This paper presents a novel approach for solving the frequency responses of a powerline network, which is a two-parallel-conductor system with multiple junctions and branches. By correcting the reflection coefficient and transmission coefficient of each junction, a complex network can be decomposed into several, single-junction, units. Based on the Baum-Liu-Tesche (BLT) equation, we preliminarily propose the calculation method of frequency responses for single-junction network. In accordance with the direction of power transfer, we calculate the frequency responses of loads connected to each junction sequentially, from the perspective of the network structure. This approach greatly simplifies the computational complexity of the network frequency responses. To verify the proposed algorithm, networks with various numbers of junctions and branches are investigated, and the results are compared with a commercial electromagnetic simulator based on the topology. The analytical results agree well with the simulated ones.

In this paper, we consider different types of networks, and investigate the characteristics of the frequency responses of loads, which are distributed in the networks. Without loss of generality, both frequency-independent and frequency-dependent loads are discussed, respectively. Beginning with a transmission-line (TL) network with frequency-independent loads, via the TL theory and Baum-Liu-Tesche equation, we demonstrate that the frequency responses are periodic in the frequency domain, where the periodicity is derived and verified. Subsequently, our study is extended to the complex networks that consist of multiple junctions and branches. By using the statistical method, we generate random loads with different attributes, i.e., resistive, inductive, or capacitive, and mainly study the effect of the number of branches and junctions on the frequency response of targeted load in various networks. From the perspective of protections for the targeted load in networks, results indicate that, for lossless and good dielectric (i.e., low-loss) media, it is crucial to consider the frequency responses at the critical frequencies in a periodical manner, rather than at a single frequency. Furthermore, it is worth noting that, the frequency response of targeted load behaves differently when varying the attributes of other loads in the network. The variation of network topology, i.e., increasing the number of junctions or branches, also influences the frequency response.

In this paper, we consider a conducted UWB disturbance due to intentional electromagnetic interference (IEMI), and evaluate impact quantifiers at the loads of a network. Through FFT, we characterize the time-domain transient and apply a Baum-Liu-Tesche (BLT) approach. The EMIreceived at the loads, which can be inductive, capacitive or resistive, is calculated and via the inverse FFT, we get the load responses in time-domain. To perform an impact evaluation of the loads, five quantifiers, i.e., time-domain peak, total signal energy, peak signal power, peak time rate of change and peak time integral of the pulse, are employed. It is seen that the impact evaluation of different kinds of loads, in a particular network, should be based on different attributes depending upon the characteristics of the transient.

In this paper, the frequency responses of the loads in different types of electrical networks subjected to intentional electromagnetic interference (IEMI), are analysed with a method based on the Baum-Liu-Tesche (BLT) equation. The networks can be multi-conductor systems with multiple junctions and branches. To verify the calculation results, a commercial electromagnetic simulator based on electromagnetic topology was used. The calculation results agree well with the numerical simulations.

Nowadays, faced with the ever-increasing dependence on diverse electronic devices and systems, the proliferation of potential electromagnetic interference (EMI) becomes a critical threat for reliable operation. A typical issue is the electronics working reliably in power-line networks when exposed to electromagnetic environment. In this paper, we consider a conducted ultra-wideband (UWB) disturbance, as an example of intentional electromagnetic interference (IEMI) source, and perform the impact evaluation at the loads in a network. With the aid of fast Fourier transform (FFT), the UWB transient is characterized in the frequency domain. Based on a modified Baum–Liu–Tesche (BLT) method, the EMI received at the loads, with complex impedance, is computed. Through inverse FFT (IFFT), we obtain time-domain responses of the loads. To evaluate the impact on loads, we employ five common, but important quantifiers, i.e., time-domain peak, total signal energy, peak signal power, peak time rate of change and peak time integral of the pulse. Moreover, to perform a comprehensive analysis, we also investigate the effects of the attributes (capacitive, resistive, or inductive) of other loads connected to the network, the rise time and pulse width of the UWB transient, and the lengths of power lines. It is seen that, for the loads distributed in a network, the impact evaluation of IEMI should be based on the characteristics of the IEMI source, and the network features, such as load impedances, layout, and characteristics of cables.

In this paper, we investigate the targeted load frequency responses of Intentional Electromagnetic Interference (IEMI) in low voltage power line network, which consists of multiple junctions and branches. A disturbance that is injected at a random position in the network is considered in our work, and we study the impact of the position of the injection point in the sense of probability distribution, through the Monte Carlo method. To increase the precision, in the Monte Carlo simulation model, we introduce three variance reduction techniques, namely, complementary random numbers, correlated sampling and stratified sampling, and we use them in combination. Results show that they can significantly reduce the variance and increase the simulation precision. More importantly, simulations quantitatively show that, controlling the probability of criminal accessing the targeted load can effectively reduce the influence level, which is crucial for ensuring the security and robustness of whole networks.