Search

Abstract Recently, deep learning (DL) based dynamic security assessment (DSA) methods have been very successful. However, although a DSA model can be trained well for a specific topology, it often does not perform well for other topologies. Since the topology in real‐world power systems is frequently changing, the performance reduction of DL‐based DSA methods is very serious, which is a challenging and urgent problem. This paper proposes a novel DSA method based on a convolutional neural network (CNN) to solve this problem. In the proposed method, a strong yet simple domain adaptation approach named adaptive batch normalization (AdaBN) is used, which significantly enhances the extensibility and generalizability of the DSA model when the topology changes and eliminates the need to train a large number of models. This approach achieves a deep adaptation effect by modulating the statistics from the source domain to the target domain in all batch normalization layers across the model. Unlike other domain adaptation methods, this method is parameter‐free, requires no additional components, and has advanced performance despite its simplicity. In addition, this paper introduces TGAN‐based data augmentation to deal with the difficulty of costly data collection and labelling. This data augmentation makes the proposed model applicable to small databases. The test results of the proposed method on IEEE 39‐bus and IEEE 118‐bus systems show that this method can evaluate system dynamic security during topology changes and in the face of data noise with high accuracy.

Abstract In recent years, there has been a notable increase in the frequency and intensity of natural disasters related to global warming. Large‐scale blackouts caused by natural disasters demonstrate the extreme vulnerability of power systems to these devastations. Resilience planning is the key to preparing power systems for natural disasters by enhancing the vital infrastructure's robustness. This paper proposes a new resilience improvement framework based on resiliency assessment and optimal hardening to improve the distribution system's resiliency against hurricanes. The main goal is to find the optimal distribution system line hardening solution according to reconfiguration potentials after the hurricane to minimize the distribution system cost of energy not supplied. Fragility curves and Monte Carlo simulations are used for the distribution system resilience assessment. Poles and conductors are vulnerable components of distribution system lines; therefore, two hardening strategies have been outlined using measures like replacing old poles with new poles, upgrading pole classes, and vegetation management. This method is modelled as an optimization program considering budget limitations and load priorities and implemented by a genetic algorithm on the IEEE 33‐bus standard network. The results show that optimal line hardening according to network reconfiguration potentials significantly increased the distribution system's resilience.

Abstract Due to increasing the intricacies of cyber‐physical systems (CPSs) and the severity of natural phenomena, upgrading network planning is vital to reduce the vulnerability of these systems. This study develops a novel preventive‐corrective resilient energy management strategy (PC‐REMS) for a CPS in two stages, exploiting the network reconfiguration (NR) and energy storage systems (ESSs) capacity. The first stage of the proposed PC‐REMS follows preventive actions based on contingency faults. In contrast, the second stage applies corrective measures for improving the CPS resilience to cope with natural physical disasters. Vulnerability assessment data is sent to the physical power system daily through the communication network. The first stage of preparing the CPS for predictable faults focuses on pre‐scheduled ESSs and preventive NR to minimise the expected energy curtailment cost. The second stage involves the network recovery in real‐time through corrective NR to minimise energy curtailment cost after the faults. Three resistance, recovery, and resilience indices are introduced for evaluating the effectiveness of the model. The proposed model is examined by performing multiple simulations on the 33 and 118‐bus radial test systems. The simulation results show the efficiency of the proposed PC‐REMS model in dealing with predictable disasters to improve the CPS resilience.

Abstract Due to the expansion of the decarbonization in modern power systems, particularly the high integration of renewable energy resources, more and more uncertainties and variabilities are brought to electricity systems. These uncertainties change the system operating conditions and face the power system with unknown issues. The secure operation of modern power systems requires developing security assessment techniques that effectively mitigate the variation of the operating conditions. This paper proposes a novel continuous learning scheme for online static security assessment (SSA) that enables the periodic renovation of the assessment model. The scheme consists of a Mondrian forest (MF)‐based training model and database generation approach conducting characteristics of operating conditions. The employed security index is a new weather‐depended performance index whose ambient temperature can affect line ratings. After the new training set is generated in the update procedure, the MF algorithm is updated using this data. Eventually, the SSA model is enriched with current circumstances in the power system. The proposed scheme is examined using real‐world data mapped to the New England 39‐bus and 118‐bus standard test systems. The effectiveness and superior performance of the proposed method are demonstrated using various criteria and measures.