Identification-based power unit model for load frequency control purposes

Hain, Y.; Kulessky, R.; Nudelman, G.

doi:10.1109/pess.2000.867620

Cited by 8 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed method is simple and practically feasible for implementation. A new method for process transfer function identification (TFI) and a new approach for developing a power unit's dynamic model based on new applications for the presented TFI method are given in [217]. The power unit dynamic model, identified using three simple experiments, is applied to load frequency control.…”

Section: Optimal Controlmentioning

confidence: 99%

A Comprehensive Review of Load Frequency Control Technologies

Rasolomampionona,

Połecki,

Zagrajek

et al. 2024

Energies

View full text Add to dashboard Cite

Load frequency control (LFC) is one of the most important tools in power system control. LFC is an auxiliary service related to the short-term balance of energy and frequency of power systems. As such, it allows the acquisition of a central role in enabling electricity exchanges and providing better conditions. The classification of LFC can be carried out from different angles: we can enumerate, among others, the type of control used. The following types of control are presented in this review: classical, optimal, and robust control. More advanced controls can also be used for classification: fuzzy logic control, ANN control, genetic algorithms, PSO control, etc. The influence of renewables and power control tools like FACTS is also considered as a category to be analyzed. The last classifications are related to two important subjects—the influence of DC links on LFC efficiency and the dangers of cyberattacks on the LFC.

show abstract

Section: Optimal Controlmentioning

confidence: 99%

A Comprehensive Review of Load Frequency Control Technologies

Rasolomampionona,

Połecki,

Zagrajek

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…For the AGC system depicted in Fig. 1 operating in steady-state condition, the relation between Q, U and P can be described by a multiple-inputs single-output model [16]- [18],…”

Section: Seady-state Data Segmentation and Model Parameter Estimamentioning

confidence: 99%

Steady-State Interval Detection and Nonlinear Modeling for Automatic Generation Control Systems

2019

View full text Add to dashboard Cite

Automatic generation control systems are designed to adjust electric power outputs of multiple generators simultaneously in accordance with the current load. However, the control instruction and the main steam pressure have significant impacts on the resulting active power generation of a conventional thermal generator, and the impacts may be associated with nonlinear characteristics. As a result, the control instruction requires an accurate modeling of the relationship between these three variables for a satisfactory control performance. This paper proposes a method to build a piecewise linear model for the nonlinear relationship from steady-state data hidden in historical data samples. The proposed method is composed by two main steps of steady-state interval detection and steady-state data segmentation. Historical data samples are grouped using the k-means clustering algorithm, and the time domains of each cluster are merged in a specific way to obtain the steady-state intervals. The steady-state data are taken as the samples means of data in the steady-state intervals. A bottom-up algorithm is utilized to partition the steady-state data into numbers of sets iteratively, and the parameters of the piecewise linear model for each data set are estimated by the least squares algorithm. The effectiveness of the proposed method is illustrated via industrial applications to two thermal power generation units.INDEX TERMS Automatic generation control system, piecewise linear model, steady-state interval detection, k-means clustering algorithm, bottom-up algorithm.

show abstract

“…Artificial neural networks are computational tools based on the properties of biological neural systems. Artificial neural network (ANN) applied to LFC presents in [20]. In a hybrid power system ANN is used as one of the two-loop controller for maximum power point tracking.…”

Section: Artificial Neural Network (Ann) Controlmentioning

confidence: 99%

Load frequency control issues in multiarea power system: A Review

Kosser¹

2018

IJTSRD

View full text Add to dashboard Cite

The modern electric grid is one the most complex man-made control systems. Automatic generation control plays an important role in power system operation to maintain the frequency within an acceptable range and to properly respond to load changes under normal conditions. Many publications have been made in the area of Load frequency control (LFC) of interconnected power systems. Load frequency control is necessary to develop better control in order to achieve less effect on the frequency and tie line power deviations after a load perturbation. However, number of control strategies has been employed in the design of load frequency controllers in order to achieve a better dynamic response and the exact choice of the LFC controller in a particular case requires sufficient expertise because each controller has its own merits and demerits. Due to this, an appropriate review of load frequency control (LFC) mechanism is essential and a few attempts have been made in this concern. This paper presents a detailed survey on load frequency control (LFC) mechanism. In this paper detailed analysis of various control methodologies based on classical control, robust and self-tuning control and various soft computing control techniques are discussed. Finally, the investigations on incorporating fast acting energy storage devices such as Battery energy storage system (BESS), superconducting magnetic energy storage (SMES), Redox flow batteries (RFB) and Flexible AC transmission systems (FACTS) devices for mitigating the LFC problems in a deregulated power system are also addressed.

show abstract

Identification-based power unit model for load frequency control purposes

Cited by 8 publications

References 12 publications

A Comprehensive Review of Load Frequency Control Technologies

A Comprehensive Review of Load Frequency Control Technologies

Steady-State Interval Detection and Nonlinear Modeling for Automatic Generation Control Systems

Load frequency control issues in multiarea power system: A Review

Contact Info

Product

Resources

About