A distributed state monitoring and fault diagnosis method for a direct current microgrid

Abstract

The application provides a kind of distributed state monitoring and fault diagnosis method of direct current micro grid.Belongs to the technical field of direct current micro grid, solve the technical problem that existing state monitoring method cannot accurately monitor the state of direct current micro grid existing load disturbance, process uncertainty and measurement noise in practical application.The technical scheme is as follows: a.establishing the distributed state space model of each power generation unit in direct current micro grid;B.using full rank decomposition technology to design the time-invariant parameter matrix of distributed observer and determine the time-varying parameter matrix of distributed observer in the sense of minimum mean square error;C.based on local state monitoring, construct distributed residual generator.The beneficial effects of the application are: overcome the difficulty of high false alarm rate caused by fault propagation, reduce the communication and calculation amount, reduce the risk of network attack, make the system have higher reliability and scalability.

Description

Technical Field

[0001] This invention belongs to the field of DC microgrid technology, specifically relating to a distributed condition monitoring and fault diagnosis method for DC microgrids. Background Technology

[0002] DC microgrids do not suffer from phase synchronization or harmonic mitigation issues and possess advantages such as small size, simple structure, and good economy, making them widely used in data centers, electric vehicles, and other fields. Condition monitoring is a crucial component of DC microgrid energy management systems; however, the volatility of renewable energy sources and load disturbances make condition monitoring extremely challenging. Furthermore, extreme weather, equipment aging, and other factors inevitably lead to component failures, actuator failures, sensor failures, and malicious network attacks. Therefore, condition monitoring and fault diagnosis in DC microgrids have received widespread attention. DC microgrids are typical large-scale distributed interconnected systems, with each generator unit highly coupled. A sudden increase in load or a fault can affect the state of other generator units. Centralized or distributed condition monitoring methods and fault diagnosis both require interactive information, which not only affects the accuracy of condition monitoring and fault diagnosis but also consumes significant communication and computing resources and is highly vulnerable to network attacks. Therefore, centralized and distributed condition monitoring methods and fault diagnosis have inherent drawbacks.

[0003] In summary, the distributed condition monitoring and fault diagnosis method for DC microgrids has significant practical implications and application value, and a distributed condition monitoring and fault diagnosis method for DC microgrids has been proposed. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to address the above-mentioned technical problems by providing a distributed state monitoring and fault diagnosis method for DC microgrids. The distributed observer in this method decouples the influence of local load disturbances and the state of neighboring generator units, and achieves state monitoring of DC microgrids in the sense of minimum mean square error under the influence of process uncertainty and measurement noise. Furthermore, it enables fault diagnosis of DC microgrids when actuator faults, voltage and current sensor faults occur concurrently.

[0005] The inventive concept of this invention is as follows: First, considering the effects of load disturbances, system uncertainties, and faults, a distributed state-space model of each generating unit in a DC microgrid is established based on Kirchhoff's voltage and current laws and the quasi-steady-state approximation of the power line. Then, using full-rank decomposition technology, the time-invariant parameter matrix of the distributed observer is designed, and the time-varying parameter matrix of the distributed observer is determined under the meaning of minimum mean square error, achieving decoupling between the local load and the states of neighboring generating units, thus obtaining accurate DC microgrid state monitoring. Next, a distributed residual generator is constructed based on the local state monitoring, and concurrent fault diagnosis of the DC microgrid is achieved through residual evaluation. Finally, the effectiveness of the proposed method is verified using a DC microgrid composed of six distributed generating units.

[0006] To achieve the above-mentioned objectives, this invention provides a distributed condition monitoring and fault diagnosis method for DC microgrids, comprising the following steps:

[0007] a. Considering the effects of load disturbances, system uncertainties, and faults, Kirchhoff's voltage and current laws and the quasi-steady-state approximation of the power line are used to establish a distributed state-space model of each power generation unit in the DC microgrid;

[0008] b. The time-invariant parameter matrix of the distributed observer is designed using the full-rank decomposition technique, and the time-varying parameter matrix of the distributed observer is determined under the meaning of minimum mean square error, so that the distributed observer can monitor the status of the local power generation unit in real time and accurately.

[0009] c. Construct a distributed residual generator based on local condition monitoring, and realize concurrent fault diagnosis and decision-making of DC microgrid through residual evaluation.

[0010] Furthermore, step a considers the effects of process uncertainties, measurement noise, load disturbances, actuator failures, and voltage and current sensor failures. Kirchhoff's voltage and current laws and the quasi-steady-state approximation of the power line are used to establish the DGU in the DC microgrid. i The distributed state-space model is as follows:

[0011]

[0012] Where x [i] (k)=y [i] (k)=[V i I ti ] T u [i] (k)=[V ti ], d [i] (k)=[I Li ], w [i] (k), v [i](k) represent the system state variables, output variables, control signals, unknown input disturbances, process uncertainties, and measurement noise, respectively; V i and I ti V represents the voltage and filter current at the i-th common coupling point, respectively; ti Indicates the control voltage for the Buck converter; I Li Represents an unknown load current; R ti L ti C ti These represent the resistor, inductor, and capacitor of the RLC filter circuit, respectively; V j N represents the common coupling point voltage of neighboring power generation units; i DGU i The set of all neighboring power generation units j, and the total number of neighbors is denoted as |N|. i |=s i s i ≥1; R ij L ij These are the equivalent resistance and inductance of the power line, respectively; w [i] (k) and v [i] (k) are all independent Gaussian white noise, satisfying: The deviation is due to a control voltage malfunction. ΔV i It is the measurement deviation of the voltage sensor, ΔI ti This is the measurement deviation of the current sensor, and the corresponding known system parameter matrix is:

[0013]

[0014] In step b, design (DO) i Distributed observers are as follows:

[0015]

[0016] in It is DO i state, It's DGU i State estimation, F i ∈R 2×2 T i ∈R 2×2 H i ∈R 2 ×2 G is the time-invariant parameter matrix to be designed. i (k+1)∈R 2×2 It is the time-varying parameter matrix to be designed.

[0017] By utilizing full-rank decomposition techniques to decouple the load disturbances of the local generating unit from the state of neighboring generating units, the time-invariant parameter matrix satisfies:

[0018]

[0019] T i =[IH i C i ];

[0020] F i =[IH i C i A zii ;

[0021] in To achieve state monitoring of a DC microgrid under the minimum mean square error condition, the design time-invariant parameter matrix satisfies:

[0022]

[0023] Where P [i] (k) is the state estimation error covariance of the local power generation unit, which satisfies:

[0024]

[0025] The residual generator is constructed in step c as follows:

[0026]

[0027] The decision logic for achieving concurrent fault diagnosis of DC microgrids through residual evaluation is as follows:

[0028]

[0029] J [i] (k+1) is the test statistic, satisfying... J th，i It is the corresponding threshold, which satisfies

[0030] Compared with the prior art, the present invention has the following technical effects:

[0031] 1) The designed distributed observer-based state monitoring and concurrent fault diagnosis method decouples the influence of local load disturbances and the state of neighboring generator units, and achieves state monitoring of each generator unit in the sense of minimum mean square error under the influence of process uncertainty and measurement noise, and the monitoring results are more accurate.

[0032] 2) The designed method based on distributed observer state monitoring and concurrent fault diagnosis solves the problem of high false alarm rate caused by fault propagation in practical applications;

[0033] 3) The designed distributed observer-based state monitoring and concurrent fault diagnosis method solves the problem of large communication and computing resources in centralized or distributed state monitoring and concurrent fault diagnosis methods, avoids the risk of network attacks during information exchange, and makes the system reliable and scalable. Attached Figure Description

[0034] Figure 1 A flowchart illustrating the steps of the distributed condition monitoring and fault diagnosis method for DC microgrids provided by the present invention.

[0035] Figure 2 DGU in this invention i The circuit model diagram;

[0036] Figure 3 This is a diagram illustrating an example of a DC microgrid composed of six power generation units in this invention.

[0037] Figure 4 This is a schematic diagram of the DC microgrid state monitoring results based on distributed observers during a sudden load increase in this invention;

[0038] Figure 5 This is a schematic diagram of the DC microgrid state monitoring results based on distributed observers during concurrent faults in this invention;

[0039] Figure 6 This is a schematic diagram of the concurrent fault diagnosis results of DC microgrid based on residual evaluation in this invention. Detailed Implementation

[0040] Example 1

[0041] Reference Figure 1 The flowchart illustrates a distributed condition monitoring and fault diagnosis method for a DC microgrid, comprising the following steps:

[0042] Step a: Refer to Figure 2 The DGU shown i The circuit model is constructed, considering the effects of process uncertainties, measurement noise, load disturbances, actuator failures, and voltage and current sensor failures. Kirchhoff's voltage and current laws and the quasi-steady-state approximation of the power line are used to establish the DUG in the DC microgrid. i The distributed state-space model is as follows:

[0043]

[0044] Where x [i] (k)=y [i] (k)=[V i ,I ti ] T ,u [i](k)=[V ti ],d [i] (k)=[I Li ],w [i] (k),v [i] (k) represent the system state variables, output variables, control signals, unknown input disturbances, process uncertainties, and measurement noise, respectively; V i and I ti V represents the voltage and filter current at the i-th common coupling point, respectively; ti Indicates the control voltage for the Buck converter; I Li Represents an unknown load current; R ti L ti C ti These represent the resistor, inductor, and capacitor of the RLC filter circuit, respectively; V j N represents the common coupling point voltage of neighboring power generation unit j; i DGU i The set of all neighboring power generation units j, and the total number of neighbors is denoted as |N|. i |=s i s i ≥1; R ij L ij These are the equivalent resistance and inductance of the power line, respectively; w [i] (k) and v [i] (k) are all independent Gaussian white noise, satisfying: The deviation is due to a control voltage malfunction. ΔV i It is the measurement deviation of the voltage sensor, ΔI ti This is the measurement deviation of the current sensor, and the corresponding known system parameter matrix is:

[0045]

[0046] Step b: Design a distributed observer (DO) i )as follows:

[0047]

[0048] in, It is DO i state, It's DGU i State estimation, F i ∈R 2×2 ,T i ∈R 2×2 H i ∈R 2×2 G is the time-invariant parameter matrix to be designed. i (k+1)∈R 2×2It is the time-varying parameter matrix to be designed.

[0049] To ensure that condition monitoring is unaffected by load disturbances from local generating units and the status of neighboring generating units, d [i] (k) and x [j] If (k) are all considered unknown inputs, then DGU i The distributed state-space model was rewritten as:

[0050]

[0051] in And r i =rank(E i Satisfies: 0 < r i ≤min{2,1+2s i}

[0052] The following was obtained by decomposing using the full-rank decomposition technique:

[0053]

[0054] in It is a full-rank column matrix. It is a full-rank row matrix. The design-time invariant parameter matrix satisfies:

[0055]

[0056] T i =[IH i C i ];

[0057] F i =[IH i C i A zii ;

[0058] The state monitoring of the DC microgrid based on distributed observers decouples the load disturbances of local generating units from the state of neighboring generating units. The design-time invariant parameter matrix satisfies:

[0059]

[0060] Where P [i] (k) is the state estimation error covariance of the local power generation unit, which satisfies:

[0061]

[0062] This enables the state monitoring of DC microgrids in the sense of minimum mean square error.

[0063] Step c: Construct the residual generator as follows:

[0064]

[0065] The decision logic for achieving concurrent fault diagnosis of DC microgrids through residual evaluation is as follows:

[0066]

[0067] J [i] (k+1) is the test statistic, satisfying... J th，i It is the corresponding threshold, which satisfies α is a given significance level.

[0068] This embodiment, implemented in Matlab R2020a, uses a DC microgrid consisting of six distributed generation units as an example. Figure 3 As shown. The method designed in this embodiment was verified, and Table 1 provides the electrical parameters for a specific example. The covariance of the set process uncertainty and measurement noise are Q... wi = diag(0.001, 0.001), Q vi =diag(0.001,0.001), simulation time is set to 30μs.

[0069] Example 1: Initially, the initial load current of the 6 power generation units is 5A; after 5μs, the load suddenly increases and the load current increases to 10A.

[0070] Example 2

[0071] Based on Example 1, in Example 2, after 10μs, DGU1 experiences an actuator failure, and DGU4 experiences voltage sensor failure and current sensor failure, with the following failure amplitudes: Given a significance level α = 0.05, the threshold is J. th,i =5.9915, i=1,...,6.

[0072] Table 1 Specific Electrical Parameters of DC Microgrids

[0073] 1 0.2 2.2 1.8 (1,2) 0.05 2.1 2 0.3 1.9 2.0 (1,3) 0.07 1.8 3 0.1 1.7 2.2 (1,6) 0.1 2.5 4 0.5 2.5 3.0 (2,4) 0.04 2.3 5 0.4 2.0 1.2 (3,4) 0.06 1.0 6 0.6 3.0 2.5 (4,5) 0.08 1.8 (5,6) 0.08 3.0

[0074] Therefore, the time-invariant parameter matrix F of the distributed observer is calculated. i ,T i H i and the time-varying parameter matrix G that converges iteratively over time i (k+1), i=1...6 are as follows:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] Results Explanation:

[0082] Figure 4 This is a diagram showing the state monitoring results of a DC microgrid during a load surge. It can be seen that, under the influence of load disturbances, process uncertainties, and measurement noise, the designed distributed observer can accurately monitor the voltage and current status of each generator unit, while traditional state monitoring methods are significantly affected by noise. In particular, even with a load surge after 5 μs, the state monitoring method based on distributed observers can still achieve accurate state monitoring of the DC microgrid.

[0083] Figure 5 This is a diagram showing the status monitoring results of a DC microgrid during a concurrent fault. It can be seen that after the concurrent fault occurred 10μs ago, only the status monitoring of the faulty DGU1 and DGU4 showed a slight deviation, while the status monitoring of other generator units remained accurate.

[0084] Figure 6 Based on the residual assessment results of concurrent fault diagnosis of DC microgrid, it was directly diagnosed that alarms occurred in DGU1 and DGU4, and no false alarms occurred.

[0085] This embodiment, based on distributed state monitoring and fault diagnosis of DC microgrids, first establishes a state-space model of distributed generation units, considering the impacts of load surges, system uncertainties, and faults in practical applications. Then, a distributed observer is designed, and the time-invariant parameter matrix is ​​determined using full-rank decomposition technology. This decouples the influence of load disturbances in local generation units and the states of neighboring generation units. The time-varying parameter matrix is ​​determined under the minimum mean square error principle, reducing the impact of process uncertainties and measurement noise, thus making state monitoring more accurate. The distributed design method eliminates the need for information exchange, reducing communication and computing resources, significantly lowering the risk of network attacks, and overcoming the challenge of high false alarm rates caused by fault propagation. This successfully achieves concurrent fault diagnosis of DC microgrids.

[0086] The technical features of this invention not described can be implemented by or using existing technology, and will not be repeated here. Of course, the above description is not a limitation of this invention, and this invention is not limited to the examples above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of this invention should also be within the protection scope of this invention.

Claims

1. A distributed condition monitoring and fault diagnosis method for a DC microgrid, characterized in that, Includes the following steps: a. Considering the effects of load disturbances, system uncertainties, and faults, Kirchhoff's voltage and current laws and the quasi-steady-state approximation of the power line, establish a distributed state-space model of each power generation unit in the DC microgrid; Step a considers the effects of process uncertainties, measurement noise, load disturbances, actuator failures, and voltage and current sensor failures. Kirchhoff's voltage and current laws and the quasi-steady-state approximation of the power line are used to establish the i-th generating unit (DGU) in the DC microgrid. i The distributed state-space model is as follows: ； in These include system state variables, output variables, control signals, unknown input disturbances, process uncertainties, and measurement noise. V represents the voltage and filter current at the i-th common coupling point; ti Indicates the control voltage for the Buck converter; I Li Represents an unknown load current; R ti L ti C ti These represent the resistor, inductor, and capacitor of the RLC filter circuit, respectively; V j This represents the voltage at the common coupling point of neighboring power generation unit j; DGU i The set of all neighboring power generation units j, and the total number of neighbors is denoted as . ;R ij L ij These are the equivalent resistance and inductance of the power line, respectively. and All are unrelated Gaussian white noise, satisfying: , ; The deviation is due to a control voltage malfunction. , It's a measurement deviation from the voltage sensor. This is the measurement deviation of the current sensor, and the corresponding known system parameter matrix is: ； b. Design the time-invariant parameter matrix of the distributed observer using full-rank decomposition technology and determine the time-varying parameter matrix of the distributed observer under the meaning of minimum mean square error, so that the distributed observer can monitor the status of the local power generation unit in real time and accurately; In step b, a distributed observer (DO) is designed. i as follows: ； in, It is DO i state, It's DGU i State estimation, It is the time-invariant parameter matrix to be designed. It is the time-varying parameter matrix to be designed; By using full-rank decomposition technology to decouple the load disturbance of the local generating unit from the state of the neighboring generating unit, the time-varying parameter matrix during design satisfies: ； ； ； in To achieve state monitoring of a DC microgrid under the minimum mean square error condition, the design invariant parameter matrix satisfies: ； in The state estimation error covariance of the local power generation unit satisfies: ； c. Construct a distributed residual generator based on local condition monitoring, and realize concurrent fault diagnosis and decision-making of DC microgrid through residual evaluation.

2. The design of a distributed condition monitoring and fault diagnosis method for a DC microgrid according to claim 1, characterized in that, The residual generator is constructed in step c as follows: ； The decision logic for achieving concurrent fault diagnosis of DC microgrids through residual evaluation is as follows: ； in It is a test statistic that satisfies ; It is the corresponding threshold, which satisfies .

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