scispace - formally typeset
Search or ask a question

What are the reasons for the occurrence of faults in a power system? 

Answers from top 10 papers

More filters
Papers (10)Insight
Among the different categories of power distribution faults, animal-caused faults are probably the ones that can be prevented most easily and effectively if appropriate control actions are taken.
In-depth analysis shows that the SOC of atmosphere system contributes much to that of power system faults.
This technique is capable to identify the ten different types of faults with negligible effect of variation in fault inception angle, loading and other parameters of the power distribution system.
Therefore it is very important to know the increase in power losses in the machine due to the occurrence of such faults for applying corrective actions at the precise time once the fault has been diagnosed.
Proceedings ArticleDOI
T. Johannesson, F. Roos, S. Lindahl 
05 Apr 2004
18 Citations
The results indicate the occurrence of the major types and causes of incorrect protection operations that have been revealed at disturbances in the electric power system under study.
Satisfactory results are obtained, and the developed methodology can be easily generalized and used to identify other causes of faults in power distribution systems.
The simulation results show that a fault, whenever occurs in the aforementioned parts of power systems, is detected using the knowledge of the faults models proposed in this work.
Experimental studies for real power systems reveal the usefulness of the proposed technique to diagnose faults that have uncertainty.
This proposed method provides a means for both utility and consumer engineers to assess the impact of these faults and design cost effective solutions to potential power quality problems.

Related Questions

Why does power outage happens?4 answersPower outages occur due to a variety of reasons, including natural disasters like lightning, high winds, and ice storms, as well as human errors, accidents, deliberate sabotage, and equipment failures. These outages can lead to significant consequences for communities and various sectors, such as healthcare facilities, where electronic equipment and environmental control systems heavily rely on electricity. Blackouts can escalate into large-scale events due to a sequence of cascading outages triggered by low-probability events and grid stress factors like operational margins and overloaded equipment. Historical data analysis reveals that common causes of outages include animals, falling trees, thunderstorms, and wind, with preventive measures like surveillance drones being considered to mitigate such incidents. Implementing Distributed Energy Resources (DERs) like Renewable Energy Sources (RES) and storage devices can provide alternative power sources during and after disasters.
What are the primary causes of power outages in distribution systems?4 answersThe primary causes of power outages in distribution systems vary across different regions. Weather hazards, such as wind and winter storms impacting transmission lines and flooding affecting substations, are significant contributors to outages in the U.S. and Europe. In populous areas like Lagos State, transient faults and jumper cuts are identified as major causes of outages, leading to substantial energy loss and high costs. Additionally, environmental conditions and external factors like vegetation, animals, and human behaviors play a role in outage occurrences. Prioritizing outage causes through scenario-based approaches can help in addressing specific issues, such as working in power network limit zones or mitigating collisions with external objects in different cities.
What are the primary causes of power losses in power systems?5 answersThe primary causes of power losses in power systems include technical factors like conductor resistance, voltage drops, and transformer losses, as well as non-technical factors such as theft and defective equipment. Additionally, inappropriate conductor sizing, long distribution lines, low power factor, and overloading of lines contribute significantly to power losses in electrical power systems. These losses not only result in economic losses and reduced system reliability but also impact the voltage profile and overall efficiency of the distribution network. Strategies to mitigate power losses involve improving equipment and maintenance practices, combating theft and illegal connections, implementing distributed generation systems, and optimizing grid strategies through methods like real load sharing, reactive power injection, and transformer tap changing.
What are the problems that can occur in a power system if HVDC is connected?4 answersThe problems that can occur in a power system if HVDC is connected include vulnerability to system faults and changes in operational conditions, adverse effects of low DC-side impedances and sensitive semiconductor-based integrated power electronics devices, and the risk of frequency overshoot resulting from the loss of HVDC tie. Additionally, the type, quantity, and complexity of power equipment have considerably increased in the digital power grid system, making traditional fault diagnosis methods inadequate. Therefore, artificial intelligence (AI) techniques are being explored as ideal tools to simplify solutions' complexity and enhance self-learning ability for fault diagnosis in HVDC transmission systems. Furthermore, ground faults in multiterminal HVDC networks can cause failures in electrical power systems, and quick and accurate detection and actuation are essential to avoid outages. A new protection method based on directional evaluation of the ground current has been proposed, which has been validated through computer simulations and laboratory tests. Finally, commutation failure and transient over-voltage issues can arise in HVDC systems, which can endanger the safety of power grid operation. A new control strategy has been proposed to address these issues, increasing fault detection speed, reducing voltage drop, and suppressing commutation failure probability.
What are the advantages and disadvantages of using artificial neural networks for fault detection and classification in power systems?5 answersArtificial neural networks (ANNs) offer several advantages for fault detection and classification in power systems. They can detect and classify various types of faults, including line-to-ground, line-to-line, double-line-to-ground, triple-line, and triple-line-to-ground faults. ANNs can improve the efficiency of fault detection and classification by measuring performance metrics such as mean square error, root mean square error, mean absolute error, and linear regression. They provide fast and accurate fault detection and classification, even under different conditions, making them useful for transmission line protection schemes. However, there are also some limitations to using ANNs for fault detection and classification. The performance of ANNs depends on the choice of architecture, such as the number of hidden neurons, which requires a detailed analysis. Additionally, the accuracy of ANNs in detecting and classifying faults may vary depending on the training algorithm used, such as backpropagation or Levenberg Marquardt optimization.
How can artificial intelligence be used to detect faults in power grids?5 answersArtificial intelligence (AI) techniques can be used to detect faults in power grids by leveraging the capabilities of machine learning and graph neural networks. These techniques can quickly locate system failures and enable follow-up control actions. AI can also be applied to load forecasting, power grid stability assessment, and security problems in the smart grid and power systems, enhancing the reliability and resilience of the grid. Additionally, AI provides a convenient route for power grid stability assessment, saving time on model development and numerical computation. In the field of power electronics, AI offers powerful tools for fault diagnostics and fault-tolerant control of power electronic systems, which are crucial in modern smart grids and renewable energy systems. Furthermore, AI approaches such as expert systems, artificial neural networks, and multi-agent systems have been used for fault diagnosis in power grids, improving fault detection and identification.

See what other people are reading

Why is demand forecast for 6 months ideal?
5 answers
Forecasting demand for 6 months is ideal due to its crucial role in business development. Accurate demand forecasts are essential for managing various business processes effectively, such as production planning, financial planning, and sales. Businesses must optimize resources and prepare strategic plans close to reality to minimize costs and ensure sustainability. For products with short life cycles, like those in IT ecommerce, accurate demand forecasting is vital for efficient operations and competitive advantage. Techniques like Classic Decomposition, Forecast based on Fuzzy Logic, and advanced methods like LSTM models can significantly improve forecast accuracy, aiding in minimizing sales forecasting errors. Therefore, a well-developed predictive model using various methods can help businesses anticipate demand fluctuations, optimize supply chains, and enhance overall operational efficiency.
What are the current advancements in machine learning techniques for power forecasting?
5 answers
Recent advancements in machine learning techniques for power forecasting include the development of models tailored for different renewable energy sources. For wind power forecasting, machine learning algorithms like LightGBM, random forest, CatBoost, and XGBoost have been utilized, with CatBoost showing superior performance in very-short-term predictions. In solar photovoltaic (PV) power forecasting, advanced ML models such as long short-term memory and gated recurrent unit models have outperformed traditional methods, addressing the intermittent nature of solar PV-generated power. Additionally, for electricity demand forecasting, models like Bi-directional LSTM (BiLSTM) have shown better performance compared to other models like LSTM, ANN, DNN, RNN, and GRU, emphasizing the importance of accurate prediction for energy strategy formulation. Moreover, BiLSTM models have been proposed for state estimation in power systems, showcasing robustness in the presence of uncertainties and missing data points.
How may the consumer behavior of the respondents be described in purchasing habits?
4 answers
The consumer behavior of the respondents in purchasing habits can be described as influenced by various factors such as cultural, social, psychological, and demographic characteristics. The respondents' purchasing decisions are influenced by factors like flavor preference, consumption patterns, income levels, educational backgrounds, and demographic characteristics like age and gender. For instance, the research shows that flavor is a significant factor in selecting tea products, while income and education levels influence decisions on purchasing expensive foods like beef. Demographic characteristics like age and gender also play a role in the buying decision process, with different age groups relying on various sources of information and prioritizing different criteria like quality and discounts. Additionally, changes in modern lifestyles, especially for women, have led to shifts in purchasing habits due to factors like work commitments and societal roles.
What are the advantages and disadvantages of using differentiable symbolic programming for machine learning tasks?
5 answers
Differentiable symbolic programming offers advantages such as enabling the learning of worst-case-safe parameters for neurosymbolic programs, facilitating the learning of symbolic sequences through a differentiable tree interpreter for structure-to-structure transformation tasks, and providing a differentiable approach, DGP, for symbolic regression that efficiently handles high-dimensional problems. However, challenges include the unsuitability of certain fuzzy implications for differentiable learning settings, leading to imbalances in gradients. Despite these challenges, differentiable symbolic programming methods like DSE, DTM, and DGP have shown superior performance in various machine learning tasks, showcasing their potential for enhancing interpretability and performance in complex real-world scenarios.
How to build ML model genetator?
5 answers
To build an ML model generator, one can consider various approaches outlined in the provided contexts. Firstly, integrating a ML-FEM coupled model can accurately simulate and predict the transient behavior of faults in generators, enhancing protection schemes. Additionally, leveraging a hybrid gateway in a peripheral I/O device allows for dual domains, enabling communication between compute resources and hosts for ML applications. Furthermore, utilizing a deep auto-encoder model like MLP-SAE can predict gene expression changes based on genetic variants, showcasing the effectiveness of deep learning in genomics research. By amalgamating these insights, one can develop a robust ML model generator that incorporates advanced simulation techniques, domain-specific communication structures, and predictive genetic modeling methodologies.
How to create AI systems using by Python language?
5 answers
To create AI systems using Python, one can leverage various approaches outlined in the provided research contexts. One method involves developing intelligent systems through programming based on Fuzzy and Artificial Neural Networks (ANN) using Python. Another approach is to design a Python-based personal assistant for Windows systems, inspired by virtual assistants like Siri and Cortana, to perform tasks such as web searches, handling emails, mathematical calculations, and more, utilizing machine learning for user interaction. Additionally, understanding the essentials of Artificial Intelligence, even with minimal programming experience, is crucial for building AI systems effectively. It is essential to ensure compatibility of AI inputs and outputs with existing system components, allowing autonomous operation and continuous improvement within complex systems, emphasizing the importance of system architecture and neural network utilization.
What is the main methodology formulation in environmnrtal impact assessment on distribution of electrical power?
4 answers
The main methodology formulation in environmental impact assessment on the distribution of electrical power involves analyzing the electric and magnetic fields produced by high voltage overhead transmission lines and ensuring compliance with international exposure guidelines. Additionally, the integration of smart monitoring in power distribution systems plays a crucial role in enhancing overall system reliability, with a focus on communication infrastructure and real-time monitoring. Furthermore, the transition to electric vehicles for transportation introduces challenges to power distribution networks, necessitating a methodological framework to assess the impact of plug-in electric vehicles on system loading and voltage regulation. Overall, these methodologies encompass evaluating field distributions, reliability indices, and power flow calculations to address environmental impacts and enhance the efficiency and reliability of electrical power distribution systems.
How often does faults due to vibrations occur in ships?
5 answers
Faults due to vibrations in ships are a significant concern, with various studies shedding light on their occurrence. Research indicates that wave-induced vibrations can contribute up to 50% of fatigue damage in large ocean-going ships, emphasizing the impact of vibrations on ship structures. Additionally, equipment fault detection in engines for boats focuses on shock and vibration information, with a high probability (98%) of engine fault determination and 72% probability of fault detection. Furthermore, metal fatigue in vessels, caused by dynamic movement, has been diagnosed using vibration analysis, highlighting the importance of reliability engineering in detecting faults related to vibrations. Overall, these studies underscore the frequency and significance of faults attributed to vibrations in ships, necessitating effective monitoring and diagnostic strategies for ensuring maritime safety.
What are the shortcomings of wavelet packet energy theory used in protection of HVDC systems/?
10 answers
The wavelet packet energy theory, while instrumental in the protection of High-Voltage Direct Current (HVDC) systems, exhibits several shortcomings that have been identified across various studies. One primary limitation is the need for predetermined decomposition levels in wavelet packet transform, which can hinder the adaptability and efficiency of fault detection mechanisms in HVDC systems. This issue is addressed by proposing an algorithm that dynamically calculates relative wavelet energy change to decide the wavelet level, aiming to extract richer features of HVDC discharge without the need to determine the level in advance. Moreover, the sensitivity of primary protection systems, which often employ wavelet packet energy theory, is compromised under high-resistance fault conditions. This leads to maloperations and affects the protection feasibility, necessitating the development of backup protection strategies to enhance system stability. Another challenge is the accurate and quick identification of faults, especially in modular multilevel converter-based HVDC (MMC-HVDC) systems, where traditional wavelet packet energy approaches may not provide sufficient noise tolerance or may struggle with the identification of lightning strikes. Additionally, conventional fault isolation schemes relying on wavelet packet energy theory complicate engineering implementation due to their dependence on numerical simulations or analytical calculations for threshold setting. The theory's application also faces technical and economic challenges in ensuring speed and selectivity of DC fault protection, highlighting the need for adaptive solutions that can accommodate system changes without compromising performance. Criticism of existing methods based on wavelet packet energy theory points to the necessity for sensitive and automated fault identification schemes that can discriminate correctly between high-impedance faults and low-impedance faults, while also reducing the impacts of load variations and external phenomena. Furthermore, the complexity of HVDC transmission line conditions, such as vulnerability to lightning strikes, necessitates advanced signal recognition tools like wavelet entropy to improve fault identification accuracy. In the context of DC microgrids, the strict time limits for fault interruption pose a challenge for wavelet packet energy-based techniques, requiring fast and reliable fault detection and localization capabilities. Lastly, the attenuation of high-frequency signals over long distances presents a challenge for transient protection principles relying on wavelet packet energy theory, necessitating the development of criteria that can effectively distinguish faults within the protection zone.
What are OSAT in semiconductor?
6 answers
Outsourced Semiconductor Assembly and Test (OSAT) companies play a crucial role in the semiconductor manufacturing process by providing specialized services that include the assembly, packaging, and testing of semiconductor devices. These services are financially advantageous as they allow semiconductor companies to outsource key steps of the production process to firms with the necessary expertise and infrastructure, thereby reducing capital expenditure and focusing on core competencies such as design and innovation. OSAT companies are equipped to handle various aspects of semiconductor packaging and testing, including the development of fine pitch Through-Silicon Vias (TSVs) interposers, which are essential for creating high-density interconnections in multi-chip modules. This capability is significant as it provides a commercially viable technology path for semiconductor companies looking to leverage advanced packaging techniques without the need for extensive in-house facilities. However, outsourcing to OSAT companies is not without risks, particularly concerning the security of on-chip assets. The process of testing at OSAT facilities can potentially expose sensitive design and functionality details to untrusted parties. To mitigate these risks, techniques such as scan chain obfuscation are employed to hide the actual scan data from testers by inserting logic driven by a secret key between the scan cells. Despite these measures, recent studies have shown that attacks like ScanSAT can break these obfuscation techniques, highlighting the ongoing challenge of securing semiconductor designs in the OSAT environment. In summary, OSAT companies are integral to the semiconductor industry, offering essential assembly and testing services that enable the production of complex semiconductor devices. While they offer significant cost and efficiency benefits, the security of intellectual property remains a critical concern that requires ongoing attention and innovation.
Why does the mines need efficient transportation?
5 answers
Efficient transportation systems are crucial for mines due to various reasons outlined in the provided contexts. The modern mining industry heavily relies on advanced transportation methods to enhance production efficiency, reduce costs, ensure safety, and optimize logistical processes. Utilizing technologies like Industry 4.0 and digitalization in mining operations not only increases efficiency but also simplifies internal and external logistics, leading to sustainable development and improved safety measures. Implementing innovative transportation devices, such as the efficient mine transportation device described in one of the contexts, can absorb vibrations and impact forces, ensuring stability during transportation and facilitating seamless connections between multiple mine cars. Overall, efficient transportation systems play a vital role in enhancing productivity, safety, and overall operational effectiveness in the mining industry.