Recently evolutionary algorithms are used more often. Differentialevolution (DE) is a method that repeats the attempt to find optimal points by differentiating a criterion. In classical optimization methods, such as gradient and Newton, a derivative operator is used, while in results of DE the quality criterion search method is used . DE optimizes a problem by maintaining a population of candidate solutions and creating new candidate solutions by combining existing ones according to its simple formulae, and then keeping the candidate solution which has the best score or fitness on the optimization problem at hand. In this way, an optimization problem is treated as a black box that merely provides a measure of quality given by a solution, therefore the gradient is not needed.
In this study, a new grey wolf optimized LFC has been investigated for automatic loadfrequencycontrol of a two areainterconnectedpower systems. It is shown analytically and graphically that there is a substantial improvement in the time domain specification in terms of lesser rise time, peak time, settling time as well as a lower overshoot. The proposed controller using GWO algorithm with PID controller is proved to be better than the conventional PI controller. The simulation results are given to validate the disturbances for LFC. From the simulation results, the tabulated settling time of 2% and 3% disturbances are shown in graphical representation. Therefore, the proposed GWO-PID controller is recommended to generate good quality and reliable electric energy. In addition, the proposed controller is very simple and easy to implement since it does not require many information about system parameters. Comparison of the proposed GWO-PID controller with Genetic, Particle Swarm, Flower Pollination, Fire Fly, Ant Colony Optimization Algorithms along with PI-PID controllers in multi-areainterconnectedpowersystem will be subject to the future work.
2 are under unchanged condition. Fig 7 shows that the variation in frequency of area 3. The simulation proved that the presented FOPID controller improves the operation of multiareainterconnectedpowersystem by reducing the steady state error and oscillations present in the responses. Table V displays the evaluation of settling time for the change in frequency of all area, for 0.1 p.u change in load demand of area-3 with different controllers. The settling time and peek overshoot is reduced for the interconnected three areapowersystem with FOPID controller than the PID controller.
The crucial objectives of load-frequencycontrol (LFC) to a multi-areainterconnectedpowersystem are to maintain the systemfrequency at a nominal value (50 Hz or 60 Hz) and the tie-line power flows at predetermined values. Based on tie-line bias control strategy, conventional regulators, such as I, PI and PID, were initially used for solving the LFC problem. Due to the complexity, nonlinearity and uncertainty of a multi-areapowersystem in practice, the conventional regulators may not obtain the control performances good enough to bring the network back to the steady state as soon as possible. Meanwhile, intelligent controllers, such as fuzzy logic (FL)-based controllers, are able to completely replace these conventional counterparts. The superiority of the FL-based LFC controllers over the conventional ones for a typical case study of five-areainterconnectedpower grids is validated in this paper through numerical simulations implemented in Matlab/Simulink package. It should be apparent from this comparative study that the LFC controller based on FL technique is a feasible selection in dealing with the LFC problem of a multi-areapower network.
are decreased so that the regional control error will pass zero after a while to ensure the stability of the entire system. This paper constructs the model for three areas with hydro, thermal, and wind power. The IEEE standard mathematical model for hydro and thermal power used in this paper is composed of the governor, water turbine, steam turbine, and generator individually. The schematic diagrams of loadfrequencycontrol models for thermal- and hydro-powers are shown in Fig. 1 and Fig. 2 –.
In the powersystem operation and control the main constraints to be satisfied is to maintain the systemfrequency and terminal voltages within the specified limits. The powersystem should not only ensure a better quality but also reliable power supply by maintaining the above mentioned constraints. According to powersystemcontrol theory, a nominal systemfrequency depends on the balance between generated and consumed real power . If the amount of generated power is less than the demanded amount, speed and frequency of the generator units begin to decrease, and vice versa. Hence, the amount of power produced by each generators should take care in minimizing the frequency deviations occurred in the powersystem due to sudden change in load. For this purpose, loadfrequencycontrol is being adopted and the main aim of this control is that the steady state error of the systemfrequency deviations following a step load demand should be made zero in a faster manner. The stabilization of frequency oscillations in an interconnectedpowersystem becomes challenging when implemented in the future environment due to unpredictable load demand. So advance economic, high efficiency and improved control schemes [2, 3] are required to ensure the powersystem reliability. Several indices have been proposed as methods to evaluates the success of frequencycontrol between linked systems. For instance, the AR based standard
A is system matrix, B is the input distribution matrix and Γ disturbance distribution matrix,𝑥 is the state vector,𝑢 is the control vector and 𝑑 is the disturbance vector of load changes of appropriate dimensions. The typical values of system parameters for nominal operation condition are given in appendix. This study focuses on optimal tuning of controllers for LFC and tie-powercontrolusing settling time based optimization to ensure a better powersystem restoration assessment. On the other hand in this study the goals are to control the frequency and inter area tie-power with good oscillation damping using the modern control theory concept and also to obtain a good performance under all operating conditions with various loading conditions and finally to design a low-order controller for easy implementation. To achieve the above said conditions a Feasible Restoration Index (FRI) and Complete Restoration Index (CRI) based on the settling time has been formulated in this proposed methodology in TATURIPS with the SMES unit and GT unit in Area-1and
In literature, control strategies based on conventional and fuzzy logic controller are proposed . Several authors suggest variable-structure systems, various adaptive control techniques and Riccati equation approach for load a frequency controller design [6, 7]. There are many studies about different control strategies having advantages and disadvantages [1, 2, 5, 8-10]. In Reference , a loadfrequencycontrolusing a conventional PID controller is applied and it is emphasized that the controller performance is better than others. However, if a powersystem structure has nonlinear dynamics and parts, the system operating point varies and conventional controllers needing system model must not be used. In Reference , a modified dynamic neural networks controller is proposed. It is determined that the proposed controller offers better performance than conventional neural network controller. In Reference , for a single areasystem and two areas interconnectedpower systems, artificial intelligence techniques are purposed for the automatic generation control and the comparison is performed between intelligent controllers and the conventional PI and PID controllers. In Reference , a robust decentralized control strategy is used for Loadfrequencycontrol for four areapower systems to obtain robust stability and better performances. In References [1, 8], powersystemloadfrequencycontrol is realized by fuzzy logic controller.
The loadfrequencycontrol applies single areasystem and also multiareasystem. Past year loadfrequencycontrol implemented without controller and optimization technique. Today we apply modern control technique of loadfrequencycontrol. Modern technique maintains frequency when any change of load. To achieve this many controller such as PI, PID controller and many optimization technique as particle swarm optimization technique, genetic algorithm, fuzzy logic controller, artificial neural network, differentialevolution etc. are used.
Online fuzzy PI controller tuning using the bat algorithm method is presented in [ 24 ]. In addition, an artificial neural network is used to controlpower generation in a multi-area thermal power plant system in [ 25 ] and a third-order observer-based sliding mode control in [ 26 ]. An adaptive neural fuzzy controlsystem was applied to improve LFC in both hydro and thermal power systems in [ 27 ]. In [ 28 ], a novel PID-like neural network controller is proposed. The resilient back-propagation algorithm with sign instead of the gradient is used to derive the rule of updating network weights. The simulation experiment was carried out on an inverted-pendulum system. Reference [ 29 ] presents an adaptive PID-like controller using a modified neural network for learning of system dynamics. This controller applied to speed control of DC machine. Moreover, a simplified adaptive neuro-fuzzy inference system (ANFIS) structure acting as a PID-like feedback controller to control nonlinear systems is presented in [ 30 ]. In fact, all of the above-mentioned studies focused on system dynamics. However, in our proposed method in this paper, the controller is not dependent on the system dynamic parameters, and system parameters are assumed to be uncertain, and the controller is designed online. The proposed controller is adaptive and adapts itself to system new conditions. It can be applied in practical and real-world applications including the standard 39-bus New England system that has satisfied our expectations despite nonlinearity of the powersystem. Furthermore, the proposed controller is based on type-2 neural fuzzy, which performs better than type-1 fuzzy and models more uncertainties. The control parameters were trained by the descending gradient method and the error back-propagation posture so that the areacontrol error signal could be zero. Jacobian of the system model is extracted and is applied to the controller. Then, the control error signal is applied as a PID to the controller input in order to increase the speed and compensate for the slower speed due to parameter training. Then, the suggested method is compared with the PI, IMC-PID and PID controllers for evaluation and comparison. Finally, to show effectiveness of proposed control, we applied this method on New England test system 39-bus, which is widely used as a standard system for testing of the powersystem analysis and control. Compared to our previous work in [ 31 ], the distinct features of this paper are as follows:
This paper presents the FLC using PI-fuzzy controllers. The proposed controller is tuned using PSO to obtain the controller gains in order to get an efficient fuzzy control on four of an interconnected electrical powersystem. This is a new approach to optimize the fuzzy controller that differentiates to other's methods. The simulation results are carried out in term frequency response for its damping under different load conditions and compared it to the effectiveness of proposed controllers with other controllers. Simulation results show that the undershot and settling times with the proposed controller are better and guarantees robust performance under a wide range of operating conditions.
areainterconnectedsystem which consists of reheat turbines and generation constraints have been studied. The inputs to the proposed NN controller are system state variables and disturbance vector. Back propagation-through-time algorithm has been used to cope with the continuous time dynamics as the learning rule. In using neural networks for dynamic powersystemcontrol, since it contains large number of parallel input vector, the total system may be too complicated. This is initially designed for a fuzzy logic controller in the loadfrequencycontrol of the powersystem. A specified control scheme has been designed for a two areainterconnectedpowersystem with control dead zone . A combined fuzzy logic and NN based controller for LFC have also been designed using conventional AreaControl Error (ACE) Criterion. A new type of control scheme called Control Performance Criteria (CPC) has been used to evaluate LFC performance, which is of great importance as high quality control in required in the present day power transfer applications. The Control Performance Standard (CPS) is specifically designed to comply with the performance standards imposed by the North American Electric Reliability Council (NERC) for equitable operation of an interconnectedsystem. Fuzzy logic system is usually designed to assure that the control performance is in compliance with NERC’s control performance standards [8,9]. Considering the powersystemloadfrequencycontrol, this paper establishes a recurrent neural network model to predict the future frequency of the target object, thus forecasting the ACE and the CPS standard index. Based on this prediction, the optimized controller is designed, which follows the CPS performance standards through the fuzzy logic control. Simulation results show the effectiveness of the proposed method.
frequencycontrol is constructed for multiareapowersystem with matched and mismatched parameter uncertainties. The proportional and integral switching surface is designed for each area to enhance the dynamic performance through reducing the chattering and overshoot during reaching phase. The controller design process has been theoretically proved based on Lyapunov stability theorem. Robustness of the proposed controller is illustrated by implementing it on the three areainterconnectedpowersystem.
In Electrical powersystem network the efficient and stable operation round the clock is extremely important. With the growing power demand the need for more power generating station arises due to which the complexity of powersystem increased. Large number of interconnection has been made which makes the control of multipart powersystem even more tedious job to accomplish. Now a days most of the world power networks has more than two interconnected generating unit of different genera for which the reactive and active powercontrol is needed. Loadfrequencycontrol is one of the key variable to have a stable and reliable operation of the interconnectedsystem. By definition, the loadfrequencycontrol is a methodology that equates the power generation and demand while maintaining the frequency fluctuation with in the stipulated level. So a very sophisticated control is needed to maintain the systemfrequency and tie line power under control. In past time many controller has been developed which are widely based on PID control technique. But in modern time, modern controller is needed so, many research is going on in this direction.
LoadFrequencyControl is the main control problem in power systems that maintains frequency of the system will be within the specified limits and maintained the tie line power between the interconnected systems. The main electric power business at present is largely in the hands of Vertically Integrated Utility (VIU). In this scenario entire Generation-Transmission-Distribution in single hand and that supplies the power to consumer at regulated rates. But, it can’t be existed for long time in competitive environment. In an open energy market GENCOs may not participate in the LFC problem, they are independent power utilities. DISCOs contract with GENCOs or Independent Power Producers (IPPs). In this deregulated scenario Independent System Operators are maintained the systemfrequency and tie line power within the operating limits.
The LFC system investigated for present study is composed of an interconnection of two unequal system of area-1: 1600MW and area-2: 1800MW capacity. Area-1 is considered with single-stage reheats type thermal system and Area- 2 is a hydro system with Electrical type governor. Both thermal and hydro governors are studied with Dead Band type non-linearity. Overall block diagram of test system is shown in Fig. 1. 1% step load perturbation (SLP) in area-1 is considered for study. Nominal values of all parameters are given in Appendix-1. MATLAB version 2009 is used to study the dynamics of an interconnectedpowersystem. Integral (I), Proportional plus Integral (PI), Proportional plus Integral plus Derivative (PID) type of controllers are used for this study. Optimum values of controller parameters are determined by Integral Square Error (ISE) criterion through BBO algorithm.
As mentioned in previous Chapters, the study of the AGC and POD is very important in both powersystem dynamic analysis [5.1]-[5.2] and changing conventional powersystem through deregulation [5.3], existing many proposals in these important research areas [5.4]-[5.5]. In parallel to liberalization in the power industry, high penetration of renewable resources and required technology for their integration provide new research challenges [5.6]. In this sense, recent trends of research lie with the adoption of previous concepts and conventional models considering new AC/DC complex scenarios with more application of DC interconnections and renewable energy systems (RES) penetration [5.7]-[5.8]. Since last decade, many researchers tried to propose new models for loadfrequencycontrol considering the competitive environment, usually extending previous conventional approaches [5.4]-[5.5], by introducing more detailed models of generation based on renewable resources [5.5]-[5.8]. Nowadays, integration of RES is the main challenge of the industry. During the steady state operation, the generation and consumption of energy must be balanced and any imbalance could bring severe deviations on the frequency and on the transmitted power. As it is well-known, the rate of change of frequency depends on the initial power disparity and system inertia, which is a serious issue when dealing with a powersystem with high penetration of renewables with low inertia. Actually the lack of inertia, due to the high penetration of RES and systems based on power electronics, results in a big challenge for power systems control in future power systems. For example, in PV generation, there is not any mechanical parts with inertia. Likewise, in wind and wave power generation cannot contribute directly to the total inertial of the system because of decoupling the prime mover from electrical generator [5.8]. Therefore, technical solutions for providing additional inertia are very useful.
suitable objective function is very important. Conventional objective functions used in the literature are Integral of Time multiplied by Squared Error (ITSE), Integral of Squared Error (ISE), Integral of Time multiplied by Absolute Error (ITAE) and Integral of Absolute Error (IAE). Three different objective functions are used for the design purpose in the present paper. The results obtained from the simulations show that the pro- posed control strategy optimized with a new objective function achieves better dynamic performances than the standard objec- tive functions. The superiority of the proposed approach has been shown by comparing the results with a recently published Craziness based Particle Swarm Optimization (CPSO) tech- nique for the same interconnectedpowersystem. It is observed that the proposed DE optimized PI controller outperforms the CPSO optimized PI controller and the best performance is obtained with DE optimized PID controller. Finally, the study is extended to a more realistic network of two-area six unit sys- tem with different power generating units considering physical constraints such as boiler dynamics for thermal plants, Gener- ation Rate Constraint (GRC) and Governor Dead Band (GDB) nonlinearity. It is observed that the proposed approach can be applied to interconnectedpower systems with diverse sources of generation with different PID controllers for each generating unit.
Imbalances between load and generation must be corrected within seconds to avoid frequency deviations that might threaten the stability and security of the powersystem. The problem of controlling the frequency in large power systems by adjusting the production of generating units in response to changes in the load is called loadfrequencycontrol (LFC). The Objectives of LFC are to provide zero steady-state errors of frequency and tie-line exchange variations, high damping of frequency oscillations and decreasing overshoot of the disturbance so that the system is not too far from the stability. The loadfrequencycontrol of a multiareapowersystem generally incorporates proper controlsystem, by which the area frequencies could brought back to its predefined value or very nearer to its predefined value so as the tie line power, when the is sudden change in load occurs.
The objective of this study is to investigate the loadfrequencycontrol and inter area tie-powercontrol problem for a multi-areapowersystem taking into consideration the uncertainties in the parameters of system. An optimal control scheme based particle swarm optimization (PSO) Algorithm method is used for tuning the parameters of this PID controller. The proposed controller is simulated for a three-areapowersystem. To show effectiveness of proposed method and also compare the performance of these three controllers, several changes in demand of first area, demand of second area and demand of three areas simultaneously are applied. Simulation results indicate that PSO controllers guarantee the good performance under various load conditions.