Power system transient protection method based on fault information fusion

Abstract

The application mines the internal correlation between the fault condition attribute and the fault feature, and provides a power system transient protection method based on fault information fusion. The fault information fusion method adopts Kalman filtering algorithm and improved Kalman filtering algorithm, or adopts particle filtering algorithm or improved particle filtering algorithm. The standard fault feature fused with the fault condition attribute can more accurately reflect the fault. The power system transient protection method based on the fault condition attribute overcomes the adverse effects of the fault initial phase angle and the transition resistance on the protection, and improves the reliability of the transient protection.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system relay protection, and in particular relates to a power system transient quantity protection method based on fault condition attributes. Background Art

[0002] Accurate and rapid fault clearing is crucial for improving the transient stability of power systems and the transmission capacity of transmission lines. As grid voltage levels continue to rise, the requirements for grid protection performance are also becoming increasingly stringent. For ultra-high and extra-high voltage grids, research on line protection with ultra-fast operation performance and safety and reliability is an urgent task. Line protection based on power frequency parameters is susceptible to power system oscillations, overloads, and short-circuit transition resistance. Therefore, traditional power frequency parameter-based protection cannot meet the needs of grid development. Transient signals are generated at the moment a power system fault occurs. These signals carry a wealth of fault information, such as fault direction, location, type, and duration. Fault characteristics are particularly pronounced in high-frequency bands. Therefore, transient protection, by quickly capturing these high-frequency components of the fault signal, can detect faults in an extremely short time and achieve ultra-fast protection. Transient parameter-based protection, which is unaffected by power frequency oscillations, CT saturation, and features ultra-fast operation, has become a research hotspot in the field of relay protection both domestically and internationally.

[0003] A variety of modern signal processing techniques, such as the Hilbert-Huang transform, S transform, wavelet transform, mathematical morphology, and neural networks, are widely used in transient protection of power transmission lines to improve the performance of identifying fault directions using fault transient information. The Hilbert-Huang transform has advantages in processing nonlinear and non-stationary signals, and has the advantages of high time-frequency resolution, high sensitivity, strong adaptability, and strong anti-interference ability. The S transform combines the advantages of the short-time Fourier transform (STFT) and the wavelet transform, and has the advantages of high time-frequency resolution, support for multi-scale analysis, and no need for a preset mother wavelet. Its advantages are mainly reflected in its flexible time-frequency analysis capabilities, adaptability to non-stationary signals, and high-precision fault feature extraction. The advantages of the wavelet transform are reflected in its powerful time-frequency analysis capabilities, adaptability to non-stationary signals, and rapid fault detection and location. This has led to the widespread application of wavelet transforms in power systems, especially for the detection and processing of transient faults.

[0004] The performance of traditional transient protection is affected by the initial fault phase angle or transition resistance. When the initial fault phase angle is small or the transition resistance is large, the transient fault signal is significantly weakened, the fault characteristics are not obvious, and the protection performance is severely affected, potentially failing to meet requirements. Traditional traveling wave protection, which relies on the detection of the traveling wave's wave crest, has raised questions about its reliability.

[0005] Technical solution and shortcomings of existing technology 1

[0006] In the case of weak faults (small fault initial phase angle and high transition resistance), the transient signal is very weak and the fault feature extracted will be very small, which is very different from the fault feature value in other cases, resulting in low protection sensitivity and low reliability. Chinese invention patent CN201310065694.2, named Adaptive single-ended transient protection of transmission lines based on transition resistance and fault angle reduction, proposes a method and invents a reduction formula that can unify other transition resistances and fault initial phase angles to the same fault initial phase angle and transition resistance, solving the impact of transition resistance and fault initial phase angle on transient protection. However, this method relies on the bypass attenuation effect of the busbar system's distributed capacitance to the ground on the high-frequency components of the fault. In fact, because the line boundary effect of the busbar system is not obvious, its effect is not ideal and difficult to be practical. Summary of the Invention

[0007] The purpose of the present invention is to solve the defects of the above-mentioned prior art, apply the blocking and attenuation effect of line surge arresters on fault high-frequency signals, and consider the influence of fault operating conditions on fault characteristics, so as to provide a power system transient protection method based on fault information fusion.

[0008] The present invention adopts the following technical solutions:

[0009] S1. When the protection object is a circuit and the busbar connected to the circuit.

[0010] The power system transient protection method based on fault information fusion includes:

[0011] Step 1. Collect the line voltage u1 from the head end of the protected line and the bus voltage u2 from the protected bus.

[0012] Step 2. Extract the high-frequency component u of voltages u1 and u2 h1 and u h2 .

[0013] Step 3. Calculate u h1 and u h2 The transient quantity TQ1 and TQ2 can be one of energy, entropy of fault signal, signal complexity, signal singularity, Lipschitz index, instantaneous amplitude, singular value and other transient high-frequency component processing quantity; or the instantaneous integral of the transient quantity, or the instantaneous cumulative sum of the transient quantity.

[0014] Step 4. Calculate the fault condition attributes: Calculate the fault initial phase angle θ f With transition resistance R f .

[0015] Step 5. Fault information fusion: transient quantities TQ1 and TQ2 are fused to form the initial fault phase angle θ f and transition resistance R fThe standard transient quantities NTQ1 and NTQ2 are obtained. The transient quantity is integrated with the fault condition attribute using the Kalman filtering algorithm proposed in the present invention.

[0016] Step 6. Calculate the difference NTQD between the standard transient quantities NTQ1 and NTQ2 12 NTQD 12 =NTQ1-NTQ2.

[0017] Step 7. Use the standard transient quantity difference in step 6 as the fault characteristic to determine the fault. Take line L3 and bus M as the protected objects, and NTQD 12 As the fault characteristics to construct protection criteria:

[0018] When NTQD 12 When >Aset, it is judged as an internal fault of line L3;

[0019] When NTQD 12 <-Bset, it is judged as a busbar fault;

[0020] Otherwise, it is determined that there is no fault inside the protection area, i.e., line L3 and bus M;

[0021] Among them, Aset and Bset are positive real numbers for protection settings.

[0022] S2. When the protection object is a single circuit.

[0023] The power system transient protection method based on fault information fusion includes:

[0024] Step 1. Collect the line voltage u1 and the bus voltage u2 from the head end of the protected line.

[0025] Step 2. Extract the high-frequency component u of voltages u1 and u2 h1 and u h2 .

[0026] Step 3. Calculate u h1 and u h2 The transient quantity TQ1 and TQ2 are selected from the group consisting of signal energy, entropy of the fault signal, signal complexity, signal singularity, Lipschitz index, instantaneous amplitude, singular value, transient quantity gradient, and transient quantity relative gradient; or the instantaneous integral of these transient quantities, or the instantaneous cumulative sum of these transient quantities.

[0027] Step 4. Calculate the fault condition attributes: Calculate the fault initial phase angle θ f With transition resistance R f .

[0028] Step 5. Fault information fusion: transient quantities TQ1 and TQ2 are fused to form the initial fault phase angle θ f and transition resistance Rf The standard transient quantities NTQ1 and NTQ2 are obtained. The transient quantity is integrated with the fault condition attribute using the Kalman filtering algorithm proposed in the present invention.

[0029] Step 6. Calculate the difference NTQD between the standard transient quantities NTQ1 and NTQ2 12 NTQD 12 =NTQ1-NTQ2.

[0030] Step 7. Use the standard transient quantity difference in step 6 as the transient characteristic of the fault direction to determine the fault direction. 12 >Aset, it is determined that the equivalent information of "positive direction fault" is seen from busbar M to line L3; and the equivalent information of "positive direction fault" is sent to the other end. Otherwise, it is determined that there is no fault in the positive direction from busbar M to line L3;

[0031] Step 8. If the local end determines that the fault is in the positive direction and receives the equivalent information of "positive direction fault" from the other end, it determines that the fault is internal to line L3; otherwise, it determines that line L3 has no fault.

[0032] Where Aset is a positive real number for protection setting. Note that Aset here is not the same as Aset in Method 1 and there is no connection between them. The same applies to the following.

[0033] S3. When the protection object is a single circuit.

[0034] The power system transient protection method based on fault information fusion includes:

[0035] Step 1. Collect the line voltage u1 from the head end of the protected line.

[0036] Step 2. Extract the high-frequency component u of voltage u1 h1 .

[0037] Step 3. Calculate u h1 The transient quantity TQ1 is one of the following: signal energy, entropy of fault signal, signal complexity, signal singularity, Lipschitz index, instantaneous amplitude integral, instantaneous amplitude, instantaneous amplitude accumulation, singular value, transient quantity gradient, transient quantity relative gradient, etc.

[0038] Step 4. Calculate the fault condition attributes: Calculate the fault initial phase angle θ f and transition resistance R f .

[0039] Step 5. Fault information fusion: transient quantity TQ1 fusion fault initial phase angle θ f and transition resistance R fThe standard transient quantity NTQ1 is obtained. The transient quantity is integrated with the fault condition attribute using the Kalman filtering algorithm proposed in the invention.

[0040] Step 6. Use the standard transient quantity NTQ1 as the fault characteristic to determine the fault. Use NTQ1 as the fault characteristic to construct the protection judgment criterion:

[0041] When NTQ1>Aset, it is judged that there is an internal fault in line L3;

[0042] Otherwise, it is determined that line L3 has no fault;

[0043] Where Aset is a positive real number for protection setting.

[0044] S4. When one busbar is protected.

[0045] The power system transient protection method based on fault information fusion includes:

[0046] Step 1. Collect the bus voltage u2 from the protected bus.

[0047] Step 2. Extract the high-frequency component u of voltage u2 h2 .

[0048] Step 3. Calculate u h2 The transient quantity TQ2 is one of the following: signal energy, entropy of the fault signal, signal complexity, signal singularity, Lipschitz index, instantaneous amplitude, singular value, transient quantity gradient, transient quantity relative gradient, etc.; or the instantaneous integral of these transient quantities, or the instantaneous cumulative sum of these transient quantities.

[0049] Step 4. Calculate the fault condition attributes: Calculate the fault initial phase angle θ f and transition resistance R f .

[0050] Step 5. Fault information fusion: transient quantity TQ2 fusion fault phase angle θ f and transition resistance R f The standard transient quantity NTQ2 is obtained. The Kalman filtering algorithm proposed in the present invention is used to fuse the transient quantity and fault condition attribute information.

[0051] Step 6. Use the standard transient quantity NTQ2 as the fault characteristic to determine the fault. Use NTQ2 as the fault characteristic to construct the protection judgment criterion:

[0052] When NTQ2>Aset, it is judged as bus M fault;

[0053] Otherwise, it is determined that busbar M has no fault;

[0054] Where Aset is a positive real number for protection setting.

[0055] Beneficial effects of the present invention:

[0056] (1) The present invention makes a fault judgment based on the fact that the fault transient quantity is greatly attenuated when it passes through the line surge arrester, taking into account the fault operating condition attributes at the time of the fault. The protection method based on this principle is not affected by the size of the busbar-to-ground stray capacitance, the distributed capacitance and current of the transmission line, the saturation of the current transformer, the oscillation of the power system and the transition resistance, and has ultra-high-speed operation performance.

[0057] (2) The present invention integrates the fault condition attributes into the quasi-fault characteristic quantity, eliminates the influence of the fault condition attributes on transient protection, and greatly improves the protection sensitivity and protection reliability.

[0058] (3) The present invention utilizes a machine learning algorithm to judge faults based on quasi-fault characteristics and fault condition attributes, which can realize automatic fault classification, make fault judgments more intelligently, and improve protection reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a schematic diagram of the power system wiring.

[0060] Figure 2 Figure 2 is the line fault voltage and bus fault voltage diagram, (a) is the line fault voltage u1, (b) is the bus fault voltage u2.

[0061] Figure 3 is the high-frequency component of the line fault voltage and the high-frequency component of the bus fault voltage, (a) is the high-frequency component of the line fault voltage u h1 , (b) is the high frequency component u of bus fault voltage h2 .

[0062] Figure 4 is the transient energy of the high-frequency component of the line fault voltage and the high-frequency component of the bus fault voltage, (a) is the transient energy TQ1 of the high-frequency component of the line fault voltage, and (b) is the transient energy TQ2 of the high-frequency component of the bus fault voltage.

[0063] Figure 5 are the standard transient energies of the high-frequency components of the line fault voltage and the bus voltage, (a) is the standard transient energy NTQ1 of the high-frequency components of the line fault voltage, and (b) is the standard transient energy NTQ2 of the high-frequency components of the bus fault voltage.

[0064] Figure 6 is the standard transient energy difference.

[0065] Figure 7 is the bus fault voltage.

[0066] Figure 8 is the high frequency component of bus fault voltage.

[0067] Figure 9 is the transient energy of the high-frequency component of the bus fault voltage.

[0068] Figure 10 is the standard transient energy of the high-frequency component of the bus fault voltage.

[0069] Figure 11 is the transient energy difference. DETAILED DESCRIPTION

[0070] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0071] Explanation of terms:

[0072] Transient quantity: Transient quantity refers to the electrical quantity calculated from the high-frequency component of the fault voltage or fault current or fault traveling wave.

[0073] Fault condition attributes: Fault condition attributes include fault initial phase angle and transition resistance.

[0074] Quasi-fault characteristics refer to physical quantities that are used in the next step of fault information fusion and then directly or indirectly for fault diagnosis. Indirect fault diagnosis involves using quasi-fault characteristics after fault information fusion, then summing, subtracting, or calculating their ratios for fault diagnosis. Quasi-fault characteristics can be power frequency quantities or transient quantities calculated from the power frequency or high frequency components of current or voltage.

[0075] The solution of the present invention:

[0076] (1) The present invention generates standardized transient quantities by fusing transient quantities with fault condition attributes to make fault judgments, thereby effectively avoiding the impact of fault condition attributes on the protection performance. This fusion strategy improves the system's adaptability to complex conditions and the accuracy of protection.

[0077] (2). The fusion algorithm uses the Kalman filter algorithm and the improved Kalman filter fault information fusion, particle filter and the improved particle filter algorithm fault information fusion.

[0078] (3) The fault characteristics are constructed based on the transient data of voltage and current on both sides of the existing surge arrester of the transmission line. There is no need to install additional detection units, so the existing equipment can be used to quickly extract and analyze the fault information, which improves the economy and practicality of the system.

[0079] S1. When the protection object is a circuit and the busbar connected to the circuit.

[0080] In conjunction with the instructions Figure 1 Here, we take line L3 and bus M as an example to illustrate the transient unit protection method of the line and bus. The wave arrester installed on the transmission line has a strong blocking effect on the high-frequency signal in the stop band, while not affecting the transmission of the power frequency current. Therefore, the transient high-frequency signal of the fault will be greatly attenuated when passing through the wave arrester, so that the high-frequency signal before and after passing through the wave arrester has a significant difference. Based on this feature, a protection method for the transmission line and bus can be constructed. At the same time, the initial phase angle of the fault and the transition resistance have a great influence on the transient signal formed by the fault. The present invention utilizes the blocking and attenuating effect of the line wave arrester on the fault high-frequency signal, and considers the influence of the two fault working condition attributes of the initial phase angle of the fault and the transition resistance on the fault transient high-frequency signal to propose a transient protection method based on fault information fusion and power system traveling waves.

[0081] like Figure 1 As shown in FIG, a power system transient protection method based on fault information fusion includes:

[0082] Step 1. Collect the line voltage u1 from the head end of the protected line and the bus voltage u2 from the protected bus.

[0083] Step 2. Extract the high-frequency component u of voltages u1 and u2 h1 and u h2 .

[0084] Step 3. Calculate u h1 and u h2 The transient quantity TQ1 and TQ2 can be one of energy, entropy of fault signal, signal complexity, signal singularity, Lipschitz index, instantaneous amplitude, singular value and other transient high-frequency component processing quantity; or the instantaneous integral of these transient quantities, or the instantaneous cumulative sum of these transient quantities.

[0085] Step 4. Calculate the fault condition attributes: Calculate the fault initial phase angle θ f With transition resistance R f .

[0086] Step 5. Fault information fusion: transient quantities TQ1 and TQ2 are fused to form the initial fault phase angle θ f and transition resistance R f The standard transient quantities NTQ1 and NTQ2 are obtained. The transient quantity is integrated with the fault condition attribute using the Kalman filtering algorithm proposed in the present invention.

[0087] Step 6. Calculate the difference NTQD between the standard transient quantities NTQ1 and NTQ2 12 NTQD 12 =NTQ1-NTQ2.

[0088] Step 7. Use the standard transient quantity difference in step 6 as the fault characteristic to determine the fault. Take line L3 and bus M as the protected objects, and NTQD 12 As the fault characteristics to construct protection criteria:

[0089] When NTQD 12 When >Aset, it is judged as an internal fault of line L3;

[0090] When NTQD 12 <-Bset, it is judged as a busbar fault;

[0091] Otherwise, it is determined that there is no fault inside the protection area, i.e., line L3 and bus M;

[0092] Among them, Aset and Bset are positive real numbers for protection settings.

[0093] S2. When the protection object is a single circuit.

[0094] In conjunction with the instructions Figure 1 Here, we take the protection line L3 as an example to illustrate the line transient direction longitudinal protection method.

[0095] like Figure 1 As shown in FIG, a power system transient protection method based on fault information fusion includes:

[0096] Step 1. Collect the line voltage u1 and the bus voltage u2 from the head end of the protected line.

[0097] Step 2. Extract the high-frequency component u of voltages u1 and u2 h1 and u h2 .

[0098] Step 3. Calculate u h1 and u h2 Transient quantities TQ1 and TQ2. A transient quantity is one of signal energy, entropy of a fault signal, signal complexity, signal singularity, Lipschitz index, instantaneous amplitude, singular value, transient quantity gradient, and transient quantity relative gradient, etc.; or the instantaneous integral of these transient quantities, or the instantaneous cumulative sum of these transient quantities.

[0099] Step 4. Calculate the fault condition attributes: Calculate the fault initial phase angle θ f With transition resistance R f .

[0100] Step 5. Fault information fusion: transient quantities TQ1 and TQ2 are fused to form the initial fault phase angle θ f and transition resistance R fThe standard transient quantities NTQ1 and NTQ2 are obtained. The transient quantity is integrated with the fault condition attribute using the Kalman filtering algorithm proposed in the present invention.

[0101] Step 6. Calculate the difference NTQD between the standard transient quantities NTQ1 and NTQ2 12 NTQD 12 =NTQ1-NTQ2.

[0102] Step 7. Use the standard transient quantity difference in step 6 as the transient characteristic of the fault direction to determine the fault direction. 12 >Aset, it is determined that the equivalent information of "positive direction fault" is seen from busbar M to line L3; and the equivalent information of "positive direction fault" is sent to the other end. Otherwise, it is determined that there is no fault in the positive direction from busbar M to line L3;

[0103] Step 8. If the local end determines that the fault is in the positive direction and receives the equivalent information of "positive direction fault" from the other end, it determines that the fault is internal to line L3; otherwise, it determines that line L3 has no fault.

[0104] Where Aset is a positive real number for protection setting. Note that Aset here is not the same as Aset in Method 1 and there is no connection between them. The same applies to the following.

[0105] S3. When the protection object is a circuit. Figure 1 Here, the protection line L3 is taken as an example to illustrate the line single-ended transient protection method.

[0106] The power system transient protection method based on fault information fusion includes:

[0107] Step 1. Collect the line voltage u1 from the head end of the protected line.

[0108] Step 2. Extract the high-frequency component u of voltage u1 h1 .

[0109] Step 3. Calculate u h1 The transient quantity TQ1 is one of signal energy, entropy of fault signal, signal complexity, signal singularity, Lipschitz index, instantaneous amplitude integral, instantaneous amplitude, instantaneous amplitude accumulation, singular value, transient quantity gradient and transient quantity relative gradient.

[0110] Step 4. Calculate the fault condition attributes: Calculate the fault initial phase angle θ f and transition resistance R f .

[0111] Step 5. Fault information fusion: transient quantity TQ1 fusion fault initial phase angle θ f and transition resistance R fThe standard transient quantity NTQ1 is obtained. The transient quantity is integrated with the fault condition attribute using the Kalman filtering algorithm proposed in the invention.

[0112] Step 6. Use the standard transient quantity NTQ1 as the fault characteristic to determine the fault. Use NTQ1 as the fault characteristic to construct the protection judgment criterion:

[0113] When NTQ1>Aset, it is judged that there is an internal fault in line L3;

[0114] Otherwise, it is determined that line L3 has no fault;

[0115] Where Aset is a positive real number for protection setting.

[0116] S4. When one busbar is protected.

[0117] In conjunction with the instructions Figure 1 Here, the transient voltage bus protection method is explained by taking bus M as an example.

[0118] The power system transient protection method based on fault information fusion includes:

[0119] Step 1. Collect the bus voltage u2 from the protected bus.

[0120] Step 2. Extract the high-frequency component u of voltage u2 h2 .

[0121] Step 3. Calculate u h2 The transient quantity TQ2 is one of signal energy, entropy of fault signal, signal complexity, signal singularity, Lipschitz index, instantaneous amplitude, singular value, transient quantity gradient, transient quantity relative gradient; or the instantaneous integral of these transient quantities, or the instantaneous cumulative sum of these transient quantities.

[0122] Step 4. Calculate the fault condition attributes: Calculate the fault initial phase angle θ f and transition resistance R f .

[0123] Step 5. Fault information fusion: transient quantity TQ2 fusion fault phase angle θ f and transition resistance R f The standard transient quantity NTQ2 is obtained. The Kalman filtering algorithm proposed in the present invention is used to fuse the transient quantity and fault condition attribute information.

[0124] Step 6. Use the standard transient quantity NTQ2 as the fault characteristic to determine the fault. Use NTQ2 as the fault characteristic to construct the protection judgment criterion:

[0125] When NTQ2>Aset, it is judged as bus M fault;

[0126] Otherwise, it is determined that busbar M has no fault;

[0127] Where Aset is a positive real number for protection setting.

[0128] In addition, the present invention uses machine learning (such as support vector machines, neural networks, etc.) to complete the fault information fusion starting from step 5 in (1) to (4) and the fault judgment in step 6. The transient quantity, the initial phase angle of the fault, and the transition resistance are used as input vectors to output the corresponding line L3 fault, busbar M fault, no fault inside the protection zone, and line positive direction fault in (1) to (4). Before using machine learning (such as support vector machines, neural networks, etc.) for fault judgment, the machine learning is trained with a training fault sample set, and then the trained machine learning is used to perform actual fault judgment.

[0129] The fault information fusion method of the present invention adopts a Kalman filter algorithm to fuse transient quantities and fault condition attribute information. The Kalman filter algorithm can be an extended Kalman filter (EKF) algorithm, or an unscented Kalman filter (UKF) algorithm, or a particle filter (PF) algorithm, or an extended Kalman particle filter (EPF) algorithm, or an unscented Kalman particle filter (UPF) algorithm. Taking the extended Kalman filter (EKF) algorithm as an example, the state equation and the observation equation are established as formula 1 and formula (2) respectively, and the Kalman recursive formula is constructed. Then, the initial state and covariance matrix are initialized, and the recursive calculation is performed to complete the fusion of the fault characteristics and the fault condition attribute information.

[0130] X(k) = X(k-1) + W(k) (1)

[0131] Y(k) = F(θ f , R f )X(k) + V(k) (2)

[0132] Where X(k) is the standard transient quantity after the fault quantity is fused with the fault condition attribute information; W(k) is the standard transient quantity noise, Q is the standard transient quantity noise variance; Y(k) is the fault quantity before fusion with the fault condition attribute information, i.e., the quasi-fault feature; V(k) is the quasi-fault feature noise, R is the quasi-fault feature noise variance; F(θ f ,R f ) is the fault condition attribute factor, which can be taken as F(θ f ,R f )=γ·(a·exp(b·R f )+c·exp(d·R f ))·(a0+a1·cos(θ f ·w)+b1·sin(θ f·w)+a2·cos(2·θ f ·w)+b2·sin(2·θ f ·w)). θ f ,R f are the calculated fault initial phase angle and transition resistance; γ is the adjustment constant, which can be taken as 1; a, b, c, d, a0, a1, b1, a2, b2, w are real constants.

[0133] The specific fusion process steps are as follows:

[0134] Step 1. Initialize the initial states X(0), Y(0), and P(0). The initialization can be set to X(0) = 0.01, Y(0) = 0.01, and P(0) = 1; where X(0) is the initial estimate, Y(0) is the initial observation, and P(0) is the initial covariance matrix.

[0135] Step 2. State prediction.

[0136] X(k|k-1)=X(k-1)

[0137] Step 3. Observe and predict.

[0138] Y(k|k-1)=F(θ f ,R f )X(k|k-1)

[0139] Step 4. Solve the state transfer matrix Φ(k).

[0140]

[0141] Step 5. Solve the observation transfer matrix H(k).

[0142]

[0143] Step 6. Find the covariance matrix prediction P(k|k-1).

[0144] P(k|k-1)=Φ(k)P(k-1|k-1)Φ T (k)+Q

[0145] Step 7. Calculate the Kalman filter gain.

[0146] K(k)=P(k|k-1)H T (k)(H(k)P(k|k-1)H T (k)+R) -1

[0147] Step 8. Request status update.

[0148] X(k)=X(k|k-1)+K(k)(Y(k)-H(k)X(k|k-1))

[0149] Step 9. Covariance update.

[0150] P(k)=(I n -K(k)H(k))P(k|k-1)

[0151] Example 1

[0152] The protection object is a circuit and the busbar connected to the circuit. Figure 1 , assuming that a single-phase grounding fault occurs on line L3, which is 5 km away from busbar M, and the initial phase angle of the fault is θ f =45°, transition resistance R f =5Ω. Taking the protected objects as line L3 and bus M as an example, the transient unit protection method of the line and bus is described. The steps include:

[0153] Step 1. Collect the line voltage u1 from the head end of the protected line L3 and the bus voltage u2 from the protected bus M. The line voltage u1 and bus voltage u2 collected in this embodiment are as follows: Figure 2 shown.

[0154] Step 2. Extract the high-frequency component u of voltages u1 and u2 h1 and u h2 Wavelet transform is used here, and it can also be replaced by Fourier transform, classical mode decomposition, variational mode decomposition, S transform, filter bank, mathematical morphology and other methods to extract the high-frequency components u1 and u2 respectively. h1 and u h2 The high-frequency component u extracted in this embodiment h1 and u h2 as follows Figure 3 shown.

[0155] Step 3. Calculate u h1 and u h2 The transient energy TQ1 and TQ2 of u h1 and u h2 Perform Hilbert transform to find their instantaneous amplitudes, and then integrate them to get their transient energies TQ1 and TQ2. The standard transient energies TQ1 and TQ2 calculated in this embodiment are as follows: Figure 4 shown.

[0156] Step 4. Calculate the initial fault phase angle θ f and transition resistance R f . Fault initial phase angle θ f and transition resistance R f There are several ways to calculate this, two of which are listed here.

[0157] The first type, the initial phase angle of the fault θ f Calculation formula:

[0158] θ f =θ0-aL

[0159] Where θ0 is the voltage phase when the fault is detected at the protection installation, a is the phase coefficient of the transmission line, and L is the fault distance from the fault point to the protection installation. f Calculation formula:

[0160]

[0161] Where P is the active power supplied to the short-circuit point at the line protection installation, I a It is the fault phase current of the short-circuit point supplied to the line protection installation; λ is the power distribution coefficient of the power supply on the power transmission side.

[0162] The second type, the initial phase angle of the fault θ f Calculation formula:

[0163]

[0164] Where θ0 is the voltage phase when the fault is detected at the protection installation, T0, T a are the time when the phase current a and 0 components first reach the protection installation, T u For a voltage cycle, take T u =0.02s, x0 is the relative speed coefficient, take x0=0.7. Transition resistance R f Calculation formula:

[0165]

[0166] In the formula They are the positive and negative sequence current special phases, They are the positive and negative sequence voltage special phases, the special phases are A, B or C phases, are the zero-sequence current phasor values ​​at the protection installations at both ends of the line, Z m1 , Z m2 , Z m0 =0.5 times the positive, negative and zero sequence impedance of the line. f =45°, transition resistance R f =5Ω.

[0167] Step 5. Fault information fusion: transient quantities TQ1 and TQ2 are fused with fault condition attributes θ f and R fObtain the standard transient quantities NTQ1 and NTQ2. The transient quantity fusion fault condition attributes can adopt the Kalman filtering algorithm proposed in this invention. The standard transient energy difference values ​​NTQ1 and NTQ2 calculated in this embodiment are as follows Figure 5 shown.

[0168] Step 6. Calculate the difference NTQD between the standard transient quantities NTQ1 and NTQ2 12 NTQD 12 =NTQ1-NTQ2. The standard transient energy difference NTQD calculated in this embodiment is 12 as follows Figure 6 shown.

[0169] Step 7. Use the standard transient quantity difference as the fault characteristic to judge the fault. Take line L3 and bus M as the protected objects, and NTQD 12 As the fault characteristics to construct protection criteria:

[0170] When NTQD 12 When >5, it is judged to be an internal fault of line L3;

[0171] When NTQD 12 When <-50, it is judged as a busbar fault;

[0172] Otherwise, it is determined that there is no fault inside the protection area, i.e., line L3 and bus M;

[0173] In this embodiment, when the fault occurs, NTQD 12 The maximum value is 291, which is greater than 5, so it is judged that line L3 is faulty, which is correct.

[0174] Example 2

[0175] The protection object is one circuit. Figure 1 , assuming that a single-phase grounding fault occurs on line L3, which is 5 km away from busbar M, and the initial phase angle of the fault is θ f =45°, transition resistance R f =5Ω. Taking line L3 as an example, the transient direction longitudinal protection method of the line is described. The steps include:

[0176] Step 1. Collect the line voltage u1 from the head end of the protected line L3 and the bus voltage u2 of the bus M. The line voltage u1 and bus voltage u2 collected in this embodiment are the same as u1 and u2 in step 1 of embodiment 1.

[0177] Step 2. Extract the high-frequency component u of voltages u1 and u2 h1 and u h2Wavelet transform is used here, and it can also be replaced by Fourier transform, classical mode decomposition, variational mode decomposition, S transform, filter bank, mathematical morphology and other methods to extract the high-frequency components u1 and u2 respectively. h1 and u h2 In this embodiment, the high frequency component u h1 and u h2 The same as u in step 2 of Example 1 h1 and u h2 same.

[0178] Step 3. Calculate u h1 and u h2 The transient energy TQ1 and TQ2 of u h1 and u h2 Perform Hilbert transform to obtain their instantaneous amplitudes, and then integrate them to obtain their transient energies TQ1 and TQ2. The transient energies TQ1 and TQ2 in this embodiment are the same as TQ1 and TQ2 in step 3 of embodiment 1.

[0179] Step 4. Calculate the initial fault phase angle θ f and transition resistance R f θ calculated in this embodiment f =45°, R f =5Ω.

[0180] Step 5. Fault information fusion: transient quantities TQ1 and TQ2 are fused with fault condition attributes θ f and R f The standard transient quantities NTQ1 and NTQ2 are obtained. The transient quantity fusion fault condition attributes can adopt the Kalman filtering algorithm proposed in the present invention. The high-frequency component standard transient energy NTQ1 and NTQ2 of this embodiment are the same as NTQ1 and NTQ2 in step 5 of embodiment 1.

[0181] Step 6. Calculate the difference NTQD between the standard transient quantities NTQ1 and NTQ2 12 NTQD 12 =NTQ1-NTQ2. The standard transient energy difference NTQD in this embodiment 12 Same as NTQD in step 5 of Example 1 12 same.

[0182] Step 7. Use the standard transient quantity difference as the transient characteristic of the fault direction to determine the fault direction. 12 As the transient characteristic of fault direction, the protection criterion is constructed: when NTQD 12>1, it is determined that there is a positive direction fault from bus M to line L3; and the equivalent information of "positive direction fault" is sent to the other end; otherwise, it is determined that there is no fault in the positive direction from bus M to line L3. In this embodiment, when the fault occurs, NTQD 12 When the maximum value is 291 and is greater than 1, a positive fault is determined from line L3, and a "positive fault" equivalent message is sent to the N-end protection system. Similarly, the N-end protection system determines the fault direction using the same method as the M-end. In this embodiment, the N-end protection system determines a positive fault and therefore also sends a "positive fault" message to the M-end protection system.

[0183] Step 8. If the local end determines a forward fault and simultaneously receives "forward fault" equivalent information from the peer end, it determines that a fault exists within line L3. Otherwise, it determines that line L3 is fault-free. In this embodiment, the M-side protection system determines a "forward fault" and receives "forward fault" information from the peer end N-side protection system. Therefore, the judgment is correct: an internal fault exists within line L3.

[0184] Example 3

[0185] The protection object is one circuit. Figure 1 , assuming that a single-phase grounding fault occurs on line L3, which is 5 km away from busbar M, and the initial phase angle of the fault is θ f =45°, transition resistance R f =5Ω. Here, the protection line L3 is used as an example to illustrate the line single-ended transient protection method.

[0186] The power system transient protection method based on fault information fusion includes the following steps:

[0187] Step 1: Collect the line voltage u1 from the head end of the protected line L3. The line voltage u1 collected in this embodiment is the same as the u1 in step 1 of embodiment 1.

[0188] Step 2. Extract the high-frequency component u of voltage u1 h1 Here we use wavelet transform, which can also be replaced by Fourier transform, classical mode decomposition, variational mode decomposition, S transform, filter bank, mathematical morphology and other methods to extract the high-frequency component u of u1. h1 The high frequency component u of this embodiment h1 The same as u in step 2 of Example 1 h1 same.

[0189] Step 3. Calculate u h1 The transient quantity TQ1. u h1 Perform Hilbert transform to obtain its instantaneous amplitude, and then integrate it to obtain their transient energy TQ1. The transient energy TQ1 of this embodiment is the same as TQ1 in step 3 of embodiment 1.

[0190] Step 4. Calculate the initial fault phase angle θ f and transition resistance R f θ calculated in this embodiment f =45°, R f =5Ω.

[0191] Step 5. Fault information fusion: transient quantity TQ1 is fused with fault condition attribute θ f and R f The standard transient quantity NTQ1 is obtained. The transient quantity fusion fault condition attribute can adopt the Kalman filtering algorithm proposed in the present invention. The high-frequency component standard transient energy NTQ1 of this embodiment is the same as NTQ1 in step 5 of embodiment 1.

[0192] Step 6. Use the standard transient quantity as the fault characteristic to determine the fault. Use NTQ1 as the fault characteristic to construct the protection judgment criteria: when NTQ1 > 4, determine that there is an internal fault on line L3; otherwise, determine that there is no fault on line L3. Where Aset is a positive real number set for the protection setting. In this example, NTQ1 is 291, which is greater than 4, so the fault is determined on line L3, which is correct.

[0193] Example 4

[0194] The protected object is a busbar. Figure 1 , a single-phase grounding fault occurs on busbar M, and the initial phase angle of the fault is θ f =45°, transition resistance R f =5Ω. Here, bus M is taken as an example to illustrate the transient voltage bus protection method.

[0195] The fault information fusion and power system transient protection method includes the following steps:

[0196] Step 1. Collect the bus voltage u2 from the protected bus M. The bus voltage u2 collected in this embodiment is as follows: Figure 7 shown.

[0197] Step 2. Extract the high-frequency component u of voltage u2 h2 Wavelet transform is used here, which can of course be replaced by Fourier transform, classical mode decomposition, variational mode decomposition, S transform, filter bank, mathematical morphology and other methods to extract the high-frequency component u of u2 h2 The high frequency component u of this embodiment h2 as follows Figure 8 shown.

[0198] Step 3. Calculate u h2 The transient quantity TQ2. h2 Perform Hilbert transform to find its instantaneous amplitude, and then integrate it to get their transient energy TQ2. The transient energy TQ2 of this embodiment is as follows Figure 9 shown.

[0199] Step 4. Calculate the initial fault phase angle θ f and transition resistance R f θ of this embodiment f =45°, R f =5Ω.

[0200] Step 5. Fault information fusion: transient quantity TQ2 is fused with fault condition attribute θ f and R f The standard transient quantity NTQ2 is obtained. The Kalman filtering algorithm proposed in the present invention can be used to fuse the transient quantity and the fault condition attribute information. The standard transient quantity NTQ2 of this embodiment is the same as the NTQ2 in step 5 of embodiment 1. Figure 10 shown.

[0201] Step 6. Determine the fault using the standard transient quantity as the fault characteristic. Use NTQ2 as the fault characteristic to construct the protection criterion: When NTQ2 > 50, determine that bus M is faulty; otherwise, determine that bus M is not faulty. In this example, NTQ2 is 461, which is greater than 50, so the fault is determined to be line L3, which is a correct judgment.

[0202] Example 5

[0203] The protection object is a circuit and the busbar connected to the circuit. Figure 1 , assuming that a single-phase grounding fault occurs on line L3, which is 5 km away from busbar M, and the initial phase angle of the fault is θ f =45°, transition resistance R f =5Ω. Taking the protected objects as line L3 and bus M as an example, the transient unit protection method of the line and bus using SVM is described. The steps include:

[0204] Step 1. Collect the line voltage u1 from the head end of the protected line L3 and the bus voltage u2 from the protected bus M. The line voltage u1 and bus voltage u2 collected in this embodiment are the same as u1 and u2 in step 1 of embodiment 1.

[0205] Step 2. Extract the high-frequency component u of voltages u1 and u2 h1 and u h2 Wavelet transform is used here, and it can also be replaced by Fourier transform, classical mode decomposition, variational mode decomposition, S transform, filter bank, mathematical morphology and other methods to extract the high-frequency components u1 and u2 respectively. h1 and u h2 In this embodiment, the high frequency component u h1 and u h2 The same as u in step 2 of Example 1 h1 and u h2same.

[0206] Step 3. Calculate u h1 and u h2 The transient energy TQ1 and TQ2 of u h1 and u h2 Perform Hilbert transform to obtain their instantaneous amplitudes, and then integrate them to obtain their transient energies TQ1 and TQ2. The transient energies TQ1 and TQ2 in this embodiment are the same as TQ1 and TQ2 in step 3 of embodiment 1.

[0207] Step 4. Calculate the difference TQD between transient quantities TQ1 and TQ2 12 .TQD 12 =TQ1-TQ2. The transient energy difference TQD in this embodiment 12 as follows Figure 11 shown.

[0208] Step 5. Calculate the initial fault phase angle θ f and transition resistance R f θ calculated in this embodiment f =45°, R f =5Ω.

[0209] Step 6. Fault information fusion and fault judgment: Use support vector machine (SVM) to judge the fault area. The fault area is represented by fz. When line L3 is faulty, fz=1; when busbar M is faulty, fz=2; when line L3 and busbar M are not faulty, fz=0. 12 ,θ f and R f The SVM input vector is used as the support vector machine (SVM). The SVM output is the fault area determination result fz. The SVM is first trained using a training sample set. The trained SVM can then be used to determine the fault area. In this embodiment, the SVM is first trained using the training sample set to determine the fault area. The trained SVM then determines the fault in this embodiment as a line L3 fault, which is a correct determination.

[0210] Example 6

[0211] The protection object is one circuit. Figure 1 , assuming that a single-phase grounding fault occurs on line L3, which is 5 km away from busbar M, and the initial phase angle of the fault is θ f =45°, transition resistance R f =5Ω. Taking line L3 as an example, the transient directional pilot protection method using SVM is described. The steps include:

[0212] Step 1. Collect the line voltage u1 from the head end of the protected line L3 and the bus voltage u2 of the bus M. The line voltage u1 and bus voltage u2 collected in this embodiment are the same as u1 and u2 in step 1 of embodiment 1.

[0213] Step 2. Extract the high-frequency component u of voltages u1 and u2 h1 and u h2 Wavelet transform is used here, and it can also be replaced by Fourier transform, classical mode decomposition, variational mode decomposition, S transform, filter bank, mathematical morphology and other methods to extract the high-frequency components u1 and u2 respectively. h1 and u h2 In this embodiment, the high frequency component u h1 and u h2 The same as u in step 2 of Example 1 h1 and u h2 same.

[0214] Step 3. Calculate u h1 and u h2 The transient energy TQ1 and TQ2 of u h1 and u h2 Perform Hilbert transform to obtain their instantaneous amplitudes, and then integrate them to obtain their transient energies TQ1 and TQ2. The transient energies TQ1 and TQ2 in this embodiment are the same as TQ1 and TQ2 in step 3 of embodiment 1.

[0215] Step 4. Calculate the initial fault phase angle θ f and transition resistance R f θ calculated in this embodiment f =45°, R f =5Ω.

[0216] Step 5. Calculate the difference TQD between transient quantities TQ1 and TQ2 12 .TQD 12 =TQ1-TQ2. The transient energy difference TQD in this embodiment 12 Same as TQD in step 5 of Example 5 12 same.

[0217] Step 6. Fault information fusion and fault judgment: Use support vector machine (SVM) to judge the fault direction. The fault direction is represented by fz. When the line L3 fault occurs in the forward direction, fz = 1; when the line L3 fault occurs in the reverse direction, fz = -1. 12 ,θ f and R fThe SVM input vector is used as the support vector machine (SVM). The SVM output is the fault direction determination result fz. The SVM is first trained using a training sample set. The trained SVM can then be used to determine the fault direction. In this embodiment, the SVM is first trained using the training sample set to determine the fault direction. The trained SVM then determines the fault direction of this embodiment and correctly determines that the fault is in the positive direction of line L3.

[0218] Step 7. When the SVM determines a positive fault (i.e., fz = 1), it sends a "positive fault" equivalent message to the other end. In this embodiment, it determines a positive fault on line L3 and sends a "positive fault" message to the other end, N. Similarly, the N-end protection uses the same method to determine the fault direction. In this embodiment, the N-end also determines a positive fault and sends a "positive fault" message to the M-end protection.

[0219] Step 8. If the local end determines that a fault is occurring in the forward direction and simultaneously receives equivalent information indicating a "forward direction fault" from the peer end, it determines that a fault exists within line L3. Otherwise, it determines that line L3 is not faulty. In this embodiment, the local end determines that a fault is occurring in the forward direction and simultaneously receives "forward direction fault" from the peer end N. Therefore, the local end correctly determines that a fault exists within line L3.

[0220] Example 7

[0221] The protection object is one circuit. Figure 1 , assuming that a single-phase grounding fault occurs on line L3, which is 5 km away from busbar M, and the initial phase angle of the fault is θ f =45°, transition resistance R f =5Ω. Here, the protection line L3 is taken as an example to illustrate the line single-ended transient protection method using SVM.

[0222] The power system transient protection method based on fault information fusion includes the following steps:

[0223] Step 1: Collect the line voltage u1 from the head end of the protected line L3. The line voltage u1 collected in this embodiment is the same as the u1 in step 1 of embodiment 1.

[0224] Step 2. Extract the high-frequency component u of voltage u1 h1 Here we use wavelet transform, which can also be replaced by Fourier transform, classical mode decomposition, variational mode decomposition, S transform, filter bank, mathematical morphology and other methods to extract the high-frequency component u of u1. h1 In this embodiment, the high frequency component u h1 The same as u in step 2 of Example 1 h1 same.

[0225] Step 3. Calculate u h1 The transient quantity TQ1. u h1Perform Hilbert transform to obtain its instantaneous amplitude, and then integrate it to obtain their transient energy TQ1. The transient energy TQ1 of this embodiment is the same as TQ1 in step 3 of embodiment 1.

[0226] Step 4. Calculate the initial fault phase angle θ f and transition resistance R f θ calculated in this embodiment f =45°, R f =5Ω.

[0227] Step 5. Fault information fusion and fault judgment: Use support vector machine (SVM) to judge the fault area. The fault area is represented by fz. When line L3 is faulty, fz = 1; when line L3 is not faulty, fz = 0. f and R f The support vector machine (SVM) input vector fz is used as the SVM output for fault area determination. The SVM is first trained using a training sample set. The trained SVM can then be used to determine the fault area. In this embodiment, the SVM is first trained using the training sample set to determine the fault area. The trained SVM then determines the fault area in this embodiment, correctly determining that the fault is on line L3.

[0228] Example 8

[0229] The protected object is a busbar. Figure 1 , assuming that a single-phase grounding fault occurs on bus M, the initial phase angle θ f =45°, transition resistance R f =5Ω. Here, bus M is taken as an example to illustrate the transient voltage bus protection method using SVM.

[0230] The fault information fusion and power system transient protection method includes the following steps:

[0231] Step 1. Collect the bus voltage u2 from the protected bus M. The bus voltage u2 collected in this embodiment is the same as the u2 in step 1 of embodiment 4.

[0232] Step 2. Extract the high-frequency component u of voltage u2 h2 Wavelet transform is used here, which can of course be replaced by Fourier transform, classical mode decomposition, variational mode decomposition, S transform, filter bank, mathematical morphology and other methods to extract the high-frequency component u of u2 h2 In this embodiment, the high frequency component u h2 Same as u in step 2 of Example 4 h2 same.

[0233] Step 3. Calculate u h2 The transient quantity TQ2. h2Perform Hilbert transform to find its instantaneous amplitude, and then integrate it to obtain their transient energy TQ2. The transient energy TQ2 of this embodiment is the same as TQ2 in step 3 of embodiment 4.

[0234] Step 4. Calculate the initial fault phase angle θ f and transition resistance R f θ calculated in this embodiment f =45°, R f =5Ω.

[0235] Step 5. Fault information fusion and fault judgment: Use support vector machine (SVM) to judge the fault area. The fault area is represented by fz. When busbar M is faulty, fz=1; when busbar M is not faulty, fz=0. f and R f The support vector machine (SVM) input vector fz is used as the SVM output, which is the fault area determination result fz. The SVM is first trained using a training sample set. The trained SVM can then be used to determine the fault area. In this embodiment, the SVM is first trained using the training sample set to determine the fault area. The trained SVM then determines the fault area in this embodiment, correctly determining that bus M is faulty.

[0236] Example 9

[0237] A fault information fusion method is an embodiment of a method for fusing transient quantity and fault condition attribute information using a Kalman filtering algorithm.

[0238] In this embodiment, the extended Kalman filter (EKF) algorithm is used to integrate the transient quantity TQ1 in step 5 of embodiment 1 with the fault condition attribute θ f and R f Take the standard transient quantity NTQ1 as an example.

[0239] The state equation and observation equation are established as Equation (1) and Equation (2) respectively, and the Kalman recursive formula is constructed. Then, the initial state and covariance matrix are initialized, and the recursive calculation completes the fusion of fault characteristics and fault condition attribute information.

[0240] X(k) = X(k-1) + W(k) (1)

[0241] Y(k) = F(θ f , R f )X(k) + V(k) (2)

[0242] Where X(k) is the standard transient quantity integrated with fault condition attribute information; W(k) is the standard transient quantity noise, whose mean is 0, Q is the standard transient quantity noise variance, which can be taken as Q = 0.01; Y(k) is the transient quantity before integrating fault condition attribute information, i.e., the quasi-fault feature; V(k) is the quasi-fault feature noise, whose mean is 0, R is the quasi-fault feature noise variance, which can be taken as R = 0.01; θ f ,R f is the calculated fault initial phase angle and transition resistance; F(θ f ,R f ) is the fault condition attribute factor function, which includes θ f ,R f Fault information can be implemented in many ways, one of which is F(θ f ,R f )=γ·(a·exp(b·R f )+c·exp(d·R f ))·(a0+a1·cos(θ f ·w)+b1·sin(θ f ·w)+a2·cos(2·θ f ·w)+b2·sin(2·θ f w). γ is the adjustment constant, which can be taken as 1; a, b, c, d, a0, a1, b1, a2, b2, w are constants, which can be taken as a=128.3, b=-0.01102, c=163.6, d=-0.001644, a0=38.72, a1=-43.86, b1=255.5, a2=5.494, b2=1.328, w=0.01935 respectively.

[0243] The specific steps are as follows:

[0244] Step 1. Initialize the initial state X(0), Y(0), and P(0). The initialization can be set to X(0) = 0.01, Y(0) = 0.01, and P(0) = 1;

[0245] Step 2. State prediction:

[0246] X(k|k-1)=X(k-1)

[0247] Step 3. Observation prediction:

[0248] Y(k|k-1)=F(θ f ,R f )X(k|k-1)

[0249] Step 4. Solve the state transfer matrix φ(k):

[0250]

[0251] Step 5. Solve the observation transfer matrix H(k):

[0252]

[0253] Step 6. Find the covariance matrix prediction P(k|k-1):

[0254] P(k|k-1)=φ(k)P(k-1|k-1)φ T (k)+Q

[0255] Step 7. Calculate the Kalman filter gain:

[0256] K(k)=P(k|k-1)H T (k)(H(k)P(k|k-1)H T (k)+R) -1

[0257] Step 8. Request status update:

[0258] X(k)=X(k|k-1)+K(k)(Y(k)-H(k)X(k|k-1))

[0259] Step 9. Covariance update:

[0260] P(k)=(I n -K(k)H(k))P(k|k-1).

[0261] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A power system transient protection method based on fault information fusion, characterized in that: When the protection object is a circuit and the busbar connected to it, the steps of transient unit protection include: Step 1. Collect the voltage u1 of the head end of the protected line and the voltage u2 of the protected busbar; Step 2. Extract the high-frequency components u of voltages u1 and u2 respectively h1 and u h2 ; Step 3. Calculate the high-frequency components u separately h1 and u h2 The transient quantities TQ1 and TQ2 are selected from the group consisting of signal energy, entropy of the fault signal, signal complexity, signal singularity, Lipschitz index, instantaneous amplitude, singular value, transient quantity gradient, and transient quantity relative gradient; or the instantaneous integral of the transient quantity, or the instantaneous cumulative sum of the transient quantity; Step 4. Calculate the fault initial phase angle θ of the fault condition attribute f and transition resistance R f ; Step 5. Fault information fusion: transient quantities TQ1 and TQ2 are fused with fault condition attributes and the initial fault phase angle θ f and transition resistance R f Obtain standard transient quantities NTQ1 and NTQ2; Step 6. Calculate the difference NTQD between the standard transient quantities NTQ1 and NTQ2 12 ,NTQD 12 =NTQ1-NTQ2; Step 7. Use the standard transient quantity difference as the fault feature to judge the fault; when NTQD 12 >Aset, it is judged as an internal line fault; When NTQD 12 When Aset < -Bset, it is judged as a busbar fault; otherwise, it is judged as no fault inside the protection area; where Aset and Bset are positive real numbers set for protection.

2. The method according to claim 1 is characterized in that steps 5 to 7 can use support vector machine (SVM) or neural network to complete fault information fusion and fault judgment. When using support vector machine, the steps are as follows: Calculate the difference TQD between transient quantities TQ1 and TQ2 12 , TQD 12 =TQ1-TQ2, TQD 12 , initial fault phase angle θ f and transition resistance R f As the input vector of the support vector machine SVM, the SVM output is the fault area judgment result, where, First, the SVM is trained using the training sample set, and then the trained SVM is used to determine the fault area.

3. A power system transient protection method based on fault information fusion, characterized in that: When the protection object is a single-circuit line, the transient directional pilot protection steps include: Step 1. Collect the voltage u1 of the head end of the protected line and the voltage u2 of the protected busbar; Step 2. Extract the high-frequency components u of voltages u1 and u2 respectively h1 and u h2 ; Step 3. Calculate the high-frequency components u separately h1 and u h2 The transient quantities TQ1 and TQ2 are selected from the group consisting of signal energy, entropy of the fault signal, signal complexity, signal singularity, Lipschitz index, instantaneous amplitude, singular value, transient quantity gradient, and transient quantity relative gradient; or the instantaneous integral of the transient quantity, or the instantaneous cumulative sum of the transient quantity; Step 4. Calculate the fault initial phase angle θ of the fault condition attribute f and transition resistance R f ; Step 5. Fault information fusion: transient quantities TQ1 and TQ2 are fused with fault condition attributes and the initial fault phase angle θ f and transition resistance R f Obtain standard transient quantities NTQ1 and NTQ2; Step 6. Calculate the difference NTQD between the standard transient quantities NTQ1 and NTQ2 12 ,NTQD 12 =NTQ1-NTQ2; Step 7. Use the standard transient quantity difference as the transient characteristic of the fault direction to determine the fault direction; when NTQD 12 >Aset, it is determined that the line has a positive direction fault and sends equivalent information of "positive direction fault" to the other end; otherwise, it is determined that there is no fault in the positive direction of the line; Step 8. If the local protection system determines that the fault is "positive direction fault" and receives equivalent information of "positive direction fault" from the remote protection system, it determines that the fault is an internal line fault. Otherwise, it determines that the line has no fault.

4. The method according to claim 3, characterized in that Steps 5 to 7 can use support vector machine (SVM) or neural network to complete fault information fusion and fault judgment. When using support vector machine (SVM), the steps are as follows: Calculate the difference TQD between transient quantities TQ1 and TQ2 12 , TQD 12 =TQ1-TQ2, TQD 12 , initial fault phase angle θ f and transition resistance R f As the input vector of the support vector machine SVM, the SVM output is the fault direction judgment result. The SVM is first trained with the training sample set, and then the trained SVM is used for fault direction judgment; When the SVM determines that the line is faulty in the forward direction, it sends the equivalent information of "faulty in the forward direction" to the other end. When the SVM determines that the line is faulty in the forward direction, it sends the equivalent information of "faulty in the forward direction" to the other end. If the local end determines that a fault is occurring in the positive direction and simultaneously receives equivalent information indicating a "fault in the positive direction" from the remote end, it determines that a fault is occurring within the line. Otherwise, it determines that there is no fault in the line.

5. A method for transient protection of a power system based on fault information fusion, characterized in that: When the protection object is a single-circuit line, the single-ended transient protection of the line includes the following steps: Step 1. Collect the voltage u1 at the head end of the protected line; Step 2. Extract the high-frequency component u of voltage u1 h1 ; Step 3.u h1 Perform Hilbert transform to obtain its instantaneous amplitude, and then integrate it to obtain its transient quantity TQ1, where the transient quantity is one of signal energy, entropy of fault signal, signal complexity, signal singularity, Li index, instantaneous amplitude integral, instantaneous amplitude, instantaneous amplitude cumulative sum, singular value, transient quantity gradient, and transient quantity relative gradient; Step 4. Calculate the fault initial phase angle θ of the fault condition attribute f and transition resistance R f ; Step 5. Fault information fusion: transient quantity TQ1 fusion fault initial phase angle θ of fault condition attributes f and transition resistance R f Obtain the standard transient quantity NTQ1; Step 6. Use the standard transient quantity as the fault characteristic to determine the fault; use NTQ1 as the fault characteristic to construct the protection judgment criterion: when NTQ1>Aset, it is determined to be an internal line fault; otherwise, it is determined that the line is not faulty; where Aset is a positive real number set by the protection.

6. The method according to claim 5, characterized in that Steps 5 and 6 can use support vector machine (SVM) or neural network to complete fault information fusion and fault judgment. When using support vector machine, the specific steps include: taking TQ1, fault initial phase angle θ f and transition resistance R f As the support vector machine SVM input vector, the SVM output is the fault area judgment result, wherein the SVM is first trained with the training sample set, and then the trained SVM is used for fault area judgment.

7. A power system transient protection method based on fault information fusion, characterized in that: When the protection object is a bus, the transient voltage bus protection steps include: Step 1. Collect the protected bus voltage u2; Step 2. Extract the high-frequency component u of voltage u2 h2 ; Step 3. Calculate u h2 The transient quantity TQ2 is one of signal energy, entropy of fault signal, signal complexity, signal singularity, Lipschitz index, instantaneous amplitude, singular value, transient quantity gradient and transient quantity relative gradient; or the instantaneous integral of the transient quantity, or the instantaneous cumulative sum of the transient quantity; Step 4. Calculate the fault initial phase angle θ of the fault condition attribute f and transition resistance R f ; Step 5. Fault information fusion: transient quantity TQ2 fusion fault initial phase angle θ of fault condition attributes f and transition resistance R f Get the standard transient quantity NTQ2; Step 6. Use the standard transient quantity as the fault characteristic to determine the fault, and use NTQ2 as the fault characteristic to construct the protection judgment criterion: when NTQ2>Aset, it is determined to be a bus fault; otherwise, it is determined to be a bus no fault; where Aset is a positive real number set by the protection.

8. The method according to claim 7, characterized in that Steps 5 and 6 can use support vector machines or neural networks to complete fault information fusion and fault judgment. When using support vector machines, the steps are as follows: TQ2, θ f and R f As the support vector machine SVM input vector, the SVM output is the fault area judgment result, wherein the SVM is first trained with the training sample set, and then the trained SVM is used for fault area judgment.

9. The power system transient protection method based on fault information fusion according to claim 1, 3, 5 or 7, characterized in that: Step 5: Fault information fusion adopts Kalman filter algorithm or particle filter algorithm.

10. The method according to claim 9, characterized in that: When using the Kalman filter algorithm, the steps include: Step 1. Initialize the initial state X(0), Y(0), and P(0), and set X(0) = 0.01, Y(0) = 0.01, and P(0) = 1; Step 2. State prediction: X(k|k-1)=X(k-1) Step 3. Observation prediction: Y(k|k-1)=F(θ f ,R f )X(k|k-1) Step 4. Solve the state transfer matrix Φ(k): Step 5. Solve the observation transfer matrix H(k): Step 6. Find the covariance matrix prediction P(k|k-1): P(k|k-1)=Φ(k)P(k-1|k-1)Φ T (k)+Q Step 7. Calculate the Kalman filter gain: K(k)=P(k|k-1)H T (k)(H(k)P(k|k-1)H T (k)+R) -1 Step 8. Request status update: X(k)=X(k|k-1)+K(k)(Y(k)-H(k)X(k|k-1)) Step 9. Covariance update: P(k)=(I n -K(k)H(k))P(k|k-1); Among them, X(k) is the standard transient quantity after the fault quantity is integrated with the fault condition attribute information; Q is the standard transient quantity noise variance; Y(k) is the fault quantity before integrating the fault condition attribute information, i.e., the quasi-fault feature; R is the quasi-fault feature noise variance; F(θ f ,R f ) is the fault condition attribute factor, θ f ,R f are the initial phase angle of the fault and the transition resistance respectively.

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