Active Power Estimation Research Articles

Multilayer LSTM Model for Wind Power Estimation in the Scada System

Wind energy is clean energy that does not pollute the environment. However, the complex and variable operating environment of a wind turbine often makes it difficult to predict the instantaneous active power generated. In this study, a wind turbine active power estimation system based on a short-term memory network (LSTM) using time series analysis is proposed. The data obtained from the wind turbine SCADA system is used as input variables. In the proposed method, a multilayer LSTM architecture is designed to train the model. The first LSTM network consists of 64 units, and the second one consists of 32 units. This is followed by a dense layer consisting of 16 neurons. In the last layer, the architecture is finalized by using a linear activation function for the prediction process. The proposed deep learning (DL)-based LSTM prediction model takes into account environmental factors such as wind speed and wind direction for active power forecasting. The results show that the LSTM-based time series analysis method is capable of effectively capturing time series features among the data. Thus, the proposed architecture can realize high-accuracy active power forecasting.

A Wide-Area Measurement System-Based Load Encroachment Identification Method Applicable to Power Systems with High Wind Power Penetration

One of the main factors in triggering cascading outages is the unexpected tripping of backup protection caused by load encroachment (LE). Hence, LE should be identified quickly to avoid unexpected tripping. This paper proposes a fast LE identification method based on the wide-area measurement system (WAMS), applicable to transmission grids with high wind power penetration. Firstly, based on the improved shortest path algorithm, the method searches for critical lines greatly affected by the LE to determine the collected range of wide-area information. Then, by analyzing the hidden dangers of the existing calculation method for the active power estimation value (APEV) in the power system with wind farms, a fast calculation method of the APEV based on the WAMS is proposed to ensure that the existing LE identification criterion is still applicable in the high-penetration system of wind power. Finally, based on the PSASP platform, the wind farm model is integrated into the IEEE 39-bus New England system for simulation. The simulation results show that the method proposed in this paper can quickly search for the critical line set (CLS) and accurately identify the LE under high wind power penetration, effectively avoiding the unexpected tripping of backup protection and preventing cascading outages.

Developing an Optimal Framework for PMU Placement Based on Active Distribution System State Estimation Considering Cost-Worth Analysis

Due to low levels of observability and automation in active distribution networks (ADNs), the deployment of accurate measurement devices would be inevitable to increase the network observability. This work develops an optimal framework for phasor measurement unit (PMU) placement considering the accuracy of the distribution system state estimation (DSSE) process. In this regard, first, considering the significance of active power in supplying network loads, an active power sensitivity analysis is conducted in which the accuracy of active power injection at each bus is multiplied by its value of lost load. In this way, the accuracy of active power estimation could be transformed into a monetary value. Then, based on the determined sensitivity criterion, the optimal placement of PMUs has been performed with the aim of appropriate accuracy for the DSSE. On the other hand, the accuracy of the results obtained in the state estimation procedure can affect the estimation of line-loadings and lead to load shedding due to the low accuracy of estimated line-loadings. It is noteworthy that due to the high integration of distributed energy resources in distribution systems, ADNs would be prone to congestion issues. Therefore, in the next stage, this perspective is also used for determining the optimal number and location of PMUs in ADNs to improve their observability based on the operational conditions of the network. Finally, the developed algorithm is applied on the 77-bus-UK-test distribution network to investigate its effectiveness in improving the DSSE accuracy and mitigating interrupted loads. The numerical results indicate that implementing the proposed framework not only increases the DSSE accuracy but also decreases the cost of compensating the accuracy of active power by 7% compared to the typical network without PMUs. Further, the results show that the proposed model significantly mitigates the total curtailed loads due to the low accuracy of estimated line-loadings.

Decentralized Frequency Control of AC Microgrids: An Estimation-Based Consensus Approach

In this article, a decentralized secondary control (SC) based on the active power estimation (APE) is presented. This achievement is realized by employing the unique feature of frequency as a global variable in autonomous ac microgrids (MGs). The APE is merely based on the droop coefficient of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P-{\omega }$ </tex-math></inline-formula> characteristics. The decentralized SC, utilizing a consensus protocol, restores the MG frequency to the nominal value while maintaining accurate power sharing of the droop mechanism. The consensus protocol is estimation-based and does not require communication infrastructure. In addition to the proposition of stability analysis method, experimental results with four distributed generation units (DGUs) also verify the effectiveness of the proposed SC structure.

Signal quality based power output prediction of a real distribution transformer station using M5P model tree

Forecasting the output power of transformer stations is a prominent analysis of power systems, which is key aspect of load balancing. Moreover, power forecasting may lead to maximized energy efficiency in the electrical grid. In this study, a fully operational real distribution transformer station which is resident in Istanbul is investigated. Transformer’s electrical characteristics which are strictly related with signal quality, are recorded every fifteen minute for four months, are employed to predict power output of the transformer. As a signal quality concern, a local electrical distribution company must supply stable and distortion free signal waveforms to the consumers where frequency-voltage stability, reduced THD values are satisfied. In order to estimate power output of the transformer, these parameters are subjected to well-known machine learning (ML) methods and consequently most effective methods are selected. In this paper M5P tree algorithm is promoted for total active power estimation of the analyzed transformer station. The M5P results have been compared with the recorded results using Root Mean Square Error (RMSE) and Correlation Coefficient (R) as 78.55 and 0.986, respectively. According to the results, the M5P model is performed quite efficiently in power estimation for the output power of distribution transformer station.

A Novel VSC HVDC Frequency Control Strategy based on Neural Network Power Estimation using ROCOF

This paper introduces a novel VSC HVDC frequency control strategy by Active Power Estimation. Although VSC HVDC has a much faster control speed than synchronous generator, frequency control of VSC is similar to that of governor. If VSC can use the fast-control character in frequency control, frequency stability can be improved. For fast control, proper power order is important and to estimate the power, this paper uses a neural network that solves the nonlinear relationship between input and output easily. To make quick control before the frequency reaches nadir, ROCOF is used as an input variable of the neural network. It can be seen that when the load of the system is greatly changed, the frequency fluctuation is significantly lowered when the VSC changes the output by the estimated power. Control of the VSC through the neural network is expected to enable faster frequency control than previously possible.

Intelligent optimization of active power output of wind farm during fault recovery with wake effect

In order to improve analytical accuracy of active power in wind power plant group during the period of failure recovery, a kind of optimization method for Nataf point estimation of active power for wind power plant group during the period of failure recovery was proposed in the thesis. Firstly, reliability model for wind speed was researched; modified Jensen was used to amend wake flow model; the lower wind speed of the draught fan was calculated; non-uniform wind speed field under complex terrain was accurately described. Secondly, three-point estimation algorithm based on Nataf conversion was improved so as to make the incomplete multivariable information be handled by the algorithm; what’s more, the accuracy of estimation for power variable was improved by taking advantages of correlation for random variable. Finally, through the simulation experiment, performance advantages of the algorithm are verified.

Influence of the Wake Effect on Electrical Dynamics of Commercial Tidal Farms: Application to the Alderney Race (France)

Marine renewable energies (MRE) allow the diversification of the energy mix and are thus developing rapidly. The extraction of tidal energy is one of the most promising MRE and is nowadays in a preindustrial phase. The Alderney Race (Raz Blanchard in French), situated between the island of Alderney (Channel Islands) and the Cap de la Hague in France, capitalizes a resource estimated between 5 and 14 TWh/year. This great tidal resource attracts tidal energy developers. Harnessing the high potential of the Alderney Race requires deploying numerous turbines in arrays. The purpose of this paper is to investigate the influence of the wake effects on the electrical dynamics of commercial tidal farms. The proposed methodology is applied to a hypothetical tidal farm in the Alderney Race. The influence of the wake effects on the power produced by a tidal farm is assessed by comparing simulations where the effect is neglected to simulations with wake effects. Two operational conditions are analyzed: Normal operation (one-day simulation with varying tidal speed); grid disturbances (voltage dip at the tidal farm point of common coupling). Simulations show that neglecting the wake effect implies significant errors in active power estimation at the point of common coupling during normal operation. Moreover, differences in the behavior of the system during voltage dip occur if the wake effect is not taken into account.

Active power estimation of photovoltaic generators for distribution network planning based on correlation models

For the last years, PV (photovoltaic) generation has been having an important impact on LV (low voltage) and MV (medium voltage) grids in Spain, due to the increasing number of installations. As power monitoring of the generation is in most cases not required, utilities are forced to make assumptions on PV power generation in order to perform network planning studies for both peak demand and contingency analysis. These assumptions increase errors committed during the analysis, as the number of PV installations increases. This paper presents a methodology for estimating PV active power generation values for planning purposes in MV and LV power systems, from historic generation data, based on the development of correlation models. This methodology is applied to three different examples, using predictors based on real registered data. The methodology was also applied in a typical grid study and it's error was determined.

Estimation of the Amplitude Square Using the Interpolated Discrete Fourier Transform

Estimation of the Amplitude Square Using the Interpolated Discrete Fourier Transform To improve the estimation of active power, the possibility of estimating the amplitude square of a signal component using the interpolation of the squared amplitude discrete Fourier transform (DFT) coefficients is presented. As with an energy-based approach, the amplitude square can be estimated with the squared amplitude DFT coefficients around the component peak and a suitable interpolation algorithm. The use of the Hann window, for which the frequency spectrum is well known, and the three largest local amplitude DFT coefficients gives lower systematic errors in squared interpolated approach or in better interpolated squared approach than the energy-based approach, although the frequency has to be estimated in the first step. All investigated algorithms have almost the same noise propagation and the standard deviations are about two times larger than the Cramér-Rao lower bound.

Active Power Measurements - An Overview and a Comparison of DSP Algorithms by Noncoherent Sampling

Active Power Measurements - An Overview and a Comparison of DSP Algorithms by Noncoherent Sampling This paper presents an overview of algorithms for one-phase active power estimation using digital signal processing in the time domain and in the frequency domain, and compares the properties of these algorithms for a sinusoidal test signal. The comparison involves not only algorithms that have already been published, but also a new algorithm. Additional information concerning some known algorithms is also included. We present the results of computer simulations in MATLAB and measurement results gained by means of computer plug-in boards, both multiplexed and using simultaneous signal sampling. The use of new cosine windows with a recently published iterative algorithm is also included, and the influence of additive noise in the test signal is evaluated.

Active power measurement algorithm for power system signals under non-sinusoidal conditions and wide-range frequency deviations

A simple approach in the design of digital algorithms for active power under non-sinusoidal conditions and wide-range frequency deviations is presented. In order to accurately estimate the fundamental component of the active power, the pure sinusoidal voltage model was assumed. Adaptive finite-impulse-response (FIR) filters are used to decompose out the fundamental components of the input voltage and current signals and to minimise the noise effects. The total active power estimation was carried out by applying adaptive low-pass FIR filters for averaging of the instantaneous power. A simple algorithm for simultaneous frequency and both fundamental and total active power estimation, in a wide range of frequency changes, with benefits in a reduced complexity and computational efforts is obtained. To demonstrate the performance of the developed algorithms, the results of computer simulations and laboratory testing are presented. The algorithm shows a very high level of robustness, as well as high measurement accuracy over a wide range of frequency changes. The algorithm convergence provided fast response and adaptability, thus, this technique provides an accurate active power estimates in about one period. It has been found that the proposed algorithm really meets the needs of online applications.

UPS Parallel Balanced Operation Without Explicit Estimation of Reactive Power—A Simpler Scheme

A novel scheme of the well-known technique for parallel operation of uninterruptible power supply systems, namely frequency and voltage droop control method, is presented. The innovation relies on active power estimation, together with the use of a piecewise-continuous nonlinear function, in order to eliminate the need for costly cascaded low-pass filters usually employed to estimate the reactive power. Moreover, the whole scheme is derived in the time domain on a state-space approach. As a consequence, models that are lower order and more amenable to nonlinear analysis are obtained. Simulated results for two UPS parallel operation connected to a resistive load are presented. The results indicate that the novel technique leads to a proper load sharing.

Measurement of active power by time domain digital signal processing

This paper compares DSP time domain algorithms of one-phase active power measurement by non-coherent sampling that are superior to some frequency domain algorithms in active power estimation of non-sinusoidal signals. The measurement bias, uncertainty and sampling time necessary for the required accuracy are compared for various data windows with the use of simulations and measurements. Both monofrequency and multifrequency signals are dealt with. Analytical formulas are presented for the active power bias for rectangular and general cosine windows and for standard uncertainty of active power measurement. A simple method is described for correction of the multiplexing delay in power measurement using multiplexing DAQ boards. The information from this paper can also be used for measuring energy consumption and three-phase power.