Due to the rapid growth of renewable energy and its integration into the existing grid, the future power grid may face several stability challenges. Sub-synchronous oscillation (SSO) is one of the critical stability aspects that can disrupt the normal operation of the grid within a few grid cycles. Although the methods of SSO detection and its mitigation in conventional power systems are well defined, the interfacing of large number of power electronics-based devices at generation, transmission and distribution level has regenerated SSO issue introducing several new factors that can trigger different types of SSO. Thus, this paper proposes an energy based SSO assessment that analyzes the energy of the measured signal before and after the disturbance to effectively determine SSO in a timely manner. This approach is validated on both simpler and more complex power systems including single AC bus integrated with type-4 wind farm and standard IEEE 39 Bus network with type 4 wind farm, respectively. Various simulation case studies are presented to validate the effectiveness of proposed scheme in determining different types SSOs.

Recently, incidents of the sub-synchronous oscillation (SSO) occurrence have significantly increased in the power system integrated with renewable energy resources. In the current power system, as more distributed renewable energy resources replace conventional synchronous generator-based sources, the grid is transformed into a low-inertia power system. The low inertia and distributed nature of renewable energy systems contribute to weak grid conditions. In the type-4 wind turbine generators (WTG) SSO can originate due to the interaction of fast dynamics of power converter’s control and weak AC grid. If SSO is not detected and mitigated in a timely manner, it can cause severe damage to the WTG’s shaft, turbine structure, and can pose severe type of instability in the power system that may lead to cascaded tripping. Therefore, this paper presents a machine learning (ML) based scheme for fast and accurate detection of SSO and localizing the WTG’s control parameter that triggers SSO under the influence of weak AC grid. The proposed SSO detection and localization scheme features a dual neural network structure (DNNS) based on long short-term memory (LSTM). The first NN structure detects SSO and triggers second NN structure when SSO is detected. The second multiclass NN identifies the control parameter of WTG that triggered this SSO and provides a recommendation/ reference for the SSO mitigation scheme. The effectiveness of the proposed ML based SSO detection and localization scheme is verified via confusion matrix and simulation that analyzes different cases related to the ML scheme validation.

The modern power system dominated by renewable energy resources such as Type-4 wind farms (WF) has recently seen significant increase in cases of sustained oscillations at the sub-synchronous frequency range that is typically referred to sub-synchronous oscillations (SSOs) in the existing literature. Although thorough studies have been conducted on prior types of WFs to understand the triggering factors for SSO, however, in the case of Type-4 WFs the triggering factors and causes still remain unclear. Therefore, this paper studies the SSO characteristics exhibited by Type-4 WFs operating within a weak grid. To emulate real-world grid conditions, an extensive AC network modeled after the IEEE 39-bus system is employed. Through simulations incorporating the integration of Type-4 WFs at various locations within the grid, the resulting effects on SSO triggering factors are analyzed. The investigation is structured around three distinct case studies, conducted at buses 31, 37, and 38. These studies involve varying network reactance to explore and establish the relationship between SSO events and the short-circuit ratio (SCR) of the network. The consistent findings across diverse case studies underscore the general relationship between SCR, grid strength, and SSOs. The analysis reaffirms the significant impact of increased reactance on SSO characteristics and confirms that weaker grids are more susceptible to SSOs.

Grid-forming converter establishes a stable and controllable voltage at its output terminal without requiring external angle reference, which enables the GFC to be a candidate for providing black start services. However, this attribute poses significant challenges to the conventional power hardware-in-the-loop (PHIL) simulation, which incorporates the physical power converter by regulating its voltage angle to be synchronized with that of an interfacing power amplifier mimicking the real-time emulated power grid. The lack of voltage synchronization at the coupling point of GFC and interfacing power amplifier leads to instability. To address this challenge, the current-type interfacing method with compensation and scaling scheme is proposed to interface a GFC with soft black-start capability into a PHIL setup. Analytical assessment and experimental results involving interfacing a 90 kVA power converter implemented with grid-forming control are presented to verify the methodology.