Measurement based method for online characterization of generator dynamic behaviour in systems with renewable generation

This paper introduces a hybrid-methodology for online identification and clustering of generator oscillatory behavior, based on measured responses. The dominant modes in generator measured responses are initially identified using a mode identification technique and then introduced, in the next step, as input into a clustering algorithm. Critical groups of generators that exhibit poorly or negatively damped oscillations are identified, in order to enable corrective control actions and stabilize the system. The uncertainties associated with operation of modern power systems, including Renewable Energy Sources (RES) are investigated, with emphasis on the impact of the dynamic behavior of power electronic interfaced RES

Photovoltaic Power Plants as a Source of Electromagnetic Interference to Metallic Agricultural Pipelines

AbstractThe electromagnetic interference of power lines to nearby metallic pipelines has been a subject of research for many decades. Usually attention was given to gas or oil pipelines that shared the same rights-off-way with a power line for large distances. However, the recent advancement of renewable energy sources and specifically Photovoltaic (PV) power, due to generous incentives provided in many countries, has resulted in installations of large PV power stations even in agricultural areas. This brought up cases where such power stations in the MWp level, typically connected in medium voltage through buried cables, are located in the vicinity of metallic irrigation pipelines. Under certain conditions, these situations may result in induced voltages and currents on the pipeline that can pose threats to operating personnel. This work presents an analysis of the problems through a quasi-real case study adapted from a real case of a PV power station. The calculation methodology involves a hybrid method that is used in a way to reduced computational time. Results are presented both for normal operating conditions and faults in the power station and may be useful for both agriculture professionals and engineers

Measurement-based analysis of the dynamic performance of microgrids using system identification techniques

The dynamic performance of microgrids is of crucial importance, due to the increased complexity introduced by the combined effect of inverter interfaced and rotating distributed generation. This paper presents a methodology for the investigation of the dynamic behavior of microgrids based on measurements using Prony analysis and state-space black-box modeling techniques. Both methods are compared and evaluated using real operating conditions data obtained by a laboratory microgrid system. The recorded responses and the calculated system eigenvalues are used to analyze the system dynamics and interactions among the distributed generation units. The proposed methodology can be applied to any real-world microgrid configuration, taking advantage of the future smart grid technologies and features

Dynamic performance of a low voltage microgrid with droop controlled distributed generation

Microgrids are small-scale highly controlled networks designed to supply electrical energy. From the operational point of view, microgrids are active distribution networks, facilitating the integration of distributed generation units. Major technical issues in this concept include system stability and protection coordination which are significantly influenced by the high penetration of inverter-interfaced distributed energy sources. These units often adopt the frequency-active power and voltage-reactive power droop control strategy to participate in the load sharing of an islanded microgrid. The scope of the paper is to investigate the dynamic performance of a low voltage laboratory-scale microgrid system, using experimental results and introduce the concept of Prony analysis for understanding the connected components. Several small disturbance test cases are conducted and the investigations focus on the influence of the droop controlled distributed generation sources

Methodology for evaluating equivalent models for the dynamic analysis of power systems

The increasing penetration of distributed renewable energy sources drastically alters the dynamic characteristics of distribution networks (DNs). Therefore, several equivalent models have been recently proposed, to analyze more accurately the complex behavior of modern DNs. However, relatively simple models are still commonly used in practice for dynamic power system studies. In addition, dynamic equivalent models for DNs are sensitive to different operating conditions and there is lack of systematic understanding of their performance. Scope of this paper is to propose a methodology for identifying the applicability range in terms of accuracy and generalization capability of several conventional and newly developed equivalent models for the dynamic analysis of modern DNs. A set of metrics is used for the modelling accuracy assessment and a sensitivity analysis framework is introduced to fully quantify the generalization capabilities of DN equivalent models. Based on the above, guidelines and recommendations for the development of robust equivalent models for DN analysis are proposed

A three-level distributed architecture for the real-time monitoring of modern power systems

To monitor network operation in real-time, power system operators have developed wide-area monitoring systems (WAMS). However, the centralized communication and information processing architecture of WAMS cannot be extended easily to distribution networks. In this aspect, a three-level distributed network monitoring architecture is proposed in this paper, concerning the dynamic analysis of transmission, primary and secondary distribution networks by exploiting measurements of ambient data and transient responses. In the proposed architecture, operators are responsible for the operation and analysis of their own grid but also can share an overview of the system performance to facilitate their operational coordination. Different online and offline applications are supported within the architecture, including small-signal, transient and frequency stability analysis as well as dynamic equivalencing and real-time inertia estimation. Measurement-based algorithms and models are proposed for each case. Finally, the performance of the developed algorithms has been tested by using a combined transmission and distribution power system model

Development of measurement-based load models for the dynamic simulation of distribution grids

The advent of new types of loads, such as power electronics and the increased penetration of low-inertia motors in the existing distribution grids alter the dynamic behavior of conventional power systems. Therefore, more accurate dynamic, aggregate, load models are required for the rigorous assessment of the stability limits of modern distribution networks. In this paper, a measurement-based, input/output, aggregate load model is proposed, suitable for dynamic simulations of distribution grids. The new model can simulate complex load dynamics by employing variable-order transfer functions. The minimum required model order is automatically determined through an iterative procedure. The applicability and accuracy of the proposed model are thoroughly evaluated under distinct loading conditions and network topologies using measurements acquired from a laboratory-scale test setup. Furthermore, the performance of the proposed model is compared against other conventional load models, using the mean absolute percentage error

Power hardware-in-the-loop setup for developing, analyzing and testing mode identification techniques and dynamic equivalent models

During the last decades, a significant number of mode identification techniques and dynamic equivalent models have been proposed in the literature to analyze the dynamic properties of transmission grids and active distribution networks (ADNs). The majority of these methods are developed using the measurement-based approach, i.e., by exploiting dynamic responses acquired from phasor measurement units (PMUs). However, there is lack of a common framework in the literature for the performance evaluation of such methods under real field conditions. Aiming to address this gap, in this paper, a power hardware-in-the-loop setup is introduced to generate dynamic responses, suitable for the testing and validation of measurement-based mode identification techniques and dynamic equivalent models. The setup consists of a high voltage transmission grid, two medium voltage distribution grids as well as a low voltage ADN. Using this setup, several disturbances are emulated and the resulting dynamic responses are recorded using PMUs. The measurements are made available to other researchers through a public repository to act as benchmark responses for the evaluation of measurement-based methods

Artificial-intelligence method for the derivation of generic aggregated dynamic equivalent models

Aggregated equivalent models for the dynamic analysis of active distribution networks (ADNs) can be efficiently developed using dynamic responses recorded through field measurements. However, equivalent model parameters are highly affected from the time-varying composition of power system loads and the stochastic behavior of distributed generators. Thus, equivalent models, developed through in situ measurements, are valid only for the operating conditions from which they have been derived. To overcome this issue, in this paper, a new method is proposed for the derivation of generic aggregated dynamic equivalent models, i.e., for equivalent models that can be used for the dynamic analysis of a wide range of network conditions. The method incorporates clustering and artificial neural network techniques to derive robust sets of parameters for a variable-order dynamic equivalent model. The effectiveness of the proposed method is evaluated using measurements recorded on a laboratory-scale ADN, while its performance is compared with a conventional technique. The corresponding results reveal the applicability of the proposed approach for the analysis and simulation of a wide range of distinct network conditions