Islanding Detection and Power Quality Analysis in Microgrid

芝浦工業大学2019年

Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples

AN INTELLIGENT PASSIVE ISLANDING DETECTION AND CLASSIFICATION SCHEME FOR A RADIAL DISTRIBUTION SYSTEM

Distributed generation (DG) provides users with a dependable and cost-effective source of electricity. These are directly connected to the distribution system at customer load locations. Integration of DG units into an existing system has significantly high importance due to its innumerable advantages. The high penetration level of distributed generation (DG) provides vast techno-economic and environmental benefits, such as high reliability, reduced total system losses, efficiency, low capital cost, abundant in nature, and low carbon emissions. However, one of the most challenges in microgrids (MG) is the island mode operations of DGs. the effective detection of islanding and rapid DG disconnection is essential to prevent safety problems and equipment damage. The most prevalent islanding protection scheme is based on passive techniques that cause no disruption to the system but have extensive non-detection zones. As a result, the thesis tries to design a simple and effective intelligent passive islanding detection approach using a CatBoost classifier, as well as features collected from three-phase voltages and instantaneous power per phase visible at the DG terminal. This approach enables initial features to be extracted using the Gabor transform (GT) technique. This signal processing (SP) technique illustrates the time-frequency representation of the signal, revealing several hidden features of the processed signals to be the input of the intelligent classifier. A radial distribution system with two DG units was utilized to evaluate the effectiveness of the proposed islanding detection method. The effectiveness of the proposed islanding detection method was verified by comparing its results to those of other methods that use a random forest (RF) or a basic artificial neural network (ANN) as a classifier. This was accomplished through extensive simulations using the DigSILENT Power Factory® software. Several measures are available, including accuracy (F1 Score), the area under the curve (AUC), and training time. The suggested technique has a classification accuracy of 97.1 per cent for both islanded and non-islanded events. However, the RF and ANN classifiers\u27 accuracies for islanding and non-islanding events, respectively, are proven to be 94.23 and 54.8 per cent, respectively. In terms of the training time, the ANN, RF, and CatBoost classifiers have training times of 1.4 seconds, 1.21 seconds, and 0.88 seconds, respectively. The detection time for all methods was less than one cycle. These metrics demonstrate that the suggested strategy is robust and capable of distinguishing between the islanding event and other system disruptions

Wide-Area Time-Synchronized Closed-Loop Control of Power Systems And Decentralized Active Distribution Networks

The rapidly expanding power system grid infrastructure and the need to reduce the occurrence of major blackouts and prevention or hardening of systems against cyber-attacks, have led to increased interest in the improved resilience of the electrical grid. Distributed and decentralized control have been widely applied to computer science research. However, for power system applications, the real-time application of decentralized and distributed control algorithms introduce several challenges. In this dissertation, new algorithms and methods for decentralized control, protection and energy management of Wide Area Monitoring, Protection and Control (WAMPAC) and the Active Distribution Network (ADN) are developed to improve the resiliency of the power system. To evaluate the findings of this dissertation, a laboratory-scale integrated Wide WAMPAC and ADN control platform was designed and implemented. The developed platform consists of phasor measurement units (PMU), intelligent electronic devices (IED) and programmable logic controllers (PLC). On top of the designed hardware control platform, a multi-agent cyber-physical interoperability viii framework was developed for real-time verification of the developed decentralized and distributed algorithms using local wireless and Internet-based cloud communication. A novel real-time multiagent system interoperability testbed was developed to enable utility independent private microgrids standardized interoperability framework and define behavioral models for expandability and plug-and-play operation. The state-of-theart power system multiagent framework is improved by providing specific attributes and a deliberative behavior modeling capability. The proposed multi-agent framework is validated in a laboratory based testbed involving developed intelligent electronic device prototypes and actual microgrid setups. Experimental results are demonstrated for both decentralized and distributed control approaches. A new adaptive real-time protection and remedial action scheme (RAS) method using agent-based distributed communication was developed for autonomous hybrid AC/DC microgrids to increase resiliency and continuous operability after fault conditions. Unlike the conventional consecutive time delay-based overcurrent protection schemes, the developed technique defines a selectivity mechanism considering the RAS of the microgrid after fault instant based on feeder characteristics and the location of the IEDs. The experimental results showed a significant improvement in terms of resiliency of microgrids through protection using agent-based distributed communication

Inter-Microgrid Operation: Power Sharing, Frequency Restoration, Seamless Reconnection and Stability Analysis

Electrification in the rural areas sometimes become very challenging due to area accessibility and economic concern. Standalone Microgrids (MGs) play a very crucial role in these kinds of a rural area where a large power grid is not available. The intermittent nature of distributed energy sources and the load uncertainties can create a power mismatch and can lead to frequency and voltage drop in rural isolated community MG. In order to avoid this, various intelligent load shedding techniques, installation of micro storage systems and coupling of neighbouring MGs can be adopted. Among these, the coupling of neighbouring MGs is the most feasible in the rural area where large grid power is not available. The interconnection of neighbouring MGs has raised concerns about the safety of operation, protection of critical infrastructure, the efficiency of power-sharing and most importantly, stable mode of operation. Many advanced control techniques have been proposed to enhance the load sharing and stability of the microgrid. Droop control is the most commonly used control technique for parallel operation of converters in order to share the load among the MGs. But most of them are in the presence of large grid power, where system voltage and frequency are controlled by the stiff grid. In a rural area, where grid power is not available, the frequency and voltage control become a fundamental issue to be addressed. Moreover, for accurate load sharing a high value of droop gain should be chosen as the R/X ratio of the rural network is very high, which makes the system unstable. Therefore, the choice of droop gains is often a trade-off between power-sharing and stability. In the context, the main focus of this PhD thesis is the fundamental investigations into control techniques of inverter-based standalone neighbouring microgrids for available power sharing. It aims to develop new and improved control techniques to enhance performance and power-sharing reliability of remote standalone Microgrids. In this thesis, a power management-based droop control is proposed for accurate power sharing according to the power availability in a particular MG. Inverters can have different power setpoints during the grid-connected mode, but in the standalone mode, they all need their power setpoints to be adjusted according to their power ratings. On the basis of this, a power management-based droop control strategy is developed to achieve the power-sharing among the neighbouring microgrids. The proposed method helps the MG inverters to share the power according to its ratings and availability, which does not restrict the inverters for equal power-sharing. The paralleled inverters in coupled MGs need to work in both interconnected mode and standalone mode and should be able to transfer between modes seamlessly. An enhanced droop control is proposed to maintain the frequency and voltage of the MGs to their nominal value, which also helps the neighbouring MGs for seamless (de)coupling. This thesis also presents a mathematical model of the interconnected neighbouring microgrid for stability and robustness analysis. Finally, a laboratory prototype model of two MGs is developed to test the effectiveness of the proposed control strategies

Design Methodology and Stability Analysis for a PhotoVoltaic (PV) Plant Interfaced with a • Distribution Network.

In recent years, there has been significant interest in utilization of PhotoVoltaic (PV) solar systems of single- and double-digit MW power ratings, at sub-transmission and distribution voltage levels of the power system, under the umbrella of Distributed Generation (DG). Despite this interest, however, inadequate investigation has been dedicated to the connection of PV systems to distribution networks, and there is little experience regarding the operation of such PV systems. The study reported in this thesis attempts to address this gap. This thesis proposes a control strategy and provides stability analysis for a typical singlestage, three-phase, PV system that is connected to a (residential/commercial) distribution network. The control is based on an inner current-control loop and an outer DC-link voltage regulator. The current-control strategy decouples the dynamics of the PV system from those of the network and the loads. The DC-link voltage control scheme enables the control and maximization of the output real power of the PV system. Further, a feed-forward control strategy is employed for the DC-link voltage regulation scheme, to enhance the stability of the PV system. The feed-forward compensation scheme makes the PV system dynamics independent of the nonlinear characteristic of the PV panels. This, in turn, permits the design and optimization of the PV system controllers for a wide range of operating conditions. In this thesis, a mathematical model and a control design methodology are presented for the PV system, and it is shown that, under proposed control, the PV system fulfills the operational requirements for a grid-connected PV system. The effectiveness of the proposed control strategy and the most important transients of the PV system are evaluated through simulation studies conducted on a detailed switched model of the PV system, in the PSCAD∕EMTDC software environment. Based on the developed mathematical model, a modal/sensitivity analysis is conducted in this thesis on a linearized model of the overall system, i.e. the PV system, the distribution network, and the local loads, to characterize the dynamic properties, to evaluate the robustness of the controllers, and to identify the nature of interactions between the PV system and the network/loads. The modal analysis confirms that, under the proposed control strategy, dynamics of the PV system are decoupled from those of the distribution network/loads. This, alternatively, means that, if its controllers are designed based on the proposed structure and methodology, the PV system iii does not destabilize the distribution network. It is also shown that the PV system dynamics are not influenced by those of the network. Thus, the PV system maintains its stability and dynamic properties despite major variations in the line length, line X/R ratio, load type, and load distance from the PV system

Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

Contributions for microgrids dynamic modelling and operation

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Power Converter of Electric Machines, Renewable Energy Systems, and Transportation

Power converters and electric machines represent essential components in all fields of electrical engineering. In fact, we are heading towards a future where energy will be more and more electrical: electrical vehicles, electrical motors, renewables, storage systems are now widespread. The ongoing energy transition poses new challenges for interfacing and integrating different power systems. The constraints of space, weight, reliability, performance, and autonomy for the electric system have increased the attention of scientific research in order to find more and more appropriate technological solutions. In this context, power converters and electric machines assume a key role in enabling higher performance of electrical power conversion. Consequently, the design and control of power converters and electric machines shall be developed accordingly to the requirements of the specific application, thus leading to more specialized solutions, with the aim of enhancing the reliability, fault tolerance, and flexibility of the next generation power systems