Search

In this paper, feedback linearization (FBL) technique together with disturbance observer (DO) approach is proposed to mitigate the effect of the resonant frequency of grid-tied LCL filter systems. The state-feedback control law is employed to achieve stabilization of the LCL filter system under a wide range of resonant frequency variation. The DO is designed to counteract the effect of model uncertainty and unknown disturbance aiming to achieve asymptotic stability under FBL control. Specifically, the observer is designed to estimate an additional input, representing uncertainty and unknown disturbance, from measurable variables. Then, the FBL control utilizes the disturbance estimate to compensate for its effect. An interesting feature of the composite controller is its ability to meet the transient response specifications even in the presence of model uncertainty and external disturbance. The composite controller was implemented for simulation and experimental evaluation, and performance testing. High performance with respect to disturbance rejection and parameter variation has been demonstrated. [ABSTRACT FROM AUTHOR]

Taylor–Fourier (TF) filters represent a powerful tool to design phasor measurement unit (PMU) algorithms able to estimate synchrophasor, frequency, and rate of change of frequency (ROCOF). The resulting techniques are based on dynamic representations of the synchrophasor, and hence, they are particularly suitable to track the evolution of its parameters during time-varying conditions. Electrical quantities in power systems are typically three-phase and weakly unbalanced, but most PMU measurement techniques are developed by considering them as a set of three single-phase signals; on the contrary, this peculiarity can be favorably exploited to improve accuracy and reduce the computational cost. In this respect, this paper proposes to directly perform the TF expansion of the space vector (SV) samples obtained from three-phase measurements. A new paradigm allows to independently estimate positive and negative sequence synchrophasors along with system frequency and ROCOF, leveraging the three-phase characteristics. The performance of the proposed technique is assessed by using test signals inspired by the standard IEEE C37.118.1-2011, including noise as well as magnitude and phase unbalance. Achieved results highlight the flexibility of the enhanced SV-based approach, which is capable to combine excellent dynamic performance together with an accurate estimation of both positive and negative sequence components. [ABSTRACT FROM AUTHOR]

Effective monitoring and management applications on modern distribution networks (DNs) require a sound network model and the knowledge of line parameters. Network line impedances are used, among other things, for state estimation and protection relay setting. Phasor measurement units (PMUs) give synchronized voltage and current phasor measurements, referred to a common time reference (coordinated universal time). All synchrophasor measurements can thus be temporally aligned and coordinated across the network. This feature, along with high accuracy and reporting rates, could make PMUs useful for the evaluation of network parameters. However, instrument transformer behavior strongly affects the parameter estimation accuracy. In this paper, a new PMU-based iterative line parameter estimation algorithm for DNs, which includes in the estimation model systematic measurement errors, is presented. This method exploits the simultaneous measurements given by PMUs on different nodes and branches of the network. A complete analysis of uncertainty sources is also performed, allowing the evaluation of estimation uncertainty. Issues related to operating conditions, topology, and measurement uncertainty are thoroughly discussed and referenced to a realistic model of a DN to show how a full network estimator is possible. [ABSTRACT FROM AUTHOR]

Voltage sags and swells are power quality events commonly observed in power systems; however, none of the existing methods allows determining their point-on-wave inception and recovery instants (and consequently, their duration) accurately in all cases. The primary goal of this paper is to determine these instants with little or no delay, by calculating the absolute rms voltage difference between two adjacent sliding windows. The proposed method is based on the assumption that this difference is maximum when the sample under analysis corresponds to either the inception or the recovery instant. This method is valid for both sag and swell events, with or without transients. Evaluation of the proposed method performance for different types of events shows that it is robust and highly accurate in determining the inception and recovery instants. The estimation error for the majority of the events analyzed is either zero or one sample (each sample corresponds to a phase angle difference of 2.81 $^\circ$), while the worst performance is three samples. [ABSTRACT FROM AUTHOR]

Linearized models of power systems are often desirable to formulate tractable control and optimization problems that still reflect real-world physics adequately under various operating conditions. In this paper, we propose an approach that can make use of known data concerning the distribution of demand, and/or intermittent supply, to minimize expected model inconsistency with respect to the original non-linear model. The optimal linearization is obtained by approximating a generalized moment problem with a hierarchy of sparse semi-definite relaxations. The output is a linearization point that minimizes the expected absolute constraint violation with respect to the uncertain supply and demand, under optimal power flow solutions. Numerical results for different power systems demonstrate the accuracy and scalability of our linearization method. [ABSTRACT FROM AUTHOR]

This paper examines the operation of distribution networks that have large aggregations of thermostatically controlled loads (TCLs) providing frequency regulation to the bulk power system. Specifically, we assess the prevalence of distribution network constraint violations, such as over- or under-voltages and overloading of transformers. Our goal is to determine the set of constraints that are at increased risk of being violated when TCLs provide regulation. We compare network operation in two cases: first, with TCLs operating freely; and second, with TCLs controlled to track a regulation signal. Using GridLAB-D, we run power flow simulations of five real distribution networks. Our results indicate that voltage limits are at increased risk of violation when TCLs provide regulation because of increased voltage variation. Effects on transformer aging are more nuanced and depend on the method used for dispatching TCLs. Overall, we find that, for the networks studied, the set of constraints at risk of violation is a small subset of all network constraints. Therefore, network-protecting load control algorithms may only need to consider a small set of constraints, enhancing their computational efficiency. [ABSTRACT FROM AUTHOR]

This paper develops a computationally efficient method to bound the impact of multiple uncertain parameters in a dynamic load model. Load model trajectory sensitivity is first conducted on regional dynamics only (e.g., a large industrial load bus and its model for adequately representing the bus voltage dynamics) to identify critical and correlated load parameters. Systemwide trajectory sensitivity on the entire power system model is then evaluated for this reduced set of parameters, and finally, the impact of multiple uncertain parameters on the representation of power system dynamics is bounded. To reinforce this reasoning, we elaborate on the conceptual meaning of the load model trajectory sensitivity, its implication, and its applicability to the entire power grid analysis. This research also develops a fluctuation index of trajectory sensitivity to effectively rank and select the model parameters based on the impact of their perturbations on the system's dynamic performance. Case studies for the Korean power system demonstrate the validity and efficacy of the developed methods for adequately bounding the uncertainty impacts with reference to the comprehensive time-domain dynamic simulation approach. [ABSTRACT FROM AUTHOR]

Since forced oscillations are exogenous to dynamic power system models, the models by themselves cannot predict when or where a forced oscillation will occur. Locating the sources of these oscillations, therefore, is a challenging problem which requires analytical methods capable of using real time power system data to trace an observed oscillation back to its source. The difficulty of this problem is exacerbated by the fact that the parameters associated with a given power system model can range from slightly uncertain to entirely unknown. In this paper, a Bayesian framework, via a two-stage maximum a posteriori optimization routine, is employed in order to locate the most probable source of a forced oscillation given an uncertain prior model. The approach leverages an equivalent circuit representation of the system in the frequency domain and employs a numerical procedure, which makes the problem suitable for real time application. The derived framework lends itself to successful performance in the presence of phasor measurement unit measurement noise, high generator parameter uncertainty, and multiple forced oscillations occurring simultaneously. The approach is tested on a four-bus system with a single forced oscillation source and on the WECC 179-bus system with multiple oscillation sources. [ABSTRACT FROM AUTHOR]

This paper proposes a decentralized control based on static var compensator (SVC) devices to improve the frequency control of high voltage transmission systems. For a realistic structure preserving power system with voltage dependent loads, we propose a sufficient condition based on a strict Lyapunov energy function, which is utilized to design a nonlinear adaptive SVC controller. This controller only requires local bus measures and parameters. Furthermore, we also present an SVC with a conventional frequency droop controller. The performance of the two considered SVC control systems are evaluated on the well known IEEE New England 39-bus 10-machine power system and compared with conventional SVC controllers. [ABSTRACT FROM AUTHOR]

This paper presents a framework to carry out multi-area stochastic reserve scheduling (RS) based on an ac optimal power flow (OPF) model with high penetration of wind power using distributed consensus and the alternating direction method of multipliers (ADMM). We first formulate the OPF-RS problem using semidefinite programming (SDP) in infinite-dimensional spaces that is in general computationally intractable. Using a novel affine policy, we develop an approximation of the infinite-dimensional SDP as a tractable finite dimensional SDP, and explicitly quantify the performance of the approximation. To this end, we adopt the recent developments in randomized optimization that allow a priori probabilistic feasibility guarantees to optimally schedule power generating units while simultaneously determining the geographical allocation of the required reserve. We then use the geographical pattern of the power system to decompose the large-scale system into a multi-area power network, and provide a consensus ADMM algorithm to find a feasible solution for both local and overall multi-area network. Using our distributed stochastic framework, each area can use its own wind information to achieve local feasibility certificates, while ensuring overall feasibility of the multi-area power network under mild conditions. We provide numerical comparisons with a new benchmark formulation, the so-called converted dc (CDC) power flow model, using Monte Carlo simulations for two different IEEE case studies. [ABSTRACT FROM AUTHOR]

This paper presents some further results for multi-parameter robust stability analysis of large-scale power systems. A finite frequency test is first applied to check robust stability of the system. The parametric stability margins are then calculated to measure the degree of robust stability at different frequencies. The uncertainty's effects on stability are revealed by tracing the value set movements at the worst case frequency. The proposed approach allows the value set plot to be generated automatically, and is applicable to a substantially large number of parameters. The advantages of the approach derive from the affine uncertainty of the characteristic polynomial; for large-scale power systems, we explain that such affine uncertainty usually exists. Case studies of the 547-machine 8647-bus China system are reported, revealing that the suggested value set method appears to be a practical tool for real-world stability analysis. [ABSTRACT FROM AUTHOR]

Battery energy storage is becoming an important part of modern power systems. As such, its operation model needs to be integrated in the state-of-the-art market clearing, system operation, and investment models. However, models that commonly represent operation of a large-scale battery energy storage are inaccurate. A major issue is that they ignore the dependence of the charging power on the battery state of energy. Consequently, market players might suffer great monetary losses for not being able to follow their day-ahead schedule and/or deliver the scheduled reserves. In order to bridge the gap between very detailed low-level battery charging constraints and high-level battery operation models used in the literature, this paper examines a dependence of battery charging ability on its state of energy. It proposes a laboratory procedure, which can be used for any battery type and technology, to obtain this dependence. It also formulates an accurate linear battery charging model, which closely approximates the real-life battery charging constraints. The proposed battery charging model is compared against the models commonly used in the literature. Battery operation schedules obtained by all the models are compared against experimentally obtained results in order to assess the value of the proposed model in real life. [ABSTRACT FROM AUTHOR]

Measurements from the on-board systems of marine vessels are increasingly available for data analysis and are growing in importance as the ship industry enters a phase of digitalization. The purposes of the data analysis from vessels in operation include the verification of the power system design in general and improvement of the electrical power load analysis in particular. In this paper, we show how to extract valuable information from the data-driven operational profile analysis, which reveals the real power demand, and how the vessel was operated. Using the real power range analysis, we emphasize the significance of rarely occurring high power demands, which are critical for power system design and optimization. We propose a methodology for the quantification of variability in the generated power, which explains the tails of probability distributions of a power signal based on signal decomposition. The proposed methodology makes use of the data and it can facilitate the selection of the optimal size, number, and configuration of generators or batteries when designing new power systems. Measurements from a data collection system are used to demonstrate the methodology for dynamic positioning mode of the operation of a platform supply vessel. [ABSTRACT FROM AUTHOR]

Linear power flow models are widely used in power systems to simplify the nonlinear power flow equations. The DC power flow model is one of the representatives. There are many other linear power flow models that improve the DC power flow model with the inclusion of $\boldsymbol{Q}$ and $\boldsymbol{v}$. However, existing linear models are derived based on empirical mathematical approximation without a general methodology guidance. In this paper, we found that the fundamental difference among different linear power flow models lies in the formulation of “independent variables.” Based on this finding, a general formulation of linear power flow models is proposed. The linearization error is theoretically analyzed. In particular, the case when $\boldsymbol{ \theta }$ and $\boldsymbol{v^{\scriptscriptstyle k}}$ are regarded as independent variables is thoroughly investigated. Method for finding the linear power flow with the minimum error is presented. The formulation of the independent variables associated with the minimum linearization error is determined by the distribution of state variables $\boldsymbol{v}$ and $\boldsymbol{\theta }$. It is shown that the linearization error when $\boldsymbol{v^{\scriptscriptstyle 2}}$ is regarded as an independent variable is normally smaller than that for $\boldsymbol{v}$ because of the special properties of the distribution of $\boldsymbol{v}$ in power grids. [ABSTRACT FROM AUTHOR]

Combined transmission and distribution systems (CoTDS) simulation for power systems requires development of algorithms and software that are numerically stable and at the same time accurately simulate dynamic events that can occur in practical systems. The time scales of simulation can be orders of magnitude apart making the combined simulation extremely challenging. This has led to increased research in applying co-simulation techniques for integrated simulation of the two systems. In this paper, two methods for dynamic co-simulation of CoTDS are proposed using parallel and series computation of the transmission system and distribution systems. The convergence of numerical methods in co-simulation is addressed from a fundamental mathematical perspective and the impact of the integration time-step is studied on proposed CoTDS dynamic co-simulation methods. Both these co-simulation methods are validated against total system simulation in a single time-domain simulation environment. The proposed CoTDS co-simulation method is also validated against commercial EMTP dynamic simulation. CoTDS dynamic co-simulation is further demonstrated on a New England 39-bus transmission system with ten load buses replaced by distribution systems including a 5780-node distribution system on one load bus for a detailed fault-induced delayed voltage recovery simulation study. [ABSTRACT FROM AUTHOR]

During the last decades, significant advances have been made in the area of power system stability and control. Nevertheless, when this analysis is carried out by means of decentralized conditions in a general network, it has been based on conservative assumptions such as the adoption of lossless networks. In this paper, we present a novel approach for decentralized stability analysis and control of power grids through the transformation of both the network and the bus dynamics into the system reference frame. In particular, the aforementioned transformation allows us to formulate the network model as an input–output system that is shown to be passive even if the network's lossy nature is taken into account. We then introduce a broad class of bus dynamics that are viewed as multivariable input/output systems compatible with the network formulation, and appropriate passivity conditions are imposed on those that guarantee stability of the power network. We discuss the opportunities and advantages offered by this approach while explaining how this allows the inclusion of advanced models for both generation and power flows. Our analysis is verified through applications to the two area Kundur and the IEEE 68-bus test systems with both primary frequency and voltage regulation mechanisms included. [ABSTRACT FROM AUTHOR]

Hardening components in transmission systems is a practice to improve system resilience against possible disturbances caused by natural disasters. In a power system with a very high penetration of renewable energy, the system hardening will be further complicated by the uncertainty and variability of renewable energy. In this paper, we study the transmission line hardening planing problem in the context of probabilistic power flows injected by the high penetration of renewable energy. We assume that the probabilistic information of renewable energy is incomplete and ambiguous and propose a data-driven approach to approximate the renewable uncertainty sets. We then extend the $N-1$ security criteria to multiple simultaneous contingencies and seek to prepare a hardening plan for the worst-case scenarios. A two-stage data-driven stochastic model is formulated by considering the joint worst-case wind output distribution and transmission line contingencies. Then, we apply the Column-and-Constraints generation method to solve the proposed model. To test the effectiveness of the proposed approach, we conduct experiments on 24-bus and 118-bus test systems. We numerically show that the data-driven approach can effectively address the uncertainty ambiguity and the proposed approach can produce effective hardening plans that improve the system resilience. [ABSTRACT FROM AUTHOR]

Substantial challenges in power systems operation and control as a result of the intermittent and stochastic nature of wind power generation can be significantly alleviated by proficient very short-term wind power prediction interval (WPPI) models. In WPPI models, minimization of cost functions is conducted to train prediction engines and consequently tune their parameters. The prevalent cost functions of prediction engines in WPPI models are mainly non-differentiable and non-convex, and therefore the training process becomes problematic. To transcend such a crucial barrier, this paper addresses a new very short-term WPPI framework based on a bi-level formulation and benefiting from a differentiable and convex cost function. The prediction engine is trained by classical global optimization of the cost function in the lower-level problem, while hyperparameters that control the quality of the WPPIs are injected thereto from the upper-level problem. The hyperparameters can be tuned such that the most useful WPPIs are constructed from the lower-level problem depending on the power system operator's preferences. Lessening the need to heuristically tune a large number of prediction engine parameters is the foremost contribution of this work to the WPPI literature. The superior performance of the proposed WPPI is verified in the multistep ahead prediction of real wind power generation data in comparison to well-tailored benchmark models. [ABSTRACT FROM AUTHOR]

Cyber-attack is recognized as an emerging risk in smart grids. In this paper, we reveal a potential link between data attack and physical consequences and analyzed how the attacker can launch a malicious data attack to trigger sequential outages and thus impose large damages to the grid. In this attack mechanism, the attacker constructs an optimal false data injection attack to intentionally trigger a targeted branch outage sequence that trips multiple branches and then leads to subsequent failures. The studied attack mechanism integrates constructing an optimal data attack and identifying critical lines, and imposes a substantial security impact with a high chance of occurring. Simulations on the IEEE 118-bus system verify that the introduced attack mechanism and highlight the risk of such attacks in today's smart grids. [ABSTRACT FROM AUTHOR]

Future changes in the generation mix may reduce the overall amount of system inertia. It becomes crucial to provide response services more quickly to secure postfault transient frequency dynamics. This paper proposes a novel mixed integer linear programming unit commitment formulation that simultaneously optimizes energy production and the allocation of inertial response (IR), enhanced frequency response (EFR), and primary response (PR) against largest plant outage. A set of linearized inertia-dependent and multispeed constraints on frequency evolution explicitly recognizes the quicker provision of EFR compared to PR and their mutual interplay with IR. The proposed model is applied to analyze a typical 2030 GB low-carbon scenario. Results demonstrate the value, as reduction of system operational cost, when battery storage provides EFR, facilitating a cost-efficient transition toward the low-carbon electricity sector. [ABSTRACT FROM AUTHOR]

Optimal power management of shipboard power system for failure mode (OPMSF) is a significant and challenging problem considering the safety of system and person. Many existing works focused on the transient-time recovery without consideration of the operating cost and the voyage plan. In this paper, the OPMSF problem is formulated considering the mid-time scheduling and the faults at bus and generator. Two-side adjustment methods including the load shedding and the reconfiguration are coordinated for reducing the fault effects. To address the formulated non-convex problem, the travel equality constraint and fractional energy efficiency operation indicator limitation are transformed into the convex forms. Then, considering the infeasibility scenario affected by faults, a further relaxation is adopted to formulate a new problem with feasibility guaranteed. Furthermore, a sufficient condition is derived to ensure that the new problem has the same optimal solution as the original one. Because of the mixed-integer nonlinear feature, an optimal management algorithm based on Benders decomposition (BD) is developed to solve the new one. Due to the slow convergence caused by the time-coupled constraints, a low-complexity near-optimal algorithm based on BD is proposed. The results verify the effectivity of the proposed methods and algorithms. [ABSTRACT FROM AUTHOR]

Virtual inertia has been extensively implemented in power systems with distributed energy resources, especially wind energy conversion systems. This paper proposes an optimal virtual inertia planning strategy for stability enhancement of power system, which is penetrated with large volume of renewable resources provided by doubly fed induction generators (DFIGs). The stability of DFIG is first analyzed and the concept of critical frequency drop is proposed. Moreover, the intersection area between the two predefined operation curves of DFIG is defined as the stability margin and it is calculated in the form of a function of local virtual inertia. Subsequently, a virtual inertia planning strategy is proposed, wherein a coherency index that considers the homogeneity of the stability margin at each node is presented, followed by a nonconvex optimization problem. The problem is simplified with the Lyapunov function method and resolved. Two case studies are conducted to verify the effectiveness and performance of the proposed approach: a 12-bus system and the IEEE-118 bus system. Both cases show that with the proposed approach, the system stability margin coherency level is enhanced and the general stability is promoted. [ABSTRACT FROM AUTHOR]

This paper considers high voltage direct current (HVDC) links for the primary frequency control of interconnected time-delay power systems with electric vehicles (EVs). Novel distributed control of HVDC links is proposed to minimize the differences in the frequency deviations between the connected power areas. A power system with EVs, HVDC links and multiple time delays is first presented. Then, we propose a new robust distributed HVDC controllers design method to stabilize the closed-loop system with a guaranteed $H_{\infty }$ performance. The design method can be flexibly extended to design static output feedback controllers of various structures to cater for different power system topologies. The performance and usefulness of our proposed controller design method is validated by extensive simulations. [ABSTRACT FROM AUTHOR]

In planning and operation of electric power systems, it is essential to perform several evaluations using the power flow algorithm to estimate and manage the operating point of the network being studied. The probabilistic load flow (PLF) algorithm aims to overcome the limitations imposed by the use of the conventional deterministic tool, allowing the consideration of input uncertainties. This paper proposes a new method for calculating risk indices (e.g., probability of overloads in transmission equipment), based on Monte Carlo simulation (MCS) using importance sampling techniques via the cross-entropy method. In fact, the probability associated with any target region of the output PLF variables can be precisely evaluated. The flexibility and accuracy of the conventional MCS are maintained, while the computational time required is greatly reduced. The proposed method is applied to different IEEE test systems and the obtained results are widely discussed. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive method for tuning a proportional-resonance controller for synchronization of the grid-connected inverters is presented. In the proposed approach, the grid frequency is obtained by minimizing the error signal using a frequency-locked loop mechanism that consists of a resonant adaptive filter and a perturbation-based extremum seeking algorithm. Simulations and experimental studies are presented to demonstrate performance of the proposed controller in face of the frequency variations of an emulated grid voltage waveform. The results are compared with conventional nonadaptive methods, which indicate that performance characteristics for voltage/frequency tracking and power factor can be achieved based on the IEEE 1547 standard. [ABSTRACT FROM AUTHOR]

In past studies on cascading failure assessment, the risk of N – x initiating contingencies (ICs) regarding their initiating effect and hidden contingency risk (a high-risk x-component outage sequence within the IC) may not be fully investigated, which may underestimate the risk of IC-triggered cascading overload failures (COFs). In addition, the research on the efficient screening of N – x IC (x ≥ 2) and the identification of the cascading with the largest system impact is limited. To undertake this challenge, this paper proposes a COF identification approach (CIA) which integrates the IC screening and the COF identification for finding the high-impact COFs initiated by N – x ICs with hidden risks. The CIA is validated by simulations on the IEEE 118-bus system. The simulation results demonstrate that the CIA can efficiently screen out the high-risk N – 2 and N – 3 ICs that have an optimal outage sequence and trigger COF chains. The results also show that the CIA can identify the IC-triggered high-impact COFs and quantifies their impacts. The approach can be used as either a cascade-triggering event screening tool (by applying the IC screening module) or a COF risk assessment tool for ensuring system reliability. [ABSTRACT FROM AUTHOR]

The vessel’s pulse load can intermittently produce pretty high power in a very short time. Considering their periodical charge-and-discharge process, the system variables of integrated power system (IPS) are characterized by a periodic alternating process rather than a single-equilibrium point. It is not suitable to evaluate the operational performance according to the equilibrium point. In this paper, a periodic orbit model of the IPS and its algorithm is proposed according to sensitivity analysis of end value of system variables to their initial value. The algorithm can be used for distributed compute of system variables, which adjust system variables according to the sensitivity to make the end value equal to the initial value. And, the calculation speed of the algorithm can be increased greatly. The model and the algorithm lay the foundation for assessing the operational performance of the IPS with pulse load. The proposed method has been verified by the simulation test. [ABSTRACT FROM AUTHOR]

Recently, energy conservation has been concerned, and there are several aspects to improve the energy consumption issues. Therefore, many research results were proposed to mitigate the related problems, one of these is to eliminate the extra standby power of household appliances. This paper employed an effective method to identify load characteristics of appliances in different operation states by support vector machines, and a smart home electric energy saving system was built up to implement the method. Furthermore, a virtual living room was simulated in the laboratory as an experimental object, and a prototype system was built up. The performance test results are proposed to validate the feasibility of the proposed system. [ABSTRACT FROM AUTHOR]

The excitation system plays a critical role in the operation of synchronous generators. An equipment failure could impact the voltage quality for smaller grids. Furthermore, it can lead to cost penalties and reduced production for the power plant owner. Recently, a new high-speed-response rotating brushless exciter was developed that employs remote control of the rotating thyristors on the generator shaft. This has led to new possibilities for improving the performance of brushless exciters. This contribution investigates the failure modes of a dual-star outer pole exciter that feeds two separate thyristor bridges connected in parallel during a normal operation. The possibility of a redundant postfault operation due to open-thyristor or open-phase faults is demonstrated using experimental testing. The system is compared with the fault performance of a conventional three-phase system. This paper includes the implementation and validation of a fault-predicting double d–q exciter model. In addition, the dangerous effects of a shorted-thyristor fault are investigated. A “skip firing” protection technique is briefly demonstrated for the fast isolation of such faults, yielding nondestructive postfault recovery and redundant failure-mode operation. The evidence shows that the dual-star exciter is a competitive choice for the future development of fault-tolerant brushless exciters. [ABSTRACT FROM AUTHOR]

In previous research, the variable conductance of a parallel active power filter has been used for antiparallel resonance with parameter variations in power systems. Its disadvantages are many sensors, complicated harmonic detection, and complexity of the algorithm for successful accomplishment in antiparallel resonance. In order to eliminate such drawbacks, this paper presents the parallel active power filter based on source current detection for antiparallel resonance with robustness to parameter variations for power factor correction using capacitor banks. The proposed method employs only single harmonic detector and an open-loop shaping design for robustness to variations of source impedance and a sudden change of capacitor bank values for power factor correction. The proposed antiparallel resonance controller can be readily applied to both large-scale and laboratory-scale power systems without any change. The large-scale simulation and the laboratory-scale experiment results confirm the validity, the accuracy, and good performance for antiparallel resonance, harmonic suppression, and robustness to parameter variations in power systems. [ABSTRACT FROM AUTHOR]

In this paper, new techniques are presented to improve the performance of the optimal switching vector model predictive control. A well-defined analytic technique is proposed for the real-time adjustment of dynamic weighting factors, as well as a new objective function formulation. These techniques provide a dynamic controller behavior, automatically adapted to distinct operating points. This ensures better steady-state performance in different conditions while improving simultaneously the dynamic response. The proposed controller presents a lower computational load than the conventional formulation and its design requires less extensive testing. General guidelines are presented to guide the user in the controller design. Simulation and experimental results are presented, demonstrating the advantages and superior performance of the proposed controller. [ABSTRACT FROM AUTHOR]

The modular multilevel converter (MMC) has become the most promising converter topology for medium- and high-power applications. In this paper, a sliding mode control (SMC) based method is proposed for the MMC. Based on analysis of the system dynamics under the proposed control method, relations among control parameters and their validity conditions are obtained, which provide a guidance for systematic controller design. Compared with the conventional proportional-integral based method, the proposed SMC-based method provides comparable steady state performance and faster dynamic responses, without compromising the computational effort or requirement for precise system model. Simulation studies in the PSCAD/EMTDC software environment are carried out to validate the performance and effectiveness of the proposed control method in the context of an HVdc transmission system. A laboratory prototype is also developed to experimentally validate the proposed control method. [ABSTRACT FROM AUTHOR]

Future multi-terminal dc networks are envisioned for the large-scale integration of renewable energy sources into today's power systems. An essential aspect for the reliable operation of these systems is the fast and selective dc-side fault handling and separation of faulted lines. The objective of this paper is the development and evaluation of a multi-terminal dc protection strategy based on fault-blocking converters (e.g., full-bridge based modular multi-level converters) and high-speed switches. Both a suitable fault control and a new high-speed switch design for a fast separation of faulted dc lines are elaborated. The novel concept is intended to reduce the requirements on the high-speed switches compared to dc circuit breakers and to ensure a fast restoration of the active power transmission capability, as well as the dc voltage. The fault-clearing strategy is analyzed using electromagnetic transient (EMT) simulations under variation of the fault location, type, and resistance. The investigations are carried out in a cable-based multi-terminal HVdc system with full-bridge modular multi-level converters in symmetrical monopole configuration. [ABSTRACT FROM AUTHOR]

Residential transformer population is a critical type of asset that many electric utility companies have been attempting to manage proactively and effectively to reduce unexpected transformer failures and life loss that are often caused by overloading. Within the typical power asset portfolio, the residential transformer asset is often large in population, has the lowest reliability design, lacks transformer loading data, and is susceptible to customer loading behaviors, such as adoption of distributed energy resources and electric vehicles. On the bright side, the availability of more residential service operation data along with the advancement of data analytics techniques has provided a new path to further our understanding of residential transformer overloading risk statistically. This paper develops a new data-driven method that combines transformer temperature rise and insulation life loss simulation model with clustering analysis technique. It quantitatively and statistically assesses the overloading risk of residential transformer population in one area and suggests proper risk management measures according to the assessment results. Multiple application examples for a Canadian utility company have been presented and discussed in detail to demonstrate the applicability and usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

A lot of work has been done for the effects evaluation of electronic equipment due to high-power electromagnetic environments. The focus of the evaluation usually stays on whether the effects occur or not (“1” or “0”) during tests. However, in addition to such kind of either-or effects, multilevel effects happen in many practical cases as well. In this paper, we propose a kind of statistical approach for the evaluation of multilevel effects. By means of multinomial regression, the occurrence probabilities of each level of effects can be estimated simultaneously. Through maximum likelihood estimation (MLE), both the Softmax model and the mutually independent normal distribution assumption model are built and solved in a numerical way. We also talk about how the data affect the goodness of the regression. As a consequence, the Monte Carlo simulation gives a quasi-function relationship about model stability and estimation error. A case study on computer communication system has been conducted in the laboratory to assess the validity and applicability of the proposed models. The result is satisfactory for the three-level cases and gives all the probability prediction of the malfunction occurrences. [ABSTRACT FROM AUTHOR]

The paper describes an algorithm for parameter identification of a dynamic equivalent for an external subsystem, based solely on the available online measurements in boundary buses and branches. Static equivalent part is represented by equivalent impedances from boundary buses (ones that separate the internal and external subsystems) and calculated using the modified (minimum loss) radial, equivalent, and independent method. Parameter identification of synchronous generator (SG)-based equivalent (for predominantly production external areas), dynamic load (DL)-based equivalent (for predominantly load external areas), or (SG + DL)-based equivalent (for mixed external areas) in fictitious buses is performed by Levenberg–Marquardt weighted least-square nonlinear optimization, which minimizes the variances between available online measurements and transient responses of the reduced power system. The IEEE 14-bus and 441-bus real-world test systems are used to illustrate and test the proposed power system equivalent derivation technique. [ABSTRACT FROM AUTHOR]

Total transfer capability (TTC) is an effective indicator to evaluate the transmission limit of the interconnected systems. However, due to the large-scale wind power integration, operation conditions of a power system may change rapidly, yielding time-varying characteristics of the TTC. As a result, the traditional time-consuming transient stability constrained TTC model is unable to assess the online transmission margin. In this paper, we propose an online measurement-based TTC estimator using the nonparametric analytics. It consists of three major components: the probabilistic data generation, the composite feature selection, and the group Lasso regression-based training scheme. Specifically, we present a probabilistic data generation approach to take into account the uncertainties of the day-ahead generation scheduling and to reduce the number of redundant or infeasible data. Then, the composite feature selection is used to reduce the dimension of the generated data and identify the features which are highly correlated with TTC. The features are determined by the maximal information coefficients and nonparametric independence screening approach. Finally, these selected features are trained by the group Lasso regression to learn the correlation between the TTC and the online measurements. Once real-time measurements are available, the TTC can be assessed immediately through the learned correlation relationship. Extensive numerical results carried out on the modified New England 39-bus test system demonstrate the feasibility of the proposed TTC estimator for online applications. [ABSTRACT FROM AUTHOR]

Multienergy systems are flexible energy systems that can benefit from energy resources to supply different energy demands. Due to the capabilities of multienergy systems in generating different energy carriers, these systems have been rapidly expanded in power systems. After restructuring in power system in recent years and appearance of competent energy markets, energy systems operated within such environments have been usually exposed to uncertainties of various parameters, such as price, demand, etc. In this paper, a novel optimization framework based on hybrid scenario-based/interval/information gap decision theory method is developed to investigate the optimal operation of smart energy hubs (S. E. Hubs) subject to economic priorities, technical constraints of the distribution network and uncertainties. Considering energy hubs equipped to smart facilities, demand-side management programs including price response and load response services have been available to motivate electrical consumers to revise their consumption pattern in order to satisfy economic priorities of energy hubs. By using the results of employed hybrid uncertainty modeling approach, the operator of S. E. Hubs can decide either to take risk-averse or risk-seeking strategy against the uncertainties. Uncertainty based integration of S. E. Hubs into distribution network is evaluated regarding the IEEE 33-bus test system and the results obtained from simulations are presented for comparison. [ABSTRACT FROM AUTHOR]

High penetration of wind energy in the modern power system exposes the need of smoothing the fluctuating output power in an effective and conducive way. In this context, this paper proposes two novel control strategies that utilize the self-capability of permanent magnet synchronous generator-based wind turbine to realize power smoothing. The first strategy pursues to offer power smoothing support via simultaneous utilization of dc-link voltage control, rotor speed control, and pitch angle control. The second control strategy seeks to coordinate the three concerned individual control schemes in a hierarchical manner, where the power smoothing tasks are allocated to individual control modules or their combinations dynamically in line with WT's operation status. Both two strategies are able to provide power smoothing support by fully exploiting wind turbine's self-capability, whereas the second strategy has the merits on 1) reducing the activation frequency of pitch angle control, and 2) enhancing wind energy harvesting. Case studies of the proposed control strategies are carried out to compare and verify their effectiveness in achieving power smoothing. [ABSTRACT FROM AUTHOR]

Supervised machine learning has been successfully used in the past to infer a system's security boundary by training classifiers (also referred to as security rules) on a large number of simulated operating conditions. Although significant research has been carried out on using classifiers for the detection of critical operating points, using classifiers for the subsequent identification of suitable preventive/corrective control actions remains underdeveloped. This paper focuses on addressing the challenges that arise when utilizing security rules for control purposes. Illustrative examples and case studies are used to show how even very accurate security rules can lead to prohibitively high risk exposure when used to identify optimal control actions. Subsequently, the inherent tradeoff between operating cost and security risk is explored in detail. To optimally navigate this tradeoff, a novel approach is proposed that uses an ensemble learning method (AdaBoost) to infer a probabilistic description of a system's security boundary. Bias in predictions is compensated by the Platt Calibration method. Subsequently, a general-purpose framework for building probabilistic and disjunctive security rules of a system's secure operating domain is developed that can be embedded within classic operation formulations. Through case studies on the IEEE 39-bus system, it is showcased how security rules derived from supervised learning can be efficiently utilized to optimally operate the system under multiple uncertainties while respecting a user-defined balance between cost and risk. This is a fundamental step toward embedding data-driven models within classic optimisation approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a bilevel programming model for the planning of tidal current farms (TCFs). The micrositing strategy of tidal current turbines (TCTs) and the collector system planning scheme are coordinated to achieve a better balance of energy yields, TCF capital investment and power systems economic operation using the proposed method. The power output of the TCF is modeled considering the characteristics of tidal current velocity and wake effects. A coordinated planning model consisting of one upper level model and two lower level models is developed to maximize comprehensive profit. Not only the investment and maintenance costs of TCTs and submarine cables, but also the operation cost of the collector system and the impact of the TCF integration on the operation of power systems are all taken into account to ensure the long-term benefits of both TCF owners and power systems. An efficient solution for the proposed planning model is developed by combining a genetic algorithm with a mixed integer programming. The effectiveness and adaptability of the proposed method are demonstrated using the measured data of tidal current velocity with distinct characteristics and the IEEE 30- and 118-bus test systems. [ABSTRACT FROM AUTHOR]

During the out-of-step oscillation process, the frequency of voltage at every point in power system changes continuously. In this paper, the voltage frequency variation regulation is analyzed in an equivalent two-terminal system whose two-terminal potential amplitudes are not equal and all electrical component impedance angles are different. It finds the bus voltage frequencies at different sides of out-of-step center own different characteristics. In order to use the frequency variation characteristics to construct the criterion of power system out-of-step protection, a fast frequency calculation algorithm is proposed, in which the frequency is calculated by toggling different formulas at different sampling points and the calculation accuracy is improved by the aid of coordinate rotation digital computer (CORDIC) algorithm. The algorithm is easy to implement and measurement errors can keep under a low level. Finally, a novel frequency based out-of-step protection with wide area information is described. It can judge the occurrence of out-of-step oscillation in the first out-of-step oscillation cycle and determine the electrical component where the out-of-step center locates, which lays the foundation for the determination of out-of-step splitting section for complex power systems. The test results with IEEE39 system in PSCAD verify the correctness of the proposed protection. [ABSTRACT FROM AUTHOR]

The proliferation of technologies such as combined heat and power systems and smart meters has accelerated the multienergy trading. A two-stage game model is built in this paper to analyze the optimal strategy of each player and their interactions. Specifically, in consumer game model, we present an autonomous and distributed micro energy management system among users in which power and heat are traded at the same time. In supplier game model, integrated energy suppliers receive energy demand information and share the market according to the game result. In addition, we present a detailed proof of the existence and uniqueness of the Nash equilibrium, which lays the foundation for researchers to build an effective model using game theory. Further, a distributed algorithm specifically designed for this model is proposed. The players can retain a certain level of anonymity without revealing the details of their energy consumption or generation schedules to other players. Lastly, simulation results confirm the feasibility of two-stage game model and the effectiveness of the proposed distributed algorithm. [ABSTRACT FROM AUTHOR]

A shortage of large power system data sets, as well as the frequent restrictions on sharing such models, have led to newfound interest in creating synthetic data that can be easily shared among researchers. This paper considers the problem of forming a power system test case from the constituent parts of realistic power grid samples. Starting from a topology, and samples of generation, load, and branches, we assemble systems while respecting the constraints imposed by a typical Optimal Power Flow problem. Expressed in this manner, the problem involves solving for permutations of the input data. Since permutations matrices are binary, the problem is linearized, allowing for a Mixed Integer Linear Problem formulation. The problem is further decomposed using the Alternating Direction Method of Multipliers as well as an Evolutionary Algorithm to facilitate scaling to larger system sizes. A post processing step is used to add shunt elements for reactive power planning. The resulting systems demonstrate statistically similar power flow behavior to reference systems. Finally, new analysis avenues, opened by synthesizing test cases according to the proposed method, are briefly introduced by creating fictitious systems with different topology models and examining how these affect power flow behavior. [ABSTRACT FROM AUTHOR]

Robust simulation is essential for reliable operation and planning of transmission and distribution power grids. At present, disparate methods exist for steady-state analysis of the transmission (power flow) and distribution power grid (three-phase power flow). Due to the nonlinear nature of the problem, it is difficult for alternating current power flow and three-phase power flow analyses to ensure convergence to the correct physical solution, particularly from arbitrary initial conditions, or when evaluating a change (e.g., contingency) in the grid. In this paper, we describe our equivalent circuit formulation approach with current and voltage variables, which models both the positive sequence network of the transmission grid and three-phase network of the distribution grid without loss of generality. The proposed circuit models and formalism enables the extension and application of circuit simulation techniques to solve for the steady-state solution with excellent robustness of convergence. Examples for positive sequence transmission and three-phase distribution systems, including actual 75k+ nodes Eastern Interconnection transmission test cases and 8k+ nodes taxonomy distribution test cases, are solved from arbitrary initial guesses to demonstrate the efficacy of our approach. [ABSTRACT FROM AUTHOR]

This paper addresses the distributed optimal frequency control of power systems considering a network-preserving model with nonlinear power flows and excitation voltage dynamics. Salient features of the proposed distributed control strategy are fourfold, first, nonlinearity is considered to cope with large disturbances, second, only a part of generators are controllable, third, no load measurement is required, fourth, communication connectivity is required only for the controllable generators. To this end, benefiting from the concept of “virtual load demand,” we first design the distributed controller for the controllable generators by leveraging the primal-dual decomposition technique. We then propose a method to estimate the virtual load demand of each controllable generator based on local frequencies. We derive incremental passivity conditions for the uncontrollable generators. Finally, we prove that the closed-loop system is asymptotically stable and its equilibrium attains the optimal solution to the associated economic dispatch problem. Simulations, including small and large-disturbance scenarios, are carried on the New England system, demonstrating the effectiveness of our design. [ABSTRACT FROM AUTHOR]

Despite substantial improvement in the development of forecasting approaches, conditional and dynamic uncertainty estimates ought to be accommodated in decision-making in power system operation and market, in order to yield either cost-optimal decisions in expectation, or decision with probabilistic guarantees. The representation of uncertainty serves as an interface between forecasting and decision-making problems, with different approaches handling various objects and their parameterization as input. Following substantial developments based on scenario-based stochastic methods, robust and chance-constrained optimization approaches have gained increasing attention. These often rely on polyhedra as a representation of the convex envelope of uncertainty. In this paper, we aim to bridge the gap between the probabilistic forecasting literature and such optimization approaches by generating forecasts in the form of polyhedra with probabilistic guarantees. For that, we see polyhedra as parameterized objects under alternative definitions (under $L_1$ and $L_\infty$ norms), the parameters of which may be modeled and predicted. We additionally discuss assessing the predictive skill of such multivariate probabilistic forecasts. An application and related empirical investigation results allow us to verify probabilistic calibration and predictive skills of our polyhedra. [ABSTRACT FROM AUTHOR]

This paper describes the use of large electric grids in university electric power system courses. Since much actual power system information is not publicly available, the application of public domain synthetic grids developed by the authors is presented including a 2000-bus grid. Discussion of the educational applications utilized in a senior level class are given for power flow analysis and sensitivity, economic dispatch, contingency analysis, optimal power flow (OPF), security-constrained OPF, transient stability, and real-time dynamic operations. In each of these, the application of the large synthetic grids give students insights and experience with cases closer to actual power systems in complexity and size. [ABSTRACT FROM AUTHOR]

The increasing uncertainty that surrounds electricity system operation renders security assessment a highly challenging task; the range of possible operating states expands, rendering traditional approaches based on heuristic practices and ad hoc analysis obsolete. In turn, machine learning can be used to construct surrogate models approximating the system's security boundary in the region of operation. To this end, past system history can be useful for generating anticipated system states suitable for training. However, inferring the underlying data model, to allow high-density sampling, is problematic due to the large number of variables, their complex marginal probability distributions and the nonlinear dependence structure they exhibit. In this paper, we adopt the C-Vine pair-copula decomposition scheme; clustering and principal component transformation stages are introduced, followed by a truncation to the pairwise dependency modeling, enabling efficient fitting and sampling of large datasets. Using measurements from the French grid, we show that a machine learning training database sampled from the proposed method can produce binary security classifiers with superior predictive capability compared to other approaches. [ABSTRACT FROM AUTHOR]

Petrochemical industry not only consumes a large amount of electricity every year but also requires high-quality power supplies. Any unexpected power failure could shut down the entire production line and cause significant financial loss. Meanwhile, due to the large electricity consumption and intensive power demand, in-house cogeneration plants have been equipped for most petrochemical facilities. Thus, any power interruption or disturbance between a petrochemical facility and interconnected power grids could affect both sides of the power systems. Therefore, having high power quality becomes one of the most critical parts for petrochemical facilities. To avoid unexpected power failure and to provide a better understanding of the power system at petrochemical facilities, a fast, precise, and reliable power monitoring system is required. A novel real-time web-based power monitoring system on field-programmable gate array platform is developed in this paper for a power substation at the petrochemical facility. Besides the data collecting with event-triggering mechanism and measurement data recording functions, subsynchronous oscillation detection application is also developed in this system. This monitoring system could provide precise data to help engineers with insightful analysis of the electric system to prevent a power failure, and it also could help system operators to have a better understanding of the system operation characteristics. [ABSTRACT FROM AUTHOR]