Sensitivity‐based reliability coordination for power systems considering wind power reserve based on hybrid correlation control method for wind power forecast error

Summary This work demonstrates the impact and robust coordination control among the Doubly Induction Generator (DFIG) and Synchronous Generators (SGs) with Power Oscillation Damping (POD), Power System Stabilizer (PSS),and STATic synchronous COMpensator (STATCOM) on the critical Low‐Frequency Oscillations (LFOs). These modes occurred due to system uncertainty, which leads to power flow interruption and experiences instability. It is mitigated by the proposed location and optimal coordinated optimized gain parameters of the controller. The locations are determined using the deterministic method and power flow sensitivity analysis (PSS and STATCOM). The objective function for the Genetic Algorithm (GA) is chosen as the real part and damping ratio of eigenvalue deviation of rotor speed. The small‐signal stability is assessed by using eigenvalue analysis via linearization of a nonlinear system. The transient stability is performed through time‐domain simulation by creating a three‐phase fault. Both analyses are examined with and without POD, PSS, and STATCOM and their combination by substituting SG with the same capacity of Wind Farm (WF). It is also indicated that a combination of POD, PSS, and STATCOM enhances system stability. It is also observed that the WF could not significantly involve in the modes of oscillation alike to the SG but provides the damping and beneficial toward the stability. GA and optimal location improve the system damping characteristics over a wide range of system uncertainty. The effectiveness and robustness of the proposed approaches are verified by considering a two‐area modified test system. Highlights This work demonstrates the impact and robust coordination control among the DFIG and SGs with POD, PSS and STATCOM on the critical LFOs caused by the intermittent nature of wind power. The locations are determined using the deterministic method and power flow sensitivity analysis (PSS and STATCOM). The objective function for the GA is chosen as the real part and damping ratio of eigenvalue and rotor speed deviation. The obtained results show enhancement in system stability.

“…The behavior of the dynamic test system is calculated at the equilibrium operating point, and the system stability is distinctly observed 14 that is expressed as:

\dot{x} = f ((),,, x, ε, u), 0 = normalg ((),,, x, ε, u), normaly = normalh ((),, x, ε)

where x, ε, u, and y are the system state variables, algebraic variables, input and output variables.…”

Section: Linearization Of the Nonlinear Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parameter tuning of PSS and STATCOM controllers using genetic algorithm for improvement of small‐signal and transient stability of power systems with wind power

Bhukya

Mahajan

2021

“…2 The rapid movements in the wind power outputs are closely related to the rapid movements in the load power and other power resources during the regulation process. 3 Thus, the regulation of the conventional generators to follow the loads is affected by the wind power outputs. Due to the insufficient ramp rate, the conventional generators are usually difficult to respond to the rapid power fluctuations, which in turn significantly limits the widely integration of the wind power.…”

Section: Introductionmentioning

confidence: 99%

Smoothing control of wind power fluctuations with battery energy storage system of electric vehicles

Wang

Dong

et al. 2021

With the rapid development of wind industry, its integration causes inevitable and rapid power fluctuations in the power grid. To promote the integration of wind power and investigate the energy storage capability of electric vehicles (EVs), a smoothing control strategy is proposed to smooth the wind power fluctuations considering the battery energy storage system of EVs (EESS). Based on a proposed maximum operation area of an individual EV considering its traveling behaviors and traveling comfort level, the response parameters are defined to describe the response characteristics of the EV aggregator (EVA). The EESS can operate as an energy storage system composed of several EVAs with different response parameters. A smoothing control strategy is then developed to smooth the wind power fluctuations with the EESS. Considering the power fluctuation rate and the response capability of the EESS, the required power change of each EVA in the EESS is determined in the smoothing control. Then the required power output change is allocated to individual EVs in the EVA with the developed SOC-adaptive method and the power output limits constrained by the maximum operation area of each EV. Comparative results are provided to validate the effectiveness of the proposed smoothing control strategy. The EESS is able to smooth the wind power fluctuations, and the power output limits and the traveling comfort levels of individual EVs are ensured with the SOC-adaptive method and the constraints from the maximum operation areas. K E Y W O R D Selectric vehicle (EV), energy storage system of EVs (EESS), power fluctuation, smoothing control, SOC-adaptive method, wind power

“…The sensitivity analysis method is split into two methods such as perturbation method 17 and partial derivative method, 18 which are used to evaluate the reliability indices. Several reliability indices are calculated in Reference 19 and are given as ENS (energy not supplied), SAIDI (system average interruption duration index), SAIFI (system average interruption frequency index), and ASAI (average service availability index) 20,21 …”

Section: Introductionmentioning

confidence: 99%

Islanding‐based reliability enhancement and power loss minimization by network reconfiguration using PSO with multiple DGs

Ramachandradurai

Kumarappan

2021

The distributed generation (DG) units are used more in the electrical networks to reduce the energy demand along with reducing the system losses and increasing the voltage profile of the load points. This paper presents an unintentional islanding detection, reliability enhancement, and power loss minimization of the restored system after an islanding event. The detection of islanding is performed by passive detection method of simultaneous measurement of voltage and frequency (vf)-based technique. The reliability of the system is increased along with the power loss minimization through reconfiguration by Particle Swarm Optimization (PSO) technique. The network reconfiguration is performed by identifying the most vulnerable buses, and reliability indices of the system are evaluated after the islanding event. The reliability indices act as a marker for validating the proposed reconfiguration strategy. The proposed method for islanding detection, reliability enhancement, and power loss minimization is compared with the existing methods available in the literature. The proposed method is tested on two different radial distribution system such as IEEE-69 and 118 bus systems, and the results obtained after the validation are promising.