Operating Conditions Of Power System Research Articles

ПОРІВНЯЛЬНИЙ АНАЛІЗ ДИНАМІЧНИХ ВЛАСТИВОСТЕЙ ГАЗОТУРБІННИХ УСТАНОВОК ШИРОКОГО ДІАПАЗОНУ ПОТУЖНОСТЕЙ ПРИ РОБОТІ НА ЧАСТКОВИХ ЕЛЕКТРИЧНИХ НАВАНТАЖЕННЯХ

Abstract. The article considers the possibility of using gas turbine power plants when operating at partial electrical loads. This option of using gas turbine plants can help reduce the unevenness between electricity production and consumption, which is associated with the difficult operating conditions of the Ukrainian power system. The main disadvantages of nuclear power plants (the main source of electricity generation in Ukraine), which do not allow them to compensate for the unevenness of the power system in transient and dynamic modes, are considered. The paper analyses the dynamic properties and characteristics of gas turbine units (GTUs) of a wide range of capacities: small, medium and large installed capacities. As a result of the modeling, it was found that the inertial properties of a gas turbine unit increase with the increase in the rated power of the unit. The paper presents the results of modeling the dynamic characteristics of gas turbine plants of small, medium and large capacity when the electrical load changes. The paper presents the transient processes of controlling the rotor speed of a gas turbine engine with optimal controller settings and settings calculated for partial loads of a gas turbine installation. The change in the quality of the transient processes of controlling the rotor speed of a gas turbine unit when operating at partial electrical loads (100%, 75%, and 50%) compared to the installed capacity is analyzed. Keywords: gas turbine installation, partial electrical load, dynamic characteristics, transient control processes, control quality indicators.

Neyman-Pearson Umbrella Algorithm-Based Static Voltage Stability Assessment With Misclassification Restriction: An Integrated Data-Driven Scheme

The online static voltage stability assessment (VSA) has attracted more attention due to the complexity of the operating conditions of modern power systems. Considering that the first category error of VSA misclassification has more serious influence than the second category error, an integrated data-driven scheme for real-time VSA with misclassification restriction is proposed to make it more practical for industrial applications. In the scheme, bagging nearest-neighbor prediction independence test (BNNPT) is employed in the feature selection process, and the Neyman-Pearson umbrella (NPU) algorithm is adopted to construct the VSA model. Benefiting from the ability of BNNPT to explore the correlations between variables, the crucial variables strongly related to the voltage stability index (VSI) are selected from the operation data. For the VSA model, an efficient model updating strategy is designed to achieve fast VSA. Moreover, the VSA model can satisfy different practical VSA demands through adjusting the first category error threshold and the circular splitting mechanism. When the PMU data is sent to the VSA model, the assessment results can be calculated by the model quickly. The tests on a 23-bus test system and a practical 7917-bus system illustrate the satisfactory performance.

A Distributionally Robust Defender-Attacker-Defender Model for Resilience Enhancement of Power Systems Against Malicious Cyberattacks

The cybersecurity of electric power grids is emerging as a critical challenge for the power industry in the transformation of modern power systems towards the future smart grid. It is of great importance to enhance the resilience of power systems against potential cyber threats. In this paper, a distributionally robust recovery resource allocation method based on the tri-level defender-attacker-defender (D-A-D) model is proposed to enhance the resilience of power systems in the face of malicious cyberattacks. The proposed recovery resource allocation method is able to optimally distribute the recovery resources among the substations in the grid to mitigate the impacts of successful cyberattacks during the recovery process, and improve the resilience performance of power systems against the attacks. In the proposed model, the recovery resource represents the available resource that can be expended to support the recovery efforts of the cyber-physical power system. It is expected to accelerate the recovery process of the system after successful attacks. The recovery resource may include necessary software, hardware, and labor force. Meanwhile, a distributionally robust optimization (DRO) model is proposed to address the uncertainty of the power system operation conditions, e.g., renewable energy resources (RESs). In order to verify the proposed system defense method, case studies were conducted on the IEEE Reliability Test System RTS-79. The results of the case studies show that the proposed method can provide a robust solution to recovery resource allocation for mitigating the risk of potential cyberattacks.

State Identification of Power System Based on Visual Geometric Group Network

Accurate and timely identification of power system operation states are requisite for taking control strategies and ensuring power system security. With the high grid penetration of renewable energy generation and wide application of power electronic equipment, power system is becoming more complicated and facing more uncertainties. Traditional model based power system state identification methods are faced with challenges and data driven methods have great prospects. This paper proposes a new data-driven method for identifying power system operating states based on visual geometric group network VGG16 (16-layer Visual Geometry Group (VGG16). Firstly, the Lasso algorithm is used to select the operating state features of the power system to realize the features dimensionality reduction. Then, the weights of the loss functions are adjusted according to the difficulty of model for identifying power system states. Finally, the IEEE39 node system is used as an example, the effectiveness and robustness of the proposed method is verified.

An online power system transient stability assessment method based on graph neural network and central moment discrepancy

The increasing penetration of renewable energy introduces more uncertainties and creates more fluctuations in power systems. Conventional offline time-domain simulation-based stability assessment methods may no longer be able to face changing operating conditions. In this work, a graph neural network-based online transient stability assessment framework is proposed, which can interactively work with conventional methods to provide assessment results. The proposed framework consists of a feature preprocessing module, multiple physics-informed neural networks, and an online updating scheme with transfer learning and central moment discrepancy. The t-distributed stochastic neighbor embedding is used to virtualize the effectiveness of the proposed framework. The IEEE 16-machine 68-bus system is used for case studies. The results show that the proposed method can achieve accurate online transient stability assessment under changing operating conditions of power systems.

Mitigating Subsynchronous Oscillation Using Model-Free Adaptive Control of DFIGs

Incorporating subsynchronous damping controllers (SSDCs) into the control loops of doubly-fed induction generators (DFIGs) is one of the most cost-effective methods to suppress subsynchronous oscillation (SSO). However, it is difficult to achieve satisfactory control performance of SSDCs due to the time-varying structure and operating conditions of wind integrated power systems. In this paper, an improved model-free adaptive control (MFAC) method is developed for DFIGs to overcome the limitations of conventional methods for SSO mitigation. First, the structure of the MFAC-based SSDC (M-SSDC) for DFIGs is designed. Then, an improved MFAC predictive control algorithm is proposed to achieve SSO mitigation. Moreover, the stability of the closed-loop system with the proposed M-SSDC is analyzed, which provides some guidelines for determining controller parameters. Additionally, the input-output signal pair of M-SSDC is selected by using geometric measure for the optimum damping performance. Case studies under various operating conditions of the test power system validate the effectiveness of the proposed M-SSDC as well as its superior damping performance over conventional approaches.

A New Adaptive Switching Control Method for the Complex Operating Condition of the Grid-Connected Power System

Due to the increase in wind power accessing power system in recent years, the low-frequency oscillation of power system is more complicated. Therefore, a new adaptive switching control method combined with a nonlinear system is proposed for the complex operating conditions of the grid-connected power system. Firstly, this method decomposes the low-frequency oscillation control problem of the power system into two parts, which are the linear system control and the nonlinear system control. Then, in the linear system control part, the iterative identification method is used to control this part. In the nonlinear system control part, the nonlinear term is decomposed into a combination of the previous beating measurable and the nonlinear estimation increment. Secondly, the nonlinear system is divided into three different operating conditions by the nonlinear estimation increment. Thus, four different adaptive controllers are obtained. Then, the designed controller is switched according to the controlled object with four controllers by the switching function, which not only ensures the stability of the closed-loop system, but also improves the control performance of the controller under different operating conditions. Finally, the feasibility and effectiveness of this method are verified, and the controller can fit the control efficiency of the real power system well.

Integration of RES-based Power Plants into the Unified Energy System of Russia: Problematic Issues and Approaches to Solving Them

Recent years have seen mass-scale integration of wind (WPP) and solar (SPP) power plants for capacities ranging from a few to several hundred megawatts into the Unified Energy System (UES) of Russia. Wind and solar power plants for a total capacity of 5.28 GW are to be commissioned by the end of 2024, and around 6.7 GW else are to be commissioned within the next decade. WPPs and SPPs are constructed in the framework of capacity supply agreements, which guarantee the return of investments in the wholesale electricity and capacity market. The aim of the study is to analyze problematic matters concerned with integrating RES into the UES of Russia and to outline a list of first-priority and further steps to be taken for securing the optimal control of power system operation modes under the conditions of a growing share of RES in the mix of generating capacities. In view of the fact that certain power districts operating within the UES of Russia have been islanded for various reasons, the problematic matters of RES integration are analyzed also for these power system operation conditions. Experience gained around the world with integrating RES into power systems is considered, and the RES introduction scales for which certain organizational and technical measures must be developed and taken are ranked. Statistical data from Russian and foreign sources on the reliability indicators of wind farms and photovoltaic systems for various capacities are given. The equipment of modern WPPs and SPPs features high availability indicators; however, it is quite frequently disconnected, which leads to load surges on the adjacent grids and the need to cover the emergency active power shortage by conventional power plants. The article suggests a list of first-priority steps to be taken for correctly integrating RES into distribution grids that will make it possible to avoid grid equipment failures and incidents involving mass-scale loss of power supply to consumers in the course of power system operation. The implementation of the plans that have been set forth will facilitate smooth integration of the growing RES capacities into the UES of Russia, while keeping the ability to control the power systems operation modes. The article substantiates the need of introducing certain amendments to the legislation, statutory, regulatory and technical documents that set out technical requirements for RES equipment, its technological connection to and operation as part of power systems. If implemented in a goal-seeking and stage-by-stage manner, the suggested measures will help rule out excessive disconnections of RES, engage them in control of power system operation modes, guarantee reliable power supply to consumers, and secure safe and reliable performance of the UES of Russia.

An adaptive time–frequency approach based secured back-up distance protection scheme for system stress conditions

Critical operating conditions of power system may lead to distance relay security issues. Distance relay back-up protection is offered with long reach setting and due to this under sudden change in voltage and current parameters during non-fault situations also considered as a fault by the protection function in an integrated network. The conventional technique fails to discriminate system stress conditions such as power swing, voltage instability and load encroachment from an actual symmetrical fault in the back-up zone (third zone (Z3)) of the distance relay. To mitigate this, a Modified Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (MCEEMDAN) technique is used to decompose the instantaneous power and to develop a reliable index. The most significant intrinsic mode function (IMFs) is extracted to estimate the energy as used as detection index. An adaptive statistical threshold selection process is developed to detect and discriminate all system stress conditions from symmetrical fault in the back-up zone of the distance relay. The proposed algorithm is a supervisory scheme for the back-up zone setting (Z3) of the distance relay. Simulation results for different stress conditions, presence of noise, sudden switching of load and symmetrical fault are presented to highlight the strength of the method.

Intelligent Design of Multi-Machine Power System Stabilizers (PSSs) Using Improved Particle Swarm Optimization

In this paper, an improved version of the particle swarm optimization algorithm is proposed for the online tuning of power system stabilizers in a standard four-machine two-area power system to mitigate local and inter-area mode oscillations. Moreover, an innovative objective function is proposed for performing the optimization, which is a weight function of two functions. The first part of fitness is the function of the angular velocity deviation of the generators, and the other part is a function based on the percentage of undershoot and maximum overshoot, and also the damping time of the power system oscillations. The performance of the proposed stabilization method is compared with the genetic algorithm and bacteria foraging algorithm results. Simulations are made in three different power system operation conditions by changing the system load. The simulation results indicate the superiority of the proposed method over the genetic algorithm and bacteria foraging algorithm. In all the scenarios, power system oscillations are damped faster and with lower amplitude when the power system stabilizers coordinate with the proposed optimization method.

Electric Vehicles Aggregation for Frequency Control of Microgrid under Various Operation Conditions Using an Optimal Coordinated Strategy

This paper presents a novel optimal coordinated strategy for frequency regulation (FR) through electric vehicles (EVs) under variable power system operation states (PSOSs). The methodology ensures a secure and economical operation of the power system through the coordination of the frequency regulation, the power of the electric vehicles and generators with multiple optimization objectives. In the normal state of operation of the power system, the battery degradation cost is taken into account and accordingly the minimum FR cost is utilized as an objective. On the other hand, for abnormal operation, the optimization objective considers the minimum frequency restoration duration. Different scenarios have been investigated to validate the proposed method. The simulation results confirm the usefulness and superior performance of the proposed optimized coordinated control strategy.

Safe Operation Conditions of Electrical Power System Considering Power Balanceability among Power Generators, Loads, and Storage Devices

The introduction of an energy storage system plays a vital role in the integration of renewable energy by keeping a stable operation and enhancing the flexibility of the power flow system, especially for an islanding microgrid which is not tied to a grid and for a self-contained microgrid which tries to stay independent from a grid as much as possible. To accommodate the effects of power fluctuations of distributed energy resources and power loads on power systems, a power flow assignment under power balance constraint is essential. However, due to power limitations of power devices, the capacity of storage devices, and power flow connections, the power balance may not be achieved. In this paper, we proposed a system characterization which describes the relation among power generators, power loads, power storage devices, and connections that must be satisfied for a system to operate by keeping SOC limitations of power storage devices. When we consider one power generator, one power load, and one power storage device connected at a single node, the generated energy by the generator minus the consumed energy by the load from some start time will increase/decrease the state of charge (SOC) for the storage device; hence, keeping SOC max/min limitations relies on whether the difference between the generated energy and the consumed energy stays within a certain range or not, which can be computed from the capacity Ess and other parameters. Our contribution in this paper is an extension and generalization of this observation to a system that consists of multiple fluctuating power generators, multiple fluctuating power loads, multiple storage devices, and connections that may not be a full connection between all devices. By carefully enumerating the connection-dependent flow paths of generated energy along the flow direction from generators to storages and loads, and enumerating the connection-dependent flow paths of consumed energy along the counter-flow direction from loads to storages and generators, we have formulated the increase/decrease of SOCs of storage devices caused by the imbalance between generated energy and consumed energy. Finally, considering the max/min limitations of SOCs and fluctuations of power generators and power loads, the conditions that the power generators and the power loads must have for SOCs of storage devices to maintain individual max/min limitations have been derived. The system characterization provides guidelines for a power flow system that can continue safe operation in the presence of power fluctuations. That is, in order for a system to have a feasible power flow assignment, there are the issues of how large the capacity of a power storage device should be, how large/small the maximum/minimum power/demand levels of the power generators and the power loads should be, and how the connection should be configured. Several examples using our system characterization are demonstrated to show the possible applications of our results.

Application Analysis of Power System Control and Fault Diagnosis Based on Ant Colony Algorithm

Due to the frequent occurrence of power system faults and the complexity of fault types, the speed of traditional control methods is slow, so a power system control method based on ant colony dispersion walk algorithm is proposed. Artificial neural network is a method and technology in machine learning, which has been widely used in engineering and technology. The problem of power system fault diagnosis is complex and changeable, so it is difficult to achieve accurate diagnosis by conventional methods. At present, the commonly used neural networks include perceptron, single-layer network, BP network and radial basis network, each of which has different characteristics and uses. BP network and radial basis network are commonly used in power system fault diagnosis. The network can be created, trained and simulated in MATLAB, and the power system fault model can also be established and simulated. According to the characteristics of the system nodes, a nonlinear control model composed of a set of differential-algebraic equations is established to establish the steady-state model of the power system structure, and the ant walk algorithm is used to solve the model and analyze the node information of the power system. The control output principle of power system power node is set to judge whether the operation state of power system is normal or not, so as to complete the design of power system control method based on ant colony dispersion walk algorithm. The experimental results show that the proposed method has fast control speed for power system faults. The synchronous frequency signal and voltage signal provided by the power system are extracted, the characteristics of the power system signal are classified, and the hidden fault location is determined by the time-frequency characteristic of the power system voltage signal. The experimental results verify the robustness of the above methods for fast hidden fault location.

DNN-Based Distributed Voltage Stability Online Monitoring Method for Large-Scale Power Grids

With the increase of power load demand and the complexity of power grid structure, the problem of voltage stability is becoming more serious, and it is urgent to study the countermeasures. In this paper, a DNN-based distributed voltage stability online monitoring method for large-scale power grids is proposed. Unlike the traditional load margin methods, the proposed method uses a local index load impedance modulus margin (LIMM) index to determine the optimal installation locations of PMUs, which is more efficiently. Moreover, the DNN is applied to learn the nonlinear relationship between the power system operation state and its corresponding LIMM. By this way, the corresponding LIMM value can be predicted through the state variables of nodes from the installed PMUs. This method can greatly improve the calculation speed of LIMM and assess the system voltage stability level in real-time, which help the system operator to judge the operating state and take measures in time. Finally, the proposed method is tested on the 14-bus system and then on the 118-bus system respectively, and the simulation results verify the effectiveness and correctness of the proposed method.

Tie-line Power Adjustment Method Based on Proximal Policy Optimization Algorithm

The tie-line power adjustment is an essential part of the power system operation state calculation. Various existing algorithms to solve the tie-line power adjustment problem are mainly implemented by introducing tie-line power equation constraints into conventional power flow calculations. Such methods have low calculation efficiency, not enough automation, and are prone to non-convergence in the power flow calculation. In this paper, the tie-line power adjustment problem is formulated as a Markov decision process, and the proximal policy optimization algorithm is introduced to optimize the decision policy. In order to enhance the effectiveness of the proposed method, a new deep neural network structure suitable for the proximal policy optimization algorithm is designed. The proposed method is verified with the IEEE 39-bus system.

Surge arrester operation investigation in low‐ and high‐current regions using MATLAB GUI: Implementing an educational goal for undergraduate students

AbstractMetal oxide surge arresters (MOSAs) have been widely used as protective devices against switching and lightning overvoltages in power electrical systems. MOSAs have dynamic characteristics in low‐ and high‐current regions. The unique model of MOSA, which is able to evaluate all the possible operating conditions of power systems, would surely be so complicated. Therefore, by the selection of an operating condition, the appropriate model of MOSA has been created. Many parameters affect MOSA performance in low‐ and high‐current regions. As a result, MOSA's performance under different operating conditions has been investigated by using a MATLAB GUI environment. To make a software environment to simulate MOSA performance in a high voltage laboratory, temperature variations influence on leakage current and its harmonic components under power frequency applied voltages and also the efficacy of impulse parameters variations on MOSA's residual voltage have been investigated. The designed graphical environment has been used by 40 electrical engineering students. The results showed that the use of the graphical environment had a great impact on the concepts' understanding of MOSA's performance.

Impact identification of DFIG model on transient security analysis in power system

With the increase of wind power penetration level, the operation state of power system is more complex and changeable. The transient security region (TSR) has unique advantages for analyzing power system with doubly fed induction generators (DFIGs) model. In this paper, the influence of wind power penetration level, location of DFIGs, and uncertainty of DFIGs on TSR are studied, and the theoretical analysis is carried out in combination with the extended equal area criterion. The results are verified on the New England 39-bus test system.

Parallel deep reinforcement learning‐based power flow state adjustment considering static stability constraint

To solve the problem of manpower and time consumption caused by power flow state adjustment in a large-scale power grid, a power system operation state adjustment method considering the static stability constraint based on parallel deep reinforcement learning is proposed. By introducing the process of adjusting the power flow state that satisfies static stability, the Markov decision-making process of adjusting power flow is constructed. Then, based on the positioning of the adjustment target, the selection of actionable devices and the calculation of the amount of action, a power flow state adjustment strategy is developed. The adjustment process is accelerated through sensitivity, transfer ratio and load margin. Then, a parallel deep reinforcement learning model is established, and it maps actions to power flow adjustment to form a pair of generator actions and realises parallel adjustment of multi-sectional objectives. In addition, the reinforcement learning strategy and the deep learning network are improved to promote learning efficiency. Finally, the New England 39-bus standard system and actual power grid are used to verify the effectiveness of the method.

Economic Analysis of a Power System Network using Optimal Placement of PMUs and DULRs Through Complete and Incomplete Observability Analysis

For power system operation and control State estimation has become essential in recent days. To evaluate this State estimation, to reach out and recorder system status Supervisory Control and Data Acquisition and Energy Management System are intended due to which are operated at specified time. Phasor Measurement Unit is one of the crucial techniques for auditing a system. To make system performance is honest, keeping Phasor Measurement Units in a system is very much helpful, and that will be intended to help operating time reduced by very much. Due to this another problem may arise, that is substation updating cost may increase due to these Phasor Measurement Units. In this paper an optimal set of Phasor Measurement Units along with optimal locations Dual Use Line Relays are achieved for complete economic analysis of a power network using both complete and incomplete observability analysis. No of Phasor Measurement Units required to attain the complete perceptibility of a network are considerably diminished using incomplete observability analysis. To test IEEE 14, 24, 30, 57 and both SR and sub systems 1, 2 with a size of 270 and 444 buses, the recommended complete and incomplete observability analysis are used to place Phasor Measurement Units. Similarly IEEE 14, 30 and 57 bus systems are considered for both Phasor Measurement Units and Dual Use Line Relays placement. Also single Phasor Measurement Unit loss condition is considered by using complete observability analysis.

Load stability analysis of Kazakhstan photovoltaic power system based on data visualization

Traditional methods can not track the load migration of Kazakhstan photovoltaic power system dynamically, and the analysis results have limitations. Therefore, this paper introduces the data visualization method to analyze the load stability of Kazakhstan photovoltaic power system. The simulation model of photovoltaic power system is built to set the model parameters. The system stability node is obtained by using the principle of power flow calculation. The load stability analysis of the system is realized by controlling the output impedance of boost converter. In order to verify the effectiveness of the proposed method, a comparative experiment was designed. The results show that this method can control the operation state of Kazakhstan photovoltaic power system, and has a good effect on the load stability analysis of the system.