Operation Of Electric Power Systems Research Articles

Toward the Prediction Level of Situation Awareness for Electric Power Systems Using CNN-LSTM Network

Situation awareness (SA) has been recognized as a critical guarantee for the stable and secure operation of electric power systems, especially under complex uncertainties after renewable energy integration. In this article, an artificial-intelligence-powered solution is presented to reach a full realization of SA covering perception, comprehension, and prediction, the last of which is more advanced but challenging and hence has not been discussed in any literature before. A novel SA model is proposed by aggregating two powerful deep learning structures: convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network. The proposed CNN-LSTM model has superiority to achieve collaborative data mining on spatiotemporal measurement data, i.e., to learn both spatial and temporal features simultaneously from phasor measurement units data. Two functional branches are designed within the SA model: a contingency locator to detect the exact fault location at present and a stability predictor to predict stability status of the system in the future. Test results have shown high performance (accuracy) of the model even on a low level of data adequacy. The proposed SA model can promisingly facilitate very fast postfault actions by the system operators to prevent the power system from any unstable operational status.

Autonomous Operation of Grid Synchronizing Breakers with Low Voltage Ride Through (LVRT) Capabilities

With rapidly increasing penetration level of renewable energy sources (RES) in electric grids, it is noteworthy that stable and reliable operation of electric power systems is urgently needed, given the inverter interfaced RES has less inertia compared to conventional rotational generation units. Meanwhile, it should be also noted that grid operation can be categorized as normal and abnormal conditions. For each type of grid operation condition, the inverter interfaces should produce reliable power generation into the grid. Especially during fault operation, low-voltage ride through (LVRT) is a critical operation condition that needs to be achieved in each interface inverter. Furthermore, when the system is operated in severe conditions, the faulty areas should be effectively isolated from the rest of the system. Considering the operation requirements in electric grids, in this paper, an autonomous operation of grid synchronizing breakers is designed and implemented. Particularly, the developed grid synchronizing breakers can autonomously detect grid operation condition by measuring the grid-connected current and grid side voltage. An extended LVRT curve is designed to support the original LVRT curve so that enhanced fault mitigation and isolation can be achieved. In the meantime, the designed circuit breaker can automatically synchronize with the external grid voltage. Therefore, transient process when breaker is turned on or off can be successfully controlled without violating the existing standards. A simulation model is implemented in MATLAB/Simulink to verify the effectiveness of the proposed method.

Networked Microgrid Operations: Supporting a Resilient Electric Power Infrastructure

The fundamental requirements for electric power-system operations are the same regardless of the system size. While specific operational implementations will change based on the measurements-regardless of whether the power system spans a continent or a single building, it is necessary to regulate the frequency and voltage, adjust generating unit outputs, and to protect equipment from electrical faults. In this way, the operational requirements of electric power systems exhibit fractal behaviors. Because of this operational characteristic, it is technically feasible to separate or segment any portion of an electric power system and operate it independently, if sufficient generating assets and control systems are present. However, the generating assets and controls necessary to implement this type of operation have not traditionally been deployed; centralized generation and control is currently the predominant operational paradigm. For example, the frequency is typically regulated at a regional level by balancing the generation at large central units with the consumption of power by end-use customers and system losses. Small changes in the frequency are used to coordinate the sharing of the load between generators on a regional level.

Analysis of Communication Time Delayed Automatic Generation Control via SMA with 2 DOF PIλDµ Controller for Interconnected Power System

Automatic generation control (AGC) is a vital process for the design and operation of electrical power systems. Quality of electrical energy is generated with effective AGC and sent to the consumers. Interconnected power systems are large and complex systems since they consist of more than one control area and they are connected to each other. Therefore, it is very difficult to control these systems. In this study, two different interconnected power systems are considered for AGC. First, an AGC system having reheat without time delay is analyzed. Secondly, an AGC system with communication time delay (CTD) is examined in order to make control analysis closer to real system. These CTDs are observed the AGC systems because of communication networks, phasor measurement units (PMUs), wide - area measurement - monitoring systems (WAMSs), supervisory control and data acquisition (SCADA) units etc. AGC becomes much more complicate and complex with the addition of CTDs to the system. Because of high flexibility and capability ratio, two degree of freedom fractional order proportional – integral - derivative (2 DOF PIλDµ) controller has been used for both reheated and time delayed power systems. A new meta heuristic Slime Mold Algorithm (SMA) is used to set of the 2 DOF PIλDµ controller parameters. SMA is based on nature of oscillation mode of slime mould and this algorithm is developed in 2020. System performances are examined in terms of settling time (for %0.005 band width), % overshoot and % undershoot for frequency deviation of each region and tie line power deviation. All results are expressed both numerically and graphically. It is clear that the results obtained with the proposed 2 DOF PIλDµ and SMA are more successful than the defined as the more realistic AGC systems in literature and also improved the system performance.

Mathematical modelling of mutual electromagnetic influences of related power transmission lines in a transition process mode

A mathematical model is obtained using the variable state method, allowing the simulation of electromagnetic processes in transmission lines and communication lines (both homogeneous and heterogeneous) taking into account the reciprocal inductive and capacitive bonds in transition and established modes. The resulting mathematical model can solve a number of important scientific and practical problems related to the design and operation of electric power systems (EEC). The model also allows for a variety of situations and tasks related, for example, to the calculation of circuit breakage, short circuit (SC) or single-phase SC in isolated neural networks. In particular, it is possible to calculate the steered voltage of the line from high-voltage or high-precision power lines. Based on this methodology, it is also proposed to develop an algorithm for the machine formation of mathematical models for the study of transition processes in complex electrical networks, where the initial data will be their parameters and the structure of graphs of the studied networks.

Evaluation of Social and Economic Benefits of Demand Response

The rapid development of demand response has realized the transformation of power system operation from isolated decision to interconnected communication, which is of great significance for improving power network flexibility, security and reliability. Also, by introducing demand response, power network investment for satisfying peak load could be delayed, while renewable energy consumption and power network economic operation could be promoted simultaneously. Based on the practice of demand response in China as well as to further encourage the application of demand response, this paper proposes a social and economic benefit evaluation model for demand response. Combined with the reality of China’s electric power system operation, social and economic benefits of demand response are scientifically evaluated. Also, capital requirements for China to promote the application of demand response is analysed and practical measures for demand response application are put forward.

Получение законов распределения вероятностей параметров установившегося режима электроэнергетических систем

Stable operation of electrical power systems is one of the crucial issues in the power industry. Current vo­lumes of electricity consumption cause the need to constantly increase the generated capacity, repeatedly modifying and complicating the original circuit. In addition to this, given the current trend towards the use of digital power systems and renewable energy sources, more and more uncertainties difficult to predict by standard mathematical methods appear. Events in the power system are deterministic, i.e. random. Thus, it is difficult to fully assess the system stability, voltage levels, currents, or possible power losses. Finding the probability distribution laws can give us an understanding of all the possible states in which an object can exist. Obtaining them is complicated by the difficulty of accounting for all the correlations between the random arguments of the source data. These laws are necessary to determine the optimal operating modes, the possibility of solving the problem of determining the optimal renewable energy sources installation locations and the required amount of generated energy in a non-deterministic way. The purpose of this article is to test the developed SIBD method for obtaining the full probabilistic characteristics. This method, unlike the Monte Carlo methods, does not use a random sample of initial data, but completely covers the studied functional dependence. The problem was solved using the provisions of probability theory and mathematical statistics, numerical optimization methods in particular. The MATLAB Matpower application package was also used to solve technical computing problems.

Adaptive Hybrid Fuzzy PI-LQR Optimal Control using Artificial Immune System via Clonal Selection for Two-Area Load Frequency Control

Load frequency control (LFC) problem has been a foremost issue in electrical power system operation and is becoming more important recently with growing size, changing structure, and complexity in interconnected power systems. In general, LFC system utilizes simple proportional integral (PI) controller. However, due to the PI control parameters are commonly adjusted based on classical or trial-error method (TEM), it is incapable of obtaining good dynamic performance for a wide range of operating conditions and various load change scenarios in a multi-area power system. This paper introduces a novel control scheme for load frequency control (LFC) using hybrid fuzzy proportional integral (fuzzy PI) and linear quadratic regulator (LQR) optimal control, where fuzzy logic control (FLC) is used to adjust the gains KP and KI of PI controller which called fuzzy PI in this paper, while the LQR optimal control method is employed to obtain the feedback gain KOP through Algebraic Riccati Equation (ARE). The merit of both control strategies is to tune their control feedback gains, which are KP, KI and KOP, regarding various system operating conditions. Artificial immune system (AIS) via clonal selection is utilized to optimize the membership function (MF) of fuzzy PI and weighting matrices Q and R of LQR optimal control in order to obtain their optimal feedback gains. To examine the efficacy of the proposed method, LFC of two-area power system model is utilized as a test system. The amalgamation of fuzzy PI-LQR is applied to improve the dynamic performance of two-area LFC. Other control schemes such as PI controller, hybrid PI controllerLQR, and hybrid fuzzy PI-LQR are also investigated to the studied a test system. The obtained simulation results show that the proposed method could compress the settling time and decrease the overshoot of LFC which is better than other approaches that are also employed to the tested system in this study.

Research on key technologies of multi-task-oriented live maintenance robots for Ultra High Voltage multi-split transmission lines

Purpose The purpose of this study is to solve the key technical problems of the practical application of electric robots. The UHV multi-split transmission line power cable operation robot is an important equipment to ensure the reliable operation of high voltage lines and is a useful exploration to realize high-quality power transmission. As the robot system platform equipment mature and operation environment gradually become more complex, the double arm coordination motion control in extreme environment becomes one of the main bottleneck for its practical in power system. Design/methodology/approach This paper summarizes the key technologies related to power cable robots, and aims at key technical indicators such as operation reliability, operation efficiency and operation quality in the robot’s practical process. The dynamic evolution mechanism of the robot’s mechanical configuration, the multi-physics information fusion algorithm in extreme environments, the robot’s autonomous positioning and its error compensation control, the robot’s robust motion control in extreme environments and the dual-arm force-position hybrid coordination control and the dynamic distribution and elimination mechanism of internal forces in the closed chain between robots and operating objects, all the research methods and solutions of the key technologies are proposed, respectively. Findings Finally, a new control architecture for power cable robots in the background of the Ubiquitous Power Internet of Things is proposed so as to manage the operation and maintenance of electric power systems. The above key technologies are a new exploration of the operation and maintenance management of EHV (Extra High Voltage) multi-split transmission lines which have laid a solid theoretical foundation for the power cable robot. Originality/value High voltage transmission line is the main channel of power transmission. It is an important means to improve the integration of operation and maintenance management of power system to use robot instead of manual inspection and maintenance of power line, in the promotion and application of electric robot. The authors pay attention to the practicability, and the breakthrough of key technologies of robot is the premise of the practicability of robot. In this paper, the robot operation and control in multi-task and complex scenes are studied. The research and implementation of the main key technologies, such as the dynamic evolution mechanism of robot configuration, the coupling and fusion law of multi physical fields in the extreme electric power environment, the autonomous positioning control of manipulator, the robust control of robot in the super electromagnetic field environment and the cooperative operation control of multi manipulator, are discussed.

Embedded Digital Twins in future energy management systems: paving the way for automated grid control

Abstract Emerging real-time applications in information technology, and operational technology enable new innovative concepts to design and operate cyber-physical systems. A promising approach, which has been discovered recently as key technology by several industries is the Digital Twin (DT) concept. A DT connects the virtual representation of a physical object, system or process by available information and sensor data streams, which allows to gather new information about the system it mirrors by applying analytic functions. Thereby the DT technology can help to fill sensor data gaps, e. g., to support anomaly detection, and to predict future operating conditions and system states. This paper discusses a dynamic power system DT as a cornerstone instance of a new generation of EMS, and a prospective new EMS architecture, to support the increasingly complex operation of electric power systems. Unlike in traditional offline power system models, the parameters are updated dynamically using measurement information from the supervisory control and data acquisition (SCADA) and a wide area monitoring system (WAMS) to tune the model. This allows to derive a highly accurate virtual representation of the mirrored physical objects. A simulation engine, the Digital Dynamic Mirror (DDM) is introduced, in order to be able to reproduce the state of a reference network in real-time. The validation of the approach is carried out by a case study. In a closed loop within EMS applications, the DDM can help to assess contingency mitigation strategies, thus it can support the decision-making process under variable system conditions. The next generation of control centre Energy Management System (EMS) can benefit from this development by augmentation of the dynamic observability, and the rise of operator situation awareness.

Active Power Control of Grid tied PV system using Fuzzy Controller

Historically, electric power system operators have seen photovoltaic (PV) power systems as potential sources of problems due to intermittency and lack of controllability. However, the flexibility of power electronic inverters allows PV to provide grid-friendly features including volt-VAR control, ramp-rate control, high-frequency power curtailment, and event ride-through. Commercially available smart PV inverters can further provide frequency down-regulation by curtailing power, but they are unable to provide true frequency regulation through active power control (APC) because they are unable to increase power on command. This paper proposes a coordinated DC-link voltage control and deloading control for two-stage PV system to offer frequency support in an islanded microgrid without energy storage system (ESS). This paper proposes a predictive fuzzy logic based PV inverter control method for very fast and accurate control of active power

Formula omitted] Multi-contingency transient stability constrained AC optimal power flow with volt/var controllers

In order to guarantee the continuous operation of electric power systems (EPS), a proper planning of these networks in steady and transient state must be performed. This paper presents an N−1 multi-contingency AC optimal power flow (OPF) that embeds, in the same mathematical optimization model, a set of transient stability constraints (TSC) that guarantee rotor angle and angular velocity variation within technical limits, at given N−1 contingencies. Furthermore, the proposed model considers the operation of volt/var controllers, such as shunt elements and OLTC transformers, to further improve the operation of the EPS, under given levels of demand and generation. Taking advantage of the classical first-order transient stability model of synchronous machines and the implicit trapezoidal integration rule, the proposed model can be formulated as a stand-alone mixed-integer nonlinear programming (MINLP) model. Then, through well-established linearization techniques, the initially proposed MINLP model is transformed into a new mixed-integer linear programming (MILP) model, which can be implemented via algebraic programming languages, such as AMPL, and solved using convex optimization solvers, such as CPLEX. Three systems with dissimilar number of synchronous machines and nodes have been used for tests (i.e., the 9-Bus/3-Generator Western System Coordinating Council (WSCC) system, the 39-Bus/10-Generator New England system and the 68-Bus/16-Generator IEEE system). The efficiency of the proposed linearization techniques and the stability requirements of the solutions have been validated using an exact AC power flow and the transient stability analysis program PSAT-Matlab. Results show the ability of the proposed MILP model to provide stable operating points under N−1 contingencies, at minimum production cost.

Gain Schedule PI Fuzzy Load Frequency Control for Two-Area Electric Power System

The use of proportional integral (PI) load frequency control (LFC) to ensure the stable and reliable operation of electric power system is practical important. Any imbalance between synchronous generators and consumption loads will cause frequency unstable within the complete power system. The purpose of the load frequency control (LFC) is to keep the power system frequency and the inter-area tie power as near equilibrium point. This article introduces a gain schedule PI fuzzy load frequency control (GLFC) applying to two area electric power system. The GLFC consists of two level control systems, where the PI controller in the conventional form and its parameters are tuned in real time by fuzzy system. A fuzzy rule base is constructed in the form set of IF-THEN that describe how to choose the PI parameters under different operating conditions. The simulation has been conducted in MATLAB Simulink package. The effectiveness of the GLFC is measured by comparison with conventional PI load frequency controller.

Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System

Electric power grids are a crucial infrastructure for the proper operation of any country and must be preserved from various threats. Detection of illegal electricity power consumption is a crucial issue for distribution system operators (DSOs). Minimizing non-technical losses is a challenging task for the smooth operation of electrical power system in order to increase electricity provider’s and nation’s revenue and to enhance the reliability of electrical power grid. The widespread popularity of smart meters enables a large volume of electricity consumption data to be collected and new artificial intelligence technologies could be applied to take advantage of these data to solve the problem of power theft more efficiently. In this study, a robust artificial intelligence algorithm adaptive neuro fuzzy inference system (ANFIS)—with many applications in many various areas—is presented in brief and applied to achieve more effective detection of electric power theft. To the best of our knowledge, there are no studies yet that involve the application of ANFIS for the detection of power theft. The proposed technique is shown that if applied properly it could achieve very high success rates in various cases of fraudulent activities originating from unauthorized energy usage.

Optimized fuzzy self-tuning PID controller design based on Tribe-DE optimization algorithm and rule weight adjustment method for load frequency control of interconnected multi-area power systems

The reliable load frequency control (LFC) is addressed as one of the most important services in the modern electric power system operation and planning. Since the power systems have various structural and non-structural uncertainties, using control methods with fixed parameters may not yield the optimal performance of the system. Thus, in this research, a novel fuzzy PID controller is introduced to LFC for interconnected multi-area power systems in parametric uncertainties as well as external disturbances existence. The proposed algorithm adjusts the scaling factors and the modal parameters of input and output membership functions as well as the weights of fuzzy PID controller rule values. According to the transient response of the area control error (ACE) variable partitioning, the fuzzy rule weight values are obtained. Furthermore, in order to enhance the quality of the response, the scaling factors and the modal parameters of the input and output membership functions of fuzzy PID controllers are optimized by Tribe-DE (TDE) algorithm. The method is examined on the two and three area interconnected power systems at several conditions and an Integral of Time multiplied Absolute Error (ITAE) less than 0.0108 as well as an absolute maximum undershoot less than 0.0210 Hz for Δf1 regulation are obtained in the two area interconnected power system. Good transient behavior, disturbance rejection capability and insensitivity to parameter changes are advantages of the proposed controller.

Multi-step least squares support vector machine modeling approach for forecasting short-term electricity demand with application

Electricity demand forecasting plays a crucial role in the operation of electrical power systems because it can provide management decisions related to load switching and power grid. Thus, there have been models developed to estimate the electricity demand. However, inaccurate demand forecasting may raise the operating cost of electric power sector, which means that it would waste considerable money. In this paper, a novel modeling framework was proposed for forecasting electricity demand. Sample entropy was developed to identify the nonlinearity and uncertainty in the original time series, after that redundant noise was removed through a decomposition technique. Besides, the most optimal modes of original series and the optimal input form of the model were determined by the feature selection method. Finally, electricity demand series can be conducted forecasting through least squares support vector machine tuned by multi-objective sine cosine optimization algorithm. The case studies of Australia demonstrated that the proposed framework can ensure high accuracy and strong stability. Thus, it can be considered as a useful tool for electricity demand forecasting.

Identification of critical variables in conventional transformers in distribution networks

Transformers are essential equipment to the operation of electrical power systems, a failure causes the lack of electricity supply to end-users, affecting the operating indicators of companies in the distribution sector. The investigation presents an identification of the faults in transformers through a fishbone diagram, an evaluation of the variables that cause the identified failure using the cross-impact matrix method and a proposal to improve the performance. The results will enable a plan to be developed for taking action with monitoring plans to avoid faults that could put the electrical asset at risk and achieve a better performance of the distribution network.

Valuing intra-day coordination of electric power and natural gas system operations

Increasing renewable energy penetrations and low natural gas prices have increased the reliance of U.S. power systems on natural gas-fired generation. Increased reliance has sparked concerns over and resulted in natural gas deliverability constraints. To mitigate these constraints, research and regulatory reform have tried to improve coordination between power and gas systems. The U.S. Federal Energy Regulatory Commission (FERC) issued Order 809 in 2015 to improve day-ahead and intra-day coordination of power and gas systems. Given little research on intra-day coordination and FERC's recent action, we quantify the value of improved intra-day coordination between gas and electric power systems. To do so, we co-simulate coordinated day-ahead, intra-day, and real-time operations of an interconnected power and natural gas test system using a dynamic natural gas simulation model and a power system optimization model. We find intra-day coordination reduces total power system production costs and natural gas deliverability constraints, yielding cost and reliability benefits. Sensitivity analysis indicates improved intra-day renewable energy forecasts and higher renewable energy capacities increase intra-day coordination benefits for gas network congestion. Our results indicate FERC Order 809 and other policies aimed at enhancing intra-day coordination between power and gas systems will likely yield cost and reliability benefits.

Laboratory setup for determining residual magnetic flux value using low voltage DC source

Residual magnetic flux can make significant problems in electric power system operation if it is not taken into consideration. The laboratory setup for determining residual magnetic flux value is presented in this paper. The experimental procedure includes only electrical measurements and quantities (voltage and current). Residual magnetic flux value is determined using a low voltage DC source for energization of an unloaded transformer. A simple model of a magnetic core coil or unloaded transformer which consists of a linear resistance in series with a nonlinear inductance is used. Various de-energization magnetic flux values are set before each measurement in order to prove validity of proposed experimental procedure independently of initial condition of magnetic flux. In total, 25 measurements are carried out. Also, the pros and cons of the laboratory setup and experimental procedure are given, as well as plans for future research.

Settings determination for numerical transformer differential protection via its detailed mathematical model

Currently, an issue of relay protection (RP) settings determination that ensures its correct operation in electric power systems (EPSs) has not been resolved completely. The main reason for this is the lack of methods and tools for adequate consideration of transients during a fault existence for RP settings calculation. In this study, a novel approach for RP settings determination is presented. Its main feature is the application of detailed mathematical models reproducing simultaneously processes in the entire set of elements in the RP scheme, including instrumental transformers, and in large-scale EPS mathematical model as a whole. This key feature can be achieved via the hybrid power system simulator - hybrid real-time power system simulator - implementing a methodically accurate analogue-digital solution of the EPS mathematical model. The numerical transformer differential protection (NTDP) based on this approach was set up via the standard dual-slope tripping characteristic. Also, a novel approach to the formation of the tripping characteristic is proposed. The key feature of this approach is applying a flexible curve that enveloping all fault characteristics I diff = f ( I rest ) of those modes in which the NTDP should not trip.