Automatic generation control in power plant using PID, PSS and Fuzzy-PID controller

Automatic generation control or AGC system is significant controlling system that operates efficiently to balance the load and generation in power system at minimum cost for economical operation. System frequency will vary from nominal value if there is mismatch occurs between generation and demand. Due to this high frequency deviation system may breakdown. A very fast, reliable and accurate controller is needed to maintain the system frequency within the range to maintain stability. In this paper the proposed model consisting of PID controller whose parameters have been optimized using PSO tuned Fuzzy Logic Controller and it’s been compared with conventional PSO-PID controller. Each control area in power systems includes the dynamics response of the systems. The results contained in this paper present the strength of the particle swarm optimizer for tuning the Fuzzy based PID controller parameter for two area power system network, for better performance PSO trained SSSC and TCPS has been introduced to the system. The enhancement in the dynamic response of the power system network is verified. The output response of the proposed work is compared with conventional PSO-PID & PSO Fuzzy-PID based AGC system. Simulation experiments so conducted in MATLAB showed that the proposed system outperformed the conventional one by achieving better response.

The research work focuses on the automatic generation control (AGC) of a multi-source two area power system with vehicle-to-grid technology under deregulated environment. The electric vehicles are the future for the clean automobile sector and hence the charging and discharging process for the vehicles have an important role in the power system stability. The main role of AGC is to minimize the system frequency variations and hence attempt has been made to design novel DUAL mode control scheme. The dual mode control basically takes the magnitude of the error and effectively chooses between proportional and integral control for better action. This paper has proposed a Dual mode PID controller whose performance is put to comparison with PID and 2 DOF PID controller. The controller gains are tuned by a newly proposed Dragonfly algorithm. The system is to be simulated under base case and bilateral contract market scenario of deregulated environment. Analysis is also made for both the charging and discharging modes of electric vehicles. The robustness of the proposed controller is to be established by analyzing the dynamic system performances in terms of settling time, maximum overshoot, and undershoot.

This paper presents a real-time implementation of a genetic-based hybrid fuzzy-PID controller for industrial motor drives. Both the design of fuzzy-PID controller and its integration with the conventional PID in global control system to produce a hybrid design is demonstrated. A genetic optimization technique is used to determine the optimal values of the scaling factors of the output variables of the fuzzy-PID controller. The objective is to utilize the best attributes of the PID and fuzzy-PID controllers to provide a controller, which will produce better response than either the PID or the fuzzy-PID controller. The principle of the hybrid controller is to use a PID controller, which performs satisfactorily in most cases, while keeping in the background, a fuzzy-PID controller, which, is ready to take over the PID controller when severe disturbs occur. The hybrid controller is formulated and implemented in real-time, using the speed control of a brushless drive system as a testbed. The design, analysis, and implementation stages are carried out entirely using a dSPACE DS1104 digital signal processor (DSP)-based real-time data acquisition control (DAC) system, and MATLAB/Simulink environment. Experimental results show that the proposed fuzzy- PID controller-based genetic optimization produces better control performance than the conventional PID controllers, particularly in handling nonlinearities and external disturbances.

The photovoltaic (PV) system’s nonlinear nature, uncertainty in temperature, and light intensity in the environment are responsible for the fluctuation of the maximum power point (MPP). Many maximum power point tracking (MPPT) algorithms are proven effective in the literature, including use of artificial intelligence. Proportional integral derivative controller and fuzzy controllers found effective to track MPP. Using the combination of Fuzzy-PID controller, swiftly adapt to changes in the atmosphere to confirm the solar array is continually operating at its highest efficiency with MPP. Easy and effective oscillation elimination is possible with PID control. To regulate the maximum power of the PV system combination of Fuzzy-PID self-tuning controllers is found effective.The goal is to enhance a PV system’s dynamic response to in presence of irradiation and temperature fluctuations. For tuning Fuzzy logic controller (FLC), gain scheduler values of the proportional, integral, and derivative constants are fundamental. The design processes for non-linear conventional PID controllers, FLC, and series combination of Fuzzy-PID controller are discussed. It compares the traditional PID controller, FLC, and Fuzzy-PID controller. The comparison of outputs produced for PID, FLC, and series Fuzzy-PID controller confirms the effectiveness of the algorithm. Use of Fuzzy controller helps in self-tuning of PID controller constant value. MATLAB/Simulink based environment used to perform the simulations.

Fuzzy logic control based on the Takagi–Sugeno inference method has been applied for the yeast alcoholic fermentation running in a continuous-time biochemical reactor in this paper. A type-1 fuzzy PID controller was designed to temperature control in a biochemical reactor. The fuzzy PID controller was also designed using type-2 fuzzy sets. The advantage of the fuzzy control is that it can be used very successfully for control of strongly non-linear processes and processes that are difficult to model because of complicated reaction kinetics. Obtained simulation results confirm this fact. The disadvantage of the fuzzy control design lies in the time-consuming tuning of controllers. The subtractive clustering method was used to identify the rule base. This approach was chosen to minimize the number of rules of the designed fuzzy logic controllers and to simplify the fuzzy controller design. Simulation results confirm that fuzzy PID controllers can assure better performance than conventional PID controllers. Yeast alcoholic fermentation is an exothermic process and the cooling is necessary to maintain the optimal temperature in the reactor. Fuzzy PID control reduces coolant consumption in comparison with conventional PID control and so it assures energy-efficient control of continuous-time alcoholic fermentation. Using type-2 fuzzy sets in the fuzzy PID controller design is very promising as the type-2 fuzzy controller offers even better results than the type-1 fuzzy controller.

Approaching hybridized GWO-SCA based type-II fuzzy controller in AGC of diverse energy source multi area power system

With the development of Smart Grids worldwide, efforts have been focused on optimizing the role of Smart Grid in the present electrical system. The integration of renewable energy resources to the grid presents a great challenge since they are intermittent and vary constantly causing frequency fluctuations. Automatic Generation Control (AGC) solves this problem by keeping the frequency close to nominal value and maintaining the balance between generation and demand. AGC has been implemented in various softwares as an added functionality. Power Systems Analysis Toolbox (PSAT) is a MATLAB toolbox designed for power flow computations but it lacks AGC implementation. This paper presents AGC implementation on PSAT using Artificial Neural Networks (ANN) controller and PID controller. The goal is to optimize PSAT by adding extra functionalities to it. The AGC design is implemented on IEEE 14-bus system on PSAT. A comparison of ANN controller and PID controller is also presented. The results show successful addition of AGC to PSAT library. The AGC design helps to control the frequency, has high performance and can be extended to other power systems.

In power system any unpredictable variations of consumption load will cause fluctuation in frequency and the transferred power between the lines. Load frequency control is one of the main problems in the power system which synchronizes with automatic generation control. Checking the different factors that cause unbalance in power system is necessary and one of the important factors of them is the load that randomly varies. In this paper, a three area power system in deregulated environments with participation of synchronized generation of wind with Doubly Fed Induction Generator (DFIG), hydro and thermal power plants are studied. The load in this power system is considered as a random variable which periodically and randomly has frequency of 0.25 around nominal amount. Fluctuations are caused due to considering load as a random variable and using wind-powered turbine with DFIG in power system. To improve the response of power system, the optimal fuzzy logic, PID controller and FACTS devices will be used. Imperialist Competitive Algorithm (ICA) has been used for optimizing the parameters of a fuzzy logic controller. PID Controller parameters have been optimized with three intelligent algorithms, ICA, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm and the results will be compared with each other. With comparison the result it is observed that in the case of using optimal fuzzy logic controller (OFLC) we have the fastest response with the minimum over shoot and under shoot, also in this case the transferred power between the areas is settled in the lowest value. The results show a significant reduction in losses in transmission lines.

Excitation system, an integral part of the synchronous generator, performs control and protection functions that include control of voltage and reactive power. Power system stability can also be enhanced by supplying additional signal through the excitation system. This function is performed by the power system stabilizer (PSS) that generates a control signal introduced as a supplementary input into the excitation system. Due to the nonlinear characteristics, wide operating conditions, and unpredictability of perturbations in a power system, the conventional fixed parameterPSSgenerally cannot maintain the same quality of performance under all operating conditions. To improve power system performance and stability, various approaches have been proposed. An overview of the development and successful implementation of various approaches, including adaptive PSSs based on analytical and artificial intelligence techniques, is provided.

The traditional manual operation mode has problems such as high manpower cost, low efficiency, low safety, low operating precision, etc. Achieving unmanned intelligent systems in the field of mechanical and electrical engineering has become the key to transforming and upgrading relevant enterprises and improving their overall competitiveness. In this paper, an improved fuzzy PID control system is constructed based on a PLC control system combined with a fuzzy PID intelligence algorithm. It is applied to the pumping motor, coagulation dosing system, and other specific devices for simulation experiments, compared to the conventional PID algorithm, and evaluated the control system in this paper. The fuzzy PID control algorithm of this paper reduces the steady-state adjustment time of the response curve by 30% compared to the conventional PID algorithm, and the overshooting amount is reduced by 25%. With almost no overshoot, the pumping motor that was equipped with this paper’s PID algorithm reached its stable target speed in 0.3 seconds. In addition, this study found that in the coagulation dosing system model incorporating the fuzzy PID algorithm of this paper, when the model is accurate, only 20% of the overshooting amount occurs after the system has been running for some time. The conventional PID algorithm’s 50% overshoot is significantly less than this, and the time it takes to reach a steady state is also 50 minutes earlier than the conventional PID control. When the model is imprecise, fluctuations occur in both conventional PID control and fuzzy PID control, but the amplitude of the fluctuations in fuzzy PID control is relatively small. In conclusion, this paper shows that the fuzzy PID control algorithm in this paper has higher control accuracy, response speed, and stability than the conventional PID control algorithm.

Automatic generation control (AGC) of four-area interlinked power system with generation rate constraints (GRC) is presented in this paper. The four-area interconnected power system having two areas with steam turbines and other areas are nuclear and hydro turbine tied together with power lines is considered in this paper. The AGC of multi-area power system using PID, Fuzzy and ANFIS controllers, and their comparative performance analysis are presented in this paper. ANFIS is a hybrid intelligent controller formed by parallel combination of fuzzy logic and neural network. This controller is able to control the frequency deviation efficiently and at minimum time, so ANFIS controller gives improved result over fuzzy and PID controllers.

Cascade fuzzy control of a tubular chemical reactor

The research on active control of the pantograph has been intensively studied, however, the control results in practice are not satisfactory as expected, for the existence of time delays coming from the input and output measurement. This paper focuses on the problem of time delays in the designing of the control strategy, including the sensor signal input and the action of the actuator. A multibody dynamic pantograph model is established based on the relative coordinate method, combined with the rigid catenary established by the modal superposition method. Firstly, the performance of the traditional PID controller with signal time delay is studied, including the separation or combination of sensor signal input delay and actuator signal output delay. Then, a novel fuzzy PID controller with an improved rule library is proposed, which further improves the control performance of the PID controller. Finally, to deal with the time delay of sensor and actuator well, a dynamic predictive fuzzy adaptive PID (DMC-Fuzzy-PID) controller is proposed by combining dynamic matrix control (DMC) with the improved fuzzy PID controller, which may support the engineering application of active control of pantograph.KeywordsActive controlTime delayRigid catenaryMultibody dynamicsFuzzy PID controller

Researching optimization problems of controller is need of industrial process control ,in which PID controller is widely used,which its parameters can be equivalent to optimization problems. In the industrial control the PID controller is widely used in excitation control system to improve its control performance.In order to find the optimal PID controller parameters effectively ,a kind of Adaptive Particle Swarm Optimization method (CAPSO) based on Cloud Theory is applied to fuzzy PID controller. Through the establishment of particle swarm algorithm of fuzzy PID controller parameters optimization model, which it can be used to optimize the membership function of fuzzy PID controller. Particle code adopts real coding. Particle dimension is related with the number of the input variables divided by fuzzy set and the number of control rules of whole fuzzy control system, thus the parameters of PID control are optimized on real time. The result from the simulation shows that compared with PID control and fuzzy control this system has several advantages which are small overshoots ,fast response ,and good stable performance to improve the control performance of excitation control system.

The fuzzy PID controller is proposed to improve the performance of the network pressure-superposed water supply (NPSWS) system. Taking the pump as the load of the asynchronous motor, the whole system can be simplified as a second-order model. On the basis of the model, fuzzy rules are designed to complete on-line parameters adjustments of PID controller. Comparisons are made between the fuzzy PID controller and the conventional PID controller in terms of dynamic performance, steady state performance and noise immunity. The simulation results show that the fuzzy PID controller not only has faster response, slighter overshoot, stronger noise immunity, but also has wider adaptation range and higher accuracy in stability than PID controller.