This paper incorporates the flexible optical-layer resource scheduling capability of software-defined optical networks (SDONs) and proposes an SDON-enabled CPPS model along with a control network optimization method, and proposes a multidimensional vulnerability assessment method for CPPSs.
Abstract
The risk of cross-domain cascading failures in cyber–physical power systems (CPPSs) has become increasingly significant. The existing studies generally employ communication networks with static routing and fixed bandwidth allocation, which are insufficient to cope with dynamic load fluctuations and unexpected faults. To address these limitations, this paper incorporates the flexible optical-layer resource scheduling capability of software-defined optical networks (SDONs) and proposes an SDON-enabled CPPS model along with a control network optimization method. First, a three-layer CPPS architecture based on the SDON framework is constructed to characterize the interaction mechanism between the control layer and the data forwarding layer, as well as the fault propagation paths. Second, a control network optimization configuration model with the objective of minimizing energy consumption is established in which primary–backup routing schemes and wavelength resources are jointly designed using a mixed-integer linear programming approach. Finally, with load shedding rate adopted as the evaluation metric, a multidimensional vulnerability assessment method for CPPSs is proposed. The simulation results demonstrate that, compared with random control networks, the optimized CPPS reduces the average energy consumption by 53.6% and 34.2% under single-fault and multiple-fault scenarios, respectively, while the load shedding rate is reduced by 23% and 37.2%, thereby verifying the effectiveness of the proposed method.
This thesis provides an end-to-end mathematical and machine learning framework for designing dependable, low-latency, and scalable SDN infrastructures.
As the core hub of the modern power system, the dynamic disturbance suppression and connection stability of the virtual circuit in the process layer of the intelligent substation are the key challenges to ensure reliable grid operation. Aiming at the problems such as the limited adaptability of traditional feedback-based strategies under nonlinear communication disturbances such as communication delay and packet loss, and the lack of real-time adaptability of static configuration mechanisms, this paper proposes an LADRC -based disturbance-estimation and supervisory scheduling method for process-layer virtual circuit connection and verification. By constructing the Linear Extended State Observer (LESO), multi-source disturbances (such as delay jitter, packet loss, and queue congestion) manifested across the network layer, protocol layer, and underlying physical infrastructure are estimated in real time as a filtered total-disturbance signal. The experimental results show that: Compared with the traditional baseline strategies, the proposed method improves communication delay-related performance, reducing the observed average end-to-end latency from 2.47 ms to 1.28 ms under typical disturbance scenarios, decreases the packet loss rate by 88.5% (0.06% vs. 0.52%), and increases the jitter suppression rate by 127%. The research results provide theoretical and technical support for improving the communication robustness and connection stability of smart substations.
Jiesheng Chen, Shidan Liu, Wei-Ming Luo et al.· Frontiers in Energy Research· 0 citations
The increasing penetration of distributed energy resources and networked microgrids (MGs) has amplified the vulnerability of cyber-physical energy systems to operational disturbances and control-dependent interactions. While microgrid reconfiguration (MR) is widely used to enhance system resilience, existing approaches rarely quantify how different control architectures influence physical vulnerability under identical network conditions. This paper proposes a unified cyber-physical vulnerability assessment framework for MR based on a composite integrated system vulnerability (ISV) index. The ISV combines dynamic instability, operational limit violations, and frequency deviation into a single physically interpretable metric. MR is formulated as an optimization problem that minimizes ISV, subject to AC power flow feasibility, radiality, operational constraints, and a bounded number of switching actions. The framework is applied to a benchmark multi-microgrid system operating under both centralized and decentralized control architectures. For each candidate topology, tie switch positions are evaluated by recomputing ISV under the corresponding control mode, enabling a fair and architecture-aware comparison. Simulation results demonstrate that identical reconfiguration actions can yield markedly different vulnerability levels depending on the control structure. Additional stress scenarios with increased loading further reveal how vulnerability escalates as operational margins shrink. The results highlight the critical role of control-topology interaction in MG vulnerability and provide a systematic methodology for control-aware reconfiguration of cyber-physical energy systems.
Kiarash Pourramezani, B. Vahidi, H. Baghaee et al.· Scientific Reports· 0 citations
An Adaptive SDN-Edge 5G Architecture (ASE-5G) is proposed that integrates SDN programmability with edge-assisted control-plane coordination while preserving compatibility with the 3rd Generation Partnership Project (3GPP) service-based architecture.
Vivi Monita, Naufal Hanan, Lutfianto et al.· 0 citations
The practical architecture known as software‐defined networking (SDN) enables the Internet of Things (IoT) to function in various applications. Also, SDN has been adopted for effective routing in wireless networks. The controller intends to work using an algorithm to offer secure routing. However, some existing algorithms must provide optimized and secured routing paths. This study presents a new method for selecting the most suitable route by combining the Markov Chain Model (MCM) with reinforcement learning techniques (MCM‐RLA). The aim is to ensure that the chain and reward functions align with the Quality of Service (QoS). The reward regarding the following successive routing path is analyzed where SDN‐enabled IoT enhances the routing based on the prior routing ideas. Moreover, the entire network is managed via the network remotely. The performance of the anticipated is compared with various prevailing approaches. Multiple metrics like packet delivery rate (PDR), network lifetime, routing overhead, energy efficiency, and delay are compared to attain suitable WSN performance via efficient routing.
M. Meenakshi Dhanalakshmi, M. Karthiga· International Journal of Com...· 0 citations
Industrial thermal-storage systems require communication mechanisms that can prioritize control traffic according to both network conditions and the physical urgency of the associated thermal process. This study proposes a Thermal-State-Aware Dynamic Routing (TSDR) method that integrates thermal criticality with link delay, jitter, packet-delivery probability, congestion, energy cost, and fault risk in a constrained multi-criteria path-selection framework. The proposed method is deterministic and rule-based; it combines multi-criteria routing, deterministic queue scheduling, threshold-based fault detection, precomputed backup paths, route hysteresis, and explicit recovery-confirmation rules, and it does not employ a trained artificial-intelligence or machine-learning model. Evaluation was conducted in a hybrid physical–emulation Hardware-in-the-Loop environment comprising three physical 500-L thermocline tanks, 45 measurement devices, PLC-based control and industrial networking hardware, 14 physical communication endpoints, 146 NS-3-emulated nodes, and synchronized MATLAB/Simulink and reduced-order CFD thermal models. A total of 480 independently initialized runs were completed across four routing architectures and ten operating or fault conditions. Compared with the strongest baseline, namely TSN-enabled network-state adaptive routing, the proposed method reduced P95 end-to-end latency from 98 to 64 ms (34.7%), jitter from 32 to 24 ms (
25.0
%
), and fault-recovery time from 6.1 to 2.9 s (
52.5
%
). Packet Delivery Ratio increased from
97.3
%
to
99.1
%
, while deadline misses decreased from
5.8
%
to
1.9
%
. Physical temperature-tracking RMSE decreased from
0.81
to
0.62
∘
C
, Energy Utilization Factor increased from
0.84
to
0.95
, and measured auxiliary power decreased by
9.1
%
. Mixed-effects analyses identified statistically significant differences under the evaluated HIL conditions, with moderate-to-large effect sizes for the principal outcomes. Emulation-based scalability tests maintained the 100 ms routing deadline at 640 logical nodes; this result is EMU evidence and does not represent a physically deployed 640-node system. Under the base techno-economic scenario, the projected incremental investment produced an NPV of approximately USD 104,000, an IRR of
41.8
%
, a simple payback of
2.32
years, and an estimated annual reduction of
68.3
tCO
2
e
. These financial and environmental outcomes are scenario-based projections, while the technical findings apply only to the evaluated hybrid HIL configuration.
Abed Saif Ahmed Alghawli, Ali Raza, Altahir Saad Ahmed et al.· Frontiers in Human Dynamics· 0 citations
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