Ahmed M. A. Haidar

Vulnerability control of large scale interconnected power system using neuro-fuzzy load shedding approach

Vulnerability control is becoming an essential requirement for security of power systems in the new utility environment. It is a difficult task for system operator who under economic pressure may be reluctant to take preventive action against harmful contingencies in order to guarantee providing continued service. For power systems which are operated closer to their stability limits, it is desirable to use load shedding as a form of vulnerability control strategy. This paper presents a neuro-fuzzy approach for determining the amount of load to be shed in order to avoid a cascading outage. The objective is to develop fast and accurate load shedding technique to control the vulnerability of power systems by means of using a neuro-fuzzy controller. A case study is performed on the IEEE 300-bus test system so as to validate the performance of neuro-fuzzy controller in determining the amount of load shed. Test results prove that the neuro-fuzzy controller provides accurate and faster vulnerability control action.

Transient stability evaluation of electrical power system using generalized regression neural networks

Transient stability evaluation (TSE) is part of dynamic security assessment of power systems, which involves the evaluation of the systems ability to remain in equilibrium under credible contingencies. Neural networks (NN) have been applied to the security assessment of power systems and have shown great potential for predicting the security of power systems. This paper proposes a generalized regression neural networks (GRNN) based classification for transient stability evaluation in power systems. In the proposed method, learning data sets have been generated using time domain simulation (TDS). The GRNN input nodes representing the voltage magnitude for all buses, real and reactive powers on transmission lines, the output node representing the transient stability index. The proposed GRNN was implemented and tested on IEEE 9-bus and 39-bus test systems. NN results show that the stability condition of the power system can be predicted with high accuracy and less misclassification rate.

An Intelligent Load Shedding Scheme Using Neural Networks and Neuro-Fuzzy

Load shedding is some of the essential requirement for maintaining security of modern power systems, particularly in competitive energy markets. This paper proposes an intelligent scheme for fast and accurate load shedding using neural networks for predicting the possible loss of load at the early stage and neuro-fuzzy for determining the amount of load shed in order to avoid a cascading outage. A large scale electrical power system has been considered to validate the performance of the proposed technique in determining the amount of load shed. The proposed techniques can provide tools for improving the reliability and continuity of power supply. This was confirmed by the results obtained in this research of which sample results are given in this paper.

A computational intelligence-based suite for vulnerability assessment of electrical power systems

This paper discusses the feasibility of implementing computational intelligence algorithms for power system analysis in an open source environment. The scope is specially oriented to education, training and research. In particular, the paper describes a software package, namely Computational Intelligence Applications to Power System (CIAPS), that implements a variety of heuristic techniques for vulnerability assessment of electrical power systems. CIAPS is based on Matlab and suited for analysis and simulation of small to large size electric power systems. CIAPS is used for solving power flow, optimal power flow, contingency analysis based on artificial neural networks and fuzzy logic techniques. A variety of illustrative examples are given to show the features of the developed software tool.

The Influences of T-Joint Core Design on No-Load Losses in Transformers

The main objective of this article is to compare the performance of transformers with respect to different core designs based on T-joint configurations of 23o, 45o, 60o, and 90o. The quantitative analysis of localized flux distribution and loss calculation is determined through the significant progress of numerical field calculations using the finite element method. The transformer core has been designed and simulated using QuickField software. From the results, it was found that the best core design is a 60o T-joint configuration in terms of the no-load losses and flux distribution.

Electrical Defect Detection in Thermal Image

Electrical and Electronic objects, which have a temperature of operating condition above absolute zero, emit infrared radiation. This radiation can be measured on the infrared spectral band of the electromagnetic spectrum using thermal imaging. Faults on electrical systems are expensive in terms of plant downtime, damage, loss of production or risk from fire. If the threshold temperature is timely detected, the electrical equipment failures can be avoided. This paper presents a straightforward approach for thermal analysis that examines power loads and large area thermal characteristics. A thermal imaging camera was used to collect thermal pictures of the tested system under various operating conditions. These pictures are analyzed using thermal diagnosis system in order to detect the fault location that may occur and improve inspection efficiency.

Behavioral characterization of electric vehicle charging loads in a distribution power grid through modeling of battery chargers

Plug-in Electric Vehicle (PEV) is a new atypical load in power systems. In future, PEV load will play a significant role in the distribution grids. This integrated load into the power grid may overload the system components, increase power losses and may violate system constraints. Currently, the most common method of Electric Vehicle (EV) modeling is to consider the EV loads as constant power elements without considering the voltage dependency of EV charging system during state of charges (SOC). EV load demand cannot be considered as a constant power, as modeling as a constant power load will not provide accurate information about the behavior of charging system during charging process. As several research projects on smart grids are now looking into realistic models representing the realistic behavior of an EV loads, this paper proposes a methodology for modeling of EV charger integrated to an electricity grid in order to understand the impacts of EV charging load. A charging system was designed to capture the EV load behavior and extract the coefficients of the EV ZIP load model. A comparative study was carried out with different types of load models. The results indicate that the assumptions of load demand as a constant power to analysis the effect of PEVs on power grid would not be effective in real time application of PEVs.

Artificial Intelligence application to Malaysian electrical powersystem

Vulnerability assessment and control of a power system is important to power utilities due to the blackouts in recent years in many countries which indicate that power systems today are vulnerable when exposed to unforeseen catastrophic contingencies. A fast and accurate technique to assess the level of system strength or weakness is some of the essential requirements for maintaining security of modern power systems, particularly in competitive energy markets. This paper presents intelligent artificial techniques for vulnerability assessment of Malaysian power system and recommends preventive control measures. Accurate techniques for vulnerability assessment and control of power systems are developed. In vulnerability assessment, power system loss index is used as a vulnerability parameter, neural network weight extraction is employed as the feature extraction method and the generalized regression neural network is used to predict vulnerability of a power system. As for vulnerability control, load shedding is considered by using the neuro-fuzzy technique. Finally, the paper presents and discusses the results from this research with recommendations.

A Coordinated Control Approach for DC link and Rotor Crowbars to Improve Fault Ride-Through of DFIG-Based Wind Turbine

Nowadays, most double fed induction generators (DFIGs)-based wind turbines are equipped with a rotor crowbar connected in parallel with the rotor side converter (RSC). The parallel rotor side crowbar (PRSC) is used to protect the RSC and dc-link capacitor by dissipating the rotor energy during grid fault condition. In this paper, two types of crowbar protections are used, one in the rotor winding and the second in the dc link. During the fault condition, the rotor winding crowbar connects in series with the rotor winding and RSC to decrease the RSC current and dissipate the rotor energy. The general PRSC does not have the ability to significantly decrease the over-current. To protect the semiconductor switches of RSC, DFIG should not be kept connected with the utility grids under severe faults. The dc-link capacitor crowbar (DCCC) operates only if the dc capacitor voltage exceeds a threshold level. Both the series rotor side crowbar (SRSC) and the DCCC operate in coordination with each other to protect RSC and dc link during fault condition, and improve the fault ride through of the DFIG. Using the proposed SRSC, RSC continues its operation to control the DFIG during fault condition. Thereby, the reactive power can be injected to support the voltage at the point of common coupling. The behavior of the DFIG is investigated when the combined crowbars are operating with the proposed coordinated control approach and results are presented.

Impact assessment of electric vehicle demand through load modeling

The primary aim of this paper is to develop an analytical framework for evaluating the impact of Electric Vehicles (EV) on distribution systems. EV load demand cannot be considered as a constant load and hence modeling as a single-stage, constant load will not provide accurate information. In the past, a constant load model was used to examine the effect of EV charging without considering the voltage dependency of EV load models. However, inappropriate modeling may lead to misleading results. In this paper, EV charging is appropriately modeled considering the voltage dependency of the EV load. Simulation studies have been carried out to assess the EV load impacts in terms of changes in power losses, bus voltages, and real and reactive power demands at the distribution systems with different types of load models. Results demonstrate that the proposed voltage dependency model of EV load can provide better understanding of the impacts of EV in the distribution systems.