60 results about "Second-order cone programming" patented technology

The invention provides an active power distribution network multi-time scale rolling optimization method driven by multi-agent graph reinforcement learning. The method comprises the following steps: dividing slow time scale equipment and fast time scale equipment; the slow time scale is optimized and modeled into a multi-agent Markov game, and a global state space and a combined action space corresponding to switch actions, reactive compensation equipment gears and voltage regulation equipment gears are defined; switching action candidate vectors are generated based on a basic loop of the power distribution network, and action space dimensions are compressed through a scheme of excluding conflicting actions of the same common branch; fitting an action value function of each agent by adopting a graph neural network, generating a dimensionality-reduced discrete action strategy according to the global state space, and carrying out collaborative optimization through a distributed decision and a global reward mechanism; and on the basis of the slow time scale optimization result, the fast time scale optimization problem is converted into a mixed integer second-order cone programming model, and an instruction is solved in a rolling manner.

A multi-energy coupling aggregator feasible region rapid identification method relates to the technical field of comprehensive energy system operation optimization, and comprises the following steps: firstly, establishing an optimization operation model which aims at minimizing the total operation cost and covers electricity-gas-heat multi-energy coupling and equipment operation constraints; secondly, on the basis of the model, defining an interactive energy feasible region (IEFR) and a flexible climbing feasible region (FRFR) to be identified; then, a max-min robust optimization model used for depicting a feasible region boundary is constructed, and the max-min robust optimization model is converted into a mixed integer second-order cone programming MISOCP model capable of being efficiently solved through a strong dual theory and a KKT condition; and finally, carrying out iterative solution by adopting a polyhedral projection algorithm based on a dichotomy and a plane cutting mechanism, and rapidly obtaining an accurate boundary of the IEFR and the FRFR.

The invention provides a power distribution network comprehensive planning method and system in electric power engineering design. The method comprises the following steps: firstly, acquiring geographic space data and historical operation data in a planning area and cable vibration spectrum data monitored by an optical fiber sensing network; carrying out distributed storage and correlation analysis on the geographic space data and the cable vibration spectrum data through a mixed integer programming parallel acceleration algorithm, and generating a cable aging risk distribution diagram based on spatial topology constraints; performing multi-target collaborative optimization based on a second-order cone programming model, and generating a high-voltage distribution network line capacity increasing scheme and a cable replacement priority sequence; and finally, in combination with a partial discharge frequency domain characteristic change trend in the cable vibration spectrum data, correcting a line corridor extension parameter, and outputting a dynamic planning map including a cable life prediction node and a capacity increasing path. According to the technical scheme provided by the invention, precise planning and optimization of the high-voltage power distribution network are realized, and the accuracy and efficiency of cable life prediction and capacity increasing path planning are improved.

The invention discloses an energy storage type intelligent soft switch double-layer optimization site selection method based on improved whale algorithm and second-order cone programming cooperation, and belongs to the technical field of power distribution networking and optimization operation, and the method comprises the steps: collecting and preprocessing the basic data of a power distribution network; a double-layer optimization model with the minimum daily operation cost of the system as the target is established, the upper layer takes the installation position of the energy storage type intelligent soft switch as a decision variable, and the lower layer takes the three-port power transmission value as a decision variable and constructs constraints based on the power flow equation after second-order cone relaxation and the energy storage operation limitation; an upper layer site selection problem is solved by adopting an improved whale optimization algorithm introducing a self-adaptive inertia weight, a lower layer operation problem is solved by adopting a second-order cone programming method, and collaborative optimization is realized through double-layer interaction. According to the method, global collaborative optimization of planning and operation is realized, the solving precision and efficiency of the site selection problem are improved, and the system operation cost and the network loss are effectively reduced.

The invention discloses a probabilistic optimal power flow calculation method based on a second-order cone and unscented transformation, belongs to the field of power system optimization, and efficiently processes the uncertainty of new energy output and load by coupling the probabilistic sampling capability of unscented transformation (UT) and a convex optimization framework of second-order cone programming (SOCP). According to the method, unscented transformation efficient sampling is utilized, a covariance matrix is directly embedded to simplify correlation processing, and accurate probability distribution of network loss, voltage and line power can be output. Test results show that the calculation efficiency is improved by dozens of times compared with Monte Carlo simulation, the error is lower than 3%, and the method is suitable for risk assessment and operation optimization of the power system.

The invention provides a power distribution network reactive power optimization method and device based on deep reinforcement learning, and the method comprises the steps: building a mixed integer second-order cone programming model according to the state parameters of low-speed reactive power regulation equipment in a power distribution network, solving the mixed integer second-order cone programming model, and obtaining the day-ahead optimal scheduling result of each piece of low-speed reactive power regulation equipment; based on the day-ahead optimal scheduling result and the operation characteristics of the current power distribution network, establishing a multi-agent reinforcement learning model with the goal of minimizing the current operation cost of the power distribution network; a multi-agent depth deterministic strategy gradient algorithm is adopted to train the multi-agent reinforcement learning model, and an intra-day multi-agent real-time scheduling strategy is generated; and fusing the day-ahead optimal scheduling result with the intra-day multi-agent real-time scheduling strategy, and outputting a multi-time-scale coordinated power distribution network reactive power optimization control instruction. According to the technical scheme, a multi-time-scale coordinated reactive power optimization mechanism considering day-ahead economical efficiency and intra-day real-time performance is realized.

This invention discloses a distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication, belonging to the technical field of radar resource allocation. It aims to solve the performance balance problem under multi-task coordination in distributed MIMO radar air combat scenarios. An integrated reconnaissance, interception, and communication adversarial scenario is constructed, using the lower bound of position estimation error (SPEB), missile detection probability, and inter-node communication capacity as performance evaluation indicators for reconnaissance, interception, and communication, respectively. Then, with the goal of minimizing the total system transmit power, a model is constructed by combining relevant performance constraints and node task rules. Finally, the model is transformed into a mixed-integer second-order cone programming problem, and the non-convex terms are convexified using the McCormick envelope method. A numerical solution is obtained based on the CVX solver in MATLAB, achieving joint optimization of node task allocation and power allocation. This invention reduces the total transmit power while satisfying three types of performance requirements, achieving efficient waveform resource scheduling and improving the practicality and robustness of radar countermeasures.

This invention discloses an intelligent control system for a space-type atomic layer deposition equipment, belonging to the field of intelligent control technology. The system of this invention includes: an acquisition module that acquires the pressure difference and flow rate values ​​of the gas curtain pipeline and performs low-pass filtering to obtain a pure signal matrix; a feature extraction module that calculates the flow field impedance and impedance residual based on this matrix and converts them into an empirical probability distribution vector; a model building module that constructs a degenerate distribution uncertainty set and a scheduling objective cost function based on this vector, and converts them into a mixed integer second-order cone programming model; an offline solution module that discretizes and classifies the dynamic distribution divergence tolerance and solves the model, and pre-generates a scheduling strategy library; and an online control module that looks up tables in the strategy library according to the real-time divergence tolerance and outputs substrate transmission speed control and channel switching commands. This invention transforms the minimization-maximization probability model into a convex optimization problem for offline solution, breaking the computing power bottleneck and balancing control response speed and scheduling decision-making through efficient table lookup.

The invention provides an off-grid ReP2A integrated system topology capacity collaborative planning method, and relates to the technical field of renewable energy source hydrogen production and synthesis ammonia, and the method comprises the steps: constructing a mixed integer second-order cone programming model covering a power network, energy storage, hydrogen production, hydrogen storage, hydrogen transmission and synthesis ammonia full-chain coupling constraint; wherein the mixed integer second-order cone programming model takes the minimum annual average comprehensive cost of the system as a target to collaboratively optimize equipment capacity configuration, power transmission and hydrogen transmission network topology and a system operation strategy; and solving the mixed integer second-order cone programming model by adopting a branch and bound Benders decomposition algorithm, and outputting a programming construction scheme. The scheme can solve the problems of high comprehensive cost and low operation efficiency of the system.

The invention discloses a power distribution network new energy bearing capacity determination method and device, a medium and electronic equipment, and relates to the technical field of new energy access, and the method comprises the steps: obtaining a plurality of transformer area topology networks of a target power distribution network and operation parameters of multilevel new energy bearing capacity evaluation indexes corresponding to the transformer area topology networks; performing constraint condition construction by adopting the operation parameters corresponding to the topological networks to obtain constraint conditions corresponding to the topological networks; based on each constraint condition and a target function taking maximization of the photovoltaic access capacity of the target power distribution network as a target, a mixed integer second-order cone programming model is adopted to carry out model construction, and an optimization model of the target power distribution network is obtained; and solving the optimization model by adopting a branch and bound algorithm to obtain the new energy bearing capacity of the target power distribution network. The new energy bearing capacity of the power distribution network determined by the method is more accurate.

The invention relates to a train energy-saving optimization method and device and a storage medium. The method comprises the following steps: acquiring train operation line data and train parameters; a train energy-saving convex optimization model is determined, the train energy-saving convex optimization model comprises an objective function and constraint conditions, the constraint conditions comprise constraint conditions for convex relaxation auxiliary variables, and the convex relaxation auxiliary variables are used for replacing non-convex terms in the train energy-saving optimization model; and based on the line data and the train parameters, solving the train energy-saving convex optimization model by taking the minimum train operation energy consumption as an optimization target, and determining train operation state information. According to the embodiment of the invention, an energy-saving optimization problem of the train can be converted into a second-order cone programming problem (namely a convex optimization problem), the effect of stable and rapid solution can be realized, a globally optimal solution can be obtained, various complex route constraint conditions can be processed, the calculation efficiency is improved, and calculation resources are saved.

The invention discloses an MPC-based urban power system multi-state-multi-time scale optimization scheduling method, and belongs to the technical field of power system optimization scheduling. The method comprises the following steps: S1, dividing a system operation state based on a main and distribution network connection state and a node voltage out-of-limit condition; s2, establishing corresponding optimization scheduling models for different states; s3, based on a model prediction control framework, adaptively adjusting a prediction time domain and a control time domain according to the state emergency degree, and introducing a state switching hysteresis mechanism to suppress frequent switching; s4, constructing a multi-state-multi-time scale collaborative optimization scheduling model; and S5, solving by adopting a mixed integer second-order cone programming method to obtain an optimal scheduling plan of each distributed power supply, energy storage and load. According to the method, the voltage safety, the economical efficiency and the operation stability of the urban power system containing the high-proportion renewable energy sources in various operation states can be effectively improved, the problem of short view of energy storage is solved, and fine scheduling is achieved.

The invention provides a voltage out-of-limit suppression type power distribution network dynamic reconstruction method and system, and relates to the technical field of power distribution network dynamic reconstruction, and the method comprises the steps: obtaining basic data of a power distribution network, including a topological structure, system impedance, DG output and load parameters; establishing a radial operation constraint of the power distribution network based on the quantitative relationship among the branch number, the node number and the root node of the power distribution network; based on the single commodity flow thought, constructing a power distribution network dynamic reconstruction constraint based on the single commodity flow thought; the method comprises the following steps: introducing a branch state variable on the basis of a traditional Distflow power flow model, converting the branch state variable into a power distribution network power flow model in a mixed integer nonlinear programming form, and converting the model into a mixed integer second-order cone programming model by adopting a second-order cone relaxation and Big-M method to form a complete voltage out-of-limit suppression type power distribution network dynamic reconstruction model; and solving the dynamic reconstruction model, generating a switching operation sequence meeting voltage constraints, and completing topological optimization of the power distribution network.

The invention relates to the technical field of power grid optimization scheduling, and discloses a power distribution network distributed photovoltaic consumption capability improvement method, system and device and a medium, and the method comprises the steps: obtaining the operation side data of a power distribution network to construct a power distribution network distributed photovoltaic consumption capability improvement model, converting the model into a mixed integer second-order cone programming model, and carrying out the optimization scheduling of the power distribution network; a KSCH-Homotopy algorithm formed by a Homotopy algorithm introducing a KSCH smoothing function is adopted for solving, so that when the cone relaxation deviation of the obtained solving result does not meet the preset precision requirement, the power distribution network cut plane constraint introducing a dynamic adjustment multiplier is added in the mixed integer second-order cone programming model; and based on the updated mixed integer second-order cone programming model, iteratively executing the model construction step and the model solving step until the cone relaxation deviation formed by solving meets the preset precision requirement, and executing the solving result as a consumption capability improvement strategy, thereby effectively improving the distributed photovoltaic consumption capability.

The invention relates to the technical field of reactive power optimization of a power system, and discloses a low-voltage power distribution network reconstruction method, system and device based on demand side response, and a medium, and the method comprises the steps: enabling a demand side response strategy to be embedded into a power distribution network reconstruction model, and enabling the adjustment effect of demand side resources to be fully exerted, and randomness and volatility caused by access of high-proportion clean energy are effectively balanced. In the optimization solution process, second-order cone relaxation and reasonable variable setting are adopted, a complex non-convex problem is converted into a mixed integer second-order cone programming problem capable of being efficiently solved, and the defect that a traditional method is prone to falling into a local optimal solution is overcome. In the reconstruction process of the power distribution network, strict topology constraint verification and switch operation frequency limitation ensure the safety and reliability of the power distribution network after reconstruction.

The application discloses a distributed decoupling optimization method for a comprehensive energy system considering carbon trading, and comprises the following steps: establishing a centralized optimization framework of an electrically coupled system, initializing global data and information of a power system and a gas system; constructing a centralized model of a regional comprehensive energy system, wherein an objective function of the model comprises the power system, the gas system and carbon emission cost, and corresponding operation constraints are added to the objective function; converting an original non-convex problem into a mixed integer second-order cone programming based on SOCP relaxation, and performing reactive power optimization by using an ADMM consistent distributed algorithm; introducing consensus variables to represent the synergistic effect between the power and natural gas networks based on the ADMM method of consistent variables, so as to solve a distributed optimization problem of the regional comprehensive energy system; and finally, a case analysis of a regional electric and gas comprehensive energy integrated system is performed, and the distributed decoupling optimization of the system is realized by using the ADMM method of consistency. The application also comprises a distributed decoupling optimization system for a comprehensive energy system considering carbon trading.

The present application belongs to the technical field of trajectory optimization of morphing aircraft, and specifically discloses a reentry trajectory optimization method of morphing aircraft, comprising: establishing a reentry dynamics model and a multi-constraint condition model of the morphing aircraft; constructing a minimum drag coefficient configuration-Mach number profile and a reentry height flight corridor, and converting the trajectory optimization problem of the morphing aircraft into a trajectory optimization problem with a fixed aerodynamic shape; performing dimensionless, linearization and discretization processing on the reentry dynamics model, and introducing a control quantity transformation, so as to convert the trajectory optimization problem into a standard second-order cone programming problem; using a primal-dual interior point method to discretely solve the second-order cone programming problem, so as to obtain an optimal reentry trajectory satisfying all constraint conditions, and complete the reentry trajectory optimization method of the morphing aircraft. The present application avoids calculating the configuration parameter derivative in the trajectory optimization, and solves the problem that the derivative information of the configuration parameter cannot be obtained from the aerodynamic data.

The invention provides a quasi-regular double six-discontinuous triangulation network construction method for potential sliding surface search of a side slope. The method comprises the following steps: iteratively generating a quasi-regular initial triangulation network adapted to the boundary of a side slope model; dividing the local part into six grids, and encrypting to generate a six-direction array arrangement topological grid; constructing a six-node triangular unit and a high-order discontinuous line data format; and solving and searching the potential sliding surface of the slope by a second-order cone programming model. The method not only adapts to the geometric boundary of a slope discrete model, but also can meet the special requirements of a quasi-regular double six-discontinuous triangulation network, and a six-direction through discontinuous grid structure in any tiny area is formed; meanwhile, local partitions can be further preset through the double six-discontinuous grids, resource consumption of grid units in non-critical areas is reduced, the double six-discontinuous grids are applied to limit analysis of upper limit finite elements, high-precision search of the potential sliding surface of the side slope is achieved through rapid solving of a second-order cone programming problem, and the search precision can be guaranteed while the calculation burden can be reduced.

A variable fractional delay FIR filter optimization design method based on an intelligent optimization algorithm comprises the steps that S1, the design problem of a Farrow structure variable fractional delay filter is converted into solving of two ill-conditioned linear equation sets; s2, solving the ill-conditioned linear equation set by adopting an improved principal component weighted iterative algorithm, wherein weighting factors of the principal component weighted algorithm are associated with optimization parameters through an exponential mapping relation; s3, constructing a collaborative optimization framework by utilizing a Runge-Kutta optimization algorithm, and performing joint optimization on parameters and orders of a filter by taking an error norm of a filter coefficient vector solution as an optimization target; and iteratively solving the ill-conditioned linear equation set to obtain an optimal filter coefficient sum, and completing the filter design. According to the method, the defect of high calculation complexity of a traditional linear programming method and a second-order cone programming method is overcome, the order of the filter is further optimized while the same performance is obtained, and 28% of hardware resources can be saved.

This invention discloses a convex relaxation modeling method for optimal power flow in DC microgrids based on distributed observers. The method includes: establishing an accurate optimal power flow model with non-convex characteristics of MPPT-DGs and CPLs and performing accurate convex relaxation using second-order cone programming; deriving synchronization terms based on KKT conditions to form a distributed control objective; designing a distributed state observer under sparse communication to estimate the global average voltage; deriving the maximum communication delay stability tolerance; and adopting a dynamic event-triggered communication strategy to reduce the communication burden. This solves the problems of difficulty in solving non-convex problems, reliance on fully connected communication networks, communication delay affecting stability, and insufficient heterogeneous energy storage management in existing optimal power flow control methods for DC microgrids. It achieves high-precision, low-communication, and high-reliability operation of the globally optimal power flow under sparse communication and uncertain delay conditions.

The invention provides an energy storage multi-objective optimization method and system based on second-order cone relaxation, and the method comprises the steps: constructing a power flow model of a power distribution network, and constructing a node power balance constraint, a voltage drop constraint, and an operation boundary constraint; adopting second-order cone relaxation to constrain the alternating current power flow relation of the power flow model to obtain a second-order cone relaxation constraint; by taking network loss minimization as a first target and power generation cost minimization as a second target, summarizing all constraint and optimization targets to obtain a second-order cone programming model; constructing a loop phase angle consistency constraint about the mesh set according to the adjacent matrix; the second-order cone programming model is solved, a solving result is verified according to the loop phase angle consistency constraint, and if verification is not passed, the solving result is adjusted, and solving is carried out again until the newest solving result meets the loop phase angle consistency constraint. According to the invention, high-precision, high-efficiency and high-real-time energy storage optimization can be considered.

The application discloses a multi-zone flexible supply and consumption capacity evaluation method considering power distribution network source and load probability, and belongs to the technical field of power system operation and planning. Firstly, a multivariate normal distribution probability model of distributed photovoltaic and load is established to quantize the spatial correlation of power of each node. Secondly, a maximum local consumption rate of the distributed photovoltaic is taken as an objective function, and a large M method is introduced to calculate the reverse power transmission. Then, the linearized power flow of the medium-voltage network, the multi-level energy storage operation, the SOP intelligent soft switch and the interaction constraint of the medium and low-voltage network are comprehensively considered, and the chance constraint model is established for the node voltage and branch capacity respectively. Finally, the chance constraint analysis containing uncertainty is converted into a linear constraint and a second-order cone constraint to form a single-layer mixed integer second-order cone programming model for solving. The application can accurately quantize the photovoltaic consumption potential of the multi-zone flexible interconnected power distribution network, and improve the reliability and solving efficiency of the evaluation result under actual fluctuation conditions.

The present application relates to neuromodulation and brain-computer interface technical field, propose a kind of robust optimization method and system under the condition of parameter uncertainty, robust optimization method includes constructing five-layer head tissue model including skin, skull, cerebrospinal fluid, grey matter and white matter based on the magnetic resonance image of patient, and the independent conductivity uncertainty radius of each layer of tissue is set, multi-dimensional ellipsoid uncertainty set is constructed;Min-Max worst case cost function is constructed and is solved, guarantee in ellipsoid set under the worst case, electrode current weight meets safety constraint;Worst case cost function is converted into second-order cone programming form using dual principle;Pseudo-trace perception sliding window mechanism is introduced to monitor the continuity of physiological signal or impedance data, when detecting non-physiological mutation, trigger re-optimization by calling historical stable parameters and amplifying uncertainty radius;When re-optimization has no solution, trigger safety derating mechanism, and the current instruction of previous period is scaled proportionally.

The invention provides an angle-measurement-only multi-pulse self-adaptive closed-loop rendezvous guidance method based on second-order cone programming, and belongs to the relative navigation and rendezvous guidance technology of spacecrafts. According to the method, firstly, an angle-only-measurement relative navigation dynamical model and a measurement model based on a CW equation are constructed under LVLH coordinates, system state observability analysis is carried out, the orbit maneuvering condition enabling the state to be observable is obtained, and the condition is used for constructing an optimal guidance index. Secondly, analyzing and modeling the rendezvous guidance error source, and deducing to obtain a relative orbit guidance error covariance model through error covariance analysis; thirdly, on the basis of a second-order cone programming method, guidance fuel consumption and system state observability conditions are fused, and a multi-target weighted cost function of optimal rendezvous guidance is jointly constructed; meanwhile, based on the established guidance error covariance model, a cost function weight index system with a self-adaptive adjustment function is designed, and real-time self-adaptive adjustment of a guidance strategy along with change of system performance is achieved. The method solves the problems of high fuel consumption, poor flexibility and the like caused by separate design of the relative navigation and the rendezvous guidance model in the traditional only-sight-line measurement rendezvous guidance method, and has the ability of adaptively adjusting and optimizing the rendezvous fuel consumption and the relative navigation system observability.

This application provides a method and system for comprehensive planning of distribution networks in power engineering design. First, it acquires geospatial data, historical operational data, and cable vibration spectrum data monitored by a fiber optic sensor network within the planning area. Then, it uses a mixed-integer programming parallel acceleration algorithm to perform distributed storage and correlation analysis on the geospatial data and cable vibration spectrum data, generating a cable aging risk distribution map based on spatial topology constraints. Next, it uses a second-order cone programming model for multi-objective collaborative optimization, generating high-voltage distribution network line capacity expansion schemes and cable replacement priority sequences. Finally, it combines the partial discharge frequency domain characteristic variation trend in the cable vibration spectrum data to correct the line corridor expansion parameters, outputting a dynamic planning graph including cable life prediction nodes and capacity expansion paths. The technical solution provided in this application achieves accurate planning and optimization of high-voltage distribution networks, improving the accuracy and efficiency of cable life prediction and capacity expansion path planning.

The application discloses a kind of joint estimation indoor positioning method, system, medium and equipment, it is related to indoor positioning technical field, including each anchor node sends signal to neighbor anchor node and collects RSSI data, fitting to collection data, estimate the path loss index of each anchor node;It is carried out k-means clustering analysis, and the most centroid of index is taken as the final path loss index estimation value;Maximum likelihood estimation method and Huber loss are used to build positioning problem, and the positioning problem is converted into the joint optimization problem of mixed semi-definite quadratic cone programming;Through EM algorithm, mixed gaussian noise parameters are dynamically updated, the position of target node and anchor node transmission power are optimized synchronously each time iteration, and the final positioning result is output.The application realizes the positioning of target node in the case where there is malicious anchor node and gaussian mixture noise, improves the accuracy and efficiency of positioning.

Disclosed in the present invention are a power distribution network soft open point siting and sizing method and system based on mixed second-order cone programming. The method comprises: acquiring interconnected flexible power distribution system data; on the basis of the interconnected flexible power distribution system data and interconnected flexible power distribution system constraints, establishing a flexible power distribution network soft open point siting and sizing programming model by using minimum daily total operation cost as an objective function; combining an improved sparrow algorithm and second-order cone programming to solve and optimize the flexible power distribution network soft open point siting and sizing programming model, to obtain a soft open point siting and sizing solution; and calculating the objective function corresponding to the soft open point siting and sizing solution to obtain an optimal soft open point siting and sizing solution. In the present invention, the economies of different soft open point configuration solutions are more accurately evaluated, so as to select an optimal soft open point optimization configuration solution, thereby achieving flexible power distribution network operation programming that takes both the power supply reliability and economy into consideration, and achieving coordination and complementarity between system devices.

This invention discloses a fault reconfiguration method for digital microgrids, specifically a method for fault reconfiguration of digital microgrids considering uncertain renewable energy generation and demand-side resource aggregators. The method establishes a demand response model for load aggregators based on price elasticity coefficients and effectively characterizes the uncertainty of renewable energy generation integrated into new distribution networks through chance constraints. Furthermore, a power flow model suitable for reconfiguration of new distribution networks is established, and then a reconfiguration model for the new energy distribution system is established considering network losses and switching costs. To address the non-convexity of the reconfiguration model, the Big M method and second-order cone relaxation are employed, and intermediate variables are introduced to transform the reconfiguration model into a mixed-integer second-order cone programming problem. Simulation experiments and detailed verification results demonstrate the reliability and effectiveness of this invention in fault reconfiguration of new energy distribution systems with demand-side resources and uncertain renewable energy generation.

The invention provides an active power distribution network reconstruction and island division unified recovery method and system, and belongs to the technical field of power system operation and control and intelligent power distribution networks. The method comprises the following steps: modeling an active power distribution network; establishing a multi-source collaborative fault recovery objective function which minimizes the total generalized cost during fault recovery; constructing a unified constraint model based on mixed integer second-order cone programming; substituting the data model into the unified constraint model and the fault recovery objective function, solving a formed mixed integer second-order cone programming problem, and obtaining a network reconstruction strategy and an island division boundary in a fault recovery period; and carrying out voltage and power flow safety verification on the optimal decision variables (including a reconstruction strategy, an island boundary and power supply output) by utilizing an alternating current power flow algorithm, and generating a fault recovery execution plan. The method overcomes the defects that in the prior art, a fractional step method is locally optimal, a heuristic algorithm is low in efficiency, and a traditional model is difficult to give consideration to dynamic maintenance and unified topological constraints.

A locating and sizing planning method for a switch matrix control series-parallel hybrid transformer comprises the following steps: forming a planning scene set, corresponding to each time period power and an SCHT to-be-selected installation position set based on distributed power supply and load historical typical day data; based on the planning scene set, the power corresponding to each time period and the SCHT to-be-selected installation position set, combining a pre-constructed double-layer planning mathematical model, and adopting a hybrid algorithm combining a heuristic optimization algorithm and second-order cone programming for solving to obtain an optimal switch matrix control series-parallel hybrid transformer planning scheme; wherein the pre-constructed double-layer planning mathematical model is a planning layer model constructed by taking the minimum comprehensive cost as the target, an operation layer model constructed by taking the maximum flexibility index as the target, and the pre-constructed double-layer planning mathematical model is constructed by the planning layer model and the operation layer model. According to the method, the SCHT system model suitable for planning is established, and optimal power flow optimization of the power distribution system is considered, so that an optimal configuration scheme considering both economical efficiency and flexibility is obtained.