Research Article  Open Access
FengJih Wu, ChihJu Chou, Ying Lu, JarmLong Chung, "Modeling Electromechanical Overcurrent Relays Using Singular Value Decomposition", Journal of Applied Mathematics, vol. 2012, Article ID 104952, 18 pages, 2012. https://doi.org/10.1155/2012/104952
Modeling Electromechanical Overcurrent Relays Using Singular Value Decomposition
Abstract
This paper presents a practical and effective novel approach to curve fit electromechanical (EM) overcurrent (OC) relay characteristics. Based on singular value decomposition (SVD), the curves are fitted with equation in state space under modal coordinates. The relationships between transfer function and Markov parameters are adopted in this research to represent the characteristic curves of EM OC relays. This study applies the proposed method to two EM OC relays: the GE IAC51 relay with moderately inversetime characteristic and the ABB CO8 relay with inversetime characteristic. The maximum absolute values of errors of hundreds of sample points taken from four time dial settings (TDS) for each relay between the actual characteristic curves and the corresponding values from the curvefitting equations are within the range of 10 milliseconds. Finally, this study compares the SVD with the adaptive network and fuzzy inference system (ANFIS) to demonstrate its accuracy and identification robustness.
1. Introduction
Power generation systems generally have few large generators connected directly to their subtransmission networks and distribution networks. Thus, the fault currents of buses do not differ much from those of transmission lines. This makes lowcost, reliable, and easily coordinated electromechanical (EM) overcurrent (OC) relays suitable for protection coordination relay in the subtransmission networks and distribution networks. Although some older relays have been replaced by new digital ones, there are still many EM OC relays in service.
The operation principle of the EM OC relay is to introduce an electric current into the coil of an electromagnet to produce eddy currents with phase differences. This in turn generates induction torque on the rotation disc of the relay. The proper contact closing time can be set by adjusting the distance between the fixed and the movable contacts, achieving protection coordination between upstream and downstream. However, due to the mechanical nature of the relay, there are inertial and frictional effects. Therefore, unlike a digital relay [1], it is not possible to describe the characteristic curve of an EM OC relay using a single precise equation. Manufacturer manuals generally provide families of characteristic curves with different time dial (TDS) values. These are all piecewise nonlinear continuous smooth descending curves.
Accurate representations of the inversetime EM OC relay characteristics play an important role in the coordination of power network protection schemes. In the early days, researchers were interested in fitting EM OC relay characteristics curves [2] to facilitate protection coordination in conventional power systems. After the introduction of the digital OC relays, better curvefitting of EM OC relay characteristic [3â€“6] is even more important for proper protection coordination. Most of the literature about curve fitting shows the absolute values of errors [7â€“9], while some show the averages of absolute values of errors [9, 10]. Only a few studies show the maximum absolute values of percentage errors [10], which are harder to curve fit, and no studies show the maximum absolute values of errors, which are the hardest to curve fit. For EM OC relays at small values of (multiples of tap value current), for example, 1.3â€“3.5, the relay operating time changes nonlinearly and drastically, and only one study [10] shows the curve fitting results in this range of values.
This paper applies the Hankel matrices and the singular value decomposition (SVD) [11â€“13] to obtain the curvefitting equation of the characteristic curves of two EM OC relays under state space with modal coordinates [14, 15]. To demonstrate the accuracy of the curvefitting results, the current study not only shows all the maximum absolute values of errors, maximum absolute values of percentage errors, average of absolute values of errors, and average of absolute values of percentage errors, but also considers smaller values of where the relay operating time changes nonlinearly and drastically. For even better accuracy, this paper reduces the maximum absolute values of errors to less than an alternating current cycle in the range of a few milliseconds (ms), as opposed to 3 cycles in [3] or 2 cycles in [8].
The paper proposed a new application algorithm to fit the characteristic curves of the EM OC relays, using one unified equation to represent their characteristics. Finally, this study uses the SVD method to fit the characteristic curves of EM OC relays. Comparing the results with those obtained by [9] demonstrates the accuracy and identification robustness of the SVD method.
The content of this paper is as follows: Section 2 introduces the representations for the characteristic curves of EM OC relays, the mathematical derivation of the curve fitting of the characteristic curves of the EM OC relays using SVD method is outlined in Section 3, two cases are studied in Section 4 to verify the approach, the performance of the SVD and the wellbehaved ANFIS algorithm are compared in Section 5, and the conclusion is in Section 6.
2. Models of the Electromechanical (EM) Overcurrent (OC) Relay Characteristic
The characteristic of an EM OC relay is determined by its magnetic circuit design, and the manufacturers provide the characteristics in the relay manuals with curves in a twodimensional plot with the abscissa and operating time the ordinate. Some typical models for the characteristic of an EM OC relay are as follows.
2.1. Exponential and Polynomial Forms
Various exponential and polynomial forms of equations are summarized and recommended by the IEEE Committee [3], for example, (2.1)â€“(2.5) below, for EM OC relay characteristic curve fitting. In some studies [7, 8, 10] that apply numerical methods to determine the best coefficients of the curvefitting equations, the maximum absolute values of percentage errors are as large as 15% [10], so there is still much room for improvement. Consider where : relay operating time. : time dial setting. : fault current on the secondary side of the CT. : current tap setting. : multiples of tap value current, . : constants.
2.2. Customized Characteristic Equation
A customized characteristic Equation (2.6) is obtained by modifying in [16] for simulation. The IEEE normal standard inversetime digital relay characteristic representation is obtained by letting [5], and the IEC normal standard inversetime digital relay characteristic representation is obtained by letting both and [17].
Take the characteristic curves of the ABBâ€™s EM OC relay CO8 as an example [18]. The recommended values of , , , and in (2.6) are 8.9341, 0.17966, 0.028, and 2.0938, respectively in [16]. Figure 1 shows the actual and the fitted characteristic curves with TDS settings of 0.5, 2, 5, and 10. The averages of absolute values of errors of the 488 sampling operating times for TDS settings 0.5, 2, 5, and 10 are 99.95, 189.18, 382.16, and 449.94â€‰ms, respectively. The fitted curve differs considerably from the actual characteristic curve and cannot be used directly as a good replacement.
Consider the case [19] in which the TDS settings in (2.6) are modified to 0.3, 1.5, 4, and 8.7, while their manufacture data counterparts remain the same as 0.5, 2, 5, and 10, respectively (expressed as Figure 2). The averages of absolute values of errors of the four sets of 488 sampling operating times taken between the modified fitted curve and the actual characteristic curve are 28.64, 32.54, 90.97, and 168.74â€‰ms, respectively. Although this modified (2.6) has better accuracy, it is still not good enough to represent the actual characteristic curves provided by the manufacturer.
2.3. Data Base Method
The values of and the corresponding operating times are stored directly [3, 4]. This type of representation is commonly used today, but requires large data storage. Because the relay characteristics cannot be represented by an equation, interpolation method is usually applied to estimate data points not stored.
2.4. Artificial Intelligence Techniques
Researchers are applying more artificial neural network and fuzzy model techniques [7, 9] to optimal curve fitting. Among these approaches, the ANFIS algorithm developed by Geethanjali and Slochanal [9] shows promising results.
3. The Singular Value Decomposition (SVD) Method
Based on the concept of transfer function, this paper proposes an algorithm to represent the characteristic curves of EM OC relays to calculate Markov parameters [20, 21]. After the Hankel matrices [20] are constructed, they are decomposed by SVD and transformed to state space system under modal coordinates. Finally, the state space solution is found, and the fitting equation is obtained by transformation back to the continuous domain system [21]. The proposed algorithm processes the domain data directly and is thus an identification method in the domain.
The SVD method is as follows [11â€“15, 20, 21].
Step 1. Find the estimated operating time on the EM OC relay characteristic curve corresponding to a specific multiple of tap value current as in (3.1). Repeat this process to the right with incremental step to form the sample sequence , , where : minimum multiple of tap value current. : increment sampling step.
Step 2. Use , to form the Hankel matrices and in (3.2) as follows:
Step 3. Apply SVD to the Hankel matrix to obtain matrices , , and in (3.3)
Step 4. Determine the proper dimension for the modal coordination system in (3.3) and obtain matrices , , and in (3.4), where is also the number of fitting waveform components and its range is form 1 to the rank of
Step 5. Calculate the matrices , , and , which are the estimates of the matrices , , and in the state space system (3.5), as (3.6) and (3.7) show where is shown as (3.8) and the system matrix is as shown in (3.9)
Step 6. Transform the state space Equation (3.5) into modal coordinate system to find , , and as (3.10), (3.12), (3.13), and (3.14) show where is as shown in (3.11) where : eigenvalues of , . : matrix whose columns are the eigenvectors of .
Step 7. Obtain the unit impulse response sequence from (3.10), the state space equations under modal coordinates, as known system Markov parameters in (3.15) below:
Step 8. Derive the equation of the fitted relay characteristic curve by transforming back to continuous domain system (3.16)
where is the operating time with as its variable. is the number of the smooth waveform components. is the number of the paired oscillation waveform components (thus the coefficient 2). is the number of the independent oscillation components. is the number of the unpaired oscillation waveform components. , and are the constants. is the oscillation frequency, in Hertz. is the oscillation phase shift, in radian.
Equation (3.17) describes the relationship among , , , and
The characteristic curves of an EM OC relay can be represented by a digital state space model, and the desired bound of the maximum absolute value of errors may be established by selecting an appropriate system model order . Equation (3.16) is an unified equation by SVD method to fit EM OC relays characteristic curves, all of which are piecewise nonlinear continuous smooth descending curves. Any of such characteristic curves can be fitted by simply changing the values of the parameters in the equation.
4. Cases Study
The following case study involves two EM OC relays: the GE moderately inversetime relay IAC51 [22] and the ABB inversetime relay CO8 [18]. To be both accurate and reasonable, the constraint set for the fit is that the maximum absolute value of errors between all the fitted sampling points and the actual characteristic curves for each TDS be less than 10 milliseconds. The calculations in this study were made using MATLAB.
4.1. Case 1: GE IAC51 Relay
Four characteristic curves corresponding to TDS 1, 4, 7, and 10 were selected for curve fitting and 486 sample points of relay operating time are taken for ranging from 1.5 to 50.0 with steps of 0.1. Table 1 lists the calculated mathematic parameters, where the selected number of fitted waveform components is the minimum number of waveform needed to construct the TDS curve. Equation (3.16) can be rewritten as (4.1) to represent the characteristic curves corresponding to the four TDS as
 
TDS: time dial setting; : number of fitted waveform components. 
The number of the smooth waveform components was set to the maximum of the fitted result for the four TDS characteristic curves, and likewise for and . The parameters of the waveform components not used in the fitted result were set to zero. This simple operating equation contains 26 parameters and includes smooth waveform components, paired oscillation waveform components and unpaired oscillation waveform components. Since the minimum value of the samples of the multiples of tap value current of the characteristic curve is 1.5 and the sampling step , .
Table 2 compares the 17 relay operating times and the corresponding SVD fitted values for each TDS. The average of absolute values of errors for each TDS is in the range 0.40â€“1.27â€‰ms.
 
TDS: time dial setting; M: multiples of tap value current; top: actual operating time in milliseconds; SVD: fitted value by singular value decomposition; Err: absolute value of difference between top and SVD value; AV: average of absolute values of errors in milliseconds. 
Table 3 summarizes the complete fitting results. The maximum absolute values of errors range from 3.62 to 8.73â€‰ms, and all occur at smaller values (1.8, 2.2, 2.8, and 2.2). The maximum absolute values of percentage errors range from 0.15 to 0.48, and mostly occur in the smaller onethird of the range of (i.e., 41.4, 3.9, 3.9, and 12.1). The average of absolute values of errors range from 0.28 to 0.89â€‰ms, and the average of absolute values of percentage errors ranges from 0.03 to 0.19.
 
TDS: time dial setting; Max_Err/M: maximum absolute values of errors in millisecond in multiples of tap value current; Max_Err%/M: maximum absolute values of percentage errors in multiples of tap value current; AV: average of absolute values of errors in milliseconds; AV%: averages of absolute values of percentage errors. 
Finally, Figure 3 shows the samples of the actual relay operating times and the corresponding values from the curvefitting equations obtained by SVD. This figure shows that two sets of values are so closely matched that they are virtually indistinguishable. This clearly demonstrates the accuracy and identification robustness of the SVD method.
4.2. Case 2: ABB CO8 Relay
Four characteristic curves corresponding to TDS 0.5, 3, 6, and 9 were selected for curve fitting, and 488 sample points of relay operating time were taken for ranging from 1.3 to 50.0 with steps of 0.1. Table 4 lists the calculated mathematic parameters. Equation (3.16) can be rewritten as (4.2) to represent the characteristic curves corresponding to the four TDS as
