Four-area Interconnected Power System Research Articles

Distributed model predictive load frequency control of multi-area power system with DFIGs

Reliable load frequency control U+0028 LFC U+0029 is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control U+0028 DMPC U+0029 based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints U+0028 GRCs U+0029, load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed–loop performance, and computational burden with the physical constraints.

A new intelligent online fuzzy tuning approach for multi-area load frequency control: Self Adaptive Modified Bat Algorithm

The primary aim of the Automatic Generation Control (AGC) is to maintain system frequency and tie-line interchanges in a predestine limits by regulating the power generation of electrical generators, in case of fluctuations in the system frequency and tie-line loadings. This paper proposes a new online intelligent strategy to realize the control of multi-area load frequency systems. The proposed intelligent strategy is based on a combination of a novel heuristic algorithm named Self-Adaptive Modified Bat Algorithm (SAMBA) and the Fuzzy Logic (FL) which is used to optimally tune parameters of Proportional–Integral (PI) controllers which are the most popular methods in this context. The proposed controller guaranties stability and robustness against uncertainties caused by external disturbances and impermanent dynamics that power systems face. To achieve an optimal performance, the SAMBA simultaneously optimizes the parameters of the proposed controller as well as the input and output membership functions. The control design methodology is applied on four-area interconnected power system, which represents a large-scale power system. To evaluate the efficiency of the proposed controller, the obtained results are compared with those of Proportional Integral Derivative (PID) controller and Optimal Fuzzy PID (OFPID) controller, which are the most recent researches applied to the present problem. Simulation results demonstrate the successfulness and effectiveness of the Online-SAMBA Fuzzy PI (MBFPI) controller and its superiority over conventional approaches.

Model Predictive Control for Load Frequency Control with Wind Turbines

Reliable load frequency (LFC) control is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control (DMPC) based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The scheme incorporates the two critical nonlinear constraints, for example, the generation rate constraint (GRC) and the valve limit, into convex optimization problems. Furthermore, the algorithm reduces the impact on the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and that without the participation of the wind turbines is carried out. Good performance is obtained in the presence of power system nonlinearities due to the governors and turbines constraints and load change disturbances.

Interval type-2 fuzzy PID load frequency controller using Big Bang–Big Crunch optimization

This paper proposes an optimization based design methodology of interval type-2 fuzzy PID (IT2FPID) controllers for the load frequency control (LFC) problem. Hitherto, numerous fuzzy logic control structures are proposed as a solution of LFC. However, almost all of these solutions use type-1 fuzzy sets that have a crisp grade of membership. Power systems are large scale complex systems with many different uncertainties. In order to handle these uncertainties, in this study, type-2 fuzzy sets, which have a grade of membership that is fuzzy, have been used. Interval type-2 fuzzy sets are used in the design of a load frequency controller for a four area interconnected power system, which represents a large power system. The Big Bang–Big Crunch (BB–BC) algorithm is applied to tune the scaling factors and the footprint of uncertainty (FOU) membership functions of interval type-2 fuzzy PID (IT2FPID) controllers to minimize frequency deviations of the system against load disturbances. BB–BC is a global optimization algorithm and has a low computational cost, a high convergence speed, and is therefore very efficient when the number of optimization parameters is high as presented in this study. In order to show the benefits of IT2FPID controllers, a comparison to conventional type-1 fuzzy PID (T1FPID) controllers and conventional PID controllers is given for the four-area interconnected power system. The gains of conventional PID and T1FPID controllers are also optimized using the BB–BC algorithm. Simulation results explicitly show that the performance of the proposed optimum IT2FPID load frequency controller is superior compared to the conventional T1FPID and PID controller in terms of overshoot, settling time and robustness against different load disturbances.

Four-Area Load Frequency Control of an Interconnected Power System Using Neuro-Fuzzy Hybrid Intelligent Proportional and Integral Control Approach

AbstractThis article presents a novel control approach, hybrid neuro-fuzzy (HNF), for the load frequency control (LFC) of a four-area interconnected power system. The advantage of this controller is that it can handle nonlinearities, and at the same time, it is faster than other existing controllers. The effectiveness of the proposed controller in increasing the damping of local and inter-area modes of oscillation is demonstrated in a four-area interconnected power system. Areas 1 and 2 consist of a thermal reheat power plant, whereas Areas 3 and 4 consist of a hydropower plant. Performance evaluation is carried out by using fuzzy, artificial neural network (ANN), adaptive neuro-fuzzy inference system, and conventional proportional and integral (PI) control approaches. Four different models with different controllers are developed and simulated, and performance evaluations are carried out with said controllers. The result shows that the intelligent HNF controller has improved dynamic response and is at the same time faster than ANN, fuzzy, and conventional PI controllers.

Automatic Generation Control Using ANN Technique for Multi-Area Power System with SMES Units

This paper presents an application of layered artificial neural network (ANN) controller to study automatic generation control (AGC) problem of a four-area interconnected power system in which three areas include steam turbines and the other area includes a hydro turbine, and all of the areas include superconducting magnetic energy storage(SMES) units. In the study, back propagation-through-time algorithm is used to cope with the non-linear dynamics. As the system equations are satisfactory, no ANN emulator is used. The performance of the power system is simulated by using conventional integral controllers and ANN controller. By comparing the results of simulations, the performance of the ANN controller is better than conventional controllers.

A robust controller design of SSSC for stabilization of frequency oscillations in interconnected power systems

As an AC interconnected power system is subjected to a large load with rapid change, system frequency may be severely disturbed and becomes oscillatory. To stabilize the frequency oscillations, the dynamic power flow control of the Static Synchronous Series Compensator (SSSC) located in series with the tie line between interconnected power systems, is employed. By regarding the system interconnection as the control channel, the power flow control by an SSSC via the interconnection creates a sophisticated method of frequency stabilization. To implement this concept, the robust design method of the lead/lag controller equipped with the SSSC is proposed. In the design process, not only the attenuation performance of system disturbances, but also the robust stability against system uncertainties are taken into consideration. The optimal parameters of the lead/lag controller are obtained by using the tabu search algorithm (TSA), so that both performance and robustness are satisfied. In addition, the technique of overlapping decompositions is applied to reduce the order of the study power system, meanwhile the physical characteristic is still preserved. Simulation study exhibits the significant effect of designed controller on the study four-area interconnected power system under different load disturbances and variation of system parameters.

The application of ANN technique to automatic generation control for multi-area power system

This paper presents an application of layered artificial neural network controller (ANN) to study automatic generation control (AGC) problem in a four-area interconnected power system that three areas include steam turbines and the other area includes a hydro turbine. Each area of steam turbine in the system contains the reheat effect non-linearity of the steam turbine and the area of hydro turbine contains upper and lower constraints for generation rate. Only one ANN controller, which controls the inputs of each area in the power system together, is considered. In the study, back propagation-through-time algorithm is used as ANN learning rule. By comparing the results for both cases, the performance of ANN controller is better than conventional controllers.

Area load frequency control using fuzzy gain scheduling of PI controllers

This paper presents a new approach to study the area load frequency control (LFC) problem using fuzzy gain scheduling of proportional-integral (PI) controllers. The control scheme adopts a formulation for the area control error which always guarantees zero steady state time error and inadvertent interchange. The proposed control has been designed for a four-area interconnected power system with control deadbands and generation rate constraints. Simulation results confirm the designed control performance of the proposed controller.