Reactive power transfer allocation method with the application of artificial neural network

2008 ◽  
Vol 2 (3) ◽  
pp. 402 ◽  
Author(s):  
M.W. Mustafa ◽  
S.N. Khalid ◽  
H. Shareef ◽  
A. Khairuddin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reactive Power ◽  
Power Transfer ◽  
Allocation Method ◽  
Artificial Neural

scholarly journals Artificial-neural-network based unified power flow controller for mitigation of power oscillations

2021 ◽  
Vol 24 (3) ◽  
pp. 1323
Author(s):  
Vireshkumar Mathad ◽  
Gururaj Kulkarni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Flow ◽  
Reactive Power ◽  
Pi Controller ◽  
Unified Power Flow Controller ◽  
Power Oscillations ◽  
Artificial Neural ◽  
Flow Controller ◽  
Power Flow Controller

The series and shunt control scheme of unified power flow controller (UPFC) impacts the performance and stability of the power system during power swing. UPFC is the most versatile and voltage source converter device as it can control the real and reactive power of the transmission system simultaneously or selectively. When any system is subjected to any disturbance or fault, there are many challenges in damping power oscillation using conventional methods. This paper presents the neural network-based controller that replaces the proportional-integral (PI) controller to minimize the power oscillations. The performance of the artificial neural network (ANN) controller is evaluated on IEEE 9 bus system and compared with a conventional PI controller.

Neural network–based improved active and reactive power control of wind-driven double fed induction generator under varying operating conditions

2018 ◽  
Vol 42 (5) ◽  
pp. 381-396 ◽  
Author(s):  
Debirupa Hore ◽  
Runumi Sarma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reactive Power ◽  
Active Power ◽  
Operating Conditions ◽  
Induction Generator ◽  
Active And Reactive Power ◽  
Control Scheme ◽  
Artificial Neural ◽  
Double Fed Induction Generator

Artificial neural network–based power controllers are trained using back propagation algorithm for controlling the active and reactive power of a wind-driven double fed induction generator under varying wind speed conditions and fault conditions. Vector control scheme is used for control of the double fed induction generator. Here stator flux–oriented vector control scheme is implemented for the rotor side converter and grid voltage vector scheme is used for control of grid side converter using tuned proportional–integral active and reactive power controllers, which is later replaced by artificial neural network–based controllers. The artificial neural network controllers are trained using the data obtained from simulation of conventional proportional–integral controllers under varying operating conditions. The intelligent controller makes the generated stator active power to track the reference active power more precisely at specified power factor in both sub-synchronous and super-synchronous modes of operations. Simulation results reveal that the neural network–based controller significantly improves the performance of variable speed wind power generating double fed induction generator under various conditions.

Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network

2022 ◽  
pp. 728-748
Author(s):  
Gummadi Srinivasa Rao ◽  
Y. P. Obulesh ◽  
B. Venkateswara Rao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Load Flow ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this chapter, an amalgamation of artificial bee colony (ABC) algorithm and artificial neural network (ANN) approach is recommended for optimizing the location and capacity of distribution generations (DGs) in distribution network. The best doable place in the network has been approximated using ABC algorithm by means of the voltage deviation, power loss, and real power deviation of load buses and the DG capacity is approximated by using ANN. In this, single DG and two DGs have been considered for calculation of doable place in the network and capacity of the DGs to progress the voltage stability and reduce the power loss of the system. The power flow of the system is analyzed using iterative method (The Newton-Raphson load flow study) from which the bus voltages, active power, reactive power, power loss, and voltage deviations of the system have been achieved. The proposed method is tested in MATLAB, and the results are compared with particle swarm optimization (PSO) algorithm, ANN, and hybrid PSO and ANN methods for effectiveness of the proposed system.

scholarly journals Power quality improvement in an AC network using artificial neural network and hysteresis band current controller

2018 ◽  
Vol 38 (3) ◽  
pp. 42-49
Author(s):  
Abdul Mutal Sulehri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reactive Power ◽  
Phase Error ◽  
Reference Signal ◽  
Harmonic Distortion ◽  
Shunt Active Power Filter ◽  
Current Controller ◽  
Non Linear ◽  
Artificial Neural

This paper demonstrates a study to improve the total harmonic distortion (THD) originated due to excessive use of power electronic (PE) equipment and non-linear loads. Shunt active power filter (SAPF) is used to mitigate the harmonics from the system because it has the capability of minimizing the harmonic problems initiated by non-linear loads. The instantaneous reactive power (IRP) p-q theory is used for the generation of reference signal and for the extraction of compensating components of the current. The proportional integral (PI) controller and artificial neural network (ANN) have been employed in the DC-link controller and for current errors adjustments. In this paper, both conventional hysteresis and adaptive hysteresis band current controller (HBCC) have been used for the generation of gate pulses for the SAPF, which reduces THD in the source current to a value within IEEE specified standards, without any phase error over the extensive range of adaptive HBCC strategy. Simulation results confirm that the SAPF with HBCC and ANN performs the harmonic mitigation efficiently and maintains power factor (PF) close to unity. 

scholarly journals Islanding Detection in Distributed Generation Integrated Thimi – Sallaghari Distribution Feeder Using Wavelet Transform and Artificial Neural Network

2019 ◽  
Vol 15 (2) ◽  
pp. 55-61
Author(s):  
Basanta Pancha ◽  
Rajendra Shrestha ◽  
Ajay Kumar Jha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Power Quality ◽  
Distributed Generation ◽  
Reactive Power ◽  
External Signal ◽  
Islanding Detection ◽  
Detection Zone ◽  
Artificial Neural

In response to the problem of increased load demand, efforts have been made to decentralize the power utility through the use of distributed generation (DG). Despite the advantages of DG integration, un-intentional islanding remains a big challenge and has to be addressed in the integration of DG to the power system. Islanding condition occurs when the DG continues to power a part of the grid system even after the connection to the rest of the system has been lost, either intentionally or un-intentionally. The unintentional islanding mode of operation is not desirable as it poses a threat to the line workers’ safety and power quality issues. There are many methods which may be used to detect the islanding situation. Passive methods such as under/over voltage and under/over frequency work well when there is an imbalance of power between the loads and the DG present in the power island. However, these methods has larger Non Detection Zone (NDZ) and fail to detect the islanding condition if there is a balance of power supplied and consumed in the island. Remote technique of islanding detection is reliable but is not economical in small network area. Active technique of islanding detection distorts the power quality of the system as it introduces external signal in the system. This paper uses the Wavelet Transform (WT) to extract the features of voltage signal at PCC (Point of Common Coupling) and these features have been used to train Artificial Neural Network (ANN). The ANN model trained by these WT features, which understands the pattern of input feature vector, have been used to classify the islanding and non-islanding events. In this proposed method, NDZ has been efficiently eliminated which is created due to difference between active and reactive power during islanding condition. No power quality problem exists in this method as there is no disturbance injection. Hence, this proposed method is better than conventional passive and active methods.

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