Application of fuzzy logic to power system stabilizer

Power systems stability is a complex problem which was first recognised in 1920 and has been widely investigated by power system engineers ever since. The first laboratory test on a practical power system was conducted in 1924, followed by the first field test in the following year. The models and method of analysis were relatively simple, with long fault clearing times (0.5 to 2.0 seconds). In 1930, network analysers (which were analogue simulators of the power system) were developed and this led to the improvement of stability analysis. In early 1950’s, they were used to analyse problems which required detailed models of the synchronous machine, excitation system and speed governor. In the mid 1950’s, the first digital computer program for power systems stability was developed. Since the 1960's most of the industry efforts and interests relating to system stability have been concentrated on transient stability. Power systems are designed and operated to criteria concerning transient stability (Kundur, 1994). There have been significant developments in equipment modelling and testing, for synchronous machines, excitation systems and loads. In addition, using high speed fault clearing, fast exciters and special stability aids have been used to improve the transient stability of power systems. The high speed exciters adversely affect the small signal stability associated with local plant mode of oscillations by introducing negative damping of the rotor angle oscillations. Such problems have been solved using power systems stabilisers (PSS). The incorporation of a power systems stabiliser (PSS) into the excitation controller is to improve the system’s performance where the system’s damping is low. At the same time, it can also combat the damping reductions introduced by an AVR (Hughes, 1991). The damping of the rotor angle oscillations can be improved by adding a supplementary signal to the excitation control system to produce a component of the electrical torque on the rotor in phase with speed variations (Larsen and Swann, 1981). Figure 5.1 shows the block diagram of a power system stabiliser added to the excitation control system. The rotor angle oscillations of a generator feeding power to a large inter-connected power system occur in the frequency range of 0.2 to 2 Hz. Different signals have been used as the input to the PSS including: the rotor speed deviation, the bus frequency, the electrical power deviation and the accelerating power (Padiyar, 1996). When a speed signal is employed as an input for the PSS, then a phase lead compensator is required to provide sufficient phase lead (Hughes, 1991). A transient gain or washout is normally used to remove any steady state offset in the speed signal. This filter acts as a high pass filter and is required to ensure that the stabilising signal (PSS output) does not affect the steady state regulation characteristics

Artificial neural networks in surrogate modeling

Offline optimization of controller parameters for complex non-linear processes can be time consuming, even with high performance computers. This chapter demonstrates how a Radial Basis Function ANN can be utilized to tune the controller parameters for a non-linear process quickly. The ANN strategy used is basically to approximate the relationship between the controller parameters and the values of the objective function used. This strategy is called metamodeling or surrogate modeling (Gorissen et. al., 2006). The process used in this chapter is the mixing process, which is a multivariable and intrinsically non-linear plant. The Radial Basis Function Neural Network surrogate model used was able to give a good approximation to the optimum controller parameters in this case. In the design of control systems, one often has a complicated mathematical model of a system that has been obtained from fundamental physics and chemistry. The system will usually consist of inputs and outputs and in practice; it is normally desired to find the optimum controller parameter values that would give optimal outputs of the system. The simulations needed when applying optimization algorithms might be very expensive computationally owing to the complexity of the actual model. In spite of the advances in computer technology, the computational time to simulate the actual model might still be long and thus it becomes impractical to rely exclusively on simulation for the purpose of design optimization. Thus there is a need for metamodeling, that is, for the determination of simpler models that involve less computation but are good approximations to the complicated model

Optimization of an Intelligent Controller for an Unmanned Underwater Vehicle

 Underwater environment poses a difficult challenge for autonomous underwater navigation. A standard problem of underwater vehicles is to maintain it position at a certain depth in order to perform desired operations. An effective controller is required for this purpose and hence the design of a depth controller for an unmanned underwater vehicle is described in this paper. The control algorithm is simulated by using the marine guidance navigation and control simulator. The project shows a radial basis function metamodel can be used to tune the scaling factors of a fuzzy logic controller. By using offline optimization approach, a comparison between genetic algorithm and metamodeling has been done to minimize the integral square error between the set point and the measured depth of the underwater vehicle. The results showed that it is possible to obtain a reasonably good error using metamodeling approach in much a shorter time compared to the genetic algorithm approach

Flood Warning and Monitoring System Utilizing Internet of Things Technology

Flooding is one of the major disasters occurring in various parts of the world including Malaysia. To reduce the effect of the disaster, a flood warning and monitoring are needed to give an early warning to the victims at certain place with high prone to flood. By implementing Internet of Thing technology into the system, it could help the victim to get an accurate status of flood in real-time condition. This paper is develop a real-time flood monitoring and early warning system using wireless sensor node at a high prone area of flood. This system is based on NodeMCU based technology integrated using Blynk application. The wireless sensor node can help the victims by detecting the water levels and rain intensity while giving an early warning when a flood or heavy rain occurs. Basically, the sensor node consists of ultrasonic sensor and rain sensor controlled by NodeMCU as the microcontroller of the system which placed at the identified flood area. Buzzer and LED started to trigger and alert the victim when the flood had reached certain level of hazard. Data detected from the sensors are sent to the Blynk application via wireless connection. Victim will get to know the current status of flood and rain by viewing the interface and receiving push notification that available in Blynk application via IOS or Android smartphones. The flood level’s data sent to the email could help various organizations for further improvement of the system and flood forecasting purposes. After a test had been conducted, it was found that this prototype can monitor, detect and give warning with notification to the victim earlier before the occurrence of floods

Investigation and Evaluation of Low cost Depth Sensor System Using Pressure Sensor for Unmanned Underwater Vehicle

This paper presents the investigation and evaluation of low cost depth sensor system design for unmanned underwater vehicle (UUV) using pressure sensor. Two types of low cost pressure sensor system design are proposed for underwater vehicle. The pressure sensors are expected to prevent buckling or damaging to the UUV. The first design uses barometric pressure sensor, while the second design uses MPXAP which is an integrated silicon pressure sensor on-chip signal conditioned and temperature compensated. There are two different sub model of MPXAP put forward in this research namely, MPX4250AP and MPX5700AP. These pressure sensors are tested in three different conditions: in water tank, lake and swimming pool to study their effect on various densities. Details of the designs are discussed and implementations of these sensors on UUV are analyzed. Experimental results showed these pressure sensors have different performances. Based on the analysis of the results, MPX AP sensor is more suitable to be applied to UUV with low cost budget. For the depth from 0 to 30 meter, MPX 4250 AP is selected while MPX 5700 AP is for the range of depth up to 70 meter

Review on auto-depth control system for an unmanned underwater remotely operated vehicle (ROV) using intelligent controller

This paper presents a review of auto-depth control system for an Unmanned Underwater Remotely operated Vehicle (ROV), focusing on the Artificial Intelligent Controller Techniques. Specifically, Fuzzy Logic Controller (FLC) is utilized in auto-depth control system for the ROV. This review covered recently published documents for auto-depth control of an Unmanned Underwater Vehicle (UUV). This paper also describes the control issues in UUV especially for the ROV, which has inspired the authors to develop a new technique for auto-depth control of the ROV, called the SIFLC. This technique was the outcome of an investigation and tuning of two parameters, namely the break point and slope for the piecewise linear or slope for the linear approximation. Hardware comparison of the same concepts of ROV design was also discussed. The ROV design is for smallscale, open frame and lower speed. The review on auto-depth control system for ROV, provides insights for readers to design new techniques and algorithms for auto-depth control

Multi-objective Optimization of PID Controller using Pareto-based Surrogate Modeling Algorithm for MIMO Evaporator System

Most control engineering problems are characterized by several objectives, which have to be satisfied simultaneously. Two widely used methods for finding the optimal solution to such problems are aggregating to a single criterion, and using Pareto-optimal solutions. This paper proposed a Pareto-based Surrogate Modeling Algorithm (PSMA) approach using a combination of Surrogate Modeling (SM) optimization and Pareto-optimal solution to find a fixed-gain, discrete-time Proportional Integral Derivative (PID) controller for a Multi Input Multi Output (MIMO) Forced Circulation Evaporator (FCE) process plant. Experimental results show that a multi-objective, PSMA search was able to give a good approximation to the optimum controller parameters in this case. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) method was also used to optimize the controller parameters and as comparison with PSMA

Flood forecasting of Malaysia Kelantan river using support vector regression technique

The rainstorm is believed to contribute flood disasters in upstream catchments, resulting in further consequences in downstream area due to rise of river water levels. Forecasting for flood water level has been challenging, presenting complex task due to its nonlinearities and dependencies. This study proposes a support vector machine regression model, regarded as a powerful machine learningbased technique to forecast flood water levels in downstream area for different lead times. As a case study, Kelantan River in Malaysia has been selected to validate the proposed model. Four water level stations in river basin upstream were identified as input variables. A river water level in downstream area was selected as output of flood forecasting model. A comparison with several benchmarking models, including radial basis function (RBF) and nonlinear autoregressive with exogenous input (NARX) neural network was performed. The results demonstrated that in terms of RMSE error, NARX model was better for the proposed models. However, support vector regression (SVR) demonstrated a more consistent performance, indicated by the highest coefficient of determination value in twelve-hour period ahead of forecasting time. The findings of this study signified that SVR was more capable of addressing the long-term flood forecasting problems

Review on Auto-Depth Control System for an Unmanned Underwater Remotely Operated Vehicle (ROV) using Intelligent Controller

This paper presents a review of auto-depth control system for an Unmanned Underwater Remotely operated Vehicle (ROV), focusing on the Artificial Intelligent Controller Techniques. Specifically, Fuzzy Logic Controller (FLC) is utilized in auto-depth control system for the ROV. This review covered recently published documents for auto-depth control of an Unmanned Underwater Vehicle (UUV). This paper also describes the control issues in UUV especially for the ROV, which has inspired the authors to develop a new technique for auto-depth control of the ROV, called the SIFLC. This technique was the outcome of an investigation and tuning of two parameters, namely the break point and slope for the piecewise linear or slope for the linear approximation. Hardware comparison of the same concepts of ROV design was also discussed. The ROV design is for smallscale, open frame and lower speed. The review on auto-depth control system for ROV, provides insights for readers to design new techniques and algorithms for auto-depth contro

Review on Auto-Depth Control System for an Unmanned Underwater Remotely Operated Vehicle (ROV) using Intelligent Controller

This paper presents a review of auto-depth control system for an Unmanned Underwater Remotely operated Vehicle (ROV), focusing on the Artificial Intelligent Controller Techniques. Specifically, Fuzzy Logic Controller (FLC) is utilized in auto-depth control system for the ROV. This review covered recently published documents for auto-depth control of an Unmanned Underwater Vehicle (UUV). This paper also describes the control issues in UUV especially for the ROV, which has inspired the authors to develop a new technique for auto-depth control of the ROV, called the SIFLC. This technique was the outcome of an investigation and tuning of two parameters, namely the break point and slope for the piecewise linear or slope for the linear approximation. Hardware comparison of the same concepts of ROV design was also discussed. The ROV design is for smallscale, open frame and lower speed. The review on auto-depth control system for ROV, provides insights for readers to design new techniques and algorithms for auto-depth contro