“…This part produces almost 70% of the total power of the turbine. In Figure 2(b), the inlet steam chest, reheater, and crossover models are shown with transfer functions T 1 , T 2 , and T 3 , respectively as follows [52], [53], [54], [55], [56]:…”
Section: A Modeling Of Steam Turbine Componentsmentioning
confidence: 99%
“…and transmission systems is not considered in the primary frequency control. In generator-turbine i, the relationship between the input-output power and its frequency will be as Equation ( 4) according to the fluctuation equation per unit [52], [53], [54], [55], [56].…”
Section: B the Conventional Structure Of Over-frequency Control Based...mentioning
confidence: 99%
“…Equation ( 4) around the working point can be written as Equation ( 5) for electrical and mechanical power changes. Assume M = 2H [52], [53], [54], [55], [56].…”
Section: B the Conventional Structure Of Over-frequency Control Based...mentioning
confidence: 99%
“…Therefore, system load changes will include a component proportional to the frequency change and an independent component as Equation ( 6). Here, D is the load-damping constant or loadfrequency sensitivity coefficient [52], [53], [54], [55], [56].…”
Section: B the Conventional Structure Of Over-frequency Control Based...mentioning
confidence: 99%
“…In [52], to maintain the voltage-frequency stability of the power system, an ANNbased PD controller is used and its results are compared with the magnetotactic bacteria optimization algorithm. In [53], to maintain the network frequency within the allowed range due to resonance attacks, ANN based on an observer sliding mode controller is used, in which resonance uncertainties are considered as input and it is tried to use ANN to detect and reduce them. In [54], the neural PID controller is used to control the frequency of the network, where the ANN consists of three hidden layers with 15 neurons, and its results are compared with the fuzzy controller.…”
Secondary frequency control systems, such as Automatic Generation Control (AGC), are used in interconnected power grids. However, when a system failure causes systems to separate into zones (islands), AGC can no longer be used, and the primary frequency control is the only control available. Moreover, load changes may cause frequency drop in some areas and over-frequency in other areas. Therefore, the goal in this article will be to design a neural network-based proportional, integral, and derivative (PID) controller in the primary control architecture to control the over frequency condition. The proposed controller is adaptively optimized in two stages by the honey badger algorithm (HBA). In the first stage, the PID controller gain values are optimized by the HBA algorithm for different values of load loss. While in the second stage, a feed-forward artificial neural network (ANN) is trained to match the tie-line measured power to the corresponding optimized HBA-PID gains obtained in the first stage. Finally, the proposed controller is implemented on a two-area interconnected thermal power system. The proposed controller results qualitatively outperform one of the best tuning methods, the Ziegler-Nichols (ZN) approach, and they show that the proposed controller has better dynamic responses with minimal frequency deviations and fast settling time, creating and guaranteeing a margin of stability for the closed loop.
“…This part produces almost 70% of the total power of the turbine. In Figure 2(b), the inlet steam chest, reheater, and crossover models are shown with transfer functions T 1 , T 2 , and T 3 , respectively as follows [52], [53], [54], [55], [56]:…”
Section: A Modeling Of Steam Turbine Componentsmentioning
confidence: 99%
“…and transmission systems is not considered in the primary frequency control. In generator-turbine i, the relationship between the input-output power and its frequency will be as Equation ( 4) according to the fluctuation equation per unit [52], [53], [54], [55], [56].…”
Section: B the Conventional Structure Of Over-frequency Control Based...mentioning
confidence: 99%
“…Equation ( 4) around the working point can be written as Equation ( 5) for electrical and mechanical power changes. Assume M = 2H [52], [53], [54], [55], [56].…”
Section: B the Conventional Structure Of Over-frequency Control Based...mentioning
confidence: 99%
“…Therefore, system load changes will include a component proportional to the frequency change and an independent component as Equation ( 6). Here, D is the load-damping constant or loadfrequency sensitivity coefficient [52], [53], [54], [55], [56].…”
Section: B the Conventional Structure Of Over-frequency Control Based...mentioning
confidence: 99%
“…In [52], to maintain the voltage-frequency stability of the power system, an ANNbased PD controller is used and its results are compared with the magnetotactic bacteria optimization algorithm. In [53], to maintain the network frequency within the allowed range due to resonance attacks, ANN based on an observer sliding mode controller is used, in which resonance uncertainties are considered as input and it is tried to use ANN to detect and reduce them. In [54], the neural PID controller is used to control the frequency of the network, where the ANN consists of three hidden layers with 15 neurons, and its results are compared with the fuzzy controller.…”
Secondary frequency control systems, such as Automatic Generation Control (AGC), are used in interconnected power grids. However, when a system failure causes systems to separate into zones (islands), AGC can no longer be used, and the primary frequency control is the only control available. Moreover, load changes may cause frequency drop in some areas and over-frequency in other areas. Therefore, the goal in this article will be to design a neural network-based proportional, integral, and derivative (PID) controller in the primary control architecture to control the over frequency condition. The proposed controller is adaptively optimized in two stages by the honey badger algorithm (HBA). In the first stage, the PID controller gain values are optimized by the HBA algorithm for different values of load loss. While in the second stage, a feed-forward artificial neural network (ANN) is trained to match the tie-line measured power to the corresponding optimized HBA-PID gains obtained in the first stage. Finally, the proposed controller is implemented on a two-area interconnected thermal power system. The proposed controller results qualitatively outperform one of the best tuning methods, the Ziegler-Nichols (ZN) approach, and they show that the proposed controller has better dynamic responses with minimal frequency deviations and fast settling time, creating and guaranteeing a margin of stability for the closed loop.
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