Wave energy resources, particularly the incident wave power density, are critical to the design and evaluation of wave energy conversion systems. Based on the operational principles of the Eagle wave energy converter, this study analyzes the incident wave power density under realsea conditions, develops a methodology for measuring input wave energy power, and establishes an input wave power model. Utilizing wave data including significant wave height, mean period, and wave direction collected over 217 consecutive hours from June 8 to 18, 2016, during the realsea state testing of the "Wanshan" Eagle wave energy converter, the realtime input wave energy power was measured and compared with published literature data. The results indicate that the measured values are generally lower than those reported in the literature, and the temporal variation in wave power is more gradual, providing a more accurate representation of actual marine environmental changes. Consequently, the proposed incident wave power measurement method can offer precise and reliable reference data for the design and performance assessment of wave energy conversion systems or wave power stations.

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.

This paper takes Spirodela polyrhiza as the experimental object to study the effects of the dilution multiple of aquaculture wastewater and the initial inoculation amount on the growth and crude protein accumulation of Spirodela polyrhiza, as well as the absorption and purification patterns of nitrogen and phosphorus nutrients in aquaculture wastewater by Spirodela polyrhiza. The results show that with the increase of the dilution multiple of aquaculture wastewater, the removal rates of NH4+N, NO3N, and PO43P by Spirodela polyrhiza gradually increase, and the absorption and purification of NH4+N by Spirodela polyrhiza is earlier than that of NO3N. With the increase of the initial inoculation amount of Spirodela polyrhiza, the removal rates of PO43P and NO3N both increase. When the initial inoculation amounts of Spirodela polyrhiza are 50% and 75%, the removal rates of NH4+N reach 97.6% and 94.1%, respectively. The crude protein content of Spirodela polyrhiza decreases with the increase of the dilution multiple of aquaculture wastewater. The highest growth amount and growth rate of Spirodela polyrhiza is achieved in the aquaculture wastewater diluted 20 times. Under the conditions of 20 times dilution of aquaculture wastewater and an initial inoculation amount of 75% of Spirodela polyrhiza, the annual protein yield of Spirodela polyrhiza can reach 0.91 t/hm².

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.

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.

To evaluate the importance of the startup process of the compressed air energy system (CAES), a mathematical model of the entire system was established based on a 300 MW CAES power plant. A series of analyses were conducted to evaluate the changes in the main parameters during the startup and the corresponding dynamic responses were obtained. These analyses include the antisurge operation of the compressors, 1drive4 variable frequency startup of the compressor train, the programmed startup of the turbine train, and the power regulation process under the airdistribution scheme. The results indicate that the optimization of startup process can shorten the start time of the compressor and turbine trains and improve the system efficiency. In addition, the qualitative data during many important operation process was determined. The study provides valuable date and theoretical basis for the safe and efficient operation of the CAES unit.

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.

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 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.

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.