Public beliefs about lightning in Malaysia

Outcomes of a comprehensive survey on the public beliefs in Malaysia with regard to the nature of lightning and lightning safety have been presented. A questionnaire survey is done among 100 respondents, comprised of an equal number of participants from each gender. Despite the sample is biased towards educated layers of the society, the concepts of a sizable fraction of the sample are in contradiction with accepted scientific norms. Only 54% of the sample indicated disagreement with incorrect statements in the questionnaire. The percentage that agreed with correct statements is slightly below 50%. Such information plays a vital role in developing lightning safety guidelines for a given region. It is advisable to plan the safety modules in line with these beliefs, at least at the initial stage, rather than having myth-busting type approaches from the right beginning

A novel implementation for generator rotor angle stability prediction using an adaptive artificial neural network application for dynamic security assessment

This paper addresses a new approach for predicting the generator rotor angle using an adaptive artificial neural network (AANN) for power system stability. The aim of this work is to predict the stability status for each generator when the system is under a contingency. This is based on the initial condition of an operating point, which is represented by the generator rotor angle at a certain load level. An automatic data generation algorithm is developed for the training and testing process. The proposed method has been successfully tested on the IEEE 9-bus test system and the 87-bus system for Peninsular Malaysia

Simulation of an adaptive artificial neural network for power system security enhancement including control action

This paper presents a new method for enhancing power system security, including a remedial action, using an artificial neural network (ANN) technique. The deregulation of electricity markets is still an essential requirement of modern power systems, which require the operation of an independent system driven by economic considerations. Power flow and contingency analyses usually take a few seconds to suggest a control action. Such delay could result in issues that affect system security. This study aims to find a significant control action that alleviates the bus voltage violation of a power system and to develop an automatic data knowledge generation method for the adaptive ANN. The developed method is proved to be a steady-state security assessment tool for supplying possible control actions to mitigate an insecure situation resulting from credible contingency. The proposed algorithm is successfully tested on the IEEE 9-bus and 39-bus test systems. A comparison of the results of the proposed algorithm with those of other conventional methods reveals that an ANN can accurately and instantaneously provide the required amounts of generation re-dispatch and load shedding in megawatts

The influence of a curved on copper-type down conductor

The objective of this paper is to investigate the influence of curved copper conductor angle under current transient and voltage using numerical analysis approach. A thorough evaluation for copper down-conductor attainable in lightning protection system with a recommended cross-sectional area of conductor based on the standards under different numerous angles will be examined. The results in terms of field values were reviewed and considered in resemblance with the critical breakdown value of air. Although the comparison is by no means rigorous, it may shed some light on how the geometrical modelling and the physical parameters weighted in the computational modelling and how further refinement could be synthesized. In the end, a realistic approach for the optimal angle of down-conductor contributed to the installation design of a down-conductor in confined area is set and establish

Transient security assessment for power system stability: a review on artificial intelligence approach

The growth of large interconnected electricity networks requires a high degree of security for normal operation. This paper attempts to overview several available techniques for assessing the Transient Security Assessment (TSA) of a power system. Different algorithms are explained in details (Static and Dynamic Security Assessment) with the uses of Artificial Intelligent (AI) method. Transient effects can be roughly described as undesired voltage/ current that may result a contingencies in the power system. However, it is only considered lightning and switching as the main causes of TSA. In this proposed method, Artificial Neural Network (ANN) and Fuzzy techniques are able to use in term of classification, prediction and to determine the system security status. Time domain analysis is performed for each credible contingency using signal processing method; than an AI model is proposed for the TSA analysis. The novelty of the proposed approach is that the fast ability to detect and classify any disturbance (lightning (or) switching) in the electric power system using AI techniques

Backflashover analysis for 132 kV Kuala Krai-Gua Musang transmission line

Lightning has been a major concern to the power system researchers because it can cause damage to the connected electrical equipment and transmission failure. One study carried out is that where the level of voltage at a substation is observed following a backflashover analysis on a transmission line. Sample of worst case transmission line was taken from Tenaga National Berhad, which is 132 kV Kuala Krai-Gua Musang line for the purpose of simulation using PSCAD software. An integral part of this study is the model of transmission line components such as insulator coordination gap flashover, tower model, nonlinear current dependent resistance as footing model. All models are verified by accurate analysis of previous researchers. Results were analyzed and influences of line parameters to backflashover rate were discussed. Practical recommendations and conclusions based on the results are made for an improvement in the lightning performance of high voltage transmission line

Dissolved gas analysis of transformers based on rough set and fuzzy logic methods

Dissolved Gas Analysis (DGA) is one of the common approaches that can be used to detect incipient faults in transformers. According to IEEE C57/104-2008 and IEC 60599 standards, there are many DGA techniques that can be used to interpret the type of faults. In this paper, an alternative method to diagnose faults in transformers based on Rough Set (RS) and Fuzzy Logic (FL) is proposed. The rules for the FL are generated from the attributes of RS. Based on this method, it was found that the efficiency of the fault interpretation based on RS/FL is improved compared to the conventional methods in standards

A simulation tool to assess the lightning induced over-voltages on dc cables of photovoltaic installations

The development of large scale photovoltaic (PV) plants in rural areas is constantly increasing. This paper describes appropriately scaled laboratory tests and geometrically accurate (real scale) simulation models in an attempt to assess the induced over-voltages on long dc cabling loops

On the comparison of lightning fatality rates between states in Malaysia from 2008-2019

This study briefly canvasses lightning fatalities in Malaysia. Acquiring lightning fatality statics are a challenge as mandatory reporting of such events is seldom required, and as such, many lightning incidences were unreported. For that reason, numerous cases were undocumented in the official records. However, for injury prevention efforts and calling a government's attention to these as a severe problem, information on numbers, exposed locations and their backgrounds are essential to determine vulnerable areas. A theoretical approach using Gomes-Kadir equation was also tested to estimate annual lightning deaths in each state of Malaysia using lightning density and sociological factors and the results compared against known fatality data

Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications

The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system’s modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model