Offshore wind farms are growing in complexity and size, expanding deeper into maritime environments to capture stronger and steadier wind energy. Like other domains in the energy sector, the wind energy domain is continuing to digitalize its systems by embracing Industry 4.0 technologies such as the Industrial Internet of Things (IIoT), virtualization, and edge computing to monitor and manage its critical infrastructure remotely. Adopting these technologies creates dynamic, scalable, and cost-effective data-acquisition systems. At the heart of these data-acquisition systems is a communication network that facilitates data transfer between communicating nodes. Given the challenges of configuring, managing, and troubleshooting large-scale communication networks, this review paper explores the adoption of the state-of-the-art software-defined networking (SDN) and network function virtualization (NFV) technologies in the design of next-generation offshore wind farm IIoT–Edge communication networks. While SDN and NFV technologies present a promising solution to address the challenges of these large-scale communication networks, this paper discusses the SDN/NFV-related performance, security, reliability, and scalability concerns, highlighting current mitigation strategies. Building on these mitigation strategies, the concept of resilience (that is, the ability to recover from component failures, attacks, and service interruptions) is given special attention. The paper highlights the self-X (self-configuring, self-healing, and self-optimizing) approaches that build resilience in the software-defined IIoT–Edge communication network architectures. These resilience approaches enable the network to autonomously adjust its configuration, self-repair during stochastic failures, and optimize performance in response to changing conditions. The paper concludes that resilient software-defined IIoT–Edge communication networks will play a big role in guaranteeing seamless next-generation offshore wind farm operations by facilitating critical, latency-sensitive data transfers.

This article implements and validates the performance of a virtual intelligent electronic device (vIED) framework for digital substations using a real-time (RT) simulation environment. The work looks toward the future design of protection, automation, and control systems, an evolution of the digital substation design based on IEC 61850 and virtualization technology. An RT simulation setup was developed to speed up and enhance the deployment and maintenance of vIEDs with a novel well-defined test methodology. Several scenarios were tested by varying the number of vIEDs and relevant (communication, scalability, and functionality) configuration criteria. In addition, we assessed the efficiency of a software-defined communication network for the vIED framework and its adaptability to dynamic scaling of the network under transient data traffic loads. The tests demonstrated the efficient performance of vIEDs across various system configurations, particularly for requirements on the response time and network transfer latency. The findings showcase the significance of proper design and testing methodology that can be benchmarked against other virtualization platforms for substation systems.

Next-generation offshore wind farms are increasingly adopting vendor-agnostic software-defined networking (SDN) to oversee their Industrial Internet of Things Edge (IIoT-Edge) networks. The SDN-enabled IIoT-Edge networks present a promising solution for high availability and consistent performance-demanding environments such as offshore wind farm critical infrastructure monitoring, operation, and maintenance. Inevitably, these networks encounter stochastic failures such as random component malfunctions, software malfunctions, CPU overconsumption, and memory leakages. These stochastic failures result in intermittent network service interruptions, disrupting the real-time exchange of critical, latency-sensitive data essential for offshore wind farm operations. Given the criticality of data transfer in offshore wind farms, this paper investigates the dependability of the SDN-enabled IIoT-Edge networks amid the highlighted stochastic failures using a two-pronged approach to: (i) observe the transient behavior using a proof-of-concept simulation testbed and (ii) quantitatively assess the steady-state behavior using a probabilistic Homogeneous Continuous Time Markov Model (HCTMM) under varying failure and repair conditions. The study finds that network throughput decreases during failures in the transient behavior analysis. After quantitatively analyzing 15 case scenarios with varying failure and repair combinations, steady-state availability ranged from 93% to 98%, nearing the industry-standard SLA of 99.999%, guaranteeing up to 3 years of uninterrupted network service.

Stochastic disruptions such as flash events arising from benign traffic bursts and switch thermal fluctuations are major contributors to intermittent service degradation in software-defined industrial networks. These events violate IEC 61850-derived quality of service requirements and user-defined service-level agreements, hindering the reliable and timely delivery of control, monitoring, and best-effort traffic in IEC 61400-25-compliant wind power plants. Failure to maintain these requirements often results in delayed or lost control signals, reduced operational efficiency, and increased risk of wind turbine generator downtime. To address these challenges, this study proposes a threshold-triggered Deep Q-Network self-healing agent that autonomically detects, analyzes, and mitigates network disruptions while adapting routing behavior and resource allocation in real time. The proposed agent was trained, validated, and tested on an emulated tri-clustered switch network deployed in a cloud-based proof-of-concept testbed. Simulation results show that the proposed agent improves disruption recovery performance by 53.84% compared to a baseline shortest-path and load-balanced routing approach, and outperforms state-of-the-art methods, including the Adaptive Network-based Fuzzy Inference System by 13.1% and the Deep Q-Network and Traffic Prediction-based Routing Optimization method by 21.5%, in a super-spine leaf data-plane architecture. Additionally, the agent maintains switch thermal stability by proactively initiating external rack cooling when required. These findings highlight the potential of deep reinforcement learning in building resilience in software-defined industrial networks deployed in mission-critical, time-sensitive application scenarios.

Stochastic disruptions such as flash events arising from benign traffic bursts and switch thermal fluctuations are major contributors to intermittent service degradation in software-defined industrial networks. These events violate IEC~61850-derived quality-of-service requirements and user-defined service-level agreements, hindering the reliable and timely delivery of control, monitoring, and best-effort traffic in IEC~61400-25-compliant wind power plants. Failure to maintain these requirements often results in delayed or lost control signals, reduced operational efficiency, and increased risk of wind turbine generator downtime. To address these challenges, this study proposes a threshold-triggered Deep Q-Network self-healing agent that autonomically detects, analyzes, and mitigates network disruptions while adapting routing behavior and resource allocation in real time. The proposed agent was trained, validated, and tested on an emulated tri-clustered switch network deployed in a cloud-based proof-of-concept testbed. Simulation results show that the proposed agent improves disruption recovery performance by 53.84% compared to a baseline shortest-path and load-balanced routing approach and outperforms state-of-the-art methods, including the Adaptive Network-based Fuzzy Inference System by 13.1% and the Deep Q-Network and traffic prediction-based routing optimization method by 21.5%, in a super-spine leaf data-plane architecture. Additionally, the agent maintains switch thermal stability by proactively initiating external rack cooling when required. These findings highlight the potential of deep reinforcement learning in building resilience in software-defined industrial networks deployed in mission-critical, time-sensitive application scenarios.