• 专利附录
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    • 专利摘要:
    Procedure and system for analyzing the quality of energy and 2S2PQ quality index for characterization of the signal at a point of the power supply. The procedure comprises: acquiring a voltage signal u (t), obtaining a preprocessed signal ue (t) adaptable to the processor, processing the signal to obtain a series of statistical features S1, S2, ..., SN characteristic of it, store said features, graphically represent the relative frequencies of said features associated with a temporal window of analysis Δ t selected by the user, compute the 2S2PQ index of power supply quality. The invention also comprises a system for statistical characterization at a point of supply and quality index, as well as a computer program incorporating code adapted to perform the steps of the procedure, when said program is executed on a computer or any other form of hardware programmable. (Machine-translation by Google Translate, not legally binding)
    公开号:ES2711204A1
    申请号:ES201700746
    申请日:2017-10-30
    公开日:2019-04-30
    发明作者:Perez Agustín Agüera；De La Rosa Juan José Gonzalez；Oliveros Olivia Florencias；Salas José Carlos Palomares；Gavira Manuel Jesús Espinosa；Pro Alvaro Jimenez；Fernandez José María Sierra
    申请人:Universidad de Cadiz；
    IPC主号:
    专利说明:

    [0001]
    [0002] Procedure and system for quality analysis of energy and quality index 2S2PQ, characterization of the signal at a point of the power supply.
    [0003]
    [0004] Sector of the technique
    [0005]
    [0006] According to its characteristics, this invention is located in the field of research of Industrial Metrology and Instrumentation, and more specifically in the field of intelligent instrumentation applied to the Power Quality Analyzers (Power Quality, PQ), which are currently a tool in full development in the context of the smart grid (Smart Grid).
    [0007]
    [0008] State of the art
    [0009]
    [0010] This section includes the objectives of current instrumentation equipment in the field of energy monitoring, energy quality indices that implement current equipment, and patents related to measurement methods associated with energy quality. closer to the object of the proposed invention.
    [0011]
    [0012] 1.- Objectives of the current instrumentation equipment in energy monitoring.
    [0013] With the inclusion of distributed generation systems, unconventional types of loading and storage (for example, electric vehicles) and microgrids (microgrids) arise, so it is necessary to propose new indices that take into account the energy that is delivered to the end user and their behavior on the network. In the new context of research in the Smart Grid, proposals for monitoring procedures that seek to optimize quality indices are emerging. However, many of these methods have not been validated under real operating conditions but have been validated based on numerical simulations.
    [0014]
    [0015] The modernization of the electrical network is assuming the integration of intelligent electronic devices, which allow integrating solutions for energy meters increasingly affordable, as exposed Apparatus, E., 2008. The key role of Intelligent Electronic Devices (IED) in Advanced Distribution Automation (ADA). In CICED2008. Technical Session 3. Protection, control, communication and automation of distribution network, pp. 1-7).
    [0016]
    [0017] However, the monitoring of the network still has a large number of challenges, mainly associated with the necessary management of an increasing number of monitoring sites, and the adequate management of the large volume of data that this entails, as well as the fact that that the new electronic devices used in electrical installations are more sensitive to small problems of energy quality and variations in supply conditions, according to Bollen, MHJ et al., 2010. Trends, challenges and opportunities in power quality research. European Transactions on Electrical Power, 20 (1), pp.3-18. 2010
    [0018]
    [0019] In this sense, during the last two decades there has been a marked interest on the part of the scientific community in reaching a consensus on the development of these new quality indices and the procedures that should be incorporated in the measurement instrumentation of the electrical network ( CIRED / CIGRE working group C4.112 "Guidelines for power quality monitoring").
    [0020]
    [0021] In this context, the quality level of the network depends on the price that customers are willing to assume, being necessary to quantify the state of the network under normal conditions, of good quality, as well as in critical conditions. Due to this, new standards of quality of energy are created, associated with standards, which are mostly applied for contractual purposes to demand compliance with the system according to the good quality of the energy. The standards EN 50160, UNE-EN 50160 and IEC 61000-3-6 are related to the characteristics that must be met by the voltage supplied by the general distribution networks, and the evaluation of the emission limits for the connection of distortion systems to systems of power MV, HV and EHV.
    [0022]
    [0023] It is known that there are phenomena that cause electromagnetic disturbances classified by the International Electrotechnical Commission (IEC), which condition the behavior of certain networks. The voltage variation caused by these events is quantified in two different ways: through descriptive indices, and through reliability indices. The former are used for energy quality events that are notoriously random in nature, may vary over time and are highly dependent on the topology of the system; they can not be limited and only approximate figures are given after their occurrence. The most commonly monitored variables are frequency, RMS voltage (Root Mean Square or Root Mean Square), imbalances between phases, harmonics of voltage and current, voltage gaps (sags) and surges (swells). ).
    [0024]
    [0025] Second, practices such as those of the Institute of Electrical and Electronics Engineers, 2009 IEEE - Recommended Practices, as well as by organizations such as CENELEC and CIGRED, are recommended, where they propose the measurement of current parameters as well as current-tension based on reliability indexes (Reliability Indices). The reliability indices allow the operator of the network to compute the number of interruptions, the sum of the duration of all of them and the average duration of all interruptions during a year. All records are calculated through the SAIFI, SAIDI, CAIDI indices that quantify the system's energy performance for all customers. For example, at an industrial level, electric companies usually report indices such as the System Average RMS Variation Frequency (SARFI), which is essentially a count of the number of times the magnitude and duration fall below a predetermined threshold.
    [0026]
    [0027] However, the reliability indices are not representative of the perturbations directly suffered by the end users of the system. At the consumer level, descriptive indices are established, which are measured at a specific connection point of a transmission or distribution system; the measured levels are influenced by disturbances from all other parts of the interconnected system. In this sense, the statistical values depend mainly on the behavior of the network during a time, reflecting the situation in a given site or in a system as a whole. The following are proposed: descriptive indexes of sites and descriptive indexes of the system.
    [0028]
    [0029] In this way, the daily and weekly percentiles are calculated in 3 or 10 minute intervals, without necessarily storing unmanageable amounts of raw data, as well as in weekly and annual readings. For example: in the case of voltage sags, the site indices are used for the evaluation of the compatibility between the sensitive equipment and the power source, and can be used as an aid for the election of a mitigation method of the voltage drop, offering information to local customers about voltage drops in a site during a certain period of time (3-5 year follow-up incorporating interannual variations, according to the Institute of Electrical and Electronics Engineers, 2014. IEEE Std 1564 - Guide for Voltage Sag Indices).
    [0030]
    [0031] Site indices can be presented in different ways: in a tension table, in a contour plot, as the number of more severe events in a certain curve (ITIC curve or SEMI curve F47) or below a certain residual stress when they occur sag events gravity is calculated from the residual stress (from 90-10%) and the duration of the voltage drop in combination with the reference curve, as expressed by Gallo, D., Landi, C. & Luiso , M., 2009. Accuracy Analysis of Voltage Dip Measurement. XIX IMEKO World Congress, Portugal, pp.743-748.
    [0032]
    [0033] The system index is the value of the site index not exceeded for a high percentage of sites. The percentages are defined for each index and individual parameter. Examples of high percentile sites are 90, 95 and 99% (IEEE 2009).
    [0034]
    [0035] However, the information obtained by consumers does not include measurements of the quality of the supply, since currently these indices can only be used to detect typical levels of disturbances, and it is the network operator who can dispose of them.
    [0036]
    [0037] 2. - energy quality indices that implement the current equipment and optimization of the existing methods and indices.
    [0038]
    [0039] The standard UNE-EN 6100-4-30: 2015 on testing and measurement techniques, establishes methods of measuring the quality of energy for different types of instruments, mainly class A. It includes a group of parameters to be calculated with specification of the performance, such as: frequency, RMS value, flashing, voltage interruptions, overvoltages, phase decompensation, harmonics of voltage and current, as well as inter-harmonics. The network analyzers that are currently on the market measure the energy based on these values, according to the characteristics and types of distortions typically detected in the distribution networks. The monitoring through these procedures and indexes, seeks to classify the quality of energy for contractual purposes, resulting in the reading of the instrument a percentage of adequacy to the parameters measured in the standards, as a method to verify the state of the system. However, these data contain a large amount of potentially valuable information about the behavior of the quality of the electrical supply, which are not treated or stored.
    [0040]
    [0041] Next, the procedures related to the current invention are exposed, all based on the FFT (Fast Fourier Transform, Fast Fourier Transform).
    [0042]
    [0043] The first determines the response of the system loads to voltage drops, based on the maximum current values of an event type Molnar-Matei, F., Moga, M. & lovan, M., 2011. Procedure for determining The response of the system load to voltage sags. 2011 IEEE EUROCON - International Conference on Computer as a Tool, (April), pp. 1-4 In this case you gain in the detection of maxima, but you lose information.
    [0044]
    [0045] Cheng et al. (Cheng et al 8th Conference on Industrial Electronics and Applications, 2011) propose a high resolution method in order to extract the flicker components that make up the signal. This is a robust method, however it requires a high mathematical computation.
    [0046]
    [0047] The methods of estimation of the high resolution spectrum, MUSIC and ESPRIT proposed by Leonowicz, Z & Lobos, T., 2006. Power Quality Evaluation using Advanced Spectrum Estimation Methods. 2006 International Conference on Power System Technology, (April), pp.
    [0048] 1-6. As an alternative to those based on traditional Fourier analysis, they can improve the precision of spectral parameters of distorted power signals.
    [0049]
    [0050] Another antecedent spectral method is that developed by Meyer, J. & Schegner, P., 2006. Characterization of power quality in low voltage networks based on modeling by mixture distributions. 2006 International Conference on Probabilistic Methods Applied to Power Systems, (January 2015), pp. 1-6. It is focused on the classification and detection of anomalies, Starting from the study of current harmonics in several networks connected to the same operator of the system. From the evolution of these intensities, qualitative criteria are established for the analysis of the behavior of the network. The method proves to be scalable since it is capable of analyzing supply characteristics both in domestic networks and in industrial networks. Carry out a focused study at the point of delivery of the consumer, and over a predefined time interval.
    [0051]
    [0052] In addition, Bucci, G., Fiorucci, E. & Landi, C., 2003. Digital measurement equipment for steadystate PQ measurements. 2003 IEEE Bologna PowerTech - Conference Proceedings, 4, develop equipment for steady state PQ measurements, based on an algorithm for the measurement of VRMS and VTHD% based on the sliding window technique. Their results have been obtained from simulations and experimental tests.
    [0053]
    [0054] With respect to the higher order statistics (Higher-Order Statistics, HOS) and their use in the detection of disturbances in the voltage signals for the analysis of the quality of energy, the studies carried out by Ribeiro, M V. et al., 2007. Detection of disturbances in voltage signais for power quality analysis using HOS. Eurasip Journal on Advances in Signal Processing, 2007 , where it is demonstrated that the analysis based on HOS, with respect to other aforementioned methods, requires less computation, achieving a good temporal resolution of analysis below a cycle, as well as detecting parameters stable during all the time of computation.
    [0055]
    [0056] The HOS also have the peculiarity of remaining constant over time for an undisturbed signal, as shown in Aguera-Perez et al. 2011. Characterization of electricaI sags and swells using higher-order statistical estimators, Measurement, 44, pp.1453-1460. This feature is usable by the instrument that incorporates this invention. It should be noted that the present invention addresses the statistical study of the signal with a method applicable to any type of network (LV, MV, HV) and with a resolution capable of detecting not only events, but also the state of the network in an interval of time predefined by the user, as well as compute indexes of site and indexes of system, aligned all this to the norm and the state of the art in the matter.
    [0057]
    [0058] In relation to artificial intelligence techniques, the first background aligned with the present invention consists of a total energy quality index for electric networks using neural networks: Raptis, T E. et al., 2015. Total Power Quality Index for Electrical Networks Using Neural Networks. Energy Proceia, 74, pp.1499-1507 , which incorporates different time scales. The second selected method has been developed by Monedero, et al., 2005. Classification of Electrical Disturbances in Real Time Using Neural Networks. IEEE Transactions on Power Delivery, 23 (11), pp.1-7, which classifies electrical disturbances in real time (based on standardized indices) using neural networks. An electric pattern generator has been developed as a training tool. The system is integrated into a software tool for a PC with hardware connected to the acquisition. Based on synthetic signals, this work proposes a methodology that takes into account the behavior of the network, however it is not able to adequately compute the indices, according to the same time scale.
    [0059]
    [0060] 3.- Patents relating to measurement methods associated with energy quality
    [0061]
    [0062] In the invention "Fundamental frequency stability and harmonic analysis" (US 2015/0355249 A1), the method, apparatus and computer support to measure the frequency of an electrical signal is developed, through a main sampling window that allows detecting the level harmonic that contains the signal. The accuracy of the fundamental frequency is estimated through a group of subwindows of the main window, comparing the estimated fundamental frequency of each window.
    [0063] Another instrument and techniques for non-intrusive monitoring of the network is explained in the international patent WO 2017/066658 A1, which performs the detection of physical parameters such as electric and / or magnetic fields to monitor and / or control electric discharges, allowing monitoring the consumption of electricity in homes or businesses.
    [0064]
    [0065] For the real-time harmonic detection of electric power systems, based on the method of determination of direct weight, the patent CN103383413 (A), proposes a network structure of neutral sinusoidal function. With this method, redundant network training in traditional harmonic detection methods is avoided; network weights containing harmonic amplitudes and phase information are obtained by one-step calculation, resulting in high detection efficiency.
    [0066]
    [0067] Another method of analysis in the time domain for the detection of harmonic components in a distorted sine wave (voltage and current) which proposes to reduce the calculation capacity of a processor to ensure storage capacity is proposed in patent KR20100049412 (A ). The wave distortion is estimated based on a weighted value. The distorted waves of the estimated voltage and current are compared to the input signal shape. A harmonic extraction is performed using a relationship equation.
    [0068]
    [0069] The patents mentioned above show that it is common for manufacturers to develop tools with insufficient temporal scalability. Likewise, there is a lack of knowledge and agreement on a number of aspects of the monitoring process, and in particular on the processing of the data.
    [0070]
    [0071] A more convenient method of verifying the performance of the clients (through the indexes of sites), consists of measuring the parameters of quality of the energy in parallel with the smart energy meters, reducing costs in the installation. Institute of Electrical and Electronics Engineers, 2009. IEEE - Recommended Practices.
    [0072]
    [0073] In that sense, new proposals are emerging that consist of incorporating new functionalities and procedures for measuring the quality of energy in smart meters, in order to guarantee a mapping of the system based on a more intuitive representation of statistics and widely accepted indices. , according to Meyer, J., Klatt, M & Schegner, P., 2011 in: Power quality challenges in future distribution networks. 20112nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, pp. 1-6.
    [0074] Precisely in this group of proposals the present invention is framed as a new procedure and system based on the obtaining of graphic representations of the statistical behavior of the signal, accompanied by a quality index scalable in time, so that said system is more intuitive for users and easily incorporated into the new intelligent instrumentation to undertake on-line monitoring.
    [0075]
    [0076] Explanation of the invention
    [0077]
    [0078] The present invention seeks to solve the drawbacks raised above by a method of characterization of the supply signal where specifically the data of the electrical signal u (t) is obtained, according to the characteristics of the point of the network under test. Said signal is preprocessed in the time domain, resulting in a vector ue (t) adapted to the processing conditions. Subsequently, a series of statistical features S1, S2, ..., SN characteristic of the vector ue (t) are calculated. The statistics are stored in a database (data base). Next, graphical representations are made showing the relative frequencies of the statistics during the time interval selected. Based on these statistical features, a quality index representative of the selected time interval is formulated.
    [0079]
    [0080] In a possible realization, the preprocessing of the signal u (t) includes the adaptation of the signal as a function of the nominal values of the supply signal.
    [0081]
    [0082] In a possible embodiment, the procedure comprises the calculation of statistical statistics S1, S2, ... SN, to characterize a certain period T of sampling the signal (hereinafter this set of values will be called N-up). . These statistics fulfill that, for a preprocessed nominal (or healthy) signal, they take a known and fixed value (Sj nominal value). Thus, S1, S2 ..., SN will be the nominal N-upla associated with a healthy signal.
    [0083]
    [0084] In a possible embodiment, the procedure comprises the calculation of the previous statistics for M sampling periods of the signal, such that they cover a time interval At, where At = M T. Thus, an M number of N-uplas are obtained, which they make up a set of statistics denoted by {Sij} where i = 1,. , M and j = 1,. , N
    [0085]
    [0086] In a possible embodiment, the procedure comprises the representation of the relative frequencies of the N-uplas associated to a certain selected time interval and corresponding calculation of the 2S2PQ index that is detailed below.
    [0087]
    [0088] In a possible embodiment, the representation of relative frequencies comprises the use of histograms of one or more dimensions.
    [0089]
    [0090] In a possible embodiment, the electric supply quality index comprises the calculation of the deviations of the statistics with respect to their nominal values. (Statistical Signal Processing Power Quality Index, 2S2PQ). Following the expression:
    [0091]
    [0092] 2S2PQAt = f (S11 - S11, S2 - Sl2, Sij - S j., SMN - SMN ^
    [0093]
    [0094] where At corresponds to the selected time interval, S11, S12,. , S MN, are the statistics associated with the signal under test; S1, S2, ..., SN represent the nominal statistics that correspond to a healthy signal; and 2S2PQ the quality index associated with the i-th computation period of the signal under test.
    [0095]
    [0096] In another aspect of the present invention, there is provided a system for the statistical characterization at a point of supply and quality index, comprising: means for sampling and preprocessing the electric supply signal u (t), means for processing the signal ue (t), means to determine the statistics S11, S12,. , S MN, means of storage for the creation of a record of the signal under test, means for the user to record (a time interval associated with the representation of frequencies and the 2S2PQ index, critical thresholds), means for the graphic representation of the relative frequencies of the statistics according to one or several time intervals and the use of histograms of one or more dimensions, means for the computation of the 2S2PQ index, means so that the process can be executed continuously if the user so decides, means for the communication of alarms to the user.
    [0097]
    [0098] In another aspect of the invention, a computer program is provided comprising code means adapted to perform the steps of the characterization procedure of the electrical supply signal and quality index 2S2PQ, when said program is executed in a computer or any other form of programmable hardware.
    [0099] Finally, they provide multiple uses for this invention according to the industrial applications that are applicable in this sector.
    [0100]
    [0101] Brief description of the figures
    [0102]
    [0103] Fig. 1. The procedure for measuring the instrument is described: The steps by which the procedure must be applied by a conventional instrument are listed. Includes the different stages of the analysis for obtaining the characteristics of the electrical supply signal, at a point in the network: 1. Acquisition and preprocessing of the signal, 2. Processing, 3. Data storage, 4. Graphic representation and Computation of the 2S2PQ index.
    [0104]
    [0105] Fig. 2. 2D histogram: Possible representation of frequencies and 2S2PQ calculation for the analysis in a point of the network based on the following conditions: At = 24 h, considering two statistics (kurtosis and variance) than in a signal without distortion they should obtain the values S1 = 1.5 and S2 = 0.5, respectively. Thus the cut point of the axes in the coordinates (0.5, 1.5) establishes the nominal operation point.
    [0106]
    [0107] Fig. 3. 2D histogram: For comparison with Fig.2, another possible representation of frequencies and calculation of 2S2PQ for the analysis in a point of the network based on the following conditions: At = 24 h, considering two statistics (kurtosis and variance) that in a signal without distortion should obtain the values S1 = 1.5 and S2 = 0.5 respectively. Thus the cut point of the axes in the coordinates (0.5, 1.5) establishes the nominal operating point.
    [0108]
    [0109] Fig. 4 2D Histogram: Changing the time scale with respect to Fig. 2 and Fig. 3, another possible representation of frequencies and calculation of 2S2PQ for the analysis in a point of the network based on the following conditions: At = 2 h, considering two statistics (kurtosis and variance) that in a signal without distortion should obtain the values S1 = 1.5 and S2 = 0.5 respectively. Thus the cut point of the axes in the coordinates (0.5, 1.5) establishes the nominal operating point.
    [0110]
    [0111] Fig. 5. 2D histogram: For comparison with Fig 4, another possible representation of frequencies and calculation of 2S2PQ for the analysis in a point of the network based on the following conditions: At = 2 h, considering two statistics (kurtosis and variance) ) that in a signal without distortion they should obtain the values S1 = 1.5 and S2 = 0.5 respectively. Thus the cut point of the axes in the coordinates (0.5, 1.5) establishes the nominal operating point.
    [0112]
    [0113] Fig. 6. 2D histogram: Changing again the time scale to the order of the seconds, another possible representation of frequencies and calculation of 2S2PQ for the analysis in a point of the network based on the following conditions: At = 2 s, considering two statistics (kurtosis and variance) that in a healthy signal should obtain the values S1 = 1.5 and S2 = 0.5 respectively. Thus the cut point of the axes in the coordinates (0.5, 1.5) establishes the nominal operating point.
    [0114]
    [0115] Mode of realization of the invention
    [0116]
    [0117] The present invention characterizes the supply of the network at a specific delivery point. The proposed method opens a range of possibilities in relation to the study at different time scales and voltage ranges (LV-MV-HV).
    [0118]
    [0119] The following referenced embodiments are provided by way of examples, and are not intended to be limiting of the present invention.
    [0120] A procedure is proposed that increases the flexibility of the monitoring system, giving answer to several essential problems of the measurement of the quality of the energy. The procedure allows the equipment to work under the concept of scalability, monitoring different points of the network with the same method, as well as enabling the analysis in different time scales and measurements in different types of networks and points of the same.
    [0121] The procedure offers a characterization of the electrical supply signal in a selected time interval, which reflects whether the statistics of the signal have experienced significant, transient or constant variations with respect to its nominal range. Likewise, a new quality index of the electric supply is proposed, which brings together in a numerical value the information of the statistics represented, and which is computed according to the selected time interval.
    [0122] The development of the process comprises several stages. Figure 1 shows a diagram of the method of the invention where the different stages that must be carried out consecutively are illustrated:
    [0123] 1. - Stage 1. Acquisition and preprocessing of the signal
    [0124] 1.1. - The data of the electrical signal u (t) is obtained, according to the characteristics of the point of the network under test.
    [0125] 1.2. - Subsequently, the signal is preprocessed in the time domain, resulting in a vector ue (t). The nominal operation area of the network is defined, which is determined by a set of specific statistics: S1, S2, ..., SN or nominal N-up.
    [0126] 2. - Stage 2. Processing of the signal
    [0127] The signal ue (t), is sold with the purpose of cutting it into a number of M periods of computation. The M N-uplas that characterize the signals are calculated (t), obtaining the set of statistics {Sij}, where i = 1, ..., M (mdice relative to the period) and j = 1, ..., N (index relative to the statistic considered).
    [0128] The following is obtained the deviation of each statistic Sij with respect to the nominal value of said statistic Sj associated with the nominal operation area of the network: Sij - Sj. This allows to define indices based on the deviations observed in the voltage signal over their nominal values.
    [0129] 3. - Stage 3. Data storage
    [0130] The data is dumped in real time in a register that contains the information associated with the supply point under test.
    [0131] 4. - Stage 4. Graphic representation and computation of the 2S2PQ index.
    [0132] The analysis is defined according to the time interval preselected by the user. In the same way, the analysis can be updated as new data are incorporated into the registry. As for the graphical representations, the histograms characterize the statistical behavior of the signal describing the relative frequencies of the N-uplas associated to the predefined measurement time At, offering a graphic summary of the state of the network during the. The representations show the dispersion of the statistics with respect to the zone of nominal operation of the network. The figures from 2 to 6 are conceived as examples considering two statistics in the analysis: variance and kurtosis. A preprocessed nominal signal according to this example yields values of 0.5 for the variance and 1.5 for the kurtosis (Aguera-Perez et al., 2011; Characterization of electrical sags and swells using higher-order statistical estimators, Measurement, 44 , pp.1453-1460). The nominal 2-up is defined by the values (0,5, 1,5). Under these conditions, Figures 2-6 present 2D histograms for the 2-uplas corresponding to different time intervals. The white axes on the figure with the cutoff point at (0.5, 1.5) mark the nominal operation area; that is, a perfect signal during the interval studied would result in a 100% frequency on that point. Any deviation on the perfect signal causes the computed statistics (and therefore the 2- uplas) to vary, appearing new frequencies on other coordinates of the graph depending on the characteristics of the perturbation.
    [0133]
    [0134] Next, the 2S2PQ index associated to each predefined measurement time At is calculated. This numerical index synthesizes the deviations observed in the statistics with respect to their nominal values. Thus, a perfect signal throughout the selected interval is characterized by a fixed 2S2PQ value, since all differences are zero. In the same way any deviation on the perfect signal triggers changes on the nominal values and therefore the 2S2PQ will acquire other values.
    [0135]
    [0136] Consequently, based on the statistics associated with the predefined time interval, the 2S2PQ index is formulated for the quality of the energy at that point of the electric network according to the expression:
    [0137]
    [0138] 2S2PQAt = f (S11 - S11, S2 - S ^ --.- Sij - S ^ ..., SMN - SMnX
    [0139]
    [0140] Where:
    [0141]
    [0142] At = selected time interval.
    [0143]
    [0144] Sij = jth statistic corresponding to the i-th period.
    [0145]
    [0146] Sj = nominal j-th statistic.
    [0147]
    [0148] M = number of periods considered in the interval At.
    [0149]
    [0150] N = number of statistics used for the characterization of the signal.
    [0151]
    [0152] The index is calculated according to the number of periods M of the signal contained in the interval At. In the same way, the representation in histograms is done by relative frequencies. Thus, the results obtained are independent of the duration of the considered At interval, allowing the comparison of different time scales in the same context. However, when At is small (that is, the analysis is based on few N -uplas), point deviations decisively influence both the graphical representation and the computation of the index, allowing a characterization of transient events. When At grows, punctual phenomena are masked, making explicit the long-term behavior of the signal. By selecting different At it is possible to compare, in a same qualitative and quantitative framework, the behavior of the signal at different time scales.
    [0153]
    [0154] For a better explanation of the invention, the figures shown below are analyzed as examples, where the relative frequencies of the 2-uplas, defined by variance and kurtosis, are represented by 2D histograms.
    [0155]
    [0156] Figure 2 represents the trend of the statistical behavior of the signal over a 24-hour day at a point in the network. The point of operation corresponds to the statisticians nominal (point of intersection of the coordinate axes). The 2S2PQ index has been defined for this example as the average of deviations in absolute value, and yields a value of 0.019.
    [0157]
    [0158] Figures 2 and 3 are made in the same 24-hour interval, on two significantly different days as described below. Although they have the same value of the 2S2PQ index (0.019), the graphical representation shows that the behavior is qualitatively different. Figure 2 shows that many of the measurements fall in the nominal zone, although a significant amount of them has been scattered to the left. This scattering causes the decrease in the value of the 2S2PQ index. The signal in Figure 3 exhibits a more stable behavior (greater number of points concentrated in the same region of the plane), but there is an important bias regarding the geometric center associated with the nominal behavior. All this demonstrates that, the procedure of characterization of the signal by means of the histograms offers complementary information to the numeric value of the 2S2PQ index.
    [0159]
    [0160] Below are three figures (from 4 to 6) that show the scalability of the procedure (different measurement intervals At) from the information contained in figure 2. Figure 4 shows a sampling interval of 2 h (out of 8). -10 AM). The 2S2PQ index reflects acceptable behavior of the electrical supply signal, around 0.005.
    [0161]
    [0162] On the other hand, figure 5 also presents a sampling interval of 2 h, now in the time slot of 13-15 PM. When comparing both figures 4 and 5, it is concluded that in figure 5 the signal is more deteriorated, reflecting a value of the 2S2PQ index of 0.017.
    [0163]
    [0164] Finally, in figure 6, the representations are made during a measurement time of 2 s. A marked dispersion of the measurements with respect to the nominal geometric center is observed, which denotes a behavior associated with a transient event of the supply signal, reflecting a 2S2PQ index of 0.05, which is even worse than that associated to the period of 24 h, and represented in figure 2. This temporal scale of 2 s, consequently allows a greater resolution of the representations of the absolute frequencies; therefore it is suitable for the detection of transient events that occur in the electrical supply signal.
    [0165]
    [0166] Industrial application
    [0167]
    [0168] - The present invention is a procedure designed for smart measuring instruments (smart meters) and network analyzers.
    [0169]
    [0170] - Given the low computational requirements enables the detection of continuous events with a temporary resolution of up to one period (0.02 seconds).
    [0171]
    [0172] - It offers the possibility to perform network analysis in greater depth by allowing the comparison between different time scales. The following results could be obtained: "point A of the network historically offers a poor quality of electrical energy, although in the hourly section of Monday from 6:00 a.m. to 8:00 a.m. this is perfect." This is a determining factor for the programming of processes with the least possible impact on the network, for the identification of problematic equipment and for the selection of places where to connect sensitive equipment.
    [0173] - The comparison of different time scales allows the implementation of slogans of the type: "if the PQ index in the last cycles (0.2 seconds) has a variation of 5% with respect to the index observed in the last month, execute action X" .
    [0174]
    [0175] - Surveillance for claims to the electric system operator for contractual purposes.
    [0176] - According to the implementation on an industrial scale, the inputs of the procedure, characteristics of the system or network, are conveniently scaled, increasing the flexibility of the method and its ability to adapt to new environments and monitoring campaigns.
    [0177] - Allows easy interoperability between different instruments that incorporate this method of measurement.
    权利要求:
    Claims (19)
    [1]
    1. Procedure for characterizing an electrical supply signal, characterized by:
    - Sample an electrical supply signal u (t);
    - Preprocess the signal to obtain a scalable signal ue (t);
    - Obtain a series of statistics S1, S2, ... SN associated with the signal ue (t);
    - Create a temporary record of the statistics;
    - Represent the relative frequencies of the W-uplas, stored in the registry and associated to a certain measurement time;
    - Offer a quality index of the electrical supply signal;
    [2]
    2. Method according to claim 1 wherein the preprocessing of the signal u (t) is done according to the nominal values of the supply signals.
    [3]
    3. Procedure according to claims 1-2 where the statistics S1, S2, ... SN take a known and fixed value (nominal value) for a preprocessed sound (or nominal) signal S11, S12,. , S mn
    [4]
    4. Method according to claims 1-3 wherein the representation of the relative frequencies and the calculation of the index are associated to a certain interval of time.
    [5]
    5. Method according to claims 1-4 wherein the representation of relative frequencies is performed by the use of histograms of one or more dimensions.
    6. Procedure according to claims 1-5 wherein the quality index of the electrical supply signal is calculated according to the deviations of the statistics from their nominal values. (Statistical Signal Processing Power Quality Index, 2S2PQ).
    2S2PQAt = f (S11 - S11, S2 - S12, - Sij - ^ j ^ -, SMN - SM n X
    [6]
    6. Method according to claims 1-6 wherein the quality index 2S2PQ is compared with previously defined thresholds for the monitoring of the signal of the electrical supply through alarms.
    [7]
    7. System for the statistical characterization of higher order of the energy footprint of a supply point and quality index for electrical voltage, according to claims 1-6 comprising:
    - means for sampling the electrical supply signal u (t).
    - means to preprocess the signal u (t).
    - means to process the ue (t) signal.
    . means to determine the statistics S1, S2, ... SN.
    - means of storage for the creation of a record of the signal under test.
    - means for the user to record:
    - Temporal interval associated with the representation of frequencies and the 2S2PQ index. - Critical thresholds.
    - means for the graphic representation of the relative frequencies of the statistics according to one or more time intervals.
    - means for the graphical representation of the relative frequencies of the statistics by histograms of one or more dimensions.
    - means for computation of the 2S2PQ index.
    - means for the process to be executed continuously if the user so decides.
    - means for the communication of alarms to the user.
    [8]
    8. A computer program comprising code means adapted to perform the steps of the characterization procedure of the electrical supply signal and quality index 2S2PQ, according to claims 1 to 6, when said program is executed in a computer or any other form of programmable hardware
    [9]
    9. Use of the procedure of characterization of a signal of the electrical supply that according to claims 1-8 allows, to determine the quality of the energy delivered to the consumer at a point of supply.
    [10]
    10. Use of the procedure of characterization of a signal of the electrical supply that according to claims 1-8 allows, propose the follow-up based on daily and seasonal patterns.
    [11]
    11. Use of the characterization procedure of an electrical supply signal that according to Claims 1-8 allows scalability in a temporary space (from 1 signal period to infinity), and in different voltage ranges (LV-MV-HV ).
    [12]
    12. Use of the characterization procedure of an electrical supply signal that according to claims 1-8 allows, determine a site index, as well as a system index, which may be comparable with the indexes at the operator level of the network, in accordance with current international standards, for the location of supply failures.
    [13]
    13. Use of the procedure of characterization of a signal of the electrical supply that according to claims 1-8 allows, to implement the generation of alarms for the monitoring at industrial, public and domestic levels, when pre-defined critical thresholds are exceeded, with instructions in function of behavior at different time scales.
    [14]
    14. Use of the procedure of characterization of a signal of the electrical supply that according to claims 1-8 allows, conveniently adapted to new environments and monitoring campaigns.
    [15]
    15. Use of the characterization procedure of a signal of the electrical supply that according to claims 1-8 allows, to propose specifications adjusted to the supply of electrical energy, in order to incorporate it to contractual conditions.
    [16]
    16. Use of the characterization procedure of an electrical supply signal that according to claims 1-8 allows, be incorporated into intelligent measuring instruments, providing information to the producer-consumer on the behavior of its installation and for the surveillance of claims to the operator.
    [17]
    17. Use of the procedure of characterization of a signal of the electrical supply that according to claims 1-8 allows, to guarantee the interoperability between different intelligent measurement instruments, both fixed and portable, that incorporate this method of measurement, graphic representations and the 2S2PQ index.
    [18]
    18. Use of the characterization procedure of an electrical supply signal that according to Claims 1-8 allows, to compare the quality of the energy at different time scales as well as the comparison between these to determine slogans at the operational level.
    [19]
    19. Use of the characterization procedure of a signal of the electrical supply that according to claims 1-8 allows, to make decisions for the programming of processes with the least possible impact on the network, for the identification of problematic equipment and for the selection of places where connect sensitive equipment.
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    同族专利:
    公开号 | 公开日
    ES2711204B2|2021-01-11|
    引用文献:
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