Operating Conditions Of Power System Research Articles

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.

Adaptive differential protection scheme for wind farm integrated power network

In this paper, an adaptive differential protection scheme is proposed for wind farm integrated power network. Any fixed threshold setting based protection scheme is not always suitable for the different operating conditions of power system. Considering the possible modulations in the fault signal, a particle swarm optimization (PSO) based differential relay algorithm is proposed in this paper. The positive sequence current differential principle is used. For different operating conditions, fault conditions and configurations of the system, the threshold value can be calculated online. So, practically the method can be applied to any power system model and the impact of dynamic operation of wind farm on the protection function is negligible. The threshold setting is designed considering the fault location and fault inception angle. Initially, the most effective faults among all the possible faults are identified with a single variable (fault location) objective function. Later, only single-phase-to-ground fault is considered for threshold setting with all possible parameters. The method is tested using a standard power system model integrated with wind farm and simulated using MATLAB R2016a software. Results prove the efficacy of the method.

Monitoring Technology of Power System Operation State Based on Stereo Sensing

In recent years, the development of computer technology, automation control technology and communication technology has provided new hardware and software support for the implementation of distribution network operation monitoring. In the daily production of the oil field, the reliability and stability of the power supply are the key links to ensure the completion of the production tasks of the oil field. This paper studies the problem of scene depth reconstruction in panoramic technology. On this basis, the panoramic depth map is combined with the panoramic image, and the three-dimensional panoramic characteristics are reconstructed from the visual and environmental aspects of the stereo perception model suitable for its performance. This paper develops an oilfield distribution network operation monitoring system based on stereo perception, and develops corresponding system management software. This paper first determines the mathematical model of the distribution network line loss calculation, and introduces the principle of GPRS technology to formulate a specific network plan for distribution network operation monitoring. Finally, based on the terminal data collection method of CDD-IDT / GPRS, the development of terminal system management software is finally completed. Field test results show that the stereoscopic perception-based oil field distribution network operation monitoring system developed in this paper has high accuracy of accident prediction, strong real-time performance, stable and reliable operation, and the average transmission time of each data in the stereoscopic perception based monitoring network transmission It is 1.88 seconds, which can be displayed immediately after pre-loading.

Online Detection of Events With Low-Quality Synchrophasor Measurements Based on $i$Forest

In this article, we propose an online data-driven approach that leverages the isolation mechanism for fast event detection with low-quality data measurement. The proposed adaptive and online isolation forest ( $i$ Forest)-based detection (AOIFD) method adopts a hierarchical subspace feature selection scheme to design two levels of detectors. As such, it is capable of differentiating events from low-quality data measurements, preventing false alarms in the presence of low-quality data measurements. We further propose a data augmentation method to address the training data imbalance, which is caused by the rare occurrence of events. Moreover, we propose an adaptive training process to update the AOIFD method so that it can adapt to the time-varying operating conditions of power systems. The proposed AOIFD algorithm is practical in the sense that it is a fast-response method that requires no system modeling information and no global communications. Case studies with both synthetic and realistic PMU data are conducted to validate the effectiveness of the proposed method.

Realization of A Knowledge-based Intelligent System for Power Dispatching Plan Management

With the expanding of power grid scale in Chinese metropolis, the task intensity of power dispatchers increases rapidly in regulation of the power system operation structure and states to deal with everyday scheduled maintenance. In this paper, we propose a knowledge-based intelligent system developed to deal with daily management of the power dispatching plans. The system will analyse all the operation state changing tasks arranged for the next day and group the plans according to their association. It will automatically check the security of each power dispatching plan and generate the corresponding dispatching-order tickets. The proposed system builds up power grid ontology knowledge and first-order logic rules and integrates techniques of knowledge reasoning, natural language understanding and network topology analysis. Application shows that it can effectively realize the day-ahead power dispatching plan management (PDPM) instead of the human dispatchers.

A Self-Adaptive Contractive Algorithm for Enhanced Dynamic Phasor Estimation

In this paper, a self-adaptive contractive (SAC) algorithm is proposed for enhanced dynamic phasor estimation in the diverse operating conditions of modern power systems. At a high-level, the method is composed of three stages: parameter shifting, filtering and parameter unshifting. The goal of the first stage is to transform the input signal phasor so that it is approximately mapped to nominal conditions. The second stage provides estimates of the phasor, frequency, rate of change of frequency (ROCOF), damping and rate of change of damping (ROCOD) of the parameter shifted phasor by using a differentiator filter bank (DFB). The final stage recovers the original signal phasor parameters while rejecting misleading estimates. The most important features of the algorithm are that it offers convergence guarantees in a set of desired conditions, and also great harmonic rejection. Numerical examples, including the IEEE C37.118.1 standard tests with realistic noise levels, as well as fault conditions, validate the proposed algorithm.

Real-Time Application of an Indirect Power-System Contingency Screening Method Based on Adaptive PCA

This paper presents a real-time application of a data-mining method with the purpose of indirect contingency screening, by rapid recognition of hazardous, reoccurring power-system operating conditions. The method, suitable for real-time applications, is demonstrated using the Slovenian power-system model for first-swing stability issues. The presented demonstration is segmented into a few parts. First, a database containing a set of pre-fault operating conditions (represented by a measurement matrix) and critical clearing times corresponding to several contingencies is constructed. Measurements matrix from the database is decomposed using the principal component analysis method and represented in a coordinate system defined by the principal components as dense clusters of points. Consequentially, monitoring real-time steady-state conditions in terms of seeking similarity to existing database clusters is facilitated (global screening). This is established by identifying the shortest Euclidean distance metric in the mentioned coordinate system. Finally, a second-stage (local) screening is applied in order to adaptively account for the fluctuating sensitivity of contingencies to variations in pre-fault operating conditions. In this manner, an accurate indication of each contingency impact is provided rapidly as long as a similar operating state exists within the database. Otherwise, the case is thoroughly investigated and added to the database as a new entry. This approach is applicable to a wide spectrum of dynamic problems, considering that problem-relevant sets of input data are available.

Multi-class support vector machines for static security assessment of power system

Abstract Accurate determination of present operating state of power system takes utmost importance for utility operator for reliable and secure operation of power system. Conventional power flow methods have the limitations of larger memory requirement, longer computational time and infeasible options for real time applications for security assessment. Moreover, using performance index based on bus voltage deviations and line loadings for security assessment suffers with masking problem and fails to discriminate the contingency cases with the closer violations. Hence, composite security index has been formulated in this work addressing hyper-ellipse encompassed within a hyper-box concept to address multiclass classification problem of power system security using support vector machine (SVM). Sequential forward selection has been implemented for optimal feature selections to achieve the highest classification accuracy and the least mis-classification rate. The results of the proposed method have been validated and compared with existing method for three IEEE standard test systems.

Distributed Framework for Detecting PMU Data Manipulation Attacks With Deep Autoencoders

Phasor measurement unit (PMU) data manipulation attacks (PDMAs) may blind the control centers to the real-time operating conditions of power systems. Detecting these attacks accurately is essential to ensure the normal operation of power system monitoring and control. Using long-term accumulated historical PMU measurements to train a machine learning model to detect PDMAs has shown promising results. In this paper, deep-autoencoder-based anomaly measurers are deployed throughout the power system to build a distributed PDMA detection framework. The architecture of a deep autoencoder and its training process are introduced. How to convert the historical PMU measurements into data samples for learning is also elaborated upon. Once trained, an anomaly measurer can assess the PDMA existence possibility of the new PMU measurements. By integrating the results of different anomaly measurers, the proposed distributed PDMA detection framework can detect PDMAs in the whole power system. The effectiveness and detection performance of the framework are discussed through experiments.

Full theoretical analysis with simulation-based verification of thyristors-bridge-type SFCL operation modes

The paper presents a full theoretical analysis of thyristors-bridge-type superconducting fault current limiter (SFCL) operational modes, supported by MATLAB/SIMULINK-based simulations. Both normal and fault current operation conditions of a SFCL-based power system under continuous conduction mode (CCM) and discontinuous conduction mode (DCM) are treated. The developed equations are shown to be valid for all possible thyristor-firing angles. Derived analytical relations are useful for both comprehensive design of thyristors-bridge-type SFCLs and analysis of circuit simulator based outcomes.

탐라해상풍력발전단지의 확률적 시뮬레이션 방법을 이용한 Capacity Credit의 평가

This study is described an algorithm for probabilistic production cost credit evaluation of wind turbine generators (WTG) with multi-state and aims to analysis the reliability of wind power generator in Jeju Island power system in order to maintain the operational state of power system at the reliability level. An analysis on generated capacity of wind power generator with Tamla offshore wind farm, which owned by KOEN(Korea South-East Power Co.Ltd.) and without Tamla offshore wind farm has been done in terms of Effective Load Carrying Capability(ELCC) and Capacity Credit(C.C.) in power system from the perspective of Loss of Load Expectation(LOLE). Case studies in this paper demonstrates how it changes the ELCC and C.C. according to the changing of wind speed in the Tamla offshore wind farm.

A Contribution Analysis of Tamla Offshore Wind Farm by Using of Probability Simulation Method

Abstract This study aims to analyze the reliability of wind power generator in Jeju Island power system in order to maintain the operational state of power system at the reliability levels. It is also described an algorithm for probabilistic production cost credit evaluation of Wind Turbine Generators (WTG). An analysis on generated capacity of wind power generator with Tamla offshore wind farm according to two different average wind speeds such as 11.8 [m/s] and 12.8 [m/s], and without Tamla offshore wind farm have been done in terms of Effective Load Carrying Capability (ELCC) and Capacity Credit (C.C.) in power system from the perspective of Loss of Load Expectation (LOLE). According to the changing of wind speed in the Tamla offshore wind farm, this paper demonstrates how it changes the ELCC and C.C. shown as case studies.

Deep Reinforcement Learning-Based Tie-Line Power Adjustment Method for Power System Operation State Calculation

Operation state calculation (OSC) provides safe operating boundaries for power systems. The operators rely on the software-aid OSC results to dispatch the generators for grid control. Currently, the OSC workload has increased dramatically, as the power grid structure expands rapidly to mitigate renewable source integration. However, the OSC is processed with a lot of manual interventions in most dispatching centers, which makes the OSC error-prone and personnel-experience oriented. Therefore, it is crucial to upgrade the current OSC in an automatic mode for efficiency and quality improvements. An essential process in the OSC is the tie-line power (TP) adjustment. In this paper, a new TP adjustment method is proposed using an adaptive mapping strategy and a Markov Decision Process (MDP) formulation. Then, a model-free deep reinforcement learning (DRL) algorithm is proposed to solve the formulated MDP and learn an optimal adjustment strategy. The improvement techniques of “stepwise training” and “prioritized target replay” are included to decompose the large-scale complex problems and improve the training efficiency. Finally, five experiments are conducted on the IEEE 39-bus system and an actual 2725-bus power grid of China for the effectiveness demonstration.

A novel data-driven approach for transient stability prediction of power systems considering the operational variability

Data driven methods are playing an increasingly important role in transient stability assessment, primarily because of the availability of large annotated datasets. Nevertheless, training data cannot cover all the possible operating conditions of a modern power system with variable power generations and loads. The classifier should adjust to the near-future operation condition in limited time, and this adjustment may be hindered by the computational time of the simulations and classifier training. To dramatically reduce the computational cost, this paper presents a systematic approach for building and updating an accurate transient stability classifier. First, the time-series trajectories of generators after disturbance are used as the inputs, and then a convolutional neural network (CNN) ensemble method is proposed to generate the transient stability predictor using these multi-dimensional data. To reduce the misclassification of instability, different cost weights are considered for the stable and unstable instances in the loss function. When the operating condition changes substantially and makes the pre-trained classifier unavailable, the active learning and fine-tuning techniques are integrated to update the classifier with good performance using fewer labelled instances and short computational time. The simulation results of two power systems illustrate the effectiveness of the proposed approach.

Nodal market power assessment of flexible demand resources

With the incorporation of higher shares of intermittent renewable energies (RES), more flexible resources are required in power systems to keep load balance. Under some extreme circumstances, the flexible demand resources (FDRs) may have the potential to dominate and obtain excess benefits, preventing other FDRs from participating in the electricity markets. Therefore, it is of great significance to identify the key FDR market power locations and implement some corresponding regulations. However, the relevant researches in power systems focused on the supply side, rather than the demand side. In this paper, a novel nodal market power analysis method is proposed to evaluate the potential influence of FDRs on electricity markets. Firstly, a multi-state model is established to present the multiple power system operation states including the random failures of system components. Then, the nodal market power assessment model is established under each specific state and new indices are proposed to evaluate the nodal market power of FDRs quantitatively. Furthermore, the key FDR nodes in demand side with stronger power in capturing excess revenue are identified. The 24-bus IEEE Reliability Test System is modified to demonstrate the feasibility of the proposed method. The numerical results of the proposed method are capable to display the existence of market power in demand side, and provide some valuable guidance for classification and operation of electricity markets.

Online Static Security Assessment of Power Systems Based on Lasso Algorithm

As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton–Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.

OPTIMAL POWER FLOW WITH TCSC DEVICE USING CUCKOO OPTIMIZATION ALGORITHM

Optimal Power Flow (OPF) problem is an optimization tool through which secure and economic operating conditions of power system is obtained. In recent years, Flexible AC Transmission System (FACTS) devices, have led to the development of controllers that provide controllability and flexibility for power transmission. Series FACTS devices such as Thyristor controlled series compensators (TCSC), with its ability to directly control the power flow can be very effective to power system security. Thus, integration TCSC in the OPF is one of important current problems and is a suitable method for better utilization of the existing system. This paper is applied Cuckoo Optimization Algorithm (COA) for the solution of the OPF problem of power system equipped with TCSC. The proposed approach has been examined and tested on the IEEE 30-bus system. The results presented in this paper demonstrate the potential of COA algorithm and show its effectiveness for solving the OPF problem with TCSC devices over the other evolutionary optimization techniques.

An Optimal Coordinated Method for EVs Participating in Frequency Regulation Under Different Power System Operation States

This paper proposes an optimal coordinated method for electric vehicles (EVs) participating in frequency regulation (FR) under different power system operation states (PSOSs). In the proposed methodology, the FR power of EVs and generators is coordinated with different optimization objectives for power system secure and economic operations. When a power system operates in normal state, the minimum FR cost is used as an optimization objective considering the battery degradation cost. In the abnormal state, the minimum frequency restoring time is considered in the optimization objective. Based on the optimized results in different scenarios, the output power coordinated control rule between EVs and generators is drawn. Simulations on an interconnected two-area power system have validated the superiority of the proposed optimized coordinated control strategy.

Robust Dispatch of High Wind Power-Penetrated Power Systems Against Transient Instability

High-level wind power integration can dramatically affect a power system's dynamic performance and introduce significant uncertainties to system's operation. This paper proposes a robust dispatch method to optimize the power system's operation state while sustaining its transient stability with highly variable and stochastic wind power generation. The problem is first modeled as an augmented optimal power flow model with uncertain variables and differential-algebraic equations. Then, the stability constraints are converted to approximately-equivalent algebraic equations based on one-machine-infinite-bus equivalence technique and trajectory sensitivity analysis. Next, the uncertain wind power generation is represented by a small number of strategically selected testing scenarios. Finally, a decomposition-based computation strategy is developed to divide the original model into a master problem and a series of slave problems which are solved iteratively. Using industry-grade system dynamic models and simulation software, the proposed method is tested on the New England 39-bus system and Nordic32 system, showing high performance on economic optimality, stability robustness, and computational efficiency.