Salman Mohagheghi

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

Modeling distribution automation system components using IEC 61850

IEC 61850 was proposed as a standard protocol for communications within the substation. In its current edition, the standard does not cover communications outside the substation, either with the control center or with other substations, for instance for remote protection. However, during the recent years, there has been a general belief that some features of the standard can be efficiently utilized for applications outside the substation as well. With the advent of new monitoring and control technologies the idea of power system automation at the distribution system and feeder level is crossing new boundaries. In such applications, accessing the accurate data is a necessity. With its future-proof object oriented structure, IEC 61850 can provide comprehensive and accurate information models for various components of distribution automation systems. This paper provides some examples on how the standard can be employed for this purpose, and what measures need to be taken to enable it to efficiently respond to some of the emerging technologies in distribution automation systems.

Autonomous Self Commissioning Method for Speed Sensorless Controlled Induction Machines

Speed sensorless control of ac machines at zero speed so far is only possible using signal injection methods. Especially when applied to induction machines the spatial saturation leads to a dependence of the resulting control signals on the flux/load level. Usually this dependence has to be identified on a special test stand during a commissioning procedure for each type of induction machine. In this paper an autonomous commissioning method based on a neural network approach is proposed that does neither depend on a speed sensor present as a reference nor on a load dynamometer coupled to the machine and guaranteeing constant speed. The training data for the neural network is obtained using only acceleration and deceleration measurements of the uncoupled machine. The reliability of the proposed autonomous commissioning method is proven by measurement results. When comparing the resulting sensorless control performance, the proposed commissioning method reaches the same level of performance as a manual identification using load dynamometer as well as speed sensor.

A fuzzy cognitive map for data integrity assessment in a IEC 61850 based substation

The abundance of data available under the smart grid paradigm makes it possible for the system operator to enhance the situational awareness across the power network. More accurate data logging and reporting, especially in the newly adopted object oriented based communication protocols such as IEC 61850, allows for more efficient analysis and interpretation of the state of the network. This paper explores one of such applications that arise from employment of the standard IEC 61850 in substations. It is proposed here that the integrity of the data available from across the substation can be verified using the quality and validity data available from different devices and control/protection functions in the substation. A fuzzy cognitive map is used here that derives relationships and associations between various functions within the substation and determines a level of confidence on the data available. Although this scheme is developed for a substation in this paper, it can be equally well applied to other applications beyond substations and across the distribution automation domain.

Optimal wide area controller and state predictor for a power system

An optimal wide area controller is designed in this paper for a 12-bus power system together with a static compensator (STATCOM). The controller provides auxiliary reference signals for the automatic voltage regulators (AVR) of the generators as well as the line voltage controller of the STATCOM in such a way that it improves the clamping of the rotor speed deviations of the synchronous machines. Adaptive critic designs theory is used to implement the controller and enable it to provide nonlinear optimal control over the infinite horizon time of the problem and at different operating conditions of the power system. Simulation results are provided to indicate that the proposed wide area controller improves the damping of the rotor speed deviations of the generators during large scale disturbances. Moreover, a robust radial basis function network based identifier is presented in this paper to predict the states of a multimachine power system in real-time. This wide area state predictor (WASP) compensates for transport lags associated with the present communication technology for wide area monitoring of the electric power grid. The WASP is also robust to partial loss of information caused by larger than expected transport lags or even failed sensors throughout the network.

A Static Neural Network for Input-Output Mapping of Power Electronic Circuits

This paper investigates the effectiveness of a static neural network for input-output mapping of power electronic circuits. The neural network is a multilayer perceptron (MLP) that is trained to form a mapping between the inputs and outputs of a power electronic circuit. The circuit consists of a full bridge diode rectifier, together with the source inductance and the output filter. Dynamic models have been used for the rectifier diodes. The ultimate objective of the designed neural network is to provide an indication when the performance properties of one or more components in the rectifier circuit have changed. Simulation results are provided that indicate the neural network is capable of mapping the inputs and outputs of the circuit and detect operating conditions that are different from the original condition.

Evolutionary Approaches to the Linear Machine Layout Problem

Flexible Manufacturing Systems (FMSs) cope with multi-product, usually small sized production. In this research work we investigate the use of evolutionary methods to solve the linear or single-row layout problem, which is the most commonly implemented layout in FMSs. Three different approaches, based on Ant Colony Optimization, Genetic Algorithms and Particle Swarm Optimization are tested. The experimental results show that a near optimal solution can be found for all three methods, exploiting only a small portion of the feasible solution space, pinpointing once more the merit of using evolutionary algorithms to tackle difficult combinatorial problems.