Stochastic small disturbance stability analysis of nonlinear multi-machine system with Itô differential equation

Mi, Xiao; Wang, Jie; Wang, Rui

doi:10.1016/j.ijepes.2018.03.029

Cited by 14 publications

(4 citation statements)

References 25 publications

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“…Literature [91] established a reduced-order SDE model of the DC distribution system, derived the analytical solution of SDE, gave the mean and mean square stability criteria, and comprehensively characterized the relationship between stochastic excitation and system mean stability and mean square stability. Literature [92] conducted a stochastic small-disturbance stability analysis based on a nonlinear SDE model that is closer to the actual system, proved the mean stability and mean square stability under stochastic small disturbances, and derived the stability criteria of SEI.…”

Section: Stochastic Differential Equations Withmentioning

confidence: 99%

Classification Study of New Power System Stability Considering Stochastic Disturbance Factors

Li,

Duan,

Gao

et al. 2023

Sustainability

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Power system instability causes many local or large-scale power outage accidents. To maintain sustainable development, a new power system construction aimed at maximizing new energy consumption is being put on the agenda. However, with a large increase in stochastic disturbance factors (SDFs), the system gradually shows strong stochasticity, and the stability presents greater complexity. It is necessary to analyze the impact on the system based on different processing methods of SDFs to maintain system stability. This paper delves into the impact of SDFs on system stability by analyzing and summarizing both stochastic variables and processes. Initially, the SDFs in the power system are meticulously analyzed and categorized. When the SDFs are treated as stochastic variables, the probabilistic stability is classified and evaluated based on a probability analysis method, which includes the probabilistic small-disturbance stability, the probabilistic transient stability, and the probabilistic voltage stability. When the SDFs are treated as stochastic processes, the stochastic stability is classified and evaluated by using a stochastic analysis method, including the stochastic small-disturbance stability, the stochastic transient stability, and the stochastic voltage stability. Finally, the research perspectives on SDFs and system stability are discussed and prospected.

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Section: Stochastic Differential Equations Withmentioning

confidence: 99%

Classification Study of New Power System Stability Considering Stochastic Disturbance Factors

Li,

Duan,

Gao

et al. 2023

Sustainability

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“…The rotor stability is classified into small disturbance angle stability and transient stability. The rotor stability of machines in power systems can be determined analytically using Equation (9) (Mi, Wang & Wang, 2018).…”

Section: Rotor Stability In Power Systemmentioning

confidence: 99%

Unified Power Flow Controller: A Brief Review on Tuning and Allocation for Power System Stability

Sule

2023

ejtas

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The Power System can become unstable due to disturbances. To enhance system stability the Unified Power Flow Controller (UPFC) is tuned and allocated in the System. In this paper, a brief review of UPFC tuning and allocation studies for power systems stability is presented. The databases consulted for literature are the IEEE Xplore, ScienceDirect, Google Scholar and IOP Publications. The search terms used are Allocation, Tuning, UPFC, Power System and Stability to find the literature used in this review. A total of 26 Journal articles and conference papers were found and reviewed based on tuning and allocation studies. The Researchers applied Fuzzy coordination, Genetic Algorithm (GA), Particles Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Linear Quadratic Tracker (LQT) to tune the UPFC for enhancing power system stability. For studies on UPFC allocation in power systems, the Researchers applied frequency response of power system transfer function, power flow, Tabu Search (TS), PSO and GA. For allocation based on optimization, the Researchers minimized power losses, voltage index and investment costs considering equality and inequality constraints.

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“…Here, the power system is modeled with CPSS coordinated by SSSC which is tuned using GWO and to compare it was also simulated with CPSS only and without it. The results have been tabulated and compared with different conditions [7].…”

Section: A Introductionmentioning

confidence: 99%

Small Signal Stablity Analysis in SMIB & 3 Machine 6 Bus System using GWO Optimization Technique

Lodha¹

2019

IJRASET

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A power system is a complicated network which has the task of fulfilling various consumer power demands continuously. But in its process too many disturbances occurs. These disturbances may be a common single line to ground fault, double line, double line to ground fault, three phase fault or it could be small signal disturbances. To eliminate it is utmost importance to resume the continuity of power supply which in turn ensures its reliability. Hence, a power system is always equipped some kind of controllers to come back to its original operating status after this disturbances. Here, a SMIB system more specifically a Modified Phillip-Heffron's model is presented which has Static Synchronous Series Compensator (SSSC) as the controller. To further improve its dynamic performance a nature inspired optimization technique is utilized. This is Grey Wolf Optimization (GWO). The system is tested for electrical power deviation, power angle deviation and speed deviation under two different cases. The first case is when reference voltage setting is increase by 5% step and the second case is the step increase of 10% in mechanical torque. The results of GWO based SSSC are compared and tabulated with SMIB without any controller and with Gravitational Search Algorithm (GSA) based SSSC. The further analyses consider the more real scenario of multi machine system (MMPS) which has 3 machine and 6 bus. It utilizes GWO based coordinated damping controller consisting of SSSC and PSS. This model is tested under different fault conditions as mentioned above and then its results are compared with MMPS without any controller and with conventional power system stabilizer (CPSS). For simulation both the above model of SMIB and MMPS MATLAB/SIMULINK is used.

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Stochastic small disturbance stability analysis of nonlinear multi-machine system with Itô differential equation

Cited by 14 publications

References 25 publications

Classification Study of New Power System Stability Considering Stochastic Disturbance Factors

Classification Study of New Power System Stability Considering Stochastic Disturbance Factors

Unified Power Flow Controller: A Brief Review on Tuning and Allocation for Power System Stability

Small Signal Stablity Analysis in SMIB & 3 Machine 6 Bus System using GWO Optimization Technique

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