scholarly journals Load Frequency control(LFC) of a Multiarea Restructured Hybrid Powersystem on Multi objective SSA

2019 ◽  
Vol 9 (2S3) ◽  
pp. 395-402
Keyword(s):  
Frequency Control ◽  
Test System ◽  
Optimization Techniques ◽  
Load Frequency Control ◽  
Dynamic Responses ◽  
Double Integral ◽  
Load Frequency ◽  
Multiobjective Approach ◽  
Integral Controller ◽  
Frequency Controller

The aimof the paper tune the paramters of the load frequency controller using a latest and novel algorithm named as Salp sarm of algorithm with multiobjective approach. The test system choosen is a Two area interconnected hybrid power system under deregulated-environment integrated with Distributd genertion (DG) resource.The DG systems consists of Windturbine generator(WTG), SolarPV systems, Diesels engines generators(DEG), Fuelcells with Aqua electrolyzers and Energy storages like Batteries energy storage systems(BESS). To minimise the frequency of oscillations, Secondarycontroller opted was an optimal Fuzzy PID plus double integral controller (FPID-II). The effectiveness of proposed controller is determined with the comparison of nominal PI, PID and Two degree freedom PID (TDOFPID) controller. Furthermore the dynamic responses of SSA tuned FPID-II controller are been compared with other optimization techniques. The results depit the superiority of the proposed controller in suppressing the deviations of frequency

scholarly journals Central Tunicate Swarm NFOPID-Based Load Frequency Control of the Egyptian Power System Considering New Uncontrolled Wind and Photovoltaic Farms

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3604
Author(s):  
Hady H. Fayek ◽  
Panos Kotsampopoulos
Keyword(s):  
Power System ◽  
Fractional Order ◽  
Frequency Control ◽  
Optimization Techniques ◽  
Load Frequency Control ◽  
The Novel ◽  
Operating Condition ◽  
Load Frequency ◽  
Non Linear ◽  
Fractional Order Pid

This paper presents load frequency control of the 2021 Egyptian power system, which consists of multi-source electrical power generation, namely, a gas and steam combined cycle, and hydro, wind and photovoltaic power stations. The simulation model includes five generating units considering physical constraints such as generation rate constraints (GRC) and the speed governor dead band. It is assumed that a centralized controller is located at the national control center to regulate the frequency of the grid. Four controllers are applied in this research: PID, fractional-order PID (FOPID), non-linear PID (NPID) and non-linear fractional-order PID (NFOPID), to control the system frequency. The design of each controller is conducted based on the novel tunicate swarm algorithm at each operating condition. The novel method is compared to other widely used optimization techniques. The results show that the tunicate swarm NFOPID controller leads the Egyptian power system to a better performance than the other control schemes. This research also presents a comparison between four methods to self-tune the NFOPID controller at each operating condition.

scholarly journals PI and LQR controllers for Frequency Regulation including Wind Generation

2018 ◽  
Vol 8 (5) ◽  
pp. 3711 ◽  
Author(s):  
Semaria Ruiz ◽  
Julian Patiño ◽  
Jairo Espinosa
Keyword(s):  
Power Systems ◽  
Wind Turbines ◽  
Frequency Control ◽  
Test System ◽  
Performance Comparison ◽  
Load Frequency Control ◽  
Superior Performance ◽  
Frequency Regulation ◽  
Control Effort ◽  
Load Frequency

<pre>The increasing use of renewable technologies such as wind turbines in power systems may require the contribution of these new sources into grid ancillary services, such as Load Frequency Control. Hence, this work dealt with the performance comparison of two traditional control structures, PI and <span>LQR</span>, for secondary regulation of Load Frequency Control with the participation of variable-speed wind turbines. For this purpose, the doubly-fed induction generator wind turbine was modeled with additional control loops for emulation of the inertial response of conventional machines for frequency regulation tasks. Performance of proposed strategies was verified through simulation in a benchmark adapted from the <span>WSCC</span> 3 machines 9-bus test system. Results showed overall superior performance for <span>LQR</span> controller, although requiring more strenuous control effort from conventional units than PI control.</pre>

BFO Optimized Automatic Load Frequency Control of a Multi-Area Power System

2016 ◽  
pp. 369-412
Author(s):  
Pravat Kumar Ray ◽  
Sushmita Ekka
Keyword(s):  
Resource Utilization ◽  
Optimization Algorithm ◽  
Frequency Control ◽  
Load Frequency Control ◽  
Simultaneous Optimization ◽  
Computational Burden ◽  
Load Frequency ◽  
Speed Regulation ◽  
Integral Controller ◽  
Bacteria Foraging Optimization Algorithm

This chapter presents an analysis on operation of Automatic Load Frequency Control (ALFC) by developing models in SIMULINK which helps us to understand the principle behind ALFC including the challenges. The three area system is being taken into account considering several important parameters of ALFC like integral controller gains (KIi), governor speed regulation parameters (Ri), and frequency bias parameters (Bi), which are being optimized by using Bacteria Foraging Optimization Algorithm (BFOA). Simultaneous optimization of certain parameters like KIi, Ri and Bi has been done which provides not only the best dynamic response for the system but also allows us to use much higher values of Ri than used in practice. This will help the power industries for easier and cheaper realization of the governor. The performance of BFOA is also investigated through the convergence characteristics which reveal that that the Bacteria Foraging Algorithm is quite faster in optimization such that there is reduction in the computational burden and also minimal use of computer resource utilization.

scholarly journals Design of 2-Dof Pid Controller for Load Frequency Control of Two Area Power System using Mfo Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 158-161
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Algorithm ◽  
Pid Controller ◽  
Frequency Control ◽  
Load Frequency Control ◽  
The Other ◽  
Load Frequency ◽  
Integral Controller ◽  
Moth Flame Optimization Algorithm ◽  
Tie Line

The paper endeavours to analyse the load frequency control for two area system. In this paper, two areas has been considered in which non-reheated type of turbine in both area are used and whose secondary loop consists a latest controller called 2 degree-of-freedom PID (2-DOF-PID) controller. The parameter of the this controller is been optimized by the latest meta heuristic algorithm also called Moth flame optimization algorithm (MFO) to minimize the deviation in frequency of area and tie-line power respectively. The same processes are repeated with PID controller and Integral controller whose parameters are also optimized by MFO. A comparison is made among the result of these and 2-DOF-PID controller prove its superiority over the other controller for minimizing the deviation which occurs in frequency of the area as well as the tie-line power.

scholarly journals Multivariable predictive control considering time delay for load-frequency control in multi-area power systems

2016 ◽  
Vol 26 (4) ◽  
pp. 527-549 ◽  
Author(s):  
Sabah Daniar ◽  
Mojtaba Shiroei ◽  
Rahmat Aazami
Keyword(s):  
Time Delay ◽  
Power Systems ◽  
Predictive Control ◽  
Frequency Control ◽  
Test System ◽  
Load Frequency Control ◽  
Generation Rate ◽  
Economic Allocation ◽  
Load Frequency ◽  
New Formulation

Abstract In this paper, a multivariable model based predictive control (MPC) is proposed for the solution of load frequency control (LFC) in a multi-area interconnected power system. The proposed controller is designed to consider time delay, generation rate constraint and multivariable nature of the LFC system, simultaneously. A new formulation of the MPC is presented to compensate time delay. The generation rate constraint is considered by employing a constrained MPC and economic allocation of the generation is further guaranteed by an innovative modification in the predictive control objective function. The effectiveness of proposed scheme is verified through time-based simulations on the standard 39-bus test system and the responses are then compared with the proportional-integral controller. The evaluation of the results reveals that the proposed control scheme offers satisfactory performance with fast responses.

scholarly journals Design of various Automatic Control Topologies for Load Frequency Control of Six Area Hybrid Multi-Generation Power System

2019 ◽  
Vol 9 (2) ◽  
pp. 1922-1928
Keyword(s):  
Hybrid Model ◽  
Fuzzy Inference ◽  
Control Strategies ◽  
Frequency Control ◽  
Load Frequency Control ◽  
Dynamic Responses ◽  
Inference System ◽  
Load Frequency ◽  
Neural Network Controller ◽  
Load Unit

The automatic load frequency control of six unequal areas hybrid model consist of thermal, reheat thermal plants, hydraulic governor system, nuclear, diesel, gas turbine plants. These generating units are represented from area one to area six. If any disturbance occurs in load results to variation of frequency and tie-line. When the generating area doesn’t meet load unit gives abnormal limits in response. This can be solved by the implementation of Proportional integral controller(PI), Artificial Neural Network controller(ANN), Adaptive Neuro Fuzzy Inference System controller (ANFIS).But still there is a chance to achieve better responses can be done by the proposed Genetic Algorithm (GA) based PI controller. In the MAT LAB environment hybrid model is developed. The Step varying input is applied that will leads to creating the disturbances and controlled by applying controller respectively. The evaluation of responses will be carried out by the control strategies of frequency responses and tie-lie power responses in all six areas hybrid model system. This work gives the information about the dynamic responses achieved by GA controller is more efficient than that of existing controllers and evaluated by MATLAB Simulink results.

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