CN112346439B - Micro-grid sensor fault tolerance control method based on PD type learning observer - Google Patents
Micro-grid sensor fault tolerance control method based on PD type learning observer Download PDFInfo
- Publication number
- CN112346439B CN112346439B CN202011309721.2A CN202011309721A CN112346439B CN 112346439 B CN112346439 B CN 112346439B CN 202011309721 A CN202011309721 A CN 202011309721A CN 112346439 B CN112346439 B CN 112346439B
- Authority
- CN
- China
- Prior art keywords
- fault
- output
- microgrid
- sensor
- current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000005259 measurement Methods 0.000 claims abstract description 13
- 238000012544 monitoring process Methods 0.000 claims abstract description 5
- 239000011159 matrix material Substances 0.000 claims description 35
- 239000003990 capacitor Substances 0.000 claims description 15
- 238000006243 chemical reaction Methods 0.000 claims description 13
- 238000012546 transfer Methods 0.000 claims description 6
- 230000003321 amplification Effects 0.000 claims description 3
- 230000003190 augmentative effect Effects 0.000 claims description 3
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 3
- 239000011541 reaction mixture Substances 0.000 claims description 3
- 230000009466 transformation Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000010248 power generation Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 239000004576 sand Substances 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000003416 augmentation Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000001687 destabilization Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0262—Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Inverter Devices (AREA)
Abstract
The invention belongs to the technical field of microgrid fault tolerance control, and particularly relates to a microgrid sensor fault tolerance control method based on a PD type learning observer. The method comprises the following steps: step 1, establishing a system closed-loop output feedback controller according to a state space model of a micro-grid inverter type distributed power supply; step 2, establishing a sensor fault model of the microgrid system, and converting the sensor fault model into an actuator fault model form; and 3, establishing a sensor fault-tolerant PD type learning observer, solving a reconstructed fault signal, feeding back an output compensated by the reconstructed signal to an output feedback controller, and ensuring the stable tracking of the output power. Real-time monitoring of faults is realized, the faults of the sensors are accurately reconstructed, and measurement errors of the sensors are corrected by using the reconstructed fault signals; the IIDG can run safely and reliably when grid-connected power of the micro-grid system is transmitted, and the method has wide adaptability to various faults of the micro-grid sensor.
Description
Technical Field
The invention belongs to the technical field of microgrid fault tolerance control, and particularly relates to a microgrid sensor fault tolerance control method based on a PD type learning observer.
Background
In recent years, distributed power generation technology based on renewable energy has been widely used due to shortage of fossil energy and increasing environmental problems. However, the distributed power supplies have inherent defects of high output power randomness, high regulation difficulty and the like, so that the safe and stable operation of the power system is influenced to a certain extent by the access of a large number of distributed power supplies, and a new problem is brought to the operation and management of the traditional power system.
The distributed power supply in the microgrid is an inverter power supply which takes power electronic elements as grid-connected interfaces, and mathematically, the distributed power generation system of the microgrid is a nonlinear system with all parts strongly coupled with each other. In a grid-connected operation mode, the distributed power supply mainly adopts PQ control, and the output power of the distributed power generation system can be ensured to follow a given value to transmit power with a large power grid. The stable operation of the microgrid system determines the stable transmission of power between the microgrid system and a large power grid, and the fault problem in the system can cause the microgrid to be in a destabilization state, so that the construction of a fault-tolerant control strategy is an important component of the design of the distributed power generation system of the microgrid. However, the protection technology is still in the theoretical research stage as the application bottleneck of the micro-grid system. For a typical complex nonlinear system of a microgrid, domestic and foreign scholars apply various fault-tolerant control methods such as fault-tolerant control, adaptive fault-tolerant control, neural network control, sliding-mode fault-tolerant control and the like by adopting an artificial intelligence control technology when researching faults of the microgrid.
A sensor is one of the most important elements in a control system, and a small fault of the sensor may cause misoperation of the control system or false alarm of a fault diagnosis system, resulting in degradation of the performance of the system. Therefore, the sensor fault diagnosis research is very important for improving the performance of the microgrid system. In recent years, observer-based fault reconstruction has attracted more and more attention, and various fault reconstruction observers are widely applied. Such as a sliding mode observer, an adaptive observer, a learning observer, and the like.
Therefore, under the fault state of the micro-grid, a method for realizing real-time monitoring of the fault by the fault-tolerant learning observer and correcting the measurement error of the sensor by using the reconstructed fault signal to form a power stable tracking reference value is researched, and the method has good engineering practical significance.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and aims to ensure the safe and reliable operation of an IIDG (inter-integrated differential) during grid-connected power transmission of a microgrid system and provide a microgrid sensor fault tolerance control method based on a PD (PD type learning observer) which has wide adaptability to various faults of a microgrid sensor.
In order to achieve the purpose, the invention adopts the following technical scheme:
the fault tolerance control method of the micro-grid sensor based on the PD type learning observer comprises the following steps:
step 1, establishing a system closed-loop output feedback controller according to a state space model of a micro-grid inverter type distributed power supply;
step 2, establishing a sensor fault model of the microgrid system, and converting the sensor fault model into an actuator fault model form;
and 3, establishing a sensor fault-tolerant PD type learning observer, solving a reconstructed fault signal, feeding back an output compensated by the reconstructed signal to an output feedback controller, and ensuring the stable tracking of the output power.
As a preferred technical scheme of the invention: the specific steps of the step 1 are as follows:
step 1.1, converting three-phase output into two phases by using a park module according to a three-phase converter structure of the microgrid, and deducing a state space model of an IIDG in the microgrid system under a dq coordinate system;
step 1.2, according to the constructed state space model of the IIDG, a closed-loop output feedback controller is established, and the control law is as follows:
udq＝Ky (1)
in the formula (1), y is the system output current and is defined as y ═ iod ioq]TK is a control rate gain matrix;
step 1.3, establishing an augmented micro-grid system model under the control of an output feedback controller, and determining the value of a control law gain matrix K by using a linear matrix inequality method; the inequality is defined as:
in the formula (2), χ ═ Aa+BaKCa，AaRelates to the amplification of the inverter output current i in a microgrid systemtd、itqTerminal voltage v of filter capacitorcfd、vcfqAnd bus terminal output current iod、ioqParameter (c) ofaIs a parameter relating to an input variable z, CaIs a unit matrix parameter related to the output current and voltage at the bus terminal, FaIs a parameter related to the system disturbance d, I is the identity matrix.
As a preferred technical scheme of the invention: the specific steps of the step 2 are as follows:
step 2.1, on the basis of the inverter type distributed power supply model, establishing an IIDG sensor fault model as follows:
in equation (3), A is the inverter output current i in IIDGtd、itqTerminal voltage v of filter capacitorcfd、vcfqAnd bus terminal output current iod、ioqB is a parameter relating to the input vector z, C is a unit matrix parameter relating to the bus bar terminal output current and voltage, D is a parameter relating to the system disturbance vector w, and the state variable x ═ itd itq iod ioq vcfd vcfq]TZ is the input vector, w is the perturbation vector, Fs＝I2As a matrix of error vector coefficients, is a selection of faults, fsRepresents the current measurement error on the dq axis and can be expressed as:
fs＝[Δd Δq]T (4)
in the formula (4), ΔdAnd ΔqWhen the sensors are in fault, fault signals of current on a d axis and a q axis are output;
step 2.2, establishing a fault conversion filter, and defining the fault conversion filter as follows:
in the formula (5), xsIndicating the state of the filter current of the fault-transfer filter, AsIs a Hurwitz matrix;
step 2.3, integrating the filtered sensor fault model as follows:
in the formula (6), the first and second groups,
indicating the state of the IIDG inverter output current and the fault transfer filter current,
which are its state matrix parameters, m represents the input and disturbance of the system,
for its state matrix parameters, fsRepresenting the current measurement error on the dq axis,
is its state matrix parameter;
and integrating the filtered sensor fault model to convert the sensor fault model into an actuator fault model form.
As a preferred technical scheme of the invention: the specific steps of the step 3 are as follows:
3.1, establishing a PD type learning observer according to a micro-grid system sensor fault model, and solving PD type learning observer matrixes L and S and a learning time interval tau; define the PD-type learning observer as:
in the formula (7), the first and second groups,
and
respectively estimating the system state, measuring output and estimating and reconstructing a sensor fault signal of the PD type learning observer; l and S are learning observer matrixes, sigma is a constant to be determined, and tau is a learning time interval;
step 3.2, solving a fault reconstruction signal of the PD type learning observer, and defining the state estimation error of the microgrid IIDG system
Output estimation error
And actuator fault reconstruction error
Comprises the following steps:
in the formula (8), the first and second groups,
and
respectively estimating the system state, measuring output and estimating and reconstructing a sensor fault signal of the PD type learning observer;
step 3.3, solving the estimation error kinetic equation of the system as follows:
in the formula (9), the reaction mixture,
represents the estimation error of the state of the microgrid IIDG system,
representing the output estimation error;
step 3.4, calculating the output current compensated by the reconstructed signal, and defining the output current as
The current is fed back to the output feedback controller, so that the stable tracking of the output power is ensured.
As a preferred technical scheme of the invention: and the sensor fault model is converted into an actuator fault model form through a fault conversion filter.
As a preferred technical scheme of the invention: the PD type learning observer realizes real-time monitoring of faults and corrects measurement errors of the sensor by using the reconstructed fault signals.
Compared with the prior art, the micro-grid sensor fault tolerance control method based on the PD type learning observer has the following technical effects:
1. the fault-tolerant controller can ensure that the micro-grid system can still keep the global stable output of current when the micro-grid system has a sensor fault, and the output power can accurately track an externally given reference value.
2. The PD type learning observer can autonomously detect the faults of the sensors, and can estimate and reconstruct the fault signals existing in the current system without adding extra sensors.
3. The PD type learning observer introduces a differential term of a measurement output estimation error on the basis of a P type learning observer, and can reconstruct the fault of the micro-grid sensor more quickly and accurately.
4. The fault-tolerant controller consists of a closed-loop output feedback controller, a fault conversion filter and a PD type learning observer, and compared with a single controller, the structure of the fault-tolerant controller has stronger robustness.
Drawings
FIG. 1 is a topological diagram of a main circuit of a three-phase grid-connected converter according to the present invention;
FIG. 2 is a schematic diagram of a microgrid PQ control according to the present invention;
FIG. 3 is a schematic diagram of a microgrid current output feedback control according to the present invention;
fig. 4 is a schematic diagram of fault-tolerant control of a microgrid system according to the present invention.
Detailed Description
The present invention will be further explained with reference to the drawings so that those skilled in the art can more deeply understand the present invention and can carry out the present invention, but the present invention will be explained below by referring to examples, which are not intended to limit the present invention.
In FIG. 1, vdThe direct current side voltage after the micro power supply is simplified can be supplied by wind driven generators, solar photovoltaic panels, fuel cells and other energy sources. CdIs a DC side filter capacitor, T1～T6Is an IGBT power device, and the output voltage of the inverter is va，vb，vcThe output current of the inverter is ia，ib，ic，Ra＝Rb＝RcR is the total converter and filter losses. L is1a＝L1b＝L1c＝L1Is an inverter side inductor, L2a＝L2b＝L2c＝L2Is a network side inductor, CfIs a filter capacitor, vcfa，vcfb，vcfcRespectively, the filter capacitor terminal voltages. The output current of the inverter passes through the LCL filter to obtain a network inlet current ioa，iob，ioc，vsa，vsb，vscIs the three-phase grid voltage with the midpoint O.
In fig. 2, an active reference value P is setrefAnd a reactive reference value QrefObtaining a d-axis current reference value i through calculationdrefAnd q-axis current reference value iqref。vtabcAnd itabcRespectively the output voltage and current of the converter. The output power of the converter is supplied to the grid through the LCL filter. Wherein R isnFor total losses of converters and filters, L1nAnd L2nIs the total inductance of the filter, CnIs a filter capacitor, VcfIs the capacitor voltage. Voltage and current at output terminal are respectively vsAnd io。
Referring to fig. 1 and 2, during grid-connected operation of the microgrid, an inverter of the microgrid is controlledThe manufacturing method mainly adopts a PQ control method. In the operation mode, the public power grid supports the frequency and the voltage inside the microgrid, and the inverters of the distributed micro power supplies use the frequency and the voltage of the public power grid as reference values, use the reference values as users to transmit active power and reactive power, and transmit redundant electric energy to the public power grid. The micro-grid PQ control is established based on a double-loop control mode under a dq coordinate system, and essentially detects the output voltage of an inverter, carries out dq conversion, and sets an active reference value PrefAnd a reactive reference value QrefObtaining a d-axis current reference value i through calculationdrefAnd q-axis current reference value iqref. The inverter used in the figure is a six-pulse bridge based on IGBTs, constituting a three-phase voltage source converter. v. oftabcAnd itabcRespectively the output voltage and current of the converter. The output power of the converter is supplied to the grid through the LCL filter. Wherein R isnFor total losses of converters and filters, L1nAnd L2nIs the total inductance of the filter, CnIs a filter capacitor, VcfIs the capacitor voltage. Voltage and current at output terminal are respectively vsAnd io. Three-phase alternating current obtains the phase theta of voltage and current through a phase-locked loop SPLL, and the phase theta is used for real-time tracking of reference current i by an inner loop controlled by a double loop in Park conversiondrefAnd iqref. Converting into three phases by coordinate transformation, and outputting SPWM reference voltage uabc. The phase locked loop SPLL performs tracking control of the current and provides a frequency reference for coordinate transformation.
The microgrid system comprises a distributed power supply, a sensor, a direct current side filter capacitor, a three-phase inverter, an LCL filter, a fault-tolerant controller and the like; the fault-tolerant controller consists of an output feedback controller, a fault conversion filter, a PD type learning observer and the like, can reconstruct a fault signal of output current when a micro-grid sensor fails, and inputs the corrected feedback signal into the closed-loop output feedback controller so as to ensure the stability of the output power of the micro-grid; the fault-tolerant controller is regulated by an output feedback controller and a signal regulator (comprising an inverse park converter and an SPWM modulator) through an externally given current input reference value rThe method comprises the steps of preparing a signal u, providing an SPWM pulse signal for an inverter of an inverter type distributed power supply, enabling a system output y to pass through an output filter in the state that a sensor fails, converting a sensor failure model into an actuator failure model, inputting the output into a PD type learning observer, and enabling the designed PD type learning observer to rapidly output current after filtering
Reconstructing the fault signal, and reconstructing the reconstructed fault signal
And output current
The difference value is used as a correction feedback signal and input into an output feedback controller, and then the running state of the system is regulated.
In a micro-grid system, a distributed power supply is mostly supplied by wind driven generators, solar photovoltaic panels, fuel cells and other energy sources, and is simplified into direct-current voltage serving as a direct-current source of the system, and is connected with a filter capacitor in parallel to play a role in filtering the direct-current source. The connected three-phase inverter takes IGBT as a power device, converts direct current into three-phase alternating current, and the inverter outputs current and voltage which pass through the LCL filter to obtain network access current and voltage respectively. The three-phase alternating current obtains the phase theta of voltage and current through the phase-locked loop, and the phase-locked loop is used for coordinate transformation, meanwhile, the phase-locked loop realizes tracking control of the current and provides frequency reference for coordinate transformation.
The fault tolerance control method of the micro-grid sensor based on the PD type learning observer comprises the following steps: step 1, establishing a system closed-loop output feedback controller according to a state space model of a micro-grid inverter type distributed power supply; step 2, establishing a sensor fault model of the microgrid system, and converting the sensor fault model into an actuator fault model form; and 3, establishing a sensor fault-tolerant PD type learning observer, solving a reconstructed fault signal, feeding back an output compensated by the reconstructed signal to an output feedback controller, and ensuring the stable tracking of the output power.
In the attached figure 3, a dq coordinate system model under the control of the micro-grid system PQ is established, an output feedback controller is designed, and a reference current i calculated through reference power is inputdrAnd iqrThe SPWM modulation signal u is output through the output feedback controller and coordinate transformation, the output current of the system is controlled, the stable tracking reference value of the output power is ensured, and the method comprises the following steps:
step 1.1, converting three-phase output into two phases by using a park module according to a three-phase converter structure of the microgrid, and deducing a state space model of an IIDG in the microgrid system under a dq coordinate system;
step 1.2, according to the constructed state space model of the IIDG, a closed-loop output feedback controller is established, and the control law is as follows:
udq＝Ky (1)
in the formula (1), y is the system output current and is defined as y ═ iod ioq]TK is a control rate gain matrix;
step 1.3, establishing an augmented micro-grid system model under the control of an output feedback controller, and determining the value of a control law gain matrix K by using a linear matrix inequality method; the inequality is defined as:
in the formula (2), χ ═ Aa+BaKCa，AaRelates to the amplification of the inverter output current i in a microgrid systemtd、itqTerminal voltage v of filter capacitorcfd、vcfqAnd bus terminal output current iod、ioqParameter (c) ofaIs a parameter relating to an input variable z, CaIs a unit matrix parameter related to the output current and voltage at the bus terminal, FaIs about a systemThe parameter of the disturbance d, I, is the identity matrix. Solving variable gamma in linear matrix inequality by using LMI (least mean squares) minimization method1And v and K corresponding to the minimum value. The controller designed in the steps reduces the influence of the interference on the output to the minimum through a minimization method, thereby achieving the purpose that the controller has robustness on the interference and ensuring the realization of a control target.
In the attached figure 4, a reference value r is input by a given current, a modulation signal u is obtained after the modulation by an output feedback controller and a signal regulator (including an inverse park converter and an SPWM modulator), a PWM pulse signal is provided for an inverter of an inverter type distributed power supply, when a sensor fails, a system output y firstly passes through a fault conversion filter, a sensor fault model is converted into an actuator fault model, then an output current is input into a PD type learning observer, the designed learning observer can rapidly reconstruct a fault signal in the filtered output current, a difference value between the reconstructed fault signal and an actual output current is input into a controller as a correction feedback signal, and then the operation state of the system is adjusted, and the steps are as follows:
step 2.1, on the basis of the inverter type distributed power supply model, establishing an IIDG sensor fault model as follows:
in equation (3), A is the inverter output current i in IIDGtd、itqTerminal voltage v of filter capacitorcfd、vcfqAnd bus terminal output current iod、ioqB is a parameter relating to the input vector z, C is a unit matrix parameter relating to the bus bar terminal output current and voltage, D is a parameter relating to the system disturbance vector w, and the state variable x ═ itd itq iod ioq vcfd vcfq]TZ is the input vector, w is the perturbation vector, Fs＝I2The error vector coefficient matrix is selected for faults;
in the formula (3), the first and second groups,
fsrepresents the current measurement error on the dq axis and can be expressed as:
in the formula (4), ΔdAnd ΔqWhen the sensors are in fault, fault signals of current on a d axis and a q axis are output;
step 2.2, establishing a fault conversion filter, and defining the fault conversion filter as follows:
in the formula (5), xsIndicating the state of the filter current of the fault-transfer filter, AsIs a Hurwitz matrix;
step 2.3, integrating the filtered sensor fault model as follows:
in the formula (6), the first and second groups,
indicating the state of the IIDG inverter output current and the fault transfer filter current,
which are its state matrix parameters, m represents the input and disturbance of the system,
for its state matrix parameters, fsRepresenting the current measurement error on the dq axis,
is its state matrix parameter;
in the formula (6), the first and second groups,
and integrating the filtered sensor fault model to convert the sensor fault model into an actuator fault model form. 3.1, establishing a PD type learning observer according to a micro-grid system sensor fault model, and solving PD type learning observer matrixes L and S and a learning time interval tau; define the PD-type learning observer as:
in the formula (7), the first and second groups,
and
respectively estimating the system state, measuring output and estimating and reconstructing a sensor fault signal of the PD type learning observer; l and S are learning observer matrixes, sigma is a constant to be determined, and tau is a learning time interval;
step 3.2, solving a fault reconstruction signal of the PD type learning observer, and defining the state estimation error of the microgrid IIDG system
Output estimation error
And actuator fault reconstruction error
Comprises the following steps:
in the formula (8), the first and second groups,
and
respectively estimating the system state, measuring output and estimating and reconstructing a sensor fault signal of the PD type learning observer;
step 3.3, solving the estimation error kinetic equation of the system as follows:
in the formula (9), the reaction mixture,
represents the estimation error of the state of the microgrid IIDG system,
representing the output estimation error;
step 3.4, calculating the output current compensated by the reconstructed signal, and defining the output current as
The current is fed back to the output feedback controller, so that the stable tracking of the output power is ensured.
In specific implementation, referring to fig. 1 and fig. 2, relevant parameter settings are shown in table 1
TABLE 1 microgrid system circuit
Referring to fig. 3 and 4, the relevant parameter settings are as follows:
by constructing an augmentation system model and solving the controller control rate by using an LMI minimization method, the control law of the output feedback controller, namely control gain parameters, is obtained as follows:
and optimizing each matrix parameter by using an LMI (local mean minimization) method to solve the minimum value of the inequality, wherein the solved matrixes S and L are as follows:
the invention utilizes the improved PD type learning observer to detect and reconstruct the fault in the output measurement of the micro-grid system, and feeds back the reconstructed fault signal to the controller, thereby finally stably controlling the accurate power transmission between the micro-grid and the large grid when the micro-grid is connected.
On the basis of analyzing a grid-connected structure of a micro-grid system and an operation control method thereof, aiming at the problem of sensor faults of an inverter distributed generator (IIDG) in the micro-grid system, an IIDG sensor fault model under a dq coordinate system is established to describe the influence of the IIDG sensor fault model on the system performance; the IIDG sensor fault tolerance control method based on the learning observer is provided; designing a learning observer by using the established fault model, realizing real-time monitoring of the fault, accurately reconstructing the fault of the sensor, and correcting the measurement error of the sensor by using the reconstructed fault signal; the fault-tolerant control scheme provided ensures safe and reliable operation of the IIDG when grid-connected power of the micro-grid system is transmitted, and has wide adaptability to various faults of the micro-grid sensor.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention, and are not intended to limit the scope of the present invention, and any person skilled in the art should understand that equivalent changes and modifications made without departing from the concept and principle of the present invention should fall within the protection scope of the present invention.
Claims (5)
1. The fault tolerance control method of the micro-grid sensor based on the PD type learning observer is characterized by comprising the following steps of:
step 1, establishing a system closed-loop output feedback controller according to a state space model of a micro-grid inverter type distributed power supply;
step 2, establishing a sensor fault model of the microgrid system, and converting the sensor fault model into an actuator fault model form;
step 3, establishing a sensor fault-tolerant PD type learning observer, solving a reconstructed fault signal, feeding back an output compensated by the reconstructed signal to an output feedback controller, and ensuring stable tracking of output power;
the specific steps of the step 1 are as follows:
step 1.1, converting three-phase output into two phases by using a park module according to a three-phase converter structure of the microgrid, and deducing a state space model of an IIDG in the microgrid system under a dq coordinate system;
step 1.2, according to the constructed state space model of the IIDG, a closed-loop output feedback controller is established, and the control law is as follows:
udq＝Ky (1)
in the formula (1), y is the system output current and is defined as y ═ iod ioq]TK is a control rate gain matrix;
step 1.3, establishing an augmented micro-grid system model under the control of an output feedback controller, and determining the value of a control law gain matrix K by using a linear matrix inequality method; the inequality is defined as:
in the formula (2), χ ═ Aa+BaKCa，AaRelates to the amplification of the inverter output current i in a microgrid systemtd、itqTerminal voltage v of filter capacitorcfd、vcfqAnd bus terminal output current iod、ioqParameter (c) ofaIs a parameter relating to an input variable z, CaIs a unit matrix parameter related to the output current and voltage at the bus terminal, FaIs a parameter related to the system disturbance d, I is the identity matrix.
2. The microgrid sensor fault tolerance control method based on a PD-type learning observer is characterized in that the specific steps of the step 2 are as follows:
step 2.1, on the basis of the inverter type distributed power supply model, establishing an IIDG sensor fault model as follows:
in equation (3), A is the inverter output current i in IIDGtd、itqTerminal voltage v of filter capacitorcfd、vcfqAnd bus terminal output current iod、ioqB is a parameter relating to the input vector z, C is a unit matrix parameter relating to the bus bar terminal output current and voltage, D is a parameter relating to the system disturbance vector w, and the state variable x ═ itd itq iod ioq vcfd vcfq]TZ is the input vector, w is the perturbation vector, Fs＝I2As a matrix of error vector coefficients, is a selection of faults, fsRepresents the current measurement error on the dq axis and can be expressed as:
fs＝[Δd Δq]T (4)
in the formula (4), ΔdAnd ΔqWhen the sensors are in fault, fault signals of current on a d axis and a q axis are output;
step 2.2, establishing a fault conversion filter, and defining the fault conversion filter as follows:
in the formula (5), xsIndicating the state of the filter current of the fault-transfer filter, AsIs a Hurwitz matrix;
step 2.3, integrating the filtered sensor fault model as follows:
in the formula (6), the first and second groups,
indicating the state of the IIDG inverter output current and the fault transfer filter current,
which are its state matrix parameters, m represents the input and disturbance of the system,
for its state matrix parameters, fsRepresenting the current measurement error on the dq axis,
is its state matrix parameter;
and integrating the filtered sensor fault model to convert the sensor fault model into an actuator fault model form.
3. The microgrid sensor fault tolerance control method based on a PD-type learning observer is characterized in that the specific steps of the step 3 are as follows:
3.1, establishing a PD type learning observer according to a micro-grid system sensor fault model, and solving PD type learning observer matrixes L and S and a learning time interval tau; define the PD-type learning observer as:
in the formula (7), the first and second groups,
and
respectively estimating the system state, measuring output and estimating and reconstructing a sensor fault signal of the PD type learning observer; l and S are learning observer matrixes, sigma is a constant to be determined, and tau is a learning time interval;
step 3.2, solving a fault reconstruction signal of the PD type learning observer, and defining the state estimation error of the microgrid IIDG system
Output estimation error
And actuator fault reconstruction error
Comprises the following steps:
in the formula (8), the first and second groups,
and
respectively estimating the system state, measuring output and estimating and reconstructing a sensor fault signal of the PD type learning observer;
step 3.3, solving the estimation error kinetic equation of the system as follows:
in the formula (9), the reaction mixture,
represents the estimation error of the state of the microgrid IIDG system,
representing the output estimation error;
step 3.4, calculating the output current compensated by the reconstructed signal, and defining the output current as
The current is fed back to the output feedback controller, so that the stable tracking of the output power is ensured.
4. The microgrid sensor fault tolerance control method based on a PD-type learning observer is characterized in that a sensor fault model is converted into an actuator fault model form through a fault conversion filter.
5. The microgrid sensor fault tolerance control method based on a PD-type learning observer is characterized in that the PD-type learning observer realizes real-time monitoring of faults and corrects measurement errors of a sensor by using a reconstructed fault signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011309721.2A CN112346439B (en) | 2020-11-20 | 2020-11-20 | Micro-grid sensor fault tolerance control method based on PD type learning observer |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011309721.2A CN112346439B (en) | 2020-11-20 | 2020-11-20 | Micro-grid sensor fault tolerance control method based on PD type learning observer |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112346439A CN112346439A (en) | 2021-02-09 |
| CN112346439B true CN112346439B (en) | 2021-12-21 |
Family
ID=74364659
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011309721.2A Active CN112346439B (en) | 2020-11-20 | 2020-11-20 | Micro-grid sensor fault tolerance control method based on PD type learning observer |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112346439B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112803484B (en) * | 2021-02-26 | 2023-08-11 | 上海海事大学 | Multi-level grid-connected inverter fault-tolerant control method based on mean current compensation model |
| CN113741365B (en) * | 2021-09-01 | 2022-07-08 | 南京辉强新能源科技有限公司 | Multi-harmonic interference observer-based micro-grid distributed power supply enclosure control method and device |
| CN115146707B (en) * | 2022-06-07 | 2023-07-07 | 湖南雪墨电气科技有限公司 | Multifunctional internet of things power factor detection method |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103488092B (en) * | 2013-10-16 | 2016-01-20 | 哈尔滨工业大学 | Diagnose and fault tolerant control method based on the satellite failure of T-S fuzzy model with study observer |
| CN107703742B (en) * | 2017-09-06 | 2020-09-01 | 南京邮电大学 | Flexible spacecraft sensor fault adjusting method |
| CN108415255B (en) * | 2018-03-28 | 2019-06-11 | 北京航空航天大学 | A kind of anti-interference attitude control method of spacecraft under executing agency is impaired |
| US11635734B2 (en) * | 2019-01-10 | 2023-04-25 | Dalian University Of Technology | Interval error observer-based aircraft engine active fault tolerant control method |
| CN109991857A (en) * | 2019-05-07 | 2019-07-09 | 中国民航大学 | Failure reconfiguration method based on PD type study observer |
| CN111953009A (en) * | 2019-05-17 | 2020-11-17 | 天津科技大学 | Fault diagnosis method for island multi-inverter parallel sensor |
| CN110566403A (en) * | 2019-08-08 | 2019-12-13 | 天津科技大学 | Wind power generation T-S fuzzy robust scheduling fault-tolerant control method |
| CN111610719B (en) * | 2020-07-07 | 2022-04-01 | 青岛科技大学 | Fault-tolerant control method of nonlinear actuator fault system based on observer |
-
2020
- 2020-11-20 CN CN202011309721.2A patent/CN112346439B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN112346439A (en) | 2021-02-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112346439B (en) | Micro-grid sensor fault tolerance control method based on PD type learning observer | |
| CN107528491B (en) | A kind of cascaded H-bridges multi-level converter and its control method | |
| Castilla et al. | Control design guidelines for single-phase grid-connected photovoltaic inverters with damped resonant harmonic compensators | |
| CN110581565B (en) | Control method and device in photovoltaic power generation grid-connected system | |
| CN104078976B (en) | Harmonic suppressing method, device and the photovoltaic system of a kind of photovoltaic system grid-connected current | |
| Kumar et al. | Self-adjustable step-based control algorithm for grid-interactive multifunctional single-phase PV-battery system under abnormal grid conditions | |
| KR101248593B1 (en) | Photoelectric cell system with compensating wattless power and operating method of it | |
| Munir et al. | Control of grid connected pv array using P&O MPPT algorithm | |
| CN101842956A (en) | Control method for a universal power condition system | |
| CN105449709A (en) | Grid-connected control method for photovoltaic power generation system | |
| CN107733215B (en) | Current sharing control method and device for three-level multi-module inverter | |
| CN103872703A (en) | Control system and strategy method for overcoming low voltage ride through | |
| Chatterjee et al. | Design and analysis of stationary frame PR current controller for performance improvement of grid tied PV inverters | |
| Mohammadi et al. | Controller-hardware-in-the-Loop testing of a single-phase single-stage transformerless grid-connected photovoltaic inverter | |
| Reddy et al. | Enhancement of power quality with fuzzy based UPQC in grid integrated and battery assisted PV system | |
| Zahoor et al. | Synchronization and dq current control of grid-connected voltage source inverter | |
| Ma et al. | Second-order linear active disturbance rejection control and stability analysis of energy storage grid-connected inverter | |
| Baltacı et al. | Design and experimental validation of an artificial neural network-SVPWM controller for a novel micro grid-tied fuel cell-based 3-phase boost inverter | |
| CN105552958A (en) | Constant frequency hysteresis current control method for photovoltaic grid-connected inverter | |
| Kumar et al. | ROGI with FsLMS based control technique for solar PV system under weak grid | |
| Al Essawy et al. | A grid-connected pv system based on z-source inverter with maximum power extraction | |
| Pan et al. | Fractional-order sliding mode control strategy for quasi-Z source photovoltaic grid-connected inverter | |
| Singh et al. | Adaptive based leaky LMS control technique of grid connected SPV system | |
| Luo et al. | Power quality active control research of building integrated photovoltaic | |
| Reddy et al. | Hybrid renewable energy sources based four leg inverter for power quality improvement |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20210209 Assignee: Jiangsu Indars Automation Technology Co.,Ltd. Assignor: NANTONG University Contract record no.: X2022320000288 Denomination of invention: A Fault Tolerant Control Method for Microgrid Sensor Based on PD Learning Observer Granted publication date: 20211221 License type: Common License Record date: 20221130 |
|
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20210209 Assignee: Nantong suluda Intelligent Manufacturing Technology Co.,Ltd. Assignor: NANTONG University Contract record no.: X2023980052244 Denomination of invention: Fault tolerant control method for microgrid sensors based on PD type learning observer Granted publication date: 20211221 License type: Common License Record date: 20231214 |