The high penetration of photovoltaic (PV) in power grids typically leads to the displacement of traditional synchronous generators (SGs). However, with a high penetration of PV, fewer SGs are running, and the sharing of responsibility to control the system frequency is reduced and easily exacerbates the problem of reduced inertia response in the power system. This can increase frequency deviation when there is a load-generation imbalance or unexpected disturbances. The limited amount of inertial response from the PV generation means that it cannot provide the same frequency support as SGs. Therefore, this paper suggests a fast frequency control (FFC) technique for the battery energy storage system (BESS) to reduce the instantaneous frequency deviation (IFD) in the Ethiopian grid. The authors specifically provide knowledge of the modeling of droop-type controlled BESS, which can provide additional damping, enhance the inertial ability of the system, and reduce IFD. The simulation results indicate the effectiveness of BESS in mitigating the adverse inertial impact of PV and meeting the necessary grid requirement. The proposed method was tested on the Ethiopian power system model through simulations by using DIgSILENT simulation software. To validate the model, transient frequency simulation studies are performed for various fault conditions, and comparisons are made with and without BESS for different levels of PV penetration.

A method to distinguish between forced and natural oscillations using PMU data is proposed. In PMU measurements, forced and natural oscillations can appear very similar visually, but the remedial actions are very different for them. Therefore, it is important to be able to distinguish between them. Forced oscillations do not exhibit damping in the same way as natural oscillations which are governed by the electromechanical modes of the system. This fundamental difference between forced and natural oscillations is leveraged in this paper to distinguish them. The method fits a Least Squares Autoregressive Moving Average plus Sinusoid (LS-ARMA+S) model to the measurement data, which is used to extract damping ratios of the electromechanical modes from the colored background noise. The LS-ARMA+S method is selected because it can accurately separate an oscillation from the background noise without losing the modal information in the background noise at the oscillation frequency. The damping ratio is then used to classify the oscillation as forced or natural. The method is tested in two case studies; one simple transfer function system and one small test system with 4 generators. The results show that with careful selection of damping ratio threshold value, the method achieves more than 99 % correct classifications.

In power systems, maintaining transient stability is crucial to avoid unanticipated blackouts. The role of Transient Stability Assessment (TSA) is vital for quickly identifying and promptly addressing instabilities. TSA facilitates rapid reactions to serious fault conditions. This paper pioneers the integrated comparison of two distinct methodologies-Maximal Lyapunov Exponent (MLE) methods and Convolutional Neural Networks (CNN)-in a single unified framework for transient stability assessment in power systems, uniquely evaluating their accuracy and reliability for TSA. The CNN-based method uses measured time series data from voltage magnitude, phase angle, and frequency measurements at generator buses, while the MLE approach utilizes both phase angles and frequency of generator buses. This paper provides a qualitative and quantitative comparison of the performance and accuracy of MLE and CNN. This research utilizes the same case studies conducted on the Nordic32 system for both MLE and CNN to ensure robust, unbiased comparisons and promote interdisciplinary research, aligning with current trends in AI integration in power systems.

The progressive retirement of conventional power plants is decreasing the total system inertia and making the frequency control more delicate. In this context, voltage-sensitive loads will have a more pronounced impact on frequency. Therefore, there is an interest in understanding and quantifying how voltage dynamics impact the frequency response. For this purpose, a linearization of the system and the analysis of the obtained transfer functions are in focus. The paper aims to complement the traditional eigenvalue analysis, capture a system's response to large disturbances, and estimate the frequency overshoots. The study also shows that controllers affecting the load voltage, such as Power System Stabilizers (PSSs), can impact frequency response during a large disturbance. The impact of PSS on system responses and its transfer functions is carried out by applying a general control configuration. Both conventional PSS1A and multi-band PSS designs are analyzed and compared. Thereby, the paper explicitly interlinks a PSS design, and its parameters with properties of the system's frequency response reflected in the system transfer function. Illustrating enhancements facilitated by the presented approach, the explicitly derived PSS designs demonstrate the potential to reduce frequency overshoot. As an example, this reduction is achieved by improving a common mode damping or changing the position of the dominant zero of the closed-loop system.

In power systems, ensuring transient stability is paramount to prevent unforeseen blackouts and power failures. Transient stability assessment is crucial for the early detection and mitigation of instabilities, providing a rapid response to severe fault situations. The concept of the maximum Lyapunov exponent facilitates fast predictions for transient stability assessment after severe disturbances. This paper introduces an efficient maximum Lyapunov exponent algorithm for online transient stability assessment, representing the primary contribution of this work. This approach uses the time series data from the rotor angles of generators or the phase angles of generator terminal buses. Case studies are conducted on the Nordic Power System, with simulations performed in DigSilent PowerFactory. This study contributes by offering insights into the performance and adaptability of the proposed algorithm.

Forced oscillations can cause large power oscillations in power systems, it is therefore important to be able to accurately detect them on time. This paper evaluates the accuracy of frequency and damping ratio estimation from three methods for detecting forced oscillations. The Stochastic Subspace Identification method, a spectral method using Welch periodograms, and the self-coherence method. Simulations are performed on a small test network with full and partial, respectively, PMU observability when a forced oscillation is implemented as a sinusoidal load. The results indicate that all three methods are able to detect a forced oscillation with good accuracy, even when it has the same frequency as an inter-area mode. It was also found that the average estimation error increases somewhat when there is only partial PMU observability, and that selection of PMU locations can have an impact on the estimations.

High- Voltage DC has gained popularity due to its advantages, such as transferring bulk power over long distances with low transmission losses and improved stability. As a result, choosing the appropriate HVDC system model is of paramount importance. In this contribution, the authors used DIgSILENT power-factory simulation software to model the Ethiopia-Kenya line commutated converter HVDC. The Newton-Raphson algorithm was applied for numerical analysis and calculation. To validate the model, transient simulation studies are performed for various fault conditions and comparisons are made with the case where HVDC is installed and without HVDC interconnection. Critical clearing time has been given special consideration in this comparison. According to the overall simulation results, installing HVDC between Ethiopia and Kenya improves the Ethiopian power grid's transient stability by increasing the critical clearing time in comparison to the case without HVDC links.

Real-time monitoring of transient stability is crucial in power systems to avoid sudden blackout and power failure. Previously developed algorithms based on the concept of Maximum Lyapunov Exponent and Synchrophasor Measurements claim to have fast prediction of transient stability after a severe disturbance. In this paper, formerly established techniques for real-time transient stability assessment are implemented to the IEEE-39 bus test system for various fault case scenarios. Based on the obtained results from these techniques, a comparative analysis is performed to determine accuracy, effectiveness and robustness of each technique.

This paper introduces a generic method for detection of Voltage Impasse Region(s) (V IR) that may appear when power system dynamics are represented by a set of differential algebraic equations. The recently introduced V IR denotes a singular area where voltage causality is lost due to the nonlinear modelling of static loads. Yet, the existing analytical study can predict V IR when only one load is of nonlinear type. To detect V IR for multiple nonlinear static (or ZIP) loads in a more general sense, this paper proposes an idea of a quasi-dynamic approach that maps the state-space. The newly introduced method assigns a discrete index V IRflag to each inspected state-space location and estimates V IR based on V IRflag values. By deploying the developed approach, this study structurally analyses the impact that various load nonlinearities have on V IR. As such, the paper is primarily of educational character and it thoroughly examines (and visualizes) the features of V IR by using IEEE-9 bus test system. The new method retains the accuracy of the analytical approach while becoming fairly general. Moreover, the performed study demonstrates that the cumulative amount of nonlinear load appears to be the most influential contributor to V IR existence and size.

Frequency Containment Reserves might be insufficient to provide an appropriate response in the presence of large disturbances and low inertia scenarios. As a solution, this work assesses the supplementary droop frequency-based Emergency Power Control (EPC) from HVDC interconnections, applied in the detailed Nordic Power System model. EPC distribution and factors that determine the EPC performance of an HVDC link are the focus of interest. The main criteria are the maximum Instantaneous Frequency Deviation and used EPC power. The presented methodology is motivated based on the theoretical observation concerning linearized system representation. However, the assessed and proposed properties of interest, such as provided EPC active and reactive power, their ratio, and energy of total loads and losses in the system due to the EPC, concern highly nonlinear system behavior. Finally, based on the obtained study, remarks on the pragmatical importance of the EPC distribution to the frequency nadir limitation are provided.