Requirement of green supply with higher quality has been consumers’ demand around the globe The electrical power system is expected to deliver undistorted sinusoidal rated voltage and current continuously at rated frequency to the consumers This paper presents a comprehensive review of signal processing and intelligent techniques for automatic classification of the power quality (PQ) events and an effect of noise on detection and classification of disturbances It is intended to provide a wide spectrum on the status of detection and classification of PQ disturbances as well as an effect of noise on detection and classification of PQ events to the researchers, designers and engineers working on power quality More than 150 research publications on detection and classification techniques of PQ disturbances have been critically examined, classified and listed for quick reference

A critical review of detection and classification of power quality events

This paper investigates a secondary voltage control based on a graph partitioning method. The method divides the power system into regions to eventually prevent the propagation of disturbances and to minimize the interaction between these regions. The optimized number of regions is found based on the bus voltage sensitivity to disturbances being applied to loads in each region. Then, a number of representative buses are labelled as pilot buses displaying the critical point for voltage control in each region. The control uses decentralized controllers to eliminate voltage violations resulting from load variations and disturbances in the system. The decentralized controllers are implemented by fuzzy logic which is trained via offline simulations; they inject required reactive power into the regions to correct voltage violations. The methodology is applied to the IEEE 118-bus network. The results show the performance and ability of graph partitioning and fuzzy secondary voltage control to regulate the voltage and to avoid propagation of disturbances between regions.

A Decentralized Control of Partitioned Power Networks for Voltage Regulation and Prevention Against Disturbance Propagation

This paper presents the parameter identification of an equivalent circuit-based proton-exchange membrane fuel cell model. This model is represented by two electrical circuits, of which one reproduces the fuel cell’s output voltage characteristic and the other its thermal characteristic. The output voltage model includes activation, concentration, and ohmic losses, which describe the static properties, while the double-layer charging effect, which delays in fuel and oxygen supplies, and other effects provide the model’s dynamic properties. In addition, a novel thermal model of the studied Ballard’s 1.2-kW Nexa fuel cell is proposed. The latter includes the thermal effects of the stack’s fan, which significantly improve the model’s accuracy. The parameters of both, the electrical and the thermal, equivalent circuits were estimated on the basis of experimental data using an evolution strategy. The resulting parameters were validated by the measurement data obtained from the Nexa module. The comparison indicates a good agreement between the simulation and the experiment. In addition to simulations, the identified model is also suitable for usage in real-time fuel cell emulators. The emulator presented in this paper additionally proves the accuracy of the obtained model and the effectiveness of using an evolution strategy for identification of the fuel cell’s parameters.

Identification of a Proton-Exchange Membrane Fuel Cell’s Model Parameters by Means of an Evolution Strategy

Learning paradigm based on jumping genes: A general framework for enhancing exploration in evolutionary multiobjective optimization

In this paper, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is proposed to solve the unit commitment (UC) problem as a multiobjective optimization problem (MOP) considering minimizing cost and emission as the multiple objectives. Since UC problem is a mixed-integer optimization problem, a hybrid strategy is integrated within the framework of MOEA/D such that genetic algorithm (GA) evolves the binary variables, while differential evolution (DE) evolves the continuous variables. Further, a novel nonuniform weight-vector distribution (NUWD) strategy is proposed and an ensemble algorithm based on combination of MOEA/D with uniform weight-vector distribution (UWD) and NUWD strategy is implemented to enhance the performance of the presented algorithm. Extensive case studies are presented on different test systems and the effectiveness of the hybrid strategy, the NUWD strategy, and the ensemble algorithm is verified through stringent simulated results. Further, exhaustive benchmarking against the algorithm proposed in the literature is presented to demonstrate the superiority of the proposed algorithm.

Enhanced Multiobjective Evolutionary Algorithm Based on Decomposition for Solving the Unit Commitment Problem

A new jumping genes paradigm in the format of hierarchical genetic algorithm is proposed for optimizing the power voltage control systems. The advantage of this scheme is its unique capacity for finding the required solutions that can be used for stabilizing the progressive voltage drop or even voltage collapse of a power system. Because of the multiobjective classification, all these solutions could therefore form a landscape of control pattern which is aptly applicable to the control purpose of the coordinated voltage control system. The application of the proposed paradigm is verified upon the New England 39-bus power system. A tradeoff between the recovery time and the quality of voltage control exists and largely depends upon the number of steps for control actions.

Multiobjective Coordinated Power Voltage Control Using Jumping Genes Paradigm

A new real-time power voltage control strategy is proposed in this paper. This scheme utilizes a novel offline evolutionary multiobjective optimization algorithm called jumping genes for generating the widespread control solutions and be readily stored into a knowledge data base. A separate online multiple criteria decision-making scheme is established for selecting the appropriate control solution. This concept of power voltage control has been demonstrated by the New England 39-bus power system. The system output performance was speedy and accurate.

H.M. Ma

Papers

Multiobjective Coordinated Power Voltage Control Using Jumping Genes Paradigm

A Multiple Criteria Decision-Making Knowledge-Based Scheme for Real-Time Power Voltage Control