# Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks
## 1 Introduction  
An interconnected HVDC system requires protection at both sides of the network (i.e., the AC side as well as at DC side). The DC transmission system is expected to run smoothly, and it should prevent DC blocking during AC- and DC-side faults [1, 2]. Any disturbance that occurs on the AC side may travel to the DC side and vice versa. The existing protection method available for AC system faults also needs to be revised and modified to make it faster and quicker to work in a handshake manner for overall HVDC interconnected network protection [3, 4]. The potential fault detection techniques available in the literature are the Fourier transform (FT), fast Fourier transform (FFT), short-time Fourier transform (STFT), wavelet transform (WT), discrete wavelet transform (DWT), continuous wavelet transform (CWT), Hilbert transform (HT), Hilbert-Huang transform (HHT), etc. [5, 6]. Their performance largely depends on one or other parameters mentioned in Table 1, with the nomenclature mentioned in Nomenclature part. Though vast research is available for AC fault detection techniques, the available techniques may not be suitable for all applications. The techniques respond differently when they are employed in high-voltage networks, distribution systems, or two or multiterminal networks [7, 8]. The drawbacks of the existing methods are mentioned in Table 1. With this motivation, a faster and reliable fault detection function is imperative for HVDC networks. To corroborate this, the synchro-squeezed transform (SST) is introduced in this paper. It provides a higher resolution and more accurate information about the transient events in comparison with the conventional integral transform techniques [9; 10]. The SST provides the special feature of compressing the data, which saves a great amount of storage, lessens the computation burden, and makes the operation faster without compromising the information contained in the signal. The SST can compress data by up to 40% with a 99.99% signal reconstruction capability. Both the tasks (i.e., sensing the signal and compression) go hand in hand in the case of the SST. It first reallocates the energy distribution according to the time-scale plane, similar to the wavelet transform. Then, the SST projects the time-scale plane onto the time-frequency plane. In this way, the SST provides more accurate and more concentrated time-frequency curves. The SST's orthogonality of data sensing and compression reduces the mode mixing effect, and the multi-component simulation exhibits better time-frequency characteristics. The concentrated energy distribution and sharper resolution makes the SST a powerful tool for condition monitoring and transient detection in a signal. The proposed SST algorithm suppresses the noise present in the signal accurately, traces the harmonic presence, and improves the frequency aliasing phenomenon [11; 12; 13]. In this study, a four-terminal HVDC network is analyzed for symmetrical and unsymmetrical faults in its host AC networks. The raw current signals are first decomposed using the EMD technique, and the obtained modes are passed to the improved SST for fault detection. EMD is applied to extract the different modes, which are relevant and provide the accurate information needed for the SST. A comparative study is also shown between the SST and the Hilbert-Huang transform (HHT). The organization of the paper is as follows. Section 1 provides the introduction of the paper. Section 2 explains the discrete teaser energy concept, using the SST for the purpose of real-time application, and their modeling, with a detailed mathematical explanation. Section 3 shows the simulation performed and the obtained results. Section 4 gives the performance analysis of the proposed method. Section 5 concludes the work.