Reliability analysis method and system of power distribution network considering large-scale distributed power supply access

Abstract

The method comprises the following steps: acquiring historical basic parameter data of a to-be-analyzed area of a target power distribution network, preprocessing the basic parameter data, and establishing a first model according to a preprocessing result, simulating the first model in combination with the real-time basic parameter data of the to-be-analyzed region of the target power distribution network, and obtaining the probability and average duration of the target power distribution network in each state; and solving a maximum power supply capability optimization model of the power distribution network according to the probability and the average duration of the target power distribution network in each state, and calculating the maximum power supply capability of the power distribution network in each state and the maximum load which can be provided under a specified risk level. According to the method, the state transition matrix and the observation matrix based on the hidden Markov model are constructed by using the historical operation environment data of the distributed power supply, so that the accuracy of reliability evaluation of the power distribution network containing the distributed power supply is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of reliability analysis of distribution networks, and in particular to a reliability analysis method and system for distribution networks taking into account access of large-scale distributed power sources. Background Art

[0002] The distribution system is an important part of the power system. Its main task is to effectively distribute the electricity generated by power plants to various users to ensure the stability and continuity of power supply. With the advancement of science and technology and the improvement of environmental awareness, the traditional distribution network is undergoing a profound change and gradually evolving into an intelligent distribution network that includes distributed power sources. This new type of distribution network, also known as a distributed energy system, can more effectively utilize and integrate various clean energy sources such as solar energy, wind energy, geothermal energy, etc.

[0003] Distributed generation has significant advantages. First, it is usually built close to the load, which can reduce the loss during power transmission and improve energy efficiency. Second, distributed generation is highly flexible and can quickly adjust power generation according to demand to adapt to load changes. Furthermore, since it mainly relies on renewable energy, it has little pollution to the environment and is in line with the concept of sustainable development. In addition, the construction cost of distributed generation is relatively low, which can disperse investment risks and reduce dependence on large centralized power plants.

[0004] However, the output characteristics of distributed power sources also bring new challenges. Since the generation of solar and wind energy is affected by weather and seasonal changes, their output is intermittent and volatile. This instability may affect the voltage stability and frequency regulation of the distribution network, posing a threat to power supply reliability. Traditional distribution network reliability analysis methods are mainly based on the stable output of centralized power sources, and their applicability is challenged for this new complex situation.

[0005] Therefore, it is particularly urgent to research and develop distribution network reliability analysis methods that are suitable for the access of distributed generation. Summary of the invention

[0006] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0007] In view of the above existing problems, the present invention is proposed.

[0008] Therefore, the present invention provides a reliability analysis method and system for a distribution network taking into account large-scale distributed power supply access, which can solve the problems mentioned in the background technology.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0010] In a first aspect, the present invention provides a reliability analysis method for a distribution network considering large-scale distributed power access, comprising:

[0011] Acquire historical basic parameter data of the target distribution network area to be analyzed, wherein the historical basic parameter data at least includes distributed power supply parameters and historical operation data, historical meteorological data, distribution network structure, and interconnected feeder capacity limitation data;

[0012] Preprocessing the basic parameter data, and establishing a first model according to the preprocessing result, wherein the first model includes a transfer matrix and an observation matrix of the output state of the distributed power source based on a hidden Markov model, and a two-state reliability model of the main transformer and the distribution line;

[0013] The first model is simulated in combination with the real-time basic parameter data of the target distribution network area to be analyzed to obtain the probability and average duration of the target distribution network in each state;

[0014] According to the probability and average duration of the target distribution network in each state, the maximum power supply capacity optimization model of the distribution network is solved, and the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under a specified risk level are calculated.

[0015] As a preferred solution of the reliability analysis method of the distribution network considering large-scale distributed power access described in the present invention, the preprocessing of the basic parameter data and establishing the first model according to the preprocessing result includes:

[0016] Given a set of observation sequences, i.e. historical basic parameter data, the output probabilities of different states are compared, and the maximum probability is the output state of the wind turbine;

[0017] To establish a prediction model for wind turbine output state, we first preprocess the environmental parameters and conduct model training. We use the hidden Markov model to learn the wind turbine output state changes, thereby generating multiple sub-state classifiers, each of which has its own model parameters and maximum probability value.

[0018] The second is state assessment. The trained hidden Markov model is used as a classifier for state assessment. The real-time observed environmental parameters are input and the most likely state under the observation sequence is calculated. This completes the classification, determines the state of the wind turbine output, and implements the evaluation of the wind turbine output state.

[0019] Finally, the wind turbine state transfer matrix and observation matrix are obtained, and the wind turbine output state, i.e., output power, is predicted. The simulation is completed and the output sequence of the wind turbine is obtained.

[0020] As a preferred solution of the reliability analysis method of the distribution network considering large-scale distributed power access according to the present invention, the preprocessing of the basic parameter data and establishing the first model according to the preprocessing result also includes:

[0021] The two-state reliability model of the main transformer and distribution line is represented by the component operation state probability. The component operation state probability p U and the failure state probability p D , the calculation formula is as follows:

[0022]

[0023] Among them, λ and μ represent the failure rate and repair rate of the component respectively.

[0024] As a preferred solution of the reliability analysis method of the distribution network considering large-scale distributed power access described in the present invention, wherein: the simulation of the first model in combination with the real-time basic parameter data of the target distribution network to be analyzed area to obtain the probability and average duration of the target distribution network in each state includes:

[0025] The probability and average duration of the target distribution network in each state are expressed as:

[0026] The probability p(s) of occurrence of system state s is calculated as follows:

[0027]

[0028] Among them, p z (s) represents the probability that component z is in system state s; N represents the number of components.

[0029] As a preferred solution of the reliability analysis method of the distribution network considering large-scale distributed power access described in the present invention, wherein: the simulation of the first model in combination with the real-time basic parameter data of the target distribution network to be analyzed area to obtain the probability and average duration of the target distribution network in each state also includes:

[0030] The average residence time T of the system state d (s) is calculated by the following formula:

[0031]

[0032] Among them, if component z is in the running state, then λ z represents the failure rate, otherwise it represents the repair rate.

[0033] As a preferred solution of the reliability analysis method of the distribution network considering large-scale distributed power access described in the present invention, the maximum power supply capacity optimization model of the distribution network includes:

[0034] The maximum power supply capacity optimization model of the distribution network is expressed as:

[0035]

[0036] Among them, R Ni 、T i Respectively represent the nominal capacity and load of the main transformer i; D NI , β I Respectively represent the nominal capacity and capacity factor of distributed power source I; N T 、N R They represent the number of main transformers and distributed generation respectively.

[0037] As a preferred solution of the reliability analysis method of the distribution network considering large-scale distributed power access described in the present invention, wherein: according to the probability and average duration of the target distribution network in each state, solving the maximum power supply capacity optimization model of the distribution network, calculating the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under the specified risk level includes:

[0038]

[0039] Among them, rpsc i Represents the reliable power supply capability of the main transformer i; T d (k) represents the average duration of system state k; N represents the number of system states; Ω i is the distributed power source connected to the low-voltage busbar of the main transformer i. SI represents the risk level index of reliable power supply of the distribution system. SI r Indicates the risk level threshold of reliable power supply of the power distribution system.

[0040] In a second aspect, the present invention provides a reliability analysis system for a distribution network considering large-scale distributed power access, comprising:

[0041] A data acquisition module is used to acquire historical basic parameter data of the target distribution network area to be analyzed, wherein the historical basic parameter data at least includes distributed power supply parameters and historical operation data, historical meteorological data, distribution network structure and interconnected feeder capacity limitation data;

[0042] A model building module, used for preprocessing the basic parameter data and building a first model according to the preprocessing result, wherein the first model includes a transfer matrix and an observation matrix of the output state of the distributed power source based on a hidden Markov model and a two-state reliability model of a main transformer and a distribution line;

[0043] A simulation solution module, used to simulate the first model in combination with the real-time basic parameter data of the target distribution network area to be analyzed, and obtain the probability and average duration of the target distribution network in each state;

[0044] The solution module is used to solve the maximum power supply capacity optimization model of the distribution network according to the probability and average duration of the target distribution network in each state, and calculate the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under the specified risk level.

[0045] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0047] Compared with the prior art, the invention has the following beneficial effects: the invention proposes a reliability analysis method and system for a distribution network considering large-scale distributed power access, obtains historical basic parameter data of the target distribution network to be analyzed, and the historical basic parameter data at least includes distributed power parameters and historical operation data, historical meteorological data, distribution network structure and interconnected feeder capacity limit data; preprocesses the basic parameter data, and establishes a first model based on the preprocessing result, the first model includes a transfer matrix and an observation matrix of the output state of the distributed power source based on the hidden Markov model, and a two-state reliability model of the main transformer and the distribution line; simulates the first model in combination with the real-time basic parameter data of the target distribution network to be analyzed, and obtains the probability and average duration of the target distribution network in each state; solves the maximum power supply capacity optimization model of the distribution network according to the probability and average duration of the target distribution network in each state, and calculates the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under the specified risk level. The invention uses the historical operating environment data of the distributed power source to construct the state transfer matrix and observation matrix based on the hidden Markov model, and has high reliability analysis accuracy and strong versatility, which effectively improves the accuracy of reliability assessment of the distribution network containing distributed power sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0049] Figure 1 A method flow chart of a reliability analysis method and system for a distribution network considering large-scale distributed power access provided by an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of a double random process of a hidden Markov model of a reliability analysis method and system for a distribution network considering large-scale distributed power access provided by an embodiment of the present invention;

[0051] Figure 3 A reliability analysis method for a distribution network considering large-scale distributed power access and a schematic diagram of a wind turbine operating environment and hidden state of the system provided by an embodiment of the present invention;

[0052] Figure 4 A general framework diagram of a wind turbine output prediction model based on HMM, provided by an embodiment of the present invention, for a reliability analysis method and system for a distribution network taking into account large-scale distributed power access;

[0053] Figure 5 A reliability analysis method for a distribution network considering large-scale distributed power access provided by an embodiment of the present invention and a schematic diagram of a large-capacity distributed power access to a substation low-voltage busbar via a dedicated line;

[0054] Figure 6 A reliability analysis method and system for a distribution network considering large-scale distributed power access provided by an embodiment of the present invention; A detailed flow chart of a reliability analysis method for a distribution network considering large-scale distributed power access based on a hidden Markov model;

[0055] Figure 7 A schematic diagram of a test system for a reliability analysis method and system for a distribution network considering access of large-scale distributed power sources provided by an embodiment of the present invention;

[0056] Figure 8 An internal structural diagram of a computer device of a reliability analysis method and system for a distribution network taking into account large-scale distributed power access provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0058] Example 1

[0059] Reference Figure 1-Figure 8 , which is the first embodiment of the present invention, provides a reliability analysis method and system for a distribution network considering large-scale distributed power access, including:

[0060] In the related technology, the reliability analysis method based on the N-1 criterion has the advantages of clear physical concepts and small data requirements. However, any system state must be checked by the N-1 criterion, and the result is too conservative; it cannot reflect the reliability level of the components, ignores the uncertain factors in the distribution network, does not consider the random failure of distributed power sources and the intermittent and fluctuating problems of output power, and the reliability analysis results are less accurate. The failure mode and effect analysis method takes into account that each part of the distribution network has a backup and redundancy mechanism, and considers the management mode of flexible management, control and transmission of power by means of switches and switching, with high flexibility and coordination; however, for complex systems, it is difficult to handle all failure modes and consequences. The increase in the number of components leads to a sharp increase in the calculation amount of failure mode and effect analysis, and the analysis process is complex and time-consuming. The reliability analysis method based on the random state model is suitable for non-steady-state processes and may be close to the actual operation of the system; however, the random state model has high computational complexity, large data requirements, complex model construction, and time-consuming analysis process; it takes a lot of cost to obtain more accurate reliability analysis results, and the cost performance is low.

[0061] The present application provides a method that can effectively solve the above-mentioned problems. Next, a reliability analysis method of a distribution network considering large-scale distributed power access will be described in detail in combination with multiple embodiments.

[0062] Figure 1 A method flow chart of a reliability analysis method and system for a distribution network considering large-scale distributed power access is shown, including:

[0063] S101, obtaining historical basic parameter data of the target distribution network area to be analyzed, where the historical basic parameter data at least includes distributed power generation parameters and historical operation data, historical meteorological data, distribution network structure, and interconnected feeder capacity limitation data;

[0064] It should be noted that by obtaining a variety of historical basic parameter data of the target distribution network area to be analyzed, comprehensive and accurate input information can be provided for subsequent reliability analysis. These data cover the characteristics of distributed power sources, historical operating status, environmental factors, network structure, and capacity limitations of equipment, ensuring the comprehensiveness and accuracy of the analysis.

[0065] S102, preprocessing the basic parameter data, and establishing a first model according to the preprocessing result, the first model including a transfer matrix and an observation matrix of the output state of the distributed power source based on the hidden Markov model and a two-state reliability model of the main transformer and the distribution line;

[0066] It should be noted that a Markov process refers to a random process that satisfies the Markov property. Under the condition that the state of the event is known, its future evolution does not depend on its past evolution. Under the condition that the "present" is known, the characteristic that the "future" and the "past" are independent is called the Markov property. A random process is defined by the conditional probability formula as follows:

[0067]

[0068] Where X represents the random variable of the process. If the following equation is satisfied, the process is called a Markov process:

[0069]

[0070] Furthermore, a Markov chain is a Markov process with a discrete time parameter, which can be represented as a sequence of random variables X t The parameter t representing time can be 1, 2, 3, etc. For a random test sequence, the possible discrete states of the system are X1 = x1, X2 = x2, X3 = x3, etc. If the following equation is satisfied, the random test sequence is called a Markov chain.

[0071] P{X n =x n |(X1=x1)∩…∩(X n-1 =x n-1 )}=P{X n =x n |(X n-1 =x n-1 )}

[0072] It can be seen from the formula that the state of "future" is only related to the state of "present" and has nothing to do with "past". It is called "memoryless".

[0073] It should be noted that the hidden Markov model (HMM) is a unique and innovative dynamic Bayesian network, which is based on the Markov random process and describes a process of randomly extracting a series of unknown state random sequences from a hidden Markov chain and converting these random sequences into observable observation random sequences. With the development of things, the complexity and uncertainty of phenomena make it difficult to accurately grasp their essence. Therefore, it is necessary to use the hidden Markov model to better understand these complex phenomena.

[0074] It should be noted that the hidden Markov model derives a dual random process based on the Markov chain, such as Figure 2 As shown in the figure, the process includes state sequence and observation sequence. The state sequence cannot be observed, that is, it is the sequence of states generated by the implicit Markov chain, while the observation sequence is a visible sequence, where each state generates an observation, and the random sequence of observations generated thereby, each position of the sequence can be regarded as a moment. The first random process of the double random process is the random transition of states to generate state sequences, and the second random process is the random transition of state sequences to generate observation sequences.

[0075] It should be noted that the hidden Markov model is expressed as λ = (N, M, π, A, B), and its basic parameters are N, M, π, A, B. Where N represents the number of hidden states of the model, then the hidden state sequence at time t is i t ∈{i1,i2,…i n}; M represents the observed state corresponding to the hidden state of the model, then the observation sequence at time t is o t ∈{o1,o2,…o m}; π represents the initial state probability π∈{π1,π2,…π N}; A represents the state transition probability matrix, A=(a ij ) N×N ; B represents the observation probability matrix, B = {b i (ν m )} N×M There are three basic problems in the Hidden Markov Model, namely, the evaluation problem, the decoding problem, and the learning problem. The evaluation problem refers to the problem that the model λ is known and the unknown observation sequence o is t and its corresponding hidden state sequence i t , solved using the forward-backward algorithm.

[0076] In an optional embodiment, the algorithm calculation process is as follows:

[0077] Initialize the forward variable. When t=1, the forward variable is as follows:

[0078] α1(i)=πi b i (o1),1≤i≤N

[0079] According to α1(i), we can get α t+1 (j), as shown below:

[0080]

[0081] Recursively backward, we get {α T (i)}, then the probability of the observed sequence is as follows:

[0082]

[0083] Initialize the backward variable, and the probability of the observation value after time t is as follows:

[0084] β T (i)=1,1≤i≤N

[0085] According to β t+1 (j) Recursively obtain β t (i), as shown below:

[0086]

[0087] Recursively forward, we get {β T (i)}, then the probability of the observed sequence is as follows:

[0088]

[0089] The decoding problem refers to finding the corresponding hidden state sequence based on the observation sequence to make the observation sequence optimal when the model λ is known. Using Bayesian estimation, the following formula can be obtained:

[0090]

[0091] The key to the decoding problem is to find the optimal sequence I * , using the Viterbi algorithm, the main calculation process is as follows:

[0092] Initialize the intermediate variables as shown below:

[0093]

[0094] Among them, ψ t (i) indicates that the state is S at time t i The optimal state at time t-1 under the condition of state.

[0095] The variable value at time t is obtained by recursively deducing the parameter value at time t-1 as shown in the following formula:

[0096]

[0097] After completing the variables at time T, we get the optimal state at the final moment. As shown below:

[0098]

[0099] Path backtracking, according to the above formula, the optimal state at each moment can be obtained As shown below:

[0100]

[0101] By calculating the above formula, we can get the optimal state sequence The learning problem refers to the unknown model λ. The known observation sequence is used to estimate the parameters so that the probability of generating the observation sequence is maximized. Using Bayesian estimation, the following formula can be obtained:

[0102]

[0103] In an optional embodiment, the main computational process of solving the learning problem using the Baum-Welch algorithm is as follows:

[0104] Initialize model parameters λ = (N, M, π, A, B);

[0105] Define the intermediate variable ξ t (i,j) and substitute the forward and backward variables as shown below:

[0106]

[0107] Among them, P(O|λ j ) represents the probability of observing the known model parameters.

[0108] Model parameter update, π i When t=1, the state is S i The conditional probability of

[0109]

[0110] The update is terminated until the observation value probability converges, thereby obtaining the optimal observation value probability and model parameters.

[0111] In the embodiment of the present application, the output of the fan is closely related to environmental parameters such as actual wind speed, temperature, humidity, weather conditions (i.e., sunny, cloudy, rainy and snowy days). Therefore, environmental parameters such as wind speed, temperature, humidity, weather conditions, etc. are regarded as observable quantities, while the output state of the fan is regarded as an unobservable quantity. The relationship diagram is shown in FIG. Figure 3 As shown, the state of the fan output is determined by the changes in environmental parameters.

[0112] In the embodiment of the present application, preprocessing the basic parameter data and establishing the first model according to the preprocessing result includes:

[0113] Given a set of observation sequences, i.e. historical basic parameter data, the output probabilities of different states are compared, and the maximum probability is the output state of the wind turbine;

[0114] To establish a prediction model for wind turbine output state, we first preprocess the environmental parameters and conduct model training. We use the hidden Markov model to learn the wind turbine output state changes, thereby generating multiple sub-state classifiers, each of which has its own model parameters and maximum probability value.

[0115] The second is state assessment. The trained hidden Markov model is used as a classifier for state assessment. The real-time observed environmental parameters are input and the most likely state under the observation sequence is calculated. This completes the classification, determines the state of the wind turbine output, and implements the evaluation of the wind turbine output state.

[0116] Finally, the wind turbine state transfer matrix and observation matrix are obtained, and the wind turbine output state, i.e., output power, is predicted. The simulation ends and the output sequence of the wind turbine is obtained.

[0117] Specifically, given a set of observation sequences (environmental parameters of wind turbine operation), the output probabilities of different states are compared, and the maximum probability is the wind turbine output state. Based on this principle, a wind turbine output state prediction model is established. The overall framework of the model is as follows: Figure 4 As shown. The overall framework is mainly divided into three parts. First, the environmental parameters are preprocessed and the model is trained. That is, the environmental parameters are normalized and input into the model. The HMM (Hidden Markov Model) model is used to train and learn the change of wind turbine output state, thereby generating multiple sub-state classifiers, each of which has its own model parameters and maximum probability value. Secondly, for state evaluation, the trained HMM is used as the classifier for state evaluation, and the real-time observed environmental parameters are input to calculate the most likely state under the observation sequence. The classification can be completed, the state of the wind turbine output can be determined, and the evaluation of the wind turbine output state can be realized. Finally, the wind turbine state transfer matrix and observation matrix are obtained, and the wind turbine output state, that is, the output power, is predicted. The simulation ends and the output sequence of the wind turbine is obtained; then the unit output of the entire wind farm is added to obtain the total output of the wind farm.

[0118] In the embodiment of the present application, the output of the photovoltaic unit is affected by environmental parameters such as light intensity, temperature, humidity, weather conditions (i.e., sunny, cloudy, rainy and snowy days). Similar to wind power, environmental parameters such as light intensity, temperature, humidity, weather conditions, etc. are regarded as observable quantities, while the output state of the photovoltaic unit is regarded as an unobservable quantity. The state of the photovoltaic unit output is judged by the change of environmental parameters, and a wind turbine output state prediction model is established. Given a set of observable sequences (environmental parameters for the operation of the photovoltaic unit), the output probabilities of different states are compared, and the maximum probability is the output state of the unit. Similar to wind turbines, the model method is also divided into three parts. First, the environmental parameters are preprocessed and the model is trained, that is, the environmental parameters are normalized and input into the model, and the HMM model is used to train and learn the changes in the output state of the photovoltaic unit, thereby generating multiple sub-state classifiers, each of which has its own model parameters and maximum probability value. The second is state assessment. The trained HMM is used as a classifier for state assessment. The real-time observed environmental parameters are input and the most likely state under the observation sequence is calculated to complete the classification, determine the state of the photovoltaic unit output, and evaluate the output state. Finally, the photovoltaic unit state transfer matrix and observation matrix are obtained, and the photovoltaic unit output state, i.e., output power, is predicted. After the simulation is completed, the photovoltaic unit output sequence is obtained; then the unit output of the entire photovoltaic array is added to obtain the total photovoltaic output.

[0119] In the embodiment of the present application, the large-capacity distributed power source is connected to the low-voltage busbar of the nearby substation through a dedicated line, such as Figure 5 As shown. Due to the instability of the output power of distributed power sources, voltage fluctuations may occur, resulting in unstable operation of the distribution network. Therefore, large-capacity distributed power sources connected to the low-voltage bus of the substation through dedicated lines are usually dispatchable. The dispatchable distributed power source operates in constant power factor mode, and its output power injected into the grid is as follows:

[0120]

[0121] Among them, P max , D max , β and D represent the maximum active power, maximum adjustable capacity, power factor, capacity factor and output capacity of the distributed power supply, respectively; the capacity factor ranges from [0,1].

[0122] In the embodiment of this application, the present invention focuses on the random faults of the main transformer, distributed power supply, and distribution line, assuming that they are all repairable. Using the Markov method, the two-state reliability model of the main transformer and distribution line is represented by the component operating state probability, and the component operating state probability p is obtained. U and the failure state probability p D , the calculation formula is as follows:

[0123]

[0124] Among them, λ and μ represent the failure rate and repair rate of the component respectively.

[0125] It should be noted that by establishing a prediction model based on HMM environmental parameters and output status, the output status of wind turbines and photovoltaic units can be accurately evaluated and predicted, thereby improving the operational stability of the power system. This prediction mechanism can identify possible power fluctuations in advance, provide a decision-making basis for grid dispatching, effectively suppress voltage fluctuations caused by unstable output of distributed power sources, and ensure the stable operation of the distribution network. At the same time, by calculating the output power of distributed power sources, multiple factors such as maximum output power, adjustable capacity, power factor and capacity factor are considered, making the control of power supply more refined and improving the utilization rate and dispatching efficiency of power supply. In addition, by establishing a two-state reliability model of the main transformer and distribution line, the fault status of the equipment can be monitored and predicted in real time, maintenance can be carried out in advance or the operation strategy can be adjusted, the power outage time caused by equipment failure can be reduced, and the reliability of power service can be improved.

[0126] S103, simulating the first model in combination with the real-time basic parameter data of the target distribution network area to be analyzed, and obtaining the probability and average duration of the target distribution network being in each state;

[0127] The first model is simulated in combination with the real-time basic parameter data of the target distribution network to be analyzed area to obtain the probability and average duration of the target distribution network in each state, including:

[0128] The probability and average duration of the target distribution network in each state are expressed as:

[0129] The probability p(s) of occurrence of system state s is calculated as follows:

[0130]

[0131] Among them, p z (s) represents the probability that component z is in system state s; N represents the number of components.

[0132] Furthermore, simulating the first model in combination with the real-time basic parameter data of the target distribution network to be analyzed area to obtain the probability and average duration of the target distribution network in each state also includes:

[0133] The average residence time T of the system state d (s) is calculated by the following formula:

[0134]

[0135] Among them, if component z is in the running state, then λ z represents the failure rate, otherwise it represents the repair rate.

[0136] S104, according to the probability and average duration of the target distribution network in each state, solve the maximum power supply capacity optimization model of the distribution network, calculate the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under the specified risk level.

[0137] In the embodiments of the present application, the reliability indicators used are expected energy not supplied (EENS), distribution network energy curtailment index (DNECI) and severity index (SI). EENS is defined as the expected number of load demand reductions caused by power shortages or grid constraints in a given time interval. DNECI is defined as the ratio of EENS to the maximum load L of the system. SI is defined as the number of minutes of interruption of the entire system under maximum load, also known as the "distribution system reliable power supply risk level index". The calculation formulas for DNECI and SI are as follows:

[0138]

[0139] SI=DNECI×60

[0140] In the embodiment of the present application, SI is the equivalent duration of all load losses during peak load, and the International Electrotechnical Commission divides SI into 4 levels. The maximum power supply capability (MPSC) of the distribution network refers to the maximum load that the distribution network can provide under a certain system state, which takes into account factors such as the capacity of substation transformers, distributed power sources and feeders, network topology and operation constraints. The maximum power supply capability optimization model of the distribution network includes: The maximum power supply capability optimization model of the distribution network is expressed as:

[0141]

[0142] Among them, R Ni 、T i Respectively represent the nominal capacity and load of the main transformer i; D NI , β I Respectively represent the nominal capacity and capacity factor of distributed power source I; N T 、N R They represent the number of main transformers and distributed generation respectively.

[0143] In the embodiment of the present application, the constraints of the optimization model are as follows:

[0144]

[0145] in, Respectively represent the main transformer sets in operation and shutdown states; They represent the set of internal and external communication units of the substation centered on the main transformer i; i' is the number of distributed power sources connected to the low-voltage busbar of the main transformer i; Respectively represent the distributed power collection in the running and shutdown states; t ii' Indicates the load of the distributed power source in the same busbar when the faulty main transformer i is transferred to the operating state; t ij represents the load of the faulty main transformer i when it is transferred to the operating state main transformer j; K represents the safety overload factor of the main transformer; c ij represents the limited interconnection capacity between main transformer i and main transformer j. The first constraint is the load transfer balance constraint; the second and third constraints are the distributed power transmission capacity constraints; the fourth and fifth constraints are the main transformer transmission capacity constraints; the sixth constraint is the main transformer interconnection capacity constraint; the seventh and eighth constraints are the capacity constraints of the main transformer and distributed power under the operating state. The decision variable of the MPSC model is T i and β i , the optimal solution of the optimization problem is T i * and Indicates the optimal load of the main transformer and the optimal capacity factor of the distributed generation under this state. The maximum value of the objective function is MPSC * , the calculation formula for the maximum energy that the low-voltage busbar of the main transformer i can provide is as follows:

[0146]

[0147] Among them, Ω i is the distributed generation set connected to the low-voltage busbar of the main transformer i. The state probability reliability index I of MPSC PRI,MPSC It is the product of the state probability and the severity, and the calculation formula is as follows:

[0148] I PRI,MPSC =p(s)·MPSC(s)

[0149] Among them, p(s) and MPSC(s) represent the probability and severity of system state s respectively. The system probability reliability index of MPSC It is the sum of all state indicators of MPSC, and the calculation formula is as follows:

[0150]

[0151] Among them, S A It is a system status collection, created by enumeration; It is also the expected value of MPSC. The reliable power supply capability (RPSC) of the distribution network refers to the maximum load that the distribution network can provide under a certain reliable power supply risk level (such as SI), and takes into account the capacity of each component (such as the capacity of substation transformers, distributed power sources and feeders), network topology and operation constraints. The calculation formula of SI is as follows:

[0152]

[0153] Among them, rpsc i Indicates the reliable power supply capability of the main transformer i; represents the maximum power supply capacity of main transformer i in system state k; T d (k) represents the average duration of system state k; N represents the number of system states; p(k) represents the probability of system state k.

[0154] According to the probability and average duration of the target distribution network in each state, the maximum power supply capacity optimization model of the distribution network is solved, and the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under the specified risk level are calculated, including:

[0155]

[0156]

[0157] Among them, rpsc i Represents the reliable power supply capability of the main transformer i; T d (k) represents the average duration of system state k; N represents the number of system states; Ω i is the distributed power source connected to the low-voltage busbar of the main transformer i. SI represents the risk level index of reliable power supply of the distribution system. SI r Indicates the risk level threshold of reliable power supply of the power distribution system.

[0158] In an optional embodiment, the maximum load that the distribution network can provide under different risk levels can be analyzed to determine the power supply capacity of the system under a specific risk level, which helps to understand the performance of the system under extreme conditions. Consider the impact of the access point, capacity and different types (wind power, solar power) of distributed power sources on system reliability, and evaluate the changes in system performance under various scenarios. Based on the results of the reliability analysis, make suggestions to improve system reliability, such as adjusting the access strategy of distributed power sources, strengthening the grid structure, increasing backup capacity or improving emergency response plans. Combine the improvement of system reliability with cost investment, evaluate the economic benefits of improvement measures, and ensure a reasonable return on investment. Summarize the analysis results and write a detailed technical report, including system reliability indicators, risk analysis, sensitivity analysis, optimization suggestions, and economic evaluation, for reference by decision makers.

[0159] In summary, the present invention proposes a reliability analysis method for a distribution network considering large-scale distributed power access, obtains historical basic parameter data of the target distribution network to be analyzed, and the historical basic parameter data at least includes distributed power parameters and historical operation data, historical meteorological data, distribution network structure and interconnected feeder capacity limit data; preprocesses the basic parameter data, and establishes a first model based on the preprocessing result, the first model includes a transfer matrix and an observation matrix of the distributed power output state based on the hidden Markov model and a two-state reliability model of the main transformer and the distribution line; simulates the first model in combination with the real-time basic parameter data of the target distribution network to be analyzed, and obtains the probability and average duration of the target distribution network in each state; according to the probability and average duration of the target distribution network in each state, solves the maximum power supply capacity optimization model of the distribution network, and calculates the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under the specified risk level. The present invention uses the historical operating environment data of distributed power sources to construct a state transfer matrix and an observation matrix based on the hidden Markov model, and performs reliability analysis with high accuracy and strong versatility, which effectively improves the accuracy of reliability assessment of the distribution network containing distributed power sources.

[0160] This embodiment also provides a reliability analysis system for a distribution network considering large-scale distributed power access, including:

[0161] A data acquisition module is used to acquire historical basic parameter data of the target distribution network area to be analyzed, where the historical basic parameter data at least includes distributed power supply parameters and historical operation data, historical meteorological data, distribution network structure, and interconnected feeder capacity limitation data;

[0162] A model building module, used for preprocessing basic parameter data and building a first model according to the preprocessing result, wherein the first model includes a transfer matrix and an observation matrix of the output state of the distributed power source based on a hidden Markov model and a two-state reliability model of the main transformer and the distribution line;

[0163] A simulation solution module, used to simulate the first model in combination with real-time basic parameter data of the target distribution network to be analyzed, and obtain the probability and average duration of the target distribution network in each state;

[0164] The solution module is used to solve the maximum power supply capacity optimization model of the distribution network according to the probability and average duration of the target distribution network in each state, and calculate the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under the specified risk level.

[0165] The above-mentioned unit modules may be embedded in or independent of the processor in the computer device in the form of hardware, or may be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0166] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 8 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a reliability analysis method for a distribution network considering large-scale distributed power access is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0167] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0168] Obtain historical basic parameter data of the target distribution network area to be analyzed, the historical basic parameter data at least including distributed power supply parameters and historical operation data, historical meteorological data, distribution network structure and interconnected feeder capacity limitation data;

[0169] Preprocessing the basic parameter data, and establishing a first model based on the preprocessing results, the first model including a transfer matrix and an observation matrix of the output state of the distributed power source based on the hidden Markov model and a two-state reliability model of the main transformer and the distribution line;

[0170] The first model is simulated in combination with the real-time basic parameter data of the target distribution network area to be analyzed to obtain the probability and average duration of the target distribution network in each state;

[0171] According to the probability and average duration of the target distribution network in each state, the maximum power supply capacity optimization model of the distribution network is solved, and the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under the specified risk level are calculated.

[0172] Example 2

[0173] Reference Figure 2-Figure 7 , which is an embodiment of the present invention, provides a reliability analysis method and system for a distribution network considering large-scale distributed power supply access. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0174] An example of the power distribution network of the present invention is Figure 7 As shown, it is used for the calculation and analysis of reliable power supply capacity. There are two 35 / 10 kV substations (S1, S2) and one 110 / 10 kV substation (S3). The total capacity of all substations is 286MVA. The limited capacity of each feeder is 10.4MVA. The bus numbers of each substation are I, II, III, IV, V, and VI. The safety overload factor of the main transformer is 1.3. The reliability parameters of the main transformer are shown in Table 1. It is assumed that all distributed power sources have the same type and reliability parameters: Failure rate λ DG is 5 (times / year), repair rate μ DG It is 175.2 (times / year).

[0175] Table 1 Main transformer reliability parameters

[0176]

[0177] In order to analyze the impact of distributed power generation on reliable power supply capacity, 5MVA or 2.5MVA distributed power generation is connected to low-voltage busbars I, III, and V, respectively, as shown in Table 2. It is divided into 6 scenarios: no distributed power generation configuration in scenario 1, centralized distributed power generation configuration in scenarios 2-4, and decentralized distributed power generation configuration in scenarios 5-6. Under the same distribution system reliable power supply risk level indicator, the evaluation results of reliable power supply capacity are shown in Table 2.

[0178] Table 2 Reliability evaluation of different distributed generation access scenarios under the same reliable power supply risk level

[0179]

[0180] As can be seen from Table 2, under the same reliable power supply risk level, the access of distributed power sources to the low-voltage bus of the main transformer can improve the reliability level of the distribution network, and the distributed power sources can ensure the power supply of the load when the main transformer is out of power. Comparing the reliability evaluation of scenario 4 and scenario 5, it can be seen that the efficiency of decentralized configuration of distributed power sources is not always better than that of centralized configuration. Therefore, it is necessary to optimize the decentralized distributed power sources under the premise of considering economic factors to improve the power supply reliability of the distribution network.

[0181] Therefore, the reliability analysis method of the distribution network considering large-scale distributed power access based on the hidden Markov model proposed in the present invention can comprehensively consider and quantify the intermittent and fluctuating effects of the distributed power output, analyze the power supply reliability of the distribution system, and improve the accuracy of the reliability assessment of the distribution system containing distributed power sources.

[0182] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0183] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0184] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0185] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0186] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0187] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0188] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A reliability analysis method for a distribution network considering large-scale distributed power generation access, characterized in that: include: Acquire historical basic parameter data of the target distribution network area to be analyzed, wherein the historical basic parameter data at least includes distributed power supply parameters and historical operation data, historical meteorological data, distribution network structure, and interconnected feeder capacity limitation data; Preprocessing the basic parameter data, and establishing a first model according to the preprocessing result, wherein the first model includes a transfer matrix and an observation matrix of the output state of the distributed power source based on a hidden Markov model, and a two-state reliability model of the main transformer and the distribution line; The first model is simulated in combination with the real-time basic parameter data of the target distribution network area to be analyzed to obtain the probability and average duration of the target distribution network in each state; According to the probability and average duration of the target distribution network in each state, the maximum power supply capacity optimization model of the distribution network is solved, and the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under a specified risk level are calculated.

2. The reliability analysis method of the distribution network considering large-scale distributed power access as claimed in claim 1, characterized in that: The preprocessing of the basic parameter data and establishing the first model according to the preprocessing result includes: Given a set of observation sequences, i.e. historical basic parameter data, the output probabilities of different states are compared, and the maximum probability is the output state of the wind turbine; To establish a prediction model for wind turbine output state, we first preprocess the environmental parameters and conduct model training. We use the hidden Markov model to learn the wind turbine output state changes, thereby generating multiple sub-state classifiers, each of which has its own model parameters and maximum probability value. The second is state assessment. The trained hidden Markov model is used as a classifier for state assessment. The real-time observed environmental parameters are input and the most likely state under the observation sequence is calculated. This completes the classification, determines the state of the wind turbine output, and implements the evaluation of the wind turbine output state. Finally, the wind turbine state transfer matrix and observation matrix are obtained, and the wind turbine output state, i.e., output power, is predicted. The simulation ends and the output sequence of the wind turbine is obtained.

3. The reliability analysis method of the distribution network considering large-scale distributed power access as claimed in claim 2, characterized in that: The preprocessing of the basic parameter data and establishing the first model according to the preprocessing result also includes: The two-state reliability model of the main transformer and distribution line is represented by the component operation state probability. The component operation state probability p U and the failure state probability p D , the calculation formula is as follows: Among them, λ and μ represent the failure rate and repair rate of the component respectively.

4. The reliability analysis method of the distribution network considering large-scale distributed power access as claimed in claim 3 is characterized in that: The step of simulating the first model in combination with the real-time basic parameter data of the target distribution network to be analyzed area to obtain the probability and average duration of the target distribution network being in each state includes: The probability and average duration of the target distribution network in each state are expressed as: The probability p(s) of occurrence of system state s is calculated as follows: Among them, p z (s) represents the probability that component z is in system state s; N represents the number of components.

5. The reliability analysis method of the distribution network considering large-scale distributed power access as claimed in claim 4, characterized in that: The step of simulating the first model in combination with the real-time basic parameter data of the target distribution network to be analyzed area to obtain the probability and average duration of the target distribution network being in each state further includes: The average residence time T of the system state d (s) is calculated by the following formula: Among them, if component z is in the running state, then λ z represents the failure rate, otherwise it represents the repair rate.

6. The reliability analysis method of the distribution network considering large-scale distributed power access as claimed in claim 5, characterized in that: The maximum power supply capacity optimization model of the distribution network includes: The maximum power supply capacity optimization model of the distribution network is expressed as: Among them, R Ni , T i Respectively represent the nominal capacity and load of the main transformer i; D NI , β I Respectively represent the nominal capacity and capacity factor of distributed power source I; N T 、N R They represent the number of main transformers and distributed generation respectively.

7. The reliability analysis method of the distribution network considering large-scale distributed power access as claimed in claim 6, characterized in that: The method of solving the maximum power supply capacity optimization model of the distribution network according to the probability and average duration of the target distribution network being in each state, and calculating the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under a specified risk level includes: Among them, rpsc i Represents the reliable power supply capability of the main transformer i; T d (k) represents the average duration of system state k; N represents the number of system states; Ω i is the distributed power source connected to the low-voltage busbar of the main transformer i. SI represents the risk level index of reliable power supply of the distribution system. SI r Indicates the risk level threshold of reliable power supply of the power distribution system.

8. A reliability analysis system for a distribution network considering large-scale distributed power access, characterized in that: include: A data acquisition module is used to acquire historical basic parameter data of the target distribution network area to be analyzed, wherein the historical basic parameter data at least includes distributed power supply parameters and historical operation data, historical meteorological data, distribution network structure and interconnected feeder capacity limitation data; A model building module, used for preprocessing the basic parameter data and building a first model according to the preprocessing result, wherein the first model includes a transfer matrix and an observation matrix of the output state of the distributed power source based on a hidden Markov model and a two-state reliability model of a main transformer and a distribution line; A simulation solution module, used to simulate the first model in combination with the real-time basic parameter data of the target distribution network area to be analyzed, and obtain the probability and average duration of the target distribution network in each state; The solution module is used to solve the maximum power supply capacity optimization model of the distribution network according to the probability and average duration of the target distribution network in each state, and calculate the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under the specified risk level.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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