Detecting the single line to ground short circuit fault in the submarine’s power system using the artificial neural network

The electric marine instruments are newly inserted in the trade and industry, for which the existence of an equipped and reliable power system is necessitated. One of the features of such a power system is that it cannot have an earth system causing the protection relays not to be able to detect the single line to ground short circuit fault. While on the other hand, the occurrence of another similar fault at the same time can lead to the double line fault and thereby the tripping of relays and shortening of vital loads. This in turn endangers the personals' security and causes the loss of military plans. From the above considerations, it is inferred that detecting the single line to ground fault in the marine instruments is of a special importance. In this way, this paper intends to detect the single line to ground fault in the power systems of the marine instruments using the wavelet transform and Multi-Layer Perceptron (MLP) neural network. In the numerical analysis, several different types of short circuit faults are simulated on several marine power systems and the proposed approach is applied to detect the single line to ground fault. The results are of a high quality and preciseness and perfectly demonstrate the effectiveness of the proposed approach

Using the Modified Shuffled Frog Leaping Algorithm for Optimal Sizing and location of Distributed Generation Resources for Reliability Improvement

Restructuring the recent developments in the power system and problems arising from construction as well as the maintenance of large power plants lead to increase in using the Distributed Generation (DG) resources. DG units due to its specifications, technology and location network connectivity can improve system and load point reliability indices. In this paper, the allocation and sizing of distributed generators in distribution electricity networks are determined through using an optimization method. The objective function of the proposed method is based on improving the reliability indices, such as a System Average Interruption Duration Index (SAIDI), and Average Energy Not Supplied (AENS) per customer index at the lowest cost. The optimization is based on the Modified Shuffled Frog Leaping Algorithm (MSFLA) aiming at determining the optimal DG allocation and sizing in the distribution network. The MSFLA is a new mimetic meta-heuristic algorithm with efficient mathematical function and global search capability. To evaluate the proposed algorithm, the 34-bus IEEE test system is used. In addition, the finding of comparative studies indicates the better capability of the proposed method compared with the genetic algorithm in finding the optimal sizing and location of DG’s with respect to the used objective function

Passive Islanding Detection Using Adaptive Threshold Based on Instantaneous Frequency Droop

Islanding is one of the important challenges in power networks in the presence of distributed generation which is considered an undesirable incident due to the possibility of damage to operators, network equipment and consumers. Therefore, it is necessary to quickly detect islanding and make a decision regarding the connection status of local distributed generation units in the network. In this paper, a passive islanding detection method is proposed using adaptive threshold-based instantaneous frequency droop characteristic. The proposed threshold limit is dynamically changed depending on under studied conditions in order to discriminate the island from load changes, capacitor bank switching, motor start-up and short circuit types. Two medium voltage networks of Cigre and 34-bus IEEE are used to evaluate the proposed method. The simulations are performed in Digsilent software and implementation of the proposed method carried out in MATLAB software. The simulation results indicate that the island is correctly detected with appropriate accuracy in a short period of time from other disturbances in the presence of wind, solar and diesel types of distributed generations

Robust state estimation in power systems using pre-filtering measurement data

State estimation is the foundation of any control and decision making in power networks. The first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. This paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual calculation. The estimator is equipped with a filter formed in different times according to Principal Component Analysis (PCA) of measurement data. In addition, the proposed estimator employs the dynamic relationships of the system and the prediction property of the Extended Kalman Filter (EKF) to estimate the states of network fast and precisely. Therefore, it makes real-time monitoring of the power network possible. The proposed dynamic model also enables the estimator to estimate the states of a large scale system online. Results of state estimation of the proposed algorithm for an IEEE 9 bus system shows that even with the presence of bad data, the estimator provides a valid and precise estimation of system states and tracks the network with appropriate speed

Quantification of Damping Contribution from Loads

A method to identify the effect of load dynamics on inter-machine oscillations is developed. This contributes to the main aim of finding the sensitivity of the oscillatory modes with respect to the presence of particular loads in a multi-machine power system. In this method the sensitivity is found by using the identified oscillatory frequencies of the inter-machine oscillations, the identified load model, and the right and left modal matrices. Simulation of a test power system, including three generators and nine buses, is used to validate the algorithm. This approach is applied to measurements of the inter-machine oscillations of the Australian network and sensitivity to one particular load is identified

Identification of damping contribution from power system controllers

With the growth of power system interconnections, the economic drivers encourage the electric companies to load the transmission lines near their limits, therefore it is critical to know those limits well. One important limiting issue is the damping of inter-area oscillation (IAO) between groups of synchronous machines. In this Ph.D. thesis, the contribution of power system components such as load and static var compensators (SVC) that affect the IAO of the power system, are analysed. The original contributions of this thesis are as follows: 1-Identification of eigenvalues and mode shapes of the IAO: In the first contribution of this thesis, the eigenvalues of the IAO are identified using a correlation based method. Then, the mode shape at each identified resonant frequency is determined to show how the synchronous generators swing against each other at the specific resonant frequencies. 2-Load modelling and load contribution to damping: The first part of this contribution lies in identification of the load model using cross-correlation and autocorrelation functions . The second aspect is the quantification of the load contribution to damping and sensitivity of system eigenvalues with respect to the load. 3- SVC contribution to damping: In this contribution the criteria for SVC controller redesign based on complete testing is developed. Then the effect of the SVC reactive power on the measured power is investigated. All of the contributions of this thesis are validated by simulation on test systems. In addition, there are some specific application of the developed methods to real data to find a.) the mode shape of the Australian electricity network, b.) the contribution of the Brisbane feeder load to damping and c.) the effect of the SVC reactive power of the Blackwall substations on the active power supplying Brisbane