155 research outputs found
Protection of Electrical Power Systems with Full Penetration of Converter-Interface Generation
Mención Internacional en el título de doctorSince the advent of generation with converter-interface, mainly wind and solar photovoltaic (PV), power system operators have deal with some problems to maintain system stability and security. However, due to its low penetration in the system, it had barely any consequences and its study lack of interest. But over the years the generation scheme has changed, and converter-interface generators have been increasing their presence due to their low energy costs and policies against climate change.
When the penetration rate is 100 %, protection systems have detection problems in the overcurrent scheme and pick-up problems in the distance scheme, jeopardising the safety of the electrical power system. This thesis proposes to use the Wavelet transform analysis method to solve these problems in full penetration scenarios of converter-interface generation. It can detect high and low frequency variations in voltage and current signals, and classify them in time and magnitude when they occur.
In order to be able to propose a satisfactory solution, this thesis has carried out a study of the main key factors to be considered for fault detection. Analysing the differences between synchronous generators and generators with converter-interface, and the consequences of each of them for the protection systems. Describing the main converter control architectures and defining the equivalent model of converter short-circuit. Introducing the different types of faults in power systems. And describing the fundamental criteria for protection, and the most common protection schemes.
The model used to obtain the results and check the feasibility of the proposal is the IEEE nine-bus system in a ring layout. It has been modelled including all power system elements (transmission lines, transformers, and loads) and both generation technologies (synchronous generators and converter-interface generators). In addition, the converter control strategy and its current limiting have also been considered. The results show a correct and immediate fault detection.Desde la aparición de los sistemas de generación de energía eléctrica con interfaz de convertidor electrónico, mayoritariamente eólica y solar fotovoltaica, los operadores de red han tenido que lidiar con los diferentes problemas que estos provocan para mantener la estabilidad y la seguridad del sistema. Aunque debido a su baja penetración en el sistema apenas tenía consecuencias y su estudio carecía de interés. Pero con el paso de los años ha ido cambiando el esquema de generación y los generadores con interfaz de convertidor electrónico han ido incrementando su presencia debido a sus bajos costes de la energía y a las políticas de lucha contra el cambio climático.
Cuando se alcanzan niveles de penetración del 100 %, los sistemas de protección tienen problemas de detección en el esquema de sobrecorriente y de arranque en el esquema de distancia, poniendo en riesgo la seguridad del sistema eléctrico. Esta tesis propone utilizar el método de análisis de la transformada de Wavelet para solventar estos problemas en escenarios con máxima penetración de generación con interfaz de convertidor. El cual permite detectar variaciones de alta y baja frecuencia en las señales de tensión y de corriente, y clasificarlas tanto en tiempo como en tamaño cuando se producen.
Para poder presentar una solución con garantías de ser satisfactoria, en esta tesis se ha realizado un estudio de los principales factores clave para tener en cuenta para la detección de faltas. Analizando las diferencias entre generadores síncronos y generadores con interfaz de convertidor electrónico, y qué consecuencias tiene cada uno de ellos para los sistemas de protección. Describiendo las principales arquitecturas de control de convertidores y definiendo los modelos equivalentes de cortocircuito del convertidor. Presentando los diferentes tipos de faltas en los sistemas eléctricos. Y describiendo los criterios fundamentales de las protecciones y los esquemas de protección más comunes.
El modelo utilizado para la obtención de los resultados y comprobar la viabilidad de la propuesta es el sistema de nueve nudos del IEEE dispuesto en anillo. El cual ha sido modelado incluyendo todos los elementos del sistema (líneas de transmisión, transformadores y cargas) y ambas tecnologías de generación (generadores síncronos y generadores con interfaz de convertidor electrónico). Además, también se ha tenido en cuenta la estrategia de control del convertidor y su limitación de corriente. Los resultados muestran una correcta e inmediata detección de la falta.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidenta: Mónica Chinchilla Sánchez.- Secretario: Joaquín Eloy-García Carrasco.- Vocal: Roberto Lorenzo Alves Baraciart
Wavelet Analysis to Detect Ground Faults in Electrical Power Systems with Full Penetration of Converter Interface Generation
The requirements for the increased penetration of renewable energy sources in electrical power systems have led to a dominance of power electronic interfaces. As a result, short-circuit currents have been reduced by the thermal limitations of power electronics, leading to problems associated with the sensitivity, selectivity, and reliability of protective relays. Although many solutions can be found in the literature, these depend on communications and are not reliable in all grid topologies or under different types of electrical fault. Hence, in this paper, the analysis of ground fault currents and voltages using a wavelet transform in combination with a new algorithm not only detects such ground faults but also allows them to be cleared quickly and selectively in scenarios with low fault current contribution due to a full penetration converter-interface-based generation. To verify and validate the proposed protection system, different ground faults are simulated using an arc ground fault model in a grid scheme based on the IEEE nine-bus standard test system, with only grid-forming power converters as generation sources. The test system is modelled in the MATLAB/Simulink environment. Therefore, the protection relays that verify all the steps established in the new algorithm can detect and clear any ground defect. Simulations are also presented involving different fault locations to demonstrate the effectiveness of the proposed ground fault protection method.This work was supported by the Autonomous Community of Madrid under the PROMINT-CM project (S2018/EMT-4366)
Selected Papers from 2020 IEEE International Conference on High Voltage Engineering (ICHVE 2020)
The 2020 IEEE International Conference on High Voltage Engineering (ICHVE 2020) was held on 6–10 September 2020 in Beijing, China. The conference was organized by the Tsinghua University, China, and endorsed by the IEEE Dielectrics and Electrical Insulation Society. This conference has attracted a great deal of attention from researchers around the world in the field of high voltage engineering. The forum offered the opportunity to present the latest developments and different emerging challenges in high voltage engineering, including the topics of ultra-high voltage, smart grids, and insulating materials
Neural Network Fault Recognition in Power Systems with High Penetrations of Inverter-Based Resources
The growing demand for renewable energy resources (RER) has led to increased integration of inverter-based resources (IBRs), into existing power distribution and transmission networks. However, RER locations are often not ideally suited for direct integration, necessitating a restructuring of the grid from a traditional radial network to a more complex mesh network topology. This transition presents challenges in terms of protection and coordination, as IBRs exhibit atypical responses to power system anomalies compared to conventional synchronous generation.
To address these challenges and support existing power system protection infrastructure, this work explores the incorporation of machine learning algorithms. Specifically, an optimized convolutional neural network (CNN) is developed for real-time application in power system protection schemes. The focus is on prioritizing key performance metrics such as recall, specificity, speed, and the reduction of computational resources required for effective protection.
The machine learning model is trained to differentiate between healthy system dynamics and hazardous conditions, such as faults, in the presence of IBRs. By analyzing data retrieved from an IEEE 34-bus 24kV distribution network, the model's application is demonstrated and its performance is evaluated.
A photovoltaic (PV) source was incorporated into the IEEE 34-bus distribution feeder model at the end of the feeder. By adding a PV source at the end of the feeder, IBR characteristics, such as its response to system anomalies can be monitored through the model.
Once the modified IEEE 34-bus distribution feeder model with the PV source was set up, various system anomalies were simulated to create a diverse dataset for training the machine learning (ML) model. These anomalies included; load rejection - a sudden and complete removal of load from the distribution network, simulating a scenario where a significant portion of the load disconnects from the grid, load addition - a sudden and significant increase in load demand, representing a scenario where new loads are connected to the grid, islanding - a scenario where the distribution feeder becomes electrically isolated from the main grid, with the PV source acting as a microgrid and supplying power to the local loads, and various types of faults, such as short-circuits or ground faults, occurring at different locations along the distribution line.
To create a diverse dataset, model parameters were varied through 50 different iterations of each simulated anomaly scenario. These parameters included the PV system's capacity, the location of the anomaly on the feeder, the severity and duration of the anomaly, and other relevant grid parameters.
For each iteration and anomaly scenario, the responses of the system were recorded, including voltage levels, current flows, and other relevant synchorphasors at the PV source's point of common coupling (PCC). These responses formed the dataset for training the ML model.
The accumulated dataset was then used to train the various ML models, including the optimized convolutional neural network (CNN), to identify patterns and hidden characteristics in the data corresponding to different system anomalies. The training process involved feeding the model with input data from the various iterations and scenarios, along with corresponding labels indicating the type of anomaly present.
By exposing the ML model to diverse scenarios and varying parameters, the model learns to generalize its understanding of system dynamics and accurately distinguish between healthy system states and hazardous conditions. The models in this work were specifically trained to recognize the various fault characteristics on the system. The trained model's ability to process time-series data and recognize anomalies from the accumulated dataset enhances power system protection infrastructure's capability to respond rapidly and accurately to various grid disturbances, ensuring the reliable and stable operation of the distribution network, especially in the presence of PV and other IBRs.
The results show that the optimized CNN outperforms traditional machine learning models used in time-series data analysis. The model's speed and reliability make it an effective tool for identifying hidden characteristics in power system data without the need for extensive manual analysis or rigid programming of existing protection relays. This capability is particularly valuable as power grids integrate a higher penetration of IBRs, where traditional protection infrastructure may not fully account for their unique responses.
The successful integration of the optimized CNN into power system protection infrastructure enhances the grid's ability to detect and respond to anomalies, such as faults, in a more efficient and accurate manner. By leveraging machine learning techniques, power system operators can better adapt to the challenges posed by the increasing presence of IBRs and ensure the continued stability and reliability of the distribution network
Modelling of a protective scheme for AC 330 kV transmission line in Nigeria
Transmission lines play a vital role in the reliable and efficient delivery of electrical power over long distances, and these lines are affected by faults that occur due to lightning strikes, equipment failures, human, animal or vegetation interference, environmental factors, ageing equipment, voltage sag or grid faults adverse effects on the line. Therefore, protecting these transmission lines becomes crucial with the increasing demand for electricity and the need to ensure grid stability.
The modelling process involves the development of a comprehensive protection scheme utilising modern technologies and advanced algorithms. The protection scheme encompasses various elements, including fault detection, fault classification, fault location, and fault clearance. It incorporates intelligent devices, such as protective relays and communication systems, to enable rapid and accurate fault identification and isolation.
First, a 330 kV, 500 km three-phase Delta transmission line is modelled using MATLAB/SIMULINK. A section of the Delta network in Delta State Nigeria was used since the entire Nigeria 330 kV network is large. Faulty current and voltage data were generated for training using the CatBoost, 93340 data sizes comprising fault data from three-phase current and voltage extracted from the Delta transmission line model in Nigeria were designed, and twelve fault conditions were used. The CatBoost classifier was employed to classify the faults after different machine language algorithm was used to train the same data with other parameters. The trainer achieved the best accuracy of 99.54%, with an error of 0.46%, at 748 iterations out of 1000 compared to GBoost, XBoost and other classification techniques.
Second, the Artificial Neural Network technique was used to train this data, and an accuracy of 100% was attained for fault detection and about 99.5% for fault localisation at different distances with 0.0017 microseconds of detection and an average error of 0% to 0.5%. This model performs better than Support Vector Machine and Principal Component Analysis with a higher fault detection time. The effect of noise signal on the ANN model was studied, and the discrete wavelet technique was used to de-noise the signal for better performance and to enhance the model’s accuracy during transient.
Third, the wavelet transforms as a data extraction model to detect the threshold value of current and voltage and the coordination time for the backup relay to trip if the primary relay does not operate or clear the fault on time. The difference between the proposed model and the model without the threshold value was analysed. The simulated result shows that the trip time of the two relays demonstrates a fast and precise trip time of 60% to 99.87% compared to other techniques used without the threshold values. The proposed model can eliminate the trial-and-error in programming the instantaneous overcurrent relay setting for optimal performance.
Fourth, the PSO-PID controller algorithm was used to moderate the load frequency of the transmission network. Due to the instability between the generation and distribution, there is always a switch in the stability of the transmission or load frequency; therefore, the PSO-PID algorithm was used to stabilise the Delta power station as a pilot survey from the Nigerian transmission network. Also, a hybrid system with five types of generation and two load centres was used in this model. It has been shown that the proposed control algorithm is effective and improves system performance significantly. As a result, the suggested PSO-PID controller is recommended for producing high-quality, dependable electricity. Moreover, the PSO-PID algorithm produces 0.00 seconds settling time and 0.0005757 ITAE. It’s essential to carefully consider potential drawbacks like complexity and computational overhead, sensitivity to algorithm parameters, potential parameter convergence and limited interpretability and assess their impact on the specific LFC application before implementing a PSO-PID controller in a power system.
When implemented with the model in this research, the Delta transmission line network will reduce the excessive fault that occurs in the transmission line and improve the energy efficiency of the entire network when replicated with the Nigerian network.
Generally, for the effective design and implementation of the protection scheme of the 330 kV transmission line, the fault must be detected and classified, and the exact location of the fault must be ascertained before the relay protection and load frequency control will be applied for effective fault management and control system
Detection of faults in a scaled down doubly-fed induction generator using advanced signal processing techniques.
The study ventures into the development of a micro-based doubly fed induction generator (DFIG) test rig for fault studies. The 5kW wound rotor induction machine (WRIM) that was used in the test rig was based on a scaled-down version of a 2.5MW doubly fed induction generator (DFIG). The micromachine has been customized to make provision for implementing stator inter-turn short-circuit faults (ITSCF), rotor ITSCF and static eccentricity (SE) faults in the laboratory environment. The micromachine has been assessed under the healthy and faulty states, both before and after incorporating a converter into the rotor circuit of the machine. In each scenario, the fault signatures have been characterised by analyzing the stator current, rotor current, and the DFIG controller signals using the motor current signature analysis (MCSA) and discrete wavelet transform (DWT) analysis techniques to detect the dominant frequency components which are indicative of these faults. The purpose of the study is to evaluate and identify the most suitable combination of signals and techniques for the detection of each fault under steady-state and transient operating conditions. The analyses of the results presented in this study have indicated that characterizing the fault indicators independent of the converter system ensured clarity in the fault diagnosis process and enabled the development of a systematic fault diagnosis approach that can be applied to a controlled DFIG. It has been demonstrated that the occurrence of the ITSCFs and the SE fault in the micro-WRIM intensifies specific frequency components in the spectral plots of the stator current, rotor current, and the DFIG controller signals, which may then serve as the dominant fault indicators. These dominant components may be used as fault markers for classification and have been used for pattern recognition under the transient condition. In this case, the DWT and spectrogram plots effectively illustrated characteristic patterns of the dominant fault indicators, which were observed to evolve uniquely and more distinguishable in the rotor current signal compared to the stator current signal, before incorporating the converter in the rotor circuit. Therefore, by observing the trends portrayed in the decomposition bands and the spectrogram plots, it is deemed a reliable method of diagnosing and possibly quantifying the intensity of the faults in the machine. Once the power electronic converter was incorporated into the rotor circuit, the DFIG controller signals have been observed to be best suited for diagnosing faults in the micro-DFIG under the steady-state operating condition, as opposed to using the terminal stator or rotor current signals. The study also assessed the impact of undervoltage conditions at the point of common coupling (PCC) on the behaviour of the micro-DFIG. In this investigation, a significant rise in the faulted currents was observed for the undervoltage condition in comparison to the faulty cases under the rated grid voltage conditions. In this regard, it could be detrimental to the operation of the micro-DFIG, particularly the faulted phase windings, and the power electronic converter, should the currents exceed the rated values for extended periods
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
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