An extreme learning machine based adaptive VISMA for stability enhancement of renewable rich power systems
- Authors: Setiadi, Herlambang , Shah, , Rakibuzzaman , Islam, Md Rabiul , Asfani, Dimas , Nasution, Tigor , Abdillah, Muhammad , Megantoro, Prisma , Krismanto, Awan
- Date: 2022
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 11, no. 2 (2022), p.
- Full Text:
- Reviewed:
- Description: Maintaining power system stability in renewable-rich power systems can be a challenging task. Generally, the renewable-rich power systems suffer from low and no inertia due to the integration of power electronics devices in renewable-based power plants. Power system oscillatory stability can also be affected due to the low and no inertia. To overcome this problem, additional devices that can emulate inertia without adding synchronous machines can be used. These devices are referred to as virtual synchronous machines (VISMA). In this paper, the enhancement of oscillatory stability of a realistic representative power system using VISMA is proposed. A battery energy storage system (BESS) is used as the VISMA by adding an additional controller to emulate the inertia. The VISMA is designed by using Fruit Fly Optimization. Moreover, to handle the uncertainty of renewable-based power plants, the VISMA parameters are designed to be adaptive using the extreme learning machine method. Java Indonesian Power Grid has been used as the test system to investigate the efficacy of the proposed method against the conventional POD method and VISMA tuning using other methods. The simulation results show that the proposed method can enhance the oscillatory stability of the power system under various operating conditions. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Sensitivity analysis for vulnerability mitigation in hybrid networks
- Authors: Ur‐rehman, Attiq , Gondal, Iqbal , Kamruzzaman, Joarder , Jolfaei, Alireza
- Date: 2022
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 11, no. 2 (2022), p.
- Full Text:
- Reviewed:
- Description: The development of cyber‐assured systems is a challenging task, particularly due to the cost and complexities associated with the modern hybrid networks architectures, as well as the recent advancements in cloud computing. For this reason, the early detection of vulnerabilities and threat strategies are vital for minimising the risks for enterprise networks configured with a variety of node types, which are called hybrid networks. Existing vulnerability assessment techniques are unable to exhaustively analyse all vulnerabilities in modern dynamic IT networks, which utilise a wide range of IoT and industrial control devices (ICS). This could lead to having a less optimal risk evaluation. In this paper, we present a novel framework to analyse the mitigation strategies for a variety of nodes, including traditional IT systems and their dependability on IoT devices, as well as industrial control systems. The framework adopts avoid, reduce, and manage as its core principles in characterising mitigation strategies. Our results confirmed the effectiveness of our mitigation strategy framework, which took node types, their criticality, and the network topology into account. Our results showed that our proposed framework was highly effective at reducing the risks in dynamic and resource constraint environments, in contrast to the existing techniques in the literature. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.