Small Signal Stability Research Articles

Distributed Unified Controller Design for Parallel Battery Storage System in DC Shipboard Microgrid

In this paper, a novel distributed unified controller is designed to solve the problems of unbalanced State of Charge (SoC), unreasonable load current sharing, and unstable DC bus voltage for parallel battery storage systems (BSSs) in DC shipboard microgrid (DC-SMG). Different from the droop-based secondary controller, the designed distributed unified controller is a droop-free primary controller, which can incorporate SoC balancing, current sharing, and voltage regulation functions in the primary control layer. On this basis, each BSS uses an improved dynamic diffusion algorithm (DDA) to iteratively estimate the average information, and completes information exchange by a neighbor-to-neighbor communication network. Furthermore, the small-signal stability, large-signal stability, and global steady-state performance of the system is investigated using eigenvalue analysis, mixed potential theory (MPT), and matrix theory, respectively. Finally, the Matlab/Simulink simulation model and the StarSim HIL experimental platform for DC-SMG are built. The results show that the designed distributed unified controller can simultaneously achieve dynamic SoC balancing, proportional load current sharing, and smooth average bus voltage regulation, and has a faster SoC convergence speed and more stable control effect than the state-of-the-art methods.

Online Monitoring of Oscillation Modes for Small-Signal Security Assessment

This paper presents the main concepts of small-signal security assessment of power systems, focusing on the online monitoring of small-signal stability. In this work, a computational tool for online monitoring of oscillation modes (OLMO) in power systems is proposed. The objective of the methodology consists of determining small-signal stability margins, security levels, and corrective measures that can improve the damping factor of oscillation modes present in such systems. These corrective measures can be associated with possible redispatches of power plants. In this context, the Closest Security Boundary for Generation Redispatch using Eigenvalue Sensitivities, known as CSBGRES Method, could be applied to determine an optimized redispatch for the power plants of the system to ensure a desired minimum damping factor, previously chosen, for oscillation modes. The proposed tool can be applied for controlling power plant dispatches on Smart Grids, focusing on the damping factor of oscillation modes. A small test system is used to evaluate the proposed computational feature and the results were presented to demonstrate its importance to power system security. Results of the utilization of OLMO on the Brazilian Interconnected Power System (BIPS) are presented to prove the viability of using this method in large-scale power systems.

Development of Small Signal Model and Stability Analysis of PV-Grid Integration System for EV Charging Application

Development of Small Signal Model and Stability Analysis of PV-Grid Integration System for EV Charging Application

An Analytical Formula for Small Signal Stability Sensitivity Using SDP Dual

An Analytical Formula for Small Signal Stability Sensitivity Using SDP Dual

Optimum Coordination of Multi‐Band Power System Stabilizers and STATCOM Using the Modified Particle Swarm Algorithm

In this paper, to improve the small signal stability of the power system, the optimal coordination of multi‐band power system stabilizers (MB‐PSSs) and static synchronous compensator (STATCOM) has been done. The study is conducted on a standard four machines and two areas power system. To create coordination, a modified version of the particle swarm algorithm as well as the integral time absolute error (ITAE) criterion are proposed. To evaluate the proposed stability method in different operating conditions and in the form of two scenarios, a three‐phase fault with a duration of 200 ms has been applied in the communication line between the two areas. After the simulation, the values of overshoot, undershoot, settling time, and ITAE criteria for the proposed stability method have been calculated and their values have been compared with the results of power system stabilizer (PSS) and MB‐PSS stabilization methods. The results indicate that the proposed combined method of MB‐PSS and STATCOM performed better than the other two methods in improving the dynamic stability of the power system and has the lowest value of the criteria. The values of ITAE for the PSS, MB‐PSS stabilization methods and the proposed MB‐PSS and STATCOM combined method in the first scenario are equal to 0.002354, 0.0.001496, and 0.001242 p.u. and in the second scenario it is equal to 0.0.007418, 0.004015, and 0.003435 p.u., respectively.

Computing the Load Margin of Power Systems Using Golden Jackal Optimization

The Load Margin (LM) is an important stability index applied in power systems. The calculation of LM has always been associated with voltage stability studies, but the increase in load can also cause the emergence of poorly damped oscillation modes studied in stability at small signals that can destabilize the power system. This article proposes an optimization model to determine the LM considering jointly the voltage stability and small signal stability thresholds. The bio-inspired algorithm called Golden Jackal Optimization is applied to solve the optimization model. The IEEE 39-bus test system is used for case studies and discussions.

Performance Comparison of MIMO Gain/Phase Analysis Methods for Sub-Synchronous Instability

Recently, there has been great interest in the development of stability criteria for grid-connected power converters and generators using frequency-domain impedance models. We consider two recently introduced stability analysis methods and compare their small-signal stability performance for a DFIG wind farm connected to a series-compensated grid. The first method, known as the Low-Complexity Global Nyquist Condition, uses Gershgorin disks derived from the loop gain matrix to provide a stability criterion. The second method, introduced by the authors, uses a combined matrix gain and phase stability analysis. The two methods are compared by using them to predict the onset of instability in a series-compensated wind farm under a tripped line. The results reveal that the gain/phase based approach achieves less conservatism than the Nyquist-based method.

Effect of Control Damping on Small-Signal Stability of Grid-Forming VSCs Considering Interaction Between Inner and Outer Loops

Effect of Control Damping on Small-Signal Stability of Grid-Forming VSCs Considering Interaction Between Inner and Outer Loops

Probabilistic Analysis of Small-Signal Stability of Power Systems Affected by Wind Power Generation Uncertainties Considering Wake Effects

Probabilistic Analysis of Small-Signal Stability of Power Systems Affected by Wind Power Generation Uncertainties Considering Wake Effects

Aggregating a Renewable Power Station into a Single Generator for Quick Small-Signal Stability Evaluation

Aggregating a Renewable Power Station into a Single Generator for Quick Small-Signal Stability Evaluation

Enhancing the Small-Signal Stability of Power Grids via Optimally Coordinating Inverter-Based Resources

Enhancing the Small-Signal Stability of Power Grids via Optimally Coordinating Inverter-Based Resources

Inverter Intensive Hybrid Power Plant Modeling with Small-Signal Stability Augmentation through Flexible Operation Mode Transition

Inverter Intensive Hybrid Power Plant Modeling with Small-Signal Stability Augmentation through Flexible Operation Mode Transition

Impedance-Based Stability Analysis: Nodal Admittance or Bus Admittance?

Impedance-based stability analysis is widely adopted for small-signal stability demonstration of power electronics dominated power systems. Stability assessment can be performed using either nodal admittance or bus admittance (i.e. equivalent admittance of a network viewed from a specific bus). This paper demonstrates that stability analysis using the bus admittance may not always produce correct results, even though it is easier to implement. Unstable multi-bus systems can be wrongly identified as stable by the single-bus analysis, in the cases where other buses play a dominant role in causing instabilities. A detailed comparison between nodal and bus admittance stability analysis is presented, leading to recommendations for their practical use. A phase-based method is proposed to facilitate the acquisition of eigenloci encirclement around the critical point, which can be used in the stability analysis based on either nodal or bus admittance. Following the conclusion of our work, we recommend multi-bus analysis in white-box model applications for instability mode analysis and stability-oriented design. Single-bus analysis is preferable in black-box model applications due to its simplicity. It is also suggested to select a bus for analysis from those connected to a high share of inverter-based resources, to ensure the correctness of results.

State-Space Modeling and Small-Signal Stability Analysis of an Independent Microgrid with Multiple Distributed Generation Resources

State-Space Modeling and Small-Signal Stability Analysis of an Independent Microgrid with Multiple Distributed Generation Resources

Small-Signal Stability Analysis for Large-Scale Power Electronics- Based Power Systems

Small-Signal Stability Analysis for Large-Scale Power Electronics- Based Power Systems

Grid Strength Constrained Cooperative Control of Renewable Power Plants for Guaranteed Small-Signal Stability

Grid Strength Constrained Cooperative Control of Renewable Power Plants for Guaranteed Small-Signal Stability

Holistic Small-Signal Stability Analysis for Large-Scale Inverter-Intensive Power Systems with Coupled and Full-Order Dynamics from Control Systems and Power Networks

Holistic Small-Signal Stability Analysis for Large-Scale Inverter-Intensive Power Systems with Coupled and Full-Order Dynamics from Control Systems and Power Networks

Adaptive Stability Contingency Screening for Operational Planning Based on Domain-Adversarial Graph Neural Network

Compared to contingency screening (CS) techniques that focus on thermal/voltage limit violation, CS that addresses the transient and small-signal stability contingencies in the existing references requires a higher computational cost, limiting its use to off-line contingency analysis. Therefore, it poses the challenge of developing an effective CS scheme for fast recognition of critical contingency, which is one of the major concerns of system operators. To deal with this problem, this paper proposes an adaptive stability CS (SCS) scheme for operational planning. One challenge in developing the adaptive SCS for different cardinal points is data distribution discrepancy resulted from load/topology changes. To align data distribution between different domains (i.e., cardinal points), a domain-adversarial graph neural network (DAGNN) is developed to learn the domain-invariant features, so that the DAGNN model trained on the labeled source domain (e.g., peak cardinal point 2B) data can be applied to the unlabeled target domain (e.g., trough cardinal point 1B) data for SCS. To make the proposed SCS more efficient in dealing with power system data, a graph learning approach combined with graph transformer and graph isomorphism network is used in DAGNN to provide feature representations considering the graph properties of power systems, where nodes and edges refer to buses and transmission lines, respectively. Experiments on IEEE 39 Bus system and IEEE 118 Bus system have verified the effectiveness of the proposed model.

Account of additional factors for damping torsional oscillations

Some parameters are influenced by the turbine unit's torsional oscillations. The fundamental comes from damping these oscillations, which are brought on by a departure in the turbine blades' speed from the device's prediction of the steam volume, and attenuation of fluctuations due to the distribution of energy in the turbine's productive components. The usual single-machine infinite bus system is used for the analysis. For various turbine-generator shafts and various generator operating situations, rotating mass mechanical system evaluations for small-signal stability and large disturbance are conducted. It is demonstrated that the shaft's “structural' damping (H) and “steam' damping (Kn) coefficients have a considerable impact on the damping of torsional modes. The goal of this work is to determine the effect of changing the damping factors in the mathematical model of the steam turbine shaft on the system's static stability, as well as the extent to which these variables' limits on damping rotational oscillations on the maximum torsional torques generated in the shaft masses. The mathematical model of the steam turbine shaft with a single machine and transmission line to an infinite bus system was simulated using Dymola software, and the static and dynamic effects of damping factors (H) and (Kn) on system stability were demonstrated. By evaluating the best case for parameters with the least influence on the system's stability, the results were obtained by changing the factors (Kn) from 0.005 to 0.5 and (H) from 0.005 to 0.2 and the extent of its effect on the maximum torque of the steam turbine masses and reducing it by 8.4 %, as well as by reducing the settling time of the system after disturbances occur and reaching to Steady state by about 90 %.

Small Signal Stability Analysis of Kalimantan 500 KV Electricity System

Small signal stability refers to the ability of a system to return to equilibrium after experiencing a small disturbance. In this research, the Kalimantan electricity system will be analyzed for the stability of its small signal. Analysis of the stability of small signals in electrical systems including local disturbances and inter-area disturbances. The Kalimantan system will be analyzed using Power Factory software. System analysis was carried out by evaluating the eigenvalues ​​(real part and imaginary part), oscillation frequency (damped frequency and frequency ratio) produced in the Kalimantan 500 KV electricity analysis. In the analysis results, the Kalimantan system is categorized as stable as indicated by the real part and imaginary part values ​​located on the negative side of the Cartesian coordinate curve. Then, analyzing small signals, there are 117 modes categorized as local mode and 4 modes categorized as inter area mode.