Abstract:To effectively study the dynamics of powersystems with large-scale wind farms (WFs), an equivalentmodel needs to be developed. It is well known that back-tobackconverters and their controllers are important for thedynamic responses of the wind turbine (WT) under disturbances.However, the detailed structure and parametersof the back-to-back converters and their controllers areusually unknown to power grid operators. Hence, it isdifficult to build an accurate equivalent model for the WFusing the component model-based equivalent modelingmethod. In this paper, a transfer function based equivalentmodeling method for the WF is proposed. During modeling,the detailed structure and parameters of the WF are notrequired. The objective of the method is reproducing theoutput dynamics of the WF under the variation of the windspeed and power grid faults. A decoupled parameter-estimationstrategy is also developed to estimate the parametersof the equivalent model. A WF that consists of 16 WTsis used to test the proposed equivalent model. Additionally,the proposed equivalent modeling method is applied tobuild the equivalent model for a real WF in NorthwestChina. The effectiveness of the proposed method is validatedby the real measurement data.

Abstract:The paper provides a short history of the phasor measurement unit (PMU) concept. The origin of PMU is traced to the work on developing computer based distance relay using symmetrical component theory. PMUs evolved from a portion of this relay architecture. The need for synchronization using global positioning system (GPS) is discussed, and the wide area measurement system (WAMS) utilizing PMU signals is described. A number of applications of this technology are discussed, and an account of WAMS activities in many countries around the world are provided.

Abstract:This paper proposes the use of the unscented Kalman filter to estimate the equivalent model of a photovoltaic (PV) array, using external measurements of current and voltage at the inverter level. The estimated model is of interest to predict the power output of PV plants, in both planning and operation scenarios, and thus improves the efficient operation of power systems with high penetration of renewable energy. The proposed technique has been assessed in several simulated scenarios under different operating conditions. The results show that accurate estimates are provided for the model parameters, even in the presence of measurement noise and abrupt variations under the external conditions.

Abstract:The creation of a suitable wide area monitoring system (WAMS) is widely recognized as an essential aspect of delivering a power system that will be secure, efficient and sustainable for the foreseeable future. In Great Britain (GB), the deployment of the first WAMS to monitor the entire power system in real time was the responsibility of the visualization of real time system dynamics using enhanced monitoring (VISOR) project. The core scope of the VISOR project is to deploy this WAMS and demonstrate how WAMS applications can in the near term provide system operators and planners with clear, actionable information. This paper presents the wider scope of the VISOR project and the GB wide WAMS that has been deployed. Furthermore, the paper describes some of the WAMS applications that have been deployed and provides examples of the measurement device performance issues that have been encountered during the project.

Abstract:This paper is concerned about the impact of network parameter errors on the reliable operation and management of electricity markets. Specifically, the paper investigates the so-called critical parameters in a network model whose errors cannot be detected or estimated due to the lack of local measurement redundancy. Due to this property of critical parameters, it will be impossible to detect, identify and correct errors in these parameters. Given the fact that electricity market applications are heavily model-dependent, the locational marginal prices (LMPs) can be shown to be seriously distorted in the presence of critical parameter errors. Furthermore, if such errors are maliciously injected by adversaries, they will go undetected. Meanwhile, prices and revenues associated with power transactions may be strategically manipulated. An approach for quantifying the impact of critical parameters on the management of electricity markets is proposed. Conditions related to network topology and measurement configuration leading to the appearance of critical parameters are classified, and meter placement strategies for avoiding critical parameters are presented as well. Simulation results obtained by using IEEE test systems are given to verify the proposed analysis and design methods.

Abstract:This paper presents a parameter estimation technique for the hot-spot thermal model of power transformers. The proposed technique is based on the unscented formulation of the Kalman filter, jointly considering the state variables and parameters of the dynamic thermal model. A two-stage estimation technique that takes advantage of different loading conditions is developed, in order to increase the number of parameters which can be identified. Simulation results are presented, which show that the observable parameters are estimated with an error of less than 3%. The parameter estimation procedure is mainly intended for factory testing, allowing the manufacturer to enhance the thermal model of power transformers and, therefore, its customers to increase the lifetime of these assets. The proposed technique could be additionally considered in field applications if the necessary temperature measurements are available.

Abstract:In recent years, with increasing amounts of renewable energy sources connecting to power networks, sub-/super-synchronous oscillations (SSOs) have occurred more frequently. Due to the time-variant nature of SSO magnitudes and frequencies, as well as the mutual interferences among SSO modes with close frequencies, the accurate parameter estimation of SSO has become a particularly challenging topic. To solve this issue, this paper proposes an improved spectrum analysis method by improving the window function and a spectrum correction method to achieve higher precision. First, by aiming at the sidelobe characteristics of the window function as evaluation criteria, a combined cosine function is optimized using a genetic algorithm (GA). Furthermore, the obtained window function is self-convolved to extend its excellent characteristics, which have better performance in reducing mutual interference from other SSO modes. Subsequently, a new form of interpolated all-phase fast Fourier transform (IpApFFT) using the optimized window function is proposed to estimate the parameters of SSO. This method allows for phase-unbiased estimation while maintaining algorithmic simplicity and expedience. The performance of the proposed method is demonstrated under various conditions, compared with other estimation methods. Simulation results validate the effectiveness and superiority of the proposed method.

Abstract:Residential heating, ventilation and air conditioning (HVAC) provides important demand response resources for the new power system with high proportion of renewable energy. Residential HAVC scheduling strategies that adapt to real-time electricity price signals formulated by demand response program and ambient temperature can significantly reduce electricity costs while ensuring occupants ’ comfort. However, since the pricing process and weather conditions are affected by many factors, conventional model-based method is difficult to meet the scheduling requirements in complex environments. To solve this problem, we propose an adaptive optimal scheduling strategy for residential HVAC based on deep reinforcement learning (DRL) method. The scheduling problem can be regarded as a Markov decision process (MDP). The proposed method can adaptively learn the state transition probability to make economical decision under the tolerance violations. Specifically, the residential thermal parameters obtained by the least-squares parameter estimation (LSPE) can provide a basis for the state transition probability of MDP. Daily simulations are verified under the electricity prices and temperature data sets, and numerous experimental results demonstrate the effectiveness of the proposed method.

Abstract:The aging of lines has a strong impact on the economy and safety of the distribution network. This paper proposes a novel approach to conduct line aging assessment in the distribution network based on topology verification and parameter estimation. In topology verification, the set of alternative topologies is firstly generated based on the switching lines. The best-matched topology is determined by comparing the difference between the actual measurement data and calculated voltage magnitude curves among the alternative topologies. Then, a novel parameter estimation approach is proposed to estimate the actual line parameters based on the measured active power, reactive power, and voltage magnitude data. It includes two stages, i.e., the fixed-step aging parameter (FSAP) iteration, and specialized Newton-Raphson (SNR) iteration. The theoretical line parameters of the best-matched topology are taken as a warm start of FSAP, and the fitted result of FSAP is further renewed by the SNR. Based on the deviation between the renewed and theoretical line parameters, the aging severity risk level of each line is finally quantified through the risk assessment technology. Numerous experiments on the modified IEEE 33-bus and 123-bus systems demonstrate that the proposed approach can effectively conduct line aging assessment in the distribution network.