State Of Electric Power System Research Articles

Methodology for Analysis of System Reliability When Planning States and Balances of Electric Power Systems

The article presents the main provisions of the methodology for analyzing the system reliability of electric power systems when planning their states and balances. The type of system reliability for solving problems when planning the states and balances of electric power systems is called planned reliability. A substantive statement of the problem of assessing planned reliability is formulated, where the main requirements for taking into account the factors influencing the determined indicators are noted. In the general structure of the methodology, based on the sequential Monte Carlo method, three main stages are distinguished: the stage of forming pseudo-random processes of functioning of electric power systems, the stage of analyzing the states of functioning of electric power systems, the stage of calculating the indicators of planned reliability.

Grey Wolf Optimizer and Cuckoo Search Algorithm for Electric Power System State Estimation with Load Uncertainty and False Data

State estimate serves a crucial purpose in the control centre of a modern power system. Voltage phasor of buses in such configurations is referred to as state variables that should be determined during operation. A precise estimation is needed to define the optimal operation of all components. So many mathematical and heuristic techniques can be used to achieve the aforementioned objective. An enhanced power system state estimator built on the cuck search algorithm is described in this work. Several scenarios, including the influence of load uncertainty and the likelihood of false data injection as significant challenges in electrical energy networks, are proposed to analyse the operation of estimators. The ability to identify and correct false data is also assessed in this regard. Additionally, the performance of the presented estimator is compared to that of the weighted least squares, Cuckoo Search algorithm and grey wolf Optimizer. The findings demonstrate that the grey wolf Optimizer overcomes the primary shortcomings of the conventional approaches, including accuracy and complexity, and is also better able to identify and rectify incorrect data. On IEEE 14-bus and 30-bus test systems, simulations are run to show how well the method works.

Универсальный способ превентивной разгрузки судовой электроэнергетической системы

The method of preventive protection for the purposes of safe operation of the ship's electric power system in emergency situations associated with malfunctions of its elements arising during operation is considered. It is shown that existing devices form a command to unload power sources at the moment of overload, when an emergency has already occurred. At the same time, the disconnection of consumer groups occurs with a time delay. For this reason, stopping even one of the working generator sets by protection often leads to deenergization of the vessel due to overload of the remaining operational machines. A notable place in the work is given to the consideration of the method of unloading the network, in which the automatic mode is used to form a command to turn off selected electricity consumers at a time when at least one of the parallel generator sets is already inoperable, but the quality indicators of the generated electrical energy are still within acceptable limits. Within the framework of the proposed approach, the energy state of the entire ship's electric power system is predicted in the event of a decrease in its generating capacity and, if necessary, a team is formed to unload the network. The conditions under which unloading should be carried out are formulated and presented in the form of logical expressions. It is shown that the proposed approach is universal, since it does not depend on the number of generator sets operating in parallel, nor on the number of machines that have failed, nor on the type of malfunctions that have occurred. The functional diagram and description of the original device for preventive unloading of the ship's electric power system, implementing the proposed approach of preventive protection in practice, are presented.

Programming of marine engineers' simulator training

Continuous improvement of the technical equipment of sea vessels is inevitably accompanied by the development and complication of the training base for marine specialists. Simulator training based on a computer model of operating the units of the liquefied natural gas tanker of the Veliky Novgorod type includes interactive computer programs for learning management, created using the e-Tutor computer module included in the simulator delivery set. In the course of systematizing the experience of using specialized programming tools for educational and research activities of students in organizing and conducting simulator training for electrical engineers on computer simulation models of sea vessels, a system of conventional graphic symbols for the elements of the flowchart of the training exercise program has been formed and their description has been given. Training and control exercises include familiarization with the mnemonic diagrams of the integrated marine vessel automation system and assessment of the state of the ship's electrical power system with their help, starting the propulsion electrical installation, providing energy to the operating propulsion electrical installation under load changes, switching on the backup diesel generator unit for parallel operation with the network in normal and emergency modes, acceleration of the vessel to the given speed in accordance with the tasks of the instructor. The advantages of training programming using the e-Tutor computer module are the ability to use the simulator in self-study mode with automated teaching support.

Medium-term scheduling of operating states in electric power systems

This article presents a mathematical model for the medium-term scheduling of the operating states of electric power systems. The scheduling period is divided into several time intervals. The model can be used to determine the equilibrium state in which each supplier earns maximum profit from supplying electricity to the wholesale market. We estimated the maximum value of public welfare, which indicates the total financial gains of suppliers and consumers, to determine the prices at the nodes of the power system. This was done by considering the balance constraints at the nodes of the power system and constraints on the allowable values of generation, power flows, and volumes of energy resources consumed over several time intervals. This problem belongs to the class of bi-level Stackelberg game-theoretic models with several leaders. The market equilibrium is modeled simultaneously in several intervals, given the multiplicity and duration of interactions. We considered two approaches for solving the multi-interval equilibrium state problem. The first approach involved directly solving a system of joint optimality conditions for electricity suppliers and consumers. The second approach involved iterative searches until the equilibrium state was reached. This article presents the results of medium-term scheduling using a case study of a simplified real-world power system.

Development of a methodology for assessing the adequacy of electric power systems

This study presents the results of a research into the developing a methodology for assessing the adequacy of advanced electric power systems characterized by the integration of various innovative technologies, which complicates their analysis. The methodology development is aimed at solving two main problems: 1) increase the adequacy of modeling the processes that occur in the electric power system and 2) enhance the computational efficiency of the adequacy assessment methodology. This study proposes a new mathematical model to minimize the power shortage and enhance the adequacy of modeling the processes. The model considers quadratic power transmission losses and network coefficients. The computational efficiency of the adequacy assessment methodology is enhanced using efficient random- number generators to form the calculated states of electric power systems and machine learning methods to assess power shortages and other reliability characteristics in the calculated states.

A GIC Estimator for Electric Grid Monitoring During Geomagnetic Disturbances

Electric power system state estimation is a vital part of real-time monitoring and management for power grid operations and control. With increased interest in the effects of geomagnetically induced currents (GICs) and effective mitigation strategies therein, similar tools that provide situational awareness during GMD events are increasingly necessary. Therefore, a GIC estimator is developed by relating the known system parameters and measurements to the current “GIC state,” from which the GIC currents in transformers can be quickly calculated. Due to the linearity of GIC relationships, the estimator solves quickly and efficiently. The proposed methods are tested on a small example and a synthetic Texas 2000 bus scenario. These studies show the effectiveness of a GIC estimator to provide situational awareness for real-time operations, as well as sensitivity to measurement uncertainty and availability.

Математическая модель асинхронного генератора в задаче оценивания состояния электроэнергетической системы

Математическая модель асинхронного генератора в задаче оценивания состояния электроэнергетической системы

Use of phasor measurements for modeling FACTS at the electric power system state estimation

As the Smart Grid is being developed in the Russian Federation, a need in FACTS arises at resolving the issues of increasing the transmission capacity of electric power lines, voltage control, etc. The paper considers the issues of installing phasor measurement units (PMU) at the FACTS for the electric power system state estimation of (EPS SE). The relevance of the paper is explained by the mandatory introduction of PMUs at newly commissioned power engineering facilities (PEF) scheduled for 2020 and farther. PMUs registering voltage and current phasors at FACTS allow increasing the precision of results of the EPS SE problem and improving the electric modes at the considered PEF.

SMALL HYDROPOWER PLANTS AS A PART OF ELECTRIC POWER INDUSTRY AND ENERGY MARKET OF UKRAINE

Globalization and sustainable development necessitate joint analysis of technological and environmental trends in energy development taking into account current economic and legal provisions. Based on synthesis of the current state of Ukrainian electric power system in general and hydropower in particular, there have been underscored the importance of comprehensive approach in identifying the key tasks of building the pathway of further electric power industry development. The authors provide the characteristics of today’s organizational and technological structure of the electric power system, including the legal provisions regulating its activity. As currently there are significant legislative changes, particular attention was paid to the review of electricity market. Features of the new liberalized electricity market have been determined in comparison with the current market, and also in connection with renewable energy characteristics. Thus, amendments introduced by the new Law “On electricity market” to the green tariff policy have been detailed with the regard to incentive mechanism for development of renewable energy and liability of electricity producers operating under Green Tariff for imbalances. The article focuses on the analysis of the state and growth potential of small hydropower. Еconomic incentives are systematized according to the current legislation. Also, here is presented the potential for possible growth of small hydropower, namely, quantitative result of the exploration of the technical potential of hydropower resources of small rivers. There have been identified a number of barriers and impediments to the construction of small HPPs. Environmental restrictions to the construction of small hydropower stations have been formulated according to the accurate analysis of various branches of national legislation such as the Land Code, the Water Code, the Law “On the Nature Reserve Fund of Ukraine”, etc., as well as valid ratified documents and other key soft-low instruments at the international level. Additionally, provisions of Сustoms and Tax legislation on small hydropower regarding to incentives for technology development have been defined. Consideration of small hydropower projects successfully implemented on the territory of the country confirms the possibility of the future development of small hydropower.

Development of a mathematical model for cost distribution of maintenance and repair of electrical equipment

The research is devoted to the development of a model for cost distribution of maintenance and repair of electrical equipment when making decisions on the management of the electric power system state. The decrease in the reliability of electric power system operation, caused by the objectively existing aging of electrical equipment, requires consideration of equipment significance when planning the maintenance and repair. For this purpose, it is proposed to use the theory of fuzzy sets, Saati’s method and Boolean programming method. The result of solving the optimization problem of multicriteria analysis is a vector of the best alternatives, built on the principle of dominance. The developed algorithm of complex simulation of the electric power system state and cost distribution of maintenance and repair for making decisions on the determination of priority of electrical equipment out of service allows for effective decision­making. The results of probabilistic and statistical simulation of electric power system states using the Monte Carlo method allow us to take into account the probabilistic nature of emergency situations in the electric power system when determining its weakest elements that require priority replacement. The advantage of the proposed approach is taking into account the technical condition of electrical equipment for risk assessment of the electric power system emergency situation. A comparative analysis of ranking results of electrical equipment based on the emergency risk assessment of the electric power system confirmed the high efficiency of the planning of electric power system states when solving the problems of preventive control. The developed model will be used for further research and development of the algorithm for making effective decisions regarding the operation strategy of the electrical equipment and preventive control of the subsystem operation of the electric power system. The obtained results of complex simulation of the electric power system state and technical condition of the electrical equipment give grounds to assert the possibility of software implementation of operation risk analysis of the electric power system for power supply companies

Cyber Security-Oriented Smart Grid State Estimation

Development of Smart Grid involves the introduction of Wide Area Measurement System (WAMS), which provides the use of information, computing and digital technologies for measuring, transmitting and processing operating parameters when solving control problems. In this regard, the increased vulnerability to cyberattacks of the control system was noted. The control of Smart Grid includes monitoring, forecasting and planning of the system operation based on its Electric Power System state estimation results. Therefore, the goal of the paper is to develop a mathematical instrument to bad data detection under cyberattacks. Particular attention is paid to false data injection attacks which result in distortion of state variables estimates. The result of the research is an algorithm developed for state estimation based on the interior point method and test equation obtained by Crout matrix decomposition. The obtained results showed effectiveness of the algorithm in state estimation.

Generation of random states of electric power systems at assessment of their reliability by the Monte Carlo method

In this paper we deal with the problem related to effective assessment of reliability of electric power systems, particularly, the efficiency of pseudorandom number generators while using the Monte Carlo method for generation of random states of electric power systems. We consider the efficiency of four pseudorandom number generators: linear congruential generator, lagged Fibonacci generator, Mersenne Twister and Sobol sequence. The Kolmogorov-Smirnov test for analysis of randomness of sequences was used to analyze the above pseudorandom generators. Based on this analysis, it has been determined that the most random sequence may be obtained while using the Sobol sequences generator. Next, visual analysis of these pseudorandom number sequences was conducted. It showed that 2d points gained from the Sobol sequence filled the surface more evenly than other generators, where as the linear congruential generator and the lagged Fibonacci generator would form voids. In the final part of the research, the analyzed pseudo random generators were used in a model for reliability assessment using the Monte Carlo method. We modeled a three-node electric power system test scheme. As a result, the linear congruential generator and the lagged Fibonacci generator did not stabilize even after ten thousand random states of the system. These results correlate with the Kolmogorov-Smirnov test and show that these generators are barely random and unsuitable for reliability assessment using the Monte Carlo method. The Mersenne Twister and the Sobol sequence results were much better. The expected value of power shortage stabilized by the 4000th rando state with the Sobol sequence. In conclusion, we can say that the Sobol sequence is the most suitable pseudorandom generator for reliability assessment using the Monte-Carlo method than the other generators considered above.

Обнаружение некорректных данных при управлении интеллектуальной энергосистемой методами динамического оценивания состояния

Bad data detection in the initial information on the electric power system state is one of the most topical problems in the state estimation problem solution. The paper presents a method for bad data detection which is based on the analysis of the retrospective and forecasting information on state variables. The retrospective information is understood as the values of measurements and estimates taken from the previous snapshot. The forecasting information is obtained as a result of dynamic state estimation which rests on the extended Kalman filter. The suggested method demonstrates satisfactory results at measurement testing in the context of low information redundancy.

Expansion of Power System Corridors Using Tier-1 Technique for Reactive Power Compensation

This paper develops a novel strategy for the expansion of the power system corridors for the release of the embedded transmission capacity. Both theoretical and practical network models are presented with a focus on power flow studies which concentrates on the steady state or static behavior of electrical power system. The methodology involves the power flow analysis revalidation of the existing standard IEEE 14 bus system and simulation using Newton- Raphson method in both MATLAB and Powerworld simulator (PWS) environment. This paper therefore establishes that an original designed network could be modified to take more loads without building new generators or transmission lines. The expansion of the existing IEEE 14 bus network to accommodate more load involves the use of static compensators incorporated at the transmission lines. This technique is then analyzed extensively when distributed along the lines through the use of a distributed capacitors compensators, (DCC). DCC can affect significant change in power line impedance to improve the power transfer capacity of an interconnected power system. The application of the DCC on the line is the tier-1 technique. The results obtained show that by applyingthe tier-1 techniques to the transmission line, the system’s capacity will remarkably improve and the transmission line will accept extra loading.

Gain Matrix Distributed Computing Technique for Power System State Estimation

The Electric Power System State Estimation problem involves large sparse matrices. The Jacobian matrix is highly sparse in nature and the computational efforts can be enhanced by avoiding arithmetic operations resulting in ‘zero’. The researchers have introduced sparse matrix techniques so as to store only non-zero elements of the matrix and thereby reducing the huge dynamic memory requirements, which intern reduce the computational time. A few such techniques [2],[3], [4] are listed in the reference. The primary focuses of these sparse techniques are on the memory/storage space reduction. This paper elaborates a different technique to obtain the “effective operation” with the focus on the computational time and the storage space reduction. The “effective operation” can be achieved without applying conventional compact storage techniques to find the Jacobian product. A different style for multiplication of two large sparse Jacobian matrices is adopted to obtain this novel approach. As a result, computational time is reduced and also Jacobian array size is reduced form two dimensional array to single dimensional array. The solution gives scope for distributed/parallel computing without disturbing the network structure [6].

Preventive Control of Electric Power System State Variables by the Methods of Probabilistic Load Flow

The paper considers a technique for selection of preventive control actions to provide feasibility of electric power system (EPS) state variables, taking into consideration random character of variation in loads. The authors suggest that sensor variables in EPS should be identified and potential ranges of change in their values be estimated from the analysis of their numerical and probabilistic characteristics that are obtained by the analytical methods of probabilistic load flow.

Impact of PMU Measurement Buffer Length on State Estimation and its Optimization

State estimation is applied to process a set of measurements which are assumed to be taken at the same snapshot in time to obtain a best estimate of the states of electric power systems. The beneficial impacts of incorporating phasor measurement unit (PMU) measurements into the state estimator have been studied by many researchers. However, the availability of PMU measurements and many practical implementation issues still need to be resolved. This paper addresses one specific and important issue when integrating PMU measurements into the state estimator, namely, determining the optimal buffer length for PMU data. Due to different reporting rates of the conventional measurements and PMU measurements, a memory buffer of PMU measurements is recommended to be processed ahead of the state estimation. The impact of PMU measurement buffer length on state estimation is discussed. A procedure to determine the optimal buffer length of PMU measurements is presented. Processing a buffer of PMU measurements is also helpful for determining the corresponding weights in state estimation.

Electric power system state estimation

This paper discusses the state of the art in electric power system state estimation. Within energy management systems, state estimation is a key function for building a network real-time model. A real-time model is a quasi-static mathematical representation of the current conditions in an interconnected power network. This model is extracted at intervals from snapshots of real-time measurements (both analog and status). The new modeling needs associated with the introduction of new control devices and the changes induced by emerging energy markets are making state estimation and its related functions more important than ever.

Identification of multiple interacting bad data via power system decomposition

This paper presents a new, highly robust bad data identification algorithm for electric power system state estimation. A system decomposition scheme is coupled with the least median of squares estimator to allow identification of multiple interacting bad data even in cases of conforming errors. The algorithm is inherently resistant to bad measurements in positions of leverage, makes no a priori measurement error probability distribution assumptions, and is applicable in a real-time environment.