Aiming at the problem that there are a large number of horizontal or vertical distribution outliers in the wind speedpower data collected by SCADA system when wind turbine is in abnormal operation, an abnormal data processing method based on median absolute deviation method (MADM) and quartile method (QM) is proposed to solve it, namely MADM –QM algorithm. Firstly, based on the relationship model of wind speedpitch angle, the wind speedpitch angle data outside of ±4.5 MAD are discarded by solving the median absolute deviation (MAD) in the wind speedpitch angle data set of the wind speed interval. Secondly, based on the wind speedpower relationship model, the abnormal values in the wind speedpower data set of the power interval are eliminated, and then the abnormal values in the wind speedpower data set of the wind speed interval are eliminated to complete the abnormal data processing. Finally, the actual operation data of wind turbine under complex working conditions of a wind farm are taken as examples for verification, and comparison with MADM, QM and densitybased spatial clustering (DBSCAN) method. The results indicate that the proposed method can not only effectively identify abnormal data but also efficiently and stably clean them. Compared with the other three methods, to a certain extent, it proves that MADMQM can achieve good efficiency of abnormal data processing and optimal cleaning quality on the abnormal data.

Due to the wide variety of wind farm equipment and complex operating environment, it is usually unattended and difficult to find faults in time. The traditional inspection method takes a long time and has low identification accuracy. As a result, the fault is not handled in time, which affects the stable operation and power generation efficiency of wind farms. Therefore, a robot centralized inspection scheme based on improved pattern recognition is proposed for unattended wind farm groups. For transformer faults, equipment temperature anomalies and gearbox sound anomalies in wind farms, BP neural network algorithm, fuzzy pattern recognition algorithm and empirical mode decomposition algorithm are used to carry out inspection, and the proposed method is tested experimentally in a large wind power station. The results show that the proposed method can realize the inspection of various faults in wind farms. The first time to obtain the fault signal, to avoid the occurrence of security accidents; The recognition accuracy rate remains above 92.3%, and the recall rate and F1 score are also better than the comparison method, indicating that the proposed method is more comprehensive in identifying fault samples and can detect faults more effectively.

SeriesResonant ThreePortConverter (SRTPC) applies the traditional phase shifting control strategy when the port voltage mismatch which has the problems of large reflow power and small soft switching range, and this paper will propose a reflux power optimization method based on the phaseshifting plus duty cycle (PWM) control strategy. The complex power model of SRTPC is given by fundamental analysis method and phasor method, and the optimal control strategy of the converter when the reactive power is zero is given and the optimal control variable is solved through the analysis of the SRTPC reactive power (reflux power) model under the premise of ensuring the transmission of certain active power. Furthermore, the conditions for realizing soft switching under the optimized control strategy are further analyzed, and the soft switching range under the two control modes is compared. Finally, the Matlab/Simulink simulation results show that compared with the traditional phase shift control strategy, the SRTPC reflow power under the optimized control strategy has a smaller reflux power, a wider soft switching range, and higher efficiency under the condition of port voltage mismatch.

With the development of distributed generation technology on the user side, there is an urgent need to improve the reliability and economy of community power consumption. This paper introduces a community operator to manage the energy of the community and constructs an energy trading model centered on the community operator with energy storage devices. Firstly, considering the shortcomings of the existing pricing mechanism, an improved supplydemand ratio pricing mechanism is proposed to promote energy sharing in the community. Then, by coordinating energy storage devices and considering their loss costs, an online energy scheduling algorithm with low complexity is proposed based on the improved Lyapunov optimization method to maximize the revenue of the community operator under the premise of meeting the power consumption demand of the community. Theoretical analysis results show that the proposed algorithm can achieve the asymptotically optimal value of the optimization objective based only on the current system state, without the need for prior statistical knowledge of photovoltaic output, user load demand, and realtime electricity prices. Simulation results show that compared with reinforcement learning algorithms and greedy algorithms, the revenue of the community operator under the algorithm proposed in this paper is increased by 5% and 20.9% respectively, effectively promoting the local consumption of photovoltaic power.

In response to the issues of limited carbon reduction methods on the load side and poor coordination of carbon reduction methods across generation, load, and storage in current lowcarbon dispatching of power systems, a multidimensional carbon reduction coupling strategy based on carbon potential indicators is proposed. This involves the establishment of a duallayer optimization dispatch model for the power system, which includes lowcarbon economic objectives. Initially, a carbon flow tracing model for loads and energy storage is developed based on the theory of carbon emissions flow in power systems. Subsequently, a dual lowcarbon demand response model integrating carbon flow theory is established on the load side, and a lowcarbon dispatch model based on nodal carbon potential is developed for the energy storage side. Then, a duallayer optimization dispatch model for the power system characterized by time ofuse electricity pricing and nodal carbon potential is constructed, with the upper and lower layers aimed at optimal economic and lowcarbon objectives, respectively. Finally, the strategy is tested using a modified IEEE14node system, and the simulation results demonstrate that this dispatch strategy can effectively tap into the system's carbon reduction potential, enhance its carbon reduction capability, and improve its economic benefits.

Considering the lack of pumped storage power plant smoke regularly send scheduling flexibility, system problem such as carbon emissions calculation is not comprehensive, is put forward based on the permeability of pumped storage power station high energy low carbon power system optimization scheduling method, introduced the thermal power unit desulfurization, climbing to produce carbon emissions calculation factor, system of carbon emissions calculation model is established. Through the Xinjiang power grid and Fukang pumped storage power station actual data, considering different grid characteristics of winter and summer, simulation of the pumped storage power station in the high permeability of the power system operation, system for carbon emissions, abandoned electric rate etc. Comparative study the pumped storage power plant smoke regularly send, low carbon's influence on the system optimal operation way, and analyzes the causes of different influence, It is verified that the proposed lowcarbon optimal scheduling method can effectively reduce the carbon emission and the power discard rate of new energy, improve the positive and negative reserve capacity of the system, and smooth the power supply output fluctuation. This paper provides an analysis method for lowcarbon power supply dispatching in areas with high and new energy penetration, and puts forward some suggestions for the subsequent construction and development of Xinjiang power grid pumped storage power station, and provides reference for the selection of dispatching mode after the completion and operation of Xinjiang Fukang pumped storage power station.

With the continuous development of userside distributed energy resources, interactions among multiagent resources have gradually emerged. Due to autonomous regulation of distributed energy equipment and diversification of operational methods among renewable energy and load entities, it is imperative to establish multiagent gametheoretic optimization models to satisfy diverse interests. This paper focuses on multiparklevel integrated energy systems and constructs a twolayer gameoptimized scheduling model. First, a ladder carbongreen certificate trading model incorporating an equivalent offset mechanism is proposed, considering carbon emissions generated by parks during production and operational activities. Second, based on actual cooperative scenarios among parks, a multipark gametheoretic optimization model is developed to study dynamic pricing strategies of integrated system operators and the optimal operational scheduling of parks. Finally, case studies demonstrate that the proposed model achieves economic efficiency while reducing system carbon emissions, unifying economic and carbon reduction benefits.

The uneven illumination intensity causes the output curve of the photovoltaic array to be a multimodal curve, and the traditional maximum power point tracking (MPPT) control algorithm cannot track the global maximum power. Based on this, a MPPT control method for photovoltaic power generation systems is proposed, which is based on the improved sparrow search algorithm (ISSA) and disturbance and observation method (P&O). Firstly, in the early stage of tracking, chaotic mapping is used to increase the diversity of ISSA population and enhance the algorithm's wide search ability. To prevent the algorithm from getting stuck in local optima, the firefly perturbation algorithm is used to perturb and update individual sparrows. Secondly, in the later stage of tracking, P&O is used to prevent the system from oscillating near the maximum power point, ensuring stable output at the maximum power point. Finally, through numerical analysis, the proposed MPPT control method achieves fast tracking and accurate output in different scenarios, and can be well applied in photovoltaic hybrid power generation systems.

With the largescale integration of distributed power sources, the shortcircuit current characteristics of large power grids become more complex and difficult to predict. Based on this, this article proposes a new energy grid shortcircuit current prediction technology based on improved convolutional neural networks. Firstly, analyze the characteristics of shortcircuit current, perform variational mode decomposition on shortcircuit current, and obtain the intrinsic mode function; Secondly, the convolutional neural network is improved by utilizing multiscale feature extraction to maximize the features of current fault data, introducing attention mechanisms to extract important information, and using skip connections during the convolutional process to prevent information loss during forward transmission, which is beneficial for improving the accuracy of prediction. A shortcircuit current prediction model based on the improved convolutional neural network is constructed; Finally, the PSCAD/EMTDC power grid model was validated, and the experimental results showed that the proposed method has high accuracy in predicting the peak shortcircuit current. Compared with common limit learning machines and support vector machines, the average relative error decreased by 0.61% and 1.09%, respectively. This verified the effectiveness of the proposed method and laid the foundation for limiting shortcircuit current in large power grids.

The development of highfrequency AC distribution systems poses new challenges to the requirements of inverters. Traditional inverters are no longer suitable due to their complex structure, high switching frequency, and lack of boost capability. Based on this, a novel singlephase capacitor self balancing five level inverter topology is proposed. This topology achieves five level output through a series parallel mechanism combining capacitors and power sources, and has the advantage of capacitor self balancing; Improve transmission efficiency by simplifying the structure and reducing switching frequency; Using specific harmonic elimination methods to further reduce output harmonic distortion. This article introduces the working principle of inverters and the calculation method of related parameters, and conducts simulation verification. The results indicate that the theoretical analysis is correct, indicating that the inverter is suitable as a power side device in the highfrequency field.