Reliability Of Electric Power System Research Articles

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.

Optimal Unit Commitment and Generation Scheduling of Integrated Power System with Plug-In Electric Vehicles and Renewable Energy Sources

The integration of wind energy sources and plug-in electric vehicles is essential for the efficient planning, reliability, and operation of modern electric power systems. Minimizing the overall operational cost of integrated power systems while dealing with wind energy sources and plug-in electric vehicles in integrated power systems using a chaotic zebra optimization algorithm (CZOA) is described. The proposed system deals with a probabilistic forecasting system for wind power generation and a realistic plug-in electric vehicle charging profile based on travel patterns and infrastructure characteristics. The objective is to identify the optimal scheduling and committed status of the generating unit for thermal and wind power generation while considering the system power demand, charging, and discharging of electric vehicles, as well as power available from wind energy sources. The proposed CZOA adeptly tackles the intricacies of the unit commitment problem by seamlessly integrating scheduling and the unit’s committed status, thereby enabling highly effective optimization. The proposed algorithm is tested for 10-, 20-, and 40-generating unit systems. The empirical findings pertaining to the 10-unit system indicate that the amalgamation of a thermal generating unit system with plug-in electric vehicles yields a 0.84% reduction in total generation cost. Furthermore, integrating the same system with a wind energy source results in a substantial 12.71% cost saving. Notably, the integration of the thermal generating system with both plug-in electric vehicles and a wind energy source leads to an even more pronounced overall cost reduction of 13.05%. The outcome of this study reveals competitive test results for 20- and 40-generating unit systems and contributes to the advancement of sustainable and reliable power systems, fostering the transition towards a greener 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.

Power Smoothing Strategy for Wind Generation Based on Fuzzy Control Strategy with Battery Energy Storage System

This work discusses the use of a battery energy storage system applied to the smoothing of power generated at the output of wind turbines based on a fuzzy logic power control. The fuzzy control logic proposed can perform the aforementioned activity while the state of charge of the energy storage system is maintained within operational limits. In order to assess the fuzzy logic power control’s effectiveness at maintaining the state of charge levels within the allowed range, two operating situations are explored: one in which the state of charge is above the upper limit allowed, and another in which the state of charge is in the minimum value allowed. The numerical results show that, when using the battery energy storage system in conjunction with the control logic proposed, the active power provided as the point of common coupling by the wind turbines can be smoothed, thus contributing to the Electric Power System reliability and stability. The main results of this paper are based on measurements of wind and active power associated with a wind generation plant installed in the northeast region of Brazil and equipped with 2.1 MW wind turbines.

Capacity Management in Smart Grids Using Greedy Randomized Adaptive Search Procedure and Tabu Search

Over time, distribution systems have progressed from small-scale systems to complex networks, requiring modernization to adapt to these increasing levels of active loads and devices. It is essential to manage the capacity of distribution networks to support all these new technologies. This work, therefore, presents a method for evaluating the impact of optimal allocation and sizing of DGs and load shedding for response demand programs on distribution networks to improve the reliability and financial performance of electric power systems. The proposed optimization tool uses the Greedy Randomized Adaptive Search Procedure and Tabu Search algorithms. The combined optimization of DG allocation simultaneously with load shedding, reliability indices, load transference, and the possibility of islanded operation significantly improves the quality of the planning proposals obtained by the developed method. The results demonstrate the efficiency and robustness of the proposed method, improving the voltage profile by up to 2.02%, relieving the network capacity, and increasing the load restoration capability and reliability. Statistical analysis is also carried out to highlight the performance of the proposed methodology.

Review of Methods for Reducing Power Losses in Electric Grids Containing Renewable Energy Sources

In recent decades, there has been an increase in the use of renewable energy sources, which serve as the basis for the concept of distributed generation, that is, facilities that produce electricity at the location of consumers. The large-scale introduction of distributed generation affects the reliability and stability of electric power systems, including by reducing power losses in them by reducing the current in the lines. Taking into account the forecasts of many experts on the growth of distributed energy resources, the issue of studying methods for reducing electrical energy losses in distribution grids containing renewable energy sources is relevant. (Research purpose) The research purpose is determining methods for reducing electricity losses in electric grids containing renewable energy sources. (Materials and methods) Presented an information search, including an overview of Internet sources and sources of scientific literature devoted to methods of reducing electricity losses in electric grids. (Results and discussion) They used Yandex and Google search engines, Scopus, Web of Science and Google Scholar scientific databases, Cyberlenink electronic libraries as sources of information, eLIBRARY.ru , Elsevier, Springer, IEEE Xplore. It was shown that the literary analysis included 59 sources most suitable for the research topic, of which 8 sources of information are given in Russian, 51 in English. In the course of the information search, we determined methods for reducing electricity losses both in traditional electric grids and in electric grids containing renewable energy sources. (Conclusions) It was found out that the considered methods inherent in traditional power supply grids are also relevant for networks with renewable energy sources. It was revealed that the use of renewable energy sources in itself also allows to significantly reduce electricity losses in electric grids due to distributed generation

The Management of Power System Reliability in the Offshore Oil and Gas Field

The reliability of electric power systems is needed in offshore oil and gas field operations because disruptions can have a direct impact on oil production, costs, and company profits.Objectives: to determine the evaluation of reliability performance and propose a model for managing the power system reliability of the PHE OSES offshore oil and gas field.Methodology: A case study with data collected through observation, interviews, and analysis of relevant documents.Finding: Reliability in 2015-2016 has not reached the target but from 2017-2021 has reached the target of 97.5%, as well as availability in 2015, 2018, 2019 & 2020 has not reached the target but in 2016, 2017 & 2021 has reached the target of 95%. This resulted in the highest production losses in 2015 at 266,965 and the lowest in 2021 at 21,388 barrels of oil. The model is proposed by combining elements in risk-based asset management method, RCM method, and redesign.Conclusion: In recent years, reliability has been on target but availability is volatile. The model is proposed to maintain reliability & availability on target so that production losses can be minimized.

Leveraging Deep Learning Algorithms for Predicting Power Outages and Detecting Faults: A Review

Power outage prediction and fault detection play crucial roles in ensuring the reliability and stability of electrical power systems. Traditional methods for predicting power outages and detecting faults rely on rule-based approaches and statistical analysis, which often fall short of accurately capturing the complex patterns and dynamics of power systems. Deep learning algorithms, with their ability to learn automatically representations from large amounts of data, have emerged as promising solutions for addressing these challenges. In this literature review, we present an overview of deep learning algorithms applied to power outage prediction and fault detection. The purpose of this literature review is to explore the uses, effectiveness and advantages and disadvantages of utilizing deep learning algorithms in this domain. Various deep-learning models were explored in the context of power outage prediction and fault detection. Convolutional Neural Networks (CNNs) are effective in analyzing spatial dependencies and patterns in power system data, such as voltage levels and load distributions. Recurrent Neural Networks (RNNs), particularly Long Short Term Memory (LSTM) networks, excel in capturing temporal dependencies and patterns in time series data such as power demand and line currents. Generative Adversarial Networks (GANs) offer a unique approach by generating synthetic power system data for training purposes. This literature review involve collecting historical power system data from various sites. Deep-learning algorithms demonstrated promising results in power outage prediction and fault detection. It achieved a high accuracy rate of 95% in predicting power outages and accurately classified various fault types with an average precision of 92%. The findings highlight the advantages of deep learning algorithms in power outage prediction and fault detection. This literature review provides a comprehensive review of deep learning algorithms in the context of power outage prediction and fault detection.

Life cycle economic viability analysis of battery storage in electricity market

Battery storage is essential to enhance the flexibility and reliability of electric power systems by providing auxiliary services and load shifting. Storage owners typically gains incentives from quick responses to auxiliary service prices, but frequent charging and discharging also reduce its lifetime. Therefore, this paper embeds the battery degradation cost into the operation simulation to avoid overestimated profits caused by an aggressive bidding strategy. Based on an operation simulation model, this paper conducts the economic viability analysis of whole life cycle using the internal rate of return(IRR). A clustering method and a typical day method are developed to reduce the huge computational burdens in the life-cycle simulation of battery storage. Our models and algorithms are validated by the case study of two mainstream technology routes currently: lithium nickel cobalt manganese oxide (NCM) batteries and lithium iron phosphate (LFP) batteries. Then a sensitivity analysis is presented to identify the critical factors that boost battery storage in the future. We evaluate the IRR results of different types of battery storage to provide guidance for investment portfolio.

Optimization of Hybrid Energy Storage System Sizing With Considering Energy Management Strategy for High-Power Pulsed Load in Aircraft

High-power pulsed load (HPPL) such as airborne laser weapon and radar poses a severe challenge to aircraft electrical power system. Since peak power cannot be satisfied only by generator, hybrid energy storage system (HESS) should be well sized to match their energetic performances versus instantaneous power requirement and improve reliability of electrical power system. To deal with these challenges, a sizing optimization for HESS with considering energy management strategy (EMS) based on virtual-droop control in aircraft is proposed in this paper. By analyzing the spectrum characteristics of HPPL profile, splitting frequency between different types of energy storage devices is specially analyzed and designed to give full play to HESS. HESS weight, loss and parameters in EMS are taken into account in this multi-objective optimization based on Non-dominated Sorting Genetic Algorithms II (NSGA-II). Series of Pareto fronts can be obtained through the optimization process, and the most suitable solutions of different HESS combinations are selected by a decision-making means which combines subjective and objective weighting methods. Finally, optimization results of four different combinations are compared. Simulation results are obtained in MATLAB/Simulink environment to prove the rationality and applicability of the optimal sizing tool. The research has resulted in a solution of HESS and EMS optimization for HPPL used in aircraft.

Improvement of power transformer fault diagnosis by using sequential Kalman filter sensor fusion

Power transformers are one of the most important and costly equipment for the reliability and continuity of electrical power systems. For this reason, continuous monitoring of power transformers during normal operating conditions of the power grid and early fault diagnosis from existing parameters before a fault occurs is an important task. One of the most common analysis is the Dissolved Gas Analysis (DGA) method. In the DGA analysis, the concentrations of gases formed in the transformer insulating fluid are measured, classified and used to predict failures. It is observed that the classification and diagnosis are performed by classical and artificial intelligence-based methods in the relevant literature and applications. In this study, gas data classified by machine learning method is combined with sensor fusion methods to increase the diagnosis accuracy. It has been determined that the Sequential Kalman filter, which is first used differently from the literature, increases the estimation accuracy over 90% according to the results obtained by the Majority Voting and Dempster Shafer Evidence Theory fusion methods and the other results with IEC-TC-10 dataset mentioned in the literature.

Diminishing Active Power Loss and Improving Voltage Profile Using an Improved Pathfinder Algorithm Based on Inertia Weight

Part of the widely discussed problem in electrical power systems is the optimal reactive power dispatch (ORPD) due to its reliability and economical operation of electrical power systems. The ORPD is a complex and nonlinear optimization problem. The pathfinder algorithm (PFA) is a newly developed algorithm that inspires the group movement of prey with a leader called a pathfinder when hunting for food. The inertia weight is added to the PFA and is called an improved pathfinder algorithm (IPFA) to support the proper random work of the swarm to avoid the decrease in searchability of the PFA. The IPFA was proposed in this work to diminish the active power loss while improving the voltage profile. The IPFA was validated on the IEEE 30 and 118 bus systems along with particle swarm optimization (PSO) and the teaching–learning-based optimizer (TLBO). The proposed IPFA provides the best result as the losses of the IEEE 30 and 118 test systems were reduced to 16.035 and 115.048 MW from the initial base of 17.89 and 132.86 MW, respectively. The losses of PSO and the TLBO were 16.1568 and 16.1607 MW for the IEEE 30 bus system, respectively, while for the IEEE 118 bus system, the PSO provided 117.9129 MW and the TLBO provided 118.0524 MW. The two test systems’ reduction percentages (%) were 10.37% and 13.41%, respectively. The results were compared with those of other algorithms in the literature, and the IPFA provided a superior result, thereby suggesting the superiority of IPFA methods in diminishing the power loss and improving the system’s voltage profile.

About the Planned Reliability of Electric Power Systems

The article discusses the issue of expanding the methodological foundations of system reliability of electric power systems as part of the development of a new type of system reliability - planned reliability. Planned reliability is a type of system reliability intended for the analysis of electric power systems when planning their operation for a period of up to 1-2 years. Planned reliability includes some signs of security and adequacy. The need for the development of methodological foundations for this type of system reliability is associated with the need to solve a number of important problems in electric power systems, such as planning repairs of power equipment, long-term planning of power system modes, etc.

Formation of power equipment repair schedules using indicators of planned reliability

This article discusses the problem of planning maintenance of power equipment during long-term planning of the operation of electric power systems. Here we propose a mathematical model for the formation of maintenance schedules for power equipment, which takes into account two aspects: the current technical condition of power equipment and maximizing the planned reliability of electric power systems when carrying out maintenance of power equipment. Provided methodology for generating maintenance schedules for power equipment was tested on using the scheme of three reliability zones.

Justification of the structure of the autonomous electric power system of the agro-industrial enterprise

Purpose. Increasing the efficiency of agricultural production and improving the living conditions of the rural population by creating a reliable autonomous power supply system based on its own renewable energy sources. Methods. The substantiation of the structure and parameters of the autonomous electric power system of the economic level was carried out on the basis of the provisions of probability theory, mathematical statistics and simulation modeling using a specially developed reliability assessment model of various variants of autonomous electric power systems for agricultural purposes. Results. The model for assessing the reliability of autonomous electric power systems of the economic level was developed on the basis of the structural and functional organization of systems with the determination of the probability of failure-free operation. The reliability of the system is determined by the reliability of its subsystems and their interconnection – structure. The main method of increasing the reliability of power supply systems is the redundancy method, which consists in introducing additional subsystems into the system. Serial and parallel connection systems are considered. An important element of ensuring high reliability of autonomous power supply systems of agricultural production facilities is appropriate means of energy accumulation. The structure of the specific electricity supply system of the agroindustrial economy should be formed on the basis of the characteristics of production and household needs, assessment of the potential and characteristics of renewable energy sources and the conditions of safe, economically and ecologically efficient operation Conclusions 1. Effective operation of a modern farm with complex automated human activity in close harmony with living objects is possible only under the condition of absolutely reliable power supply, including on the basis of own renewable energy sources. 2. Among the natural RES, the most reliable source for farming conditions is the energy of solar radiation. 3. From the point of view of organization of application, availability of raw materials and possibilities of necessary maintenance, solid biofuel has certain advantages in the conditions of rural areas (the energy cost-output ratio is 1:16). 4. A rational option for a farm can be considered an autonomous power supply system consisting of: a solar power plant, a biofuel power plant, a storage battery – with an estimated reliability index of 0.9997. Keywords: autonomous electric power systems, reliability, solid biofuel, renewable energy sources, power supply, heat supply.

Решение задач электромагнитной совместимости специализированными программными средствами

Reliability and stability of electric power systems depend on compliance with electromagnetic compatibility of electrical equipment, which requires compulsory and objective monitoring. Performing an analysis of power quality in electrical networks is a challenging task, so it is advisable to do it using specialized software. This article gives a brief overview of LabVIEW programming environment and MatLab software package. The possibilities of application of these products for solving the problems of electromagnetic compatibility are considered.

Recloser Placement Optimization In The PDP-03 Radial Network Distribution System Using Ant Colony Optimization (ACO)

The increasing demand for electricity demanded that the power distribution system has a good reliability. To improve the reliability of electric power systems, PT. PLN Area Semarang use Recloser on the electricity network. Nowadays, the determination of recloser position is very important in reliability of electrical power system. Reliability index used in distribution systems are SAIFI and SAIDI. The value of SAIFI and SAIDI can be calculated from the outage time and the failure rate occurred in one year. Reliability can be concluded better if SAIFI and SAIDI smaller. This research aims to determine the optimal placement of recloser location on the electrical network in feeder PDP 03 Pudak Payung substation. The method used for the recloser location placement is Ant Colony Optimization (ACO) using Matlab Software R2014a. From simulation result, it can be concluded that the reliability value before and after placement recloser changed. SAIFI and SAIDI before recloser placement is 1.0172 times/year and 2.5435 hours/year. Optimal placement location for 1 recloser is at a location 4 with a value of SAIFI and SAIDI is 0.3361 times/year and 1.0456 hours/year. While for 2 reclosers is at a location 2 and 7 with a value of SAIFI and SAIDI is 0.26755 times/year and 0.86069 hours/year.

Energy Harvesting Methods for Transmission Lines: A Comprehensive Review

Humanity faces important challenges concerning the optimal use, security, and availability of energy systems, particularly electrical power systems and transmission lines. In this context, data-driven predictive maintenance plans make it possible to increase the safety, stability, reliability, and availability of electrical power systems. In contrast, strategies such as dynamic line rating (DLR) make it possible to optimize the use of power lines. However, these approaches require developing monitoring plans based on acquiring electrical data in real-time using different types of wireless sensors placed in strategic locations. Due to the specific conditions of the transmission lines, e.g., high electric and magnetic fields, this a challenging problem, aggravated by the harsh outdoor environments where power lines are built. Such sensors must also incorporate an energy harvesting (EH) unit that supplies the necessary electronics. Therefore, the EH unit plays a key role, so when designing such electronic systems, care must be taken to select the most suitable EH technology, which is currently evolving rapidly. This work reviews and analyzes the state-of-the-art technology for EH focused on transmission lines, as it is an area with enormous potential for expansion. In addition to recent advances, it also discusses the research needs and challenges that need to be addressed. Despite the importance of this topic, there is still much to investigate, as this area is still in its infancy. Although EH systems for transmission lines are reviewed, many other applications could potentially benefit from introducing wireless sensors with EH capabilities, such as power transformers, distribution switches, or low- and medium-voltage power lines, among others.

A novel methodology for optimal location of reactive compensation through deep neural networks

The present investigation proposes a methodology for the optimal location of reactive compensation in an electrical power system (EPS) through deep neural networks for voltage profile improvement. One of the main parameters to consider regarding EPS reliability is the voltage profile, a parameter that can be affected due to unexpected increases in impedance and loads in the system that translate as overloads in the system and an increase in the number of users. A voltage profile below the minimum or above the maximum accepted in the regulations of each country puts at risk the correct operation of equipment connected to the electrical network and, in turn, can cause economic losses and human lives (e.g by not guaranteeing reliability for hospitals and similar institutions).Economically, one of the most viable alternatives for improving voltage profiles is reactive compensation which in itself is carried out through capacitor banks. Therefore, this work proposes to find the correct location of capacitor banks in an electrical power system (using IEEE 14, 30 and 118 bus-bars systems as cases of study). In each system, the highest reactive load is identified, thus three values for reactive compensation are established as 80%, 50% and 25% of this maximum.Then, with these values, power flows are generated by locating each one of the reactive compensators’ possible values in each one of the bars of the system, hence generating a large number of training data so that finally the neural network is capable of providing a quantitative classification highlighting which compensation and in which bus-bar produces the best result. The result is assessed by applying a modified standard deviation which evaluates the separation of the voltage profiles from the ideal desired value of 1pu.