CN110412417B - Micro-grid data fault diagnosis method based on intelligent power monitoring instrument - Google Patents
Micro-grid data fault diagnosis method based on intelligent power monitoring instrument Download PDFInfo
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Abstract
The invention discloses a microgrid data fault diagnosis method based on an intelligent power monitoring instrument, which comprises the following steps: (10) constructing a physical constraint equation: generating a physical relation constraint equation according to the topological connection relation and the physical relation; (20) and (3) timing initiation diagnosis: initiating a diagnosis task regularly by setting a diagnosis period; (30) acquiring microgrid data: obtaining original data and power parameters of system operation, including voltage and current monitoring values; (40) establishing a data restoration and fault diagnosis model: setting a target function based on the power parameters, and establishing a centerless data restoration and diagnosis optimization model; (50) and (3) distributed optimization solution: solving an optimization equation, and solving an estimated value of the electric power parameter of the micro-point network; (60) fault diagnosis and repair: and comparing the fault database to obtain the fault type, repairing the error data and issuing a warning notice. The micro-grid data fault diagnosis method has strong robustness and high operation efficiency.
Description
Technical Field
The invention belongs to the technical field of remote monitoring of power supply and distribution networks, and particularly relates to a micro-grid data fault diagnosis method based on an intelligent power monitoring instrument, which is strong in robustness and high in operation efficiency.
Background
With the increasing severity of global energy crisis and environmental pollution, Distributed Generation (DG) has attracted widespread attention worldwide due to its unique advantages of reliability, high efficiency, etc. The micro-grid is a small-sized power generation and distribution system which is based on a distributed power generation technology and integrates units such as a distributed power supply, an energy storage device, an energy conversion device, a related load, a monitoring device and a protection device. Compared with a traditional centralized power system, the micro-point network has many advantages, such as: the system can realize self control, protection and management, has low transmission loss, flexible power generation and installation, reliable and safe power supply and the like. The micro-grid technology is a key support technology for the future intelligent power grid reformation and transformation, and is a core technical means for accessing distributed clean energy into a power system and even the whole energy system. With the development of smart power grids, a large amount of data is generated and deposited in various aspects of operation, equipment state monitoring, power utilization information acquisition and the like of micro power grids. On the basis of maintaining the uncertainty balance relationship between the energy and the load of the microgrid, the method for accurately acquiring the operation data of the microgrid system is one of the core problems of controlling and optimizing the system to realize various functions. However, due to the characteristics of randomness, volatility, intermittency and the like of the microgrid system, poor measurement data is often included in the acquired operation data. The data can cause the system to generate false alarm, so that the system can not operate normally, the system performance is influenced, the energy consumption is increased, the subsequent control and decision are seriously influenced, and even inestimable loss is caused. Therefore, in the operation process of the micro-grid, the fault data can be found and positioned in time, and the method has important significance.
The current bad data fault diagnosis method for the power system generally adopts a state estimation algorithm, and estimates the real state of the system, namely the load flow distribution on each bus by using a state estimator under the condition that the measurement has errors by setting the system structure and the measurement configuration. A great deal of research has been carried out at home and abroad on power system state estimation algorithms, and the target of the research is mainly directed to the traditional large-scale power grid. One of the calculation methods is a static estimation algorithm represented by a weighted least squares algorithm (WLS) or a weighted minimum absolute value algorithm (WLAV), and the other is a dynamic estimation method represented by kalman filtering. However, the research on the state estimation algorithm of the microgrid system is not deep enough, which is due to the characteristics of the microgrid, such as large monitoring data volume and flexible and variable control mode. The existing state estimation algorithm is applied to a micro-grid, and the phenomena of low diagnosis efficiency and poor robustness can occur.
In summary, the prior art has the following problems:
1. the robustness of the fault diagnosis system is poor. The existing fault diagnosis method is to set a microgrid dispatching center and complete the diagnosis and repair of fault data of a microgrid through a centralized state estimation algorithm. Once the central base station fails, the fault diagnosis process cannot be carried out.
2. Diagnostic methods are inefficient. Because the state estimation algorithm is completed in the dispatching center, the collected and processed data occupies a large amount of memory space. And for a micro-grid with large scale and large data volume, a transmission channel can be blocked, so that the operation efficiency of the algorithm is influenced.
Disclosure of Invention
The invention aims to provide a microgrid data fault diagnosis method based on an intelligent power monitoring instrument, which is strong in robustness and high in operation efficiency.
The technical solution for realizing the purpose of the invention is as follows:
a microgrid data fault diagnosis method based on intelligent power monitoring instruments is applicable to a microgrid with distributed intelligent power monitoring instruments, each data monitoring point in the power grid is provided with at least one intelligent power monitoring instrument, signals of adjacent intelligent power monitoring instruments are communicated in a two-way mode, and each intelligent power monitoring instrument establishes an intelligent power monitoring instrument measuring network in a self-organizing and plug-and-play mode according to line physical topology; the method comprises the following steps:
(10) constructing a physical constraint equation: generating a physical relationship constraint equation according to the topological connection relationship of the adjacent nodes and the physical relationship between the local node and the adjacent nodes;
(20) and (3) timing initiation diagnosis: initiating a diagnosis task regularly by setting a diagnosis period, and sending an equipment fault alarm signal if the hardware fault of the microgrid equipment occurs; if no fault exists, the data monitored by each node is diagnosed;
(30) acquiring microgrid data: obtaining original data of system operation through an intelligent power monitoring instrument, and obtaining various power parameters of a micro-grid of the system, including voltage and current monitoring values;
(40) establishing a data restoration and fault diagnosis model: setting a target function based on the power parameters of the intelligent power monitoring instrument network and the microgrid, and establishing a centerless data restoration and diagnosis optimization model;
(50) and (3) distributed optimization solution: each intelligent instrument sets an optimization equation based on a penalty function, the optimization equation is solved through a gradient descent method, and then distributed and parallel collaborative calculation is carried out through communication and coordination with adjacent nodes to jointly solve the estimated value of the power parameter of the microgrid;
(60) fault diagnosis and repair: and judging whether the micro-grid has faults by using the fault database, establishing the fault database according to common electrical faults in engineering practice, obtaining fault types through comparison, repairing error data and issuing warning notifications to workers.
Compared with the prior art, the invention has the following remarkable advantages:
1. the robustness is strong: (1) aiming at the defect of poor robustness of the traditional method, the intelligent chip is arranged in the power monitoring instrument, so that the power monitoring instrument is upgraded into the intelligent power monitoring instrument, each intelligent power monitoring instrument in the micro-grid is used as a network node, and adjacent nodes are connected by a network cable. Once a fault node exists in the microgrid, the whole fault diagnosis system can still establish a fault diagnosis equation again according to the constraint, and the normal operation of the fault diagnosis system is ensured.
(2) The method is a distributed parallel computing mode, and the fault diagnosis process is not dependent on a microgrid dispatching center and is completely carried out by self-organization of each node.
(3) The mathematical model established by each node is an exponential objective function, and fault data can be effectively diagnosed when small-scale disturbance and large-scale faults are faced.
2. The operation efficiency is high: (1) aiming at the defect of low efficiency of the diagnosis method, each intelligent electric power detection instrument is internally provided with a micro-processing unit for solving the local optimization problem. The global state estimation problem of the microgrid is decomposed into optimization subproblems of each node, and a global optimal solution can be obtained by solving a local optimal solution of each node, so that the efficiency of the algorithm is improved.
(2) The method completes the diagnosis of fault data through mutual cooperation and communication among the nodes, and the algorithm is not influenced by the size of the micro-grid and the number of branches.
(3) The intelligent power monitoring instrument only needs to diagnose whether local data has problems or not, does not need to collect global data, and occupies a small memory compared with the traditional method.
The invention is described in further detail below with reference to the figures and the detailed description.
Drawings
Fig. 1 is a main flow chart of a microgrid data fault diagnosis method based on an intelligent power monitoring instrument.
FIG. 2 is a flowchart of the steps of constructing the physical constraint equations of FIG. 1.
Fig. 3 is a flowchart of the timed initiation diagnostic step of fig. 1.
FIG. 4 is a flowchart of the steps in FIG. 1 for building a data repair and fault diagnosis model.
Fig. 5 is a flowchart of a solution for distributed optimization in fig. 1.
FIG. 6 is a flow chart of the data repair and diagnosis steps of FIG. 1.
Fig. 7 is a modified intelligent microgrid architecture.
Fig. 8 is a distributed algorithm simulation image.
Detailed Description
The invention discloses a microgrid data fault diagnosis method based on intelligent power monitoring instruments, which is suitable for a microgrid with distributed intelligent power monitoring instruments.
As shown in fig. 1, the method for diagnosing the data fault of the microgrid based on the intelligent power monitoring instrument comprises the following steps:
(10) constructing a physical constraint equation: generating a physical relationship constraint equation according to the topological connection relationship of the adjacent nodes and the physical relationship between the local node and the adjacent nodes;
as shown in fig. 2, the step (10) of constructing a physical constraint equation comprises:
(11) and (3) identifying a logical relationship: judging the connection relation of each intelligent electric power monitoring instrument in the microgrid according to the inherent physical connection relation and the equivalent model of components among the intelligent electric power monitoring instruments of the microgrid, and finishing the identification of the topological connection relation by utilizing the ad hoc network capacity of the intelligent electric power monitoring instruments;
(12) and (3) generating a physical relation constraint equation: and according to the basic physical equation, the kirchhoff theorem and the network logical relationship, each node obtains the physical relationship between the local node and the adjacent node, and generates a physical relationship constraint equation. (20) And (3) timing initiation diagnosis: initiating a diagnosis task regularly by setting a diagnosis period, and sending an equipment fault alarm signal if the hardware fault of the microgrid equipment occurs; if no fault exists, the data monitored by each node is diagnosed;
as shown in fig. 3, the (20) timing initiation diagnostic step includes:
(21) setting a diagnosis period: according to a set diagnosis period, triggering a task to start at fixed time;
(22) and (3) judging the equipment state: judging the actual running state of each device according to the device running state flag bit in the intelligent power monitoring instrument, and jumping to the step (24) if the devices have no fault;
(23) and (3) sending out a maintenance alarm: sending an alarm to prompt equipment failure, prompting that manual maintenance is needed, and then jumping to (21) a diagnosis period setting step to wait for next period triggering;
(24) initiating a diagnosis task: and (4) initiating a data diagnosis task when the hardware equipment has no fault.
(30) Acquiring microgrid data: obtaining original data of system operation through an intelligent power monitoring instrument, and obtaining various power parameters of a micro-grid of the system, including voltage and current monitoring values;
(40) establishing a data restoration and fault diagnosis model: setting a target function based on the power parameters of the intelligent power monitoring instrument network and the microgrid, and establishing a centerless data restoration and diagnosis optimization model;
as shown in fig. 4, the step of establishing (40) a data repair and fault diagnosis model includes:
(41) setting an objective function: setting a data restoration and diagnosis objective function according to the power parameters;
wherein J (x) is an objective function, I is a unit vector, x is a data correction vector, xmeasureMonitoring values for actual voltage and current on the instrument;
(42) establishing an optimization model: establishing a centerless data restoration and diagnosis optimization model,
wherein, Ji(xi) For the objective function, x, corresponding to node iiFor the data correction vector of node i, xjA data correction vector, X, for node jiModifying the vector space for the data of node i, gi(xi|xj0) is sectionConstraint equation formed by point i and adjacent node j, NiI, and n is the total number of nodes.
(50) And (3) distributed optimization solution: each intelligent instrument sets an optimization equation based on a penalty function, the optimization equation is solved through a gradient descent method, and then distributed and parallel collaborative calculation is carried out through communication and coordination with adjacent nodes to jointly solve the estimated value of the power parameter of the microgrid;
as shown in fig. 5, the (50) distributed optimization solving step includes:
(51) setting an optimization equation: an optimization equation based on a penalty function is set according to the data restoration and diagnosis optimization model,
Fi(xi,ρi|xj)＝Ji(xi)+ρigi(xi|xj) (3)
wherein, Fi(xi,ρi|xj) For optimisation equations based on penalty functions, piFor the penalty factor of the node i, when a fixed penalty factor is set for operation, the solution calculated by the algorithm is in local optimum and is not in global optimum, so the optimization equation is improved by adopting a variable penalty factor, the penalty factor is changed along with the increase of the iteration times in the iterative process of the optimization equation solution, the update equation of the penalty factor is,
wherein k is the number of iterations,
is the corresponding learning step length k of the i node at the k iterationmaxIn order to be the maximum number of iterations,
is the corresponding penalty factor value at the kth iteration of the inode,
the penalty factor value corresponding to the k +1 th iteration of the i node;
(52) solving an optimization equation: the optimization equation is solved by a gradient descent method, the iterative process is,
(53) neighbor interaction iteration: and sending the local calculation result to all adjacent nodes, performing calculation iteration according to the received adjacent node data, and finishing optimization solution when the node data is converged or the maximum iteration times is reached.
(60) Fault diagnosis and repair: and judging whether the micro-grid has faults by using the fault database, establishing the fault database according to common electrical faults in engineering practice, obtaining fault types through comparison, repairing error data and issuing warning notifications to workers.
As shown in fig. 6, the (60) data diagnosing and repairing step includes:
(61) and (3) fault judgment: each node substitutes the estimated value of the electric power parameter solved in the step (50) into the formula (1) to further solve J (x), if the value of J (x) is less than or equal to epsilon, the local equipment is judged to be free of fault, and the step (66) is skipped; if the value J (x) is larger than epsilon, judging that the equipment has a fault, and jumping to the step (63);
(62) and (3) fault type diagnosis: establishing a fault diagnosis database according to the operation parameters of the actual common electrical fault types of the engineering, comparing the intelligent power monitoring instrument of the fault node with the fault diagnosis database according to the measurement data and the current network state information to further judge the fault types, and if the fault types are soft faults, skipping to the step (63); if the fault is a hard fault, jumping to a step (64);
(63) soft fault repair: identifying the specific situation of the soft fault in the measurement process of the intelligent power monitoring instrument by further comparing the fault diagnosis rule base, restoring the original measured value by utilizing the estimated value of the power parameter calculated in the step (50), generating a data evaluation and restoration result, and skipping to the step (65);
(64) and (4) hard fault alarming: generating an alarm result by using irreparable hard faults of hardware equipment;
(65) and (3) fault point information issuing: the fault point issues data evaluation and repair results and alarm results generated by the intelligent power monitor meter to workers, and corresponding fault alarms are issued according to different types;
(66) and (3) completing diagnosis: and completing the data fault diagnosis for one time, and waiting for executing the next period of diagnosis task.
Simulation experiment verification
Fig. 7 shows a micro-grid system architecture embedded with an intelligent power monitoring instrument. Each smart meter manages a microgrid in a certain area and only monitors the operation state of a local power grid. The nodes communicate with each other through circuit transmission lines, and a series of fault diagnosis tasks are completed through mutual cooperation with the neighbor nodes. In the simulation verification experiment, taking the current state quantity as an example, the lines 2, 4 and 6 are selected as fault lines, and Gaussian noise is added to the respective lines to generate fault samples. The fault diagnosis is performed by the method provided by the invention, and as shown in fig. 8, a current error correction curve of each line obtained by the intelligent power monitoring instrument through a distributed optimization algorithm is obtained. As can be seen from the figure, the invention has the following characteristics:
1. and the robustness is strong. For a small microgrid system with only 6 transmission lines as shown in fig. 7, the fault samples of the experiment were set up with different deviations in the current measurements for 3 lines. The probability of failure has reached 50% and the deviation set by line 4 is-10A. For the traditional state estimation algorithm, more fault points have great influence on the estimation result. According to the fully distributed algorithm provided by the invention, each line is operated by the intelligent electric power instrument, and the final current error value can be converged to 0, so that the constraint is met and the target function reaches the minimum value. And the method adopts an exponential type objective function form, can carry out rapid diagnosis and repair on small disturbance and large deviation, and has stronger algorithm robustness.
2. The algorithm is efficient. It can be seen from the simulation curve that each node can achieve convergence only after 30 iterations, i.e. the error value of the monitored current on the line is stable. For a large-scale micro-grid, the number of nodes of the intelligent power detection instrument is only increased. For each node, the data to be processed is only the data of the node and the surrounding neighbor nodes, and the computation amount is greatly reduced, so that the efficiency of the algorithm is improved.
Claims (6)
1. A microgrid data fault diagnosis method based on intelligent power monitoring instruments is applicable to a microgrid with distributed intelligent power monitoring instruments, each data monitoring point in the power grid is provided with at least one intelligent power monitoring instrument, signals of adjacent intelligent power monitoring instruments are communicated in a two-way mode, and each intelligent power monitoring instrument establishes an intelligent power monitoring instrument measuring network in a self-organizing and plug-and-play mode according to line physical topology; the method comprises the following steps:
(10) constructing a physical constraint equation: generating a physical relationship constraint equation according to the topological connection relationship of the adjacent nodes and the physical relationship between the local node and the adjacent nodes;
(20) and (3) timing initiation diagnosis: initiating a diagnosis task regularly by setting a diagnosis period, and sending an equipment fault alarm signal if the hardware fault of the microgrid equipment occurs; if no fault exists, the data monitored by each node is diagnosed;
(30) acquiring microgrid data: obtaining original data of system operation through an intelligent power monitoring instrument, and obtaining various power parameters of a micro-grid of the system, including voltage and current monitoring values;
(40) establishing a data restoration and fault diagnosis model: setting a target function based on the power parameters of the intelligent power monitoring instrument network and the microgrid, and establishing a centerless data restoration and diagnosis optimization model;
(50) and (3) distributed optimization solution: each intelligent instrument sets an optimization equation based on a penalty function, the optimization equation is solved through a gradient descent method, and then distributed and parallel collaborative calculation is carried out through communication and coordination with adjacent nodes to jointly solve the estimated value of the power parameter of the microgrid;
(60) fault diagnosis and repair: and judging whether the micro-grid has faults by using the fault database, establishing the fault database according to common electrical faults in engineering practice, obtaining fault types through comparison, repairing error data and issuing warning notifications to workers.
2. The fault diagnosis method according to claim 1, wherein the step of (10) constructing a physical constraint equation comprises:
(11) and (3) identifying a logical relationship: judging the connection relation of each intelligent electric power monitoring instrument in the microgrid according to the inherent physical connection relation and the equivalent model of components among the intelligent electric power monitoring instruments of the microgrid, and finishing the identification of the topological connection relation by utilizing the ad hoc network capacity of the intelligent electric power monitoring instruments;
(12) and (3) generating a physical relation constraint equation: and according to the basic physical equation, the kirchhoff theorem and the network logical relationship, each node obtains the physical relationship between the local node and the adjacent node, and generates a physical relationship constraint equation.
3. The fault diagnosis method according to claim 1, characterized in that said (20) timed initiation diagnosis step comprises:
(21) setting a diagnosis period: according to a set diagnosis period, triggering a task to start at fixed time;
(22) and (3) judging the equipment state: judging the actual running state of each device according to the device running state flag bit in the intelligent power monitoring instrument, and jumping to the step (24) if the devices have no fault;
(23) and (3) sending out a maintenance alarm: sending an alarm to prompt equipment failure, prompting that manual maintenance is needed, and then jumping to (21) a diagnosis period setting step to wait for next period triggering;
(24) initiating a diagnosis task: and (4) initiating a data diagnosis task when the hardware equipment has no fault.
4. The fault diagnosis method according to claim 1, wherein said step (40) of establishing a data repair and fault diagnosis model comprises:
(41) setting an objective function: setting a data restoration and diagnosis objective function according to the power parameters;
wherein J (x) is an objective function, I is a unit vector, x is a data correction vector, xmeasureMonitoring values for actual voltage and current on the instrument;
(42) establishing an optimization model: establishing a centerless data restoration and diagnosis optimization model,
wherein, Ji(xi) For the objective function, x, corresponding to node iiFor the data correction vector of node i, xjA data correction vector, X, for node jiModifying the vector space for the data of node i, gi(xi|xj0) is a constraint equation formed by the node i and the adjacent node j, NiI, and n is the total number of nodes.
5. The fault diagnosis method according to claim 1, characterized in that said (50) distributed optimization solving step comprises:
(51) setting an optimization equation: an optimization equation based on a penalty function is set according to the data restoration and diagnosis optimization model,
Fi(xi,ρi|xj)＝Ji(xi)+ρigi(xi|xj) (3)
wherein, Fi(xi,ρi|xj) For optimisation equations based on penalty functions, piFor the punishment factor of the node i, when a fixed punishment factor is set for operation, the solution solved by the algorithm can be trapped into local optimum and is not global optimum, so that variable punishment is adoptedThe penalty factor improves the optimization equation, in the iterative process of solving the optimization equation, the penalty factor changes along with the increase of the iterative times, the updating equation of the penalty factor is,
wherein k is the number of iterations,
is the corresponding learning step length k of the i node at the k iterationmaxIn order to be the maximum number of iterations,
is the corresponding penalty factor value at the kth iteration of the inode,
the penalty factor value corresponding to the k +1 th iteration of the i node;
(52) solving an optimization equation: the optimization equation is solved by a gradient descent method, the iterative process is,
(53) neighbor interaction iteration: and sending the local calculation result to all adjacent nodes, performing calculation iteration according to the received adjacent node data, and finishing optimization solution when the node data is converged or the maximum iteration times is reached.
6. The fault diagnosis method according to claim 1, characterized in that said (60) data diagnosis and repair step comprises:
(61) and (3) fault judgment: each node substitutes the estimated value of the electric power parameter solved in the step (50) into the formula (1) to further solve J (x), if the value of J (x) is less than or equal to epsilon, the local equipment is judged to be free of fault, and the step (66) is skipped; if the value J (x) is larger than epsilon, judging that the equipment has a fault, and jumping to the step (63);
(62) and (3) fault type diagnosis: establishing a fault diagnosis database according to the operation parameters of the actual common electrical fault types of the engineering, comparing the intelligent power monitoring instrument of the fault node with the fault diagnosis database according to the measurement data and the current network state information to further judge the fault types, and if the fault types are soft faults, skipping to the step (63); if the fault is a hard fault, jumping to a step (64);
(63) soft fault repair: identifying the specific situation of the soft fault in the measurement process of the intelligent power monitoring instrument by further comparing the fault diagnosis rule base, restoring the original measured value by utilizing the estimated value of the power parameter calculated in the step (50), generating a data evaluation and restoration result, and skipping to the step (65);
(64) and (4) hard fault alarming: generating an alarm result by using irreparable hard faults of hardware equipment;
(65) and (3) fault point information issuing: the fault point issues data evaluation and repair results and alarm results generated by the intelligent power monitor meter to workers, and corresponding fault alarms are issued according to different types;
(66) and (3) completing diagnosis: and completing the data fault diagnosis for one time, and waiting for executing the next period of diagnosis task.
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