Traditional methods for power system analysis and control are facing more and more challenges with the increasing penetration of systems containing VSCs (voltage source converters, e.g., high voltage DC transmission systems, PV or wind farms). Considering the difference between VSCs and synchronous generators, researchers have proposed a control scheme named virtual synchronous generator (VSG) to emulate the inertial characteristics of a real synchronous machine (SM) to tackle the problem of frequency stability. When disturbances occur in the system, VSGs may exhibit oscillatory behaviors in the same way as real SMs. At the same time, it also interacts with SMs due to power exchange as well as frequency and angle synchronization. Under this circumstance, how the existence of VSGs influences subsynchronous resonance (SSR) becomes an important issue. Based on the equivalence theorem, this article focuses on the impact on the SSR of a VSG interconnected with a multi-mass SM. After a VSG is equivalently integrated into the SM, it is evident that there are changes in the equivalent inertia and armature impedances, both of which affect the behaviors of the oscillation as a result.

Non-intrusive load monitoring (NILM) can infer load profiles for each individual appliance from aggregated power consumption signals without installing extra sub-meters. However, performance of traditional energy disaggregation methods deteriorates in complex environments, especially susceptible to the presence of other high power consumption appliances. Practicalities are also limited by diversity of household load patterns and measurement errors. In order to address these problems, a hybrid deep learning model consisting of two steps is proposed in this paper. First, an improved variational auto-encoder (VAE) structure is introduced for preliminary energy disaggregation, where the encoder and decoder layers are long short-term networks (LSTM) to extract temporal characteristics of active power signals. Afterward, a post-processing method based on Siamese one-dimensional convolutional neural network (S-1D-CNN) is adopted to remove incorrectly predicted activation segments of target appliances. Experiments are conducted on two public datasets, and results show remarkable improvements on prediction accuracy over other deep learning methods. Both transferability and stability of the proposed model are verified under different working conditions.

Multiarea parallel computing has emerged as a powerful tool for improving operation and control efficiency in the context of expanding scale of new power systems. However, existing partitioning methods cannot handle the problem of three-phase imbalance; results are not stable enough and have not been verified in large-scale distribution networks. Driven by this motivation, this paper presents a two-stage algorithm that can be applied to large-scale distribution networks to produce non-overlapping and overlapping zoning results. The algorithm is split into two sections: sketchy partitioning and multi-region merging. A voltage/admittance matrix must first be used to determine electrical distance between nodes. The Louvain algorithm is used to obtain the result of coarse partition. The flow algorithm or hierarchical flow method is then employed, depending on limitations of the number of partitions, to obtain sub-regions with comparable scales. Finally, IEEE 123-node test feeder and IEEE 8500-node test feeder are used to validate the algorithm. Simulation results and comparisons show the proposed partition algorithm can produce more reliable and efficient partition results by contrast with existing methods, in case of unbalanced three-phase and large-scale distribution networks.

A rational partition is the key prerequisite for the application of distributed algorithms in distribution networks. This paper proposes community-detection-based approaches to a distribution network partition, including a non-overlapping partition and a border-node partitioning method. First, a novel electrical distance is defined to quantify the coupling relationships between buses and it is further used as the edge weight in a transformed equivalent graph. Then, a vertex/link partition community detection approach is applied to over-partition the network into high intra-cohesive and low inter-coupled subregions. Following this, a greedy algorithm and a tabu search method are combined to merge these subregions into target numbers according to the scale similarity principle. The proposed approaches take the influence of three-phase imbalance into consideration and they are decoupled from the power flow. Finally, the approaches are tested on an IEEE 123-bus distribution system and the results verify the effectiveness and the credibility of our proposed methods.