scholarly journals ANN based LFC with Coordination strategies of DERs in Hybrid Isolated Micro-Grid Environment

2021 ◽  
Vol 309 ◽  
pp. 01036
Author(s):  
Srikanth Boyini ◽  
Srividya Devi Palakaluri ◽  
Rekha Mudundi
Keyword(s):  
Power System ◽  
Frequency Control ◽  
Active Power ◽  
Load Frequency Control ◽  
Injection System ◽  
Renewable Energy Resources ◽  
Interconnected System ◽  
Load Frequency ◽  
Power Injection ◽  
Micro Grid

This paper provides a load frequency control (LFC) of a micro grid with renewable energy resources (RES). The operation of micro grid with a low inertia system leds to disturbances in power system. The disturbances in frequency is more in micro grid than conventional power system. So there should be a fast recovery of changes in frequency with existing system and interconnected system (RES). Active power injection is the main scheme to control frequency of a system. The matlab simulink tells us that different active power injection system contribute for the fast control of grid frequency with PID controller. The use of ANN technology to this system the load frequency control can be illustrated in faster rate of its recovery. An ANN controller is investigated which handles the inputs collectively in each sector of the power system. Back-transmission time is normally used in the study for neural network education. The performance of the power system is simulated independently with a typically integrated conventional controller and ANN controller. A complete spectrum of small signals is introduced for RESs in the isolated microgrid and a correct role in frequency control studies is taken into account.

scholarly journals STUDI ANALISIS GOVERNOR SEBAGAI LOAD FREQUENCY CONTROL PADA PLTG MENGGUNAKAN FUZZY LOGIC CONTROLLER

2018 ◽  
Vol 17 (1) ◽  
pp. 107
Author(s):  
Gusti Made Ngurah Christy Aryanata ◽  
I Nengah Suweden ◽  
I Made Mataram
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Fuzzy Logic Controller ◽  
Frequency Control ◽  
Pi Controller ◽  
Active Power ◽  
Load Frequency Control ◽  
Electrical Power System ◽  
Load Frequency ◽  
Frequency Changes

A good electrical power system is a system that can serve the load in a sustainable and stable voltage and frequency. Changes in frequency occur due to the demand of loads that change from time to time. The frequency setting of the PLTG power system depends on the active power charge in the system. This active power setting is done by adjusting the magnitude of the generator drive coupling. The frequency setting is done by increasing and decreasing the amount of primary energy (fuel) and carried on the governor. Simulation in governor analysis study as load frequency control at PLTG using fuzzy logic controller is done by giving four types of cultivation that is 0,1 pu, 0,2pu, 0,3 pu and 0,4 pu. The simulation is done to compare the dynamic frequency response output and the resulting stability time using fuzzy logic controller with PI controller. Based on the results of comparative analysis conducted to prove that governor as load frequency control using fuzzy logic control is better than using PI controller. This can be seen from the output response frequency and time stability.

scholarly journals Load frequency control of a hydro dominating interconnected power system

2020 ◽  
Author(s):  
◽  
Milan Joshi
Keyword(s):  
Power System ◽  
Closed Loop ◽  
Frequency Control ◽  
Linear Quadratic Regulator ◽  
Load Frequency Control ◽  
Accurate Estimation ◽  
Renewable Energy Resources ◽  
Linear Quadratic ◽  
Random Load ◽  
Load Frequency

Energy is one of the vital figures that impact the development of civilization in the 21st century. It has been projected that by the year 2050, global energy needs will be satisfied by renewable sources. Among these renewable energy resources hydropower is available worldwide with relatively cheaper accessibility for most of the communities. Nevertheless, hydropower's control architecture raises concern for the system operators in terms of preserving the Load Frequency Control (LFC) services due to the elongated response time of hydro turbines in catering for the varying load demands. The varying load demands are inevitable in the power system due to different clients’ energy consumption patterns at different times. This, therefore, places changing control framework requests as per the requirement of diverse clients. Hence, the research proposes and demonstrates the connection of the hydro-hydro framework through the AC tie- line for LFC. The Linear Quadratic Regulator (LQR) is a plan for hydro overseeing framework in discrete mode. The application derived is displayed through closed- loop feedback gains and closed-loop eigenvalues. In the expansion model, the positive effect of a Unified Power Flow Controller (UPFC) and Redox Flow Battery (RFB) in LFC studies is investigated. This proposition moreover shows the joint endeavors of Fuzzy Logic (FL) as well as Proportional Integral Derivative (PID), with control gains well-calculated, through Particle Swarm Optimization (PSO) result into a robust FL-PSO-PID for LFC of the connected hydro framework. The different errors are defined to assess the yield as well as the execution of the FL-PSO-PID. The yield appears through a decline in blunder values as well as minimization in framework responses from accurate estimation for the LFC under various working conditions such as non- linearity, random load alteration, and parametric move as a result of a precise estimate. In the expansion, the effect of energy storage devices is also investigated to understand the enhancement provided frequency control of the hydro system, and the result obtained shows their effectiveness. Finally, the outcomes and future extent of this investigation work have been presented.

Load Frequency Control of Interconnected Power System Using Teaching Learning Based Optimization

2015 ◽  
Vol 4 (1) ◽  
pp. 102-117 ◽  
Author(s):  
Adhit Roy ◽  
Susanta Dutta ◽  
Provas Kumar Roy
Keyword(s):  
Power System ◽  
Frequency Control ◽  
Load Frequency Control ◽  
Pid Controllers ◽  
Thermal System ◽  
Interconnected System ◽  
Interconnected Power System ◽  
Load Frequency ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

This paper presents the design and performance analysis of teaching learning based optimization (TLBO) algorithm based PID controller for load frequency control (LFC) of an interconnected power system. A two area reheat thermal system equipped with PID controllers which is widely used in literature is considered for the design and analysis purpose. The design objective is to improve the transient performance of the interconnected system. The power system dynamic performance is analyzed based on time response plots achieved with the implementation of designed optimal and sub-optimal LFC regulators in the wake of 1% load disturbance in one of the areas. The results of the TLBO optimized PID controllers on a two area reheat thermal system are compared with those of artificial bee colony (ABC) and differential evolution (DE) optimized PID controllers. The TLBO optimized controllers are found to be superior in terms of peak transient deviation, settling times, and dynamic oscillations.

scholarly journals Robust exponential load frequency control for time delay power system considering wind power

2020 ◽  
Vol 53 (2) ◽  
pp. 12536-12541
Author(s):  
Li Jin ◽  
Xingchen Shang-Guan ◽  
Yong He ◽  
Chuan-Ke Zhang ◽  
Lin Jiang ◽  
...  
Keyword(s):  
Time Delay ◽  
Power System ◽  
Wind Power ◽  
Frequency Control ◽  
Load Frequency Control ◽  
Load Frequency

scholarly journals Load Frequency Control Using Hybrid Intelligent Optimization Technique for Multi-Source Power Systems

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1581
Author(s):  
Deepak Kumar Gupta ◽  
Amitkumar V. Jha ◽  
Bhargav Appasani ◽  
Avireni Srinivasulu ◽  
Nicu Bizon ◽  
...  
Keyword(s):  
Power Plant ◽  
Power System ◽  
Frequency Control ◽  
Optimization Technique ◽  
Load Frequency Control ◽  
Frequency Deviation ◽  
Source Power ◽  
Load Frequency ◽  
Tie Line

The automatic load frequency control for multi-area power systems has been a challenging task for power system engineers. The complexity of this task further increases with the incorporation of multiple sources of power generation. For multi-source power system, this paper presents a new heuristic-based hybrid optimization technique to achieve the objective of automatic load frequency control. In particular, the proposed optimization technique regulates the frequency deviation and the tie-line power in multi-source power system. The proposed optimization technique uses the main features of three different optimization techniques, namely, the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), and the Gravitational Search Algorithm (GSA). The proposed algorithm was used to tune the parameters of a Proportional Integral Derivative (PID) controller to achieve the automatic load frequency control of the multi-source power system. The integral time absolute error was used as the objective function. Moreover, the controller was also tuned to ensure that the tie-line power and the frequency of the multi-source power system were within the acceptable limits. A two-area power system was designed using MATLAB-Simulink tool, consisting of three types of power sources, viz., thermal power plant, hydro power plant, and gas-turbine power plant. The overall efficacy of the proposed algorithm was tested for two different case studies. In the first case study, both the areas were subjected to a load increment of 0.01 p.u. In the second case, the two areas were subjected to different load increments of 0.03 p.u and 0.02 p.u, respectively. Furthermore, the settling time and the peak overshoot were considered to measure the effect on the frequency deviation and on the tie-line response. For the first case study, the settling times for the frequency deviation in area-1, the frequency deviation in area-2, and the tie-line power flow were 8.5 s, 5.5 s, and 3.0 s, respectively. In comparison, these values were 8.7 s, 6.1 s, and 5.5 s, using PSO; 8.7 s, 7.2 s, and 6.5 s, using FA; and 9.0 s, 8.0 s, and 11.0 s using GSA. Similarly, for case study II, these values were: 5.5 s, 5.6 s, and 5.1 s, using the proposed algorithm; 6.2 s, 6.3 s, and 5.3 s, using PSO; 7.0 s, 6.5 s, and 10.0 s, using FA; and 8.5 s, 7.5 s, and 12.0 s, using GSA. Thus, the proposed algorithm performed better than the other techniques.

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