the PV capacity of distribution network, are constructed. Finally, based on the analytical expressions, the optimal ES capacity under different PV capacities and the mechanism of optimal allocation ratio of PV and ES affecting the economic efficiency of distribution network planning are analyzed, which can assist the establishment of distribution network planning schemes. The results of the case study in PJM 5-bus system and IEEE 141-bus system verify the effectiveness of the proposed method.