Optimization methods for electric power systems: An overview

Power systems optimization problems are very difficult to solve because power systems are very large, complex, geographically widely distributed and are influenced by many unexpected events. It is therefore necessary to employ most efficient optimization methods to take full advantages in simplifying the formulation and implementation of the problem. This article presents an overview of important mathematical optimization and artificial intelligence (AI) techniques used in power optimization problems. Applications of hybrid AI techniques have also been discussed in this article

Measurements, Models, Systems and Design

531 s.

Power system security boundary visualization using intelligent techniques

In the open access environment, one of the challenges for utilities is that typical operating conditions tend to be much closer to security boundaries. Consequently, security levels for the transmission network must be accurately assessed and easily identified on-line by system operators;Security assessment through boundary visualization provides the operator with knowledge of system security levels in terms of easily monitorable pre-contingency operating parameters. The traditional boundary visualization approach results in a two-dimensional graph called a nomogram. However, an intensive labor involvement, inaccurate boundary representation, and little flexibility in integrating with the energy management system greatly restrict use of nomograms under competitive utility environment. Motivated by the new operating environment and based on the traditional nomogram development procedure, an automatic security boundary visualization methodology has been developed using neural networks with feature selection. This methodology provides a new security assessment tool for power system operations;The main steps for this methodology include data generation, feature selection, neural network training, and boundary visualization. In data generation, a systematic approach to data generation has been developed to generate high quality data. Several data analysis techniques have been used to analyze the data before neural network training. In feature selection, genetic algorithm based methods have been used to select the most predicative precontingency operating parameters. Following neural network training, a confidence interval calculation method to measure the neural network output reliability has been derived. Sensitivity analysis of the neural network output with respect to input parameters has also been derived. In boundary visualization, a composite security boundary visualization algorithm has been proposed to present accurate boundaries in two dimensional diagrams to operators for any type of security problem;This methodology has been applied to thermal overload, voltage instability problems for a sample system

Nonlinear finite element analysis of reinforced concrete beams strengthened with textile fine grained mortar

Nowadays, there was an increasing need of repairing and upgrading the reinforced concrete (RC) structure due to the deterioration of the structure. The fibre reinforced polymer (FRP) was commonly used for structural retrofitting purposes. However, owing to the debonding of the FRP from the concrete substrate and high cost of epoxy, it was gradually replaced with textile fine grained mortar (TFGM) nowadays. The TFGM system has been widely used in the construction field nowadays to repair the structure. Our study focus on the strain performances of the concrete surface, steel reinforcement and the textile itself. There were many proven experimental results showing that the TFGM was more effective than the other strengthening method such as FRP plate method. The experimental work done by previous researcher on investigation of strain performances of the concrete surface, steel reinforcement and the textile itself which consists of eleven (11) RC beams with dimension 150 x 200 x 2500 mm. The RC beams were strengthened with FGM and TFGM with 4 layers. The investigation continued with the finite element (FE) strain performance analysis with using Advanced Tool for Engineering Nonlinear Analysis (ATENA) software. The strain of the concrete surface, steel reinforcement and the textile were measured at a mid-point of RC beam. Then, the results of the finite element analysis software ATENA compared against the experimental results. The strain performances of the concrete and steel reinforcement improved noticeably when the number of layers of textile reinforcement used increased

Assessment of voltage stability based on power transfer stability index using computational intelligence models

In this paper, the importance of voltage stability is explained, which is a great problem in the EPS. The estimation of VS is made a priority so as to make the power system stable and prevent it from reaching voltage collapse. The power transfer stability index (PTSI) is used as a predictor utilized in a PSN to detect the instability of voltages on weakened buses. A PSI is used to obtain a voltage assessment of the PSNs. Two hybrid algorithms are developed. The (CA-NN) and the (PSO-NN). After developing algorithms, they are compared with the actual values of PTSI NR method. The algorithms installed on the 24 bus Iraqi PS. The actual values of PTSI are the targets needed. They are obtained from the NR algorithm when the input data is Vi, δi, Pd, Qd for the algorithm. The results indicate that a weak bus that approaches voltage collapse and all results were approximately the same. There is a slight difference with the actual results and demonstrated classical methods are slower and less accurate than the hybrid algorithms. It also demonstrates the validation and effectiveness of algorithms (CA-NN, and PSO-NN) for assessing voltage-prioritizing algorithms (CA-NN). The MATLAB utilized to obtain most of the results

Controlling Techniques for STATCOM using Artificial Intelligence

The static synchronous compensator (STATCOM) is a power electronic converter designed to be shunt-connected with the grid to compensate for reactive power. Although they were originally proposed to increase the stability margin and transmission capability of electrical power systems, there are many papers where these compensators are connected to distribution networks for voltage control and power factor compensation. In these applications, they are commonly called distribution static synchronous compensator (DSTATCOM). In this paper we have focussed on STATCOM and the controlling techniques which are based on artificial intelligence

Improvement of Power System Small-Signal Stability by Artificial Neural Network Based on Feedback Error Learning

Electrical power systems usually suffer from instabilities because of some disturbances occurring due to environmental conditions, system failures, and loading conditions. The most frequently encountered problem is the loss of synchronization between the rotor angle and the stator magnetic angle for synchronous generators. The contribution of this study is that a nonlinear adaptive control approach called feedback error learning (FEL) is utilized to improve the small-signal stabilities of an electric power system. The power system under study is composed of a synchronous machine connected to infinite bus. Many advantages of FEL control approach makes it capable to robustly adapting with all possible operating conditions rather than using optimization algorithms for tuning the conventional power system stabilizer (CPSS) that is still unsatisfactory especially at some critical operating points. The performances of two controllers, namely the proposed FEL scheme and the conventional controller CPSS, are tested by Matlab simulations. It is found that the FEL controller can be effectively used as an alternative stabilizer for improving small-signal stabilities of the powe