Real Electric Power System Research Articles

Analysis of a Balanced Short Circuit on a Sulbagsel Electrical System by Using The FFA-ABC Method Approach

Fault studies are an important part of electrical power system analysis. The purpose of this study was to determine the amount of line current at the point of disturbance when a three-phase balanced fault occurs in the real Sulbagsel electrical system. In this paper, a new hybrid FFA-ABC (Fruit Fly Algorithm-Artificial Bee Colony) method is proposed, which is one of the new methods used to calculate balanced three-phase short circuit currents in electric power systems, especially in the real Sulbagsel electrical system of South Sulawesi, Indonesia. The real electricity system of Sulbagsel was chosen as the research object because this system consists of 15 generators, 44 buses, 52 transmission lines, and 29 load buses with system voltages varying from 30 kV, 70 kV, 150 kV, and 275 kV so that this system is included in the complex system category. To test the effectiveness of the proposed FFA-ABC method, it was implemented on a real electric power system, namely the Sulbagsel System. In addition, it can also be applied to IEEE electrical systems or other real electric power systems. The results of the new hybrid FFA-ABC method of the balanced short circuit analysis of the Sulbagsel electrical system are then compared using the FFA method, the ABC method, and the deterministic method (in this case, the bus impedance matrix (BIM) method). The simulation results obtained show that the FFA-ABC hybrid method is able to solve the problem of balanced short circuit analysis in the Sulbagsel electrical system in South Sulawesi, Indonesia which the largest fault current occurs when the fault is close to the slack bus generator (bus 2=Sidrap) of 18.9697 p.u, and the smallest fault current occurs when the fault is farthest from the slack bus generator (bus 44=Poso) of 0.8457 p.u.

Resilient control of cyber‐physical systems using adaptive super‐twisting observer

AbstractIn this work, the problem of online secure state estimation and attack reconstruction in the face of offensives that corrupt the sensor measurements and modify the actuator commands of cyber–physical systems is investigated for designing a resilient controller for the system. The states of cyber–physical system and its actuator attacks are estimated/reconstructed online using a novel adaptive line‐by‐line super‐twisting observer, whereas sparse stealth attacks on unprotected sensors are reconstructed using a sparse recovery algorithm. The estimated attacks are used for attack compensation by a resilient controller. The efficacy of the proposed technique is illustrated via simulation on a real electric power system under deception actuator attack and stealth sensor attack.

Topology Optimization of the Network with Renewable Energy Sources Generation Based on a Modified Adapted Genetic Algorithm

The article presents an adaptive genetic algorithm developed by the authors, which makes it possible to optimize the topology of a power network with distributed generation. The optimization was based on bioinspired methods. The objects of the study were a 15-node circuit of a power net-work with photovoltaic stations and a 14-node IEEE augmented circuit with distributed generation sources (three wind farms and two photovoltaic plants). The simulation of the modes of electric power systems was performed using the Pandapower library for the Python programming language, which is in the public domain. Three types of electric load of consumers were considered, reflecting the natures of electricity consumption in the nodes of real electric power systems, the results of numerical studies were presented. The proposed genetic algorithm used two different functions of interbreeding, the function of mutation, selection of the best individuals and mass mutation (complete population renewal). At the end of each iteration of the algorithm operation, statistical dependencies were de-rived that characterized its work: the best (minimal losses) and average adaptability in the population, a list of the best individuals throughout all iterations, etc. The verification was carried out in comparison with the results obtained by a complete search of possible radial configurations of the system, and it showed that the developed genetic algorithm had fast convergence, high accuracy and was able to work correctly with different configurations of electrical circuits, generation and load structures. The algorithm can be used in conjunction with renewable energy sources generation forecasting systems for the day ahead when planning the operating modes of power units in order to minimize the costs of covering electricity losses and improve the quality of electricity supplied.

Probabilistic Prediction Intervals of Wind Speed Based on Explainable Neural Network

With the rapid growth of wind power penetration into modern power grids, wind speed forecasting plays an increasingly significant role in the planning and operation of electric power and energy systems. However, the existing wind speed forecasting methods are modeled as black boxes, which are very complicated and cannot be written down explicitly due to the complex fluctuation characteristics of wind speed series. To this end, this study proposes a novel direct method based on an explainable neural network (xNN) for deterministic and probabilistic wind speed forecasting. It can theoretically extract the nonlinear mapping features in wind speed, thereby providing a clear explanation of the relationship between the input and the output of the forecasting model. Then, the uncertainties in wind speed are statistically synthesized via the kernel density estimation method. Finally, we use wind speed data from real wind farms in Belgium to verify the feasibility and effectiveness of the proposed method. The simulation results demonstrate that it is not only able to accurately extract the non-stationary feature in the wind speed series but also superior to other benchmark algorithms in prediction accuracy. Therefore, the proposed method has a high potential for practical applications in real electric power and energy systems.

ARTIFICIAL INTELLIGENCE AND NATURE-INSPIRED OPTIMIZATION ON INTEGRATIVE CAPACITY OF RENEWABLE ENERGY IN THE WESTERN BALKAN

Two artificial intelligence models for the integration of renewable energy sources are developed within this research to contribute to the European Green Plan realization. The review of renewable energy natural potential, on one hand, and installed capacity, on the other hand, in the Western Balkans and twenty-eight European countries is done within this research, as well as emissions. The analyses show that the European countries, sometimes even with lower natural potential in renewables, have installed much more renewable capacities than the Balkans countries with much higher natural potential. According to this, the first artificial intelligence model is developed based on multi-criteria linear regression analysis. This model relies on the correlation between the relevant regressors, i.e. relevant input variables for twenty-eight European countries and the same regressors for a particular Balkans country. Its goal is to find the maximum possible integrative renewable capacity in a Balkans’s country within the real socio-economic environment. The second artificial intelligence model is developed based on multi-criteria evolution genetic algorithms. Its goal is to find the maximum possible integrative renewable capacity within a real electric power system. Nature-inspired optimization is applied. From the framework of a given large number of generations, technical combinations of the degree of renewable energy sources integration, the best populations, i.e. combinations are selected. As nature selects from many generations and allows the best to survive and punishes the „weakˮ, in our case, „weak” combinations are those failing to meet the given conditions and limitations of the real electric power system. A new methodology is offered. Theoretical general formulas are given for both models. Developed models are tested on a numerical experiment of solar energy integration in the Serbia case study. Analyses of sensitivity prove that both models are applicable for all renewable energy sources and countries or regions.

Software and Hardware Decision Support System for Operators of Electrical Power Systems

The transience and unpredictability of processes in electric power systems (EPS) determine complexity of its control. This problem is especially relevant for EPS with renewable energy sources and flexible alternating current transmission systems technologies. Automatic control systems simplify the solution of this problem. Despite this, many issues related to EPS state control are solved by power system operators. Decision support systems (DSS) are applying to increase efficiency of operators work. However, existing algorithms in practice are inoperable in some cases: unrealizable decisions are formed, or decision cannot be found at all, etc. Another important problem is impossibility to preliminarily verify the proposed solution. In this regard, operators can additionally use EPS simulators, which are usually limited either by the size of simulated scheme or by the details of equipment mathematical models. The limitations are a consequence of numerical methods using, which are badly applicable for large-scale power systems simulation. In this article, a novel DSS has been developed based on a hybrid approach to EPS modeling combining properties and capabilities of several simulation techniques: analog, physical and digital. The implicit integration of differential equations in analog way makes possible modeling faster than real time. Such opportunity allows operator to verify more decisions and select the most effective ones. The algorithm for DSS application has been developed and described in the article. The properties and capabilities of the developed DSS are confirmed by experimental studies and tests in a real EPS.

Validation of Positive-Sequence Modeling of Large-Disturbance Stability in a Distribution Network With Distributed Generation Using the Hybrid Comprehensive Simulator

One of the general trends in power industry is the penetration of distributed generation (DG) units. The ongoing transformation of electric power systems (EPS) due to this penetration leads to a significant change in the properties of power systems. The problem arises in the ensuring stability of distribution system with DG units and EPS as a whole, especially in the case of large disturbances. The main way of solving this problem is positive-sequence phasor time-domain simulation. However, the known simplifications and limitations are inevitably applied at such simulation. In this regard, the obtained simulation results need to be validated. This paper proposes an alternative approach for validation of processes calculations, based on the use of a benchmark tool instead of field data. The hybrid simulator is proposed to use as a benchmark tool, because such simulator allow to obtain comprehensive information about a single spectrum of wave, electromagnetic, electromechanical processes in power system in cases of different large disturbances. That makes possible to identify the impact of the applied simplifications and limitations in the positive-sequence simulation on the reliability of power system stability assessment in the case of large disturbances. The studies presented in this paper were carried out using the scheme of distribution system with DG units, which is part of a real EPS. The obtained results demonstrate the arising errors in stability calculations, the nature of their changing and causes of occurrence, as well as factors affecting them.

An integrated ISM fuzzy MICMAC approach for modeling and analyzing electrical power system network interdependencies

Despite the attention that the modeling and analyis of infrastructure interdependencies have received over the past two decades, only a small number of methods have been proposed for modeling interdependencies among substations (S/Ss) in electric power system (EPS) networks. For this reason, this paper proposes a novel approach that integrates interpretive structural modeling (ISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC). While the ISM provides managers with a holistic visual view of interdependencies among S/Ss, fuzzy MICMAC analysis categorizes the S/Ss in terms of their driving and dependence powers. This categorization offers an advantageous tool for decision makers to distinguish between S/Ss and their mutual associations, which enables them to identify the critical S/Ss. For demonstration, the approach is applied to a model and analyzes the independencies within real EPS S/Ss.

Research of emergency cascade processes on simulation models of test and real electric power systems

In this work, a heterogeneous electric power system (EPS) is investigated for the possible occurrence of cascade emergency processes. In the process of computational experiments, simulation models of test and real EPS were created in which of voltages of 6, 35, 110, 500 kV is presented. The calculation of the trajectories of the possible occurrence and development of a cascade process in the event of a disturbance (N-1) in the EPS has been performed. For the investigated EPS, a model of visual representation of the heterogeneous surface of the parameters that affect the possibility of the occurrence and development of cascade processes in the EPS has been created. The study carried out on the basis of the developed methods (search for paths of the emergence and development of cascade processes, visual representation of the heterogeneous surface of EPS parameters) allows projecting the results obtained for making effective management decisions to improve survivability when designing the development of real power systems.

Echo state network based ensemble approach for wind power forecasting

Accurate and reliable wind power forecasting is of great significance for the economic and safe operation of electric power and energy systems. However, due to the negative factors such as inaccurate numerical weather prediction and frequent ramping events, previous wind power prediction methods are difficult to meet actual needs for practical applications. To this end, this paper originally proposes a new wind power prediction method with high accuracy based on echo state network. This method is a hybrid of wavelet transform, echo state network and ensemble technique. Wavelet transform is used to decompose raw wind power time series data into different frequencies with better outliers and behaviors. Then, we adopt the echo state network to automatically learn the nonlinear mapping relationship between input and output in each frequency. Later, ensemble technique is applied to deal with the model misspecification and data noise problems, which are common in wind power prediction, thereby reducing the uncertainty of wind power forecasting and improving prediction accuracy. Finally, we use wind power data from real wind farms in Belgium and China to verify the feasibility and effectiveness of the proposed method. The simulation results show that the proposed method is superior to other benchmark algorithms in prediction accuracy, which indicates that the method has high potentials for practical application in real electric power and energy systems.

Hybrid Real-Time Simulator of Large-Scale Power Systems

This paper is devoted to the description of a new approach for large-scale electric power systems (EPS) simulation, so-called hybrid simulation approach. The design and research of EPS require solving a large number of non-trivial tasks. Due to the well-known EPS specificity, mathematical modeling is the only way to proceed. According to the hybrid approach, several simulation techniques (analogue, digital, and physical) are used to achieve the best results. An analogue part provides the elimination of methodological integration error, which appears in a case of numerical methods usage. A digital part allows the realization of control and parameter changing algorithms on software level. In a physical part, the connection and switching of modeled elements are provided in a way as in real EPS. In addition, the paper presents hardware features of the simulator—hybrid real-time power system simulator (HRTSim), which is an alternative to the digital simulators currently used. The important part of this paper is the result of comparative analysis of transient processes oscillograms obtained by HRTSim and real disturbance recorders in Tomsk region power system, as well as HRTSim and real-time digital simulator.

Optimal Coordination of Double Primary Directional Overcurrent Relays Using a New Combinational BBO/DE Algorithm

The optimal relay coordination (ORC) problem for directional overcurrent relays (DOCRs) has been solved by many conventional and modern optimization algorithms. All these studies were based on the common assumption that all DOCRs are numerical, digital “hardware-based,” static, or electromechanical. Unfortunately, the transition between these technologies does not happen instantaneously, so protection engineers could see different models of these protective relays in some real electric power systems. Moreover, when old electromechanical, static, and digital relays are replaced with the latest state-of-the-art numerical relays, the older relays are kept as backup protective devices. Some called them “primary” and “local-backup” relays, whereas others called them “main-1” and “main-2” relays. The reason behind the second terminology is the chance that the old relays could act ahead of the numerical relays. In this paper, a realistic mathematical model of the ORC problem is formulated and solved using a new hybrid evolutionary algorithm. To judge whether this realistic ORC problem is completely/partially solvable or not, the IEEE 6-bus, 15-bus, and 42-bus test systems are simulated. The results prove that the technique is an effective tool to indicate which relay sets accept/do not accept this double primary relay strategy.

Stand-alone power supply system with DC photo-diesel source

An object of research is a diesel power supply system of the decentralized village of Tokma, Irkutsk Region, Russia. The objective is to increase the contribution of the PV part of a photo-diesel electrical system operating in parallel with a diesel power plant to a stand-alone distribution network of DC compared to parallel PV and diesel stations operating on AC; quality improvement and voltage stabilization in the electrical network; cost reduction of generated electricity. Optimization of the characteristics of a stand-alone power supply system at a DC is a difficult task, due to the impossibility of conducting tests in a real electric power system, and the use of physical modeling because of its extreme complexity. Given this fact, the task is to design software and hardware tools for optimizing the parameters of a DC photo-diesel power system. The developed models use MS Excel, MatLab/Simulink software packages, as well as weather data bases. A software tool has been developed that allows for simulation modeling of operating modes of photo-diesel power supply systems, generation, consumption and insolation level of a decentralized consumer, as well as determining rational technical and economic parameters; criteria of expediency and efficiency of constructing photo-diesel electric systems with DC have been identified. As a result, the level of voltage drop is reduced compared to the AC power supply system, the cost of 1 kWh of electrical energy is lower than in the AC power supply system, and the negative environmental impact is reduced.

The Hybrid Real-Time Dispatcher Training Simulator: Basic Approach, Software-Hardware Structure and Case Study

AbstractThe article presents the Hybrid Real-Time Dispatcher Training Simulator (HRTDTS). The main advantages of this simulator are the adequate simulation of a single spectrum of processes in the object for training – any real electric power system (EPS), as well as the real-time control of the equipment circuit-mode states in modes of the EPS operation. This is achieved by the HRTDTS development within the concept of hybrid simulation, which allows to surpass the modern widely-used simulators based purely on numerical simulation, which do not comprehensively reproduce processes in the EPS and limit the training of certain dispatchers skills. The dispatcher simulator structure and the developed specialized HRTDTS software are demonstrated. The developed dynamic dashboards for monitoring and operating, implementing the circuit-mode states visualization of the equipment or the EPS districts that correspond to specific objects for training, are presented. The special panels that display extended information about the processes in the EPS are also shown in the article. The comparison of simulation results with the data, obtained via RTDS, for the HRTDTS validation was carried out. The practical emergency scenario for the training of dispatching personnel, including the assignment of an emergency situation and the actions of dispatchers to eliminate it, was created for testing and demonstration of the HRTDTS capabilities.

Detailed simulation of distance protection for its testing and setting

Abstract A significant part of severe accidents (blackouts) in electric power systems (EPS) is associated with incorrect operation of relay protection and automation (RPA). One of the main reasons for the incorrect actions of the RPA devices is its rough settings, which often does not correspond to the real operating conditions for specific device. An analysis of currently used methods and tools for RPA setting up, shown that they are largely relied on the guidelines of previous decennaries. Respectively modern techniques have the same drawbacks associated with accounting the processes in specific RPA and primary transducers and its errors by approximate coefficients. It is possible to solve the indicated problem with a highly detailed analysis of the operation of key elements of RPA schemes in the specific operating conditions. The obtained results allow to estimate the processes in protected objects, processing errors in instrumental current (ICT) and voltage (IVT) transformers, as well as in RPA itself. Such possibility could be achieved by the detailed RPA mathematical modeling. The combination of an adequate EPS simulator and RPA models allows configuring parameters of the RPA settings ensuring its correct operation in real EPS. The article presents result of this research for distance protection

A fuzzy-stochastic power system planning model: Reflection of dual objectives and dual uncertainties

In this study, a fuzzy stochastic dynamic fractional programming (FSDFP) method is proposed for supporting sustainable management of electric power system (EPS) under dual uncertainties. As an improvement upon the mixed-integer linear fractional programming, FSDFP can not only tackle multi-objective issues effectively without setting weights, but also can deal with uncertain parameters which have both stochastic and fuzzy characteristics. Thus, the developed method can help provide valuable information for supporting capacity-expansion planning and in-depth policy analysis of EPS management problems. For demonstrating these advantages, FSDFP has been applied to a case study of a typical regional EPS planning, where the decision makers have to deal with conflicts between economic development that maximizes the system profit and environmental protection that minimizes the carbon dioxide emissions. The obtained results can be analyzed to generate several decision alternatives, and can then help decision makers make suitable decisions under different input scenarios. Furthermore, comparisons of the solution from FSDFP method with that from fuzzy stochastic dynamic linear programming, linear fractional programming and dynamic stochastic fractional programming methods are undertaken. The contrastive analysis reveals that FSDFP is a more effective approach that can better characterize the complexities and uncertainties of real EPS management problems.

Real-time comprehensive simulation of electric power systems for the task of overvoltages value determination

For a correct determining of the overvoltage limiters characteristics, as well as the optimal location of its installation is required to use reliable information about the primary electrical quantities, especially voltage level, in different operation modes of electric power systems (EPS). This problem is highly complex, due to the danger of the experiments in real EPS. Methods and tools for calculation and researches of quasi-stationary and switching overvoltages which are applied to solve this problem not always allow to obtain satisfactory results. The developed tools of comprehensive real-time simulation of electric power systems, providing adequate reproduction of processes associated with overvoltages in EPS, are presented in the article. Fragments of the researches of switching overvoltage in the electrical network of the real power system are given.

Using Controlled Shunt Reactors for Voltage Stabilization on the Example of Real Electric Power System

The article is devoted to actual task of real-time simulation controlled shunt reactors to use in the appropriate electric power system. Such development allows fully and reliably reproducing the processes running in controlled shunt reactors and electric power systems as whole. As an example of such task solution the working results of controlled shunt reactors simulation and its application for voltage stabilization are presented.

Synthesis of a hybrid model of the VSC FACTS devices and HVDC technologies

The motivation of the presented research is based on the need for development of new methods and tools for adequate simulation of FACTS devices and HVDC systems as part of real electric power systems (EPS). The Research object: An alternative hybrid approach for synthesizing VSC-FACTS and -HVDC hybrid model is proposed. The results: the VSC- FACTS and -HVDC hybrid model is designed in accordance with the presented concepts of hybrid simulation. The developed model allows us to carry out adequate simulation in real time of all the processes in HVDC, FACTS devices and EPS as a whole without any decomposition and limitation on their duration, and also use the developed tool for effective solution of a design, operational and research tasks of EPS containing such devices.

Effective Data Compression Model for On-Line Power System Applications

The main objective of this paper is to develop an efficient data compression model for online power system applications such as load flow studies, state estimation, contingency analysis etc and to calculate the round trip-time taken for sending the compressed data in client/ server architecture. Martin Burtscher algorithm is used for data compression since most of the power system data is expressed in per unit representation which is in floating point format. Many research works have been reported for representing and solving power system problems in distributed environments which include RMI, Component based, SOA and Grid computing. As the size of the power system is growing large and large due to increase in demand and the interconnections between large power systems may vary time to time due to addition of new generating units and due to geographic conditions, it becomes difficult to estimate the current operating states of the real time electric power system network and data communications between the network becomes difficult. The proposed method of power system data compression finds faster rate of data communications where the data is required for real - time analysis in a distributed environment. 