Controlling of non-minimum phase micro hydro power plant based on adaptive B-Spline neural network1 (original) (raw)
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Controlling of non-minimum phase micro hydro power plant based on adaptive B-Spline neural network
2013 International Conference on Information Technology and Electrical Engineering (ICITEE), 2013
Hydro power plant is a power generation system that have non-minimum phase model showing initial inverse response characteristic. For span of broad electrical load regulation, conventional non adaptive control techniques, such as PI and PID control would degrade the performance of this power generation system. To ensure the stability of Hydro power plant for severe load variations, we need a kind of controller that has adaptive capability. On the other hand, the utilization of conventional adaptive techniques such as Self Tuning Regulator and Model Reference Adaptive Controller will be diverge to control plants showing non-minimum phase mode. In this paper, the implementation of adaptive intelligence control based on B Spline neural network along with fo rward controller for controlling micro hydro power plant will be presented. Based on its characteristic, this adaptive control technique could be implemented directly without any prior training phase. From the simulation studies, the proposed scheme results fast transient response to load variations compared to traditional PI control and also very stable in responding to severe disturbance.
PID Control for Micro-Hydro Power Plants based on Neural Network
Modelling, Identification and Control / 770: Advances in Computer Science and Engineering, 2012
Micro-hydro power plants are power plants with small capacity, which is built in specific locations. The main problem of micro-hydro is the voltage generated is not stable at 220 VA and frequency of 50 Hz. A microhydro that was constructed by Lie Jasa et al. in Gambuk village at Pupuan sub-district, Tabanan district of Bali province, Indonesia in 2010 is still an open loop system in which spin turbine is stable when it is set from the high water level in reservoirs. This will be problematic when the generator load changes. This study will overcome the problem by proposing to build a closed loop system from the change in output frequency for the control circuit. The control circuit is a circuit constructed neural networkbased PID control by using the Brandt-Lin algorithm to control the governor. The governor function is to regulate the amount volume of water running into turbine. By applying Matlab simulation, the result shows that the best output is obtained when the the change in frequency will stabilize at about 40 seconds and using the value of Kp = 0.0637533, Ki=0.00021801 and Kd=0.00301846.
The Application of Fuzzy Adaptive PI Control for Micro Hydro Power Plants
2012
In this study, first of all, the prototype, which is very close to a real system, was setup and then the voltage control of micro hydro power plants (MHPP) was implemented with intelligent control system. MHPP could be controlled and conducted by various methods and systems. Classical controllers have many disadvantages so scientists have tried to solve these problems. While doing it, intelligent control methods and classical methods have been combined. Fuzzy-PI controller is one of these systems. This is an experience-based method and it is successful for non-linear systems. If the build of PI Control used in the voltage control is considered, the usage of fuzzy control have desirable effect on the system. So, in the voltage control strategy of synchronous generator, the estimation of K i (T i ) and K p which are parameter of the PI Controller have been implemented with the fuzzy controller. Hereby, a robust control build which can change itself according to a changing conditions have been achieved. The PLC system preferred widely in the industry is used for the system control.
New Approach in Hydropower Plant Control Based on Neural Networks
Energija, ekonomija, ekologija
A new approach to efficient, faster, and intelligent hydropower plant (HPP) control, where constituent equipment is described with highly non-linear mathematical models based on the recommendation from the working group of IEEE on prime movers, is represented in this paper. HPP stability and high efficiency are important factors dependent on the dynamic changes in the energy system demands and the starting time of the plant because the obtained energy is very flexible to those changes in the energy system. This paper is shown and analysed the implementation of the artificial neural network-based controller with PID as an auxiliary controller which helped achieve better behaviour, faster plant stabilization, and operation. The benefits of new technologies and possibilities led to improvements in HPP control and faster system operation. This is achieved by using MATLAB® – Deep Learning Toolbox whereas the simulations are prepared in Simulink. Artificial Neural Networks (ANN) as a tech...
Artificial Neural Network-based Neurocontroller for Hydropower Plant Control
TEM Journal
In this paper, the behavior of a system dynamics is represented where neuro-controller is designed, trained, and implemented. The development of the mathematical models is based on suggestions and recommendations from the literature issued by the working group of IEEE. According to the mathematical models, simulation is developed in Simulink software. MATLAB/Simulink software was used to represent the difference between the conventional PID controller and artificial neural network (ANN) neuro-controller. Nonlinear autoregressive-moving average (NARMA-L2) has been used for control simulation of the hydro-power plant (HPP) with neuro-controllers on one hand, and conventional PID control on the other hand.
IRJET-Modeling of Micro-Hydro Power Plant and Its Control Based On Neural Network
Micro hydro power plants are hydro plants with small capacity. In the present scenario, the main problem is that, the voltage generated and its frequency is not stable when there is a change in load demand. Hence, we propose to build a closed loop system with change in output frequency as the control variable which can be fed into the PID controller and necessary actions can be taken so as to maintain constant parameters. The control circuit will employ a neural network based PID control which can effectively control the governor which regulates the amount volume of water running into turbine. The neural network block is constructed using Brandt-Lin Algorithm, which enables the controller to adapt changes of plant efficiently. In this paper, it was observed that the most accurate and precise result was given by neural network based controller in minimum stipulated time which effectively improved the plant performance.
—This paper deals with a neural-network (NN)-based integrated electronic load controller (IELC) for an isolated asyn-chronous generator (IAG) driven by a constant-power small hydro uncontrolled turbine feeding three-phase four-wire loads. The proposed IELC utilizes an NN based on the least mean-square algorithm known as adaptive linear element to extract the fundamental component of load currents to control the voltage and the frequency of an IAG with load balancing in an integrated manner. The IELC is realized using zigzag/three single-phase transformers and a six-leg insulated-gate bipolar-transistor-based current-controlled voltage-source converter, a chopper switch, and an auxiliary load on its dc bus. The proposed IELC, with the generating system, is modeled and simulated in MATLAB environment using Simulink and Simpower System toolboxes. The simulated results are validated with test results on a developed prototype to demonstrate the effectiveness of IELC for the control of an IAG feeding three-phase four-wire linear/nonlinear balanced/ unbalanced loads with neutral-current compensation. Index Terms—Adaptive linear element (adaline), integrated electronic load controller (IELC), isolated asynchronous generator (IAG), small hydro generation, small hydropower generation, voltage and frequency control, voltage-source converter (VSC).
Adaptive PID Controller Design with Application to Nonlinear Water Level in NEKA Power Plant
In this paper, two novel adaptive PID-Iike controllers capable of controlling multi-variable, non-linear multi-input multiple-output (MIMO) systems are proposed. The proposed controllers are based on neural networks techniques and the learning algorithms are derived according to minimization of the error between the output of the system and the desired output. At first, two kinds of PID-Iike neural network controller named neural network PID and Neural network PID with internal dynamic feedbacks are introduced both of which can be used for controlling multivariable systems. The difference between these two controllers is mainly in the structure of their hidden layers that leads to their different performance. These controllers are applied to different kinds of black box, linear or nonlinear and time variant or time invariant systems. The stability of the proposed algorithm is also proven mathematically. Compared to conventional methods, more satisfactory results are achieved using the proposed methods. The simulation results show the quality performance of the proposed adaptive controllers and algorithms. Finally to show the performance of the proposed method, it is applied to the water level of tanks in water refinement process in NEKA Power Plant that is generally a very nonlinear system. Simulation results in this paper show the satisfying performance of the proposed adaptive controllers. I.
Design of a Load Frequency Controller of a Micro Hydro Power Plant using Fuzzy Logic Control
Final Year Project Research Paper, 2021
Micro Hydro-Power Plants are a dependable solution for serving small community power customers in places that are not saved by the national distribution network. Controlling the frequency of the generated electricity, on the other hand, is one of the primary issues in micro hydro power plants in order to maintain frequency and voltage outputs constant regardless of load variation. A comprehensive literature review on micro hydro power plant load frequency control was carried out in this research study. The paper also presents a successfully designed effective fuzzy controller and fuzzy supervisor for automatic control of power generation and electricity distribution. The optimal fuzzy membership functions of the fuzzy based controllers were determined using Genetic Algorithm. Also, a micro hydro power plant model was developed in MatLab Simulink and used in testing the effectiveness of the designed controllers through various simulations and the results were presented in this research paper. The paper also presents results of a comparison of the former PI controller and the developed fuzzy controller based on overshoot and settling time of the system response when exposed to a disturbance.
2007
Power system stabilizers are widely used to damp out the low frequency oscillations in power systems. In power system control literature, there is a lack of stability analysis for proposed controller designs. This paper proposes a Neural Network (NN) based stabilizing controller design based on a sixth order single machine infinite bus power system model. The NN is used to compensate the complex nonlinear dynamics of power system. To speed up the learning process, an adaptive signal is introduced to the NN's weights updating rule. The NN can be directly used online without offline training process. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the stability analysis. The proposed controller design is compared with Conventional Power System Stabilizers whose parameters are optimized by Particle Swarm Optimization. Simulation results demonstrate the effectiveness of the proposed controller design.