Takashi Hiyama

Neural network based estimation of maximum power generation from PV module using environmental information

This paper presents an application of an artificial neural network for the estimation of maximum power generation from PV module. The output power from a PV module depends on environmental factors such as irradiation and cell temperature. For the operation planning of power systems, the prediction of the power generation is inevitable for PV systems. For this purpose, irradiation, temperature and wind velocity are utilized as the input information to the proposed neural network. The output is the predicted maximum power generation under the condition given by those environmental factors. The efficiency of the proposed estimation scheme is evaluated by using actual data on daily, monthly and yearly bases. The proposed method gives highly accurate predictions compared with predictions using the conventional multiple regression model.

Identification of optimal operating point of PV modules using neural network for real time maximum power tracking control

This paper presents an application of a neural network for the identification of the optimal operating point of PV modules for the real time maximum power tracking control. The output power from the modules depends on the environmental factors such as insolation, cell temperature, and so on. Therefore, accurate identification of optimal operating point and real time continuous control are required to achieve the maximum output efficiency. The proposed neural network has a quite simple structure and provides a highly accurate identification of the optimal operating point and also a highly accurate estimation of the maximum power from the PV modules. >

Evaluation of neural network based real time maximum power tracking controller for PV system

This paper presents a neural network based maximum power tracking controller for interconnected PV power systems. The neural network is utilized to identify the optimal operating voltage of the PV power system. The controller generates the control signal in real-time, and the control signal is fed back to the voltage control loop of the inverter to shift the terminal voltage of the PV power system to its identified optimum, which yields maximum power generation. The controller is of the PI type. The proportional and the integral gains are set to their optimal values to achieve fast response and also to prevent overshoot and also undershoot. Continuous measurement is required for the open circuit voltage on the monitoring cell, and also for the terminal voltage of the PV power system. Because of the accurate identification of the optimal operating voltage of the PV power system, more than 99% power is drawn from the actual maximum power. >

Intelligent Automatic Generation Control

Intelligent Power System Operation and Control: Japan Case Study Application of Intelligent Methods to Power Systems Application to Power System Planning Application to Power System Control and Restoration Future Implementations Automatic Generation Control (AGC): Fundamentals and Concepts AGC in a Modern Power System Power System Frequency Control Frequency Response Model and AGC Characteristics A Three-Control Area Power System Example Intelligent AGC: Past Achievements and New Perspectives Fuzzy Logic AGC Neuro-Fuzzy and Neural-Networks-Based AGC Genetic-Algorithm-Based AGC Multiagent-Based AGC Combined and Other Intelligent Techniques in AGC AGC in a Deregulated Environment AGC and Renewable Energy Options AGC and Microgrids Scope for Future Work AGC in Restructured Power Systems Control Area in New Environment AGC Configurations and Frameworks AGC Markets AGC Response and an Updated Model Neural-Network-Based AGC Design An Overview ANN-Based Control Systems Flexible Neural Network Bilateral AGC Scheme and Modeling FNN-Based AGC System Application Examples AGC Systems Concerning Renewable Energy Sources An Updated AGC Frequency Response Model Frequency Response Analysis Simulation Study Emergency Frequency Control and RESs Key Issues and New Perspectives AGC Design Using Multiagent Systems Multiagent System (MAS): An Introduction Multiagent Reinforcement-Learning-Based AGC Using GA to Determine Actions and States An Agent for ss Estimation Bayesian-Network-Based AGC Approach Bayesian Networks: An Overview AGC with Wind Farms Proposed Intelligent Control Scheme Implementation Methodology Application Results Fuzzy Logic and AGC Systems Study Systems Polar-Information-Based Fuzzy Logic AGC PSO-Based Fuzzy Logic AGC Frequency Regulation Using Energy Capacitor System Fundamentals of the Proposed Control Scheme Study System Simulation Results Evaluation of Frequency Regulation Performance Application of Genetic Algorithm in AGC Synthesis Genetic Algorithm: An Overview Optimal Tuning of Conventional Controllers Multiobjective GA GA for Tracking Robust Performance Index GA in Learning Process Frequency Regulation in Isolated Systems with Dispersed Power Sources Configuration of Multiagent-Based AGC System Configuration of Laboratory System Experimental Results

Fuzzy logic control scheme for on-line stabilization of multi-machine power system

Abstract The paper presents a fuzzy logic control scheme to enhance the overall stability of a multi-machine power system. Several simple control rules are prepared to control generating units. Desired stabilizing signals are determined according to the rules together with the speed/acceleration states of generating units at every sampling time. The proposed fuzzy logic control scheme is easy to implement, and requires a low amount of computation because of its simple control rules, and required data. The proposed fuzzy logic stabilizer can be easily set up by using a micro-computer with the functions of A/D and D/A conversion. The efficiency of the proposed fuzzy logic stabilizers is demonstrated through simulations using a 10-machine and 39-bus power system.

Rule-based stabilizer for multi-machine power system

An application of the rule-based stabilizing control scheme to improve the overall stability of electric power systems is presented. Several simple rules are prepared for each generator in the system. The stabilizing signal for each generator is of the discrete type; it is renewed at every sampling time to control the generator excitation levels depending on the speed/acceleration state of the generator, using the measured speed deviation and the control rules. The efficiency of the proposed rule-based stabilizer is demonstrated by using a sample three-machine power system. >

Robustness of fuzzy logic power system stabilizers applied to multimachine power system

This paper investigates the robustness of fuzzy logic power system stabilizers using the information of speed and acceleration states of a study unit. The input signals are the real power output and/or the speed of the study unit. Nonlinear simulations show the robustness of the fuzzy logic power system stabilizers. Experiments are also performed by using a micro-machine system. The results show the feasibility of the proposed fuzzy logic stabilizer. >

On Load–Frequency Regulation With Time Delays: Design and Real-Time Implementation

This paper addresses a robust decentralized proportional-integral (PI) control design for power system load-frequency regulation with communication delays. In the proposed methodology, the PI-based load-frequency control (LFC) problem is reduced to a static output feedback control synthesis for a multiple-delay system. The proposed control method gives a suboptimal solution using a developed iterative linear matrix inequalities algorithm via the mixed H 2/H infin control technique. The control strategy is suitable for LFC applications that usually employ the PI control. To demonstrate the efficiency of the proposed control strategy, an experimental study has been performed at the Research Laboratory, Kyushu Electric Power Company, Japan.

Application of fuzzy logic control to a manipulator

The authors describe a control strategy for robotic manipulators that incorporates a proportional-plus-integral (PI) controller with a simple fuzzy logic (FL) controller. In the proposed strategy, the PI controller is used to ensure fast transient response and zero steady-state error for step inputs, or end-point control, whereas the FL controller is used to enhance the damping characteristics of the overall system. The main advantage of the proposed control energy is that only the current and previous measurements and a set of simple control rules are required; as such, it can be readily implemented. The proposed FL controller is described, and its effectiveness is demonstrated through simulations involving the control of a two-arm two-link manipulator. >

Predicting remaining useful life of rotating machinery based artificial neural network

Accurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as a method to improve accurate RUL prediction of bearing failure. For this purpose, ANN model uses time and fitted measurements Weibull hazard rates of root mean square (RMS) and kurtosis from its present and previous points as input. Meanwhile, the normalized life percentage is selected as output. By doing that, the noise of a degradation signal from a target bearing can be minimized and the accuracy of prognosis system can be improved. The ANN RUL prediction uses FeedForward Neural Network (FFNN) with Levenberg Marquardt of training algorithm. The results from the proposed method shows that better performance is achieved in order to predict bearing failure.