Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system

This paper presents automatic generation control (AGC) of interconnected two equal area, three and five unequal-areas thermal systems provided with single reheat turbine and generation rate constraints of 3% per minute in each area. A maiden attempt is made to apply integral plus double derivative (IDD) controller in AGC. Controller gains in the two-area system are optimized using classical approach whereas in the three and five area systems controller gains and governor speed regulation parameters (R_i) are simultaneously optimized by using a more recent and powerful evolutionary computational technique called bacterial foraging (BF) technique. Investigations reveal on comparison that Integral (I), Proportional-Integral (PI), Integral-Derivative (ID), or Proportional-Integral-Derivative (PID) controllers all provide more or less same response where as Integral-Double Derivative (IDD) controller provides much better response. Sensitivity analysis reveals the robustness of the optimized IDD controller gains and R_i of the five area system to wide changes in inertia constant (H), reheat time constant (T_r), reheat coefficient (K_r), system loading condition and size and position of step-load perturbation.     

Robust decentralized neural networks based LFC in a deregulated power system

In this paper, a decentralized radial basis function neural network (RBFNN) based controller for load frequency control (LFC) in a deregulated power system is presented using the generalized model for LFC scheme according to the possible contracts. To achieve decentralization, the connections between each control area with the rest of system and effects of possible contracted scenarios are treated as a set of input disturbance signals. The idea of mixed H₂/H_∞ control technique is used for the training of the proposed controller. The motivation for using this control strategy for training the RBFNN based controller is to take large modeling uncertainties into account, cover physical constraints on control action and minimize the effects of area load disturbances. This newly developed design strategy combines the advantage of the neural networks and mixed H₂/H_∞ control techniques to provide robust performance and leads to a flexible controller with simple structure that is easy to implement. The effectiveness of the proposed method is demonstrated on a three-area restructured power system. The results of the proposed controllers are compared with the mixed H₂/H_∞ controllers for three scenarios of the possible contracts under large load demands and disturbances. The resulting controller is shown to minimize the effects of area load disturbances and maintain robust performance in the presence of plant parameter changes and system nonlinearities.     

ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY

Abstract

This paper describes an application of fuzzy logic to design a fuzzy controller for the automatic generation control (AGC) problem in power system studies. A two area power system is considered. Frequency and tie-line power deviations for a step load increase in one area are plotted as a function of time, and are compared with available responses using the classical integral controller     

Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (N_i) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.     

Optimal tuning of PI speed controller coefficients for electric drives using neural network and genetic algorithms

This paper presents a method of tuning Proportional Integral (PI) controller coefficients in the off-line control of a nonlinear system. In this method, the first step is the identification of the system via Artificial Neural Networks (ANNs), using maximum overshoot and settling time obtained from the application circuit for different K_p-K_i pairs. With this in mind, multi-layer ANN, which uses back-propagation of the error algorithm, was used as the learning algorithm. In the second step, the purpose is to find the optimum controller coefficients using the ANN model as the objective function via Genetic Algorithms (GAs). A Digital Signal Processor (DSP-TMS320C50) was used to carry out control applications. The C++ language was used for ANN and GA, and and the Assembly language was used for the DSP. It is determined that maximum overshoot and settling time are very small if the system is controlled by control parameters obtained from the optimization process that uses GA.     

Automatic generation control of multi area thermal system using Bat algorithm optimized PD–PID cascade controller

This article presents automatic generation control (AGC) of an interconnected multi area thermal system. The control areas are provided with single reheat turbine and generation rate constraints of 3%/min. A maiden attempt has been made to apply a Proportional derivative–Proportional integral derivative (PD–PID) cascade controller in AGC. Controller gains are optimized simultaneously using more recent and powerful evolutionary computational technique Bat algorithm (BA). Performance of classical controllers such as Proportional Integral (PI) and Proportional Integral Derivative (PID) controller are investigated and compared with PD–PID cascade controller. Investigations reveal that PI, and PID provide more or less same response where as PD–PID cascade controller provides much better response than the later. Dynamic analysis has also been carried out for the controllers in presence of random load pattern, which reveals the superior performance of the PD–PID cascade controller. Sensitivity analysis reveals that the BA optimized PD–PID Cascade controller parameters obtained at nominal condition of loading, size and position of disturbance and system parameter (Inertia constant, H) are robust and need not be reset with wide changes in system loading, size, position of disturbance and system parameters. The system dynamic performances are studied with 1% step load perturbation in Area1.     

Novel schemes used for estimation of power system harmonics and their elimination in a three-phase distribution system

Estimation of power system harmonics and their elimination is an interdisciplinary area of interest for many researchers. This paper presents Variable Step Size Least Mean Square (VSS-LMS) approach for harmonics estimation and Shunt Active Power Filter (SAPF) with two-level Hysteresis Current Control (HCC) technique for their elimination in a three-phase distribution system. In the estimation process, the weight is updated using VSS-LMS algorithm. Harmonics components are estimated from the updated weights. In order to mitigate harmonics produced by the nonlinear load connected in a three-phase distribution system, SAPF with two-level HCC is proposed. A three-phase insulated gate bipolar transistor (IGBT) based current controlled voltage source inverter (CC VSI) with a dc bus capacitor is used as an active power filter. The first step is to calculate SAPF reference currents from the sensed nonlinear load currents by applying the synchronous detection method and then the reference currents are fed to the proposed controller for generation of switching signals. The nonlinear load consists of one three-phase and one single-phase diode rectifier feeding R–L load, so that the effectiveness of the two-level HCC scheme to compensate for unbalanced nonlinear load can be tested. Various simulation results are presented to verify the good behavior of the SAPF with proposed two levels HCC.     

Application of Firefly Algorithm for Load Frequency Control of Multi-area Interconnected Power System

Abstract—In this article, a firefly algorithm is proposed for load frequency control of multi-area power systems. Initially a two equal area non-reheat thermal system is considered and the optimum gains of the proportional integral/proportional integral derivative controller are optimized employing the firefly algorithm technique. The superiority of the proposed approach is demonstrated by comparing the results with some recently published techniques such as genetic algorithm, bacteria foraging optimization algorithm, differential evolution, particle swarm optimization, hybrid bacteria foraging optimization algorithm-particle swarm optimization, and Ziegler–Nichols-based controllers for the same interconnected power system. Further, the proposed approach is extended to a three-unequal-area thermal system considering generation rate constraint and governor dead-band. Investigations reveal on comparison that proportional integral derivative controller provides much better response compared to integral and proportional integral controllers. Additionally, robustness analysis is carried out by varying the operating load condition and time constants of speed governor, turbine, and inertia constant in the range of +50 to –50% from their nominal values as well as the size and position of step load perturbation to demonstrate the robustness of the proposed firefly algorithm optimized proportional integral derivative controller.     

Quasi oppositional harmony search algorithm based controller tuning for load frequency control of multi-source multi-area power system

This paper deals with the load frequency control (LFC) study of single-area and interconnected two-area power system having diversified power sources. The two areas considered in the present study are identical. Each area is having thermal, hydro and gas based power plants. Split-shaft model of gas turbine is used in the present work as one of the diversified generating unit for the purpose of LFC study. Optimal gains of the classical controllers (like integral controller, proportional–integral controller and proportional–integral–derivative (PID) controller, one installed at a time in the studied models) are obtained by using a novel music-inspired metaheuristic harmony search algorithm (HSA) which incorporates quasi opposition based learning technique for memory initialization and also for generation jumping. Single-area power system with diverse power sources is considered and its optimal transient performances are obtained and compared for step load perturbation. The same approach is further extended to two-area interconnected power system consisting of diverse power sources with nominal values of area input parameters. The performance of PID controller is found to be the best one for the studied power system models. It is also revealed that the performance of the interconnected two-area power system with AC–DC tie line is better in comparison to AC tie line.     

Robust Load Frequency Control with Dynamic Demand Response for Deregulated Power Systems Considering Communication Delays

Communication networks are used in load frequency control (LFC) for transmitting remote measurements and control commands, and in demand side response (DSR) for aggregating small-scale controllable loads. This paper investigates modeling and controller design for LFC together DSR in a deregulated environment, considering multiple time delays introduced by the usage of communication channels. Time delay model of the deregulated multi-area LFC with dynamic demand control (DDC) is obtained at first, in which a typical thermostatically controlled appliance, air conditioner, is used for DDC. A robust proportional integral derivative (PID) load frequency controller is designed, through the H_∞ performance analysis and the particle swarm optimization (PSO) searching algorithm, to deal with the load disturbances and multiple delays in the LFC loop and the DDC loop. Case studies based on a three-area deregulated LFC system demonstrate the effectiveness of the proposed load frequency controller and the performance improvement from the DDC. Simulation results show that the DDC can increase the delay margin of the LFC scheme. Moreover, several delay stable regions are revealed via simulation method.     

Investigation of the influence of network‐induced time delays on the activated sludge process behavior in the networked wastewater distributed systems

This paper examines the effects of networked induced time delays on the dissolved oxygen (DO) concentration in the activated sludge process (ASP) of a networked wastewater treatment plant (WWTP). This is a situation in which the controller and the wastewater plant are separated by wide geographical distance. This is a new type of WWTP control that allows two or more WWTPs to be controlled by a single controller placed in a remote location. The objective is to achieve flexibility of control and to reduce its cost. The communication medium between the controller and the WWTPs introduces communication drawbacks into the control system. The influences of network‐induced time delays [controller to actuator delay (τ_ca) and the sensor to controller delay (τ_sc)] over the behavior of the DO process controlled by both nonlinear linearizing and proportional‐integral controllers are investigated for constant and random delays. Investigation of the DO process under random delays was also performed with varying linear controller parameters [proportional gain (K_p) and integral time (T_I)]. Simulation results reveal that large network‐induced time delays in the closed‐loop DO process leads to depletion of the amount of oxygen available for microorganism metabolism, leading to inefficiency of the ASP. The critical delay during which the DO process becomes unstable due to communication drawbacks was also determined for constant and random delays. These values are found to vary depending on the delay type (constant/random), delay magnitude, and the linear controller parameters K_p and T_I. The results of this study would provide useful information for process performance and form the basis for the design of a robust networked control for the DO process capable of mitigating communication drawbacks in a networked wastewater distributed systems. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Analysis of cascaded multilevel inverters for active harmonic filtering in distribution networks

This paper aims at the investigation of an active power filter (APF) comprised of a transformerless multilevel inverter (MLI) for power conditioning in three-phase three-wire distribution network. The inverter topologies used here are three, five, seven and nine-level. The system configuration mainly involves cascaded MLI structure of APF, generation of compensation filter currents based on instantaneous active and reactive current component (i_d–i_q) method and dc-link voltage regulation using a PI controller. Not many papers focus on the regulation of dc-link capacitor voltage. Here we have proposed the implementation of bacterial foraging optimization (BFO) to extract the gains of PI controller. The proposed work provides improved dc-link voltage regulation, quick prevail over current harmonics and reduction of overall source current THD. Adequate MATLAB/Simulink simulation results are presented for the different cascaded MLIs discussed above. Additionally, the performance has been validated in real-time using Opal-RT Lab simulator considering three different conditions of supply i.e., balanced sinusoidal, balanced non-sinusoidal and unbalanced sinusoidal.     

Comparative performance analysis of teaching learning based optimization for automatic load frequency control of multi-source power systems

This paper presents a new population based parameter free optimization algorithm as teaching learning based optimization (TLBO) and its application to automatic load frequency control (ALFC) of multi-source power system having thermal, hydro and gas power plants. The proposed method is based on the effect of the influence of teacher on the output of learners and the learners can enhance their knowledge by interactions among themselves in a class. In this extensive study, the algorithm is applied in multi area and multi-source realistic power system without and with DC link between two areas in order to tune the PID controller which is used for automatic generation control (AGC). The potential and effectiveness of the proposed algorithm is compared with that of differential evolution algorithm (DE) and optimal output feedback controller tuning performance for the same power systems. The dynamic performance of proposed controller is investigated by different cost functions like integral of absolute error (IAE), integral of squared error (ISE), integral of time weighted squared error (ITSE) and integral of time multiplied absolute error (ITAE) and the robustness of the optimized controller is verified by its response toward changing in load and system parameters. It is found that the dynamic performance of the proposed controller is better than that of recently published DE optimized controller and optimal output feedback controller and also the proposed system is more robust and stable to wide changes in system loading, parameters, size and locations of step load perturbation and different cost functions.     

Application of modular multilevel converter for AGC in an interconnected power system

This article demonstrates the maiden application of a new Modular Multi level Converter based Series Compensation (MMCS) technique for multi area Automatic Generation Control (AGC) interconnected system. Primarily MMCS has been modeled in state space form and proposes an appropriate location in AGC to obtain the better dynamic responses in frequency, tie-line power and individual generating power; further to quench the oscillation for sudden changes in load. The system has been studied the operation of MMCS and investigated with Generation Rate Constraints (GRC) of reheat turbines used in system. Further, selection of suitable integral and proportional–integral controller gain has been investigated with Integral Square Error (ISE) technique and Particle Swarm Optimization (PSO) technique for step load perturbation (SLP) in area-1 with performance index as its objective function by making control parameters as variables. System with MMCS is compared with out MMCS and observed performance has been increased and results are explored.     

H∞ load frequency control of interconnected power systems with communication delays

This paper considers the problem of power system load frequency control design incorporating the effect of using open communication network instead of a dedicated one for the area control error signals. To have this, we appropriately consider time-delays in the ACE signals. A delay-dependent two-term H_∞ controller design has then been proposed using linear matrix inequalities. Comparison of effectiveness of the proposed two-term controller with that of existing one-term and two-term controller designs establishes the superiority as well as applicability of the present design for the LFC problem.     

Optimal Automatic Generation Control with Hydro,Thermal, Gas,and Wind Power Plants in 2-Area Interconnected Power System

Abstract

This paper explores automatic generation control (AGC) of a more realistic 2-area multi-source power system comprising hydro, thermal, gas, and wind energy sources-based power plants in each control area. The wind power plants (WPPs) have been growing continuously worldwide due to their inherent feature of providing eco-friendly sustainable energy. But, operations of WPPs are associated with system stability problems due to lack of inertia. However, WPPs do not participate in the elimination of mismatch between generation and demand by AGC but disturbance can be injected by the WPPs due to the stochastic nature of wind energy. An optimal controller based on full state feedback control theory is designed to conduct the study. The system dynamic performance analysis is carried out for 1% step load disturbance in corresponding control areas. It is observed that the system dynamic graphs of deviation in area frequency and tie-line power are significantly improved with the implementation of optimal AGC controller compared to GA tuned classical controller. It has also been shown that the WPPs aid the increase in load disturbance when the input wind power reduces but it negates the effect of increase in load disturbance for increase in wind energy to the WPPs.     

Output‐feedback H∞ quadratic tracking control of linear systems using reinforcement learning

This paper presents an online learning algorithm based on integral reinforcement learning (IRL) to design an output‐feedback (OPFB) H_∞ tracking controller for partially unknown linear continuous‐time systems. Although reinforcement learning techniques have been successfully applied to find optimal state‐feedback controllers, in most control applications, it is not practical to measure the full system states. Therefore, it is desired to design OPFB controllers. To this end, a general bounded L₂ ‐gain tracking problem with a discounted performance function is used for the OPFB H_∞ tracking. A tracking game algebraic Riccati equation is then developed that gives a Nash equilibrium solution to the associated min‐max optimization problem. An IRL algorithm is then developed to solve the game algebraic Riccati equation online without requiring complete knowledge of the system dynamics. The proposed IRL‐based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an OPFB policy and updates the OPFB gain using the information given by the evaluated policy. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. A simulation example is provided to verify the convergence of the proposed algorithm to a suboptimal OPFB solution and the performance of the proposed method.     

Sliding mode controller design for frequency regulation in an interconnected power system

In this paper, a Sliding mode controller design method for frequency regulation in an interconnected power system is presented. A sliding surface having four parameters has been selected for the load frequency control (LFC) system model. In order to achieve an optimal result, the parameter of the controller is obtained by grey wolf optimization (GWO) and particle swarm optimization (PSO) techniques. The objective function for optimization has been considered as the integral of square of error of deviation in frequency and tie-line power exchange. The method has been validated through simulation of a single area as well as a multi-area power system. The performance of the Sliding mode controller has also been analyzed for parametric variation and random loading patterns. The performance of the proposed method is better than recently reported methods. The performance of the proposed Sliding mode controller via GWO has 88.91% improvement in peak value of frequency deviation over the method of Anwar and Pan in case study 1 and similar improvement has been observed over different case studies taken from the literature.     

Bacteria foraging optimization algorithm based load frequency controller for interconnected power system

Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real world optimization problems. This paper proposes BFOA based Load Frequency Control (LFC) for the suppression of oscillations in power system. A two area non-reheat thermal system is considered to be equipped with proportional plus integral (PI) controllers. BFOA is employed to search for optimal controller parameters by minimizing the time domain objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller and PI controller tuned by genetic algorithm (GA) in order to demonstrate the superior efficiency of the proposed BFOA in tuning PI controller. Simulation results emphasis on the better performance of the optimized PI controller based on BFOA in compare to optimized PI controller based on GA and conventional one over wide range of operating conditions, and system parameters variations.