Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system

Fuzzy PID control is a process control technique combining the advantages of fuzzy logic and PID control. It maintains precise control over processes by combining fuzzy logic and proportional-integral-derivative (PID) controllers. With this method, it is possible to achieve higher levels of accuracy and stability in controlling process variables such as temperature, pressure, flow rate, and position. This makes Fuzzy PID control an ideal cordial application requiring precise process control. Fuzzy PID control uses the PID parameter tuning experience to make the fuzzy controller automatically tune its parameters, giving PID control a better control effect. In this paper, a fuzzy PID controller is designed to control the temperature of an electric boiler. The control effects of PID control, fuzzy control, and fuzzy PID control are compared by simulation analysis. The simulation results show that the unclear PID control effect is ideal and has a good application prospect.

Automatic generation control of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization algorithm

The research paper presents implementation of a fuzzy rule and membership function-based fuzzy-aided PID controller for automatic generation control (AGC) in multiarea nonlinear power system. At the initial stage of this proposed work, a three-area nine-unit installed interconnected network is considered for developing different dynamic responses in response to AGC analysis. A modified approach named modified sine cosine algorithm (M-SCA) is proposed for tuning the gain parameters of the above-proposed fuzzy controller to produce close optimum gain values. The proposed modified algorithm is developed from its original sine cosine algorithm by improving and updating few equations which is capable of making the balance between exploration and exploitation levels of this algorithm and improving the updating quality of iteration. To impose supremacy of M-SCA technique, it is examined through convergence curves and its performance is compared with host sine cosine algorithm, genetic algorithm, and particle swarm optimization algorithm. For controller supremacy analysis, the performance of the proposed fuzzy-aided PID controller is compared with conventional I, PI, and PID controllers, and it has been revealed that proposed M-SCA-tuned fuzzy-aided PID controller exhibits better performances through different deviated responses for AGC analysis. To demonstrate most standard and supremacy of proposed approaches, finally these are tested through a five-area ten-unit system considering some physical nonlinear constraints like generation rate constraint, governor dead band, boiler dynamics and time delay. At the final observation level, the proposed fuzzy controller has gone through different sensitivity analyses with variation of different system parametric conditions and different load conditions.

This paper demonstrates the advantage of the selection of the Pattern Search algorithm (PS) in the design of a Proportional-Integral controller used in Automatic Generation Control for a two area non-reheat interconnected thermal system. The design problem involves the implementation of PS algorithm to find out the value of parameters of the controller of the proposed power system. Here we have implemented three objective functions by using ITAE, damping ratio of dominant eigenvalues and settling time with weight coefficients for the design problem to get an optimized parameter of the Proportional Integral controller. The robustness of the designed PI controller using the Pattern search algorithm has been demonstrated after comparing other PI controllers using various optimization techniques like Genetic Algorithm, Bacteria Foraging Optimization Algorithm for the system under study. Further, best objective function uses the PS optimization algorithm in PI/PID controller parameters, tunning is obtained in presence of physical constraints like governor dead band non linearity of the proposed two area power system. The modified model to test the sensitivity analysis of PI/PID controller by using different loading condition to show the PID controller performance of frequency and tie line power deviation improved better as compared to the PI controller of the same power system. Further, it is extended to multi-area multi-source power for effectiveness and robustness of the system under different disturbances to check the transient stability.

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.

To improve tracking precision and increase the utilization of solar energy, the solar automatic tracking fuzzy PID control system based on ARM is designed. Embedded microcontroller 32-bit ARM processor is the core. A kind of mixed tracking mode is adopted, and it combines photoelectric tracking mode and solar trajectory tracking mode. In the solar trajectory tracking mode, the fuzzy PID segmented tracking control method is put forward. The fuzzy control and PID control are combined to control the system. If the system's angle deviation is large, PID control is adopted, else fuzzy control is adopted. And the controller can be automatically switched control mode according to the size of angle deviation. Matlab simulation results indicate that the solar automatic tracking system with fuzzy PID control has a good dynamic characteristics and high tracking accuracy, effectively improves the photoelectric conversion efficiency.

In the process of frozen food production, its refrigeration system is a typical large inertia, nonlinear, strongly coupled system, which is prone to large overshooting during operation, resulting in cost increase and quality control problems. This paper solves the problem of large overshoot in the digital PID control process by designing a fuzzy control, and designs a practical fuzzy controller so that the design method of the fuzzy PID controller can be realized in the simulation and actual control of the freezing system. A genetic algorithm optimization scheme is also designed for the fuzzy PID control system to optimize the fuzzy controller’s affiliation function, control rules, as well as proportionality and quantization factors, to enhance the model’s dynamic characteristics and robustness. Design of food quick-freezing simulation experiments and use the model in the refrigeration system control in practice, compare the performance of this model with the PID, fuzzy PID model, and found that the temperature difference set value of 4 ℃ and 3 ℃, respectively, the parameters of the traditional PID controller in the commissioning stage of the system after the end of the rectification to remain fixed, which makes the actual quality of the cooling water temperature difference control degradation. And the fuzzy genetic PID controller still has good control quality. In this paper, the model curve converges rapidly, the overshoot is only 3.9%, and the regulation time is only 450M seconds. The designed method of the study is feasible and has high industrial application value.

A fractional-order fuzzy proportional integral derivative (PID) controller is a controller that combines the benefits of fractional calculus and fuzzy logic with the conventional PID controller. In this paper, a four-stage optimization algorithm is proposed for the design of a Type-2 Fuzzy fractional-order PID controller based on the Fourier Series Method (FSM). Three distinct control structures are introduced: Type-2 fuzzy fractional PD + fractional PI controller, Type-2 fuzzy fractional PID, and Type-2 fuzzy fractional PD + Type-2 fuzzy fractional PI controller. In addition to a modified multi-performance criterion cost function, four integral performance criteria are employed as cost functions for each stage. The suggested algorithm avoids the utilization of the approximation equivalent for the fractional-order system and instead employs FSM. Furthermore, the approach optimizes the nonlinearity within the upper membership function (UMF) and the uncertainty range through the lower membership function, as opposed to arbitrary selection. By considering variations in the membership functions, the outcomes exhibit a superior response compared to previous investigations. The results of the three control structures are compared with the traditional PID controller, and simulation results demonstrate the feasibility of this technique. The findings suggest that by optimizing different integral performance criteria using this design technique, controllers for both integer and fractional-order plants can yield favorable step responses. The proposed algorithm is validated by comparing its step response performance with that of previous research, followed by a discussion on sensitivity analysis and computational requirements.

In this paper, a control method was designed based on particle swarm optimization (PSO)–fuzzy PID (PSO-Fuzzy PID). In order to address the issues of significant nonlinearity and extensive hysteresis in the vapor temperature of litchi hot vapor preservation treatment equipment, as well as the inadequacy of traditional control schemes, a PSO-Fuzzy PID controller was developed through the integration of particle swarm optimization and fuzzy logic algorithms. This controller incorporates a scale factor and quantization factor, building upon the foundation of the Fuzzy PID controller. Additionally, a transfer function model was established for both the heating and disturbance links, utilizing a combination of test data modeling methods and relevant theory. Thirdly, a system simulation model was built to compare and verify PID, Fuzzy PID, and PSO-Fuzzy PID control methods, which were carried out on the MATLAB/Simulink simulation platform. Finally, the litchi load test was conducted to further verify the control performance of the PSO-Fuzzy PID method, where the litchi variety was “Jinggang Hongnuo”. The simulation results showed that the overshoot of the optimized control system was reduced by 93.5% and 42.9% compared with the traditional PID control and Fuzzy PID, and the regulation time was shortened by 46% and 31%, which indicates a better control effect. The test results showed that PSO-Fuzzy PID showed better system stability and accuracy than the fuzzy PID control method in different processing temperature and processing time for litchi’s hot vapor treatment. The maximum temperature difference decreased by 0.56 °C on average and the interference adjustment time was reduced by 7.5 s on average. PSO-Fuzzy PID has a good control effect on the temperature of litchi hot vapor treatment, which can provide a reference for model building and temperature control of other similar fruit and vegetable heat treatment equipment.

In CNC lathe servo feed system, many parameters are uncertain, so it is difficult to establish accurate mathematical model of the feed system, and parameters of conventional PID controller can not change the parameters to adjust with the controlled object. Fuzzy PID controller, which has the advantages of conventional PID control and fuzzy control, can solve these problems very well. So we can make it applied in CNC lathe servo feed system. This paper has made a simulation of fuzzy self-turning PID controller in the MATLAB environment. The simulation shows that the fuzzy self-turning PID controller has a better control capability and a higher stability, and it improved the servo feed system dynamic performance.

Aiming at the limitation of traditional PID algorithm in the field of control, the fuzzy control theory and traditional PID control algorithm are combined to realize parameter self-tuning function, a fuzzy self-tuning PID controller, and this algorithm is analyzed in-depth study. It provides a new solution of modern industrial control problem that combining with fuzzy control technology and PID control technology. The FPGA has high integration, short development cycle, the characteristics of strong reusability; fuzzy PID controller has a good practical value and optimistic application prospect based on FPGA.

ABSTRACTIn this paper, Invasive weed optimization (IWO) algorithm is proposed for testing automatic generation control (AGC) of multi-area power systems. Initially, a two-area multi-source power system interconnected by a DC link is considered and IWO is employed to optimize the proportional integral derivative (PID) controller for AGC employing a modified objective function. The superiority of the proposed approach is shown by comparing the results with recently published optimal algorithms for the similar system under study. The comparison is made using various performance criteria such as integral of time multiplied absolute error (ITAE), minimum damping ratio (MDR) of dominant eigenvalues, settling times and maximum overshoots. Furthermore, sensitivity analysis is performed to show the robustness of the proposed approach by varying the system parameters, operating load conditions from their nominal values as well as size and locations of disturbance. The proposed approach is also extended to three area system considering both thermal and hydro units with generation rate constraints (GRC) and the superiority of the proposed approach is shown by comparing the results of Adaptive Neuro Fuzzy Inference System (ANFIS), hybrid Bacteria Foraging Optimization Algorithm with Particle Swarm Optimization (hBFOA–PSO) approaches for the identical interconnected power system.

A nonlinear nuclear reactor model is linearized at different initial powers for the purpose of controlling the reactor in load following mode. A new fractional order (FO) fuzzy proportional integral derivative (PID) controller is tuned next using Genetic Algorithm (GA) to control the reactor at different operating conditions. The effectiveness of using the fuzzy FOPID controller over conventional fuzzy PID controllers is shown with credible numerical simulations. The controllers tuned with the highest and lowest power models are shown to work well at other operating conditions also and hence are robust with respect to the changes in nuclear reactor power level.

A new optimized fuzzy FOPI-FOPD controller for automatic generation control of electric power systems

In this research, a whale-optimized fuzzy PID controller was developed to manage automatic generation control in multiple-area electrical energy systems with an availability-based tariff (ABT) pricing scheme. The objective of this work is to minimize the power production costs, area control errors (ACEs), and marginal costs of the multiple-area electrical energy system with real-time load and frequency variation conditions. The generation of power, deviation of power in the tie line, and deviation of frequency of the interconnected three-area electrical energy system, including the hydrothermal steam power plant and gas power plant, will be measured and analyzed rigorously. Based on the output from the whale optimization, the fuzzy PID controller regulates the deviation of power in the tie line and the deviation of frequency of the interconnected three-area electrical energy system. The reliability and suitability of the proposed optimization, i.e., whale-optimized fuzzy PID controller, are investigated against already presented methods such as particle swarm optimization and genetic algorithms.