Aiming at the economic benefits, load fluctuations, and carbon emissions of the microgrid (MG) group control, a method for controlling the MG group of power distribution Internet of Things (IoT) based on deep learning is proposed. Firstly, based on the cloud edge collaborative power distribution IoT architecture, combined with distributed generation, electric vehicles (EV), and load characteristics, the MG system model in the power distribution IoT is established. Then, a deep learning algorithm is used to train the features of the data model on the edge side. Finally, the group control strategy is adopted in the power distribution cloud platform to reasonably regulate the coordinated output of multiple energy sources, adjust the load state, and realize the economic operation of the power grid. Based on the MATLAB platform, a group model of MG is built and simulated. The results show the effectiveness of the proposed control method. Compared with other methods, the proposed control method has higher income and minimum carbon emission and realizes the economic and environmental protection system operation.
When a short-circuit fault occurs on the transmission lines of high voltage DC transmission system based on modular multilevel converters, the fault cannot be cleared by adjusting the converter control system, which results in longer recovery time. Aiming at the problem above, a fault self-clearing scheme based on the fault clearing module for the half-bridge converter station is proposed. Combined with the actual operating status of the flexible DC transmission project, centralized parameter models are utilized to analyze the fault self-clearing mechanism. Besides, the impact of the discharge branch on the fault clearing effect is studied in depth to provide a design consideration for the fault clearing module and improve the comprehensive benefits of the proposed scheme. PSCAD/EMTDC simulation results show that the introduction of the fault clearing module in the half-bridge converter station can effectively suppress the fault short-circuit current and shorten the fault clearing time. In addition, circuit breakers on both sides of the line do not need to be tripped, providing a reliable guarantee for the subsequent adaptive restart process.
The timing group delay parameter (TGD) or differential code bias parameter (DCB) is an important factor that affects the performance of GNSS basic services; therefore, TGD and DCB must be taken seriously. Moreover, the TGD parameter is modulated in the navigation message, taking into account the impact of TGD on the performance of the basic service. International GNSS Monitoring and Assessment System (iGMAS) provides the broadcast ephemeris with TGD parameter and the Chinese Academy of Science (CAS) provides DCB products. In this paper, the current available BDS-3 TGD and DCB parameters are firstly described in detail, and the relationship of TGD and DCB for BDS-3 is figured out. Then, correction models of BDS-3 TGD and DCB in standard point positioning (SPP) or precise point positioning (PPP) are given, which can be applied in various situations. For the effects of TGD and DCB in the SPP and PPP solution processes, all the signals from BDS-3 were researched, and the validity of TGD and DCB has been further verified. The experimental results show that the accuracy of B1I, B1C and B2a single-frequency SPP with TGD or DCB correction was improved by approximately 12–60%. TGD will not be considered for B3I single-frequency, because the broadcast satellite clock offset is based on the B3I as the reference signal. The positioning accuracy of B1I/B3I and B1C/B2a dual-frequency SPP showed that the improvement range for horizontal components is 60.2% to 74.4%, and the vertical components improved by about 50% after the modification of TGD and DCB. In addition, most of the uncorrected code biases are mostly absorbed into the receiver clock bias and other parameters for PPP, resulting in longer convergence time. The convergence time can be max increased by up to 50% when the DCB parameters are corrected. Consequently, the positioning accuracy can reach the centimeter level after convergence, but it is critical for PPP convergence time and receiver clock bias that the TGD and DCB correction be considered seriously.
Modular multilevel converter based on high voltage director current is important for large‐scale power regional interconnection. Adaptive reclosing is critical to improving the safety and economy of power transmission. The time‐frequency characteristics during fault clearance are analysed by Hilbert transform to suppress over‐voltage or over‐current resulting from short‐circuit fault. A phase synchronization compensation control algorithm is designed based on voltage and current through the virtual network. The rapid suppression of fault overvoltage is achieved by synchronously switching network attributes. Furthermore, the line recovery voltage is introduced into the fault property identification. The proposed adaptive reclosing scheme with high‐speed dynamics can raise the comprehensive benefits of the power system. Studies have verified that phase synchronization compensation control can effectively reduce the impact of oscillation. Moreover, permanent and transient faults can be quickly and reliably discriminated during de‐ionization by multi‐device coordination, which is earlier than the traditional adaptive reclosing scheme. It provides a basis for the fault recovery optimization strategy coordinating with multiple equipment.
The rapid suppression of fault current flowing through overhead transmission lines and safe restart of the system are critical problems to be solved urgently to improve the stability of the DC grid. A novel adaptive restart strategy combined with fault current suppression is proposed, which suitable for full-bridge modular multilevel converter high voltage direct current system. The resistance-capacitance energy transfer branch is used to achieve a large reduction and rapid attenuation of the fault current amplitude at restart time. The transient fault and permanent fault can be discriminated in a short interval after restarting based on the frequency domain amplitude characteristics. The proposed scheme has both restart-current protection and fault property identification functions, overcoming the high cost and low component reusability of traditional single-function fault current limiting schemes. The simulation results show that the scheme has a significant fault current suppression effect and accurately discriminates the fault property to realize rapid recovery.
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