Kaicheng Li

Against the backdrop of renewable energy integration and the large-scale integration of power electronic devices into power systems, the coupling of power quality issues within the system has become increasingly complex, leading to the formation of complex power quality disturbances (PQDs) characterized by the combined occurrence of multiple disturbances. Reliable and effective identification of disturbance types forms the foundation for the classified mitigation strategies of PQDs. This article proposes a data processing method, the variable-length distribution transfer field (VLF), which processes variable-length PQDs signal inputs, overcoming the limitations of fixed-length signal processing. VLF can process variable-length PQDs signals in a single pass, enhancing the algorithm’s tolerance to fluctuating computation times and enabling effective utilization of signals of arbitrary lengths. It thereby resolves the discontinuity issues caused by signal overwriting or loss due to fixed-length constraints. Concurrently, targeting the characteristics of PQDs, this article designs an edge-enhanced encoding method capable of achieving enhanced boundary discrimination and noise-robust encoding for disturbances. Furthermore, this article proposes a high-generalization self-balanced multitask classification architecture that enables loss balancing among different tasks within a multilabel and multitask classification framework. Through the above optimization, simulation results under 30 dB to 50 dB noise environments and twentyfold downsampling intensity demonstrate a classification accuracy of 99.57%. Meanwhile, experimental simulations combined with real-world grid validations still achieve a high accuracy of 99.43%. The classification algorithm exhibits strong robustness and generalization capability under highly downsampling and noisy conditions.

The high common-mode voltage (CMV) in two-level three-phase inverters will bring many negative effects. Many strategies are studied to reduce the CMV and eliminate the CMV spikes. However, due to the switching dead-time, there is a gap in eliminating CMV spikes when the inverter works under low modulation indices (the modulation index M_a < 0.4) with the existing space-vector modulation (SVM) strategies for reducing CMV (RCMV). This paper proposes a new strategy named all non-zero vectors SVM (ANZVSVM) to fill in the gap. In this strategy, as three pairs of reverse non-zero vectors are used to equivalent the zero vectors, the dwell times of the original middle-order vectors can be effectively prolonged under low modulation indices. Thus, the new strategy has the ability to eliminate the CMV spikes when the M_a is low enough to approach zero. This characteristic is not only proven in theory, but also verified by the simulations and experiments in a three-phase inverter. Compared to other RCMV strategies in the inverter under the low modulation indices, the ANZVSVM strategy has significantly lower THD and lower common-mode current peaks in both time-domain and frequency-domain.

Complex Power Quality Disturbances (PQDs) represent scenarios where multiple types of power quality disturbances occur in combination. Reliable and effective analysis of PQDs constitutes a prerequisite for their comprehensive management. To the best of our knowledge, this work is the first within the scope of our investigation to achieve simultaneous quantification of disturbance severity, localization of disturbance points, and classification of PQDs using a single algorithm. Furthermore, a novel architecture is proposed, which compresses the sampling window duration from at least 1 period to 1/4 period. This architecture enables real-time signal processing within 0.0019 seconds, thereby reducing the PQDs analysis latency from the original minimum of 0.02 seconds down to 0.0069 seconds. This architecture will effectively guide the active tripping of sensitive loads and facilitate fault ride-through, significantly enhancing the real-time responsiveness of the power system. Leveraging prior information, the captured quarter-period PQDs signals are standardized. Subsequently, the proposed Prior Forward Orthogonal Matching Pursuit (PFOMP) algorithm is employed. Targeting the minimization of the L0-norm, PFOMP selects atoms from the designed Overcomplete Standard Power Quality Disturbance Dictionary (OSPQDD) to match and track the constituent components of the PQDs. This process accomplishes the quantification and localization of the PQDs. Finally, an Extreme Learning Machine (ELM) is utilized to classify the fused features of the PQDs, achieving a classification accuracy of up to 99.72%.

Complex power quality disturbances (PQDs) are caused by the combination of multiple single disturbances. There exist complex mutual exclusion and combination relationships among these single disturbances, so-called mutually-exclusive labels mean that the disturbances corresponding to these labels cannot occur simultaneously (such as disturbance C2:Sag and C3:Swell). However, in the multilabel classification neural network, the fully connected layer and activation function corresponding to each label are identical, and there is no design to accommodate the mutually-exclusive and combination relationships, which allows the multilabel classification output mutually-exclusive disturbances simultaneously, which could mistakenly activate two incompatible governance devices at the same time. This is one of the reasons why this method cannot be applied in the power system, which requires an ultra-safe identification approach. A multilabel and multitask classification method with robust generalization ability and universal applicability is proposed to realize the mutually-exclusive output of PQDs mutually-exclusive labels. In principle, this method cannot output mutually-exclusive labels at the same time, to achieve an ultra-safe output. Meanwhile, this article proposes a faster and more accurate calculation method for PQDs envelope by using fast plug Hilbert transform, and uses Landau level transition field proposed in this article to achieve further feature extraction of the envelope and convert it into a two-dimensional (2-D) image, and then uses the proposed shallow double-path neural network DPN-17 to achieve classification and identification of PQDs. This methodology has strong universality and can be extensively utilized in various 1-D signal identification and feature extraction fields.

Some strategies are used to reduce the high output common-mode voltage (CMV) in three-phase inverters with the space-vector modulation (SVM). However, these reduced CMV (RCMV) strategies may still lead to high spikes in some cases. The traditional auxiliary vectors are used to eliminate the CMV spikes in some RCMV strategies, which have the lower total harmonic distortion (THD) in RCMV SVMs. This paper proposes a new auxiliary vector synthesis SVM (NAVSSVM) strategy. In the NAVSSVM, the new auxiliary vectors are a set of vectors with variable angles. Compared to the use of traditional auxiliary vectors, the new auxiliary vector synthesis expands the linear modulation range of the inverter to the maximum when the modulation index M_a > 1. Through optimization, the optimal NAVSSVM is found to expand the modulation region without the CMV spikes in the low M_a . In addition, the NAVSSVM strategy maintains the low THD characteristic of the auxiliary vector synthesis method. The feasibility and effectiveness of the NAVSSVM are verified by the simulations and experiments in a three-phase inverter.

Complex power quality disturbances (PQDs) is the concomitant occurrence of multiple single disturbances, which is a common disturbance in modern power system. In this article, a new plug Hilbert transform method is proposed, and the rotation vector modulus corresponding to the complex PQDs is obtained by using the back projection. Then, the characteristic signal of the complex PQD is obtained by tiling the modulus according to the time passing direction. Based on the Markov transfer chain and fuzzy logic, a new dimension conversion scheme, fuzzy transfer field (FTF), is proposed, and the disturbances characteristic signals are converted into two-dimensional (2-D) images by using this FTF, and the 2-D images covering complex PQDs information are classified and recognized by depth residual network. The accuracy and practical effect of this method is far superior to similar research. At the same time, the abrupt change and edge problem of the Hilbert transform is solved in principle, and the problem of ignoring the dependence of time sequence signal amplitude in the existing image conversion methods is pointed out and solved. These methods have strong universal applicability, not only for the recognition of PQDs signal, but also for other industrial signal and medical signal processing recognition.

Harmonic detection is a vital problem in power system, but the accuracy of harmonic detection is affected by noise. In order to suppress noise, this paper proposes the improved singular value decomposition (SVD) to denoise. Then, the optimal number of decomposition modes of variational mode decomposition (VMD) is determined with the aim of minimizing the residual of signal energy after Schmidt orthogonalization. The amplitude and frequency of each harmonic are detected by the Hilbert transform. The simulation shows that the algorithm can accurately identify the information characteristics of each harmonic and inter-harmonic, and has the advantages of high accuracy and small error.

In three-phase inverters, some modulation methods are used to reduce the high output common-mode voltage (CMV), which will bring many negative effects. However, the inverter with some reduced CMV (RCMV) methods may still have high spikes in some cases. This article proposes a new space-vector modulation (SVM), which is named 12-section SVM (12SSVM). The 12SSVM redivides the vector space into 12 sections to avoid the simultaneous switching of two legs in the areas of cross-section. This method can be used to eliminate the CMV spikes and be applicable in most modulation index ranges. The 12SSVM has a lower total harmonic distortion than other RCMV SVMs. The feasibility and effectiveness of the 12SSVM are verified by the simulations and experiments in a three-phase inverter.