世界経済・社会統計 = World development indicators

Load modeling has significant impact on power system studies. This paper presents a review on load modeling and identification techniques. Load models can be classified into two broad categories: 1) static and 2) dynamic models, while there are two types of approaches to identify model parameters: 1) measurement-based and 2) component-based. Load modeling has received more attention in recent years because of the renewable integration, demand-side management, and smart metering devices. However, the commonly used load models are outdated, and cannot represent emerging loads. There is a need to systematically review existing load modeling techniques and suggest future research directions to meet the increasing interests from industry and academia. In this paper, we provide a thorough survey on the academic research progress and industry practices, and highlight existing issues and new trends in load modeling.

Load Modeling—A Review

Power system load modeling is a mature and generally well researched area which, as many other in electrical power engineering at the present time, is going through a period of renewed interest in both industry and academia. This interest is fueled by the appearance of new non-conventional types of loads (power electronic-based, or interfaced through power electronics) and requirements to operate modern electric power systems with increased penetration of non-conventional and mostly intermittent types of generation in a safe and secure manner. As a response to this renewed interest, in February 2010 CIGRE established working group C4.605: “Modelling and aggregation of loads in flexible power networks”. One of the first tasks of the working group was to identify current international industry practice on load modeling for static and dynamic power system studies. For that purpose, a questionnaire was developed and distributed during the summer/autumn of 2010 to more than 160 utilities and system operators in over 50 countries on five continents. This paper summarizes some of the key findings from about 100 responses to the questionnaire received by September 2011 and identifies prevalent types of load models used as well as typical values of their parameters.

International Industry Practice on Power System Load Modeling

This paper maps the expected and possible adverse consequences for power quality of introducing several smart distribution-grid technologies and applications. The material presented in this paper is the result of discussions in an international CIGRE–CIRED joint working group. The following technologies and applications are discussed: 1) microgrids; 2) advanced voltage control; 3) feeder reconfiguration; and 4) demand-side management. Recommendations are given based on the mapping.

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Power Quality Concerns in Implementing Smart Distribution-Grid Applications

Potential of building integrated and attached/applied photovoltaic (BIPV/BAPV) for adaptive less energy-hungry building’s skin: A comprehensive review

The electric power industry sector has become increasingly aware of how counterproductive voltage sag affects distribution network systems (DNS). The voltage sag backfires disastrously at the demand load side and affects equipment in DNS. To settle the voltage sag issue, this paper achieved its primary purpose to mitigate the voltage sag based on integrating a hydrogen fuel cell (HFC) with the DNS using a distribution static synchronous compensator (D-STATCOM) system. Besides, this paper discusses the challenges and opportunities of D-STATCOM in DNS. In this paper, using HFC is well-designed, modeled, and simulated to mitigate the voltage sag in DNS with a positive impact on the environment and an immediate response to the issue of the injection of voltage. Furthermore, this modeling and controller are particularly suitable in terms of cost-effectiveness as well as reliability based on the adaptive network fuzzy inference system (ANFIS), fuzzy logic system (FLC), and proportional–integral (P-I). The effectiveness of the MATLAB simulation is confirmed by implementing the system and carrying out a DNS connection, obtaining efficiencies over 94.5% at three-phase fault for values of injection voltage in HFC D-STATCOM using a P-I controller. Moreover, the HFC D-STATCOM using FLC proved capable of supporting the network by 97.00%. The HFC D-STATCOM based ANFIS proved capable of supporting the network by 98.00% in the DNS.

An Integrated of Hydrogen Fuel Cell to Distribution Network System: Challenging and Opportunity for D-STATCOM

The accuracy of synchronous machine parameters is one of the crucial factors in correctly simulating power system dynamic events. This paper describes a technique for deriving synchronous machine parameters in which the implementation of the technique is automated in Matlab. A procedure for estimating the synchronous machine parameters is also proposed in which the method uses a gradient-based simulation to estimate the parameters. An example of the proposed method is then demonstrated to show the effectiveness of the proposed method.

Implementation of synchronous machine parameter derivation in Matlab

This research compares the performance of Competitive Over Resources (COR) optimisation method using a different type of constraint handling strategy to solve the economic load dispatch (ELD) problem. Previously, most research focused on proposing various optimisation techniques using the Penalty Factor Strategy (PFS) to search for a better global optimum. The issue using the penalty factor is that it is difficult to find the correct tune of constant value that influences the algorithm to find the solution. The other technique is using Feasible Solution Strategy (FSS), the idea of which is to locate the infeasible particle to the feasible solution and avoid being trapped by the unsuccessful condition of constraint. This paper investigates the performance of PFS and FSS on the COR optimisation method for solving ELD. Both strategies have been tested on two standard test systems to compare the performance in terms of a global solution, robustness and convergence. The simulation shows that FSS is a better solution compared to PFS.

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The performance of COR optimisation using different constraint handling strategies to solve ELD

Fault detection has drew much attention nowadays, as it can save time and operational maintenance costs, especially in the wind turbine (WT) that is becoming familiar with renewable energy. Machine learning became widespread use in fault detection methods. However, most available machine learning needs more data and much time to train. Therefore, there is a need to detect faults using a few data during the training process. This paper aims to apply Automated Machine Learning (AutoML) method for fault detection in WT systems. The fault detection in the WT system focuses on the internal stator fault in the generator as it is the main part of the WT. The AutoML model was developed using a neural network (NN) algorithm in python based on the Auto-Keras model. The model was developed using four inputs, i.e. stator and rotor currents in the d-q axis (Iqs, Ids, Iqr and Idr ) while the outputs are impedance values, i.e. stator resistance, Rs , and stator inductance, Ls . The WT system used in this research is the doubly-fed induction generator (DFIG) in MATLAB/Simulink. In the Auto-Keras model, the impedance values (Rs and L s) indicated the condition of the DFIG, either normal or fault conditions. Two fault types were applied to the WT system, i.e. inter-turn short circuit and open circuit fault. The Auto-Keras model was trained and tested with the various values of data. The accuracy and the root means square error (RMSE) value of the model were calculated. The result shows that the accuracy is high as it is more than 93% in most conditions, and the RMSE value is low, close to the zero value. Applying the AutoML method in fault detection of the WT system shows its capability to identify faults accurately.

Application of Automated Machine Learning (AutoML) Method in Wind Turbine Fault Detection

The aim of this project is to develop a dynamic model of distribution network cell (DNC) using artificial intelligence approach. The increasing number of distributed generation (DG) technology has lead to difficulty in modeling the DNC model. The simple load modeling is no longer reliable in presenting the DNC model. In this project, the equivalent dynamic model of DNC consists of the converter-connected generator and the composite load model. The model was developed in the form of seven order state-space model. The parameter estimation of the model was developed using fuzzy system. The parameter value was updated through adaptive neuro-fuzzy inference system (ANFIS). The active and reactive power response from the fuzzy model was compared with the response from the full DNC model at various type of disturbance. The response of full DNC model was obtained from the UK 11 kV distribution network model. The model was built in DigSILENT PowerFactory software. The performance of the fuzzy model was validated by calculating the value of root means square error (RMSE) and the best fit value. Later, the performance of the fuzzy model was also compare with the system identification model. The results obtained shown that the fuzzy model was more simple as only a few parameters involved in developing the equivalent model. This simplicity was reflected in the low computational time. The efficiency was also good based on the low RMSE value and high best fit value. In conclusion, the equivalent dynamic model of DNC based on fuzzy system approach was successfully developed.

Samila Mat Zali

Papers

An Integrated of Hydrogen Fuel Cell to Distribution Network System: Challenging and Opportunity for D-STATCOM

Implementation of synchronous machine parameter derivation in Matlab

The performance of COR optimisation using different constraint handling strategies to solve ELD

Application of Automated Machine Learning (AutoML) Method in Wind Turbine Fault Detection

Dynamic model of distribution network cell using artificial intelligence approach