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

Flood River Water Level Forecasting using Ensemble Machine Learning for Early Warning Systems

Flood forecasting is crucial for early warning system and disaster risk reduction. Yet the flood river water levels are difficult and challenging task that it cannot be easily captured with classical time-series approaches. This study proposed a novel intelligence system utilised various machine learning techniques as individual models, including radial basis function neural network (RBF-NN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and long short-term memory network (LSTM) to establish intelligent committee machine learning flood forecasting (ICML-FF) framework. The combination of these individual models achieved through simple averaging method, and further optimised using weighted averaging by K-nearest neighbour (K-NN) and genetic algorithm (GA). The effectiveness of the proposed model was evaluated using real case study for Malaysia's Kelantan River. The results show that ANFIS outperforms as individual model, while ICML-FF-based model produced better accuracy and lowest error than any one of the individuals. In general, it is found that the proposed ICML-FF is capable of robust forecasting model for flood early warning systems

Wavelet bump extraction (WBE) for editing variable amplitude fatigue loadings.

In durability testing of automobiles, load histories collected for laboratory testing are often lengthy in time. Therefore, a fatigue data editing technique is needed to summarise the load history. A fatigue mission synthesis algorithm, called Wavelet Bump Extraction (WBE) preserves the original load cycle sequences has been developed. The basis of WBE is to identify the important features or bumps that cause the majority of the fatigue damage. Bumps are identified in the frequency bands of the load spectrum using an orthogonal wavelet transform. Bumps are then extracted and combined to produce a mission signal with an equivalent fatigue damage as the onginal signal. The WBE validation was performed by analysing the cycle sequence effects in variable amplitude (VA) loadings. The experimental fatigue lives of the shortened VA loadings (Choi 2004) were compared to those predicted using strain-life fatigue damage models, i.e. Coffin-Manson, Morrow, Smith-Watson-Topper and Effective Strain Damage (ESD). The smallest difference was found between the experiment and ESD model, suggesting it is a suitable model for the use with WBE. Comparison between WBE and the time domain fatigue data editing was also conducted in order to observe its effectiveness for accelerated fatigue tests. Moreover, it is useful to evaluate the fatigue life of the original and mission signals by means of fatigue damage preservation in the mission signal. Finally, an analysis of the bump segments sequence effects was performed in order to determine an appropriate mission signal for accelerated fatigue tests. The WBE algorithm showed a substantial compression of the VA loadings could be achieved whilst maintaining fatigue damage and the important load sequences. The ability of the WBE algorithm to shorten fatigue loadings would be expected to prove useful in accelerated fatigue testing of automobiles. Finally, the combination of WBE and ESD provides a novel application of the wavelet-based fatigue data editing

Acoustic Emission Evaluation of Fatigue Life Prediction for a Carbon Steel Specimen using a Statistical-Based Approach.

This study was carried out to investigate the relationship between the strain and acoustic emission (AE) signals to ascertain the applicability of AE in predicting the fatigue life of metallic specimens. This paper is an extension research of previous work that has been published before. Previous paper presents the ability of AE to predict the fatigue life using statistical parameters such as root mean square (r.m.s) and kurtosis. The same approach also has been carried out in this recent paper but this time, the common parameters that can be directly extracted from the AE data acquisition system were used. To achieve the objective, the strain and AE signals were measured using a strain gauge and a AE piezoelectric transducer on SAE 1045 steel specimens. These measurements were conducted during the cyclic test at constant loadings of 570 MPa, 610 MPa, and 650 MPa. For data collection, AE parameters, i. e., count rate, hits, and duration, were extracted from specific software and were then correlated to fatigue lives calculated using the strain data. Fatigue life values were calculated using strain-life models. The correlation between the experimental and predicted values of fatigue life was then established by the so-called coefficient of correlation which is within 97.2 % and 98.5 % for the Coffin-Manson model and between 92.7 % and 94.3 % for the Smith-Watson-Topper model, respectively. As for the Morrow model, the coefficient of correlation was tabulated at approximately between 71.9 % and 73.6 %. Good correlation values expressed that the AE technique is applicable for predicting the fatigue life of metallic specimens

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

Life prediction of SAE 1045 carbon steel using the acoustic emission parameter

The competency of acoustic emission (AE) technique in order to predict the fatigue life of SAE 1045 carbon steel was discussed in this paper. The correlation of the AE parameter and the number of cycles to failure of the tested specimens were established via the statistical approach. In this paper, The AE hits were selected as the functional parameter. The fatigue life values were calculated using the strain-life approach of three models; Coffin-Manson, Smith-Watson Topper and Morrow. Both AE and strain signals used in the analysis were captured using the AE sensor and strain gauge that were attached to the specimen during the fatigue test. The results show that the AE technique has a good potential in assessing the fatigue life with the designed H-N curve (AE hits-number of cycles to failure curve)

Multiple linear regression parameters for determining fatigue-based entropy characterisation of magnesium alloy

This paper presents the development of the multiple linear regression approach based on the stress ratio and applied load that was assessed using entropy generation. The energy dissipation is associated with material degradation to determine the fatigue life with consideration to the irreversible thermodynamic framework. This relationship was developed by predicting a complete entropy generation using a statistical approach, where a constant amplitude loading was applied to evaluate the fatigue life. By conducting compact tension tests, different stress ratios were applied to the specimen. During the tests, the temperature change was observed. The lowest entropy generation was 2.536 MJm-3 K-1 when 3,000N load with a stress ratio of 0.7 was applied to the specimen. The assumptions of the models were considered through graphical residual analysis. As a result, the predicted regression model based on the applied load and stress ratio was found to agree with the results of the experiment, with only 9.3% from the actual experiment. Therefore, the entropy generation can be predicted to access the dissipated energy as an irreversible degradation of a metallic material, subjected to cyclic elastic-plastic loading. Thermodynamic entropy is shown to play an important role in the fatigue process to trace the fatigue life

Fatigue life prediction of the SAE 1045 medium carbon steel using the acoustic emission technique associated with Weibull distribution approach

This paper presents the capability of acoustic emission (AE) technique in predicting the fatigue life of the SAE 1045 carbon steel. Using the Weibull distribution approach, the specific AE parameters and the number of cycles to failure of the tested specimens were correlated. In addition, the lives of specimens were also calculated using the available empirical models. The AE and strain signals that were used were experimentally measured using the AE sensor and strain gauge, as the sensors were attached to the specimen during the fatigue test. The AE parameter was transform to the Wei bull parameter as this technique gives more accurate values. The results showed that the AE technique has a good potential in assessing the fatigue life with the designed h-N curve, correlating the AE hits-number and also cycles to failure

The perspective of mathematics in traditional Malay society with the ethnomathematics approach

This paper demonstrates the fundamentals of understanding mathematics within the traditional Malay culture. Western scholars have labelled the Malay community as those who are not able to evaluate the accuracy of mathematics. However, it is obscure to accept that the mathematical ideologies of Malays are merely based on their close observations of nature. In this respect, the study of ethnomathematics is relevant to analyse the mathematical ideas of traditional people, which were initially used in the discipline of Anthropology. Literary works are a reflection of the mind and culture of a society. Thus, this paper analyses three works of Malay oral literature; The Tales of Awang Sulung Merah Muda (Hikayat Awang Sulung Merah Muda), The Tales of Terung Pipit (Hikayat Terung Pipit) and Raja Donai Bahtera Kulit Kacang; all of which were basically presented by Malay storytellers.Each set of Malay mathematical ideas are interpreted based on a matrix system that is parallel to the thoughts and views of the Malays. Several mathematical techniques are used actively in the text; for instance, in the determination of the length, weight, predictable time and other such calculations. Indirectly, this analysis proves that the traditional Malay community is capable of assembling their mathematical ideas perfectly and in harmony with their perception of culture