Power quality events are usually associated with more than one disturbance and their recognition is typically based on multi-label learning. In this study, we propose a new method for recognizing multiple disturbances (MPQDs) based on variational mode decomposition (VMD) and a random discriminative projection extreme learning machine for multi-label learning (RDPEML). First, VMD is employed to decompose the MPQDs into several intrinsic mode functions and the standard energy differences of each mode are extracted as features that form the input vectors of the classifier. Second, a novel multi-label classifier called RDPEML is constructed by combining a random discriminative projection multiclass extreme learning machine (ELM) and a thresholding learning method-based kernel ELM. In order to obtain better classification performance, a tenfold cross-validation embedded particle swarm optimization approach is utilized to search for the optimal values of the structural parameters. Finally, a test study was conducted using Matlab synthetic signals and real signals sampled from a three-phase standard source under different noise conditions. Compared with several recent state-of-the-art multi-label learning algorithms, RDPEML achieved better classification performance with superior computational speed.

The single pole-to-ground (SPG) fault is one of critical failures which will have a serious impact on the stable operation of the multi-terminal DC distribution network based on the modular multilevel converter (MMC). It is very significant to analyze fault characteristics for detecting faults and protection design. This paper established the DC SPG fault model, which showed that in the presence of a reactor, the short-circuit current was reduced from 2.3 kA to 1 kA at 6 ms after the fault. Then, a novel SPG fault protection strategy was proposed, which detected the current derivative in connection transformer grounding branch. When the value increases past the threshold of current derivative, small resistance was switched on to increase fault current. Thus, the reliability of differential protection was enhanced. Compared with the traditional protection method, the proposed method does not need communication, and improved the speed of protection. Finally, the simulation model was established in PSCAD/EMTDC. The model included three converter stations: T1, T2 and T3. Among them, T1 outputs power, and T2 and T3 receive power. The results of RTDS showed that the DC circuit breaker operated within 3 ms, the three-port circuit breaker worked within 50 ms, which proves that the proposed strategy was effective. At this time, the system switched from the T1–T2–T3 three-terminal networking operation mode to the T1–T2 two-terminal hand-in-hand operation mode. Since the T3 terminal no longer received power, the transmission power of the T1 terminal decreased, and the received power of the T2 terminal remained unchanged.

Particle filtering algorithm has found an increasingly wide utilization in many fields at present, especially in non-linear and non-Gaussian situations. Because of the particle degeneracy limitation, various resampling methods have been researched. The estimation process of particle filtering algorithm is a series of weighted calculation processes, which can be regarded as weighted data fusion. This article proposed an improved particle filtering algorithm combining with different rank correlation coefficients to overcome the shortcomings of degeneracy. By simulating iteration operation in MATLAB, it discovers that the proposed algorithm provides better accuracy in comparison with particle filtering, Gaussian sum particle filter, and Gaussian mixture sigma-point particle filter in Gaussian mixture noise. A practical seven-dimensional harmonic model is also implemented in the simulation. After comparing the performances of different algorithms, we found that the proposed method had more accuracy than the widely used extended Kalman filtering algorithm.