A study is conducted on the transient synchronization stability of a hydropower and photovoltaic power generation system transmitted via voltage source converter high voltage direct current (VSC-HVDC) considering current limitations. Firstly, accounting for the current-limiting control strategies of photovoltaic inverters and flexible rectifiers during faults, a mathematical expression for the active power output of hydro-generating units before fault clearance is derived based on Thévenin′s theorem. Secondly, leveraging the equal area criterion, an analytical formula for the critical clearing angle of the transmission system is deduced. This critical clearing angle served as a key indicator for evaluating the transient synchronization stability limit of the system. The influence of parameters such as the saturation current angle of photovoltaic and flexible converter stations, as well as the hydro-photovoltaic power ratio, on the transient stability of the transmission system is systematically assessed. Subsequently, an optimal control parameter tuning scheme is proposed to enhance the system′s transient synchronization stability. Finally, time-domain simulations verify the accuracy of the analysis on factors affecting transient synchronization stability and the effectiveness of the proposed optimal control parameter tuning method.

Parameter variations in the photovoltaic,energy storage,direct current, and flexibility(PEPF) bipolar system will lead to unbalanced voltages between the positive and negative busbars, increasing network losses while threatening system stability. To address this, an imbalance voltage suppression strategy based on fuzzy logic control is proposed. Firstly, the impact of system parameters on inter-pole voltage imbalance is analyzed based on the structure of the photovoltaic storage direct-flexible bipolar system and current balance equations. Then, a fuzzy logic controller is employed to adjust the droop control parameters of each power source converter by collecting data on positive/negative busbar voltages and system voltage imbalance. This process modifies the output voltage and power of each power source converter, ultimately suppressing the inter-pole voltage imbalance of the photovoltaic storage direct-flexible bipolar system within an acceptable range. Simulation tests on the photovoltaic storage direct-flexible bipolar system model validate the effectiveness of the proposed method.

To reduce the impact of insufficient dual-pulse testing caused by uneven current sharing among parallel power devices, a study is conducted on the dual-pulse testing method for parallel power devices. Taking IGBT as an example, the principle of double-pulse testing is investigated, and a theoretical model for the uneven current sharing degree of parallel power devices under modified current loop paths is established and experimentally validated. The results show that modifying the current loop path can reduce the uneven current sharing among parallel power devices, consistent with the theoretical model. At 400 V, the uneven current sharing degree decreases from 43 % to 3.2 %. Under 1 500 V and at 1.2 and 1.4 times current overloads, the uneven current sharing degree is 2.8 %, enabling the successful completion of dual-pulse testing for all power devices and resolving the issue of insufficient testing due to uneven current sharing. Research results provide theoretical and experimental foundations for comprehensive dual- pulse testing of multiple parallel power devices.

In order to promote the construction and development of the new power system and enhance the regulation and storage energy ability of the new power system, this paper explores the support of pumped storage and new energy storage coordinated operation for different regulation demand scenarios of the new power system from the perspective of scheduling operation. Firstly, based on the operational characteristics of pumped storage and new energy storage, the collaborative operation mode is analyzed. Then, the pumped storage and new energy storage collaborative optimization scheduling model is constructed, and 7 types of energy storage application scenarios are constructed based on typical power consumption characteristics and extreme meteorological characteristics. Finally, the system operation effects of the power system under different energy storage development strategies are evaluated from four aspects: calming new energy fluctuations, promoting new energy consumption, enhancing power supply reliability and alleviating system peak load pressure. The results show that when the system is deployed with pumped storage and new energy storage at the same time, the system has better comprehensive operation benefits. The research can provide reference for the rational adjustment of energy storage dispatching operation modes in different regulation demand scenarios of the new power system.

The grid inertia magnitude measures the frequency stability of the system, and accurate prediction of the system inertia level in advance can avoid the risk caused by low inertia. To this end, a multi-algorithm hybrid neural network model based on modal decomposition and feature fusion for short-term prediction of system equivalent inertia is proposed. Firstly, an improved complete ensemble empirical mode decomposition with adaptive noise is used to decompose the inertia of four seasons, and a new sequence is obtained by reconstructing the new inertia based on the fine composite multi-scale fuzzy entropy of each decomposed component. Secondly, the minimum redundancy maximum relevance method is used to measure the correlation between different decomposition components and different features, and a subset of highly correlated and low redundancy features is filtered out. Finally, a bidirectional long-short-term memory network model based on Bayesian optimization algorithm is used to predict different components of different seasonal inertias, and the final prediction results are accumulated. Typical examples at home and abroad are selected for testing, which verify that the proposed method can effectively balance the prediction accuracy and prediction time, and solves the problem of seasonal differences affecting the prediction results of system inertia.

China's electricity market, which is still in the early stage of development, is in dire need of reasonable and effective price risk prevention techniques. Firstly, the SSVM-OR-EW algorithm is used to quantify the dynamic risk coefficients of individual units and the market as a whole. Secondly, the double-insurance risk prevention and control technology of "ex-ante risk prevention & ex-post risk disposal" are studied. A two-layer model of "evolutionary game-optimization clearing" is constructed. According to the dynamic risk coefficients, soften the ex-ante risk prevention, and introduce the theory of caprice to ex-post risk disposal, and construct an expert logic deduction system to further improve the price risk prevention and control. The expert logic deduction system is constructed to further explore the possibility of unit risk and provide a realistic basis for the regulatory agencies to deal with it in accordance with the regulations. Finally, an example analysis is carried out with the data of a regional spot market, and the results show that the preventive system effectively disposes of risks while avoiding the drawbacks of rigid price regulation and realizing closed-loop prevention and control.

Gas-insulated switchgear (GIS) exhibits various insulation defects during production and operation, and accurately identifying partial discharge signals caused by insulation defects is of significant importance for ensuring the safety of GIS equipment and power systems. By integrating the golden sine algorithm (golden-SA) to improve the subtraction-average-based optimizer (SABO), a fused golden sine-improved SABO optimization algorithm (GSABO) is obtained. This algorithm is applied to optimize parameters for the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the kernel extreme learning machine (KELM) to achieve recognition of GIS partial discharge faults. Firstly, to address issues such as SABO possibly falling into local optima and insufficient convergence speed, chaotic mapping and the golden sine are introduced to improve it. Then, an experimental platform is set up to collect four types of typical partial discharge signals, which are decomposed using GSABO-ICEEMDAN, and effective modal components are screened using the correlation coefficient method. Finally, the sample entropy of the selected modal components is calculated to form a feature matrix, which is input into GSABO-KELM for fault classification and recognition. Experimental analysis shows that, compared to the unimproved SABO algorithm, GSABO demonstrates significant advantages in escaping local optima, convergence speed, and accuracy. Compared with other traditional algorithms, GSABO-ICEEMDAN-KELM achieves a recognition accuracy of 99.1667%, verifying the accuracy and superiority of this algorithm, which holds reference value for engineering applications in GIS partial discharge fault diagnosis.

Aiming at the problems of low diagnostic accuracy and easy to fall into local optimization of sparrow search algorithm (SSA) for transformer fault diagnosis, a transformer fault diagnosis model is proposed based on the optimized by sine-cosine and Cauchy mutation sparrow search algorithm (SCSSA). Firstly, based on the dissolved gas analysis (DGA) method in oil, five feature quantities are used as inputs. Secondly, the sparrow algorithm is improved by using the positive cosine strategy and Cauchy variation strategy, and then the performance of SCSSA algorithm, SSA algorithm and grey wolf optimizer (GWO) are compared on four kinds of test functions for performance comparison and verified the superiority of SCSSA algorithm. Finally, SCSSA algorithm is used to optimize the parameters in the BiLSTM network, so as to improve the performance of BiLSTM network in transformer fault diagnosis. The experimental results show that the proposed SCSSA-BiLSTM fault diagnosis model has an integrated diagnostic accuracy of 95.1 %, which is 7.3 %, 12.2 %, 14.6 %, and 19.5 % higher than the SSA-BiLSTM, GWO-BiLSTM, BiLSTM, and LSTM models, respectively, and the SCSSA-BiLSTM model has better robustness.

Noise and various disturbances in the high voltage and strong magneticfield environment directly impact the accurate detection of electrical signals, as well as interfere with the time-frequency characteristics of these signals. To mitigate noise and disturbance components, an adaptive noise reduction method is proposed based on wavelet frequency division threshold and frequency division threshold function. Firstly, following wavelet packet decomposition of the signal, the node coefficients from the final two layers of the wavelet packet tree are arranged in order of frequency magnitude, and the node coefficient thresholds for different frequency bands (frequency division thresholds) are estimated. Secondly, the noise reduction adjustment coefficient is constructed by the ratio of energy of adjacent layer node coefficients, and the frequency division threshold is adaptively optimized. Subsequently, an improved threshold function (frequency division threshold function) is constructed by incorporating the benefits of both soft and hard thresholds, utilizing the noise reduction adjustment coefficient to achieve adaptive adjustment of threshold functions of different nodes. Finally, the denoised signal is obtained by reconstructing the wavelet packet tree. Simulink simulation and noise reduction results of the measured recorded waveform signal under high-voltage strong magnetism show that the method can effectively remove some effects, such as spikes, burrs, etc. The noise reduction effect is significant and the signal waveform restoration is excellent. The method holds certain application value in electrical signal detection.

The hot spot temperature of a transformer is a crucial indicator for assessing its safe operation and load capacity. Under the same operating conditions, a lower hot spot temperature signifies safer transformer operation and greater load-bearing capability. The winding structure of the transformer and the oil channel formed by the baffle plates can significantly influence the hot spot temperature. Therefore, it is necessary to establish a simulation model for the transformer's hot spot temperature. During modeling, oil channels formed by windings and baffle plates of different structures are drawn to investigate the impact of the transformer's internal structure on the hot spot temperature. A parametric modeling method based on SpaceClaim scripting language is proposed, where the transformer's relevant structural and positional parameters are set as variable inputs, and automatic modeling is achieved by running the script, enabling rapid transformer modeling. Based on the proposed parametric modeling method, transformer models with different winding and baffle plate structures are drawn, and the hot spot temperature distribution under various winding and oil channel structures is calculated using the finite volume method. The research findings are of great significance for guiding the design of transformer winding and oil duct structures.

Effectively and accurately portraying the short-term output uncertainty of renewable energy is an important foundation for the stable operation of power systems. In recent years, researchers conduct a lot of studies on generative models, among which flow-based generative models show great potential. However, flow models are rarely used to portray the uncertainty of renewable energy. Therefore, a short-term output scenario generation method of renewable energy based on conditional flow model is proposed. Firstly, a series of invertible functions are trained using real output data to map the probability distribution of the data to a standard Gaussian distribution. After the training is completed, the new output curve can be generated by inputting random noise obeying Gaussian distribution into the trained flow model. The method can be trained directly by maximum likelihood estimation and employs a series of specially designed invertible transformations, which can efficiently compute the training target. In addition, a generated scene evaluation index system is constructed to assess the quality of generated scenes, achieving interpretability of the results generated by the artificial intelligence black box model. The examples verify the effectiveness and sophistication of the proposed method.

Since the power grid emergency plan with black start and system restoration scheme being the core, which is usually very complicate, is always the most critical measure for power companies to cope with the large-scale power grid outage, and thus its effective modeling and fast solving algorithm have been the challenging issue. To focus on the issue, this paper proposes a two-stage robust optimization model and a fast solving new method for system recovery considering the uncertainty of new energy power output (NEPO). Firstly, a two-stage robust optimization model that considers the uncertainty of NEPO and the constraints of AC power flow is constructed. Then, the low nonlinear transformation of power flow equation combined with second-order cone relaxation technique is proposed to transform the mixed integer nonlinear optimization model into the mixed integer second-order cone optimization model. Finally, the column and constraint generation algorithm is used to solve the problem. The effectiveness and advancement of the proposed model and algorithm are verified by a numerical example.

Weakly connected microgrid lacks terrestrial grid support, resulting in unstable system power flow dynamics and low fault current levels, and leads to complicate protection design. To address this, virtual leakage current is constructed using node voltage information to reveal the characteristics of line-end node virtual leakage current under normal disturbances, internal fault and external fault conditions. Based on this, a fault section detection criterion and fault distance calculation model are established, a fault detection and location method suitable for weakly connected microgrid is proposed. Finally, simulation results demonstrate that the proposed method accurately identifies faulted lines and phases under varying fault resistances, locations, and types, with fault location errors between main grid lines and microgrids interconnected lines remaining below 5 %.

A novel task allocation and path planning algorithm with maximum endurance constraints is proposed for the problem of multi-UAV task allocation and path planning in electric power inspection. Firstly, the jump point search （JPS) algorithm is introduced based on the auction mechanism to estimate the shortest obstacle avoidance path lengths between UAVs and tasks and between tasks and tasks, effectively addressing the coupling problem of task allocation and path planning. Secondly, the maximum endurance constraint of UAVs is integrated into the optimization objective function to ensure that UAVs can complete the assigned inspection tasks within their maximum endurance capacities. Finally, a lazy auction strategy is proposed to speed up the algorithm's convergence without compromising the solution quality of task allocation. The simulations show that the proposed method is more suitable for the operational requirements of multirotor UAVs with limited endurance than existing methods. Moreover, the time needed to solve the task allocation is reduced by nearly 40%. The experiments demonstrate the practicality and effectiveness of the proposed method.