Compared to traditional measurement devices, the micro-synchrophasor measurement unit (D-PMU or μPMU) in the distribution power system has great differences in data acquisition frequency, data format, data dimension, time-stamped information, etc. Hence, it is imperative to research the integration mechanism of heterogeneous data from multiple sources. Based on the analysis of the current technology of multi-source information fusion, this paper proposes a novel approach, which considers two aspects: the interoperability of multi-source data and the real-time processing of large-scale streaming data. To solve the problem of data interoperability, we have modified the model of D-PMU data and established a unified information model. Meanwhile, an advanced distributed processing technology has been deployed to solve the problem of real-time processing of streaming data. Based on this approach, a smart distribution power system wide-area measurement and control station can be established, and the correctness and practicality of the proposed method are verified by an on-field project.

A soft open point (SOP) can improve the flexibility of power control and thus further enhance the reliability and economy of distribution networks. However, existing SOP regulation strategies have mainly focused on feeder imbalance regulation and line loss optimization in medium-voltage distribution networks, and the loss of SOPs and distribution transformers are often ignored, which has poor applicability in low-voltage distribution network (LDN). Therefore, a novel two-layer control strategy is proposed for SOPs working in LDN, which can improve economy and reliability of the system simultaneously. In terms of the system layer, the proposed load regulation strategy considering the economical operation area of the transformers can improve the system economy while realizing load imbalance regulation. In terms of the equipment layer, the proposed peer-to-peer control of each port in the SOP can effectively suppress the voltage fluctuation on the DC side. Thus, the system layer commands can be quickly tracked and well conducted. Finally, the feasibility and effectiveness of the proposed two-layer control strategy are verified by simulation and experimental results.

Because of the drawbacks of easy premature in initial iteration stages, the low convergence accuracy and slowed-down converging speed in final stages of the particle swarm optimization (PSO)algorithm therefore the simple particle swarm optimization (SPSO) algorithm with dynamic changes of inertia weight and acceleration coefficients (IASPSO) has been put forward. IASPSO algorithm provides a parameter optimization strategy by using exponential decreasing inertia weight and sine function acceleration coefficient to improve global exploration capacity. Simulation tests are carried out with classic Benchmark test functions. The simulation results show that compared with other PSO algorithms, IASPSO algorithm can converge to the better global optimization with a fast converging velocity and high convergence precision, promoting the optimization performance of the algorithm.

Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only depends on the regular data acquisition speed of active power measurements. Additionally, this approach brings some novelties to the traditionally used denoising Auto-Encoder (dAE), i.e., the reconfiguration of the overlapping parts of the sliding windows. The median filter is used for the data processing of the overlapping window. Two datasets, i.e., the Reference Energy Disaggregation Dataset (REDD) and TraceBase, are used for test and validation. By numerical testing of the real residential data, it proves that the proposed method is superior to the traditional Factorial Hidden Markov Model (FHMM)-based approach. Furthermore, the proposed method can be used for energy data, disaggregation disregarding the brand and model of each appliance.

Transient stability and frequency stability of virtual synchronous generator (VSG)-controlled converters during grid faults are critical for the stable operation of high-share renewable energy power systems. However, the existing transient stability methods often overlook the consideration of frequency stability. This paper presents a method to improve both frequency stability and transient stability of VSGs during grid faults. The influence mechanism of inertia and damping on transient stability and frequency stability is comprehensively analyzed under two types of faults, with or without stable equilibrium points. To address the conflict between frequency stability and transient stability of VSGs with large inertia under the first type of fault, an improved VSG control with angular frequency deviation feedback is proposed. The transient response of this control method is analyzed using small-signal and large-signal models. Furthermore, to achieve optimal dynamic performance, this improved VSG control introduces the optimal damping ratio and presents a parameter design method. Finally, the correctness of the proposed method and its theoretical analysis is verified through experimental results.