Reliability Of Electric System Research Articles

Real time conditioning monitoring of MOSFET using artificial neural network regression

The metal–oxide–semiconductor field-effect transistor (MOSFET) is a critical semiconductor device widely used in electrical and electronic systems. The performance of systems depends on the component’s reliability. More than 30% of failures of electronic and electrical systems are due to MOSFET failure. Industries are paying more apprehension towards the improvement of MOSFET reliability. Temperature stress is one of the key parameters to examine the reliability performance of semiconductor devices and to predict the lifetime of the components. This article provides a quick analysis of the MOSFET degradation models. The primary failure precursor metric that reflects the level of degradation is the MOSFET on-resistance R DS(on). In this paper, a linear, non-linear, and artificial neural network (ANN) deterioration model of MOSFETs is presented with the experimental data. The coefficient of determination (R-square), and root mean square error (RMSE) are used to compare the real data. Finally, it is demonstrated that the proposed model works well with experimental data. More than 500 data were trained for the proposed model. It can therefore predict the real-time conditioning monitoring of MOSFETs to increase the reliability of electrical systems.

Determining target levels of power distribution system reliability indices using machine learning

Electrical distribution system (EDS) reliability has always been a critical subject due to the considerable share of distribution systems in power interruptions to end-users. Meeting regulatory service quality standards requires distribution companies to achieve defined reliability levels for performance improvement. Conventionally, specialists determine target reliability based on experience, experimental knowledge, and rules of thumb. However, these methods are error-prone and may result in suboptimal investments, economic inefficiency, and inadequate resource allocation in EDS operation and planning. Accordingly, this paper presents a machine learning-based method to determine the target level for EDS's reliability indices based on its historical performance. The approach involves comprehensive analysis of historical EDS reliability data, followed by a data envelopment analysis (DEA) to assess performance across subdivisions like substations or feeders, identifying the most effective units. Subsequently, machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Radial Basis Function (RBF) network—are trained on optimal subdivisions from the DEA stage. These models accurately estimate target reliability for all subdivisions, contributing to the overall determination of the EDS target reliability level. A three-scenario case study validates the proposed method, suggesting its potential applicability as a valuable tool for regulatory agencies in setting EDS reliability targets. While admitting the potential challenges, notably the method's reliance on the quality and completeness of the historical data collected by distribution companies, the paper emphasizes the importance of collaborative efforts to ensure data accuracy and sufficiency. The proposed method leads to a fair mechanism to determine the target level for a real EDS's reliability indices in comparison with a business-as-usual scenario. Moreover, it provides a reliability performance ranking for the EDS's subdivisions.

Multi-objective capacity configuration optimization of the combined wind - Storage system considering ELCC and LCOE

The optimal capacity configuration of combined wind-storage systems (CWSSs) serves as a foundation and premise for building new electricity system. This paper proposes a novel capacity configuration model of CWSS. The model simultaneously considers economy, stability and low carbon by respectively minimizing Levelized cost of electricity (LCOE) and maximizing effective load carrying capacity (ELCC). The multi-objective optimization model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA-II). And Multi-Attributive Border Approximation Area Comparison (MABAC) method is used to score the alternative solutions on the Pareto front and make decisions under different scenarios. The results of the case study show that: (1) The evaluation of the ELCC for CWSS can quantify its ability to replace the installed capacity of thermal power, thus greatly reducing the uncertainty of wind power output. (2) Under low-carbon scenario, economic scenario and standard scenario, the results of optimal capacity configuration for the CWSS show that LCOE and ELCC are negatively correlated. When the weights of ELCC and LCOE are equal, the three goals of economy, low carbon and safety and stability are achieved. (3) The results of a single sensitivity analysis on LCOE show that the sensitivity coefficient of electricity generation is the highest, exerting the greatest impact on the capacity configuration decision of the CWSS. A 20 % increase in power generation can reduce LCOE by 0.1203RMB/kWh. This paper provides energy planning recommendations for decision-makers in CWSSs, contributing to the development of a reliable new electricity system.

Performance Evaluation of Overcurrent Relay and Ground Fault Relay Coordination in Distribution Feeder

Reliable and sustainable electrical distribution systems require effective protection measures against internal and external disturbances, especially short-circuit disturbances, to avoid damage to the equipment. Overcurrent relays (OCR) and ground fault relays (GFR) play an important role in the safety of the distribution network. To improve performance, evaluation of relay coordination is essential. During the short circuit, the nearest relay to the fault point is activated first and the backup relay is activated if the fault current exceeds the adjustment of the main relay. Coordination ensures that timely signaling is sent to interrupt the circuit. Inspection of the OCR relay revealed that it did not comply with the IEC 60255 standard and consequently adjusted the classification time to 0.402 seconds. Similarly, the results of the GFR relay inspection did not meet the standards and required an adjustment of the evaluation time to 0.492 seconds. Resolving these deviations is crucial to maintaining the reliability and compliance of the distribution network with industry standards, ensuring that the protection relays respond quickly and effectively to disturbances, thereby preventing possible damage to equipment

Improving the accuracy of diagnostic predictions for power transformers by employing a hybrid approach combining SMOTE and DNN

In power transformers, accurate diagnosis prediction plays a crucial role in ensuring the reliability and efficiency of electrical systems. This paper presents an enhanced diagnostic prediction approach for power transformers by combining the Synthetic Minority Over-sampling Technique (SMOTE) and Deep Neural Networks (DNN). Our approach addresses the challenge of imbalanced data in power transformer diagnosis prediction by leveraging SMOTE to generate synthetic samples and balance the dataset. By effectively handling rare events through the augmentation of minority class instances, our predictive model becomes more robust. Additionally, using deep neural networks enables the exploration of complex patterns and relationships within the transformer dataset. The DNN architecture integrates multiple layers of learnable features, enabling the prediction model to extract high-level representations and achieve improved accuracy and performance. To evaluate the effectiveness of our proposed approach, we conducted extensive experiments using real-world power transformer datasets. Comparative analysis of the results demonstrates that the combination of SMOTE and DNN outperforms traditional classification techniques applied to imbalanced datasets. The proposed model achieves a higher training accuracy of 98.22 % and a testing accuracy of 94.6 % and mitigates the risks associated with misdiagnosis, contributing to power systems' overall reliability and maintenance efficiency.

Effective Power Transformer Maintenance Strategy Decision-Making: Health Index, Statistical Analysis, and Life Cycle Cost

As a State-Owned Enterprise, PLN must provide sufficient quantity and quality electricity for the public interest. Still, on the other hand, according to the articles of association, PLN is required to make a profit. Power transformer failures significantly impact electrical system reliability, as they can disrupt electricity delivery. Many studies have been conducted on the health index of power transformers. However, previous studies only considered health indexes from one or several aspects of testing results. Life Cycle Cost is a method that helps select the best cost approach for establishing an effective maintenance strategy over the transformer's lifetime. A transformer lifetime uses a Weibull Analysis of a failure transformer in PLN TJBT from 2017-2022. This study uses a comprehensive health index by considering failure modes in transformers that integrate results from various aspects, such as observations while in operation, field inspections, and on-site and laboratory testing. After conducting a comprehensive health index, a risk evaluation was performed to simulate the impact of different maintenance decisions. Considering the health index and life cycle cost of the transformer, a new maintenance decision-making model was developed to choose the best maintenance strategy based on the current condition of the transformer.

End-year China wind power installation rush reduces electric system reliability

The urgent challenge posed by climate change has catalyzed global efforts to transition towards sustainable energy sources, with wind power emerging as a pivotal component. However, the rapid expansion of renewable energy sources has raised concerns about electric system reliability, given their intermittent and hard-to-forecast nature. China has provided incentives that promoted the rapid expansion of wind. However, the structure of some incentives led to the phenomenon of end-year rushes to install wind power before incentives expire. Leveraging panel data from China's provinces, we empirically estimate the impact of these installation rushes on electric reliability. We find significant adverse effects, with a one-standard-deviation increase in installation rush corresponding to a 0.767% decrease in the reliability rate and a 39.6-min increase in annual outage duration. Notably, urban areas and the northwestern grid are particularly vulnerable to the disruptions caused by year-end installation rushes. In the urban areas of the northwestern grid, we identify the potential for substantial improvements in the lower bound of the reliability rate, from 98.86% to 99.37%, or a reduction in outage duration from 11.65 h to 7.16 h. These findings show the importance of structuring incentives properly and the importance of improvements in grid infrastructure and management in the transition to a low-carbon world.

Optimizing Power Grid Stability with Enhanced Circuit Breaker Maintenance

This study aims to evaluate the effectiveness of maintenance techniques on 20 kV Power Circuit Breakers (PMTs) using the Research and Development (R&D) method. Key performance variables assessed included contact resistance, PMT synchronism, and insulation resistance. Findings revealed that contact resistance for all phases was below 100 µΩ, indicating optimal connectivity. Synchronism testing showed time differences of 0.7 ms during opening and 1.05 ms during closing, both within acceptable limits. Furthermore, insulation resistance exceeded the 20 MΩ standard, confirming the insulation's integrity. These results suggest that regular maintenance using precise measurement techniques ensures the continued reliability and safety of MV electrical delivery systems. Highlights: Rising Electricity Demand: PT PLN (Persero) faces increasing electricity needs. Advanced Protection Solutions: Innovative equipment addresses conventional limitations. Research Insights: Findings ensure safety and reliability in medium voltage systems. Keywords: Contact Resistance, Insulation Resistance, PMCB, Synchronism Testing

Testing of The Insulation Resistance and Turns Test Ratio (TTR) of The Distribution Transformers at PT PLN (Pesero) UP3 Sidoarjo

Electricity plays a vital role in human life and the economic growth of Indonesia. Therefore, a reliable electrical transmission and distribution system is essential. Transformers, especially in an industrial context, play a key role in power supply. Transformer maintenance is a critical step in maintaining a stable flow of electricity. This study adopts the Research and Development (R&D) method and focuses on measuring insulation resistance and voltage ratios in the context of transformer maintenance. The research was conducted at the Pole Transformer Substation (GTT) in P. Melati, Sidoarjo Regency, East Java, starting in August 2023. The research variables involve measuring the insulation resistance referring to IEC standards and measuring the voltage ratio to ensure proper transformer performance. The test results indicate that the insulation resistance in the transformer, both on the primary and secondary sides, meets the standards, demonstrating that the transformer functions well and safely. Voltage ratio measurements closely approximate theoretical calculations and fall within a tolerance of ± 0.5%. This study underscores the importance of transformer maintenance in maintaining a stable flow of electricity. Proper maintenance will prevent disruptions that can be detrimental, such as current leakage or short circuits. With good maintenance, the continuity of a reliable and safe electricity distribution service for consumers can be ensured.

Identification of reliable locations for wind power generation through a global analysis of wind droughts

Wind droughts, or prolonged periods of low wind speeds, pose challenges for electricity systems largely reliant on wind generation. Using weather reanalysis data, we analyzed the global distribution of and trends in wind droughts using an energy deficit metric that integrates the depth and duration of wind droughts. We identified regions with high power densities, low seasonal variability, and limited weather fluctuations that favor wind power generation, such as the American Midwest, Australia, the Sahara, Argentina, Central Asia, and Southern Africa. Northwestern Europe has high power densities but experiences more frequent and prolonged wind droughts due to higher weather variability. We found little evidence for strong trends in wind droughts over recent decades in most places. Rather, the most severe wind droughts in many places occurred before wind power substantially penetrated power systems, which suggests that historical weather data can be useful in designing reliable wind-reliant electricity systems.

Interconnected Smart Transactive Microgrids—A Survey on Trading, Energy Management Systems, and Optimisation Approaches

The deployment of isolated microgrids has witnessed exponential growth globally, especially in the light of prevailing challenges faced by many larger power grids. However, these isolated microgrids remain separate entities, thus limiting their potential to significantly impact and improve the stability, efficiency, and reliability of the broader electrical power system. Thus, to address this gap, the concept of interconnected smart transactive microgrids (ISTMGs) has arisen, facilitating the interconnection of these isolated microgrids, each with its unique attributes aimed at enhancing the performance of the broader power grid system. Furthermore, ISTMGs are expected to create more robust and resilient energy networks that enable innovative and efficient mechanisms for energy trading and sharing between individual microgrids and the centralized power grid. This paradigm shift has sparked a surge in research aimed at developing effective ISTMG networks and mechanisms. Thus, in this paper, we present a review of the current state-of-the-art in ISTMGs with a focus on energy trading, energy management systems (EMS), and optimization techniques for effective energy management in ISTMGs. We discuss various types of trading, architectures, platforms, and stakeholders involved in ISTMGs. We proceed to elucidate the suitable applications of EMS within such ISTMG frameworks, emphasizing its utility in various domains. This includes an examination of optimization tools and methodologies for deploying EMS in ISTMGs. Subsequently, we conduct an analysis of current techniques and their constraints, and delineate prospects for future research to advance the establishment and utilization of ISTMGs.

Review to Smart Grids

This paper is the result of an extensive study, comparison, and analysis of academic articles and research related to smart grids and the solutions they offer to enhance the efficiency, reliability, and sustainability of modern electrical power systems. It presents a comprehensive analysis of the progress made in recent years in smart grid technologies, including architecture, communication infrastructure, control strategies, and the integration of renewable energy sources. Additionally, the benefits and advantages of adopting smart grids are explored and presented, along with the challenges arising from their use. There is also a reference to the future prospects of using smart grids and how they can impact human life. Following this, there is an analysis of the two most critical elements of smart grids, namely Energy Management Systems (EMS) and Advanced Metering Infrastructure (AMI). Finally, some conclusions are drawn regarding the use of smart grid systems, and a personal opinion is expressed.

Artificial neural network analysis for classification of defected high voltage ceramic insulators

Partial discharge (PD) could lead to the formation of small arcs or sparks within the insulating material, which can cause damage and degradation to the insulator over time. In ceramic insulators, there are several factors that can cause PD including manufacturing defects, aging, and exposure to environmental conditions such as moisture and temperature extremes. As a result, detecting and monitoring PD in ceramic insulators is important for ensuring the reliability and safety of electrical systems that rely on these insulators. In this study, acoustic emission technique is introduced for PD detection and condition monitoring of defective ceramic insulators. A sequence of data processing techniques is performed on the captured signals to extract and select the most significant signatures for classification of defects in insulator strings. Artificial neural network (ANN) has been used to build an intelligent classifier for easily and accurately classification of defective insulators. The overall recognition rate of the classifier was obtained at 96.03% from discrete wavelet transform analysis and 88.65% from fast Fourier transform analysis. This obtained result indicates high accuracy and performance classification. The outcomes of ANN were verified by SVM and KNN algorithms.

Results of a statistical study of types of single-phase faults in low-voltage ship electrical networks

The increase in the power supply of ships is manifested in growth of ship power plants' capacity, the total length of cable lines, and the number of electrified mechanisms. With the development of marine technology, the severity of the consequences of damage to ship electrical equipment and the costs of its maintenance and repair increase. The search for ways to improve the reliability and safety of electrical equipment should be based on design and technological solutions at the stages of its development and production, as well as an analysis of various factors that may appear during the operation of marine equipment and affect the condition of ship electrical systems. During studying these factors, it has been revealed that one of the common types of damage to electrical equipment of marine equipment is single-phase short circuits, the causes of which are varied. Preventing short circuits is labour intensive and in some cases impracticable. However, understanding the characteristics of this type of damage to low-voltage equipment is necessary to improve the reliability and safety of ship electrical systems. The conducted research is based on a survey of ship electrical mechanics. The results of the surveys have made it possible to analyze the frequency of occurrence of single-phase insulation faults, the duration of their existence, to identify the most likely places of occurrence of short circuits and equipment that is more susceptible to such damage compared to other elements of the electrical power system.

(Invited) Electrolytes for Next-Generation Sodium Metal Batteries

Developing reliable and cost-effective electrical energy storage systems (EESs) for portable electronics, electric vehicles, and grid storage applications is vital. Na-ion batteries are the most promising alternative technology owing to their natural abundance and low sodium cost.[1] Solid-state batteries (SSBs) have garnered extensive attention for their ability to suppress the safety hazards of organic liquid electrolytes by replacing them with solid electrolytes.[2] Among the various solid electrolytes being explored, our group mainly focuses on NASICON (Sodium superionic conductor) type silicate and solid polymer electrolytes. Sodium silicates are a class of materials with composition, NaxMxSixOx (M = rare-earth metals, x = integer (1-10)).[3] They have the advantage of low sintering temperature (~1050 °C) and 3D framework structure containing MO6 octahedra and SiO4 tetrahedra providing facile ionic movement.[4] Solid polymer electrolytes (SPE) are known for their flexibility and electrode compatibility. Highly conductive, filler-free composite solid polymer electrolyte films were prepared using poly(vinylidene fluoride), poly(vinyl pyrrolidone) (PVP), and NaPF6.[5] PVP binder played an essential role in improving Na ion conductivity and excellent plating−stripping performance. This talk will present an overview of these solid electrolytes for next-generation sodium metal batteries.

A Comprehensive Overview of Photovoltaic Technologies and Their Efficiency for Climate Neutrality

Solar photovoltaic (PV) technology is a cornerstone of the global effort to transition towards cleaner and more sustainable energy systems. This paper explores the pivotal role of PV technology in reducing greenhouse gas emissions and combatting the pressing issue of climate change. At the heart of its efficacy lies the efficiency of PV materials, which dictates the extent to which sunlight is transformed into electricity. Over the last decade, substantial advancements in PV efficiency have propelled the widespread adoption of solar PV technology on a global scale. The efficiency of PV materials is a critical factor, determining how effectively sunlight is transformed into electricity. Enhanced efficiency, achieved through a decade of progress, has driven the global expansion of solar PV. Multi-junction photovoltaic materials have now exceeded 40% efficiency in lab tests. China leads the world in solar PV installations, boasting over 253 GW of installed capacity by the end of 2021. Other prominent countries in this sector are the United States, Japan, Germany, and India. Photovoltaic (PV) cell technologies are rapidly improving, with efficiencies reaching up to 30% and costs falling below $0.50/W, making PV a competitive source of energy in many countries around the world. Solar PV technology holds immense potential for creating a cleaner, reliable, scalable, and cost-effective electricity system. To expedite its deployment and foster a more sustainable energy future, continued investment in research and development along with supportive policies and market mechanisms is essential. This paper underscores the pivotal role of solar PV technology in the global energy transition and advocates for a concerted effort to unlock its full potential in achieving a more sustainable and resilient energy future.

Research on dual‐winding permanent magnet generator system with integrated dual‐channel controller

AbstractThis article presents a dual‐winding permanent magnet generator (PMG) system with integrated dual‐channel controller to meet the requirements of high power density, high reliability, and high output performance of aircraft electric power system. The dual‐winding of the PMG are designed to have the same phase spatially, with which the same rotor position can be applied in independent vector control. In addition, a dual‐channel controller is applied to control the two sets of windings, which achieves high‐performance control under normal state and fault‐tolerant control under fault state. The integrated dual‐channel controller integrates two sets of main power circuits into one controller and uses a main control unit, which simplifies the PMG system leading to reduction in the overall volume and weight. Reliability analysis of dual‐winding PMG system is proposed to be compared with that of the single‐winding PMG system. A dual‐winding cooperative control strategy and a fault‐tolerant control strategy are proposed to achieve high‐performance control of DC‐link voltage under normal condition and redundant functions under fault condition. As a result, the reliability of the PMG system is improved. The experimental results validate the effectiveness of the proposed PMG system and the control strategy.

The application of fault diagnosis techniques and monitoring methods in building electrical systems – based on ELM algorithm

The reliability of modern building electrical systems are receiving increasing attention as they become more intelligent and complex. As the majority of building electrical systems use neutral point grounding, earth faults or short circuits can get worse over time and damage both the distribution system and the electrical equipment. To this end, the corresponding three phases and four categories, namely three-phase voltage, three-phase current after fault, three-phase voltage distortion rate, three-phase current distortion rate, a total of 12 dimensional fault feature vectors and 10 fault simulation types, were summarised and extracted in conjunction with the actual operating conditions of the system. Using traditional fault identification ideas and neural network algorithm as reference, a 12-dimensional fault feature vector is used as the model input to construct a building electrical fault diagnosis and detection model based on ELM algorithm. Results showed that the ELM-based model’s classification accuracy for this experimental sample was 97.56 %, its AUC was 0.92, and its RMSE was 0.3521. These figures were higher than the classification accuracy and performance of the BP algorithm and GA-BP algorithm fault diagnosis models, and they also demonstrate better robustness and generalizability. The model also has a 97.27 % correct rate in fault discrimination, while the computation time is only 0.201 s, and its fault identification and diagnosis speed is faster than other algorithmic models. At the same time, this research model has a good fault monitoring accuracy of up to 98.6 % for building electrical systems. The research can provide a more sensitive, accurate and rapid fault monitoring method for the current building electrical system. It also improves the reliability of the building electrical system in a complex environment and achieves better protection of the system. This has a certain significance for the development of the building electrical industry.

Review of Concepts and Determinants of Grid Electricity Reliability

The reliability of the electricity supply is important since any interruption to the supply has direct and indirect consequences for its users. A reliable electricity supply requires a reliable electrical grid system to transmit and distribute the power from the generating plants to the consumers. This study reviewed the literature to find out how the reliability concept has been understood with a special focus on grid electricity reliability, what factors influence grid electricity reliability, what measures have been used to measure grid electricity reliability, which theories and methodologies have been applied to study grid electricity reliability and what are the likely research gaps that require future address. This review found that the literature documents four categories of factors that influence grid electricity reliability, and these are environmental, security, organizational and technical. The biggest influencers of grid electricity reliability were the technical-related factors followed by the environmental-related factors. In addition, we found that sixty studies focused on one subsystem, eleven on two subsystems while seven studies considered three subsystems. Most studies were found to address the distribution of subsystem reliability. As per the methodology adopted, this review found that eleven studies used a qualitative approach, forty-five studies used a quantitative approach, while eleven studies used a case study approach to study the concept of grid electricity reliability. In addition, we found that thirty-seven studies used the duration and frequency of power outages to measure grid electricity reliability.

Reliability Analysis on electric propulsion System Combining GO-FLOW Methodology with Multi-Valued Decision Diagrams

The electric propulsion system of an electric aircraft is the core power of the whole aircraft, which provides assurance for the safe flight of the aircraft. There are multiple stages in the flight process of electric aircraft, as well as different failure behaviors of system components at different stages. The electric motor system is a k-out-of-n system. Therefore, it is not possible to accurately describe the electric motor system with the existing operators when using the GO-FLOW method for electric propulsion system reliability calculations. In order to solve this problem, this paper models the electric motor system using the multi-valued decision diagram method (MDD method), based on which a new operator 43 is proposed to simulate the electric motor system, and then builds the GO-FLOW model and calculates the overall reliability based on the distributed electric propulsion system schematic. The example results show that the method can calculate the reliability of the distributed electric propulsion system more realistically.